This document discusses how to define key performance indicators (KPIs) for platforms. It begins by explaining why platform KPIs are important, such as improving decision making and learning unseen insights. Common pitfalls like only using qualitative data or vanity metrics are outlined. The author recommends starting by measuring your value proposition and scaling instrumentation over time. Examples of platform KPIs provided include adoption metrics, use case coverage, extensibility, technical performance, and workflow metrics. Overall, the document provides guidance on strategically defining KPIs to measure a platform's performance.

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How to Define Platform KPIs by King
Product Director

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5. How to define Platform KPIs
Ramon Martinez Orellana
Product Director at King

6. Who am I?
https://www.linkedin.com/in/ramon-martínez-orellana-46a3713a

7. Agenda
● Why should you care about defining Platform KPIs?
● Common pitfalls
● How to go about it?
● Some examples

8. A few concepts first
1. What is a Platform
https://martinfowler.com/articles/talk-about-platforms.html
“A digital platform is a foundation of self-service
APIs, tools, services, knowledge and support which
are arranged as a compelling internal product.
Autonomous delivery teams can make use of the
platform to deliver product features at a higher
pace, with reduced co-ordination.”
Evan Botcher, 2018

9. https://info.container-solutions.com/platform-as-a-product-webinar
“Treat the platform as a product (reliable, usable, fit
for purpose) for voluntary internal customers”
Matthew Skelton
A few concepts first
2. Platform as a Product

10. KPIs are your most important metrics in moving
your product forward. All KPIs are metrics. Not
every metric is a KPI.
A few concepts first
3. Metric vs KPIs

11. Why should you care about
Platform KPIs?

12. Volume 22 (2020)
Adopt: Applying product management to
internal platforms
Trial: Platform engineering product teams.
Industry trend

13. Avoid the “loudest voice in the room” pitfall.

14. Improve the overall quality of your decision making by
being data informed.

15. You will learn things that you cannot see using
qualitative data

16. Common pitfalls

17. “Why bother about KPIs, we are a platform and we can
just directly talk to our customers and ask”.

18. “We need KPIs to show the money we are bringing to the
company”.

19. Spend endless time discussing the perfect KPIs.

20. Not prioritizing instrumentation over feature
development.

21. Measuring the “what would we charge for if we were our
own company”. It leads to vanity or lagging indicators.

22. Product VS Technical performance metrics

23. Tackling this problem without analytic skills.

24. How to go about it?

25. Take time to build your case if you encounter friction.

26. Get some analytics support if you can, call in favours if
necessary.

27. Start, if you don’t have it already, with your value
proposition and try to measure that.

28. Think about how to scale up from the beginning,
get buy in from management. Especially if you are
a big platform team.

29. Start somewhere, prioritize it and iterate on
instrumentation and KPI definitions and calculations.

30. KPIs should be tightly coupled with your strategy, and
they can change over time

31. Live up to your word. Once you get going find ways to
“sell” how you are using data.

32. Avoid (as much as you can) wasting energy on
measuring what your contribution to revenue
generation is. Manage up.

33. Some examples

34. Some examples
1. Adoption metrics
a. Which customers are using
your product.
b. Not relevant if customers do
not have a choice.
c. Usually important when you
are launching a product.

35. 2. Use case coverage/variations
metrics
a. For how many different use cases
(of all of them) are customers
choosing your product.
b. How many variations of the same
thing are customers doing with
your product.
c. Important to measure penetration
across your customers as the
product matures.
Some examples

36. 3. Extensibility metrics
a. Usually a platform product
has to support extensibility if
you don’t want to become a
bottleneck.
b. How much are your users
extending your product?
c. Sometimes is even good to see
the cross sharing of
extensions between
customers.
Some examples

37. 4. Technical performance metrics
a. Uptime, #incidents, response
time...
b. Some platform products,
especially the more you go to
the core, the main value is for
them to “just” work flawlessly
Some examples

38. 5. Workflow metrics
a. Time to do X
b. If you struggle with it, flip it and
count volumes rather than round
time.
i. Be weary of the trap,
sometimes you save people
time not to do more of the
same, but to have time to do
something else.
Some examples

39. Thanks!

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