?> Frictionless product analytics with Rakam
Snowplow Outperforms GA4 in GigaOm Comparative Evaluation.
Read More
  1. Home
  2. Blog
  3. User Stories
User stories

Frictionless product analytics with Rakam

Jump to

Product analytics has evolved at a rapid pace over the last ten years. With the arrival of tools like Amplitude, Mixpanel and others, product teams can quickly get data at their fingertips, enabling them to explore user behavior and identify areas for improvement. With little time to value, these tools make it simple to work with product data and equip product managers with the insights they need to ship stronger products and to develop better features.

Solutions like Amplitude and Mixpanel are market-leading product tools for a reason – notably their powerful features that help product teams build a funnel, calculate lifetime value, analyze and reduce churn, and more. The tradeoff is that, in using these tools, you’re relinquishing control of your data. Product analytics tools typically require you to send your data through their servers, similar to how Google and Adobe Analytics work. As well as losing control over your data, you’re losing the ability to govern it. You don’t get to decide, for instance, how the data is structured and modeled and there are no guarantees on the data’s accuracy or completeness.

‘We thought why not build a BI tool, mainly for product analytics, that will just connect to your data warehouse and let you run queries on that rather than requiring you to send your data to our servers – which is how most product analytics tools work at the moment’ – Burak Emre Kabakcı, Founder at Rakam

This level of control is crucial, particularly because robust data management has become an essential part of driving a data-informed culture. With the rise of the data team and a shift towards a centralized data asset, we have seen the appetite for storing and managing behavioral data in the data warehouse – where it can be transformed and directed towards limitless use cases. With this in mind, organizations need a solution that will enable them to leverage behavioral data for product analytics, using the data warehouse as the single source of truth. 

What is Rakam? 

Rakam is a product analytics platform that draws behavioral insights from the data warehouse, enabling product teams to build reports like funnels and user flows without shipping their data out to third parties. Founded four years ago, Rakam shifted an initial focus as an on-prem BI tool towards becoming a product analytics platform that connects directly with the data warehouse. Like Snowplow, Rakam is private-SaaS, meaning that users can run Rakam on their cloud environment and never lose that all-important control over their infrastructure. 

We realized a lot of data-informed companies were leveraging data pipelines like Snowplow to get their data into the data warehouse. – Burak Emre Kabakcı, Founder at Rakam

Rakam became possible in the context of a new generation of warehousing solutions like Snowflake. More companies were sending raw event-level data into the data warehouse, and from there using business intelligence (BI) tools for reporting and decision making. Innovations to data warehousing has skyrocketed the efficiency of product analytics. With solutions like Snowflake and BigQuery, data teams can deliver low-latency data to analysts, who can run behavioral analytics queries in a matter of seconds. This wasn’t possible five years ago. 

As such, the data warehouse has emerged as the ‘brain’ of the organization – the source of truth from which multiple teams can extract and make use of behavioral data. Business intelligence tools such as Looker, Tableau, Power BI and more have made it easier to build powerful reports and dashboards. But while these tools offer much in terms of general reporting, they are lacking in terms of deeper product insights and user intelligence, and data exploration is only really accessible for those who are proficient in SQL.

Removing the barriers from product analytics 

Rakam addresses the need for an intuitive BI tool that specializes in seamless product analytics. Integrated with the data warehouse, Rakam enables product teams to dive into their user behavior, exploring granular data through complex queries. 

The great thing about Rakam is that any querying complexity is hidden behind its crisp user interface. While data engineers can build complex SQL queries, product managers are not always SQL proficient, but with Rakam they can explore behavioral data from sources like Snowplow and Segment, without having to rely on the data team. Not only does this speed up the time to value for product teams, it frees up data engineers and analytics engineers to focus on delivering value, rather than getting weighed down by ad-hoc requests. 

Over the last few years Snowplow has made data accessible to more teams within the organization. Rakam is continuing this trend. – Burak Emre Kabakcı, Founder at Rakam

As a result, Rakam boosts data productivity on all sides of the analytics process. If a product team can explore queries without friction and without having to go through data engineers, they will ask more questions and gain a deeper understanding of their users on their own. 

And while solutions like Snowplow aim to open up behavioral data as a resource to the whole organization, tools such as Rakam go a long way to democratizing data. 

‘There is no BI tool that can handle all use cases. There are always trade-offs, which is why there are BI tools for behavioral analytics, subscription analytics, product analytics. We expect to see more BI tools coming into the market that leverage the data in the data warehouse.’ – Burak Emre Kabakcı, Founder at Rakam

What’s more, Rakam makes it easy to surface the rich product insights that a product manager would expect from tools like Mixpanel and Amplitude. Rakam users can quickly build funnel reports or ask questions such as “which users opened the application over this time period” or “what is the churn propensity of these users”. Features familiar to product analysts such as user flow overview and cohort analysis are prebuilt into Rakam and ready for product teams to spin up and explore. 

Joining the dots 

As is the case with all data use cases, it’s vital to ensure high quality data enters the warehouse. Integrations with tools like Iteratively which help you stay on top of your tracking, and Snowplow’s schemas to validate data structure upfront means that Rakam users can explore product data with a high level of assurance in their data quality. 

“Events are still validated by Snowplow’s Iglu schema, Rakam just provides a helpful UI.” – Emre Semercioğlu, Product and Growth lead at Rakam

Integrations have a lot to do with that makes Rakam powerful. When connecting with Snowplow’s schemas, for example, all of Snowplow’s ready-made event properties have been mapped into Rakam’s UI. That makes it much easier to drill down into different event types and explore user behavior, because rather than searching through a list of 50 dimensions or columns in a tool like Looker, Rakam users can easily select the events they want to see from a drop-down menu. It’s features like these that makes product analytics accessible to product teams, without compromising on data quality or overall control.

The future of BI 

A single BI tool will not solve all of the data challenges and use cases in a modern organization. But a BI tool that’s designed to integrate tightly with the rest of the data stack can certainly increase data productivity and enable internal teams with valuable insights. 

Rakam’s arrival in the data ecosystem is timely. It resonates well with data teams who look to the data warehouse as the single source of truth. It empowers product managers to dive deeper into user behavior, without wrestling with lines of SQL. And its integrations with Snowplow, dbt, the data warehouse and other tools means that Rakam can deliver data quality and control over data that modern teams have come to expect. 

More about
the author

Snowplow Team
View author

Ready to start creating rich, first-party data?

Image of the Snowplow app UI