North star metric

Churn rate is the percentage of customers who stop doing business with a company during a set period. It’s calculated as customers lost during the period divided by customers at the start of the period. Logo churn counts customer accounts. Revenue churn measures dollars lost. The two figures can diverge if the customers who left were smaller than average. Churn also splits by cause: voluntary churn, where customers choose to leave, and involuntary churn, caused by failed payments or expired cards. A monthly subscription and an annual contract produce structurally different churn math, so comparisons only hold within the same industry and billing cycle.

Customer lifetime value is the total gross margin a business expects to earn from a customer across the full relationship. It is not the revenue that customer generates. A workable formula is average purchase value multiplied by purchase frequency, multiplied by average customer lifespan, multiplied by gross margin. Historic CLV sums the margin a customer or cohort has already delivered. Predictive CLV forecasts future margin and discounts it to present value. The most common error is substituting revenue for margin. That substitution inflates CLV and leads teams to justify acquisition spending that a true margin-based figure would not support.

CSAT, or customer satisfaction score, is a post-contact rating averaged across responses and usually collected with a single question immediately after an interaction closes. Because only a small fraction of customers respond, CSAT reflects the experience of people motivated to answer rather than the whole contact population.

Product analytics is the measurement of how people behave inside a product. It is built on event data: the individual actions users take, such as clicks, feature use, or completed steps, as opposed to page traffic. Standard analyses include funnels (where users drop off in a sequence), retention cohorts (whether users return over time), feature adoption (which capabilities get used and by whom), and paths (the routes users take through a product). This sets it apart from web and marketing analytics, which measure acquisition: traffic sources, campaigns, and conversions. The limit is that behavioral data shows what people did, not why. That is why teams pair it with feedback data, such as OnClarity’s Voice of Customer, to explain the patterns event data surfaces.