Data makes better products, period! Part 3/3

Johannes Schauer
sclable
Published in
3 min readAug 21, 2023

Find out how to optimize data-driven product management in your company.

How to optimize data-driven product management

Your digital product or service is live, and users are interacting with it. Well done!

The first days are always exciting, aren’t they? Analytics are flowing and provide the data necessary for your product team to start identifying room for improvement. You’re ensuring the data is coming in as expected and is of the desired quality. You’re evaluating if the dashboards and reports you created are being filled as expected, and — after sufficient time has passed — if goals are being met. You’re getting ready to implement feedback regularly.

But then, one day, the momentum dwindles. The project becomes part of day-to-day “life”, and despite good intentions, using data for continuous improvement falls to the wayside.

Assuming everything with your product or service is technically fine, in my experience the way forward is clear: you need to make working with data a habit — for the entire product team and management. At Sclable, we bake this into projects by establishing a build-measure-learn cycle that is executed over and over. The steps are simple:

  1. Use analytics and feedback data to generate insights and check goal achievement
  2. Derive improvements based on insights
  3. Review goals and modify if needed
  4. Implement improvements (and go back to step 1)
Build-Measure-Learn Cycle with Analytics & Feedback

A straightforward way to kick-start this cycle is to book regular check-ins (e.g. weekly or monthly, depending on your user base) with at least the product owner, product manager and a data/business analyst. Other stakeholders may also be valuable to integrate, like customer care, UX architects or developers. The aim of these meetings should be to jointly review the latest feedback and analytics insights (primarily but not exclusively prepared by analysts), identify potential issues and check the status of goal achievement. They also allow the team to get familiar with working with data and deriving actions from it. In these meetings, you should all be asking questions like: “What can be learned from the data?”; “Are users finding their way around?”; “Where do they get lost?”; “How many of the users are successfully completing tasks?”; “What kind of feedback are we getting?”; and “Are users raising specific issues or requesting certain features?” As the team becomes more familiar with working with data, fewer check-ins may be necessary.

Working data-driven should become a habit — ideally for the entire product team and management.

Once your insights are gathered, the team should jointly define actions that will improve your digital product or service. Doing this effectively requires defining how the success of an action is measured and if a change to the analytics must happen to measure it. It’s also important to review if the goals you’ve set are still relevant, or if they need to be modified.

If it becomes unclear why certain metrics are being reviewed, or your data/business analyst seems to be just showing insights while others watch, the team needs to work out how to make check-ins engaging for everyone.

What’s most important is ensuring you repeat this build-measure-learn cycle, so that the entire team quickly picks up on the benefits of regular interaction with data. The first iterations may be bumpy, but in my experience, they are essential to continuous improvement. Keep working with your data in a cross-functional team, together with your growing experience you will unlock great potential in your product with data-driven product management!

Key takeaways:

  1. Metrics are of real value only if they are directly tied to goals
  2. Analytics & direct user feedback are the key to continuously improve user satisfaction
  3. Working data-driven requires a cultural change of everyone involved

Want to read up on how to set goals or how to collect and use data? Check out my first two articles in this series: “Part 1: How to set relevant goals” and “Part 2: How to collect, interpret and leverage data

Follow Sclable on LinkedIn for more content like this or check out our website to see the work we do!

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Johannes Schauer
sclable
Editor for

As Director of Data & AI Transformation at Sclable, Johannes is dedicated to driving business impact by translating strategic goals into trusted data solutions.