TL;DR

• Most executives are data-adjacent, not metric-directed: data is visible in the room, but it is not changing the decision. The test is simple: would the decision look different if the data showed the opposite?
• Three signs your executive team is data-adjacent: you cannot explain why a metric moved without asking an analyst, gut feel fills the gap because the analyst queue is too slow, and metric disagreement derails meetings before strategy can begin.
• More tools and dashboards have made the problem worse, not better. Most AI analytics tools introduce a new failure mode: confident-sounding answers built on hallucinated calculations.
• Trustworthy AI analytics requires four things: plain-language interpretation, a separate computation engine running against real data, standardized metric definitions, and answers traceable to source data. Most tools deliver only the first.
• Databox Genie answers the question the room is actually asking, not just what a metric shows, but why it moved, in plain language, grounded in verified data, at the moment the question arises.

Introduction

Monday morning. The leadership sync is five minutes in and someone pulls up the CAC chart. The number is 18% higher than last month. The team reviewed the dashboard on Friday. The metric was visible. And yet nobody in the room can explain why it moved.

The data was present. The decision will still be made. Those two facts have almost nothing to do with each other.

Welcome to data-adjacent decision-making, the dominant mode of executive analytics today.

According to Databox’s State of Business Reporting research, only half of business leaders are very confident they are tracking the right KPIs in the first place. The gap is not access. Executives have dashboards, KPI reviews, and BI tools. The gap sits between seeing data and deciding from it.

What follows is a precise diagnostic: are you genuinely deciding from data, or are you operating in data-adjacent mode without knowing it? And if the answer is the latter – what does the structural fix actually look like?

What It Actually Means to Decide From Data (vs. Decide Alongside It)

Deciding from data is not a posture or a tech stack. It is a decision rule.

A decision is genuinely metric-directed if it would change when the data changes. If the decision was already formed and the data was summoned afterward to support it, that is data-adjacent.

Data-adjacent means data is present in the room, referenced in the meeting, displayed on the screen, but it is not directing the decision. Dashboards are open. Metrics are referenced. KPI decks are reviewed. The data decorates the decision rather than directing it.

Call it data science theater: the performance of being analytically rigorous without actual metric-directed decisions. Impressive dashboards that do not change behavior. Metrics reviewed in retrospect. KPI decks that describe what already happened rather than inform what happens next.

The distinction matters because data-adjacent looks like metric-directed from the outside. A CFO who opens the margin report after forming a view on pricing is operating in data-adjacent mode. A CFO who opens the margin report and lets the numbers reshape the pricing decision is operating in metric-directed mode. Same dashboard. Same metric. Entirely different decision architecture.

The clean test: Data-adjacent means you check the dashboard after you have already formed a view. Metric-directed means the dashboard is where the view forms. Data validates in the first case. Data directs in the second.

The Databox “Beyond Attribution” survey found that only 41% of go-to-market leaders are very confident their current metrics accurately reflect what’s driving pipeline growth. Confidence is a prerequisite for letting data direct decisions rather than decorate them. The majority of executives are operating without it.

For a closer look at the infrastructure required for genuine metric-directed decision-making, Databox’s AI analytics overview maps the full picture.

The Three Signs Your Executive Team Is Data-Adjacent

A diagnosis is only useful if it is specific enough to recognize. Each of the following signs is drawn from real executive behavior — the kind that reads as rigorous from inside the room while quietly producing data-adjacent outcomes.

Sign 1: You Are DRIP: Data-Rich, Information-Poor

Your team has access to data across seven platforms, three dashboards, and a weekly analyst report. Ask why conversion dropped last week and the honest answer is: no one knows yet. A solid answer requires a 48-hour turnaround.

Data scattered across systems requires substantial analyst mediation before it becomes usable. The volume of data creates fatigue rather than confidence. Zulay Regalado of Zeotap put it precisely in Databox’s research on data analysis mistakes: “Many marketers are data-rich and insight poor — meaning they struggle with the gap between having customer data and being able to act on it.” Databox’s own survey of marketing data professionals found that more than 85% reported being unsuccessful with analysis at some point — not because the data was unavailable, but because turning data presence into reliable conclusions is harder than it looks.

The paradox: more data access has produced less decision confidence, not more. When an executive cannot answer a first-principles performance question in real time, the data is present — but it is not doing the work it was supposed to do.

Sign 2: Gut Feel Is Driving; Data Is Riding Shotgun

Decisions are made in the leadership sync. The data review is scheduled for Thursday. That sequencing is diagnostic.

When data is consulted after the decision direction is already set, it functions as political cover rather than strategic input. The sequencing reveals the real relationship between the executive and the data: gut feel forms the view, and the analyst queue exists to confirm it, not challenge it. Gut feel fills the gap the analyst queue creates, and as long as answers take 48 hours, nothing changes.

Sign 3: Your Team Debates Which Number Is Right Before It Can Decide Anything

CAC from the CRM does not match CAC from the marketing platform does not match CAC from the finance model. Before the strategy conversation can begin, the meeting becomes an epistemological argument: which number do we trust?

Only half of business leaders are very confident they are tracking the right KPIs, according to Databox’s State of Business Reporting research — and nearly half selected those KPIs based on personal experience rather than validated benchmarks. 

The problem is not that the data is unavailable. The problem is that nobody agreed on what to measure before the meeting started, so the meeting becomes an argument about definitions rather than a decision about direction. If your team cannot agree on the number, they cannot decide from the number.

Why the Problem Has Gotten Worse, Not Better

More tools, more dashboards, and more data integrations have not produced more metric-directed executives. They have produced more sophisticated-looking data-adjacency.

The analyst bottleneck is an executive problem. Self-service analytics promised that COOs, VPs of Marketing, and Heads of Sales could answer routine questions without waiting. In practice, self-service meant executives could see charts – not get explanations they could run the business on.

The Databox “Time to Insight” survey found that 64% of respondents say it typically takes one to three days to gather data to answer a business question.

By the time the answer arrives, the decision window has often closed. Gut feel fills that gap because nothing else is available in time.

Most AI tools make the problem worse. The risk executives are not yet fully aware of: most AI data tools let the large language model do the calculations, producing a number that looks authoritative, reads fluently, and is wrong.

The danger is a tool that fails confidently, not visibly. A CEO who presents a hallucinated metric in a board meeting has a data-tool problem disguised as a judgment problem.

The data trust gap exists not despite all these tools, but partly because of them. When the tool meant to provide answers introduces a new failure mode instead, trust erodes further rather than building.

What Genuinely Metric-Directed Executive Decision-Making Looks Like

Genuine metric-directed decision-making is a set of behaviors, not a technology purchase. The executives who operate there do specific things differently.

Decisions would visibly change if the data showed the opposite. The clearest marker: when a metric reverses, the decision reverses. The data directs rather than decorates.

The explanation comes before the board meeting, not during it. A metric-directed executive can say why a metric moved (not just that it moved) before walking into the room. The analysis is done in advance because the tools make it available in advance.

Answers do not require the analyst queue. Questions get answered at the moment they arise: before the leadership sync, during board prep, mid-week when the anomaly surfaces. The speed of the answer matches the speed of the decision.

Every function shares one definition of every metric. CAC means the same thing in finance, marketing, and the CRM. MRR has one number. Pipeline coverage has one formula. Metric disagreement is off the table before the meeting starts.

The best analytics do not stop at showing what happened. They explain why it happened and surface what to watch next. Executives gain the ability to interact with data directly, asking questions in plain language and receiving explanations, not charts, and that interaction happens at all organizational levels, not only among those with technical staff.

The shift worth noting: metric-directed decision-making lives at a specific moment: when a senior leader forms a view and commits to a direction. Culture change matters, but the critical intervention happens at that moment, in that decision layer.

How AI-Powered Analytics Closes the Gap

Databox’s Genie is built to make genuine metric-directed decision-making operationally feasible for executives who are not data analysts. The mechanics matter because not all AI analytics are built the same way.

Natural Language Querying: From Dashboard to Conversation

The shift from passive dashboards to active querying changes what executives can do without analyst support. Genie is Databox’s AI analyst, built for exploration, analysis, and creation through plain language, with no technical skills or complex queries required.

The capability goes further than question-answering. A VP of Marketing who needs a new dashboard can describe it: “Create a dashboard showing MRR, churn rate, and trial conversions by acquisition channel” and Genie builds it. A RevOps lead who needs a new metric can describe what it should measure and Genie creates it. The analyst queue that used to handle both questions and build requests shrinks on both fronts.

The practical implication: the question that used to take 48 hours now takes seconds. “Why did CAC jump last quarter?” no longer enters an analyst queue. It gets an immediate answer. And that speed-of-answer difference is a speed-of-decision difference.

The Accuracy Distinction: Why Most AI Analytics Tools Are a Liability

Trustworthy AI analytics requires four things working together: the AI interprets the question in plain language; a separate computation engine runs actual calculations against real data; standardized metric definitions eliminate the “which number is right” debate; and answers are traceable back to source data.

Genie’s answers are grounded in standardized, trusted metrics inside Databox. Genie does not hallucinate responses: when the data needed to answer a question is not available, Genie says so rather than guessing. The separation between interpretation and computation is the architectural decision that makes the difference between a board-meeting liability and a genuine decision tool.

The “Why” Layer: Moving Past What to Why

Dashboards show what happened. Genie explains why. The functional gap between data-adjacent and metric-directed at the executive level is the gap between a metric and an explanation.

Return to the Monday morning scenario from the introduction: the CAC chart is 18% higher. A dashboard shows the number. Genie answers the question the room is actually asking “why did it move?” in plain language, with traceable source data, at the moment the question arises. The explanation reaches the executive before the meeting, not after.

What Executives Are Actually Asking And How Genie Answers

The three failure modes named above, the DRIP problem, gut feel filling the sequencing gap, and metric disagreement, each produce a specific decision moment where data-adjacent behavior takes hold. Here is what those moments look like with Genie in the picture. All of the following questions are drawn from Databox’s prompt library, 100+ real questions teams ask their data across 22 integrations.

The Monday Morning Pulse Check

Before the leadership sync, a CEO asks on their phone, on the way in, “How is the business tracking against Q2 goals?”

In a data-adjacent environment, they pull up three dashboards, scan four charts, form a rough impression, and walk into the meeting with a directional feeling rather than a defensible answer.

With Genie, the questions that used to require three separate tools get answered in one conversation, pulling from HubSpot CRM, Stripe, and QuickBooks simultaneously:

• “How many deals were created this month, and how does that compare to last month and our target?”
• “What is our MRR this month, and how has it trended over the last 6 months?”
• “What is our total income this month, and how does it compare to last month and the same month last year?”

Because Databox already has the Q2 goals defined, Genie can pull performance against them directly: no manual assembly, no analyst required. The leadership sync starts from a shared view and if anyone missed the summary, the CEO shares the Genie conversation in one tap, including to colleagues who do not have a Databox account. The DRIP problem dissolves when the interpretation is already done and shareable before the meeting starts.

The Board Prep Moment

Forty-eight hours before a board meeting, a CFO needs to explain a margin compression. The analyst is finishing two other projects.

In a data-adjacent environment, the CFO pulls last quarter’s deck and works backward, reconstructing a plausible narrative from available charts.

With Genie in Extended mode, the CFO works through the analysis in a single conversation:

• “What is our gross profit this month, and how has our gross profit margin trended over the last quarter?”
• “What are our total operating expenses this month, and which expense categories are growing the fastest?”

Genie returns a deep analysis in plain language, identifying the patterns that explain the movement, with source data traceable enough to cite in the boardroom. The AI-generated summary is editable: the CFO adds context and shapes the narrative before sharing it. The metric trust gap from Sign 3 disappears because a single source of truth removes the debate before it starts.

The Mid-Week Anomaly

Wednesday afternoon. A VP of Sales notices pipeline coverage dropped. In a data-adjacent environment, the question enters the analyst queue and the answer arrives Friday, after the window to course-correct has narrowed.

With Genie, the VP works through the anomaly immediately, asking questions directly from the HubSpot CRM and Pipedrive data already connected to Databox:

• “What is the current total value of our open pipeline, broken down by stage?”
• “Which pipeline has the highest win rate, and which has the most deals stalling in early stages?”
• “Which sales reps have the highest closed-won revenue this quarter, and which are behind pace?”

Genie’s anomaly detection may have already flagged the drop before the VP noticed it, surfacing the change as an alert rather than waiting for someone to spot it in a dashboard. And because Genie saves conversation history, the VP can return to the thread Thursday morning and ask a follow-up without rebuilding context from scratch. The gap that gut feel used to fill closes. The VP acts the same day, not three days later.

Genie does not replace a data analyst. The analyst’s role shifts from producing routine outputs to building the systems, defining metrics, and shaping the semantic layer that makes those outputs trustworthy. Genie handles the routine requests. The analyst’s strategic value increases as a result. The same principle applies to executives: Genie frees leadership to lead rather than to analyze.

The Self-Evaluation: Are You Metric-Directed or Data-Adjacent?

Answer each question honestly, not aspirationally. Scoring: 5–7 “yes” answers means genuinely metric-directed. 3–4 means transitional. Fewer than 3 means data-adjacent – and that is the starting point, not a verdict.

Can you explain why a key metric moved last week without asking an analyst?

Would your last major strategic decision have been different if the data had shown the opposite result?

Does every function use a single agreed-upon definition of CAC, MRR, and pipeline coverage right now?

When your team disagrees on a number in a meeting, is there a source of truth you all defer to – immediately?

Can you get an answer to a business performance question in under five minutes, outside of business hours, without a data team present?

In your last board presentation, did you know why every metric moved or only that it moved?

Is your data review scheduled before decisions are made or after?

Executives who score low on this checklist are exactly the executives this article was written for. The gap the checklist surfaces is a decision infrastructure gap — and it is solvable.

Conclusion

The data-adjacent problem is not a data problem. It is a decision infrastructure problem.

Executives who have dashboards, KPI reviews, and BI tools are not automatically deciding from data. The test is whether the data actually changes the decision or whether it arrives after the decision is already formed.

AI analytics built on trustworthy computation, where the LLM interprets but never calculates, where metric definitions are standardized, where answers trace back to source data, converts data presence into decision confidence. That is the structural fix.

If the checklist surfaced a gap, Genie is built to close it.

Start free — no SQL, no analyst queue, no waiting. 

The post Are Your Executives Actually Making Decisions From Data Or Just Alongside It? appeared first on Databox.

TL;DR

Self-service analytics lets SaaS operators ask a business question and get a trusted, metric-backed answer without waiting on an analyst.

Here’s what that requires in practice:

• A definition isn’t enough. Every metric needs an owner who maintains it when the business changes.
• Governance creates self-serve, not tools. Most BI rollouts fail at the metric and distribution layer, not the tooling layer.
• The hard problem is definitions. What counts as churn? Which ARR figure goes in the board deck? Settle these first.
• AI is what finally makes self-serve accessible to everyone. Natural language queries mean anyone can ask a business question without knowing which dashboard to open. But the LLM should never do your math.
• The benchmark: a decision-maker asks a question, gets a governed answer, and takes action in the same working session. Everything else is implementation detail.

The problem self-service analytics is supposed to solve

A CEO opens the Monday revenue review and sees two numbers that should agree — but don’t. Pipeline coverage is 2.1x in the board deck and 1.6x in the RevOps dashboard. She asks out loud: “Which one is right — and why are we debating the number instead of the plan?”

That moment is what self-service analytics is supposed to prevent. Not by giving everyone more charts, but by making answers fast, consistent, and defensible.

What is self-service analytics?

Self-service analytics is an operating model where non-technical business users can ask a business question, get a trusted, metric-backed answer, and take action, without waiting on an analyst, opening a ticket, or exporting to a spreadsheet.

It’s distinct from self-service BI (business intelligence), which refers to the tooling category –   Databox, Tableau, Power BI, Looker, and their peers. Self-service analytics is the outcome those tools are supposed to enable. You can have every BI tool on the market and still not have self-service analytics if nobody trusts the numbers or knows which dashboard to open.

Why it matters specifically for SaaS companies

In a SaaS business, the questions that drive decisions are fast, frequent, and cross-functional:

• Did CAC spike because paid got expensive or because our conversion rate fell?
• Is NRR slipping in a specific segment, or across the board?
• Are we at risk of missing pipeline coverage before the board meeting?

These aren’t annual strategy questions. They come up every week. Routing them through a one- or two-person analytics team (which is the reality for most mid-market SaaS companies) means the analyst bottleneck isn’t strategy or execution. It’s the analytics queue.

In Databox’s Time to Insight study, only 16% of companies describe their current process for going from data to insight as efficient and streamlined. For SaaS teams managing monthly recurring metrics, that lag is a competitive disadvantage. By the time the analyst queue clears, the decision window has often already closed.

The cost shows up at the individual level too.

How self-service analytics actually works: the four layers

Most self-serve implementations fail because one of these four layers is broken or missing:

1. The metric layer: one definition, enforced

Every governed metric needs a single authoritative definition, a named owner, and version history. Without this, you get metric drift: ARR means one thing in the board deck and something slightly different in the CRM. The result isn’t a data problem; it’s a decision problem, because two teams are optimizing for different numbers.

A Metric Library, a documented single source of truth for every metric that drives weekly decisions, is the foundation. For most SaaS companies, that starts with eight to ten metrics: ARR, NRR, pipeline coverage, churn rate, CAC, gross margin, win rate, and cash burn.

2. The access layer: the right granularity for the right role

Executives need summary views with clear variance explanations. Operators need drill-down. Giving everyone access to everything sounds democratic, but creates noise and erodes trust when numbers look different depending on how you cut them.

Role-based access is more than a security decision: it’s a design decision about what each person actually needs to make their specific decisions.

3. The distribution layer: answers where decisions happen

A dashboard that nobody opens during the Monday revenue review is shelf-ware and not self-serve. Self-serve analytics works when metrics show up inside the workflow where decisions already get made: the weekly review, the Slack channel, the board prep doc.

Distribution is the most underinvested layer. Most teams build dashboards and assume people will go look. They don’t.

4. The action layer: context built in, not bolted on

Executives act on explanations, not on numbers. If NRR dips 2 points, the metric alone doesn’t tell you whether it was driven by downgrades in one segment or broad-based churn. Self-serve analytics has to ship context alongside the number; otherwise you’ve replaced one bottleneck (waiting for the analyst) with another (figuring out what the number means).

Self-service analytics vs. self-service BI: what’s the difference?

These terms are often used interchangeably, but the distinction matters in practice.

Buying a self-service BI tool is the beginning of the process, not the end. Most SaaS teams discover this about six months after rollout, when the dashboard count has tripled but the Slack messages asking “which number is right?” haven’t stopped.

Where self-service analytics breaks down

Definitions without owners. A metric definition that nobody is accountable for maintaining will drift. When the pipeline definition quietly changes from “any open opportunity” to “opportunities with next steps logged,” every downstream report changes with it and nobody knows why the numbers shifted.

Exploration without guardrails. Giving every operator unlimited slicing and dicing without a semantic layer doesn’t democratize data – it multiplies unofficial metrics. Within months you have ten versions of “churn” and no authoritative one.

Stale or inconsistent data. SaaS executives will tolerate late data once. They won’t tolerate wrong data. If the same metric calculates differently depending on which report you open, budget and headcount decisions become political rather than analytical.

How AI makes self-service analytics work for everyone

Until recently, self-service analytics was self-service in name only. In practice, it meant self-service for power users: people already comfortable navigating BI tools, applying filters, and knowing which dashboard to open. Everyone else still sent a Slack message to the analyst.

AI changes that equation fundamentally. 

Databox CEO Pete Caputa faced exactly that choice before a leadership meeting: pull someone from marketing into an async reporting loop, or walk in without the numbers. Using our AI analyst, Genie, he pulled a full cross-platform ad spend breakdown (MTD spend by platform, Google Ads split by search vs. YouTube, branded vs. non-branded) in about 90 seconds, without involving anyone else. 

“It eliminates a lot of conversations that I used to have,” he says. “And for the ones that I do have, I don’t have to start with ‘how is this performing’, I can start with ‘what can we do to improve this.’ 

The same shift happens at the operator level. Ali Wert, Director of Content Marketing & Brand at Databox, used to spend 30 to 60 minutes manually drilling across multiple dashboards for her weekly lead and pipeline pacing report. She asked Genie to locate her custom metrics, generate a MoM comparison, drill down by original source, and produce a summary ready to paste directly into a Slack leadership update. It took three minutes. 

That’s the real promise of AI in analytics: it extends self-serve from the technically confident to genuinely everyone. A CFO, a CS lead, or a regional sales manager can ask a business question in plain English and get a governed, metric-backed answer — without SQL, without a BI training course, and without a three-day wait.

But the architecture underneath it matters enormously. There’s a critical distinction between AI that translates a question into a query against governed metrics, and AI that performs the calculation itself.

The LLM should never do your math.

When an exec asks “what changed in churn this month?”, the right architecture queries the actual churn metric, slices by segment, and returns computed results. The language model handles the translation: plain English in, structured query out, while the computation happens against trusted, governed data.

The risky path is letting the language model perform the arithmetic directly. That’s how you get confident-sounding explanations with unauditable calculations underneath them. Our research on attribution found that fewer than 1 in 3 GTM leaders are fully confident their metrics accurately reflect what’s driving pipeline growth. 

Letting an LLM do math on top of metrics that fewer than 30% of executives already trust doesn’t fix the confidence problem, it buries it deeper.

The question to ask any AI analytics vendor is simple: where does the computation happen? The answer tells you whether AI is extending your metric layer or bypassing it entirely.

Getting started with self-serve analytics: the right order of operations

Most self-serve rollouts fail because they start with the dashboard and work backward. The order that actually works:

1. Define your top ten metrics first, before anyone builds a view. ARR, NRR, pipeline coverage, churn, CAC, gross margin, win rate, burn. Write down the exact calculation for each one.

2. Assign metric ownership. One person signs off on definition changes and is the named contact when numbers conflict. A definition without an owner decays.

3. Map metrics to decision cadences. Which metrics get reviewed Monday morning, which get checked before a board meeting, which trigger action if they move 10% in either direction? Then push those metrics into the meeting, the Slack channel, or the inbox where the decision already happens.

4. Choose tooling that enforces the metric layer, not just one that makes dashboards easy to build. The question to ask any vendor: where does the computation happen?

5. Add AI queries only after the metric layer is clean. AI answers are only as trustworthy as the definitions underneath them. An exec who gets a confident AI-generated answer built on an ungoverned metric is worse off than one who waited two days for a verified number.

What good looks like: the self-serve analytics benchmark

Self-serve analytics is working when:

• A decision-maker can ask a business question and get a governed, metric-backed answer in the same working session
• The exec team spends Monday’s revenue review choosing actions, not debating definitions
• Analysts are maintaining the metric system, not producing one-off reports
• When a number looks wrong, there’s a named owner to call, not a Slack thread that ends with “can someone pull this?”

If your team can’t clear that bar, the problem usually isn’t the tool. It’s the metric layer underneath it.

The post What The Hell Is Self-Service Analytics? A Plain-English Guide for SaaS Teams appeared first on Databox.

And yet – we still hear that even the best HubSpot power users run into limitations (in functionality, pricing, or both).

And so we reached out to a handful of HubSpot Reporting pros, the real “HubSpot ninjas,” and asked, “Where do you get stuck in your HubSpot reporting?”

They cooked up some great use cases and examples, and together we built some dashboards that overcome those native limitations by layering Databox on top of HubSpot.

These all got featured in a live Show & Tell, which you can watch on-demand here: HubSpot Reporting: Live Show & Tell.

Why Databox and HubSpot?

As our CEO, Pete Caputa, said recently: “HubSpot is the system of record… and Databox is the system of insight.”

Again, HubSpot’s reporting has come a long way. But when you need custom object analysis, cross-platform views, flexible funnels, or greater historical data, the native features alone often hit a wall.

That’s where Databox unlocks your HubSpot reporting superpowers:

• Custom funnel analysis and segmentation
• Integrated goal tracking and forecasting
• Calculated and time-shifted metrics
• Visual dashboards that drive conversation
• Easy sharing across teams, clients, and execs
  

Show & Tell: Reporting Like a HubSpot Rock Star

In our recent webinar, five seasoned revenue and operations leaders walk through the actual dashboards they use to guide strategy, performance, and coaching. These aren’t generic templates—they’re real-world examples of how teams are going beyond native HubSpot reporting with Databox.

These are real-world reporting setups you can steal – plus expert tips on tracking pipeline velocity, conversion rates, forecasting ARR, and more.

So if your HubSpot dashboards feel like pulling teeth, we’ve got you covered. Let’s dig into how the pros turn HubSpot data into decisions.

How to create a QBR report that drives real conversation

Cameron Collins, Revenue operations strategist – RevPartners

Cameron showed how he replaces clunky spreadsheets with visual dashboards that map the entire funnel – from site visits to revenue. His setup unifies data from multiple HubSpot objects using calculated metrics in Databox.

What’s hard in HubSpot: Cross-object reporting and unified visualization.

“Cross-object reports are really hard to not only create, but also to display. Even with the capabilities that you do have in your native CRM… you’re going to have to create four or five or six reports in order to create the number of sessions, the number of leads, the number of MQLs, the amount of closed-won deals… All of these reports that have to be created individually do tell the same story, but now you have four or five, six reports that have to be looked at.”

Instead of executives piecing together reports on MQLs, deals, and revenue in isolation, Cameron’s dashboards surface storylines: bottlenecks, dips in deal size, conversion rate red flags. These help leaders ask sharper questions, align quickly, and course-correct in real time.

How to build a time-shifted funnel that reflects reality

Alex Lee, Senior director of business analytics – Intellect

Since B2B sales cycles rarely close in 30 days, Alex built Databoards that reflect more realistic timelines. He explained how Intellect restructured reporting using time-shifted logic: showing lead gen from 90 days ago, deals from 60 days ago, and closes today.

What’s hard in HubSpot: Time-shifted reporting across different funnel stages.

“One thing HubSpot can’t do is actually take the different date ranges for specific stages within this sort of funnel report… where you can define, in this example, new leads came in 90 days ago, deals that were created within the last 60, 65 days… and closed-won deals within this month.”

This approach exposed true pipeline velocity and lead source effectiveness – insights HubSpot alone struggles to deliver. With Databox’s Custom metric flexibility, Alex turned this into an ongoing performance indicator, not just a backward-looking report.

Mastering webinar attribution across Zoom and HubSpot

Ali Schwanke, Founder & CEO – Simple Strat

Ali tackled one of the messiest reporting challenges in marketing: webinar attribution. With multiple events and inconsistent Zoom-to-HubSpot syncs, keeping data clean is tough.

What’s hard in HubSpot: Webinar attribution and tracking behavior across multiple events (especially Zoom integrations)

“You only ever get their last registered Zoom webinar… So if you actually then create additional fields… that’s also driven by list behavior. Again, we don’t have to get in the mechanics here, but… there’s a lot of moving parts when you’re trying to report on this webinar behavior.”

She built a system combining HubSpot lists, channel UTMs, and webinar-specific survey data – all visualized in Databox. This let her identify not only who registered and attended, but which webinar converted them, and where they came from – vital for proving ROI on content and channel spend.

How to turn forecasting into a sales coaching tool

Tory Ferrall, Director of revenue operations – Databox

Tory tackled an issue familiar to many ops leaders: forecasting that’s more intuition than insight. At Databox, she improved accuracy by analyzing when deals actually entered each HubSpot stage and calculating historical win rates per stage.

What’s hard in HubSpot: Forecasting accuracy based on assumed probabilities without historical validation; lack of stage-level win rate insights without calculated metrics.

“We actually found that HubSpot had released a new property… the date that deals enter a specific deal stage… but we hadn’t changed [win probabilities] in a while… it was kind of just a gut feeling… So we decided we can and we should look deeper into this data.”

The result? Dashboards that don’t just predict revenue – they reveal where reps are strong or stuck. Managers can spot if someone excels in early stages but stalls in negotiation, and tailor coaching accordingly.

Why most dashboards fail – and how to fix yours

Crispy Barnett, Head of revenue – Supered

Crispy got straight to the point: dashboards fail when they don’t answer business-critical questions. His method? 

“Most dashboards and reports suck, honestly. Not because the data is wrong… but because they’re not built to answer real questions… And the problem is when you start with answers and not with questions, you result with nothing.”

He also leaned into HubSpot attribution models to surface what’s actually working – first, last, linear. By filtering noise and focusing on signal, his dashboards support fast, confident decision-making. Crispy suggests that attribution models (first, last, linear) require filtering and contextual layering to be useful.

 “The real rockstar reporting starts with a specific question set… The goal is not to track everything. It’s to track what matters and act on it.”

The post HubSpot Reporting Dashboards Used by Revenue Experts: Real Templates, Pro Tips appeared first on Databox.

“One of the biggest bottlenecks in our workflow is bridging the gap between raw data and actionable insights fast enough to influence real-time decisions. With so many data sources and platforms, aligning everything into a clear, unified view takes time.”

– Jonathan Aufray of Growth Hackers

Why high‑quality data matters more than you think

Whether you’re an executive shaping strategy, an analyst wrangling spreadsheets, or a team member making daily calls, clean, high-quality data is the backbone of confident decision-making. This guide will walk you through practical ways to reduce errors, streamline your workflows, and turn raw, messy data into insights you can actually use. From automating deduplication to scaling reliable reporting processes – this is your playbook for better business outcomes.

What is data cleaning? (The real definition)

Data cleaning means finding and fixing errors, inconsistencies, and junk in your datasets so you can actually trust them for analysis and decision-making. It’s not just fixing typos – it’s about getting your data ready for complex queries, dashboards, and automated reports.

Here’s how data cleaning is different from related tasks:

Data cleaning

What it is:
Data cleaning is the process of fixing errors in your existing data. That includes:

• Removing duplicates
• Filling in missing values
• Standardizing formats (like dates, text capitalization, or number types)
• Resolving inconsistencies (e.g., “NY” vs. “New York”)
• Converting incompatible field types (convert text strings that hold numbers into numeric fields so calculations don’t break)

Why it matters:
Cleaning helps ensure accuracy and consistency. Without this step, your analysis can be skewed by bad inputs  –  leading to misleading reports or dashboards.

“I want to automate and simplify the process of cleaning and validating lead data when managing datasets with thousands of records, so I can minimize manual effort and reduce mistakes…”
  –  CRM Data Analyst, mid-sized business (Marissa S., Databox client)

Data cleansing

At first glance, “data cleaning” and “data cleansing” might sound like the same thing. But while both improve data quality, they’re not identical—and understanding the difference can help you choose the right approach for your needs.

What it is:

Data cleaning is all about quick fixes. It’s the process of automatically correcting obvious issues in your data—like removing duplicates, fixing typos, standardizing formats, and filling in missing values. Think of it as tidying up a messy room. It makes your data usable and reliable for day-to-day tasks.

Most teams automate this process so it runs continuously as new data flows in

Data cleansing takes it a step further. It’s a deeper, more strategic process that involves:

• Validating data against external sources
• Collaborating with domain experts to resolve inconsistencies
• Enriching and standardizing records
• Ensuring compliance with governance rules
• Exploring and consolidating variations in specific fields

Insycle puts it well: “A huge piece of the data management puzzle is understanding what you have in your database and cleansing it so it is uncluttered, formatted correctly, and standardized. But before you can begin fixing issues, you first have to identify what those issues are.”

Why both matter:

Data cleaning keeps your data functional—it’s like routine maintenance. Data cleansing is more like a full audit and tune-up. You’ll need both to make sure your data stays useful in the short term and trustworthy in the long term.

When you’re running a quick campaign report, basic cleaning might be enough. But when you’re building a strategy based on historical trends or predictive insights, you’ll want the confidence that comes from thorough cleansing.

Data preparation

What it is:
Data preparation goes a step further than cleaning. It includes cleaning plus:

• Merging data from multiple sources (e.g., CRM + payment data)
• Reshaping or restructuring datasets (e.g., pivoting rows to columns)
• Creating new fields (like calculated metrics or categories)
  Filtering or transforming data to align with business needs
  

Why it matters:
Preparation turns raw, cleaned data into a structure that’s usable for reporting, dashboards, or analytics tools. It’s how you build a curated “source of truth” across systems.

“We need a process for accurately matching and merging datasets using common identifiers – this underpins our ability to generate actionable business reports…”
  –  Business Intelligence Lead, E-commerce Retailer

Learn more about a Data Preparation framework here.

Data wrangling

What it is:
Data wrangling is the exploratory phase. It’s about:

• Investigating your data
• Identifying potential quality issues
• Deciding what needs to be cleaned, transformed, or restructured
  

It’s a mix of profiling, testing, and tweaking before formal cleaning or preparation happens.

Why it matters:
Think of wrangling as the detective work that informs your next steps. If you skip this step, you might miss deeper issues or apply the wrong fix.

“I’d rather go through and create the cleaning process myself and from there automate it once I understand the data…”

  –  Data Engineer, Reddit discussion

Each step builds on the last. Together, they help you create clean, reliable, analysis-ready datasets that power better decisions –  especially when working across messy tools like spreadsheets, CRMs, or marketing platforms.

The stakes are high. According to Harvard Business Review, only 3% of companies have data that meets basic quality standards. When bad inputs ripple through dashboards, high‑quality data becomes more than a technical nicety – it’s the difference between credible data analysis and expensive guesswork. Treat every dataset as an asset that must be protected, validated, and refined before you risk decisions – or dollars – on it.

The real cost of messy data

According to Gartner’s 2021 research, poor data quality costs the average organization about $15 million per year. But here’s the kicker – 60% of companies don’t even measure how much bad data costs them because they don’t track it.

Your analytics team is probably spending 45% of their time just preparing and cleaning data. That means your highest-paid people are doing data janitor work instead of finding insights that actually help the business.

If you’re at an agency, dirty data creates even more problems:

• Clients lose trust when reports have obvious mistakes
• You waste billable hours on repetitive cleaning tasks
• Results aren’t consistent across similar clients
• You can’t scale your services because everything requires manual work

Teams spend way too much time double-checking numbers, trying to figure out why reports don’t match, and explaining data problems in meetings instead of actually using insights to improve the business.

According to Anaconda’s 2020 State of Data Science Survey, companies report that their analytics teams spend the highest amount of time 45% doing data cleaning, 35% analysis and 20% for other tasks.

Data cleaning challenges by role

Different roles need different approaches to data cleaning. Here’s what each type of team member faces:

Executive leaders

You need trustworthy data for big decisions and measuring performance. Your biggest worry is data blind spots – when bad data makes you overconfident or hides real problems. When your KPI dashboards show conflicting numbers, it’s hard to make confident decisions about where to spend money and what strategies to pursue.

Data analysts and BI specialists

You deal with the messiest part – working directly with raw data from multiple sources. You have to balance automation with manual checking while dealing with tool limitations and systems that don’t play nice together.

“We need a process for accurately matching and merging datasets using common identifiers  –  this underpins our ability to generate actionable business reports from disparate data sources.”

 –  Business Intelligence Lead, E-commerce Retailer (Databox internal calls archives)

The biggest challenge? Merging datasets that have different structures, formats, and quality standards. Like when your CRM customer data doesn’t line up with transaction data from your e-commerce platform.

How to do it in Databox: In Databox, you can merge Datasets from different Views within the same Data Source (like HubSpot Contacts and Deals) or across multiple Sources (like HubSpot CRM and Shopify). Similar to SQL joins, this lets you explore more complex questions by connecting data across platforms.

Marketing and sales managers

You rely on clean data to measure performance and make strategic decisions. Data quality directly affects your ability to track KPIs, measure campaign effectiveness, and optimize marketing spend and sales processes.

Operations specialists

You work with data your team already cleaned, but you need to understand what happened to it. Clear documentation and consistent formats are crucial for your analytical work.

Essential techniques for cleaning data

Let’s get into the practical stuff. Here are the core techniques that handle most data quality issues:

1. Finding and removing duplicates automatically

Duplicate records typically appear as duplicate rows in a table or dataframe. You need repeatable logic to remove duplicate entries without wiping out legitimate multi‑touch interactions. There are two types to watch for:

Exact duplicates have identical values in all fields. These are easy to spot and remove.

Fuzzy duplicates are trickier – they’re variations in spelling, formatting, or data entry. Think customer names like “John Smith” vs “Jon Smith” or “J. Smith.”

Pro tip: Create composite keys that combine multiple fields to catch duplicates more accurately while keeping your queries running fast on large datasets.

2. Handling missing values intelligently

Don’t just delete everything with missing data – you’ll throw away valuable information. Here are better approaches:

Use averages for numerical data without strong patterns (like replacing missing sales amounts with the average sale amount).

Forward/backward fill works great for time series data where you can use the previous or next value to fill gaps. In SQL, COALESCE() and similar functions let you replace NULL values on‑the‑fly while keeping your query readable.

Apply business logic to determine what makes sense. Missing transaction amounts might be zero, while missing customer segments might get labeled “Unknown” for separate analysis.

3. Making everything consistent

Inconsistent formatting breaks joins and messes up grouping. Standardize these elements:

• Text (consistent capitalization, spacing, special characters)
• Dates (pick one format and stick with it)
• Categories (group similar values under consistent labels)
• Strip nonprinting characters (line breaks, zero‑width spaces) that silently break joins or visualizations

4. Dealing with outliers

Outliers can be real extreme values or data entry mistakes. These are values that sit far outside the normal distribution, like a misplaced decimal turning “99.00” into “9900.” Use both statistical methods (like standard deviations) and business rules (like “ages can’t be negative”) to identify them. Treat each outlier as a lead to investigate, not just something to delete.

Treatment options include capping values at reasonable limits, flagging suspicious data for manual review, or using transformations to reduce the impact of extreme values.

5. Ongoing quality monitoring

Set up automated checks that run when new data comes in:

• Track missing value percentages
• Monitor for business rule violations
• Watch duplicate rates over time

Using spell checking & text normalization to get from messy text to clean data

Free‑text columns such as open‑ended survey answers, support‑ticket notes, or product‑review blurbs are equal parts goldmine and grenade. One rogue emoji or a fat‑fingered brand name can blow up a join, skew a count, or flat‑out crash your CSV export. Treat text like any other data asset: profile it, clean it, and keep it on a tight leash.

Why it matters

• “Gooogle” vs. “Google” Two extra o’s and your pie chart suddenly shows a phantom competitor.
• “USA” vs. “usa” Case differences inflate “unique” values and wreck GROUP BYs.
• Smart quotes & emojis Fancy Unicode can choke SQL loaders or turn JSON into gibberish.

Three steps to cleaner text

1. Automated spell‑check with custom dictionaries
   Pipe your column through Hunspell, TextBlob, or Amazon Comprehend – but load a domain lexicon first so you don’t autocorrect “Shopify” into “Shopping.”
   
2. Normalize casing and Unicode
   Lowercase everything, strip diacritics, and swap curly quotes for straight ones before tokenizing or running sentiment analysis.
   
3. Tokenize & fuzzy‑match near‑duplicates
   Use Levenshtein distance or fuzzywuzzy to collapse “Jon Smith” and “John Smith,” or merge hashtag variants like #BlackFridayDeals and #blackfridaydeals.

Tool tips

• Python / Pandas

df['comment'] = (
    df['comment']
      .str.lower()
      .str.normalize('NFKD')  # strip accents
      .str.replace(r'[“”]', '"', regex=True)
)

• OpenRefine → Text facet → Cluster & Edit to spot near‑duplicates in seconds.
• SQL Use SOUNDEX() or Postgres trigram extensions for in‑database fuzzy matching.

Watch‑outs

• Over‑eager corrections “H&M” turning into “Ham” is not a glow‑up. Quarantine low‑confidence suggestions for manual review.
  
• Measure the impact After every sweep, rerun your profiling stats – null counts, distinct values, duplicate rates – to confirm you fixed more than you broke.

Building workflows that actually scale to deliver clean data

Effective data cleaning needs systematic workflows that can handle more data while maintaining quality. Here’s a four-phase approach:

Phase 1: Data profiling

Start by analyzing your datasets to spot patterns and quality issues. Review stats like record counts, missing data percentages, and unique values. Then document your findings to guide your cleaning rules.

Phase 2: Rule creation

Turn your profiling insights into automated cleaning procedures. Start with high-impact, simple rules like standardizing date formats or removing obvious duplicates. Add more complex rules gradually.

Phase 3: Testing and implementation

Run your cleaning rules on sample data first before applying them across the full dataset.

Phase 4: Monitoring

Keep an eye on how your cleaning rules perform as data sources and business needs change. Set up alerts for big changes in data quality or rule performance.

Key things to monitor:

• How long it takes to process each record
• What percentage of records get changed by each rule
• Error rates and rule failures
• Data quality scores before and after cleaning

How to do it in Databox: Leverage Datasets + Calculated Columns to build repeatable logic that prepares data before it reaches your dashboards. These transformations persist as new data flows in – no manual rework needed. For recurring processes, duplicate datasets templates for similar use cases.

Empower your team with clean, reliable data and make informed decisions with confidence.

Start Your Free 14-Day Trial with Databox – No credit card required

Tools that actually work

Different tools are good at different things. Here’s what works best for various scenarios:

Business Intelligence platforms

Many BI tools now include cleaning features:

• Tableau Prep: Visual data preparation with drag-and-drop cleaning
• Power BI with Power Query: Data transformation during import
• Looker: Data transformation during query execution
• Databox: Goes beyond dashboarding with its Advanced Analytics capabilities:
  • Dataset Software: Combine multiple data sources into unified, clean datasets.
  • Calculated Metrics: Create custom formulas and logic directly in the UI – no code required.
  • Filtering and Transformation: Apply rules to cleanse, categorize, or segment data before it reaches your reports.
  • Query-based Visualization: Use SQL-like dataset queries to refine your data pipeline in real-time.
  • Automated Data Sync: Ensure that your cleaned data is always up to date across sources like HubSpot, Google Analytics, CRMs, and more.

Explore all features → Databox Advanced Analytics

Open-source solutions

For flexibility and customization:

• OpenRefine: Great for interactive cleaning with smart duplicate detection
• Python libraries (Pandas, NumPy): Programmatic cleaning with machine learning
• R packages (dplyr, tidyr): Statistical approaches to missing data

AI-powered tools

The newest category uses machine learning to spot issues:

• Trifacta Wrangler: Uses AI to find inconsistencies and suggest fixes
• TIBCO Clarity: Cloud-based cleaning with tons of data source connections

SQL for data cleaning

SQL is powerful for cleaning because it:

• Works directly on your data without moving it around
• Handles large datasets efficiently
• Creates reproducible, shareable cleaning operations
• Integrates with your existing database setup

SQL is especially good for removing duplicates, filling missing values with business logic, and running validation checks that can be automated and scheduled.

How to do it in Databox: With no-code Dataset Builder, create calculated fields, apply filters, group data, and merge multiple sources. This gives your team SQL-like control over transformation logic – without writing any code.

Agency vs. internal team approaches

Agency use cases

You face unique challenges with multiple client datasets. Each client has different data structures, quality standards, and business rules.

Focus on:

• Creating reusable transformation templates
• Building libraries of cleaning procedures for common situations
• Preventing cross-client data contamination
• Documenting common issues by industry or platform type

Internal teams use cases

You work with more consistent data sources but need to balance different departmental needs.

Focus on:

• Accommodating different analytical needs across departments
• Balancing individual team requirements with organizational standards
• Implementing monitoring to prevent quality regression
• Creating shared dataset governance

“Automating this process would free up our team to focus more on strategy and creativity, not data wrangling.”

–  Jonathan Aufray,Growth Hackers

Measuring success

Track these metrics to show the value of your data cleaning efforts:

Data quality metrics

• Accuracy rates
• Completeness levels
• Consistency scores
• Timeliness of updates

Time savings

• Hours saved through automation
• Reduction in manual cleaning tasks
• Fewer data-related support requests
• Less time spent on analysis rework

ROI calculation

ROI = (Time Savings + Error Prevention + Better Decisions) ÷ (Tool Costs + Setup + Maintenance)

Organizations typically see a return on investment ranging from 5:1 to 15:1 from data cleansing initiatives, with some companies achieving ROI exceeding 500% within two years.

Common mistakes to avoid

Over-cleaning

Don’t remove too much data in pursuit of perfection. Set business-driven quality standards, not technical perfection. Test cleaning rules on samples first, and create “quarantine” processes for questionable data instead of deleting it immediately.

Also, avoid deleting rows just because one column has a NULL value if the other fields still contain useful data. Instead, consider filling in the missing value (imputation) or adding a flag to mark it.

Too much manual work

Automate high-frequency, rule-based tasks. Save manual review for complex cases and business rule exceptions. Document manual interventions so you can find automation opportunities later.

Poor documentation

Record why you made each cleaning rule, document data sources and their specific issues, create visual workflows, and maintain change logs for rule modifications.

Vendor dependence

Understand the logic behind vendor cleaning tools, maintain internal expertise in core techniques, create backup procedures for critical operations, and regularly evaluate alternatives.

The bottom line

Good data cleaning isn’t about perfection – it’s about making your data reliable enough to support better decisions. Start with a systematic approach, measure your results, and keep refining your process based on what your business actually needs.

The key is to automate what you can, document what you do, and focus on the data quality issues that actually impact your business outcomes.

Stop struggling with inconsistent data.

Try Advanced Analytics features.

 

The post Data Cleaning Best Practices: The Foundation for Reliable Reporting Across Teams appeared first on Databox.

In SaaS, customer retention is often the difference between sustainable growth and a constantly leaking bucket. Even a small improvement in churn rate can dramatically impact your bottom line and valuation.

But the challenge is knowing exactly where to look. Is it product usage patterns? Customer support interactions? Billing issues? Or perhaps something less obvious like team adoption rates or feature usage? This is also where churn risk becomes visible—by spotting it early, you can act before customers decide to leave.

That’s what we set out to discover when we ran this survey. We collected feedback from over 40 SaaS companies to better understand the most effective strategies for spotting churn risk factors and implementing proactive retention measures.

We got valuable insights into which indicators most reliably predict churn and how top-performing companies are turning potential churn situations into opportunities for stronger customer relationships. These early signs of churn risk can help you determine which customers need proactive outreach.

Let’s check out the details.

• What Is Customer Churn
• SaaS Benchmark KPIs
• Key Insights From Our Research
• Most Effective Predictors of Customer Churning
• Most Successful Initiatives to Reduce Churn Rates
• Stay on Top of Your SaaS Performance Data with Databox

What Is Customer Churn?

Customer churn is the percentage of customers who stop using your product during a specific period. In the context of SaaS, churn typically occurs when customers cancel their subscriptions, downgrade to a free plan, or simply fail to renew.

Churn is often measured as a rate—the number of customers lost during a period divided by the total number of customers at the beginning of that period.

For example, if you start a month with 1,000 customers and lose 50 by the end of the month, your monthly churn rate would be 5 percent. This straightforward churn calculation measures how many customers you lose in relation to how many you had at the start.
While monthly churn is common, some businesses also track annual churn to measure long-term retention patterns.

While some level of churn is inevitable in any business, high churn rates can severely impact a SaaS company’s growth and profitability. Each lost customer represents not just lost recurring revenue, but also wasted acquisition costs and unrealized lifetime value. Keeping customers engaged and preventing churn has a direct impact on CLV (customer lifetime value). Every lost customer also makes your CAC (customer acquisition cost) harder to recoup, since you have to replace that lost revenue all over again.

It’s important to understand these three types of customer churn:

• Voluntary churn occurs when customers actively decide to cancel their subscription
• Involuntary churn happens due to payment failures, overlooked renewal notices or other technical issues
• Net churn factors in both lost customers and expansion revenue from existing customers

SaaS Benchmark KPIs

To better understand how SaaS companies are performing, we analyzed data from the SaaS Benchmark KPIs group, which tracks key metrics across various performance indicators.

This dataset allows SaaS companies to compare their results with the median value, as well as the top and bottom quartiles. It provides insights into essential KPIs such as sessions, new customers, churn rate, churn, recurring revenue (MRR), net profit margin, average revenue per user, and more.

According to data provided by the Benchmark Group SaaS Benchmark KPIs, in February 2025, the median churn value in February 2025 was 7.

If you’re running a SaaS business and want even more actionable insights into churn metrics and other key performance indicators, you can join our free Benchmark Group SaaS Benchmark KPIs.

By joining, you’ll be able to anonymously compare your churn rate, customer retention, MRR, and other important SaaS metrics with other similar companies at no cost.

All it takes is connecting your account to the group—completely anonymous and secure. You can even create your own Benchmark Group, giving you full control over who accesses it and shares data.

Whether you’re focused primarily on reducing churn or tracking a broader set of business metrics, Benchmark Groups cover a wide range of other categories, including marketing, finance, accounting, and many more—providing valuable performance benchmarks across all aspects of your business operations.

Key Insights From Our Research

Our research helped us find several key insights about how SaaS companies identify and address churn risks, as well as the strategies they use to improve customer retention.

Interestingly, most surveyed companies have fewer than 50 employees, meaning many teams are likely balancing customer retention efforts with limited resources.

When it comes to identifying churn risks, 76% of the companies segment and analyze churned customers to identify patterns, while 64% analyze customer feedback and reviews, and 60% monitor product usage and activity.

Pinpointing risk at the individual account level often comes down to recognizing warning signs. Tracking the count of at-risk accounts helps teams prioritize which customers need attention first.

The most common signals include negative customer feedback or public complaints (76%), decreased product usage or engagement (52%), and cancellations or non-renewals (52%).

But identifying risk is just the first step — what happens next is equally important.

Once companies spot potential churn, they often take immediate action by reviewing analytics to better understand the situation and initiating personalized outreach to gather feedback, address concerns, and offer support.

For longer-term churn prevention, companies are getting more proactive. Over the past 12 months, many have focused on collaborating with cross-functional teams to develop holistic solutions and add or improve features based on feedback from churned customers.

Measuring engagement levels also plays a major role in retention. While there’s no single method that works for everyone, 36% of respondents rely on customer surveys and feedback to assess satisfaction and engagement.

Onboarding also emerged as a crucial factor in retention, with 44% of respondents rating their onboarding process as very effective. This suggests that companies investing in clear, helpful onboarding processes are seeing stronger retention rates.

To ensure customers fully utilize their platform’s features, SaaS teams commonly lean on customer education and training (64%) as well as personalized, automated recommendations (56%). Both strategies help users discover the product’s full potential and prevent disengagement.

Finally, when it comes to feature requests, SaaS companies tend to prioritize organization and action.

Most respondents reported using regular review and consideration (72%), proactive implementation of new features (64%), and collecting and organizing requests in an internal project management tool (52%).

This structured approach ensures customer feedback is heard—and acted upon—before it turns into a churn risk.

Most Effective Predictors of Customer Churning

While churn can sometimes feel unpredictable, certain behaviors and patterns often signal when a customer is at risk of leaving.

Here are some of the most effective predictors to watch for:

• Negative Customer Sentiment Based on Call Summaries
• Engagement Level Drop
• Declining Product Usage
• Increase in Support Tickets

Negative Customer Sentiment Based on Call Summaries

Customer calls are often a goldmine of insight, especially when frustration starts to build. Negative sentiment expressed during support calls, account reviews, or success check-ins can be an early warning sign that a customer is considering leaving.

SaaS companies that track call summaries—often using AI tools that analyze tone, language, and key phrases—can flag at-risk accounts before issues escalate.

If a once-positive client starts voicing repeated concerns about product performance, pricing, or unmet expectations, it’s a clear signal that proactive intervention is needed.

Arielle Kimmer of CallTrackingMetrics says that they’ve found that “our AI-generated call summaries are among the most effective predictors of churn. These summaries can often identify unresolved support needs and negative customer sentiment, and signal potential churn months before it’s detected by our account managers.”

Engagement Level Drop

A sudden or gradual decline in how customers engage with your product often precedes churn by weeks or even months. This engagement drop can manifest in various ways, depending on your specific product.

Metrics worth monitoring include:

• Decreased login frequency
• Shorter session durations
• Lower user counts within a customer account
• Declining participation in webinars or training sessions

Particularly concerning is when admin or decision-maker engagement drops while regular users continue normal usage patterns. This often means that the account administrator may be evaluating alternatives.

Andre Oentoro of Breadnbeyond says that they’ve found “that the best indicator of a customer potentially leaving us is their level of engagement.”

“When customers actively participate in the production process, share feedback, and stay in touch, they’re usually happier with our services and less likely to churn. Engaged customers understand the value we offer and are committed to achieving their goals with us.

Conversely, if customers seem disengaged—like taking longer to respond or not participating in meetings—it often signals they’re not satisfied. By keeping a close eye on engagement levels and addressing any concerns promptly, we can reduce the risk of losing customers and maintain strong relationships.”

Mateusz Calik of Delante is another respondent who pointed out that “if the engagement rate goes down, they are going to churn.”

“There’s a very simple rule behind it – if you don’t use something it means you don’t need it or have found a better replacement for it. So I’d also divide churning cases into two categories: people who stopped needing the provided solution and people who weren’t satisfied with the product. With the first category, you can’t really prevent it from happening, but you definitely should pay attention to reasons why your product is worse than the other ones.”

Declining Product Usage

While similar to engagement drops, declining product usage focuses specifically on the core value metrics specific to your product. These are the actions that directly correlate with customers receiving value from your solution.

For example:

• A project management tool might track the number of projects created
• A communication platform would monitor message volume
• An analytics solution would measure report generation and sharing

Companies with sophisticated retention strategies map the entire customer journey and identify ‘success milestones’ for each stage. When customers fall behind on reaching these milestones, it can be a strong indicator of potential churn.

Michal Kierul of INTechHouse says that “if I had to choose one factor, it would be definitely be sustained decrease in product usage.”

“Our platform’s analytics enable us to track user activity levels, alerting us to any notable declines that could signal dissatisfaction or disengagement. This is how we deal with it effectively. We implemented these features after years of analyzing things on our own and trying to approach each issue directly.

But as our business grew, it became increasingly difficult to care about each customer in such detail. Analytics are the best solution to this, as they pose a system that’s manageable and simple. It’s easier to notice bigger mistakes and solutions of problems looking at the current situation from a wider perspective.”

Hyfa K of Empxtrack also mentioned that “one common predictor that tends to be highly effective is declining product usage or activity.”

“When customers start using our product less often or engaging with its features less actively, it’s usually a sign that they might be considering leaving. This makes sense because if someone isn’t using a product as much as before, it could mean they’re not finding it as useful or valuable as they once did. For instance, think about when you stopped using an app on your phone because you found a better one or because it just didn’t do what you needed anymore. It’s kind of like that. When customers stop using our product as much, it’s often because their needs have changed or they’ve found another solution that works better for them.

So, by keeping an eye on how much our customers are using our product and how active they are with it, we can get a good sense of whether they might be thinking about leaving. Then, we can reach out to them, see if there’s anything we can do to help, or maybe even offer them some new features or improvements to keep them happy. It’s all about making sure we’re giving our customers what they need and keeping them satisfied so they stick around.”

Increase in Support Tickets

A sudden spike in support tickets—especially for previously stable accounts—shows growing frustration that could lead to churn. However, the nature and pattern of these tickets matter as well.

An increase in “how-to” questions actually indicates healthy engagement, while tickets about product limitations, bugs, or feature requests might show growing dissatisfaction.

Another important pattern involves the seniority of ticket submitters. When executives who rarely interact with support suddenly start submitting tickets, this often indicates escalating concerns that may lead to cancellation decisions.

“An increase in support tickets, especially regarding the same issues repeatedly, can signify deeper problems with product satisfaction. We try to analyze support ticket patterns to identify and address underlying issues as often and as thoroughly as we can. I think customers will want to tell you what’s wrong, it’s human nature. If they won’t do it out of empathy, they will do it out of anger. So as a CEO, I try to urge my workers to pay close attention to listening to customers (not just literally, but through some non-obvious signals).”

Kamil Rejent

CEO at Survicate

Want to get highlighted in our next report? Become a contributor now

PRO TIP: Want an easier way to track and monitor support tickets and similar CS metrics? You can download our free HubSpot Service (Tickets Overview) Dashboard Template and compile all of your most important customer support metrics in one place.

Most Successful Initiatives to Reduce Churn Rates

Identifying at-risk customers is only half the battle. Here’s what top-performing SaaS companies are doing to actually prevent those customers from leaving:

• Create Loyalty Programs
• Personalized Email Campaigns Targeting Inactive Users
• Offering Special Deals to Users Who Stopped Using the Product
• Building a “Customer Success” Program

Create Loyalty Programs

Loyalty programs create extra value that makes customers think twice before canceling their subscriptions. For SaaS businesses, these programs can take various forms aside from traditional points systems.

They could include tiered membership benefits that activate additional features or support options based on subscription length or exclusive access to beta features for long-term customers.

Many companies find success by gamifying product usage with achievement badges, leaderboards, or status levels that users are reluctant to abandon. Others focus on tangible benefits like usage credits, expanded storage, or complimentary training sessions that increase in value the longer a customer maintains their subscription.

Manoj Kumar of Orderific says that they’ve “rolled out a loyalty program for restaurants using our SaaS technology, aimed at boosting customer retention through rewards.”

“This initiative addressed a critical challenge – helping restaurants build strong, lasting connections with their diners. The program led to increased repeat visits and higher revenue for restaurants. Plus, the insights from this program let them understand their customers better so they can offer exactly what their guests want. As a result, our clients enjoyed stronger relationships with their customers and higher overall satisfaction.”

Personalized Email Campaigns Targeting Inactive Users

Strategic, personalized outreach to dormant accounts can help re-engage users before they churn. These campaigns are successful because they respond directly to specific usage patterns and behaviors.

Re-engagement emails typically focus on specific features the user hasn’t explored, success stories from similar customers, or personalized tips based on the user’s historical interaction with the product.

Irene Graham of Spylix talked about this strategy and mentioned that they “incorporated a personalized email campaign targeting inactive users into our mix of efforts to re-engage them. Via user behavior data analysis, we found out about the customers who have not engaged with our platform for a specific period.”

“After that, we sent a number of emails with specially tailored content and deals to persuade them to re-visit. This measure brought the churn rate down almost by a quarter, also with a 20% increase in re-activation of even the previous non-users.”

PRO TIP: Do you use Mailchimp for email marketing, but still struggle with the platform’s main interface? You can use our Mailchimp Overview Dashboard to keep track of all your key metrics easily, from email open rates to deliverability.

Offering Special Deals to Users Who Stopped Using the Product

When usage has already dropped, special offers can sometimes prevent imminent churn. These actions acknowledge the customer’s disengagement and provide incentives to give the product another chance.

Successful approaches include temporary discounts tied to renewed usage commitments, free access to premium features that might better address the customer’s needs, or credits that extend the subscription period to allow more time for demonstrating value.

Some companies offer complimentary consulting sessions to understand the customer’s needs and reconfigure their product experience accordingly. Others temporarily adjust service tiers to better match current usage patterns without requiring customers to downgrade.

“To prevent customers from leaving, target those who stopped using the software after trying it for free. These are individuals who gave the software a try but didn’t continue using it. Reaching out to them again and offering special deals or extended trial periods can be quite effective.”

Hyfa K.

Marketing Manager at Empxtrack

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Building a “Customer Success” Program

Many SaaS companies assign Customer Success Managers (CSMs) to proactively guide users through onboarding, adoption, and expansion.

This team can spot potential blockers, provide tailored recommendations, and offer advice to ensure customers achieve their goals. Regular check-ins, success reviews, and proactive outreach ensure customers feel supported throughout their journey, so they’re far less likely to leave.

Michal Kierul of INTechHouse says that they’ve “launched a ‘customer success program’ designed to proactively engage with customers at every stage of their journey with our software.”

“We decided to create a separate team that does onboarding and we have a step-by-step guide of displaying and learning different options on your own. We also actively promote giving feedback on features and quality of our service.”

Stay on Top of Your SaaS Performance Data with Databox

Keeping track of key SaaS metrics—from churn rates to customer retention and product engagement —is critical for maintaining healthy growth.

But how do you know if your retention strategies are actually working or if your churn rate is competitive in your industry?

Databox can help.

Databox offers a comprehensive SaaS Benchmark Group that covers key churn metrics and other critical SaaS KPIs. By joining this group, you’ll get instant access to industry-specific benchmarks, so you can compare your churn rate, customer engagement, and retention metrics against similar companies.

This comparative analysis helps you see where you’re excelling, spot areas that need improvement, and refine your retention strategy accordingly.

That’s just one small fraction of our Benchmark Group product – there are 50+ groups you can join for free, covering everything from marketing and sales to customer success and finance.

But that’s just one part of what Databox offers. With Databox Dashboards, you can consolidate all your critical SaaS metrics—including data from tools like HubSpot, Stripe, and Google Analytics—into a centralized, real-time view. No more bouncing between platforms or struggling with manual reports.

We have 130+ integrations – all you have to do is select the key metrics you want to track and generate one-click visuals.

Sign up for a free trial and finally get the insights you need to transform your approach to customer retention and build a more resilient SaaS business.

The post Best Strategies to Identify Churn Risk Factors in SaaS (Insights from 40+ Companies) appeared first on Databox.

At Databox, we’ve been using effective strategies for managing our hybrid workplace. Learn more about how we navigate our diverse team structure.

What is a Hybrid Workplace?

A hybrid workplace combines in-office and remote work, with some employees working remotely and others based in the office. 

This model allows for flexibility in team structure and can accommodate different work preferences and needs. It aims to balance the benefits of remote and in-office work, enhancing overall productivity and employee satisfaction.

Hybrid workplaces have positive effects on work-life balance, employee satisfaction, and productivity. They also enable companies to hire talent from a broader geographical area, reducing overhead costs and promoting environmental sustainability. Having a hybrid workplace ensures business continuity and better health outcomes for employees.

What is a Hybrid Team?

A hybrid team is a group of employees who either work in an office or remotely. 

This team structure allows members to work from different locations and offers flexibility in where work is performed. 

Generally, hybrid teams rely on technology to maintain collaboration, communication, and productivity at a high level. By doing that team members can balance the benefits of remote work with the advantages of in-person interactions.

Workplace Structure at Databox

At Databox, we describe our workplace structure as a hybrid. Some teams work entirely remotely, while others are hybrid and blend both environments.

This setup allows us to access the best talent regardless of location, maintain a diverse team, cover all time zones for customer support, and offer flexibility to our team. 

Our teams’ work is interconnected and relies on cross-team collaboration. We use communication and work management tools to keep things efficient and ensure everyone is on the same page.

Our hybrid teams blend working from home with working from our head office in Ptuj, Slovenia. Every Wednesday is a designated remote day for all team members, with the flexibility to choose an additional work-from-home day each week. 

This approach enhances work-life balance and flexibility, allowing team members to focus on tasks that require deep concentration while working from home. Additionally, having a dedicated remote day ensures that everyone can participate in virtual meetings, fostering better connectivity and collaboration among teammates.

How to Manage a Hybrid Workplace

Managing a hybrid workplace involves balancing the needs of remote and in-office employees to maintain productivity and engagement. This means having a fancy office and free coffee is not going to cut it. 

Effective hybrid team management goes beyond premises, technology, and processes; it requires cultivating a positive culture that promotes collaboration, belonging, communication, trust, continuous learning, and the well-being of our people, or as we like to refer to them, Playmakers. 

It is crucial for leaders to take on the role of guiding their teams in utilizing asynchronous communication, designing optimal workspaces, and recognizing the significance of self-directed learning.

The key to a strong, healthy, and effective remote culture at Databox is creating an environment in which employees feel engaged, connected, and part of the team. We use several strategies to achieve this.

Building Trust

According to Harvard Business Review, a manager’s primary responsibility should be supporting employees. Successful leaders of remote teams must establish a foundation of trust within the team. Trustworthy leaders consistently provide feedback, making team members feel included, valued, empowered, and respected. This is especially important for a hybrid workplace.

Building trust relies on maintaining transparency, sharing important company information, frequent and open communication, and ensuring a secure working environment. Utilizing quick daily standups for syncing operationally and weekly 1-1 meetings to discuss challenges and areas of focus helps build trust and foster success.

Leveraging Technology 

Our approach emphasizes asynchronous work, which means tasks are completed independently rather than meeting-dependently. This efficient and flexible working method emerged post-COVID and has proven highly beneficial for us at Databox. However, it did require us to rely on technology to collaborate seamlessly without the need for constant meetings.

We invest in reliable tools and infrastructure that support hybrid work and allow us to collaborate successfully and efficiently. Communication and work management tools help create seamless collaboration between remote and in-office employees. Our tools include Asana, Confluence, Blogin, Slack, Zoom, Avoma, Hubspot, and more. Keeping things organized, transparent, and accessible to everyone on the team is essential for transparency.

Leadership Training

Training our leaders is crucial since they have an important impact on our company culture. 

Managers who lead hybrid or remote teams undergo several trainings per year, some focusing on leading in a remote environment. Our goal is for all our leaders to understand and use our leadership principles and guidelines for leading remote teams, which we regularly discuss in workshops and learning circles.

Additionally, we educate our leaders on challenges that can come up in a hybrid work environment and work with them so they know how to address them accordingly. We talk about approaching challenges a bit later on in this blog, but here are some general guidelines we emphasize when leading remote teams:

• Maintaining regular contact with team members through Zoom calls and Slack messages build relationships and ensure alignment.
• Establishing a clear structure for remote collaboration, such as regular check-ins, keeps everyone engaged and motivated.
• Trust team members with autonomy to accomplish their work while keep an eye out for isolation, burnout and disengagement.
• Actively developing emotional intelligence, self-awareness, and empathy through activities such as seeking feedback, coaching, and engaging in on-the-job experiences.
• Acknowledging and accommodating diverse working and communication styles by creating an open environment promotes sharing ideas without fear of judgment or criticism.

Regular Check-ins

Scheduling regular check-in meetings in the form of standups and reporting on our tasks and projects keeps our teams aligned and addresses issues promptly.

We do check-ins on all levels:

• One-on-One Meetings: Managers regularly meet with their team members to discuss ongoing projects, address potential challenges, and share feedback.
• Team Standup Meetings: Held either daily or several times a week, these meetings ensure roadblocks are removed promptly, deadlines are adjusted as needed, and the team moves forward together.
• Quarterly Business Reviews: Collaborative meetings that a team holds to evaluate its performance, discuss challenges and achievements, and develop strategies for the upcoming quarter.
• Quarterly All-Hands Meetings: We share our quarterly plans and align on overall goals, keeping everyone on the same page.

This multi-tiered approach ensures effective communication, timely issue resolution, and cohesive progress across the entire organization.

Performance Tracking

We use clear metrics and objectives to track the performance and productivity of our team members, teams, and the company as a whole. 

To track individual performance, we have each of our employees create a Personal Career Development Plan that sets the path to future growth and professional development. We also perform regular self-assessments and manager assessments that help both team members and managers align goals, provide sufficient feedback to move forward purposefully, identify potential challenges, and provide motivation for the team members to stay engaged. 

To track our company’s performance, we put our own product, Databox, to work—because if anyone’s going to keep tabs on our goals, it might as well be our own tool. Our metrics are always visible in our internal Databox account, ensuring the team is continuously updated on how we’re performing against our goals. This practice not only sets us apart from companies that don’t offer such visibility but also embodies our commitment to transparency. 

After all, if we want our teams to embrace transparency, we must lead by example and share our results openly. Using Databox not only enhances our internal operations but also helps us manage our hybrid work environment more effectively. Talk about a full-circle moment!

Common Challenges of Hybrid Workplaces

Working remotely affects people differently. Some may thrive in a remote or hybrid environment, while others need face-to-face contact to feel engaged or efficient.

Lack of personal communication

Communicating purely remotely without a chance for face-to-face communication can be challenging, especially in the area of building trust and creating a personal relationship

How Databox Overcame This Challenge
Keeping in regular contact with team members and building relationships beyond just work tasks helps everyone feel more comfortable, and fosters trust. 

To encourage this, we organize a range of team events that cater to both specific locations and the entire company. We host virtual team-building activities a few times a year, which are all about having fun. These events mix people into different groups so they can get to know each other better in a relaxed setting. In 2023, we took things up a notch with an all-company retreat in Tenerife, Spain, where over 100 of our team members from across the globe came together. 

We also make sure to celebrate the holiday season with Christmas parties at our HQ and encourage teams that can’t make it in person to set up their own festive gatherings.

Lack of engagement

Employees who are never in an office setting or work from home occasionally may experience a drop in motivation when not in the office.

How Databox Overcame This Challenge

To address the drop in motivation among remote employees or those who work from home occasionally, fostering regular check-ins and using structured communication channels are key. Maintaining connection and inclusivity through consistent feedback and recognition of accomplishments is crucial for motivating remote teams. Additionally, setting clear goals and expectations, along with providing opportunities for professional development, are essential practices we emphasize from the start of a career at Databox.

Isolation

The initial weeks can evoke feelings of isolation or loneliness for people who need social interaction or are transitioning from a traditional office setting to a remote role.

How Databox Overcame This Challenge

Leaders of remote or hybrid teams should have high emotional intelligence, self-awareness, and empathy. They should proactively seek feedback, make good use of coaching, and show interest in developing relationships with each team member. Prioritizing informal communication, such as scheduling coffee chats and social calls, can help build relationships.

For new team members, we make a concerted effort to integrate them into our team and foster social connections early on. To facilitate this, we assign each newcomer a Buddy who offers informal support during their first 90 days at Databox. The feedback we receive is overwhelmingly positive, and this program helps our Playmakers feel a strong sense of belonging and comfort, regardless of their work location.

Communication Struggles of Hybrid Workplaces

Not using written documentation or communicating only verbally or privately may reduce productivity and effectiveness.

How Databox Overcame This Challenge

Establishing a clear and structured communication plan for remote collaboration can help overcome communication struggles. We encourage our people to make a habit of documenting and writing things down, such as 1-1 agendas, tasks, and commitments. Communication and project management tools are a good way of keeping everyone on the same page and increasing transparency inside the team. 

At Databox, we streamline our workflow with a suite of tools: Confluence for documenting processes, BlogIn for staying updated on organizational changes, project plans, and product improvements, Slack for daily communication, Asana for tracking projects and tasks, BambooHR for managing HR and operational tasks, and Avoma for documenting and sharing important calls or presentations.

Remote Culture-building Activities

Company Values

Aligning everyone on company values and priorities is crucial for creating a company culture in which everyone feels like they belong, values the same things, and strives for a common goal.

Our primary mission in building a people-first culture is to continuously invest in creating an environment in which everyone is excited to join, motivated to grow, and inspired by the company’s mission. That being said, our six values help us bring together and preserve our team culture, no matter if our team members work fully remotely or in the office. By weaving them into every communication and activity, we are creating an environment in which everyone feels a sense of belonging and support from other team members.

Book Club

One of our initiatives that combines learning with fun is the Databox Book Club. Each month, we read a book (or listen to it on Blinkist—no judgment here) and then discuss its insights in a virtual workshop. This activity allows us to bond, share perspectives, learn from each other, and build relationships with colleagues who share similar interests. It also helps bring our remote team closer by fostering a sense of community and connection, bridging the physical distance with engaging and meaningful conversations. To date, we’ve hosted nearly 30 book club workshops and have no plans to stop anytime soon.

Virtual Team Building Events

By organizing virtual team-building events or workshops, you are encouraging your team to connect on a more personal level, share a laugh, and improve social relationships. At Databox, we strive for one virtual team-building activity per quarter and usually opt for online games that help us connect and relax.

Team Challenges

We strive to create team-wide challenges that everyone can participate in. Our latest initiative, “Databox Moves,” features monthly challenges promoting physical activity and healthy competition among our Playmakers. Each month, we either count our steps, run, cycle, or engage in other activities, fostering both fitness and building bonds. By giving the entire team the option to take part, brings us closer and diminishes the distance between us.

Recognition

Our quarterly All Hands meetings always include recognizing the extraordinary efforts of team members and selecting team champions. We celebrate these individuals, emphasizing that excelling in their role is not tied to their work location. Additionally, we use a Slack channel called “Quick Wins” to showcase examples of Playmakers going above and beyond.

Each holiday season, we organize a virtual activity that embodies our core values and strengthens team connections. For instance, our virtual gratitude wall allows team members to share what they’re grateful for, aligning with our value of showing gratitude and fostering a sense of connection among everyone.

Slack Channels

We encourage our team to create informal Slack channels based on their interests, such as sports, cooking, traveling, or pets. This gives members of various teams the opportunity to connect over shared passions and forge stronger bonds. Making friends is easier when you have common interests!

Conclusion

Managing a hybrid workplace efficiently requires a blend of technology, trust, clear communication, and strong leadership and culture. 

At Databox, we continuously strive to create an inclusive and engaging environment where our Playmakers feel connected and valued, regardless of their work location. By leveraging the right tools, fostering trust, and promoting continuous learning and well-being, we ensure our hybrid workplace thrives. Embracing these strategies can help other organizations navigate the complexities of hybrid work and achieve success. For us, these practices have bridged the gap between remote and in-office work, creating a cohesive, motivated, and engaged hybrid workforce.

The post Effective Strategies for Managing a Hybrid Workplace in Saas Businesses appeared first on Databox.

This principle especially rings true for software-as-a-service (SaaS) companies, where the market changes quickly. As a SaaS marketer, you might be feeling the pressure of adapting to the latest market trends.

If you’re feeling lost, we have some data-backed ideas for you to try. In partnership with First Page Strategy by RevenueZen, we asked SaaS marketers about their current challenges and wins in growth marketing. We gathered data from 100 marketers between September 2023 and May 2024 to offer some guidance. The study is ongoing, so you can also join in.

Here are the insights we have from the survey so far:

• Current Growth Marketing Priorities in SaaS
• Organic and Paid Growth Marketing Benchmarks for SaaS Companies
• 5 Top SaaS Growth Marketing Challenges (and Strategies for Tackling Them)
• 6 Growth Marketing Success Stories in SaaS
• 4 New Growth Marketing Strategies for SaaS Businesses to Try

Current Growth Marketing Priorities in SaaS

We consulted the 100 participating SaaS companies on their priorities in growth marketing.

Among the survey group, 60% work for product-led growth (PLG) companies. In other words, they let their product take the lead in marketing instead of funneling customers through sales before they use it.

Most participants (69%) perform growth marketing without an agency’s help. 31% answered that they currently work with a growth marketing agency.

The companies in our survey reported that their most successful channels for acquiring leads or users were SEO and content.

You’ll see a similar trend in which channels these companies plan to invest in. Survey respondents mainly want to invest more resources in SEO and content. Not many companies plan to decrease investments in any channel in general.

The pattern continues in the channels respondents plan to use in the future. More than 40% of participants named SEO and content channels they want to use soon.

The marketers in our survey mainly look at leads and customers when considering KPIs. They named marketing qualified leads (MQLs) (65%), sales qualified leads (SQLs) (60%), and new customers (59%) as the top KPIs they want to improve right now.

Organic and Paid Growth Marketing Benchmarks for SaaS Companies

Want to see how your SaaS marketing stacks up against the competition and get more valuable insights into the state of organic and paid marketing in SaaS companies?

Then we recommend joining First Page Strategy’s Benchmark Group: Organic & Paid Growth Marketing Benchmarks for SaaS Companies. In this Benchmark Group, members can connect their data sources and compare their performance metrics with other similar SaaS companies — anonymously and free of charge.

According to the Benchmark Group’s data, 1945 contributing members had a median of 4.5K sessions in April 2024. Among those participants, top performers had a four times higher median of 19.25K sessions.

There’s a similar disparity between average and top performers in first-page keywords. The overall median for the 94 metric contributors was 18.5 in April 2024, but the top performers in the same group had a median of 60 first-page keywords.

Instantly and Anonymously Benchmark Your Company’s Performance Against Others Just Like You

If you ever asked yourself:

• How does our marketing stack up against our competitors?
• Are our salespeople as productive as reps from similar companies?
• Are our profit margins as high as our peers?

Databox Benchmark Groups can finally help you answer these questions and discover how your company measures up against similar companies based on your KPIs.

When you join Benchmark Groups, you will:

• Get instant, up-to-date data on how your company stacks up against similar companies based on the metrics most important to you. Explore benchmarks for dozens of metrics, built on anonymized data from thousands of companies and get a full 360° view of your company’s KPIs across sales, marketing, finance, and more.
• Understand where your business excels and where you may be falling behind so you can shift to what will make the biggest impact. Leverage industry insights to set more effective, competitive business strategies. Explore where exactly you have room for growth within your business based on objective market data.
• Keep your clients happy by using data to back up your expertise. Show your clients where you’re helping them overperform against similar companies. Use the data to show prospects where they really are… and the potential of where they could be.
• Get a valuable asset for improving yearly and quarterly planning. Get valuable insights into areas that need more work. Gain more context for strategic planning.

The best part?

• Benchmark Groups are free to access.
• The data is 100% anonymized. No other company will be able to see your performance, and you won’t be able to see the performance of individual companies either.

When it comes to showing you how your performance compares to others, here is what it might look like for the metric Average Session Duration:

And here is an example of an open group you could join:

And this is just a fraction of what you’ll get. With Databox Benchmarks, you will need only one spot to see how all of your teams stack up — marketing, sales, customer service, product development, finance, and more. 

• Choose criteria so that the Benchmark is calculated using only companies like yours
• Narrow the benchmark sample using criteria that describe your company
• Display benchmarks right on your Databox dashboards

Sounds like something you want to try out? Join a Databox Benchmark Group today!

START BENCHMARKING

5 Top SaaS Growth Marketing Challenges (and Strategies for Tackling Them)

What are the top growth marketing problems on SaaS companies’ lists in 2024? We asked this question as part of our survey, and the top answer was budget constraints (52%). The next two most common problems were difficulty measuring return on investment (ROI) and market saturation, both at 44%.

In addition, we asked respondents to tell us about their challenges in the open-ended part of the survey and explain what they’re doing to address those issues. You’ll see many of the same issues mentioned in the closed-ended part of the survey. Let’s dig into each challenge and solution:

1. Planning Ahead for Consultants
2. Growing an Audience
3. Tracking ROI
4. Differentiating Content
5. Improving Data

1. Planning Ahead for Consultants

At Referral Rock, Josh Ho notices a need for better organization when working with consultants and contractors. Ho named the top problem as “Readiness to use external resources with well-defined project briefs and supporting assets so they can be plug and play.”

As for the solution, Ho says, “We need to take more time to align ourselves before we engage with consultants and not just rush in because we’re hungry.”

If this issue resonates with you, you can start managing it by creating templates for the briefs and resources you need to share. For example, if you work with freelance writers often, you can follow these tips to build a clear content brief template.

2. Growing an Audience

For growth marketing to work, you need an audience to convert into leads in the first place. Voissee’s Arum Karunianti has this problem in the company’s growth marketing efforts. “Our primary growth marketing challenge is audience expansion,” Karunianti says.

The Voissee team has a few strategies for resolving this challenge: “To address this, we’re diversifying channels and refining targeting based on data analytics. Implementing personalized marketing campaigns and leveraging social media for brand amplification is part of our strategy. Additionally, optimizing our website’s user experience and incorporating customer feedback aims to enhance conversion rates.”

3. Tracking ROI

It’s not always easy to attribute leads and customers to specific SaaS marketing efforts. Simon Bacher and the Ling team want to get better at it. “It’s nearly impossible to do this, especially when customers find us organically,” Bacher explains.

Ling is starting with new tracking methods. Bacher says, “We’ve begun using UTMs and other tracking methods to link sales to sources. This way we can have a better impression of where to invest further with our marketing efforts.”

UTM parameters help you attribute traffic to specific URLs. Learn how to use them in our comprehensive guide.

4. Differentiating Content

Market saturation is already a problem marketers named in the survey, and that challenge can get even tougher in content’s AI era. NightOwl’s Jayden Ooi calls it “Content Saturation Syndrome.”

Ooi wants to tackle this challenge with interactive content: “We are taking a different approach by focusing on creating immersive, interactive content experiences in an information-rich world. We hope to produce memorable, captivating material that breaks through the noise by utilizing augmented reality (AR) and virtual reality (VR) technologies.”

Ooi adds, “This innovative strategy not only engages our audience but also yields insightful data that helps us to continuously improve our messaging. By utilizing experience marketing to its full potential, we can make sure that our brand is unique and develops a closer bond with customers. This tactic not only deals with content saturation but also accelerates our expansion by turning inactive customers into engaged players in our brand’s story.”

Alex Boyd, Founder & Chairman of RevenueZen, also highlights the importance of balancing AI integration with maintaining content authenticity:

“One of the biggest challenges SaaS marketers are facing is the dual challenge of AI: the challenge of wanting to use it for efficiency, without their content losing its soul. And on the other hand, the challenge of their CEO and CFO pushing them to use it more and more, to cut costs.

The insight that has emerged lately from our work with SaaS VPs of Marketing is that AI is a fantastic tool for the “content operations” side of the workflow: gathering insights from SMEs, editing video, helping manage projects, gathering research, tabulating raw data into new formats. But the best marketing leaders are ruthlessly relying on insight-driven content, to win the hearts and minds of their buyers — and they’re focusing on the distribution of those insights, whether that’s through SEO or paid or organic social or any other channel.”

Alex Boyd

Founder & Chairman at RevenueZen

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5. Improving Data

Sean Lauer at Instruqt has a straightforward but crucial challenge and solution: “Our biggest challenge is a lack of data to show us what’s really working to drive qualified leads. We need to clean up our data and simplify our strategy so we can better track progress and measure ROI.”

A data analytics tool like Databox can help with this process by automatically organizing your data once you set up dashboards. From there, you can follow the basics of analyzing data to understand your progress in your growth marketing endeavors.

6 Growth Marketing Success Stories in SaaS

In addition to marketers’ current challenges, we can also learn from the success stories they have to share. If a certain tactic succeeds for another marketer, it could also work for you, or you might have a lesson to learn related to its approach.

Let’s look at six success stories shared in our survey:

1. Sequel.io: An Iconic Event Strategy
2. Spacelift: More Effective Marketing Spend
3. Content Whale: A 16-Point Increase in DR
4. JetDocs: Cold Emails, Hot Results
5. Gotomyerp: Conversion Boosting Through Social Proof
6. Grazitti Interactive: Targeted Emails for Rich Engagement

1. Sequel.io: An Iconic Event Strategy

According to our survey, SaaS marketers have SEO and content on their radars. But less common channels like event marketing can also spark success, as Sequel.io’s Allie Smith shares.

“We’ve really doubled down on our event strategies – in-person and virtual,” Smith says. “Our weekly webinar series has grown from a mere experiment to a fully-fledged conversion engine, fueling 80% of our pipeline. It’s become so well-recognized that people come to us asking to be on the series.”

Smith continues, “We’ve also turned our strategy into a playbook that anyone can leverage to create a scaleable webinar series after getting so many requests. This virtual series has gotten to a point where it’s so well recognized and has even taken an in-person form to create even more valuable content for our audience and build lasting relationships with customers, partners, and thought leaders in our space.”

You don’t necessarily have to try event marketing to try this strategy. Consider what skills and resources you have that others in your niche don’t, and build a highly targeted marketing channel based on those strengths. 

2. Spacelift: More Effective Marketing Spend

The top challenge mentioned in the closed-ended section of our survey was budget constraints, and we can look to Spacelift as a company that overcame it.

Let Kate Wojewoda-Celinska explain: “Our most recent win in growth marketing was carefully optimizing our marketing spending. By testing and adopting data-driven strategies and adjusting our marketing campaigns based on real-time analytics, we were able to relocate our budget more effectively towards the best-performing ads. This approach led to a significant reduction in cost per acquisition and an increase in conversion rates.”

Another strategy for managing spend is cutting costs in other areas, and businesses can try one or both tactics. We examined how small businesses use both methods in this guide.

3. Content Whale: A 16-Point Increase in DR

Domain ranking, the metric search engines use to measure your website’s authority, has become incredibly influential to SEO results in recent years. The Content Whale team recently increased their score through an intensive process. Bhavik Sarkhedi says, “One of our most significant recent achievements in the realm of growth marketing has been the remarkable improvement in our website’s Domain Rating (DR) from 26 to an impressive 42. This achievement is the result of a comprehensive initiative that focused on strengthening our SEO, blog content, and link-building efforts.”

According to Sarkhedi, “The initiative began by identifying areas for improvement in our existing content and website structure. We conducted a thorough SEO audit to pinpoint opportunities for optimizing on-page elements, enhancing meta tags, and improving keyword targeting. Concurrently, our content team worked diligently to produce high-quality, informative blog content that addressed the pain points and interests of our target audience. This content was not only optimized for SEO but also designed to provide genuine value to our readers.”

Link building also played a key role in this growth. “We actively sought opportunities to secure high-quality backlinks from reputable websites within our industry. These backlinks not only bolstered our website’s authority but also increased our visibility in search engine results,” Sarkhedi says.

All of these efforts led to impressive results: “The combined efforts of our SEO, content, and link-building teams resulted in a substantial boost in our website’s DR. As a result, our website’s impressions doubled, and we witnessed an impressive 10% increase in clicks for our target keywords. This success validates the effectiveness of our growth marketing strategy, emphasizing the importance of holistic and data-driven approaches in achieving our goals. Moving forward, we plan to build upon this achievement and continue refining our strategies to drive even more significant growth for Content Whale.”

Your ability to pull off this strategy will depend on the time and resources you have at hand. Out of the many tactics you have to improve your domain authority, you can try some of the top ways to get referring domains first. The number of referring domains you have affects your DR, and there are many angles you can take to earn them.

4. JetDocs: Cold Emails, Hot Results

Cold emails can be a polarizing topic in SaaS marketing and sales. Some folks swear off of them, while others find a lot of success with this tactic. But Sajwal Pageni and the Jetdocs team discovered they could fit into the latter group by targeting an audience they know needs their product.

Pageni says, “We recently started cold email campaigns where we started emailing around 1000 leads a day that fit our ideal customer profile. It was successful because the profile we have are facing the problems we are solving, but whenever they look online for solutions, they get generic search results that aren’t niched down to their industry. So, when [we] email them offering help in front of them, it’s usually a eureka moment for them.”

Keep these two factors behind the strategy in mind: the Jetdocs team targeted a niche audience, and they sent emails at a large volume. This method takes a lot of careful research and effort to pull off.

5. Gotomyerp: Conversion Boosting Through Social Proof

Social proof such as customer feedback on rating and referral platforms is another underrated avenue for lead conversion. Gotomyerp used it to greatly increase its lead conversion rate. Tom Vota shares:

“One recent win our company got from growth marketing was converting 40% of leads into customers. It was one of our best accomplishments in the past few years.”

Tom Vota

Marketing Director at Gotomyerp

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Here’s how the team executed this strategy: “We were able to achieve this through referral marketing. We have an extensive base of old and loyal clients. We requested them to recommend our service on social media. We also asked them to share their reviews and experiences on our website. As existing clients recommended our service, new clients could trust our brand.”

If your business has a base of loyal existing customers, try asking them to review and recommend your software on platforms like social media, Google, G2, and Trustpilot.

6. Grazitti Interactive: Targeted Emails for Rich Engagement

Some companies find better success with targeted warm emails over cold emails, and Grazitti Interactive is one of those businesses. Raja Setia says, “Our most recent significant win in growth marketing came from a targeted email marketing campaign. We initiated a personalized email series for existing customers, with the goal of re-engaging them and encouraging repeat purchases.”

Setia chalks up this success to multiple factors:

• “Segmentation: We meticulously segmented our customer database based on their past purchase history, preferences, and behaviors. This allowed us to send highly relevant and personalized emails to each group, increasing the chances of conversion.
• Engaging Content: The email series consisted of well-crafted, value-driven content. We offered exclusive discounts, provided helpful product tips, and shared success stories from other customers, creating a strong incentive for customers to return and make additional purchases.
• Timing and Automation: We used marketing automation tools to send emails at the right time, based on the customer’s interaction with our website or previous emails. This ensured that our emails reached customers when they were most likely to convert.
• Performance Tracking: We closely monitored the campaign’s performance, including open rates, click-through rates, and conversion rates. This allowed us to make data-driven adjustments in real time, optimizing the campaign for better results.”

According to Setia, Grazitti Interactive had a “significant increase” in customer loyalty and purchases from existing customers. You could see a boost, too, if you take the time to personalize and monitor your email campaign.

4 New Growth Marketing Strategies for SaaS Businesses to Try

Now that we know what is and isn’t working for growth marketing in SaaS right now, let’s look to the future. We asked survey participants about the SaaS growth strategies, channels, and tactics they’re excited to try, and they had four to share:

1. In-Depth SEO Strategies
2. Content Partnerships
3. Content Libraries
4. Targeted Link Building

1. In-Depth SEO Strategies

With so many respondents naming SEO as a channel they currently use or plan to use, it’s no surprise that it came up in the open-ended section of the survey. Two respondents elaborated on why they plan on investing in SEO soon.

At Rally UXR, Lauren Gibson wants to expand the brand’s SEO content with the help of freelancers. Gibson says, “We’re not very intentional about SEO, but we are trying to increase our blog content by adding a few more freelancers. (We’re a marketing team of 2 and only recently grew from a marketing team of 1 – we’re also only a 13-person startup that was founded about 2.5 years ago). We’re excited to explore SEO more and utilize these freelancers to really build up our content and start ranking more for keywords.”

BuddyCRM’s Milo Cruz has a broader approach in mind: “We’re excited to dive into SEO. We’ve only been doing the bare minimum so far, but we see that there’s a lot of untapped potential there. We’re hiring more experts to help with link-building, conversion optimization, and technical SEO to really give our online presence a boost and get more leads coming in.”

Whichever angle you come from, make sure to familiarize yourself with the most important SEO KPIs to track and which ones are the most relevant to your business.

2. Content Partnerships

Some respondents named a technique that overlaps with SEO – content partnerships. One of the main draws of collaborating with other companies on content is the backlinks you’ll receive, but you’ll also get more brand exposure overall.

Garden already had success with sponsored content in the past, so Valerie Slaughter hopes to nurture its content partnerships even more moving forward. Slaughter says:

“Our most successful initiative has been with a sponsored content placement in a reputable third-party website. I think it was so successful because our articles were educational and didn’t feel like marketing copy, which helped make our audience feel comfortable enough to learn more. This initiative increased leads by 40% QoQ. Moving into this quarter, I’m excited to try more content partnerships as this is a channel that we’ve historically underutilized and I think shows big opportunities for growth.”

Valerie Slaughter

Creative Content Strategist at Garden

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At AlphaSense, Kevin Lissandrello also wants to collaborate with more content partners. Lissandrello says, “I’m excited to partner with tier 1 publications across the industry, for our unique audiences, to push our brand, to drive awareness, and [to] drive leads. We have partners that are great, and we are doing okay getting our brand out there…I want to be partnered with the best outlets in the market to let our audience know we are a trusted source.”

3. Content Libraries

It’s one thing to invest in content, but once you have a large body of articles, you may have to think about how you present it. Boast AI’s Paul Davenport wants to repackage the company’s content for better reading.

Davenport says, “We’re embarking on a complete website overhaul that will house our robust resource library, including our 40+ episode podcast, extensive blog, and thought leadership guides. This will make it easier for our customers and the general public to leverage our expertise as R&D and tax credit leaders, while hopefully speeding up the time it takes for decision makers to navigate key areas of our website.”

4. Targeted Link Building

Link building appears again as a SaaS growth strategy to try, but this time with a targeted approach. “We’re particularly excited about diving deeper into high-quality link building as a new growth marketing strategy,” says Kamil Rejent from Survicate. “Now, I know link building isn’t exactly a ‘new’ tactic, but the way we’re approaching it is unique and tailored to the SaaS industry’s nuances.”

Here’s how Rejent explains the tactic: “ We’re not just looking for any links; we’re targeting high-authority domains within the customer feedback and user research sectors. The aim is to not only improve our SEO rankings but also to establish Survicate as a thought leader in these spaces.”

Rejent says, “What excites me about this strategy is its potential for a dual impact: boosting organic reach while simultaneously enhancing brand credibility. In the SaaS world, trust is currency, and having your content linked by reputable sources can significantly elevate your brand’s authority. It’s a long-term play that aligns perfectly with our focus on building lasting relationships with our customers. We’re leveraging our existing content — like in-depth guides, case studies, and research reports — as linkable assets. We’re also using our existing partnerships as grounds for potential link building.”

Rejent concludes, “I think the key to successful link building, especially in a saturated market, is to offer genuine value. It’s not about gaming the system; it’s about contributing meaningful insights and solutions that others find worthy of sharing. And given our expertise in customer feedback and user research, we’re in a prime position to do just that. So, in a nutshell, high-quality link building is our next big bet in growth marketing. It’s a strategy that promises not just quick wins but sustainable growth, and that’s why it’s so exciting for us.”

Track and Improve Your Growth With Databox and Benchmark Groups

One common question that comes up when you’re working on your growth marketing strategy is, “Am I growing at the same pace as my competitors?” It took heavy research to answer this question in the past, but now you have a convenient resource available: Databox Benchmark Groups. In exchange for anonymously sharing your Databox metrics, you get to see the median of everyone else’s metrics in the group.

Best of all, as part of our research with First Page Strategy by RevenueZen, we have a Benchmark Group made just for SaaS growth marketing. Join the Organic & Paid Growth Marketing Benchmarks for SaaS Companies group to see how you compare to other SaaS companies.

The post SaaS Growth Marketing Challenges and Wins in 2024 appeared first on Databox.

Why? Because converting them to paying customers becomes way easier.

Implementing a user activation framework is part of the Product-Led Growth (PLG) strategy and can significantly transform your business results. 

The good news is that it’s not hard to do.

What is Product-Led Growth?

Product-led growth is a business strategy that makes the product the main driver of acquisition, conversion, and expansion. PLG companies focus on providing a great product experience that attracts, converts, and retains users. Examples of PLG strategies include free trials, freemium versions, or straightforward onboardings that let the user experience the value of the product as soon as possible.

To prioritize customer impact and deliver exceptional value to our users, we use the Customer Lifecycle Framework (CLF), designed to streamline customer acquisition, onboarding, and retention across their journey with our product.

What is an Activation Framework?

An essential part of the CLF is the Activation Framework, an internal framework that helps us measure and improve the activation of new users. It enables us to effectively measure user engagement, identify leads that have derived real value from our product, and acquire and retain more customers. 

In this blog, you’ll learn how activation strategies can deliver value to your users while driving company growth, based on the framework we used at Databox.

How to Unlock Success with an Activation Framework

Activation is a set of self-serve flows designed to facilitate the initial discovery of our product’s value by new users. It is measured differently by different companies, for example Slack, Dropbox and Hubstpo use different activation frameworks depending on their definition or a product-qualified lead. Read more about their strategies in this article.

What is an Activation Score Framework?

An activation score framework is a method used to measure how effectively users are engaging with key features of a product. It helps identify actions that users must take to experience the core value of the product and be considered activated users.

Activation scores are typically based on user behavior. They are used to optimize onboarding and improve user retention.

At Databox, we started tracking activation scores seven years ago after introducing a basic metric called Product Qualified Lead (PQL). 

What is a Product-Qualified Lead (PQL)?

A product-qualified lead (PQL) is a user who has interacted with your product through a free trial or limited access and experienced its value. PQLs are more likely to convert into paying customers because their interest is based on firsthand experience with the product.

Activation and PQLs are closely connected. Activation refers to users reaching milestones that show they experienced the product’s value, while PQLs are users who show a likelihood of conversion. Activation scores are used to predict PQLs, which helps the company target and nurture leads with the highest potential for conversion.

There are also other types of leads that can be tracked, but not to be mistaken with PQL.

What is a Marketing-Qualified Lead (MQL)?

A marketing-qualified lead (MQL) is a user who has shown interest in your product via marketing efforts. MQLs usually engage with marketing materials and are not ready for direct sales contact.

What is a Sales-Qualified Lead (SQL)?

A sales-qualified lead (SQL) is a user that the marketing and sales teams have identified as ready for direct sales engagements. SQLs usually showcase a high level of interest and are prioritized by the sales teams.

What is the difference between a PQL, SQL, and MQL?

The difference between PQL, MQL, and SQL is in how they are identified. 

• MQLs are identified through marketing activities, such as a lead who downloads an eBook. 
• SQLs are identified through a combination of marketing and sales processes and are qualified as ready for a conversion, such as a lead who fills out a demo request. 
• PQLs are identified through product usage and are further along in the buying process, such as a lead who uses a product’s core features during a free trial.

What is a PQL Score?

A PQL score is a tool in a product-led growth strategy that provides a clear and quantifiable way to measure the potential of each lead based on their interaction with the product. By focusing on PQLs, companies can improve their sales and conversion rates.

Using PQLs and Activation Milestones to Drive Growth at Databox

At Databox, our initial strategy of using PQLs and measuring their scores was effective. However, as our company and market demands evolved, we realized we needed to update it.

We wanted to align our approach with our evolving product and business goals. Consequently, these are the lessons that we’ve learned:

• Our initial PQL definition lacked nuance and predictive power
• The introduction of new features demanded a more comprehensive scoring model, giving rise to the activation score
• Enhanced tracking quality and data acquisition enriched our insights and sparked demand for more stages in the activation process

Due to the reasons above, we expanded our activation framework by including Activation Milestones (Setup, Activated, Habit), PQLs, and Activation Score, which measures the degree of user activation on a scale of 0-100.

Activation is critical for every new Databox user as it presents the greatest opportunity for optimization. To maximize that goal, we use an approach with three activation steps (Setup, AHA, and Habit) and the PQL score. 

The new framework helps us achieve our primary goal of delivering value to users as quickly as possible and plays a vital role in driving the success of Databox, primarily through customer acquisition (new MRR) and retention (retention rate). It enables us to establish user engagement flows, conditions, and tactics for engaging with our users in the early stages of their journey.

At Databox, our activation journey begins with a series of defined steps, each crucial for users to experience the full value of our product.

Setup

The first step in the activation journey is called “Setup” and involves the user performing the necessary actions to experience the value our product can offer them. Since Databox is an analytics app where users work with their own data, connecting the first data source, creating the first dashboard (generated automatically during onboarding), and editing that dashboard are key steps to unlock further value.

AHA

This moment marks the point where users experience the “aha” moment and realize the promised (core) value of our product. Through quantitative and qualitative analysis, we’ve determined that creating at least two dashboards using multiple sources (all data in one place) is essential to unlock the more tangible benefits of Databox for the user.

Habit 

The final step encompasses a frequency of component use, where the user must be active for at least seven separate days, complete the “AHA”, and has an activation score above 40. This step emphasizes consistent engagement and sustained activation for long-term user success and retention.

PQL

In addition to our activation steps, we also use the Product-qualified leads (PQL) methodology. PQL is a potential customer that fits our target customer profile and has engaged with our product. In general, a PQL is someone who has:

• Reached a certain level of engagement within our product (has an activation score >40) and have confirmed purchase intent and suggested a sales opportunity. 

• Demonstrated characteristics that make them a good fit for our product based on demographic and firmographic data.

How to Improve Customer Focus with Activation Score

The section above discusses the activation steps, with which we measure if we delivered the value to the users as quickly as possible. Alongside these steps, we also employ the Activation Score to measure the extent of each user’s activation on a scale from 0 to 100. The Activation Score is designed to evaluate the level of engagement and quality of each interaction with our product, providing valuable insights into prospects’ behavior, which guides our customer acquisition strategy and enables us to enhance key metrics such as signup-to-paid conversion and retention rate. 

The activation score is calculated based on a range of actions performed within the product relating to its features and functionalities. Each action contributes points to the overall score, with the distribution determined by factors such as adoption rate, signup-to-paid conversion rate, and retention rate associated with each specific event.

Each user is assigned a score ranging from 0 points to 100. A score of 0 suggests a low likelihood of conversion and retention. In contrast, a score of 100 indicates that the user has fully embraced the value, has been activated, and is highly likely to convert and remain engaged.

In other words, the higher a user’s engagement, the higher their score. The chart below shows how strong a predictor of lead conversion the activation score is for Databox. It validates the effectiveness of our scoring system in accurately segmenting new signups based on their likelihood of conversion.

How Activation Framework Helps Our Teams

The Activation Framework serves as a cornerstone for our teams and enables them to segment and prioritize their initiatives based on the Activation metrics and scores. This empowers us to find the most promising accounts to optimize and engage with and when to do so. 

In general, our Activation framework helps our teams get valuable insights, including:

• early assessments of the quality of signups generated through different marketing tactics and channels
• initial assessments of the impact of new onboarding initiatives and strategies
• guidance for our Go-to-Market teams on lead prioritization, optimal timing for outreach, and required follow-up persistence.

This boosts our teams’ efficiency by improving conversion rates and enhancing overall performance while reducing the resources required to achieve better results. Here is a quick overview of our teams’ involvement in that process.

The Product team is presented throughout the whole customer lifecycle by continuously improving the product’s user experience and interface, providing support through in-app campaigns, improving Activation metrics with the PLG methodology by personalizing the onboarding experience, and conducting growth experiments to increase the customer base.

The Product Marketing Team plays a vital role in guiding users through the various stages of activation with targeted nurture campaigns. These campaigns are delivered through a variety of channels, including email and in-app messages, and provide relevant and valuable information to users, such as guidance on product usage, its benefits, and real-life case studies.

The Customer Support team engages with users through in-app chat and email, answering questions with high responsiveness, collecting and sharing user feedback on the roadmap, troubleshooting cases, escalating issues to the Technical Support team, leading the conversation towards setup services or booking calls to drive the adoption of new users and ensure a smooth customer experience. 

The Sales team is responsible for guiding prospects through the Trial period by prioritizing outreach based on Activated and PQL thresholds. They conduct video calls to help users reach business objectives, assist with plan selection, and offer account setups, new features, and paid services before handing off to the Customer Onboarding team to continue the setup process and evaluation of the product.

The Onboarding team is responsible for working with customers after the initial 90-day evaluation period. Each customer is assigned an Account Manager who proactively engages with them to introduce features, offer assistance to improve their product usage and guide them to reach PQL or either CORE CLF stage. 

The Account Management team proactively works with “older” paying customers identified as risk (not activated, etc.) of churning to eliminate potential issues and ensure a positive customer experience.

The Impact of Activation

In conclusion, the Activation framework drives process optimization and superior outcomes across teams. By prioritizing leads based on Activation metrics, we optimize funnels and effectively target potential customers with the highest conversion potential, ultimately leading to improved conversion rates, better retention, increased revenue, and customer growth. 

At Databox, we remain dedicated to refining our strategies and innovating to deliver exceptional value to our customers, with Activation being just one example of our commitment to success in today’s competitive landscape.

The impact of product-qualified leads or PQLs in our activation strategy is part of a series of technical articles that offer a look into the inner workings of our technology, architecture, and product & engineering processes. The authors of these articles are our product or engineering leaders, architects, and other senior members of our team who are sharing their thoughts, ideas, challenges, or other innovative approaches we’ve taken to constantly deliver more value to our customers through our products.

Tadej Kelc is a Data Analyst at Databox. He specializes in data quality and collection, providing customer insights, and optimizing business operations to drive business growth. He works with teams across the company to identify trends, detect anomalies, and unlock opportunities for enhanced performance. His main impact is helping our customers adopt our product, optimize performance, and reach goals.

Stay tuned for a stream of technical insights and cutting-edge thoughts as we continue to enhance our products through the power of data and AI.

The post How to Boost SaaS Growth with PQL Scores and Activation Strategy appeared first on Databox.

Your growth rate is a telling indicator of how far you’ve come in business and how soon you’ll be able to break even on investments. And if your company is shooting for an exit soon, your growth rate can be the difference between a few hundred thousand vs millions in investment dollars.

With SaaS, the stakes are even higher. Since the payment model doesn’t front-load all costs, the average SaaS company must sustain growth and keep customers for longer periods to break even. Hence, you need to benchmark growth to understand how you’re doing and if you need to make any adjustments. 

It helps predict future growth and should influence all decisions including hiring, operational setup, and resource allocation. 

Read on to learn:

• How to calculate growth rate in SaaS
• What the most important SaaS growth metrics are
• The best tools for calculating and analyzing SaaS growth rate

Let’s get started!

How Do You Calculate Growth Rate? 

The formula for calculating growth rate for any metric is:

You’ll need two figures: your initial value, and your final value. Let’s take revenue growth, for example. If you wanted to see how far you’ve grown in the last year, you’d measure your last year’s revenue against this year’s.

So if:

• Revenue from April 2020 – March 2021 (Initial Value) = $250,000
• Revenue from April 2021 – March 2022 (Final Value) = $320,000

Your revenue growth rate would be:

($320,000 — $250,000) / $250,000

That’s a 28% revenue growth rate in one year.

You can also calculate your growth rate over the specified time period based on the initial and final value with Databox’s Percentage Growth Calculator.

What Are the Most Important Growth Rates in Saas?

In our survey, Customer Lifetime Value (CLV), Costs of Acquisition (CAC), and Monthly Recurring Revenue (MRR) topped the list of important SaaS growth metrics to measure. 

Out of our 47 respondents, 35 identified as SaaS companies (54.29%) and Agencies/Consultants working for SaaS (45.71%). Here are all the metrics they voted most valuable for SaaS:

1. Customer Lifetime Value
2. Costs of Customer Acquisition
3. Monthly Recurring Revenue (MRR)
4. Signup Rate
5. Annual Recurring Revenue (ARR)
6. Churn Rate
7. Expansion Revenue
8. Net Promoter Score
9. Leads Generated
10. Annual Contract Value (ACV)
11. Lead Velocity Rate (LVR)
12. Opportunity Stage Forecasting

1. Customer Lifetime Value

The customer lifetime value (CLV) is a measure of the total revenue a business can expect from a single customer account over the course of the relationship.

It’s calculated as:

Customer Lifetime Value = (Customer Value * Average Customer Lifespan) 

The customer lifetime value is an important growth metric because it indicates product quality according to Ethan Drower of CiteMed. Drower says “We focus on lifetime customer value for the long term. The idea here is that we want to always see progress with our marketing channels (topline revenue growth), but need to be mindful of product quality (churn) and, thus, the customer LTV value becomes incredibly important.

When it came to CiteMed, we knew we were on the right path when our existing marketing channels were showing double-digit percentage growth each quarter, and our customer LTV was increasing along with it. This showed us that both our marketing efforts were successfully scaling, and our product quality/customer satisfaction was at the very least keeping pace.”

2. Costs of Customer Acquisition

Customer acquisition cost (CAC) is the amount of money a company spends to get a new customer. CAC helps measure marketing’s effectiveness. 

To calculate your CAC, you’ll need to add all the costs associated with winning new customers, i.e marketing, advertising, sales personnel… and divide that by the number of customers gained.

Customer Acquisition Cost = Cost of Sales and Marketing / Number of New Customers Acquired.

LuckLuckGo’s Ryan Yount says “we mainly focus on CAC when evaluating our performance. For example, we gauge how much money we spend on marketing, advertising, sales, and salaries, among others, and compare the number of customers we have gained over a certain period.”

3. Monthly Recurring Revenue (MRR)

The total amount of recurring revenue your business generates from all the active subscriptions in a given month is called monthly recurring revenue (MRR). This includes recurring charges from discounts, coupons, and add-ons, but not onetime fees.

It’s calculated as:

MRR = Number of subscribers under a monthly plan * average revenue per user (ARPU).

MRR helps you understand how well users love your product. It also helps you predict growth.

Jerry Han of PrizeRebel says “We check our monthly recurring revenue. It helps us understand if our software is truly meeting standards for not only us, but for our customers. We need our surveys to be quick and easy, and measuring our MRR gives us a good estimate of how accessible they are.”

Similarly, FindTheBestCarPrice.com’s Geoff Cudd says “For my performance analysis, I pay particular attention to the Lead Velocity Rate (LVR) and Monthly Recurring Revenue (MRR) growth rates (MRR). With the use of these analytics I’m able to see how well my business is doing in terms of revenue growth, forecasting future revenue, and tracking how many website visits become actual customers.”

Key Insights: Track your MRR with Databox SaaS MRR Drilldown for Leadership Dashboard template. This interactive dashboard is the perfect fit for financial teams and accounting professionals.

PRO TIP: Are You Tracking the Right Metrics for Your SaaS Company?

As a SaaS business leader, there’s no shortage of metrics you could be monitoring, but the real question is, which metrics should you be paying most attention to? To monitor the health of your SaaS business, you want to identify any obstacles to growth and determine which elements of your growth strategy require improvements. To do that, you can track the following key metrics in a convenient dashboard with data from Profitwell:

1. Recurring Revenue. See the portion of your company’s revenue that is expected to grow month-over-month.
2. MRR overview. View the different contributions to and losses from MRR from different kinds of customer engagements.
3. Customer overview. View the total number of clients your company has at any given point in time and the gains and losses from different customer transactions.
4. Growth Overview. Summarize all of the different kinds of customer transactions and their impact on revenue growth.
5. Churn overview. Measure the number and percentage of customers or subscribers you lost during a given time period.

If you want to track these in ProfitWell, you can do it easily by building a plug-and-play dashboard that takes your customer data from ProfitWell and automatically visualizes the right metrics to allow you to monitor your SaaS revenue performance at a glance.

You can easily set it up in just a few clicks – no coding required.

To set up the dashboard, follow these 3 simple steps:

Step 1: Get the template 

Step 2: Connect your Profitwell account with Databox. 

Step 3: Watch your dashboard populate in seconds.

Try this template

4. Signup Rate

Sign Up rate measures the percentage of website visitors who convert from your call to actions.

It is calculated as

ƒ Count(Sign Ups) / Count(Sessions) 

When asked what SaaS growth rate metric gives the most valuable insight, Chosen Data’s Branko Kral says “The rate of new active users. So a conversion rate is measured as the % of people who converted from marketing to signing up but also using a feature inside the product for the first time. There are other important metrics too, of course, but this lets us decide in favor of marketing that generates people who actually use the product.”

Palkita Gautam of DataToBiz shares similar views “Acquisition – where we focus on getting more users to signup on PrepAI via paid ads and content marketing. We set and achieve a 2x target of increasing our user base each month.”

Related: How Zapier Grew Signups and Activations from their Blog by 400 Percent in 1 Year

5. Annual Recurring Revenue (ARR)

ARR is the revenue generated from recurring subscriptions for an entire calendar year. It’s calculated as 

ARR = (Overall Subscription Cost Per Year + Recurring Revenue From Add-ons or Upgrades) – Revenue Lost from Cancellations.

At Explainerd, Natasha Rei says they “measure ARR to check how well our company is growing its customer base. If we see a higher ARR, that means we succeed in acquiring new customers.”

6. Churn Rate

Churn rate—also called attrition rate—refers to the percentage of people canceling their subscriptions over the course of a period of time.

Customer Churn Rate = (customers beginning of period — customers end of period ) / customers beginning of period

Measuring customer churn will help you understand how satisfied your customers are, and where you can make improvements.

“We analyze customer churn so we can calculate how many are canceling subscriptions in a given period. This metric is important because it indicates how well the company retains its customers. The lower the churn rate, the more successful the company, keeps its customers.” says Explainerd’s Rei.

Similarly, William Donnelly of Lottie says “We use Churn as the growth rate metrics to analyze the performance of SaaS.

Generally, we consider two types of churn: Revenue Churn and Customer Churn.

Customer churn evaluates the number of customers or accounts abandoning our services every month as a percentage of your overall customer count. Whereas, as a percentage of total revenue, revenue churn measures the amount of revenue paid by the customers abandoning our services every month.

As our company is in the growing phase, measuring churn becomes critical. For example, if we are experiencing a churn rate of 3% supposedly, it means that we are losing 30,000 customers every month. The replacement of such a big number of customers monthly is not at all sustainable for businesses.

Therefore, for our business, this metric becomes more important when we are losing customers. Our goal is to lower the churn rate to a point where we can start talking about this metric with respect to customers and revenue retained.”

Related: Save Your Business From Churn: 9 Churn Risk Factors to Identify

7. Expansion Revenue

When customers add new paid services to an already paid account, that extra revenue is called Expansion revenue. 

Expansion MRR rate= [(Expansion MRR at the end of the month) — (Expansion MRR at the beginning of the month) / by the Expansion MRR at the beginning of the month] x 100

Expansion revenue growth will decrease customer churn and increase LTV. This will in turn improve other SaaS metrics.

8. Net Promoter Score

Net Promoter Score (NPS) is a widely used measurement that asks respondents to rate how likely they are to recommend a product, service, or company to a friend or colleague.

NPS = Total % of promoters  – total % of detractors 

The Net Promoter Score helps businesses evaluate how well their service compares with their competitors. Net promoter scores can be used to identify any problem areas, improve customer experience, monitor loyalty trends, and increase revenue through referrals and upsells. 

9. Leads Generated

Lead generation is attracting and nurturing prospects with the goal of converting them into customers. 

For Podcast Hawk, Ray Blakney and the team focus on generated leads “We are a software that helps people get booked as a guest on podcasts on auto-pilot. By tracking the leads generated — i.e. podcasts booked — we are able to track all our most important metrics (new user sign-ups, system usage, churn, user success) all by seeing just one metric.” Blakney says.

Related: Blogging for Lead Generation: 23 Best Ways to Generate Leads from Your Blog

10. Annual Contract Value (ACV)

Annual Contract Value is the average revenue per customer contract for a year and excludes one-time fees.

ACV = Total monetary value ($) of the whole contract / Total contract length in years 

When one customer signs a 5-year contract for $30k, your ACV is $6,000. Your ACV would also be $6,000 if you had 100 customers paying $500 per month.

Claire Westbrook, LSAT Prep Hero says “At my company, the annual contract value (ACV) growth rate is the most important metric to track when measuring performance. The ACV growth rate indicates how quickly we are growing our customer base and, more importantly, how much revenue we’re generating from those customers. A high ACV growth rate means that we are acquiring new customers at a rapid pace and that those customers are spending a lot of money on our products and services.”

11. Lead Velocity Rate (LVR)

Lead velocity rate is a measure of the growth in qualified leads from one month to the next. 

Lead Velocity Rate = [(No. of Qualified Leads This Month – No. of Qualified Leads Last Month) / No. of Qualified Leads Last Month] x 100 

LVR can be an excellent predictor of a company’s growth and revenues if it is calculated from month to month. 

Ian Sells, RebateKey says “Lead Velocity Rate (LVR) It gives you a clear picture of the real-time growth of your qualified leads month by month. This gives you a glimpse of the future, giving you a more accurate projection of business growth. This is very important for most SAAS companies since we rely on recurrent revenue. This allows us to plan ahead more accurately and acts as a clear signal when we should act to improve our pipeline quality.”

12. Opportunity Stage Forecasting

Opportunity stage forecasting is a technique used by salespeople to break down their pipeline into stages. It aims to calculate the probability of winning a prospect depending on each stage of the sales pipeline.  

Mark Daoust of Quiet Light says “We look at Opportunity Stage Forecasting, where we look at which stage our prospects are in the pipeline and then we are able to forecast the chances of closing the deal. We break the pipeline down into several stages, and the further along the sales pipeline the lead is, the more likely it will become a successful deal.

Depending on the stage they are in, we give each lead a percentage, for instance, a very new prospect may have a 10% closing rate, while a prospect who is in the meeting stage would have a higher close rate, say 50%. Using this method, it’s crucial to keep the pipeline up to date to get an accurate forecast.”

PRO TIP: Looking for ways to visualize your sales forecast data from HubSpot CRM? Watch the video below to learn how to forecast sales using HubSpot CRM & Databox.

Best Tools for Calculating and Analyzing SaaS Growth Rate

More than half of our respondents analyze their growth rates by using a centralized dashboard, like Databox. Others use tools like Stripe, Quickbooks, Profitwell, and Xero to analyze growth rates. 

Let’s take a closer look at each:

Databox

A central SaaS growth rate dashboard like Databox is the best option for analyzing your growth rates.

Databox pulls all your data into one place, so you can track performance and discover insights in real-time. It integrates with tons of other growth monitoring tools like Stripe, Xero, PayPal e.t.c.

You can track, organize, and analyze financial performance to get a clear picture of revenue growth with our custom dashboard software for example.

Get started with our SaaS dashboard templates, or build your own custom dashboard.

Stripe

Payment APIs powered by Stripe allow businesses of all sizes to accept payments online.

You can use Stripe to analyze financial growth in SaaS.

Visualize your most important SaaS metrics in Databox by connecting your Stripe account here.

Quickbooks

Intuit’s QuickBooks is an accounting software package geared toward small and medium-sized businesses. The company offers both on-premises and cloud-based accounting applications that allow users to accept payments, manage and pay bills, and handle payroll.

Some of our respondents use QuickBooks to analyze their growth rate.

Visualize the most important financial metrics from Quickbooks in Databox by connecting your account here.

Profitwell

Using ProfitWell, users can see all their subscription and financial metrics in one place. 

With ProfitWell Metrics, you can track essential metrics like revenue and subscription growth rate for free.

Connect your ProfitWell account with Databox and visualize your metrics in one place.

Xero

Xero is cloud-based accounting software for small businesses. You can connect it to a live bank feed to perform bookkeeping functions such as invoicing and payroll. 

Close to 20% of our respondents say they analyze growth rate using Xero. If you combine that with the insights you get from using a small business dashboard software, you’ll be setting your business up for more success.

Visualize your most important metrics from Xero in Databox by connecting your account here.

Monitor Your Most Important Growth Rate Metrics in Databox 

SaaS companies must sustain growth and retain customers for longer periods of time to break even. As a result, you should benchmark your growth rate to understand how you’re doing and whether any changes need to be made. 

Unlike most other tools which monitor just one aspect of growth — financial, leads, traffic, or something else — Databox integrates with all your tools to give you a holistic picture of your growth rate in one place.

Get started with Databox today.

The post How to Calculate Growth Rates in SaaS: Start with These 12 Growth Metrics appeared first on Databox.