Product Analytics — the 5W1H Approach to Building Stickier Apps
The goal of product analytics is to take an evidence-based approach to development resulting in products that are sticky and keep customers engaged. In practical terms, every product owner needs to be able to answer these key questions (aka the 5W1H approach) :
- who’s using my product?
- when are they using it?
- how are they using it?
- what features are being used?
- what’s my share of habit? (the % of the time my product used to accomplish a task relative to other alternate ways)
- what attributes and behavioral traits identify the users who are most and least likely to recommend my product to their peers?
Answering these questions begins with collecting the right usage data, and incorporating the insights from that data into every aspect of your operations — from product management to customer support to account management. If you are not collecting product usage data, then you’re leaving money on the table and probably losing customers (or at least missing a BIG way you could be making your customers happier). Embracing usage analytics can make your product stickier and your customers happier.
The Power of Product Analytics
Whatever your product or service is, if you’re not digging into how your customers are using it, you’re flying blind. Focus groups, user testing, and surveys can only tell you part of the story. True usage analytics show you what people are actually doing.
Every product management decision at some level requires the optimal use of your resources. When you roll out a new product or feature, usage analytics can show you if customers are actually taking advantage of it. Netflix tracks usage analytics to figure out which movies to invest in. Spotify does the same thing, using behavioral data to sign the right artists. And Amazon clearly recognized that shoppers were buying the same items regularly and created the “Subscribe & Save” program. A deeper understanding of your users’ behavioral patterns will help you better serve them and reduce churn.
Beyond Usage Reports
Some companies have started to send usage reports to customers. These reports normally aggregate metrics over a time period but they don’t go far or deep enough. Does it have any historical data at all or is it just “here are your stats from last week!”? Data without context is not that powerful.
Trying to anticipate your users’ wants and needs by sending them reports based on those assumptions is a good start. However, these reports do not anything to change the users product experience in the short term and for most end users stats around usage is meaningless to how they perceive the value of the product.
Product Analytics with Infinity: A Winning Approach
At the minimum, your product analytics roadmap should we adopt a multi-pronged strategy that can deliver on some of the objectives listed below:
- Comprehensively capture every user interaction
- Gather granular metadata that can contextualize each user interaction
- Implement workflows to monitor and grow NPS; through automated surveys and proactive intervention based on red flag metrics
- Enable customer facing scorecards with flexibility and self-service
- Incorporate behavioral data to personalize end user experience in real time
- Elevate the role of user engagement insights in prioritizing your product roadmap
Many product teams do not pay attention to product analytics because they are primarily organized and budgeted around delivering customer functionality. A full-fledged product analytics strategy requires dedicated development resources. When customers work with Infinity, they can easily leapfrog these hurdles with minimal effort. For every app built with the Infinity platform, the system natively logs every user click, search, drill down, filter selection and any other user initiated interaction. Critical technical (e.g. device info) and user level metadata (e.g. location) are tracked and overlaid to add context. Further, Infinity uses AI to automatically personalize the product experience for each user based on the behavioral data aggregated across all users (suggested insights, search algorithm etc.)