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Product analytics

Warehouse-first analytics is presenting new frontiers for product teams, enabling them to describe product interactions in granular detail. This in turn creates highly-personalized and user-centric product experiences which set products apart from the competition.

What challenges does product analytics present?

With the economic pressure on modern product teams, there is increasing demand for data-led product decisions, as well as the use of advanced analytics and AI.

Along with this, iteration cycles are faster than ever as a response to crowded and competitive markets, requiring constant testing. Product teams always need to know where to invest their attention and whether what they have done is actually working, but the data often can’t answer these questions.

Next, there’s the increasing complexity of user journeys, which often span various platforms, devices and channels.

Product analytics tools can also create their own issues, as they come up with their own set of internal logic and assumptions, which may not apply to your business.

These tools are also limited when it comes to privacy regulation, as tracking prevention methods make it increasingly difficult to capture all user interactions, e.g. blocking tracking on Safari users after a few days.

Learn more in our Ultimate Guide to Product Analytics


Product teams can move towards a warehouse-first approach to analytics, allowing for advanced use cases which are simply not possible with traditional product analytics tools. These include predictive models and advanced segment building.

To avoid migration pains, product analytics tools – like Amplitude and Mixpanel – can become a warehouse destination, allowing product teams to continue using these tools to easily self serve – but with far richer data sets.

One major advantage of this approach is that data is not kept in a Product team silo, but is used to inform every part of the business, which in turn strengthens the product. You can, for example, use metrics from marketing, like ROAS and CLTV, to inform product updates.

With warehouse-first analytics, there is a real opportunity to differentiate from your competition with better insights into user interactions.

Use cases

Real-time A/B testing: Test, iterate and automate as well as porting known users between different experiments to discover more of their user story.

Churn Prediction: Establish the actions most predictive of churn and use them to trigger the right word to the right person at the right time.

Customer 360: Circumvent ITP restrictions and track across devices to get a true picture of the full user journey.

Business-wide metrics: Utilize metrics like ‘Customer Life Time Value’, ‘Search Intent’, as well as custom metrics from across departments to inform the product.

Value prediction: Find the behaviors shared by your highest-value users in order to understand your audience and streamline your offering.

“Now we turn to Snowplow for about 90% of our use cases; it is a really structured part of the feature development process. ”





Read our guide to advanced product analytics


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