Read the Curve
Use cohorts to find where repeat value holds—or fades
Tonight we build the retention view, follow cohorts across time and find where repeat use starts to hold or fall away. Then we connect the pattern to customer feedback and choose the strongest hypothesis to investigate.
Dates TBD — evening workshops at Workuity Biltmore. Join Discord or follow the Meetup calendar for the next cohort.
Or join the waitlist — the dates come to you.
Venue sponsor: Workuity. Format: the host builds live on screen. Bring a laptop and work along, or just watch — nobody is put on the spot.
Why this session
Top-line averages hide when customers started, what they experienced and who is actually staying. Cohorts expose those differences without pretending the chart explains the cause.
What we cover
- 01
Choose the retention definition
N-day, rolling or bracket retention; behavioral or revenue retention; select the definition and interval that match how value recurs.
- 02
Read the cohort table
Follow one cohort across time, distinguish age from calendar effects and recognize what a flattening—or falling—curve can and cannot prove.
- 03
Segment for a reason
Compare persona, plan, acquisition source and activation behavior only where the result could change the product decision.
- 04
Find the bright spots
Locate the customers already retaining, then identify the jobs, setup and repeated behaviors that distinguish them.
- 05
Locate value decay
Find the interval and journey state where use drops, then connect the quantitative pattern to support, interview or cancellation evidence.
- 06
Rank the hypotheses
Score opportunities by expected effect, evidence, effort and guardrail risk; choose one to carry into the next session.
You leave with
A product-appropriate retention metric, a segmented cohort analysis and a ranked set of hypotheses tied to observable behavior.
To follow along
A laptop and enough event history to cover at least two natural product cycles, or the supplied cohort dataset. Bring customer feedback if you have it; behavior shows where, not always why.
Afterward
Nothing is required. It compounds if you run the same work on your own product. The suggested output:
04-retention/ — retention definition, cohort views, segment comparison and ranked retention hypotheses.
Everything is open source. Missed the session? It's archived in Discord.
The session kit
Slides, a follow-along guide and skill files. Free to download, room or no room.
The community online
Recaps, archive links, questions between sessions, everyone else's work in progress.