Industry
Healthcare
Company size
50-100
Established
2016
Value
>200M USD
Location
Munich & New York
We implemented a modern data stack (Snowplow, Redshift, Looker), built the initial set of dashboards that allowed the teams to self-serve, analysed customer retention behaviour, and handed everything over to a team we helped hire.
Kaia is a companion app for people suffering from chronic back pain treatment. Patients can supplement their existing treatments with personalised exercises and get advice from professional coaches directly within the app. Their business model is subscription-based and part B2C (patients downloading and subscribing directly), part B2B2C (patients acquiring a subscription via their health insurance provider). Success is measured by the number of daily workouts patients complete, and the reduction in their self-reported pain levels. Both are particularly important for the B2B2C side of the business as the insurers want to see that Kaia can help reduce the number of visits to the therapist.
The team was suffering from major BI bottlenecks with outdated BI tool & silo-specific reporting (Apple App Store installs & subscriptions in one report, Google Analytics in another, Backend Report data for financial transactions, etc). We needed to find a way to solve that quickly and efficiently. There were also extra complications because of necessary compliance: Kaia is a healthcare product operating in the United States, and therefore needs to follow much tighter regulation than a ‘normal’ consumer app. That meant that they could not work with typical managed services (e.g. the Snowplow one), and had special requirements working with providers like Looker.
“The BI and Analytics infrastructure that Tasman set up played a critical role in our Series B fundraise and beyond.”
Manuel Thurner, Co-Founder & CO-CEO
We selected Snowplow Analytics open source event data pipeline, with a support contract from Snowplow at cost as the Managed Service but with the ownership of the data and infrastructure in Kaia’s hands, making it HIPAA compliant. We set up data collection in the apps, website, and backend—all running via Snowplow into Redshift. Data modelling was done with an Amazon EC2 server orchestrator, facilitating maintenance and transparency for the Kaia analytics engineers (who took over the data model after our engagement ended).
Insight delivery was always part of the initial spec, but the infrastructure needed to provide clean first before insight could be produced at scale. We set up Looker for visualisation, offering explores per data silo (Marketing, Product, Finance) and linking the silo’s together for explorative analysis (for example, the impact of marketing decisions on product usage and finance). We built around 15 dashboards to serve the needs of the different teams.
We delivered a set of iPython Notebooks with two goals: Simple insight delivered straight away (new conversion drivers were identified that the product team were not aware of, leading them to change their onboarding flow accordingly; user segments were defined that allowed product to better understand why some groups of customers behave differently) But also to be used as templates for future internal Kaia data science work (as we owned the structure and modelling of all the data, this was a crucial part of the deliverables).
No data team was present when we started the project, and when we finalised the engagement in July 2019 there were two full time data team members we handed over to. We designed and delivered a profile for Head of Data in a challenging recruitment environment (Munich). We assisted in the recruitment process in detail: not just the job spec but also the build of interview tasks, technical fit, etc. Delivery of job specs for the other team members was done as well:
