Consumer product company, Boston
Over two years the team shipped three consumer products, grew past 10,000 student signups with no paid media, and used funnel and cohort analysis to reach a clear conclusion about the economics of the category. This page documents the thesis, the products, the evidence and the decision.
The team at the Harvard i-lab, and the Weave Community launch site.
The problem
“Loneliness is far more than just a bad feeling. It harms both individual and societal health. Our relationships are a source of healing and well being hiding in plain sight.”
The Surgeon General’s advisory reported that about one in two American adults experience measurable loneliness, and that adults aged 18 to 24 report the highest rates of any age group. It compared the mortality impact of chronic isolation to smoking up to fifteen cigarettes a day.
Weave started from one question. Students live surrounded by peers and still report record isolation, so can digital tools move people from a screen to a room together rather than deeper into a feed.
Community in practice
The team hosted meetups and events through Weave, ran focus groups at the Harvard i-lab, and studied how shared public space creates connection.
Product evolution
The sequence matters more than any single launch. Each product was scoped by what the previous product's data ruled out.
A neighborhood social graph. Map based local posts, interest groups, events and conversations in one app.
Every Harvard event in one place. Aggregated fragmented school, club and department calendars into a single feed.
A campus scoped connection app built for one clear job, getting students to meet in person.
Go to market
Every campus launch was run the same way so results could be compared. Print a coded flyer, saturate the highest traffic locations, talk to students directly, then read the funnel.
One campus at a time until the network was usable, rather than thin coverage everywhere.
Separate scan destinations per location made it possible to compare dining halls, dorms and libraries.
Team members and student ambassadors onboarded users in person during high traffic moments.
Measurement
Signup totals hide the thing that decides whether a consumer product works. The team instrumented six stages per campus and weekly retention curves per cohort, then compared them side by side.
| Campus | Flyer scans | Signups | Profile complete | Activated | Referred a friend | Day 7 return |
|---|---|---|---|---|---|---|
| Harvard | 4,200 | 2,140 | 1,290 | 780 | 240 | 215 |
| MIT | 3,100 | 1,520 | 910 | 560 | 175 | 150 |
| Boston University | 3,600 | 1,810 | 1,080 | 640 | 190 | 175 |
| Northeastern | 2,900 | 1,420 | 855 | 500 | 145 | 135 |
| Boston College | 2,100 | 1,040 | 620 | 360 | 105 | 95 |
| Yale | 1,800 | 890 | 535 | 310 | 90 | 80 |
| All campuses | 17,700 | 8,820 50% | 5,290 60% | 3,150 60% | 945 30% | 850 27% |
Campus level figures are representative of internal tracking across the 2025 launches and are shown to illustrate the method rather than to report audited totals.
Each row is a weekly signup cohort and each column is the share of that cohort still active in the following weeks. Later cohorts held better early retention, which is what justified continued campus expansion.
| Cohort | Users | Week 0 | Week 1 | Week 2 | Week 3 | Week 4 | Week 5 |
|---|---|---|---|---|---|---|---|
| Sep 7 to Sep 13 | 72 | 100.0% | 59.7% | 52.8% | 41.7% | 40.3% | 31.9% |
| Sep 14 to Sep 20 | 92 | 100.0% | 68.5% | 55.4% | 47.8% | 25.0% | 15.2% |
| Sep 21 to Sep 27 | 109 | 100.0% | 60.6% | 52.3% | 42.2% | 18.3% | 13.8% |
| Sep 28 to Oct 4 | 60 | 100.0% | 65.0% | 55.0% | 35.0% | 21.7% | — |
| Oct 5 to Oct 11 | 35 | 100.0% | 54.3% | 45.7% | 28.6% | — | — |
| Oct 12 to Oct 18 | 29 | 100.0% | 79.3% | 62.1% | — | — | — |
| Oct 19 to Oct 25 | 24 | 100.0% | 70.8% | — | — | — | — |
| Oct 26 to Nov 1 | 20 | 100.0% | — | — | — | — | — |
Unit economics
Once acquisition was proven, the question became whether a subscription could carry the business. The team modelled conversion, price, churn and the platform fee across three scenarios.
Contribution never covers campus launch effort and support load.
Breaks even on infrastructure but not on the team required to keep launching campuses.
Profitable per campus, but requires retention behaviour that a matching product structurally works against.
In a matching product, success removes the customer. Every satisfied pair is churn, so lifetime value is capped by the thing the product is supposed to do well. With a thirty percent platform fee on subscriptions and a user base that turns over annually with graduation, the model only cleared its costs in the bull case, and the bull case required retention that the category works against.
With runway limited, the team chose to close rather than spend the remaining capital scaling a model the analysis did not support. The decision followed from the numbers.
Operating principles
A rough product in real hands produces better information than a refined plan. Experiments were designed to reach live users within one to two weeks so decisions rested on observed behaviour rather than internal opinion.
Products that serve everyone retain no one. Weave Community tried to hold events, discovery, messaging and civic action at once. Campus Dating held one job. The difference in traction was not marginal.
Scans, installs, profile completion, activation, referral and day seven return were tracked separately by campus. Growth problems and retention problems look identical in a signup count and completely different in a funnel.
Acquisition strength can disguise a structurally unprofitable model. Modelling conversion, price, churn and platform fees before scaling spend is what turned a hard outcome into a deliberate decision.
Team
Product, engineering, design, community and brand, built to run consumer experiments end to end.
