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.
What Tasman delivered
- Data strategy & platform development was crucial in the funding Kaia raised in September 2019 and June 2020. Co-founder Manuel Thurner said: “The BI and Analytics infrastructure that Tasman set up played a big role in our Series B fundraise”.
- The significant growth Kaia saw over the course of 2019 and 2020 was due to a clear product direction based on data strategy (up to 300k users by the end of October 2019, >1M in 2020).
The Problem Kaia Faced
- BI bottleneck with outdated BI tool & silo-specific reporting (Apple App Store installs & subscriptions in one report, Google Analytics in another, Backend Report data for financial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).
- Compliance challenges because of HIPAA: 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 the Snowplow Managed Service, and had special requirements working with providers like Looker.
- Investors from A Round (raised Q4 2018) were very keen on Kaia starting a data strategy with the end goal of informing marketing spend efficacy. Acquisition of new customers is crucially important as one of the company targets is to increase the % of B2C customers significantly.
- No internal understanding of the differences between BI, data science and analytics engineering.
- Build the infrastructure to collect, clean, transform and model client interactions in the mobile app and on the website (Single Source of Truth).
- Build a reporting suite in Looker covering all major business metrics (giving parity to legacy reporting systems first, and then extending from there) and offering self-service to product managers.
- Work with product to build a data strategy where data informs product design; and product is built with data collecting in mind.
- Help Kaia hire an internal Head of Data, who can take over the strategy side of the project.
- Help Kaia understand additional data team profiles, following industry best practice.
- Deliver templates for deep insight work: conversion drivers, churn drivers, customer segmentation.
The project contained three silo's: infrastructure, team, insight. Summarising per silo:
- 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)
- Data collection done in the apps, website, and backend all running via Snowplow into Redshift
- Data modeling done with an Amazon EC2 server orchestrator, facilitating maintenance and transparency for their analytics engineers (who took over the data model after our engagement ended)
- 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.
- No data team was present when we started the project, and when we closed the engagement in July 2019 there were two full time data team members we could hand over to.
- Build of a profile for Head of Data in a challenging recruitment environment (Munich)
- Assist in recruitment process: build of interview tasks, technical fit, etc.
- Build of profiles for the other team members:
- Analytics Engineering (responsible for collecting, modelling and delivering clean & structured data)
- Data Science (consuming the clean & structured data and delivering insight in the form of Looker dashboards and ad-hoc deep analysis)
- Set up of a team spec and goals for 2019 (setting them up to work off of a roadmap built at start of the project based on stakeholder interviews)
- Insight delivery was always part of the initial spec, but infrastructure (to wit, clean data) needed finalisation first before insight could be produced at scale.
- Main deliverable: 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)
- 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).
- Three insights sprints were run, and then were handed over to the internal team.