When I first started working on the OKRs feature at Databox, I interviewed customers to understand how they apply OKRs and similar frameworks in their companies today.

Again and again, I heard the same story: it’s hard enough to set the strategy and write the OKRs, but it’s even harder to align teams around them, keep them visible, and avoid the endless cycle of manual updates.

Companies invest considerable time and energy in defining objectives and key results, only for them to be forgotten by Q2. Updates quickly feel like admin work. Tools are disconnected from performance, making it hard to track progress. And what was meant to drive focus often ends up as just another “management exercise.”

That’s when it became clear: the problem isn’t with the idea of OKRs, it’s with how they’re implemented and managed. And that’s the gap we set out to close.

In the rest of this post, I’ll walk through what we learned and how those insights help shape OKRs in Databox.

The problem with OKRs today

Through interviews with business executives, team leads, and individual contributors, I’ve found that the biggest challenges with OKRs lie in how they’re put into practice.

Here are the four common themes I’ve heard in those conversations:  

1. OKRs fade after the kickoff

Teams set OKRs at the start of the quarter, but without regular visibility, they slip out of focus. By the next review, most feel outdated or disconnected from day-to-day work.

2. Manual updates kill momentum

Most teams still track OKRs in spreadsheets or slide decks. The result? Updates get skipped, data goes stale, and OKRs lose the ability to keep teams accountable.

3. Tools are disconnected from execution

Too often, OKRs live in HR systems or dedicated planning apps, while the actual performance metrics live somewhere else. This forces constant context-switching and makes it hard to connect strategy with execution. Goals exist on paper, but teams can’t see if their daily work is truly moving the needle. 

4. Complexity slows teams down

Ironically, some OKR software makes things harder instead of easier. Heavy structures, jargon-filled workflows, and added admin tasks bog teams down. Instead of helping teams focus, OKRs become another layer of process that gets in the way of execution.

Our goal: make OKRs stick

We didn’t want OKRs in Databox to become just another place where objectives get written down and forgotten. From the start, we set out to design them in a way that would actually help teams use OKRs to stay aligned, focused, and accountable.

That started with a few guiding principles: 

1. Keep it simple. Teams should be able to start small and build confidence without unnecessary overhead.
2. Connect every level. OKRs should link company strategy to team and individual goals, so alignment is always clear.
3. Automate progress. Tracking should never feel like busywork. Progress must update automatically from live performance data.
4. Grow with you. Once the basics are working, teams should be able to expand into more advanced frameworks without friction.

Or simply put: to design OKRs that truly turn strategy into execution.

Introducing OKRs in Databox

Based on what we learned and our guiding principles, here’s how we approached OKRs: 

Define strategy and align across levels
Start with your company’s vision and mission, then cascade objectives and goals down to teams and individuals, so everyone can see how their work connects to the bigger picture.

Connect objectives with goals — powered by live data
Progress updates automatically from the metrics you already track in Databox. No manual updates, no busywork.

Visualize alignment at a glance
Expand or collapse your hierarchy of objectives, goals, and sub-goals in List View, or switch to Strategy Map for a visual snapshot that’s perfect for leadership reviews and all-hands.

Stay on track together
Each objective and goal includes clear statuses, regular updates, and automatic notifications. You’ll know when progress is off track, so you can respond before it’s too late.

A familiar experience, extended
If you already use Goals in Databox, OKRs will feel like a natural extension. You can connect existing goals as sub-goals under higher-level objectives, building a complete strategy framework without starting from scratch.

What are users most excited about

When we shared the OKRs prototype with our users, the response was immediate and enthusiastic. Here’s just a glimpse of the excitement we heard:

Making OKRs work at every level of the company

Databox OKRs are designed for the people who need them to actually work, not as another management exercise, but as a daily driver for focus and accountability. 

• Executives get instant visibility into priorities and progress across the company.
• Analysts track progress against OKRs, ensure data accuracy, and surface insights that keep teams on track, so leaders can make decisions with confidence and contributors stay aligned.
• Team leaders align team goals with strategy and keep execution front and center.

Individuals see exactly how their work contributes to the bigger picture.

Going forward

This is just the beginning. OKRs are the foundation of a broader Strategy & Planning pillar in Databox, one that connects long-term vision with daily execution and performance data. 

We’re rolling out early access soon. If you’d like to be among the first to try OKRs in Databox and share your feedback:

Join the waitlist:

Want a sneak peek in the meantime?

Watch the prototype walkthrough video:

The post The Story Behind OKRs: Why We’re Building Them in Databox appeared first on Databox.

To change that, we made several improvements based on user feedback. We added support for seasonality and holidays. Introduced a confidence score to help teams understand how reliable their projections were. And made it possible to save forecasts for future comparison.

Each update made the feature more powerful, but even with all those changes, adoption barely moved. 

So, we had to ask ourselves: Were we solving the right problem in the first place?

To find out, we conducted a round of Jobs To Be Done (JTBD) interviews with people across different roles, like executives, RevOps leaders, marketers, finance managers, analysts, and agencies. 

Our goal was to understand what forecasting looked like in their day-to-day work. 

What were they trying to figure out when they built a forecast? What decisions did they need to make? And what was stopping them from using forecasting tools like ours?

In the rest of this post, I’ll share what we learned and how it led us to rebuild forecasts into a tool teams actually want to use for planning.

The problem with forecasting as we knew it

Through those interviews, we started seeing the same patterns come up again and again. No matter the role or company size, the struggles were surprisingly consistent:  

Forecasts stop at prediction

Most forecasting tools, including ours, only show what feature results might look like if past trends continue.  They don’t explain why results are trending in a certain direction or what could be done to change the outcome.

No way to test assumptions 

Teams wanted to add correlated metrics to their forecasts so they could test assumptions, explore outcomes, and see which path would get them to their goals.

Limited forecasting methods

Some teams needed advanced models with regressors. Others preferred simple trend-based forecasts. Many wanted to override the system with their own numbers. But most tools only offered one method, which didn’t always fit their data.

Assumptions were hidden

For annual planning, quarterly reviews, or campaign targets, most teams were still building scenarios in spreadsheets. While flexible, this approach buried important assumptions (like correlation factors) inside formulas.

The goal: forecasting that drives planning

We didn’t want forecasting in Databox to stop at predictions. 

We wanted to build something that actually helps teams plan: test ideas, explore different scenarios, uncover dependencies, set goals, and make more informed decisions on what to prioritize next.

Introducing Forecast Modeling.

Here’s what’s new: 

From prediction to explanation
Add impacting metrics to your forecasts and see how they correlate, so you can understand what’s driving performance and what you can do to improve it.

From static forecasts to what-if modeling
Adjust inputs and run scenarios to compare outcomes instantly, without playing with formulas in spreadsheets.

From one-size-fits-all to flexible methods
Choose the forecasting method that fits your data best. Use Databox’s advanced model, regression, smoothing, or full manual control.

From hidden assumptions to transparent modeling
See every forecasted value and correlation factor in one table. With every input and assumption visible in one place, it’s easier to explain, adjust, and keep your team on the same page.

Built for people who actually plan

Right now, most planning still happens in spreadsheets. Teams export data from their tools, move it into a spreadsheet, and build formulas from scratch before they can even start modeling outcomes. 

Forecast Modeling was built to change that. 

It’s designed for the people who actually drive planning in their orgs: marketing leaders, finance managers, RevOps teams, and executives who need to move fast, adjust often, and back decisions with data. 

It gives them the flexibility and power of spreadsheets, without the friction. Forecasts are easy to build, quick to adjust, and directly connected to the performance metrics they already track in Databox. 

This isn’t a tool for data analysts or data scientists who want to build complex models from scratch. It’s designed for people who need simplicity, but also the power and flexibility to plan with confidence, without writing code or maintaining massive spreadsheets.

What are users excited about

During validation interviews, users described exactly how they plan to use Forecast Modeling to simplify their planning. Here’s what they said: 

Finding what really drives results
A Digital Marketing Manager told me that lead generation is her primary goal. In the past, she forecasted leads based on clicks from different platforms and rough assumptions about conversion rates. With Forecast Modeling, she can connect those inputs directly, test different assumptions, and quickly see which platform has the biggest impact.

Planning with confidence across different periods
A RevOps Director shared how his team sets monthly and quarterly targets by comparing results to both the previous month and the same period last year. Until now, that meant digging through spreadsheets and building formulas for every new cycle. With Forecast Modeling, he could run those comparisons automatically, adjust assumptions, and spend more time planning instead of number-crunching.

Replacing spreadsheets with scenarios
A Sales Manager at a B2B company said he always prepares pessimistic scenarios to account for risks like rising fuel costs or staff shortages. Previously, that meant tweaking Excel formulas manually. Now, with Forecast Modeling, he can build and save those scenarios directly in Databox, making them easier to share with his team.

Turning forecasts into goals and OKRs
That same sales manager explained how frustrating it is that forecasts and goals live in separate places. His team would forecast revenue in Excel, but track goals in Databox, making it hard to stay aligned. With Forecast Modeling, he can turn any forecast into a goal, track progress automatically, and set plans that are grounded in data. 

Looking ahead

Forecast Modeling is just the beginning. It lays the foundation for a more flexible, dynamic approach to planning in Databox.

As a product manager, I know a feature only succeeds if it solves real problems. That is why we started with research, validated with a prototype, and shaped the experience around direct feedback. The first reactions have been very positive. Users are excited to try it! 

Still, nothing replaces real-world usage. I’m excited to get this into your hands, and even more excited to hear your feedback!  

If you want a sneak peek, you can watch the video here. And if you would like to be notified as soon as Forecast Modeling is available, you can join the waitlist below.

Join the waitlist

The post The Story Behind Forecasts: Why We’re Rebuilding It (and What We’re Learning) appeared first on Databox.

AI isn’t just evolving; it’s transforming entire industries.

As data-driven decision-making becomes more complex, businesses need tools that don’t just analyze data but actively guide smarter, faster decisions.

At Databox, we are embracing the shift. In 2024, AI became one of our top strategic priorities. Now, we are taking an even more significant step: transitioning into an AI-first company.

This shift is more than just adopting new technology; it’s about fundamentally changing how we operate, build our products, and empower our customers in the AI era.

Here’s how we’re investing in this transformation:

AI-Native Product Development

Our transformation starts from within. Over the past few months, we’ve made AI-powered tools like Copilot and Cursor nearly mandatory for our product development teams. We’ve also introduced internal AI workshops and coding days to help everyone explore AI’s potential firsthand.

Our goals with AI-native development are to:

• Improve Performance: Use AI-driven code generation, debugging, and optimization to boost productivity.
• Speed Up Development: Automate repetitive tasks and streamline workflows to shorten delivery times.
• Encourage Innovation: Foster a culture of experimentation with AI-driven features.

By embedding AI into our core processes, we’re not just making our team more efficient—we’re creating the foundation for continuous product innovation that benefits our customers.

Accelerating development is just the beginning. AI won’t just optimize our processes. It frees up our time to innovate more, letting us spend more time identifying customer needs and validating new product opportunities.

AI-Powered Enhancements in Our Platform

We’re not just using AI internally; we’re embedding it into our platform to help businesses analyze data more effectively. In the last year, we added Gen AI performance summaries, forecasts, data correlations, and anomaly detection to enhance our platform with data science-related work. Our vision is to go beyond traditional analytics and make data more actionable, predictive, and automated.

We are focusing on AI-driven improvements such as:

• Optimized Product Onboarding: By analyzing how our most successful users interact with Databox, we’ll help new customers set up their accounts more efficiently.
• Enhanced Data Analysis: Using machine learning to detect trends, anomalies, and patterns that might otherwise go unnoticed.
• Intelligent Insights: Providing personalized recommendations and predictive analytics to support better business decisions.

These AI-powered enhancements will help our customers improve performance, uncover trends faster, identify new opportunities, and optimize their business strategies with minimal effort.

Investing in AI Agents and Agentic Systems

AI agents are poised to transform customer interactions, autonomously handling sales, support, and retention. This approach will be a key differentiator in the future of business intelligence.

For example, we’ve employed bots in our customer support processes to take over approximately half of our customers’ questions, which increased our response time and freed up our team to offer more personalized and proactive assistance. We look forward to bringing this type of impact to our customer’s performance management and reporting processes. 

We recognize the significant role AI agents will play, so we’re investing heavily in AI Agentic systems and other AI projects to help our customers get the most out of our product. 

Agentic AI presents a huge opportunity to rethink and redefine how software is adopted, used, and leveraged. AI agents are here to stay, and they will drastically change how we interact with software tools.

Building a Dedicated AI Team

To support our transition to an AI-first company, we are creating a dedicated AI team on the same level as our core Product and Engineering (P&E) functions—product, engineering, and DevOps. This team will drive AI research, development, and integration across our P&E teams and later build things that can assist our entire organization and reinforce our commitment to embedding AI into everything we do.

As AI reshapes our business, we will also invest in continuous learning and upskilling to equip our employees with the knowledge and tools they need to succeed in an AI-first world.

—-

The Future of AI in Business Intelligence

Software development will never be the same.

Transitioning to an AI-first company isn’t just a strategic move—it’s a necessity. The world is shifting toward AI-driven automation and AI Agents, where software doesn’t just provide insights but takes intelligent action on behalf of users.

Our commitment to AI will drive efficiency, unlock new opportunities, and prepare us for the next wave of technological evolution. By embracing this shift now, Databox is ensuring that we stay ahead of the curve, delivering cutting-edge solutions that help businesses navigate the complexities of a data-driven world.

We’re excited for what’s ahead. Let’s build the AI-powered future of business intelligence—together.

The post Becoming an AI-First Company appeared first on Databox.

Last year, we started tracking exciting breakthroughs in data analysis, AI, and machine learning that can help us make advanced analysis available to every business. To help us apply these new technologies to our customers’ needs, we formed a dedicated Data Science team. Being on the lookout for the latest trends and innovative ways to integrate advanced features into the product, the team helps advance our goal of delivering cutting-edge features to our users and elevating their overall experience to the next level. So far, this has resulted in our newly released Databox Analytics platform, which also involves the Performance Summary feature, responsible for summarizing the performance of our client’s key metrics that saves them time and effort previously used for manually sifting through a large amount of data. 

Since summarizing an extensive dataset is challenging due to its complexity, we have turned to generative models for support and faced a pivotal decision: GPT-3.5 or GPT-4. This blog post examines and compares GPT-3.5 and GPT-4 and contrasts their capabilities through the lens of our newly released Performance Summary feature.

ChatGPT 101: A simple breakdown

By now, most people are familiar with ChatGPT. In simple terms, it is a language model that understands and generates human-like text based on the input received. For the model to be useful, it had to be trained on vast amounts of data. ChatGPT was trained on a diverse set of datasets, including Common Crawl, WebText2, Books1, Books2, and Wikipedia. The diverse training set is a big reason behind the general-purpose nature of the tool.

GPTs, or Generative Pretrained Transformers, operate with ‘tokens’, the fundamental units of text, using them to predict and create coherent responses. During the training phase, the model learns statistical relationships between these tokens, allowing it to generate contextually appropriate text. When a user sends a prompt, this input is first broken into tokens, a process called tokenization. For example, the sentence “Databox is amazing!” will be tokenized into [D,atab,ox, is, amazing,! ]. The tokens are processed through the model’s neural network, using learned patterns to create a fitting and coherent response as a token sequence.

OpenAI, the company behind ChatGPT, provides a simple chat interface that simplifies access to AI models, featuring GPT-3.5 and the latest addition, GPT-4. Both models have taken the world by storm by supporting functionalities such as:

• enhancing chatbot performance,
• sentiment classification,
• summarization of diverse text types,
• assisting in software development tasks like explanation, generation, verification, and transpiration,
• simplifying content by creating outlines and extracting key information,
• augmenting and creating content, or
• providing proactive suggestions based on context.

Aside from the chat interface, OpenAI exposes an Application Programming Interface (API) for accessing AI models developed by OpenAI. The API is simple to use while maintaining the flexibility required for advanced use cases. It allows teams of all sizes to focus on research and development rather than distributed systems problems. Many models are exposed via the API, the most important being an optimized variant of GPT-3.5 called GPT-3.5-Turbo and the newest addition to the OpenAI portfolio of models, GPT-4.

Picking the Perfect Fit: Model Selection for Databox Integration

When enhancing our features with ChatGPT, deciding which model to integrate was about tradeoffs. To better understand the characteristics and limitations of each model, our first step was comparing some of their differences, as found in the API documentation and the GPT-4 technical report.

The comparison above suggests that GPT-4 is supported by a significantly larger architecture. As a consequence, GPT-4 does better on the HumanEval benchmark dataset as well as provides comparatively greater results in exams across a broad spectrum of scientific endeavors such as Medicine, Computer Science, Science, Math, and Law. The significant qualitative difference is a direct consequence of the larger model size. However, processing time and costs are significantly increased, which is reflected in the higher pricing and more stringent API restrictions.

The security of the data our users entrust us with is our top priority. This is even more important when we send user data to a third party. As of the time of this writing, OpenAI has strong policies in place to ensure user data remains secure and private for both GPT-3.5-Turbo and GPT-4. Security is ensured by encrypting data at rest using AES-256 and data in transit using TLS 1.2+. OpenAI does not train models on user data, and they have been audited for SOC2 compliance. See the OpenAI Security Portal for more information on data security, privacy, and compliance on the OpenAI API platform.

OpenAI determines rate limits according to usage tiers. The higher the usage tier for an organization, the higher the rate limits. Our current limits for GPT-3.5-Turbo and GPT-4 are displayed in the table below.

One should default to using GPT-3.5-Turbo if at all possible and utilizing GPT-4 if all options are exhausted.

Using ChatGPT for Performance Summaries

Most of our clients use Databox for aggregating and visualizing their data and benefit greatly from quick data interpretation. However, having all their data in one place can be overwhelming for clients with a large amount of it. The dilemma shifts from needing more data to being inundated with too much of it, which is why we have created the Performance Summary feature. It helps turn a wealth of data into actionable insights, saving our clients time and effort. Instead of sifting through tons of metrics, Performance Summary provides our users with a concise summary of their performance.

The Anatomy of Performance Summary

To give the user a snapshot of how well a specific aspect of their business is doing, the Performance Summary consists of several components. The Generative AI-supported elements are:

1. Performance metric summary offers a descriptive and inferential overview of the data. Discusses the highlights and lowlights of individual metrics and individual causes. It goes beyond mere numbers by attempting to discern the deeper meaning of the individual metric and, as a part of a metric group.
2. Suggestions provide a list of suggestions tailored to the user.
3. Trend, which involves a circular icon with either a green, orange, or red colored symbol, represents the cumulative progress of a subset of metrics.

Set up this way, the components of a Performance Summary offer a comprehensive view of business health and are integral in driving strategic decisions. However, the generation of such a multi-faceted report is complex. It requires not only the collation of data but also a subtle comprehension of how different metrics interact with and influence one another. Crafting a meaningful performance summary that conveys status, delves into specific performance metrics, and suggests practical recommendations calls for an advanced approach. This is where the capabilities of generative models become instrumental.

Generative Models for Performance Summaries: A Smart Choice

Summarizing large and diverse datasets is tough due to their complexity. Basic rule-based or machine-learning systems can’t grasp the context needed to connect important metrics for a meaningful summary. Simpler models find it hard to handle complex ideas where key info needs to be condensed effectively. Capturing industry specifics, like jargon and knowledge, is a challenge for basic algorithms, requiring many manual adjustments. Considering these challenges, using conventional methods for this feature is a tough sell. 

The general availability of ChatGPT makes building features that require sophisticated contextual understanding possible. Because the model is trained on a massive amount of data, it is context-aware when analyzing inter-related metrics and KPIs. The model can abstract salient points from a large volume of data and add insight and recommendations based on good practices from the industry.

ChatGPT excels at making reasonable predictions even when data is sparse or incomplete. This is especially handy for Databox users navigating niche or emerging markets where data can be unpredictable. Additionally, ChatGPT is easily fine-tuned for specific needs and can be adapted to focus on particular industries or subjects, allowing Databox to customize the Performance Summary feature based on user feedback and internal analysis. This not only boosts accuracy but also ensures the summaries are more relevant to the user’s context.

The Role of the Generative API in Databox 

To utilize generative models in the Databox microservice ecosystem, we have introduced a “Generative API” microservice, which serves as a specialized intermediary between internal services and the OpenAI API. The Generative API supports all generative model use cases and exposes them to any services requiring functionality. The diagram below demonstrates the outline of the solution.

• Generative API – The focal point of the architecture, which serves as an intermediary between the Databox ecosystem and the generative model. Concretely, it abstracts the complexity of interfacing with the OpenAI API by providing a simplified and use-case-tailored interface for other services to use under the ownership of the Data Science team.
• Service X and Service Y – represent client microservices that require results of the Generative API. The Generative API does not have access to user and metric data, meaning each service is responsible for fetching data and ensuring the data is up to date.
• AMQP, Load Balancer – The service is exposed via asynchronous messaging or HTTP mechanisms. The HTTP mechanism is appropriate when a smaller amount of tokens is required and an immediate response is demanded. The asynchronous messaging (via AMQP protocol) is appropriate for lower priority requests, requests where immediate results are not necessary, or when a longer calculation is expected.
• Database – Represents repositories of user data.

This architecture highlights a separation of concerns where the Generative API focuses on providing computational intelligence while client services handle data management. It also reflects a scalable and flexible approach to integrating AI capabilities into existing systems, allowing Databox to leverage the latest advancements in AI while keeping the core services optimized for their primary functions.

Weighing Up GPT-3.5-Turbo Against GPT-4

When deciding which model to use to power our feature, there were multiple factors to consider. Quality, performance and scalability, cost, and security all played an important role in deciding which model is most suitable for the Performance Summary feature.

Quality of ChatGPT models

For us, quality refers to the ability of ChatGPT to provide clear and informative summaries, which depends on the steerability and factuality of the model. We focused on three critical things: acknowledging our instructions (steerability), giving accurate information (factuality), and providing output that does not cause harm (safety guardrails).

Through extensive experimentation, we have defined the criteria for the output for the performance metric summary as follows:

1. Limit referencing explicit metric values to a specified number to prevent information overload and maintain clarity.
2. Convey information in a formal yet conversational manner.
3. Do not explain or elaborate on the technical details of the data payload.
4. Tag paragraphs with HTML markup.
5. Tag metric names with HTML markup.
6. Limit output to a certain character length.  
7. Output a valid JSON.

The criteria for the output of the suggestions are the following:

1. Generate X suggestions (where X is a number).
2. Each suggestion must be a complete sentence.
3. Each suggestion must be limited to a certain character length.
4. Generate in the form of a list.
5. Output a valid JSON.

The criteria for the output of the trend value are the following:

1. Output must be an enumerative value of either: “positive”, “negative,” or “neutral”.

Accounting for any single criteria is not a problem for any sophisticated LLM. Accounting for all of them together while still maintaining factuality is a very difficult task. For the suggestions and trend, both GPT-3.5 and GPT-4 are compliant. This is likely because the instructions for suggestions and trends are much simpler, and the steerability demands are consequently lesser. In the following table, we briefly describe the outcome of our experiments for the performance metric summary.

Performance and scalability of ChatGPT models

The difference in capability between GPT-3.5-Turbo and GPT-4 results from the difference in the underlying architecture. GPT-4, being larger, has higher demands in terms of infrastructure, meaning it is more expensive to run for OpenAI, hence the longer response times, higher cost per token, and stricter rate limitations. Let’s look at the response times first. 

As expected, GPT-3.5-Turbo is faster at generating tokens at 23.05 milliseconds per token versus 55.36 milliseconds per token for GPT-4, making the former roughly 2.4 times faster. It is easy to see how, at even moderate amounts of completion tokens, response times can be quite high. This is additionally complicated by the rate limits introduced by OpenAI to reduce the load on the servers – serving too many requests concurrently per minute can result in an interrupted user experience. 

We improve the user experience by:

• Caching: We save and reuse responses of existing Performance Summary requests for given metrics. This reduces the request count and improves user experience 
• Rate Limit Headers: OpenAI provides a rate limit header with each response, which we use to adjust how fast we ask for data, avoiding any issues. 
• Retry Strategies: If a request fails, we try again using an exponential backoff with jitter.
• Scheduling: We generate high-priority Performance Summaries on demand and space out low-priority requests to avoid overloading the system.

Cost of ChatGPT models

As discussed earlier, GPT-4 is ten times more expensive per token than GPT-3.5-Turbo. Regarding cost breakdown, the prompt has two components: fixed and variable.

• The variable part includes request-specific data like metric details, data source type, and aggregated values. This part also encompasses few-shot prompting examples and additional context.
• The fixed part outlines the common ruleset for all requests related to the performance summary use case.

While GPT-3.5-Turbo is more economical, steering it can be challenging, requiring more tokens and advanced techniques for comparable results. Consider a specific example in the context of the Performance Summary feature.

The difference in input tokens and the number of requests made is because GPT-3.5 Turbo is significantly harder to work with. While with GPT-4 we can define all Performance Summary elements and the intricacies involved in a single request, we have to split the summary, recommendation, and trend into separate requests to improve the steerability of the model. Even considering this, GPT-3.5 Turbo is still significantly cheaper. For use cases with lesser demands on the reasoning capabilities of the model, investing some time in improving the output of ChatGPT-3.5-Turbo is often worth it.

Finally, to cap the monthly bill at a reasonable rate, it is prudent to set limitations on the product side. The key here is to determine “reasonable” use and the appropriate limitations so the user does not feel constrained by normal use while avoiding unnecessary issues regarding unexpectedly high incurred costs and load.

Reflecting on our choice

When it came to picking the right model for our Performance Summary feature, we had to find that sweet spot between the sheer power of GPT-4 and the cost-effectiveness and broader bandwidth of GPT-3.5-Turbo. Diving into the Performance Summary use case, it was unclear which model to choose without testing and reviewing our goals and limitations. If your main goal is cost efficiency and speed, GPT-3.5-Turbo might be the savvy choice, especially if your use case is straightforward and your software design is clever enough to handle the limitations. On the other hand, GPT-4 excels at delivering top-notch output with a better grasp of things, making it the go-to for cases that crave detailed and nuanced results, even if it means a bit more on the budget. Ultimately, what sealed the deal for us was delivering value to our users. GPT-4’s ability to enhance the user experience with more accurate and insightful Performance Summaries is also something we considered at Databox. As this field evolves at a rapid pace, we will stay on top of updates and adjust our strategy accordingly.

Exploration of ChatGPT models 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 deliver more value to our customers through our products constantly.

Aleksej Milosevic is a Product Scientist in the Data Science team, actively collaborating with the Product and Data Engineering teams to devise solutions that harness the power of data. His work focuses on building systems that integrate machine learning, extracting actionable insights that significantly contribute to the advancement of our products and the enrichment of the overall user experience.

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 <strong>ChatGPT Models: Choosing the Right Fit for Databox Analytics</strong> appeared first on Databox.

An all-hands meeting is even more important for a company with remote teams whose employees might, at times, not feel seen, heard, or valued as a part of the company. At Databox, our playmakers come from all corners of the globe. you know we have experience when it comes to successful all-hands, and we are going to show you just how it can be done using one of our newest features Databox Reports.

Let’s start!

Step One – All-Hands Meeting Framework

You need to have a structure in place for a company-wide meeting, and it should be one that is easily repeatable and updated. 

Let’s answer the following question:
How many All-Hands meetings do you want to have yearly? 

For some companies, once a year works, for others once every 6 months or once each quarter. It mostly depends on the timeline for your team goals and when you want to see them executed. 

At Databox, quarterly works best as most projects and goals fit within that timeframe and we can easily extend a project to two or three quarters as needed and report on the progress up to date. 

The next part of the framework creation involves deciding on the length, and format of the presentation, and who is going to moderate it. The second two we will discuss at length in the next few paragraphs, but as far as how long an all-hands meeting should be, here are some of the factors to consider –

• It is happening during work hours—don’t take up too much of your colleague’s time
• It needs to keep their attention, so keep each section focused rather than go on infinitely
• Every team deserves an equal timeshare (including introductions and conclusions)

An hour to an hour and a half depending on the size of your company would do it… just make sure that you communicate that to team leads presenting so that they can structure their presentation accordingly.

Step Two – Choose a Showrunner

It is important that everyone is clear on who creates and conducts the All-Hands meeting. In most cases, the company CEO is the one who is most knowledgeable and can easily get the information needed to create an all-hands report. We recommend that they moderate the meeting and have control over the report. Other options might include your COO, the Head of People/Culture, or any Executive with insights into high-level strategy and how each department contributes to the overarching goals

Each team lead should talk about their progress and KPIs as they know the data best and can explain in-depth anything that is related to their plans and goals.

With remote teams, All-Hands are usually held over a meeting platform like Zoom or Google Meet, while in the office the report can be presented on the screen for everyone to see. 

Step Three – Define Your All-Hands Meeting Format

Once you have settled on your meeting cadence and the showrunner, the format of the meeting should be your next consideration with the idea to make sure it is easily repeatable. Databox Reports can help with that as you can make a report once and then update it with fresh data.

The format we recommend is-

1. Introduction

An introduction sets the stage for the rest of the meeting. Go over all the numbers for the previous period (in our case, the previous quarter). Review all goals set in the previous meeting that is being monitored. Quickly go over if the current projects have performed as expected or if additional work is needed. 

At Databox, this is usually presented in the form of a dashboard. 

2. Team Deep-Dives

After the introduction, it is time for each team to give a short overview of their initiatives and goals from the previous quarter and their plans for the next.

Time is of the essence, so it is always a great idea to choose a specific meaningful metric and elaborate on it in the Side-by-Side slide within the report. This will help showcase the goals for each team because everyone gets to see their effort translated into favorable numbers. 

With Databox Reports, the numbers will be automatically updated for each new all-hands meeting, helping create a quick and repeatable process. 

The second relevant slide should contain other metrics that are crucial for the team reporting them. And with the Data Story Slide in the Databox Reports this is made even easier, not to mention that the numbers update based on the metric, so you can show the latest data for them every quarter. 

The final two slides for each team should be an introduction to new projects for the upcoming quarter and a highlight of the team member who has gone above and beyond to ensure all quarter goals are met and projects executed flawlessly. 

Highlighting the team member that has contributed the most really helps people feel seen and their actions valued. This is very important for overall morale, especially in companies with a lot of remote workers and teams.

3. Conclusion & Thanks

Finally, to conclude your All-Hands make sure to provide an overview of the plans across teams, the overall trajectory of the company, and address any concerns that might have been raised over the previous quarter.

But most importantly, take time to thank and praise teams and show them what their contribution has meant. 

Sharing Your All-Hands Meeting Report

When it comes to big teams it is more than likely that not all “hands” will be able to attend the All-Hands meeting; this is why in most cases there is a video recording of the meeting. 

However, exporting the All-Hands Report and sharing it in a PDF version within the company can also be a great way for everyone to get the updates they need on where the teams and company are in terms of their all-hands goals. 

Start Using Databox Reports for your All-Hands Meetings

If you have any questions or need help getting set up, you can visit our Knowledge Base or send us a message at help@databox.com and let us know how we can help you with setting up the Report of your choosing. 

New to Databox? Start by creating your free account. From there, you’ll be able to connect your data from more than 70 different platforms. Then you can add the metrics to your metrics screen, create data boards, automate your reporting, collaborate with your teammates, and much more. 

You can even start a trial to try out Advanced Reports. Enjoy!

The post How to Run a Successful All-Hands appeared first on Databox.

• Properly format your Google Sheets
• Use our new Google Sheet Wizard for easier metrics building

Below, you can view the full episode or keep reading for a fully transcribed version of the episode, complete with relevant screenshots.

So let’s start.

So let’s start with a brand new Google Doc, and Sheet – I will name it Test Sheet one – 

Now, let’s talk about the data table – we have two options: the vertical and the horizontal view. To get the most from your Google Sheet the vertical setup is recommended – it allows you to include all Date Ranges, values, and dimensions in columns, therefore you can create the entire Databoard with an overview and drill-down data with just a few metrics. While the horizontal has its limitations when it comes to adding more data.

That’s all about the view, easy right. And don’t worry if you already have a horizontal sheet you can easily switch it to Vertical – select – copy – paste special, paste transposed.

Now we come to date formats – as you know date formats are very important when it comes to formatting your Google Sheets, but they also play an integral role in extracting and visualizing data with Databox as the Query Builder will only recognize correct formats.

Doing this manually ensures that you will get it right more easily. So,  The Date should be formatted using a Google Sheets format to look like mm/dd/yyyy or dd/mm/yyyy. Either way, the Date needs to include information on the day, month, and year that this Metric value should be pushed to.

Now keep in mind that if you are using specific Date formats in Google Sheets that are not formatted in a way that works with Databox, you can either quickly reformat the entire column by selecting Format > Number > Date, or if it’s important to you to keep a record of the dates formatted as they are, it’s advisable to create an additional column in your Google Sheet for Dates that will be used by Databox.

So let’s reformat these so that they don’t work with Databox. I’ll choose Date time. Then, I’ll create my new column and call it Databox Dates. Type in = and the cell you’d like to transfer, so =A2 here. Then drag the setting across the other cells. You already know the next trick, select the Databox Dates column click Format > Number > Date. There we go. 

Ok, so far so good, how about we try to format the conversion of currency next.

Let’s suppose you have 3 currencies in the Google Sheet and need to get it all into 1 unified currency. In this case, we are going to use the US Dollar, the British Pound, and the Euro. And let’s say we want to report on everything in Euros.

Here’s how we can do it :

Again, I’ll create a new column. I’ll call this one Amount (Euros) and change the other to Amount (Any) since it will hold any kind of currency. To Column E I will add the formula: =GOOGLEFINANCE(“CURRENCY:USDEUR”)

Notice that when I do this, it shows us the exchange rate. To convert the currency, I will just multiply the cell in Column B and add that to my formula. Then I’ll drag the cell settings down. Right now our formula is assuming everything is converting from USD to the Euro, so I’ll just pop into each cell and make sure it’s converting the right currency. If one is already in Euros I can just delete the formula and do =cell, that way we aren’t missing any data in this column.

For the British Pound I’ll use GBR, aka Great British Pound. Alright. Done! Let’s put everything we learned together and input a new row of data.

Let’s say on April 11, 2022 Monise brought in $755 pounds. Now let’s just stretch down our date format in Column D, and then update the currency formula in Column E. Perfect! It adds very little time and will save your team a ton of headaches. Okay, this is ready to go. Let’s get all of this into Databox and build a dashboard!

Time to Build Our Metrics

This is one of my favorite parts of the process. In Databox we offer the Google Sheet Wizard to help you create metrics from Google Sheets more easily so first let’s try to do that from the sheet we have formatted together – 

So first we will connect our GS – the one we created together – here you can see we are just confirming that everything looks right and we see that it is all formatted properly – Yay Us! And now it’s time to move on and create a metric – 

Begin by selecting a metric value. This is basically just a number that you want to track. From there, you will have the option to further segment it by a dimension which I’ll explain a bit more in a second. Then you’ll add finishing touches like naming your metric, trending preferences, etc. Let’s walk through it together.

For our first one, I want to create an Amount (Euros) metric, that way I can track the money coming in. So select the column, and since we aren’t adding any additional dimensions, we’ll skip this part. For the date column, we want to choose Databox Dates since it is formatted correctly. Then I name it. Trending up is the good one.

Click Continue.

And it is that easy.

Let’s add it to our dashboard. I’m just going to drag it directly from the Metrics Library here… If you can see here, nothing is showing up… that’s because, if you remember, we don’t have any data that goes past April 2021. So I’ll choose all time so we can see all of the data, and let’s graph by year to start. Great! What would be really cool now is to be able to see who is bringing in the most money according to our data, so let’s create a new metric to do that.

In this case, we select Amounts (Euro) as the Value again. Now is the time to add a dimension, in other words, a way to slice and dice this data. We want to be able to see how much money is coming in from each team member, so column C, Owner,  would be our Dimension. Again we’ll choose the Databox Dates column.. Now let’s name it – Amount by Owner – and here is our final product. 

Don’t forget you can always choose the preferred visualization before adding it to the dashboard.

Properly formatting your Google Sheets from the get-go will save you time going forward as it will allow you to use Databox to its full potential. 

It takes a bit of time to master all the formatting but once you see how quickly your data blocks populate on your dashboard and verify the accuracy of the data, it is more than worth it.

And remember, if you lack the time to take this on you can always contact our support team and get a free Google Sheets Setup Service to help you start things off. 

If you haven’t already, just create your free Databox account and reach out to our team either via chat or email.

See you next time. 

The post How to Best Format Your Google Sheets for Databox Syncing appeared first on Databox.

Today, we’re going to talk about Landing Pages, and more specifically, how to properly track how effective your landing page and the CTAs are at converting visitors to contacts. 

In this episode of Data Snacks, I’m going to show you how to:

1. Track and visualize relevant Landing Page metrics
2. Interpret the data you have gathered
3. Apply useful tips to make your landing pages are more engaging and better at converting

Below, you can view the full episode or keep reading for a fully transcribed version of the episode, complete with relevant screenshots.

Let’s start!

As you know, a lot of work goes into planning a marketing campaign, creating a landing page for a campaign offer, and executing the campaign. Tracking conversion metrics for landing pages is an essential step in meeting your marketing goals.

Decide What You Want to Track

Here’s what I want to see––so in order to get insights into my landing pages, I want to track the following:

• New Contacts by Landing Page to see how successful each of my landing pages has been in creating new contacts
• Average Time on Page by Landing Page to see how long people stay.
• CTA Views by Landing Page so that we can see if our chosen calls to action are visible and engaging [and finally]
• Views by landing page to see if the people who get to the page decide to become new contacts. 

In Databox, New Contacts by Landing Page and CTA Views by Landing Page metrics are already created and ready to use for users who are on a Paid or above plan. 

Time to Build a Dashboard

From the Databox Designer, I’m going to the Metrics Library, and I will select HubSpot Marketing as my data source and, from there, drag and drop the metrics I want to see. 

For New Contacts by Landing Page, I will choose the table visualization so that I can easily get the order to be either ascending or descending and have a clear idea of which landing pages are performing well when it comes to bringing in new contacts. Next, let’s go with a pie chart as it can highlight better the differences between Avg. Time on Page by Landing Page, and you can see which ones are keeping the visitors engaged. For CTA Rate by Landing Page, I will go with a bar chart, and Views by Landing Page can stay in the default table visualization.

Now that the dashboard is ready, let’s analyze. There are several things that I want to look at here – first, I set the same Date Range in all the metrics so that I can compare the data accurately. Currently, it is – Last Month, you can adjust this depending on your marketing campaigns.

Let’s first look at the New Contacts by Landing Page. Once we see which are the high-performing ones, we can compare the content and the CTAs to get insight into what works better when it comes to getting new contacts. Next, compare individual CTAs to look for commonalities amongst the high performers—and under-performers—and use those insights to further optimize future calls-to-action. Here, CTA Views by Landing Page can help so that you know which ones have captured the attention of your visitors.

On this dashboard, I also want to see how long a visitor stays on average on my landing page – according to Contentsquare’s 2020 Digital Experience Benchmark report of 7 billion user sessions, the Average Time on Page across industries is 62 seconds. So that can give you a starting point. The calculation here is simple if they stay long enough to click on that CTA and turn into new contacts, your landing pages are doing their jobs.

Finally, there is Views per Landing Page, and this is a metric that can tell us a lot about which landing pages attract visitors. However, if they are entirely different than the ones creating new contacts, you need to adjust your content. Ideally, we want each viewer to become a contact, but a high percentage will do just fine.

Discover More Insights

There are certainly more insights you can pull from these metric visualizations––these examples were just a few to get you started. 

Knowing how successful each landing page is will help you understand what creates new contacts for your business. That way, you can improve other pages to meet your pre-set goals when it comes to landing page performance. 

Want our help setting this report up? Start a chat with our dashboard experts, and they’ll help build this report for you. 

If you haven’t already, just create your free Databox account and reach out to our team either via chat or email.

See you next time.

The post How Effective Are Your Landing Pages? appeared first on Databox.

Today, we’re going to talk about building a business performance dashboard that most SaaS executives want to see. But what are the most commonly asked for KPIs that these software company leaders demand in their dashboards?  

Well, let’s start with a bunch of acronyms that seem to capture their interest on a daily, weekly, monthly, quarterly, and annual basis, including:

MRR, or Monthly Recurring Revenue and ARR, or Annual Recurring Revenue

Then there’s LTV, or Customer Lifetime Value, and CAC, or Customer Acquisition Cost, and their ratio, LTV over CAC.

Next, there’s Net Retained Revenue, or NRR, which is the amount of revenue left after churn is subtracted.

After that, executives like to see ARPC, which is the Average Revenue Per Customer, or its cousin, ARPU, which is the Average Revenue Per User.  

With this information, SaaS executives can quickly assess where they are relative to goals they have set in quarterly business reviews and their annual plans. 

In this episode of Data Snacks, I’m going to show you how to:

Visualize a variety of SaaS metrics, including MRR, ARR, LTV over CAC, NRR, ARPC, and the growth of revenues compared to goals.

Below, you can view the full episode or keep reading for a fully transcribed version of the episode, complete with relevant screenshots.

Let’s get into it.

Time to Build a Board

Here’s what I want to see––I want to track MRR and ARR vs. Goals we have set for these KPIs. I want to look at total customers and MRR per customer too. I also want to see the average LTV and CAC and calculate the ratio of LTV to CAC and compare that to our goal. Then, Net Retained Revenue, total Churn and Expansion MRR, Average ARPC relative to Goal, and a chart showing revenue growth compared to our goals.

 We’re going to pull this data from a Google Sheet that our executives use to pull data from different financial sources and calculate the KPIs they want to keep track of.

From the Databox Designer, I’m going to the Metrics Library and selecting Google Sheets as the data source, then select the Google Sheet I want to use, which in this case is called Saas CEO Summary.

It’s smart to draw a sketch or at least create a list of the order and position you want to see these metrics on your final dashboard. That makes it easy to plan and drag and drop the appropriate metrics onto your data board and do any final tweaking to make your dashboard design balanced and easy to read.

I drag and drop my metrics onto the board and make sure they are set to the date range I want to assess, in this case, we’re looking at last year. Make sure your date range corresponds with the data you have in your Google Sheet, or your metrics may not be consistent and accurate. Setting Custom Date Ranges for Google Sheets is an option that’s coming soon to Databox! 

Now I can quickly assess how my SaaS company is performing, make any adjustments needed, and set new goals for upcoming quarters. This dashboard is helpful in keeping everyone aware of our goals and where we might be falling behind.

You can browse more SaaS dashboards here.

You Don’t Have to Build This SaaS C-Suite Dashboard Alone

Want help creating this report? Start a chat with one of our dashboard experts and they’ll help build this report for you for free.

Just create your free Databox account and reach out to our team either via chat or email.

See you next time.

The post What are the Most Important KPIs for the SaaS C-Suite? appeared first on Databox.

Ever feel like your Facebook ad campaigns run out of budget too quickly??

Your CPM or cost per thousand impressions might be to blame, and you didn’t even know it. So today, we’re going to talk about CPM, or cost per thousand impressions, on Facebook Ads.

Let’s take a look at how we can assess CPM for each ad and improve their performance – I’m going to show you how to:

1. Visualize your CPM by ad
2. Monitor your CPM
3. Improve your targeting. The more specific it is, the less likely you are competing with large-budget advertisers for the same audience.

Below, you can view the full episode or keep reading for a fully transcribed version of the episode, complete with relevant screenshots.

Let’s start!

Understanding Your Facebook CPM

About 15% of agencies polled in this Facebook ads report say they set goals around CPM.

If you are a Facebook Ads user, you already know about CPM and CPC (cost per click), but did you know that if you reduce Facebook CPM, in some cases, lead to a higher return on ad spend, or ROAS?

Simply put, a CPM that is higher than you expect indicates that your ads aren’t as efficient as you’d like. In other words, you are spending more money to reach a limited audience. That is why it is essential that we monitor CPM carefully.

Start Tracking It in Databox

We are starting in Databox, so let’s see how we can track CPM

better and in a more visually compelling way. 

First, we will navigate to the Databox Designer. Adding metrics is quite simple here. Simply choose the source from the Databox Metrics Library here on the left and drag-and-drop some of the available pre-built CPM metrics such as  – by ad, campaign, ad set, etc.

We can also choose different visualizations to track your CPM. For example, let’s drag and drop the line chart visualization onto the board here. And ad CPM by Ad as a metric. Now, if you have a lot of Ads set up, this might seem a bit confusing, but you can select which Ads specifically you’d like to see by using this interactive legend below the chart or the dimension window here on the right. With this visualization, you can see how your CPM is changing over time, all the peaks and valleys, and even add annotations to them so that everyone on the team can see and understand what was happening with a certain ad at a certain time. 

Another way to visualize CPM from all your ads is by showing them in a table. You can either drag and drop the metric straight from the metric library and showcase it – as the table visualization is already set. 

However, if you showcase them in an Advanced table visualization, you can track your Ads CPM alongside other relevant metrics that you need to be tracking – like CPC, ROAS, and engagement. The only thing you need to pay attention to is that each metric needs to have the same ending. In our case, that’s ‘by Ad.’ Having all this on one social media dashboard will enable quick and easy tracking of your Facebook ad performance.

Time to Interpret the Facebook CPM Data

Ok, now that we have the data in front of us, let’s see what it tells us. Next to cost per thousand impressions, we see cost per click and ROAS numbers. Now I have the ability to analyze and understand why certain ads might be more profitable than others. 

Facebook states that CPM measures the total amount spent on an ad campaign, divided by impressions, multiplied by 1,000. According to our data, on average, a good CPM is $1.39, $1.38, $1.00, $1.75, and $0.78 for the telecommunications, general retail, health and beauty, publishing, and entertainment industries, respectively. That said, it’s important to note that the average cost per thousand impressions is $11.20. This way, you now know the average spent on CPM, which should keep you from spending in excess. Of course, other factors are involved here as well, like the quality of your ads and the competition within the industry for the group targeted. 

As you can see, the desired CPM can vary, but it is the success of your overall Facebook Ad camping that determines whether it is too high, so let’s look at some other metrics. 

So what do we want to see? We want to see which one has the highest ROAS and if at the same time it has a CPM and the CPC within our goal average. That will tell us if our ads are performing well or are we just overspending without the desired results.

Once we gain this insight and are tracking it regularly, adjusting the approach and seeing what works better won’t be such an issue. You can start by improving your targeting.

Targeting the right people with the right message is the key to a successful ad campaign. So once you set up your ad, make sure that the location, age, and gender of your target market are carefully chosen. Don’t forget the interest section, as you can choose ones that are very product and service-specific, thus aiming your ads with even more precision.

PRO TIP: What’s the overall engagement of your ad campaigns?

Want to make sure your Meta ads are performing and trending in the right direction across platforms? There are several types of metrics you should track, from costs to campaign engagement to ad-level engagement, and so on.

Here are a few we’d recommend focusing on.

1. Cost per click (CPC): How much are you paying for each click from your ad campaign? CPC is one of the most commonly tracked metrics, and for good reason, as if this is high, it’s more likely your overall return on investment will be lower. 
2. Cost per thousand impressions (CPM):  If your ad impressions are low, it’s a good bet everything else (CPC, overall costs, etc.) will be higher. Also, if your impressions are low, your targeting could be too narrow. Either way, it’s important to track and make adjustments when needed. 
3. Ad frequency: How often are people seeing your ads in their news feed? Again, this could signal larger issues with targeting, competition, ad quality, and more. So keep a close eye on it. 
4. Impressions: A high number of impressions indicates that your ad is well optimized for the platform and your audience.
5. Amount spent: Tracking the estimated amount of money you’ve spent on your campaigns, ad set or individual ad will show you if you staying within your budget and which campaigns are the most cost-effective.

Tracking these metrics in Facebook Ads Manager can be overwhelming since the tool is not easy to navigate and the visualizations are quite limiting. It’s also a bit time-consuming to combine all the metrics you need in one view.

We’ve made this easier by building a plug-and-play Facebook Ads dashboard that takes your data and automatically visualizes the right metrics to give you an in-depth analysis of your ad performance.

With this Facebook Ads dashboard, you can quickly discover your most popular ads and see which campaigns have the highest ROI, including details such as:

• What are your highest performance Facebook Ad campaigns? (impressions by campaign)
• How many clicks do your ads receive? (click-through rate)
• Are your ad campaigns under or over budget? (cost per thousand impressions)
• What are your most cost-efficient ad campaigns? (amount spent by campaign)
• How often are people seeing your ads in their news feed? (ad frequency)

And more…

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 Facebook Ads account with Databox. 

Step 3: Watch your dashboard populate in seconds.

Try this template

You Are Ready to Evaluate CPM of Your Facebook Ads

We hope that we have shown you a powerful new way to evaluate the CPM of your Facebook ads without hours of preparation or drilling down on each campaign separately and compiling them into a dashboard.

The best part is that you can monitor your CPM by ad, campaign, or ad set using our social media dashboard software. And, let’s not forget that you can even monitor CPM for multiple ads from multiple different data sources. 

If you need help tracking and visualizing your Facebook Ad performance, our Support team can actually help build your first dashboard for free. 

Just create your free Databox account and reach out to our team either via chat or email.

See you next time. 

The post Is Your Facebook CPM Too High? Here’s How To Track It appeared first on Databox.