A pitch for the Product Manager, Consumer Experience role

Your teachers already win the first session.
I want to help win the fortieth.

Hi Jitendra. I studied MyYogaTeacher's website, pricing and membership rules, both apps, and your community forum. Then I built three working prototypes for the part of the journey I believe moves revenue most: the gap between a great free session and a weekly habit that lasts. Everything below is clickable.

Trial to paidFirst 30 daysCredit usage as a retention signalAmbivalence before churn
Try the prototypes Who is Smrity
01 · How the business makes money, as I read it

A membership business where usage is the product

From public pages, the engine is a recurring membership billed every four weeks. Group classes bring people in; recurring 1-on-1 credits are where the relationship, and most of the value, lives.

$0Free first session, no credit card. Lowest possible barrier to try.
4 or 81-on-1 credits per 4 week cycle. Two a week is the most popular plan.
<$20/hrPersonal teacher at a fraction of a US studio private. The core wedge.
Up to 20%Off for 3, 6 and 12 month plans. Commitment is already monetised.
LeverWhat exists todayWhat it tells me
AcquisitionFree session, $50 referral credit, gift cards, kids referral, HSA/FSA eligibilityStrong top of funnel and a health spend angle few yoga apps have
ConversionGroup unlimited plans and 1-on-1 credit plans, monthly or longer termThe decision after session one carries the whole business
RetentionUnused credits roll over while active, pause up to 3 times a year, replacement teachersYou already design against churn. Rollover also hides early disengagement
RhythmThe teacher app now prompts students into weekly repeat sessionsThe team has seen that a fixed weekly slot predicts staying. I want to extend that to day one

Sources: myyogateacher.com home, FAQ, membership FAQ and price update articles, App Store listings for the student and teacher apps, and public community threads. Where I use numbers inside prototypes, they are illustrative assumptions for the demo, not claims about your data.

02 · What I noticed as a prospective student

Five small frictions with outsized impact

The match is a moment, not a system

I pick a goal and get a teacher. My worries (a bad knee, being a beginner at 50, being seen on camera) never reach the teacher before we meet.

The best session ends in silence

After a wonderful first class there is no recap of what my teacher saw and no plan. The pricing page has to do the persuading alone.

Rollover can mask a goodbye

Unused credits rolling forward is generous, but a growing balance is often the earliest sign a student is quietly drifting.

Community lives off the app

Members on the forum asked why community is not in the mobile app, where the daily habit actually happens.

Small app trust gaps

Members reported a class type filter not working while booking and confusion over whether a rating was saved. Small, but trust compounds.

The job to be done

People do not hire MyYogaTeacher for yoga. They hire it for someone who notices them and keeps them coming back. Every build below protects that.

03 · Build one

Intent Match: the teacher meets you before you meet

A 40 second intake that captures not only the goal, but the worry and the rhythm. It matches three teachers with a reason for each, and it ends by holding a weekly slot, so the free session is booked as session one of a routine instead of a one off.

Hypothesis

  • Students who book their trial into a repeating weekly slot convert and stay at a higher rate than those who book a single session.
  • Sharing the student's worry with the teacher before class raises first session quality and confidence to continue.

Measure

  • Primary: trial to paid conversion within 14 days
  • Leading: share of trials booked as a recurring slot, intake completion rate, first session rating
  • Guardrail: sign up completion must not drop

Smallest version

  • Two extra questions on the existing flow and a "hold this time weekly" checkbox. One sprint, A/B tested.
04 · Build two

First Session Recap: the teacher's eyes, in writing

Teachers already notice everything: tight hamstrings, shallow breathing, a guarded lower back. Today that insight disappears when Zoom closes. The teacher taps a few observations; AI turns them into a warm, personal recap and a four week path in the teacher's voice. The plan offer arrives attached to proof that someone saw you.

Hypothesis

  • A personal recap with a concrete four week path, delivered within an hour of session one, lifts trial to paid more than a generic pricing nudge.
  • Recommending a rhythm (2 a week) instead of a plan name steers students to the plan most likely to build the habit.

Measure

  • Primary: paid conversion within 72 hours of first session
  • Leading: recap open rate, slot reservations, share of new members on 2 a week
  • Teacher side: time to write notes stays under 60 seconds

Edge cases I would spec

  • Health conditions in notes: AI never adds medical claims, teacher approves before send. Student no shows. Teacher on leave next week. Time zone shifts after daylight saving.
05 · Build three

Ambivalence Radar: catch the drift before the cancel click

Your JD asks for someone who can identify ambivalence. People rarely churn in a day; they drift for weeks. This turns quiet behavioural signals into a score and recommends the gentlest save, routed to the teacher first because the relationship is the product. Toggle the signals.

Maya, 47
2 a week plan · Month 3 · Goal: back pain
Member
Drift score
0

Recommended plays

Weights are illustrative. In the real build I would fit them on historical cohorts (who drifted, who left) and validate with interviews of churned students before automating anything.

06 · Why these three, in numbers

Small lifts at each step compound

An illustrative model of 10,000 free sessions a month. The baseline rates are my assumptions for the demo. Move the sliders to see how modest lifts from the three builds stack into active members.

Baseline assumptions: 80% attend the free session, 18% convert, 60% still active at month 3.

0active members at month 3
+0%vs baseline
07 · How I would decide

A starter decision log

DecisionTypeWhyAssumption under test
Add worry and rhythm to intakeFast, reversibleTwo questions, easy to removeExtra steps will not hurt sign up completion
Default the trial to a weekly slotFast, reversibleMirrors what the teacher app already nudgesRecurring booking predicts paid conversion
AI written recapResearch firstTouches health language and teacher trustTeachers will add notes if it takes under a minute
Automated save offersResearch firstWrong offer at the wrong time feels like surveillanceTeacher outreach beats discounts for drifting students
08 · My first 90 days

Listen, ship small, prove it

Days 1 to 30

  • Talk to 30 people: new trials, members, churned students, 8 teachers, sales and care
  • Sit in on live sessions and care chats
  • Map the trial journey with real drop off data
  • Fix the small app trust gaps (filters, ratings)
  • Ship the intake questions as an A/B test

Days 31 to 60

  • Read the first experiment, decide, log why
  • Pilot the recap with 10 volunteer teachers
  • Define a drift signal set with analytics
  • Build an AI synthesis loop for tickets and session feedback

Days 61 to 90

  • Scale what worked, kill what did not
  • Ambivalence Radar v1 as a teacher alert, no automation yet
  • Bring community into the app as a light weekly touchpoint
  • Share results: what moved, what we learned, what next
09 · Why me

I talk to customers weekly, and I build wellness products myself

The proof

  • 8 years in product, 6 of them owning outcomes directly with customers at Xorosoft (200 brands, US, Canada, APAC)
  • 90%+ retention across ~40 accounts I owned end to end, 80% CSAT, 22% fewer repeat tickets, 4.9/5 on G2
  • Shipped AI to production: LLM query layer on MCP, RAG assistant, ML anomaly detection, demand forecasting
  • US customer hours are my normal working rhythm today

The fit

  • I am building a yoga student platform for a teacher right now: sign up, needs based paths, programs, 1-on-1 requests, payments
  • I built and ran a social habit app (Flowify) for a year and learned the hard way that retention is designed, not hoped for
  • I use AI every day to research, synthesise and prototype. This page took a weekend, not a quarter
  • I care about helping people build habits that last. That is the job here