WHOOP

See 3 product ideas to add 1 Billion years

At a glanceIllustrative product targets. Not observed WHOOP results

The problem

WHOOP already collects tons of health data. The gap is interpretation: what does this mean for me, and what do I do next?

My role

Self-directed concept work. I framed the engagement problem, mapped the data → interpretation → action → learning loop, and designed the AI Coach and redesign concepts.

Hypothesis

Turn passive data into one clear, personal next action, and engagement and retention should follow.

Illustrative targets

+10–15% weekly engagement, +5–10% 30-day retention, +10% insight interaction. These are targets, not measured outcomes.

CURRENT WHOOP

Unlock Human Performance & Healthspan.

TODAY

Built around performance, athletes, and pushing human limits.

PROPOSED EVOLUTION

More personal. More people.

Understand your body and behavior, and what they mean for everyday life.

KNOW YOURSELF.

An evolution of the WHOOP mission: from unlocking performance to understanding the person behind the data.

How do you make WHOOP stickier?

Turn insight into action.

Problem

WHOOP gives users a lot of data. More data doesn't mean more clarity.

You see recovery, sleep, strain, stress, HRV.

But what should I actually do?

The redesign

Always-on AI Coach

Keep the AI tab visible and ready to help.

Data
Insight
Action

Ask

Should I train today?

Why is my recovery low?

What should I change tonight?

Choose your coach

Pick an AI coach based on people you already trust.

CR7. LeBron. Experts.

Their coaching style becomes an AI layer that reads your WHOOP data and turns it into personal guidance.

With permission, the coach can dig deeper into your recovery and performance patterns.

The goal

Make WHOOP something you act on, not just check.

8 80

Aspirational hypothesis: 10\u00d7 everyday engagement through actionable, personal coaching.

See the app redesign (click AFTER) →

Interactive prototype · live

Open in Figma

How I would measure it

Illustrative baselineProposed targetNot observed WHOOP data.

Recommendation comprehension

55%75%+

Can a user say what WHOOP is telling them to do — in their own words?

Median time to identify what to do

45s<20s

How much cognitive work sits between opening the app and deciding?

Action completion

20%30%+

Does understanding actually turn into behaviour?

These are the thresholds I'd use to check if the redesign creates real value, not a prediction that it will. The goal isn't more app opens. It's less work between data, understanding, and action.

Data
Understanding
Action

Primary outcomeProposed measure

Actionable decision rate

Not the outcome: App opens, session length, or streaks. Attention isn't the same as a better decision.

Candidate measures

Recommendation comprehension
Time-to-action
Action completion
Meaningful engagement
7-day retention
30-day retention
AI Coach engagement

Proposed instrumentation. None of these are current WHOOP metrics.

Decision thresholds

Actionable decision rate improves meaningfully, along with comprehension and satisfaction.

Iterate

Engagement rises but users still struggle to understand what the recommendation is asking.

Stop / revisit

The new experience doesn't improve decision quality. The problem is content or model, not interface.

Observation
Illustrative baseline: ~55% of users can restate the day's recommendation; median 45s to identify what to do.
Interpretation
The cost is comprehension, not access. More data would make it worse.
Hypothesis
One prioritized recommendation with a clear next step should raise comprehension toward 75% and cut time-to-action below 20s.
Experiment
Measure comprehension and time-to-action on the current app, then A/B the redesigned home against it.
Decision
Ship broadly only if action completion moves with comprehension. Target +10\u201315%. Needs validation.

What if performance could look as good as it feels?

WHOOP FOR HER

A more fashion-forward WHOOP, built for everyday wear.

Interactive 3D model

Rotate the WHOOP band

Open in 3D

THE OPPORTUNITY

WHOOP is built for performance.

But a wearable is also something you wear.

THE REDESIGN

Designed to blend in, not stand out.

A more fashion-forward WHOOP, built for everyday wear.

THE ECOSYSTEM

Partner with brands women already love.

Fashion × Fitness × WHOOP

More reasons to wear it.

More reasons to keep it on.

How I would measure it

Primary outcomeProposed measure

Wear consistency

Not the outcome: units sold or launch sentiment. A better-looking band only matters if it stays on the wrist.

Candidate measures

Daily wear rate
Active days / week
Wear hours
30-day retention
Accessory attachment
Conversion

Proposed instrumentation. None of these are current WHOOP metrics.

Behavioral hypothesis. Needs validation

More desirable design
Higher wear consistency
More continuous data
More product value
Potential retention benefit

I'm not claiming the redesign increases retention. Each arrow is an assumption, and only the first two can actually be tested in a pilot.

Control

Current WHOOP design

Existing band, existing accessories, no styling change.

Treatment

Fashion-forward design

Same sensor, redesigned form and materials, everyday-wear positioning.

Measure the difference in daily wear, active days, and 30-day retention over a matched cohort. The target is a directional lift, not a precise number.

Decision thresholds

Wear consistency meaningfully improves without hurting usability or satisfaction.

Iterate

Users prefer the design in surveys, but wear behavior doesn't change.

Stop / revisit

Aesthetics don't meaningfully affect wear behavior. The constraint is elsewhere.

What if every gym became a WHOOP Lab?

Concept WHOOP Labs inside a gym: members training while a facility dashboard displays live strain, recovery and research data

The offer

Try WHOOP.

Train.

Know more.

The growth loop

Gym partner
Free workout
Personalized insight
Share
New user

The bet

K 1

Inspired by Ed Baker's growth philosophy: a product-led loop where every user helps create the next.

Try WHOOP before you subscribe.

Would the economics work?

The real question isn't "will people like the trial?" It's whether WHOOP can acquire customers this way at a sustainable CAC.

Illustrative synthetic scenario

Where would the acquisition funnel break?

Assumed rates for one facility running 1,000 trials. The step that matters is engaged \u2192 paid: if the experience doesn't become intent, nothing downstream is affordable.

Synthetic / illustrative. Not WHOOP internal data.

Step	People	Rate
Trial participants Enrolled in a WHOOP-enabled class	1,000	—
Activated Band fitted and paired	700	70% of participants
Engaged Meaningful in-app session	500	71% of activated
Shared / referred Showed someone their read-out	150	30% of engaged
Paid subscribers Subscribed within 30 days	50	5% of participants
Retained (D30) Still subscribed at day 30	40	80% of subscribers

Illustrative synthetic scenario. Not WHOOP data.

Illustrative modelAssumptions, not WHOOP figures

What one trial is assumed to cost

DeviceBand amortised across a limited number of trials$45
OperationsStaff time: fitting, onboarding, reset$20
LogisticsShipping, replacement, sanitising kit$12
SupportTrial-specific support contacts$6
Partner costIncentive paid to the gym per trial$12
Loss / damageAssumed shrinkage on shared hardware$5

Cost per trial$100

The equation

CAC = total trial cost ÷ paid customers acquired

1,000 trials × $100 = $100,000 of spend. At the illustrative 5% conversion that is 50 paid customers.

Illustrative CAC

$2,000

Unlike software, a physical trial costs real money per participant. That's why viability, not desirability, is the first question WHOOP Labs has to answer.

Paid conversion	Customers	Illustrative CAC
3%	30	$3,333
5%	50	$2,000
8%	80	$1,250
10%	100	$1,000

Illustrative sensitivity analysis on the same $100,000 trial investment.

Lifetime contribution	LTV : CAC	Read
$1,000	0.5×	Not viable
$2,000	1.0×	Break-even
$3,000	1.5×	Potentially attractive
$4,000	2.0×	More attractive

Illustrative contribution scenarios against a $2,000 CAC. Not WHOOP financial data.

Conversion rate changes the economics of a physical trial a lot: the same $100,000 of spend produces a $3,333 CAC at 3% and a $1,250 CAC at 8%. LTV:CAC, not enthusiasm, is the decision framework.

Hypothetical scenario

Move the assumptions

Small changes in conversion swing the economics of a hardware-based acquisition strategy a lot. That's the case for piloting before scaling.

Cost per trial$100

Lifetime contribution$3,000

Break-even conversion

3.3%

Below this conversion rate, each customer costs more than the assumed contribution. Needs validation before any scale decision.

Break-even analysis

How does CAC move with conversion?

The dashed line is the assumed lifetime contribution. Where the curve is above it, the channel loses money at that conversion rate.

Synthetic / illustrative. Not WHOOP internal data.

Decision thresholds

Trial conversion and retention together produce an attractive CAC against assumed lifetime contribution.

Iterate

Engagement in the gym is strong but conversion or unit economics are weak. Change the offer, targeting, or cost stack.

Stop / revisit

The cost of physical acquisition can't reasonably support the resulting customer value.

Observation
Illustrative trial conversion is 5% of participants.
Interpretation
Physical acquisition is expensive at this conversion rate, roughly a $2,000 CAC on assumed costs.
Hypothesis
Higher-intent settings push conversion toward 8%, which improves CAC a lot.
Experiment
Run WHOOP Labs in high-intent fitness classes and measure conversion and 30-day retention against the funnel above.
Decision
Scale only if conversion and retention justify acquisition cost. Otherwise, iterate on the offer or stop.

WHOOP

KNOW YOURSELF.

Choose wisely

WHOOP