# Retention: day N, rolling or bracket > Three ways to measure retention and which fits a product people use weekly or monthly. - Course: [Product Analytics: From Data to Decisions](https://productovi.com/courses/product-analytics.md) - Module: Do people come back? - Lesson 13/32, ~28 min - Source: https://productovi.com/courses/product-analytics/retention-types ## 20% or 70%? **Alex Tutor:** At Halves, Theo and Maya quote retention numbers that can't both be true. Sorting that out falls to you. **Theo:** I read that good consumer apps keep at least 25% of users on day 7. Our day-7 retention is 20%. Should I stop buying installs? **Maya:** Odd. Our investor deck says week-1 retention is 70%. Same users. **Alex Tutor:** Both can be right, because “retention” is three different measurements sharing one word. Let's compute all three on ten users, so you can see each person. ![Three people watch one café door: one holds a card with a single day circled, one watches a week pass, one looks far ahead through binoculars.](https://productovi.com/courses/product-analytics/inline/retention-types-illustration-2.webp) *One word, three measurements: an exact day, a period, or ever again.* **Alex Tutor:** Each row lists the days after sign-up on which the user opened Halves. Day 0 is the sign-up day. We've watched them for 40 days. **Dataset (CSV):** [Ten Halves users and their active days](https://productovi.com/datasets/product-analytics/retention-types/halves-retention-10-users.csv) Days since sign-up with `app_opened`, separated by `;`. Observed to day 40. - `user_id`: User id - `active_days`: Days after sign-up on which the user opened the app **Alex Tutor:** First, **day-N retention**: the share of users active exactly on day N. It's what Theo's blog means by “day 7”. > **Exercise:** What is the **day-7 retention** of these ten users (active exactly on day 7)? In %. ## Rolling: “not gone yet” **Alex Tutor:** Only r01 and r04 opened the app on day 7 itself. But r02 came back on day 9 and r05 on day 12. For a bill-splitting app, that's perfectly normal. **Alex Tutor:** **Rolling retention** counts a user as retained on day N if they were active on day N **or any later day**. It answers “who hasn't left yet?” **Sofia:** So r09, who opened the app on day 0 and then only on day 31, counts as retained on day 7? **Alex Tutor:** Yes. That's generous by design, and it's why rolling is always at least as high as day-N. ![A host warmly ticks off a guest who walks back in after a long absence, as if they had never left.](https://productovi.com/courses/product-analytics/inline/retention-types-illustration-3.webp) *Rolling counts anyone who comes back later, so it's always the most generous.* > **Exercise:** What is the **rolling day-7 retention** (active on day 7 or any later day)? In %. ## Bracket: active within a window **Alex Tutor:** The third way is **bracket retention**: the share active at least once within a window, such as days 7–13, the second week. **Sofia:** So r02 on day 9 and r05 on day 12 count, but r09's day 31 doesn't. **Alex Tutor:** Exactly. It forgives the exact day but still asks whether the person showed up in that period. > **Exercise:** What is the **bracket retention for days 7–13** (active at least once in the second week)? In %. **Alex Tutor:** One more bracket so the method sticks: week 3, days 14–20. Watch the users who skip right over it. > **Exercise:** In the ten-user table, what is the **bracket retention for days 14–20** (active at least once in the third week)? In %. ## Which one fits Halves? **Alex Tutor:** Same ten people: 20% on day 7, 70% in week 2, 80% rolling. Theo's blog used day-N, the deck used a bracket. ![The same ten users give 20% day-7 retention, 70% bracket retention for days 7–13 and 80% rolling retention: name the measure next to every number.](https://productovi.com/courses/product-analytics/diagrams/m4-l1-retention-types.png) **Alex Tutor:** Pick the measure by the **natural usage rhythm**. Day-N suits products people need daily, like games or chat. For Halves, weekly brackets suit couples and flatmates, and monthly brackets suit monthly reports and rent cycles. **Maya:** And rolling? It gives the prettiest number. **Alex Tutor:** It's useful for one question: has this user gone for good? As a report it flatters, and it keeps changing after you publish it. > **Exercise:** Match each question to the retention measure that fits it best. **Maya:** So which line do I put in the deck? I don't want an investor asking why the blog says something else. > **Exercise:** Maya, the Halves product lead, is updating the investor deck. Of the ten users, 20% were active exactly on day 7 and 70% at least once on days 7–13; a blog benchmark says good apps keep 25% on day 7. Which line reports the ten users' retention **honestly**? ## A number that changes after you report it **Sofia:** What do you mean, rolling keeps changing? **Alex Tutor:** Compute rolling day-28 retention on day 35: r01, r02, r05, r08 and r09 have been back since day 28. That's 5 of 10, 50%. Then on day 40 r06 opens the app, and the same metric becomes 60%. **Alex Tutor:** Bracket and day-N retention are final once the window has passed. Rolling retention of last spring's cohort can still move next winter. ![A report already handed over keeps getting new pencil marks from a hand reaching in from the side.](https://productovi.com/courses/product-analytics/inline/retention-types-illustration-4.webp) *Rolling figures keep moving after you publish; bracket and exact-day ones stay final.* > **Exercise:** **True or false:** rolling day-28 retention reported on day 35 (50%) is final and won't change later. **Alex Tutor:** So before reporting, check the calendar. Say it's day 35 for these users: what is already safe to publish? > **Exercise:** It's day 35 after these ten users signed up. Which number can you already report as **final**? **Theo:** Fine. So what do I tell my team about that blog post? **Alex Tutor:** That the benchmark measures a different thing, and what you'll measure instead. With the numbers from these ten users. **Sofia:** And the name of the measure next to every number, so nobody compares 20% with 70% again. > **Exercise:** Write Theo, the Halves growth marketer, 3–4 sentences for his team: why the “25% on day 7” benchmark doesn't show that Halves is failing, and which retention measure Halves should report instead. Use the numbers from the ten users. > **Key ideas** > > - **Day-N retention:** active exactly on day N. Strict and noisy for products not used daily (day 7: 20%). > - **Rolling retention:** active on day N or any later day. Always the highest (80%), flattering, and it changes as new data arrives (rolling day 28: 50% on day 35, 60% on day 40). > - **Bracket retention:** active at least once in a window, e.g. days 7–13 (70%). Final once the window has passed. > - Choose by the **natural usage rhythm**: day-N for daily products, weekly or monthly brackets for Halves. > - Always name the measure next to the number; benchmarks built on another measure don't compare. **Alex Tutor:** “What's our retention?” has no answer until someone says which kind, and for which window. - Previous lesson: [Case: fix the onboarding funnel](https://productovi.com/courses/product-analytics/onboarding-funnel.md) - Next lesson: [Reading a cohort table](https://productovi.com/courses/product-analytics/cohort-table.md) --- Productovi Learning. Free, no sign-up needed to read.