KPIs are a measure to differentiate Outcome from Output. At the end of a quarter, a product manager’s success is determined by the KPI moved in the right direction. Using this quantifiable lens, what KPIs you choose for your product and how you measure them becomes super crucial to your success. Note that from here on, I’m using KPIs and metrics interchangeably.
There are different types of KPIs, and how to choose the ones relevant to your needs can get tricky. You might have questions like what features to build next or how your existing product is functioning. And to make the right decisions, you need to figure out the right KPIs. With the mere hope of helping you, I’m listing down some literature for your mental model.
North Star Metric
Before jumping into the different categories of KPIs, let’s talk about a North Star metric. For any PM at a company, the North Star metric is the hero metric that –
- Expresses value
- Represents vision and strategy
- Is actionable, understandable, and measurable
- Is not a vanity metric
Example: For Uber, the number of rides per week defines the core impact for the end-users. Similarly, for Spotify, the number of minutes spent streaming music is the critical metric determining value provided by the streaming platform.
Beyond the North Star metric, there are generally 3 different KPI buckets – Behaviour metrics, Revenue metrics, and Feedback metrics.
This category highlights how the users engage with your product or gives a sense of the user cohort actions and more. Say, in a gaming app, you look at DAUs, MAUs, Retention metrics. These user behavior metrics are defined on top of the clickstream and event data you would track for end-users. You can also further cut across metrics as per different stages of your product loops:
- Acquisition stage – Looking at CAC (Customer Acquisition Costs), CTR (Click-through rate) on certain landing pages, or VTI (Visit to Install) rates give you the idea of the top of the funnel behavior for your end-users.
- Activation stage – Now, once users have installed your app, you’d want them to sign up and perform a core action, which means that they understand the underlying value derived from your product. So you might look at the number of signups, then the number of people who played “x” songs, and finally the ratio of users who subscribed to the premium offering to users who signed up (subscription conversion rate).
- Retention stage – After users have adopted (signed up for) the product, you want to look at repeat user behavior. You want to know if users come back to your product and perform the same action again. This is answered with Cohort Analysis, i.e., figuring D7, D14, D28 retention, and so on. The crucial part here is defining the core action for tracking retention, i.e., playing a game or listening to a song for x minutes. Consequently, also defining the natural frequency of usage for your product. For example, daily frequency applies for a social app, while monthly frequency works for a flight booking app. Depending on the frequency, you would track retention for the core action in daily, weekly, or custom frequencies that apply to your product and user group.
- Referral stage – As users get used to your app, they become champion ambassadors for your product. You would want to look at referral coefficient, virality metrics, and success for referrals across channels (word of mouth, referral links, etc.)
A product needs to monetize to provide sustainable business value eventually. It is said Retention provides acquisition muscle, and it is true for products that have been able to drive monetization through strong user retention. The Average Revenue per user (ARPU) or Lifetime Value for the user (LTV) are major revenue metrics. In an e-commerce context, you also look at average cart value and average net revenue (subtracting possible taxes, refunds). For B2B/SaaS products, Annual Recurring Revenue (ARR) and Potential sales pipeline revenue on a quarterly basis are also important.
Product and service companies collect active user feedback in the form of public reviews, customer satisfaction (CSAT), net promoter score (NPS), customer effort score (CES), and more. But, as a PM, you could also collect passive user feedback for your product’s delight/experience quotient by getting specific task completion rates or performance metrics. This is especially seen in strong customer-obsessed product teams where PMs won’t just rely on global CSAT numbers but would define custom product delight metrics for the part of the product they own. This can be done by collecting customer feedback inactively as well. Or think of small ticks, thumbs up, and certain positive actions on the product (user reviews, positive sentiment score, etc.)
Lastly, LTV/CAC is an essential metric for determining a product/startup’s legitimacy and status of product-market fit.
Interpreting data and defining metrics is not easy and can be an iterative process. Note that you might pick the right metric, but how you slice it makes all the difference. For instance, if you are looking at average values instead of median values, it could skew your results. Have you looked at outliers or seasonality factors for your KPIs? Make sure you spend enough time defining and understanding KPIs. A product manager needs to be good at massaging available data and figuring out which metrics really matter.
In product management interviews, you also get questions about your ability to prioritize and differentiate between L0 and L2 metrics. L0 is your decision-making metrics, whereas L2 is your secondary metrics that provide insight but are not necessarily clinical to your global problem hypothesis.
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