Kinit Product Insights — Under the Hood
This is a technical follow up of the previous post by the Kinit team that shared some of the valuable insights we were able to gather from the closed beta.
If you haven’t read it yet I encourage you to do so!
In this post I wish to describe the process we took to define and setup the BI and data infrastructure that supports the Kinit app. This is the first of a series of posts I intend to share on other BI projects we do within the Kin ecosystem.
My perspective is that every product should be released from day 1 with the appropriate metrics and potential KPIs. Building Kinit from the ground up is a great opportunity to choose and setup BI architecture that will be best suited for the product goals. Straight from beginning we agreed that the Kinit beta must be released with product analytics.
BI goals for Kinit:
- Setup easy to use BI infrastructure that will allow us to learn as much as we can from the first beta users.
- Establish baseline metrics and KPIs.
- Set the ground for future data driven product optimization.
Metrics we set to measure:
- Activity: number of new/registered users, active users, churned users, total wallets created (on the blockchain).
- Engagement: number of earn/spend transactions, transaction volume (in KIN & in $$ value of gift cards).
- Onboarding Funnel: How many users register -> earn -> spend.
- Earning Funnel: How many users convert from one earning task to another?
- Failures: Measure onboarding failure, task completion failures, failure related to transactions on the blockchain and support engagement.
KPIs and Additional Insights:
- DAU — Daily/monthly active users (here we consider an active user as a person who completed a daily earning task).
- Earn to Spend Ratio — Learn about the inclination of our users to spend.
- Retention — How many users are still active after 7/14/21/30 days.
- Stickiness — Number of days a user performs an event in a weekly or monthly interval.
- User path: Measure how users interact with the screens of the app. Analyze paths users take within the app and their overall journey.
Events Definition
In order to support all of the above metrics and KPIs we defined 3 sets of events that will be triggered and logged:
- Analytic events: events that capture user actions (screen taps and views).
- Business events: events that are the source for KPI calculations and are decoupled from UI actions.
- Log events: events that will support debugging and technical drill downs.
For event definition and documentation we chose airtable. This very easy to use tool enables us to collaborate clearly, plus it has an API that the developers used to automate code generation directly from it (this is a topic for a dedicated post).
Here is a screenshot:
Database & Analysis Tools
All our events raw data is saved in Google BigQuery. We are also trying out Amplitude as a preferred mobile analytics tool (both tools are GDPR compliant, BTW).
So far the experience with both tools has been great and enables me and the rest of the team to ask and answer all of our required business questions. Amplitude provides us amazing ability to analyze funnels, event segmentation, retention and other KPIs. All without writing SQL queries or having technical background.
Looking ahead
As we are getting closer to the the open beta of Kinit we are thrilled to continue and improve the experience for our users. It’s exciting to learn from actual numbers how users engage with the Kinit app.
Kinit is built on top of blockchain giving its users the ability to truly be rewarded for their contribution they make in the digital world.
Our BI team will continue in this journey as we scale and learn more about our rising ecosystem.
