PEBMED case study

How PEBMED uses their event-level data to get a granular understanding of their users’ behavior


PEBMED is an app and web portal that supports doctors and healthcare professionals to make clinical decisions with informative medical content

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Key results

  • With custom tracking across their entire product suite, PEBMED can use their data to anticipate and cater to the specific content needs of different users.
  • Access to their raw data allows PEBMED to build custom data models and to get a better understanding of user behavior.
  • PEBMED’s consistent tracking methodology enables them to share data with different teams across over 100 dashboards.

The challenge

At PEBMED, analytics was disparate and multifarious. Each team conducted their tracking in siloes, with different product teams taking their own approach to data collection with different tools – leading to duplication and inconsistency. PEBMED had to find a solution that would ensure consistent tracking across the business to establish a single source of truth between multiple teams. They also needed the flexibility to model their data in line with their unique use cases, to uncover a greater understanding of their users.

The solution

Using Snowplow, PEBMED took full ownership of their raw data. PEBMED gained the freedom to to track any user event and build custom data models around user behavior, enabling them to identify key drivers of user engagement and retention across all their products. These insights could then be shared throughout the business thanks to a consistent tracking methodology, made possible with Snowplow.

Moving forward, PEBMED can use these insights to make informed decisions on what content to create next, and are already making strides to deliver even more relevant, personalized experiences to Brazil’s medical community.

“PEBMED’s data evolution is growing more sophisticated, thanks to Snowplow. We can see using Snowplow for everything from content recommendations to tracking internal applications and APIs to better, more granular joining up of data from individual users.”

Pedro Gemal Lanzieri,

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