Product Analytics Vocabulary

Activation is the moment a new user first experiences the product's core value — the "aha moment" — and it is one of the strongest predictors of long-term retention.

Different products define activation differently: for Slack it might be "sent 2,000 messages as a team"; for Dropbox it was "put one file in the Dropbox folder on two devices." The activation event should correlate with retention — users who hit it should retain significantly better than those who don't. A 45% activation rate is the starting point: the PM's job is to understand why 55% of new users don't reach the aha moment and remove the friction causing early drop-off. Common levers include onboarding redesign, time-to-value reduction, and empty-state improvements.

Key vocabulary:
• activation rate — the % of new users who complete the event(s) that define first value delivery
• aha moment — the specific point in onboarding when a user first experiences the product's core value
• time to value — how quickly a new user can reach the aha moment from sign-up

D7 retention is one of the most important early-stage product health metrics — it tells you whether the core product loop is compelling enough to bring users back a week after they first experienced it.

Retention benchmarks vary by category: social/gaming apps often target D7 > 30%; B2B SaaS tools with weekly use cases might target D7 > 40%. Below-benchmark D7 is a signal that needs diagnosis: are users understanding the product's value? Is the core loop completing? Is the product too hard to use? Retention curves are typically visualised on a graph (cohort retention chart) and the "flattening" of the curve — where it stops declining — indicates the product's retained core. If the curve goes to zero, the product has no retained core.

Key vocabulary:
• D7 retention — the % of a cohort that returns on day 7 after their first session
• retention curve — a graph showing the % of a cohort still active over time; flattening indicates a retained core
• cohort — a group of users who started using the product in the same time period, tracked together

A 60% drop at a single funnel step is an abnormally high friction point that warrants immediate investigation — it means more than half of users who reach the payment step abandon before completing it.

Funnel analysis tells you where users are dropping out, not why. The PM's job after seeing this data is to diagnose the cause. Qualitative tools help: session recordings (Hotjar, FullStory) show what users actually do at that step; exit surveys capture their stated reasons; usability tests reveal cognitive friction. Common payment-step problems include: price revealed for the first time (no pricing page earlier in funnel), too many required fields, lack of trust signals (SSL indicator, money-back guarantee), unclear next steps after payment. Each hypothesis gets an A/B test to measure causal impact.

Key vocabulary:
• conversion funnel — a sequence of steps users must complete to reach a goal, tracked to identify drop-off points
• funnel drop-off — the % of users who abandon the funnel at a specific step
• conversion rate optimisation (CRO) — the practice of systematically improving each funnel step to increase the overall conversion rate

Cohort analysis is a retention diagnostic tool — it shows not just overall churn rate but when in the lifecycle users are leaving, which points to the specific problem to solve.

Different churn points indicate different problems: Day-1 churn = onboarding/first-impression problem; Week-1 = activation or habit formation failure; Month-3 = the "sophomore slump" where initial novelty fades and competitors are evaluated at renewal; Month-12 = pricing or ROI justification at annual renewal. For month-3 churn, the PM should interview recent churners to understand the trigger ("What changed in month 3 that made you stop using the product?"), segment churners by use case or acquisition channel to find patterns, and look at usage data to see whether churners were using the product differently from retained users in months 1 and 2.

Key vocabulary:
• cohort analysis — tracking a group of users over time to understand behaviour patterns at specific lifecycle stages
• churn — the rate at which users cancel, stop using, or don't renew a product
• lifecycle stage — a defined phase of the user journey (onboarding, activation, habit, renewal) with distinct retention dynamics

When a small segment drives the majority of revenue, it is both the most valuable asset to protect and a strategic concentration risk to actively manage.

Power users are worth understanding deeply: Who are they? What workflows do they use most? What would make them increase usage? What would cause them to churn? Often the answers reveal product expansion opportunities (features that make power users even more powerful) or new user acquisition strategies (targeting companies with more power-user-like profiles). The 80/20 revenue concentration also means the product is fragile: losing a few power users has outsized revenue impact. PMs should track power user health metrics separately and establish a direct feedback channel (customer advisory board, design partner programme) with this segment.

Key vocabulary:
• power user — a user who engages with the product at significantly above-average frequency and depth
• revenue concentration risk — the business risk of depending heavily on a small number of users or customers for the majority of revenue
• customer advisory board — a structured programme giving top users early access and a direct input channel into the product roadmap