# Wellspent Financial Model

AI-coach mobile app that helps phone over-users build healthier screen habits via scientifically-validated training, bite-sized exercises, and real-time nudges.

- Canonical: https://finamodel.com/startups/wellspent
- Excel download: https://finamodel.com/startup-models/wellspent.xlsx
- Category: Health-tech
- Model type: SaaS ARR / Valuation
- Funding round: Seed
- Funding: $1.07M
- Founded: 2022
- Geography: Germany / Europe (founding team Berlin-based, RCT with Freie Universität Berlin); targeting global smartphone over-users.
- Customer: B2B

## About the company

Wellspent is an AI-coach mobile app helping phone over-users build healthier screen habits through training and real-time nudges. Its consumer proposition combines bite-sized exercises with behaviour-change coaching aimed at users seeking more intentional technology use.

The product follows a freemium consumer model in which engagement and perceived behaviour change must lead to paid conversion and retention. Commercial performance depends on free users, subscription ARPU, churn, app-store distribution, content quality, and efficient acquisition in a broad wellness category.

The model forecasts free users, paid conversion, subscription ARPU, engagement, churn, app-store fees, and acquisition cost. It includes product and AI costs, content development, marketing, gross margin, operating cash flow, cash burn, and runway.

## What's included

- 5-year monthly revenue build with stage-appropriate growth assumptions
- Full P&L, headcount plan, and operating-expense schedule
- Cash-flow statement, runway, and burn-rate tracking
- Valuation via exit multiple with a DCF cross-check
- Returns analysis with MOIC and IRR
- Unit economics including CAC, LTV, payback, and cohort retention

## Product & value proposition

- App brand name: "not less but better" (nlbb).
- Three-step product loop:
  1. Identify unhealthy habits (habit diagnostic).
  2. Replace with good habits (bite-sized exercises, specialized courses).
  3. Real-time AI nudge when phone over-use is detected.
- Scientific differentiation: first RCT-approved app in the category; trial run with Freie Universität Berlin, 232 participants.
- Alpha stage noted on slide 4.

## Market

- 310 million people worldwide fail to stick to healthy phone habits.
- Consumer spending on mindfulness apps grew 24.3x to $195m/year since 2015.
- $1.5bn funding for US mental health companies in 2020, +72% YoY.
- Company's own market sizing: 310m affected users × $81 ARPPU = **$24bn market potential** (screen time management sub-segment).
- Comparable app scale context: Fabulous 22m downloads, Forest/Focus 40m downloads.
- No formal TAM/SAM/SOM breakdown presented; $24bn is bottom-up addressable market per company math.

## Revenue model

- Model: B2C premium subscription mobile app.
- ARPPU: $81 stated in market sizing calculation - interpreted as annual subscription price point (basis for $24bn market potential math).
- Channels: Direct (App Store / Google Play); no B2B or enterprise channel mentioned.
- No pricing tiers, trial/freemium split, or billing cadence (monthly vs. annual) disclosed in deck.

## Traction & metrics

- RCT outcomes (232 participants, Freie Universität Berlin):
  - -42% reduction in problematic smartphone use.
  - -20% reduction in screen time.
  - +8% increase in overall wellbeing.
  - -40% decrease in habit strength / impulsive smartphone use.
- Product stage: Alpha.
- Pre-seed funding raised: amount redacted ("€x").
- 1 academic paper published (JMIR Publications).
- App Store testimonial: user reduced screen time from 7.5 hrs/day to 2.5 hrs/day (April 2021 premium subscriber) - qualitative only.

## Unit economics

- ARPPU of $81 used in market sizing.

## Competition / moat

Competitive landscape mapped across three habit categories:
- **Habit building:** Fabulous (22m downloads), Forest/Focus (40m downloads).
- **Habit regulation / weight loss:** Noom ($4bn market cap).
- **Mental wellbeing:** Calm ($2bn market cap).
- **Habit elimination:** Pear Therapeutics ($284m funding), Quit Genius ($78m), Tempest ($10m).
- **Learning:** Coursera ($5bn), MasterClass ($800m), Blinkist ($200m).
- **Physical / sleep:** Peloton ($37bn), Sleep Cycle ($192m).

Claimed moats:
- First RCT-approved app for screen habit change.
- AI real-time nudge technology (proprietary detection).
- Academic credibility (published paper, Freie Universität Berlin partnership).
- No direct competitor named in screen time management slot - company positions itself as the category creator.

## Team & funding ask / use of funds

**Founders:**
- Selcuk (CEO): Marketing & BizDev, 2x founder (SMACC, DerZucker Bäcker, happystrappy).
- Christina (CPO): Psychology & Design Thinking (HPI, DB, Bosch, StealthMode).
- Marius (CTO): Mobile & Software Engineering (Calm, Realm, Keepsafe).

**Team:** 6 additional members (alumni: Blinkist, Bunch, The New York Times).
**Advisors:** 3 (Freie Universität Berlin, Caracare, Tier).
**Pre-seed raised:** €x (redacted).

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## Recommended financial model

- **Archetype + why:** Consumer subscription app (mobile B2C). Revenue driven by downloads → conversion to paid → ARPPU × subscribers. Closest archetype is a DTC subscription / consumer SaaS model. Not a marketplace or GMV model.
- **Forecast horizon & granularity:** 3 years monthly (Year 1–2 monthly detail, Year 3 annual); seed-stage pre-revenue companies benefit from monthly to track cash burn and runway alongside revenue ramp.
- **Key drivers & assumptions:**
  - Total addressable users: 310m
  - ARPPU: $81/year; billing split (monthly vs. annual) 60% annual / 40% monthly, pending pricing confirmation
  - Freemium conversion rate: 3–5% (benchmark for consumer wellness apps; Calm/Headspace range)
  - Monthly app downloads / installs: ramp from ~5k/month at seed launch to ~50k/month by end of Year 2, contingent on marketing spend
  - Paid subscriber count: installs × conversion rate
  - Monthly churn: 5–8%/month (consumer habit app; high early churn typical before habit lock-in demonstrated)
  - Gross margin: ~75–85% (app store fees ~30%; minimal COGS beyond hosting / ML inference)
  - CAC: $5–15 blended (UA spend; comparable consumer app benchmarks; no deck data)
  - LTV: ARPPU / churn - highly sensitive; key model output
  - Headcount: seed funding enables team of ~10–12; model as fixed OpEx with phased hiring
  - Marketing spend as % of revenue: 40–60% in early years, declining as organic / word-of-mouth scales
  - App store revenue split: 30% platform fee
- **Scenarios (Base / Bull / Bear - which variables flex):**
  - **Base:** 3.5% conversion, 6% monthly churn, $81 ARPPU, moderate UA spend
  - **Bull:** 5% conversion, 4% monthly churn, ARPPU rises to $99 (annual pricing uplift), viral coefficient >1
  - **Bear:** 2% conversion, 9% monthly churn, CAC spikes, B2C traction slow → pivot to B2B2C (employer wellness)
- **Required sheets / outputs:**
  1. Assumptions (all drivers with toggle for Base/Bull/Bear)
  2. User funnel (installs → free → paid → churned → net subscribers)
  3. Revenue (MRR/ARR build, ARPPU × net subscribers)
  4. P&L (Revenue → Gross Profit → OpEx → EBITDA → Net Income)
  5. Cash flow & runway (burn rate, months of runway vs. raise)
  6. Unit economics summary (CAC, LTV, LTV/CAC, payback months)
  7. Sensitivity table (churn vs. conversion rate on ARR; CAC vs. LTV on payback)

## Frequently asked questions

### Is the Wellspent financial model free?

Yes. The Wellspent model is a free Excel download with live formulas.

### Can I change the assumptions?

Yes. The workbook is editable and its live formulas recalculate when assumptions change.