# Case: retention analysis by segment

> Is Halves retention healthy for each kind of group? Run a retention analysis by segment on a year of cohort data and write the verdict.

- Course: [Product Analytics: From Data to Decisions](https://productovi.com/courses/product-analytics.md)
- Module: Do people come back?
- Lesson 16/32, ~41 min
- Source: https://productovi.com/courses/product-analytics/retention-by-segment

## Retention analysis: three segments, one budget

**Alex Tutor:** Budget planning at Halves, and a big argument needs settling. Maya asks you, her product teammate, to settle it with a retention analysis by segment.

**Maya:** We're planning next year. The CEO says trip users “churn like crazy” and wants to drop the summer travel campaign. Theo wants to double it. Is our retention healthy for couples, flats and trips?

![A road splits three ways: a couple walks a steady path, flatmates another, and travelers take a loop that curves away and back.](https://productovi.com/courses/product-analytics/inline/retention-by-segment-illustration-2.webp)

*Three segments, three rhythms, so each needs its own verdict.*

**Alex Tutor:** Here's a full year for last summer's sign-ups, by the type of their first group. Two ways of counting activity, plus how many of the active users were in a group created after they signed up.

**Theo:** Before anyone asks: yes, trips are the biggest group. More than half of last summer's sign-ups came in with a trip, many of them through my travel campaign.

**Dataset (CSV):** [Halves summer 2025 sign-ups: monthly activity by group type](https://productovi.com/datasets/product-analytics/retention-by-segment/halves-retention-by-group-type.csv)

Users who signed up July–September 2025, followed for 12 months. Month 0 is the sign-up month. Staff excluded.

- `group_type`: Type of the user's first group: couple, flat or trip
- `month`: Months since sign-up
- `cohort_users`: Users in the segment's cohort
- `active_opened_app`: Users who opened the app at least once that month
- `active_any_event`: Users with any event that month, including recurring bills created by the server
- `active_in_new_group`: Of those who opened the app, users active in a group created after their sign-up month

**Alex Tutor:** Start with the measure, as in any customer retention analysis. We count monthly activity by app opens and use “any event” only as a check. Then read each segment on its own; never average them together.

**Sofia:** Couples first. 600 users; month 12 is the last row for couples.

> **Exercise:** What share of the couples cohort opened the app in **month 12**? In %, one decimal.

**Alex Tutor:** 52.7% a year later, and flat since month 6. Barely any of them needed a new group: couples keep using the one they started with.

**Maya:** So couples are the steady part. I suspected as much, but it's good to see it flat on a chart.

**Alex Tutor:** “Flat” needs a number too. How much did couples move between month 6 and month 12?

> **Exercise:** Couples: by how many **percentage points** did the share opening the app change from month 6 to month 12? Give the drop as a positive number, one decimal.

## Flats: real use or automated activity?

**Sofia:** Flats in month 12: 315 opened the app, but 403 had some event.

**Alex Tutor:** The difference is people whose only activity is a recurring rent bill the server keeps adding. How many are there, and what share of the cohort?

> **Exercise:** Flats, month 12: how many users count as active **only** because of server-created events (any event minus opened the app)?

**Maya:** 88 people, 11% of flats. Are they retained or not?

**Alex Tutor:** We can't tell from this table. Maybe they settle up outside the app, maybe they moved out and nobody switched the bill off. Report 39.4% and flag the 88 as a question worth a look.

![An empty room with packed moving boxes by the door, while a rent receipt still slides in under the door.](https://productovi.com/courses/product-analytics/inline/retention-by-segment-illustration-3.webp)

*A recurring rent bill can keep running after someone has moved out.*

**Sofia:** So the headline for flats is 39.4%, with a footnote about the 88 and a check to run.

**Alex Tutor:** Exactly. Expect someone to push back on that footnote, though.

> **Exercise:** **True or false:** in month 12, the 88 users of the 800-strong summer 2025 flats cohort who are active only through server-created rent bills should be counted as retained, because automation is value too.

## Trips: seasonal churn, then a return

**Maya:** Now the CEO's point. Trips: 1,600 users, and by month 5 only 89 open the app. That's 5.6%.

**Alex Tutor:** Now read to the end of the rows. Month 12: 349 active again, and look at the last column for them.

**Sofia:** 304 of the 349 are in a new group. They're planning next summer's trip.

**Maya:** So the CEO's “churn like crazy” is mostly people waiting between trips?

> **Exercise:** Trips, month 12: what share of the users who opened the app were active in a **new** group? In %, one decimal.

**Alex Tutor:** 87.1% of the returning trip users started a new group. The trip group ended with the trip; the users didn't leave Halves, they waited for the next one.

![A beach photo of friends pinned on a wall, while the same friends gather round a map planning their next trip.](https://productovi.com/courses/product-analytics/inline/retention-by-segment-illustration-4.webp)

*The trip group ended with the trip; its people came back for the next one.*

**Alex Tutor:** 87.1% is a share of those who came back. For the campaign debate you also need it as a share of everyone the summer brought in.

> **Exercise:** Trips: what share of the **whole trip cohort** was active in a new group in month 12? In %, one decimal.

**Theo:** So monthly retention is the wrong ruler for trips. What should I track instead?

**Alex Tutor:** Think about the rhythm first. People take one or two trips a year, so a trip metric has to look across a year, not a month.

> **Exercise:** Which metric best tracks whether trip users are retained?

## The verdict: is retention healthy in each segment?

**Theo:** Before Maya's memo: the CEO will say trips are our biggest segment with the worst retention, so the travel campaign goes. What do I answer?

**Alex Tutor:** Answer with the trip rows, and be honest about what one summer's cohort can and can't show.

**Sofia:** And not just defend the campaign. If trip users only come back once a year, that changes what the campaign is worth.

**Alex Tutor:** Good. Start with the line the memo opens on, because that's the one the CEO will quote.

> **Exercise:** Maya, the Halves product lead, is writing a budget memo: is retention healthy for couples, flats and trips among summer 2025 sign-ups? The CEO wants to cut the summer travel campaign. Which opening line for the memo **helps** the budget decision?

> **Exercise:** The CEO says: “Trips are our biggest segment and have the worst retention. Cut the summer travel campaign.” Help Theo, the growth marketer who runs that campaign, answer in 3–4 sentences with numbers from the table, including one honest limit.

**Maya:** Now the full memo: for each segment, is retention healthy, by what measure, and what we should do about it.

**Alex Tutor:** One verdict per segment, each with its own rhythm and its own numbers. And say what you'd check next.

**Sofia:** Couples, flats with the 88 flagged, trips by return, and no blended curve.

> **Exercise:** Write Maya's memo: is Halves retention healthy for couples, flats and trips?

> **Key ideas**
>
> - Fix the measure before reading: monthly activity by app opens; “any event” only as a check.
> - Read each segment in its own rhythm; don't blend them.
> - Couples: 52.7% a year later, flat from month 6, almost all in their original group.
> - Flats: 39.4% by app opens, 50.4% by any event; the 88 users kept “active” by server bills are a question, not retention.
> - Trips: 5.6% in month 5, 21.8% in month 12, and 87.1% of those in a new group. Group lifetime isn't user churn; judge trips by return for the next trip.
> - One summer's cohort is one data point: confirm with the next cohorts before big budget moves.

**Alex Tutor:** Three segments, three rhythms, three verdicts. The CEO and Theo were each right about one line of the table.

- Previous lesson: [The plateau: retention and product-market fit](https://productovi.com/courses/product-analytics/retention-plateau.md)
- Next lesson: [A/B test plan: hypothesis, metrics, guardrails](https://productovi.com/courses/product-analytics/test-design.md)

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