Mastering Conversion Analysis: A Deep Dive into Funnel Reports

In the fast-paced world of digital marketing and product analytics, understanding the intricacies of user behavior is not just advantageous—it’s essential. One of the most powerful tools at your disposal for dissecting user journeys is the funnel report. Effective use of funnel reports can illuminate the path to increased conversions, reveal bottlenecks in your user experience, and guide strategic decisions that drive business growth.

Here are the 5 unique capabilities of Kubit’s funnel report tool that have made it an indispensable asset for data-driven professionals aiming to unlock actionable insights from their user data.

Understanding Funnel Reports in Kubit

At its core, a funnel report is a visual representation of how users progress through a predetermined series of steps or actions on your website or app. This progression could relate to anything from completing a purchase to signing up for a newsletter.

Kubit’s Funnel Report offers these 5 capabilities to get the most out of your data:

  1. Multi-step Funnel Creation: Craft funnels that reflect the complexity of real user journeys.
  2. Partitioning Options: Slice your data by day, session, or custom conversion windows for nuanced analysis.
  3. Deeper Conversion Insights: Break down funnel stages by various fields to uncover underlying patterns.
  4. Advanced Visualization: Choose between step-by-step breakdowns or time-based line charts for dynamic report viewing.
  5. Cohort Analysis: Right click and build users into cohorts for targeted behavioral analysis over time.

Use Cases for Funnel Reports

The applications of funnel reports in Kubit are diverse, mirroring the myriad pathways users can take towards conversion. Here are just a few scenarios where Kubit’s funnel reports can be most valuable:

  • Enhancing User Onboarding: Track new users’ progress through your onboarding sequence to identify and rectify any stumbling blocks.
  • Optimizing Product Engagement: Discover where users disengage or drop off when interacting with specific features or content.
  • Streamlining Conversion Paths: Measure the time it takes for users to move from one stage of your funnel to the next, and deploy strategies to accelerate this progression.
  • Analyzing Behavior Pre-Conversion: Understand the actions repeat users take before finally converting, providing insights into which features or content are most influential in driving conversions.

Through these use cases and beyond, Kubit’s funnel reports offer actionable insights that can powerfully impact business strategies and outcomes.

Real-World Success with Kubit Funnel Reports

Consider Influence Mobile, a customer that leveraged Kubit’s funnel reports to uncover a costly problem. By carefully analyzing their onboarding process and identifying friction points with Kubit’s tools, they significantly improved user retention. Furthermore, Kubit’s capabilities enabled them to detect patterns indicative of fraudulent activity, ensuring a secure and trustworthy platform for their users. Their success story underlines the potential of Kubit’s funnel reports to transform challenges into triumphs.

Getting Started with Funnel Reports in Kubit

Kubit simplifies the process of building and deploying funnel reports. To get started:

  1. Define Your Conversion Goals: Determine what user actions or sequences you want to analyze.
  2. Set Up Your Funnel Steps: Using Kubit, create a funnel that reflects these steps in your user’s journey.
  3. Analyze and Iterate: Once your data starts flowing, use Kubit’s insights to refine your strategy and improve user outcomes.

Understanding how to interpret the data from your funnel reports is crucial. Look not just at where users are dropping off, but also why. This often involves cross-referencing funnel data with user feedback, usability tests, or other analytics reports.


Kubit’s funnel reports are a potent tool for anyone looking to enhance their understanding of user behavior and drive meaningful improvements in their conversion rates. Whether you’re just starting on your analytics journey or are looking to refine your approach with cutting-edge tools, Kubit offers a robust platform designed to elevate your analytics capabilities.

“The more people are looking at this data, the better. Everyone should be monitoring our most important conversions,” states a seasoned user of Kubit, underscoring the collective benefit of widespread engagement with analytics within an organization.

Ready to transform your data into actionable insights? Sign up for Kubit or reach out for a demo today, and discover how funnel reports can redefine the way you view your users’ journeys from first touch to conversion.

Unraveling the Truth About Warehouse-native Product Analytics

In recent years, warehouse-native has become a popular topic in the product analytics market. Along with the maturity of the modern data stack, it is not a coincidence that more and more companies have realized the need for customer insights coming directly from their warehouse. However, there needs to be more clarity from vendors making false claims to ride on this wave. In this blog, I will review the history of product analytics, explain the rationale behind the warehouse-native approach, and unveil the benefits of this new generation optimization tool for digital products.

Integrity vs Speed: The History of ‘Siloed’ Product Analytics

Historically, analytics has been conducted directly in a data warehouse. Consider traditional Business Intelligence (BI) tools like Tableau, Looker, and PowerBI; typically, data analysts create reports and charts in these tools to visualize the insights that ultimately stem from executing SQLs in their own data warehouse. The data control is entirely in the hands of the enterprises, though this approach requires dedicated and influential engineering and analytics teams.

With the exponential growth of digital products, from web to mobile applications, a different way of conducting analytics has emerged, starting with Omniture (later becoming Adobe Analytics) and Google Analytics. Due to the dynamics in the ecosystem, few enterprises’ data teams can keep up with the constant requirement changes and new data from different vendors. It became well-accepted to sacrifice integrity for speed by embedding SDKs and sending the data to third-party silos, and relying on a black box to get insights.

For a while, everyone was happy to rely on impression, conversion CPI/CPM, etc., and metrics from external analytics platforms to guide their marketing campaigns and product development. With the mobile era, the need for Continuous Product Design arose, along with a new breed of Growth Marketing people who rely on product insights to drive user acquisition, customer engagement, and content strategy. That’s when Mixpanel and Amplitude came into existence to provide self-service customer insights from their proprietary platforms, aiming to run fast and bypass data engineering and analytics teams.

Governance, Security, and Privacy: Rethink the Black Box   

Fairly soon, the industry started to correct itself. Sharing customers’ private data, like device identifiers, is no longer acceptable with other vendors. Many enterprises now realize that it is impossible to have complete data governance, security, and privacy control if their sensitive data has been duplicated and stored in third parties’ data silos. How can they trust the insights from a black box that can never reconcile with their data? Without a Single Source of Truth, there is no point in running fast when your insights don’t have the integrity to justify the decisions.

Let’s face it: why should anyone give up their data to third parties in the first place? With the new modern data stack, especially the development of cloud data warehouses like Snowflake, BigQuery, and Databricks, the days of having to rely on external analytics silos are long gone. More and more enterprises have taken data control as their top priority. It was time to rethink product analytics: is it possible to explore customer insights with integrity and speed at the same time?

Without a Single Source of Truth, there is no point in running fast when your insights don’t have the integrity to justify the decisions.

The Rise of Warehouse-native

Cloud warehouses have become many organization’s source of truth, leveraging millions of dollars in infrastructure investments. Scaling access to this information used to be as simple as dumping all the data into a warehouse and reverting to the BI tools. Unfortunately, reporting tools like Tableau, Looker, or PowerBI were designed exclusively for professionals answering static questions. To get insights, most product, marketing, and business people rely on analysts to build reports to answer their questions. Going through another team is tedious, slow, and even worse, highly susceptible to miscommunications. The nature of optimizing digital products necessitates ad-hoc exploration and spontaneous investigation. If each question takes hours or days to be answered, the opportunity window may have closed long before the decision is made.

This self-service demand and warehouse-native motion triggered a new generation of tools that provide SaaS product analytics directly from customers’ cloud data warehouse. It perfectly balances integrity and speed, which should be the objective of  analytics platforms.

If each question takes hours or days to be answered, the opportunity window may have closed long before the decision is made.

What is the Warehouse-native Way?

Here are four characteristics to identify a true warehouse-native solution:

Tailored to your data model

A warehouse-native solution should continually adapt to the customer’s data model instead of forcing them to develop ETL jobs to transform their data. Besides sharing data access, there should be zero engineering work required on the customer end, and all the integration should be entirely done by the vendor.

The effortless integration is one of the most significant differences from the traditional data silo approach, which mandates the customer to build and maintain heavy-duty ETL batch jobs, which could take months to develop and yet still can break frequently. One example is how Amplitude claims to be warehouse native, but in reality, it just means their application is “Snowflake Native” (running as containers) but still requires customers to transform their data into Amplitude’s schema. 

Data should never leave your control

This should be assumed under the term ‘warehouse-native’. However, some solutions are engaging in warehouse syncing or mirroring to copy customers’ data into their data silos. Some admin UI may be provided to configure the data connection and eliminate the need for custom ETL jobs, but if you see words like “load,” “transform,” or “sync,” the system is essentially making copies of customers’ data into its silos.

Besides losing control, the biggest problem with data duplication is how they adapt to customer data changes. There will be a constant struggle for backfilling, scrubbing, restating, and reprocessing when there are data quality issues, or data model changes (e.g., a new attribute or a dimension table), which are fairly common and happen regularly.

Besides reducing some engineering work, achieving a Single Source of Truth or data integrity with a data syncing method is impossible. It’s difficult to trust a black box without visibility into how insights are generated.

Complete transparency with SQL

One of the most prominent traits of a proper warehouse-native solution is to provide customers with the SQL behind every report. Since the data lives in the customer’s warehouse anyway, there should be complete transparency on how the insights are computed. Such a level of transparency can guarantee accuracy and provide reconcilability and allows customers to extend the work from product analytics platform to more advanced internal development, like machine learning and predictive modeling.

Dynamic configuration with exploratory insights

Because all reports come directly from the data in a customer’s warehouse leveraging SQL, every insight should be dynamically generated. There are several significant benefits:

  • Underline data changes will immediately be reflected in analytics. There is no data to scrub, no cache to poke, and no vendor to wait for.
  • Raw data can be analyzed on the fly in an exploratory manner. Warehouse-native analytics supports virtual events, virtual properties, dynamic functions, and analyzing unstructured data (e.g., JSON or struct), which helps in hypothesis testing before committing to lengthy data engineering work.
  • Data model improvements can be iterative and incremental. When new attributes or dimensions are added, they automatically apply to historical data. There is no data backfill required because everything happens with dynamic joins. With the multi-schema support, it is possible to have both raw and clean data schemas running in parallel to satisfy the speed and consistency requirements simultaneously.

Incorporate operational data without the need for ETL. All of the clickstream/behavior events, vendor data and operational tables can be dynamically combined for analytics, all inside the customer’s data warehouse with no data movements required.


With its unique advantages and momentum in the market, enterprises will inevitably choose warehouse-native analytics to optimize their digital products and explore customer insights with integrity. In the meantime, it is vital to look through the marketing claims and find truthful solutions. In upcoming blogs, I will cover the real-world use cases for applying true warehouse-native product analytics solutions to different teams and industries.

Meaningful Metrics for Product Analytics


In the digital age, data is the lifeblood of any business. It can transform a company’s trajectory, inform strategic decisions, and predict customer behavior. But data alone isn’t enough. It’s the application of relevant metrics that can truly drive business growth. When created and measured appropriately, metrics can help illuminate the path to better customer experiences, optimized products, and business success. However, not all metrics are created equal. The key lies in selecting ones that are meaningful, actionable, and tied to your specific business objectives.

In this blog post, we dive into the importance of metrics in product analytics, how to set the right ones, and when to measure and evolve them.

Understanding Quality Metrics

Quality metrics provide actionable insights that are specific to your business. They’re quantifiable, easy to understand, and directly linked to your key performance indicators (KPIs).

For instance, an essential metric is Viewing Time in seconds if you’re a streaming media business like ViX. That heartbeat metric is directly tied to the business goals of driving more watch time and directly impacts revenue. Please check out this case study for a more detailed overview of how ViX teams use Kubit to support and enhance their daily work.

Setting Quality Metrics

Identifying the right metrics is vital for your product’s success. Here are some common categories of metrics to consider:

  • Acquisition: how users find and start using your product. Examples include marketing campaign efficacy, traffic source breakdowns, and customer acquisition cost (CAC).
  • Activation: how effectively your product engages users after they sign up. Examples include time to the first essential action and completion of the onboarding process.
  • Engagement: how active are your users? Examples include the Viewing Time per Session or Likes per Day. Typically, these metrics should have a time factor instead of just simple counts.
  • Conversion: how effectively your product drives users towards desired actions. Examples include free to premium signup, checkout, and referring a friend. Typically a Funnel or Path (Sankey diagram) can provide visibility of the users’ journey.
  • Impact: demonstrates the effect of your product on user behavior. Examples include the impact of new product releases on user behavior and engagement.
  • Retention: how well your product keeps users coming back. Examples include how often and at what rate users are returning and how that retention rate changes over time.
  • Churn: Churn metrics help you understand why users stop using your product. Examples include churn rate, reasons for churn, and average time before churn.

Choosing the right metrics depends on your business type, product, and specific goals. There’s no one-size-fits-all approach, but keeping these categories in mind will guide you toward meaningful metrics that reflect your product’s performance and user behavior.

Measuring and Evolving Metrics

Once you’ve identified the right metrics, the next step is to measure them regularly to understand the baselines and make informed decisions. The frequency of measurement depends on the specific metric and your business needs.

For instance, DAUs (Daily Active Users) might be measured daily, while churn rate or retention might be measured monthly or quarterly. Reviewing and updating your metrics periodically ensures they remain relevant as your product and market evolve.

Also, remember that metrics should be seen as tools for learning and improvement, not just reporting. If a metric is consistently underperforming, use it as an opportunity to investigate, learn, and iterate on your product.

Metrics with Kubit

Kubit stands out in the crowded data analytics space due to its unique ability to seamlessly handle a comprehensive spectrum of data types, including but not limited to online, offline, transactional, operational, and behavioral data. Our warehouse-native approach ensures that organizations have the ability to access, analyze, and assimilate ALL of their data with Zero-ETL. This sets a new standard for creating, measuring, and adjusting metrics, offering unparalleled flexibility and precision. Unlike other solutions that mandate predefined data models in their data silos or limit the scope you can view, Kubit’s platform empowers you to explore every facet of your data and gain deep, actionable insights. This differentiation unlocks improved data-driven decision-making and gives you a competitive edge in today’s data-centric business environment.


Meaningful metrics are the guiding compass in navigating the expansive realm of product analytics. They provide a clear direction, enable informed decisions, and drive business success. By understanding what good metrics look like, how to set them, and when to evolve them, product managers and data analysts can increase their positive impact on business outcomes.

Remember, numbers tell a story. Ensure your metrics tell a story that matters to your business. Happy analyzing!

Explore your data with Multi-Dimensional Data Tables

When analyzing data, analysts often need to compare various measures simultaneously and break them down by different properties and segments. Introducing Kubit’s latest analysis chart, Data Tables, which allows for multi-measure, multi-dimensional analysis in a single view.

Tools like Excel and Sheets have provided this type of data visualization and it works! While you may still want to see data in funnels, lines, bars and pie graphs; it can sometimes be best to see it laid out in a table view.

Our Customers are using Data Tables to understand things like:

  1. Cross Tab Analysis
    • How are User engagement metrics across different user segments and features?
  2. Custom Measures and KPI Analysis
    • Compare custom-defined measures or KPIs across different dimensions
  3. Segmented A/B Testing
    • Analyze user segments by control vs. variant groups
  4. Impact of Marketing Campaigns
    • Show click through rate, conversion rate by user segments and Campaigns all in one report

Getting Started with Data Tables:

  1. Navigate to Report → Data Table.
  2. As you can see from the snapshot below, the end user can easily begin adding new events, saved measures, breakdowns and segments.
  1. Highlighted below is an example of a user selecting 3 saved measures, building 2 measures on the fly and breaking it down by Country (United States, Canada, United Kingdom), Plan Type and Platform.
  1. When executed, the below table will be displayed. Users have the ability to sort, search, adjust columns widths, export to CSV, and view the SQL behind the chart.

Take it for a Ride

Now that you have a high level overview of Kubit’s Data Table, click through the guide below and get a feel for it yourself. If you’re interested in learning more, please reach out to our team.

Click the below GIF to walkthrough the demo.

Work around 5 common Data Quality issues with Kubit


We already know the perfect data model for product analytics but even with a perfect data model you can get tripped by other data issues on your way to obtaining insights. It often happens that a data issue is uncovered while working on a report in Kubit and it suddenly blocks the task at hand. Unfortunately, data issues typically take time to fix – in the best case scenario as early as the next sprint, often a month or two and in some rare cases the issue cannot be resolved at all. So while at Kubit we advocate for data modeling and data governance best practices, we have also developed a few features to help you work around 5 typical data issues in a self-service fashion while the root cause is being addressed:

  • Missing Data
  • Duplicate Data
  • Ambiguous Data
  • Inconsistent Data
  • Too Much Data

In this blog post we’ll explore how you can leverage these features to save the day whenever a data issue tries to stop you from getting your work done!

1. Incomplete Data

Very often we have some building blocks in our data but we don’t quite have the value we want to filter by. For example, we may have a timestamp property generated when a user installs our app, but for our report we want to measure the percentage of daily active users who installed our app within 7 days. Or we might want to filter by session duration but this information is not available when each event is triggered and must be computed afterwards. Or we may even want to extract the user’s device platform from a user-agent header.

Whenever this is the case you can reach out to the Kubit team to define what we call a “virtual property” which will be computed on the fly on top of your existing data. To continue our first example, let’s call the virtual property Install Days and it will be based on a timestamp column named  install_date. Now we can think of our virtual property in SQL like this:


However, it looks like and is used as any other property within Kubit which makes our analysis very simple –  we get the amount of unique users who are active and filter by  Install Days <= 7, then divide that by the total number of daily active users like this:

2. Duplicate Data

Duplicate Data is always a pain to deal with and in the context of product insights we usually see it in the form of duplicate events. You can already leverage Kubit’s zero-ETL integration to do as much data scrubbing as you need. The results of your work will be immediately available in Kubit without any extra effort required. However, we often get asked to try and resolve some duplication on the fly – maybe the team who can fix the issue is overloaded, or there is some third-party responsible for the events generation – in both cases the process to resolve the issue will take any time between a lot and never.

Again, “virtual property” can come to the rescue, as we can generate a virtual property based on some criteria on only one of a set of duplicate events so we can distinguish it from the rest. Let’s consider the following example – imagine we have 5 purchase events for the same user, all for the same purchase but  at different timestamps:







completed purchase


2023-10-23 15:23:11



completed purchase


2023-10-23 17:05:47



completed purchase


2023-10-24 10:32:03



completed purchase


2023-10-25 22:11:59


In this case, if we want to find the number of unique users who made a purchase, the duplication is not really a problem. But if we want to count the number of purchase events or aggregate the purchase_amounts, then our results will be way off.

How does Kubit fix this?

We can advise on the best solution, but one example is to assign a boolean property Deduped with a value true on the first of a sequence of duplicate events. Kubit can easily select the first duplicate event in a time range using some SQL along those lines:

CASE row_number = 1 ROW_NUMBER() OVER(PARTITION BY user_id, purchase_id ORDER BY event_date ASC NULLS LAST) AS row_number

And once we have the first event of the sequence we can assign the virtual property. So now we can aggregate without any adverse effects caused by the event duplication:

3. Ambiguous Data

What if 2 events in our dataset are easy to confuse with one another? Perhaps the naming is not ideal and people often make mistakes when they need to use them for a report. Let’s say we see 2 Signup events in Kubit – Sign Up and sign_up

But what is the difference between the two? Maybe one is a front-end event and the other is a back-end event, but the names don’t reflect that. There is a quick fix you can make yourself in Kubit to make the difference between the two events much clearer. You can simply go to Dictionary -> Event and  Rename from the Context menu for both events to give them more appropriate names, e.g. Sign Up (server) and Sign Up (client), and a nice description:

4. Inconsistent Data

This is true especially for multi-platform apps. As soon as you start instrumenting on multiple platforms inevitably from time to time there will be discrepancies between the implementations which can result in any of the following issues:

  • the same event comes back with a different name from one or more platforms
  • property name is different on one or more platforms compared to the others
  • property value mismatch between platforms

4.1 Same event, different name

Let’s say we have the same event coming back from different platforms in 3 different flavors – FavorFavourites and Favorites.

Such a situation can be extremely frustrating as you now have to go talk to multiple teams responsible for each instrumentation, align with their release schedules, prioritize the fix and wait for it to go live so you can go back and finish your work. This is one of the reasons why we developed Virtual Events as a way to group and filter raw level events to create new entities which have exactly the meaning we want them to.

It’s super easy to create a Virtual Event, anywhere in Kubit where you have an Event Group an a Filter you can save that combination like this:

And then the Virtual Event will simply appear in any event drop-down with the rest of the regular events, so you can use it for all types of reports:

4.2 Property name mismatch

Let’s say we have a streaming platform and for all the streaming events we have a property called Stream Type. However, a typo was made when implementing the instrumentation on Android and the property is called Stream type instead. Now, for the purposes of our reports we want to treat these two as one and the same, so that our metrics don’t get skewed.

To fix this in the data warehouse properly we would need to:

  1. correct the Android instrumentation in a new app version
  2. go back in our historical data and fix the property name retrospectively

And we still haven’t solved the issue completely – what about all the people who are using older app versions and don’t have the instrumentation fix? They will keep generating data using the inconsistent property name. Turns out a simple typo will be causing us trouble for a long time in our reporting.

There’s 2 solutions which Kubit can provide to help you work around such issues:

  1. You can create Named Filters using both property names and save them for reuse
  2. The Kubit team can easily make such configurations as to treat both properties as one and the same

Let’s explore option #1. In this case we have 2 properties which are actually the same – Plan Type and PlanType. So whenever we want to filter by one of them we actually need to filter by both in order to ensure our filter is applied correctly: 

To help prevent mistakes you can then save a Named Filter which others can re-use. Also helps you save time by not having to create the same filter over and over again:

Once the filter is saved you can use it anywhere in Kubit:

4.3 Property value mismatch

This typically wreaks havoc in our report when we group by that property. For instance, a simple typo in a property value will lead to our report containing 2 groups of the same thing instead of 1 as in the example below:

To overcome issues like this on the spot you can use Kubit’s Binning feature:

Using the Value Binning option you can define custom Groups – in this case we want to merge back Winback Campaign and Winback Campaing into one group and then we want to leave  Group Othersturned off so all the other groups remain as they were:

Congratulations, you’ve successfully removed the extra group from your report:

5. Too Much Data

What if our perfect data model contains more event types than we need for our analytical purposes? Or we have an event which is still noisy and in the process of being fixed, so we want to prevent people from relying on it in their reports?

The Dictionary feature in Kubit keeps track of all your terms – Events and Fields coming from the raw data and also concepts defined by you such as Measure and Cohort. Dictionary also allows you to easily disable an event, which means it will no longer be available for selection in any event drop-down in Kubit. All you have to do is go to Dictionary -> Event and then hit Disable from the context menu of the event you want to hide:

Note that in the case where you are dealing with a noisy event you can easily enable it once the underlying issues with the event generation have been resolved.


We just explored 5 ways to overcome common data quality issues in Kubit and get to your insights on time. The best part is that all of these solutions are dynamic and the mapping happens at runtime so you can take action immediately. You don’t ever need to invest in complex ETL jobs to update and backfill data. This also gives you the ability to test some hypotheses with real data with live product insights.

At Kubit, we want our customers to have the best possible experience, so please, do let us know what else you would like to get from Kubit to tackle data quality issues!

Accurate vs. Directional: The Tradeoff that Product Leaders Need to Make… Or Do They?

One of the first questions I’ve seen asked in those big meetings, the ones we’ve all spent weeks or even months preparing for, is this: “Are these numbers accurate, or more directional?” 

You break into a cold sweat… 

I think they’re accurate?…

I think we used the same data from our main source of truth?… 

Then, you nod in full confidence. “These are more… directional… For now.”

What a relief. Weight lifted off. Now, the meeting can continue.

But something changed, without anyone saying a word. 

A giant shadow has fallen over the meeting. You realize that future facts and figures you present may be seen with that shadow hanging overhead. 

Result: you didn’t make the impact you’d hoped for.

Confidence now clouded by directionality

The tradeoff between numbers being accurate vs. being directional is an ongoing battle – and one that’s particularly challenging in Product Analytics, for reasons I’m going to explore in this article. 

But first, a quick reminder of what we mean when we say “accurate” vs. “directional.” 

“Accuracy” is when the information derived from your data can be confidently shared with internal and external stakeholders; it’s been “blessed” as delivered from your Single Source of Truth. 

“Directional,” by contrast, refers to information that’s considered “good enough” to validate or justify initial decisions. Generally, directional data is not good enough to form the basis of the final numbers you present. And it certainly should not be part of the measurable outcomes shared with your stakeholders.

Often, the outcome of the accuracy-vs.-directional struggle isn’t poor decision-making, but a lack of decision-making; if you’re unsure of the accuracy of your data, then the default is to not take any action based upon it. 

But, in Product Analytics, sometimes the worst thing you can do is nothing. Product Managers (PMs) are often expected to make decisions quickly – decisions that can have a major impact on revenue and user engagement.

Why accuracy vs. directionality is challenging in Product Analytics 

There are two reasons why Product Analytics is unique when it comes to accuracy-vs.-directionality.

First, Product has an ENORMOUS amount of data. I’m talking trillions of events per day in some cases. 

For many organizations, this data is monstrous, ever-changing, and non-standard. This means that fitting their product data into existing solutions for data governance becomes very challenging. Example: “Active User definition at Company A does not equal Active User definition at Company B.”

The second reason why the accuracy-vs.-directionality question is tricky in Product Analytics: Product people are relatively new to data being a core part of their day-to-day work, compared to teams like Business Intelligence (BI) or even Marketing – which have always been front-and-center in the data game. Building confidence in the data they use and making decisions off of it can be challenging for PMs, especially when it comes to the accuracy-vs.-directional tradeoff.

(Secret third reason… I work for a company that specializes in Product Analytics, Kubit… We all know what we are doing here, right?? 😂)

Weighted Scale Confusion

Traditionally, Product Managers shared data internally, and sometimes weren’t asked for data at all because it was too cumbersome to wrangle; however the prophecy that Product would become the profit center is coming true across many industries. With that high visibility, Product’s key numbers–like Monthly Active Users (MAU), total downloads, activated users from free-to-paid, etc.–have risen to the highest level of importance, sitting alongside the dollars and cents. This is an amazing development. 

But with higher visibility comes greater scrutiny. 

In this new Product-first world, the de facto mode must be accuracy. PMs and Data Engineers can no longer rely on directional metrics.

So, how do you make accuracy your de facto mode?

Collecting information is step one, and there are several methods that businesses use to accomplish this. Each has their own tradeoff and nuance (a topic that we can dive into in another blog), but let’s run through the highlights. 

Tools that have their own data collection methods tend to be inflexible, forcing you to conform your data into their schema. Maybe you’ve been farming the collection out to an auto-tracking tool, or you trust the logging done to monitor uptime as “events.” These collection methods can lead to data that’s potentially accurate, but that may be prone to misalignment with how YOUR business thinks about this data. 

The only way a company can fully understand where and how its product data came to be: first-party collection. But even first-party collection can be challenging! 

So what do you do?

You collect stuff, using whichever method you decide. You need this data to make decisions on your product bets, experiments, and growth strategies. My point of view aligns with what we’re seeing in the market today: an increasing awareness that product data must live inside an organization’s source-of-truth data warehouse

Typically, data warehouses and BI reports are designed, governed, and maintained to uphold a single source of truth. If we want Product Analytics data to hold the weight it deserves, then it too must live up to this standard.

So… Once your Product Analytics data is stored in the data warehouse, you have the ability to access it via multiple solutions, and you’ve achieved accuracy nirvana…right? Not so fast.

Now, you have to decide how you want to analyze this data. Tools available in the Product Analytics space typically follow the same value proposition: “send us your data, and we’ll optimize it for you so you can run high-performance queries on complex user journeys.” This is great! Until… certain limitations arise. 

When your user base grows and events balloon, now you have to pay a large bill, or begin pruning that data via sampling or excluding events. Another problem: you’ve also created another “source of truth,” because the data has left the warehouse. When it breaks, who fixes it? Now, we’re creeping into directional territory…

Before you know it, you can see the directionality shadow encroaching into your next Product leadership meeting.

With next-gen tools, directional data can be a thing of the past

To avoid the accuracy-vs.-directionality tradeoff, a new generation of tools have emerged that leverage your data warehouse directly – no sampling or pruning needed, and no alternative “truth” sources created in silos. These new solutions provide insights using existing data investments made by your organization, leveraging the cloud and removing the requirement to ship data outside your firewalls. The result of this warehouse-native approach: you have Product Managers who are enabled to work with fully accurate data.

Rachel Herrera leads Customer Success at Kubit, the first warehouse-native Product Analytics platform. Do you have thoughts on accuracy-vs.-directionality or on improving your product analytics workflows or infrastructure? Drop her a note at

Four Product Analytics Trends Worth Investigating

Product Analytics Is A Field That’s Constantly Evolving, And It’s Important For Companies To Stay Up-To-Date On The Latest Trends And Technologies In Order To Make Informed Decisions About Their Products. In This Blog Post, We’ll Explore Some Of The Latest Trends We’ve Been Observing In Both Medium-Sized And Large Companies.

Product Analytics is a field that’s constantly evolving, and it’s important for companies to stay up to date on the latest trends and technologies in order to make informed decisions about their products. In this blog post, we’ll explore some of the latest trends we’ve been observing in both medium-sized and large companies.

Holistic Data Integration

One trend that’s quickly gaining traction is the integration of qualitative and quantitative data to gain a more complete picture of product performance and customer behavior. While traditional product analytics have focused mainly on metrics such as retention and conversion rates, companies are now seeking to understand the “why” behind these numbers. This shift towards a more holistic approach has led to an increase in the use of customer feedback, surveys, and user testing to complement traditional metrics. This integration of data sources provides a more in-depth understanding of how customers use and perceive products, which can then inform product development and marketing strategies.

Machine Learning and Artificial Intelligence in Product Analytics

The second trend on our list is the use of machine learning and artificial intelligence in product analytics. Machine learning algorithms can be used to analyze large amounts of data and identify patterns and insights that would be difficult or impossible to find manually. Some examples of machine learning and AI in the product analytics world include anomaly detection and root cause analysis. Both serve to increase data quality and remove incorrect findings.

Real-time Analytics

The third trend we’re seeing: with instant insights becoming more and more of a factor, real-time analytics are on the rise. This involves collecting and analyzing data as it is generated, rather than waiting for it to be processed and analyzed later. This allows companies to make faster and more informed decisions, which can be critical in today’s fast-paced business environment. As a growing number of companies adopt real-time analytics it will become increasingly important for product analytics professionals to have a strong understanding of the work of both the data team and the business teams. This will allow them to effectively communicate the insights and recommendations generated by their analyses to stakeholders and decision-makers, and to work closely with product managers and engineers to implement changes and improvements.

Warehouse-native Platforms

The last product analytics trend on our list is the emphasis on privacy and data security. Amid growing concerns and regulations about data privacy and security, companies are looking for ways to collect and analyze customer data without compromising their customers’ rights. This has led to the development of new technologies and techniques for anonymizing and aggregating data, allowing companies to gain insights without exposing individual customers. Additionally, companies are turning to warehouse-native product analytics solutions that do not require sending a copy of their data to a third party. Warehouse-native platforms allow companies to use their own data model, and they guarantee full control of the data, with a  single source of truth. By prioritizing privacy and security in their product analytics, companies can gain valuable insights while protecting the rights of their customers.

In conclusion, the field of product analytics is constantly evolving, and companies that want to stay competitive in the future will need to stay up to date on the latest trends and technologies. By leveraging these emerging trends, companies will be able to gain a deeper understanding of their customers and their behavior and to make informed decisions that will help them improve their products and increase revenue.

‍No-Code Product Analytics — And How It Solves Your Problems

When you’re building and selling a digital product, timely analytics can make all the difference. Understanding the ways customers interact with the app can help you fine-tune the experience you deliver. A/B testing of features and campaigns can guide optimization efforts to increase engagement and satisfaction. Insight into viral adoption patterns can inform where and how to invest marketing and social media resources. By translating data into knowledge throughout the product lifecycle, you can acquire the right customers, maximize their revenue potential, drive growth, and increase retention.

With benefits like these, it’s no surprise that the product analytics market is booming. In 2021, companies spent $9.3 billion worldwide on product analytics tools—with total revenue projected to reach $29 billion globally by 2028. However, these investments may not always yield the hoped-for returns. In reality, product analytics can only deliver a meaningful business impact if you can access the right insights at the right time, quickly and easily. And first generation product analytics tools fall far short of that requirement.

Let’s dive into a few reasons why no-code product analytics can be a game-changer.

An Obstacle Course of SDKs, ETLs, and Silos 

In a business environment where speed is everything, most product analytics tools are still architected as if teams have all the time in the world. Before you can even think about insights, you need to instrument your SDK to capture your data or build an ETL pipeline to load data from your cloud data warehouse into the vendor’s siloed black box. That’s especially challenging given the way data is stored in a data warehouse like Snowflake or Big Query, which call for a structure or schema that’s hard for legacy product analytics tools to ingest without extensive transformation. These time-consuming and resource-intensive projects will add weeks or months to your timeline. Even then, you’ve got to comply with their data model, not your own.

If the word “silo” sends chills up your spine, you’re right to be wary. Creating an alternate sense of truth invites no end of complications and confusion. Keeping both sets of data in sync will now be a constant concern to avoid issues resulting from data movement, data duplication, and irreconcilable differences, and an even greater challenge when you have to ask the vendor to make changes on their end and hope that they do it. As governance and transformation take place within the vendor’s environment, that data needs to be pushed back to your own data warehouse—for yet another copy of your data. Your security and compliance teams won’t be happy about that loss of control either.

Higher storage costs add insult to injury. With a pay-as-you-go cost model, companies often worry about event volume, leading them to either sample or cut back on the different events they are capturing with the product. That limits the amount of analysis they can perform.

All that extra effort and cost might be worth it if you ended up with the product analytics tools of your dreams—but no such luck. Instead, you face additional delays and friction every step of the way. Want to build a dashboard? Add a table and backfill data? You’d better not be in a hurry. Meanwhile, product teams have no idea what’s actually happening inside that black box—how their queries are being run, how the resulting insights are being derived, or even whether the vendor understood their request in the first place.

When you’re spending millions on customer acquisition, going head-to-head with fast-moving competitors, and trying to retain customers in constantly shifting consumer markets, you need fast access to insights you can trust. That calls for a new approach to product analytics.

No-Code Product Analytics: Warehouse-Native for Fast Insights from a Single Source of Truth

Kubit DWH Single Source of Truth
Kubit DWH Single Source of Truth

The architecture of traditional product analytics tools might have made sense in the past, before companies developed their own metrics collection capabilities. These days, they want the flexibility, security, and control that comes with building their own data stack, complete with a modern cloud data warehouse. When you have your own environment, why should you have to move your data anywhere else?

No-code product analytics is built for the way companies capture and use data today. Instead of having to move your data to a vendor’s environment, and deal with all the resulting silo costs and headaches, a new generation of solutions let you connect the vendor to your live data right where it is, in your own cloud data warehouse. That means you can skip all that SDK and ETL business while maintaining a single source of truth that eliminates the need to coordinate data backfilling or scrubbing across multiple copies. Just as importantly, you know exactly how your data is being secured because you’re doing it yourself—and the ability to share data as read-only, with control over which columns to share or mask, makes regulatory compliance a lot easier too.

For users, no-code product analytics replaces inefficient workflows with direct control over both data and queries. Analysts can see exactly how each analysis is constructed, and can add new dimension tables, data, and properties as needed without a lot of time-consuming back-and-forth or development work. Queries can be performed on complete data, not just a sample, enabling more accurate and comprehensive insights. Results can be compared easily with other data in the cloud data warehouse for deeper understanding. As a result, analysts can get fast answers to key questions like:

  • Which product features are customers using most often—and which are they overlooking?
  • Where in the user journey do we tend to lose the most customers, and why?
  • Did a recent campaign bring in customers with high lifetime value or better retention, or should we rethink our targeting?
  • How long is it taking people to perform various tasks, and how did this change in our latest update?

As customers demand more personalized experiences and recommendations, digital competition continues to intensify, and data becomes a key differentiator, the importance of product analytics will only grow in the coming years. Product teams need to escape the limitations of traditional tools and embrace a faster, simpler, more flexible, and more secure way to access insights. Designed for cloud-native speed and agility, no-code analytics can help businesses make the right decisions at the right time to improve engagement, increase retention, drive growth, and succeed in the modern digital marketplace.

Funnel Analysis

What is funnel analysis?

Funnel analysis is an essential way to observe and describe a customer journey as a process with different stages that users go through. It usually involves several steps, from entering an app or web page to performing a particular action. It is called a funnel because of its shape that becomes narrower and narrower. By observing your funnel, and analyzing and adjusting its parameters, you will be able to improve conversions and customer satisfaction.

The funnel is an excellent tool for marketers, product managers, sales, and data scientists to understand user behavior better. Whether you wish to convert visitors into customers or you want your customers to buy more of your products, or even make them stay more in your app, funnel analysis is essential. Having a good understanding of your funnel is like using a GPS to guide you to a place you want to visit. It will show you the speed, the direction, and whether you’re on time or going to be late.

Conversion rates can help you understand the number of visitors who came to your website and bought a product or performed an action, such as watching a movie, downloading a document or submitting a lead form

How does funnel analysis work?

There are steps you would expect your visitor to take, from entering your website or mobile app to taking an action, such as making a purchase. With a simple funnel analysis, you can visualize your visitors’ steps to convert. Creating a funnel allows you to observe where exactly visitors or users are dropping.

First, you collect data through user tracking, SEO, email campaigns and other methods. Note that you need to have your data available and ready for funnel analysis. Then you define the steps that will be evaluated.

A simple funnel tracks how users convert from entering a landing page to checking out, or watching a movie, or another goal conversion. The funnel itself is usually presented as a bar graph. You will know where to look next when you see a decline or drop in your funnel. Usually, this is the time when an imaginary light bulb shines above your head. Understanding each step of the funnel and making necessary adjustments to see what works and what doesn’t will eventually lead to more conversions.

Funnel Analysis Stages

When creating a Funnel there are a few things you need to consider.

  1. What is the purpose of funnel analysis? Our goal may be to generate more leads, to achieve more transactions, to make people more engaged with your product, or something else? If you provide a streaming platform maybe daily active users (DAU) are your focus.
  2. What is the conversion rate you expect? This is your benchmark to work from. Let’s say you’ve finished an outstanding marketing campaign. You forecast a significant increase in conversions. What will your funnel look like? You can compare older funnels, Q1 to Q2 sales. If, for instance, you’re starting a business in a new country, you can correlate conversion rates.
  3. What could you improve to raise the conversion rate? I.e. How can you optimize your conversion rate? Maybe an ad campaign, discount offers, introduction of a new product or new service, additional benefits…. Should they do the work? A/B tests your theories with real data examples. And finally, use those improved results to achieve your goal. Segmenting users and comparing funnels will give you a deeper insight into what the next step should be.

Please refer to Kubit’s Documentation to learn more about how to create a funnel

Types of Funnels

Sometimes your customers don’t follow the path you have created for them. Users can enter your funnel in a variety of different ways. That’s why it is important to define your funnel. There are a few types of funnels:

  • Open funnel – where users could enter any step and still be counted in the analyses
  • Closed funnel – to be counted, a user should go from step 1 to step 2 to step 3, and so on. But there might be other steps, for example, between seeing a product and buying that product, a visitor might read a blog, compare products etc.
  • Strict funnel – as the name says it means there are no other steps between the steps you define and if the visitor is not following that route, he won’t be counted in the analyses.

Do’s and Don’ts of Funnel Analysis

Funnel analysis shows you whether people are dropping off, but it doesn’t tell you why they do it. Brainstorm the potential issues. Is the registration process too long and hard to fill, do you have clear messages and good product descriptions, are there too many steps and blanks to fill in before ordering a product, etc.

Quality over quantity – it is vital to not just attract visitors but also to make them stay. If you want to increase the quantity, then observe from where your audience is coming – social media, Google ads, Internet search, etc. Increasing the number of visitors doesn’t guarantee higher conversion. But, proper maintenance of the funnel will help get the most out of your new users.

Fine-tune your filtering! Some customers might be looking for a particular product while others are just browsing. Filter out those who are not your target.

Conversion windows. If you are selling shoes, from first opening a product page to placing an order, it might take a couple of minutes, but if you are comparing streaming services, it will take a little longer to complete the task. Keep that in mind.

Funnel Analysis Benefits

  • Allows you to determine key events on customers’ journey
  • Improve customer experience and satisfaction
  • Track any changes in your visitor habits
  • Helps you decide where you can increase budget and where you can scale back
  • Compare conversions between different dimensions like countries, genders, age buckets, app versions, and many other segments.


A visitor is coming to your website, and he’s seeking products, then if the products are interesting enough, he will add them to the basket and then end up buying them. Let’s explore three stages of a user journey:

  1. Awareness stage – a user is on your website or an app, and they have a problem. You get the attention of your future customer or user.
  2. Review – the visitor is on the product/service page and scrolling up and down. He gets familiar with your product or service. Maybe he is comparing prices, using provided filters, and estimating how much he needs the product. Anyway, you’ll never know what’s in his head. As a result, he sees the solution for his problem in your website or app.
  3. Finally, he makes a decision. The visitor wants your product or service and makes a purchase. Congratulations, your visitor is now a customer.

That’s the ideal route, but sometimes visitors can go back and forth on the steps. Let’s dig a little deeper. Here’s where the true funnel analysis happens: If visitors are dropping off between 1st and 2nd steps, you should check whether there is enough information about the product. Are you providing helpful information like a help menu or a chat box, or anything visitors can use as guidance? If a visitor is dropping between the 2nd and 3rd steps, you should focus on prices, check competitors, and make sure you have a unique product or service. By observing where your visitors are dropping off, you can define your weak points and discover places where improvements should be made.

There is one final step – coming back or re-engaging. After seeing a customer making a purchase, you will want to invite them on another journey of being your repeat client. You can offer a discount on their next purchase, and sign for your newsletter, and like your social media page to support your cause.

Pro Tip

High drop-off usually means UI problems. But before scratching everything and returning to the drawing board, examine the audience who are dropping. Are they teenagers or elderly? Are they your targeted audience? If not, it’s a better idea to remove them from your funnel analysis.


Funnel analysis is a useful process that will support you on your way to building an exceptional product. However, it’s not the final phase. If you want to know the middle steps that your user or a visitor takes, consider doing a Path analysis and examining where your users are getting confused. Path analysis is an integral part of conversion rate optimization. We’ll cover this in a future post.

Want to see how Kubit can help you understand your user’s behavior? Get in touch with an expert to learn more.

Self-Service: The Future of Product Analytics

Data-driven decision-making continues to become crucial for companies of all sizes. Whether you are a startup or a Fortune 500 company, understanding your users’ behavior has never been more important.

In this pursuit of growth, companies turn to product analytics to collect and analyze the information that will help them provide a better digital experience, and win over new users. The only problem with this is traditional product analytics can be labor-intensive and time-consuming.

Luckily, there are new developments in the product analytics space with regard to the collection and analysis of user data. Companies now have the option to skip the SDK or ETL integration with no-code solutions that allow product analytics to connect directly with their cloud data warehouse, creating one single source of truth.

Kubit And The Future Of Self-Service Product Analytics

Below are some ways that Kubit is helping Product Analytics move towards a Self-Service future.

Era Of Analytics Is On-Demand:

Organizations are increasingly relying on getting the insights they need when they need them. This makes for real-time decision-making and a more dynamic approach to problem-solving. But, traditional Product Analytics has not allowed for this real-time decision-making to happen.

Kubit solves this problem with their real-time user dashboard and dynamic cohorting. With Kubit, your team can get the insights they need without having to wait for a team of analysts to find it for them. To learn more about how easy it can be to get instant Product Analytics insights, click here.

The need for team-driven analysis:

Collaboration is key when an Organization is trying to get the most out of their product Analytics. But, this collaboration can be difficult when non-technical team members don’t have the tools they need to find and share the insights that they are looking for.

Kubit helps to solve this problem by providing a team-driven platform. The best analytics decisions are made by groups that work from One Single Source of Truth. Kubit’s platform fosters team cohesion, efficient communication, and data-driven cultures with a simple but elegant platform.

Bottom Line:

Whether you’re a Product Manager at an early-stage startup or a Marketing Analyst at a Fortune 500 company, you know that good data is critical for making vital business decisions.

Today, the biggest names in the consumer enterprise space are betting on Kubit’s self-service analytics platform to close this gap and give their employees greater insight into how their products and user behaviors are performing.