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Marketplace PM: balance demand, supply and sustainable matching

Practice supply-and-demand tradeoffs in marketplace contexts such as rides, stays and delivery. Start from the transaction and local constraint; shared company labels do not imply identical mechanics.

Original PMMockr learning guide · Examples are illustrative, not company interview transcripts.

Before you start

Distinguish the two sides, a request, a match and a fulfilled transaction.

What you will learn

Define liquidity for one place, category and time.
Diagnose supply and demand constraints separately.
Measure incremental fulfillment and contribution after subsidies.

Read the explanation, attempt the problem yourself, then compare the worked answer.

START WITH A QUESTION

A home-services marketplace wants to increase completed appointments. Should it subsidize demand?

Original teaching scenario. Any figures are illustrative assumptions.

Scope and assumptions

Fictional marketplace with one city and one week as the analysis scope.
Illustrative funnel: 1,000 requests, 60% matched, 75% accepted after matching and 80% completed after acceptance.

How to tackle this question

Choose a market cell: category, place and time. Define an eligible request and useful fulfilled match.
Diagnose supply availability, acceptance and cancellations before selecting acquisition or supply investment.
Assess incremental fulfillment after incentives and costs. Explain shared-inventory effects on the experiment.

Try a short outline and spoken answer before comparing.

Reveal model solution and reasoning

Locate value and bottleneck

The funnel yields 1,000 × 0.60 × 0.75 × 0.80 = 360 completed appointments. I would break matching down by time, location and required skill before assuming there are too few customers.

Compare an intervention

If suitable supply is the constraint, a broad customer discount may increase unmatched demand. I would compare more bookable provider windows with targeted provider onboarding, and choose the smaller test that addresses the evidenced constraint.

Protect economics and both sides

I would measure completed appointments and repeat use, provider earnings after travel, cancellations and contribution after incentives and servicing costs. I would choose a comparison that accounts for customers sharing the same providers.

Interviewer follow-up

Requests rise but provider cancellations also rise. Expand?

Not on request growth alone. I would inspect overbooking, travel burden and whether incentives attracted unsuitable jobs. I would pause expansion or narrow the intervention until fulfilled value and participant guardrails support it.

Check your reasoning

Did I model both sides and the local matching constraint?
Does the funnel calculate fulfilled value rather than only requests?
Do incentives and operational costs leave a sustainable outcome?

Try a changed constraint

Repeat the case for lodging and identify which supply constraints, fulfillment events and test units must change.

Go deeper

Individual-user experiments can affect untreated users through shared supply. Consider market/time assignment and measure uncertainty at the assignment unit.

A higher aggregate fill rate can be produced by excluding hard-to-serve users; report eligibility changes and compare consistent cohorts.

Start with the vocabulary

Marketplace liquidity

The ability of relevant demand and available supply to find a suitable match within the time and location users need. More total listings do not necessarily improve matching.

A city may have many providers while an evening request cannot find one with the required skill nearby.

Common mistake: Optimizing total supplier signups instead of availability and fulfilled transactions.

User segment

A group whose needs, behavior or constraints differ enough to require a different product decision. Compare segments, choose one and explain why it is worth prioritizing now.

In a navigation app, an unfamiliar-city commuter may need confidence about transfers; a familiar-route commuter may need disruption alerts.

Common mistake: Treating everyone aged 18–35 as having the same need.

Denominator and observation window

The eligible population against which an event is counted, and the period allowed for that event to occur. Rates are comparable only when eligibility and maturity are comparable.

Activation within seven days counts mature new-team cohorts; teams created yesterday have not had the same opportunity to activate.

Common mistake: Calling a rate higher after silently excluding users who struggle.

Experiment

A structured test of a hypothesis with a comparison group, a primary metric and a decision rule. Think about assignment, sample size, interference and how long outcomes need to mature.

Randomize eligible accounts into old and new onboarding, and assess activation while monitoring support burden.

Common mistake: Stopping as soon as one dashboard turns green.

Contribution and unit economics

The revenue associated with a defined unit minus its variable delivery, service and incentive costs. Identify what is excluded before using it to justify expansion.

For an illustrative account, a monthly price of 100 and variable servicing cost of 30 leave 70 before fixed costs, acquisition and churn.

Common mistake: Calling gross transaction value profit or ignoring subsidies and support effort.

Guardrail metric

A measure that tells you whether improving your main objective is causing unacceptable harm elsewhere. Name the downside, how you measure it and what action a breach would trigger.

A faster checkout experiment can improve conversion while increasing refund requests. Track refund rate alongside conversion and agree a rollback threshold before the experiment.

Common mistake: Listing “quality” as a guardrail without an observable measure or decision rule.

Build your answer, layer by layer

This is a learning sequence, not a mandatory order for presenting every answer. Adapt to the actual question.

LAYER 01

Map all participants and the local market

Explain what demand, supply and operations each need. Define geography and time because an aggregate can conceal local shortages.

Apply it: For a home-services marketplace, customers need a dependable appointment and providers need worthwhile jobs without excessive travel.

Go deeper: Ride, lodging and delivery markets have different fulfillment constraints; do not transplant one company’s operating model into another.

LAYER 02

Locate the binding constraint

Trace discovery, request, matching, acceptance, fulfillment and repeat usage. Distinguish insufficient demand from unavailable or unsuitable supply.

Apply it: If requests cannot find an available qualified provider, more customer acquisition may increase frustration.

Go deeper: Measure the constraint by location and time, and consider whether supply can move between markets.

LAYER 03

Choose one segment and intervention

Prioritize an unmet need and compare demand tools, supply tools and operational changes.

Apply it: Test bookable appointment windows against generic customer discounts when availability is the suspected problem.

Go deeper: Name who loses flexibility or bears extra work when the chosen side benefits.

LAYER 04

Define fulfilled value and economics

Track completed useful transactions, both sides’ retention and contribution after variable costs and incentives.

Apply it: Requests × match rate × acceptance rate × completion rate estimates fulfilled jobs for one consistent cohort.

Go deeper: Bookings or gross transaction value can rise while cancellations and subsidies destroy value.

LAYER 05

Test without ignoring shared supply

Explain how participants interact and choose an experimental unit that limits spillover.

Apply it: Compare appropriate market/time clusters when individual customers share the same provider pool.

Go deeper: Cluster assignment still needs sufficient comparable units, stable measurement and uncertainty analysis.

LAYER 06

Launch with both-side guardrails

Define participant protections, cancellation response, operational ownership and a stop rule.

Apply it: Expand only if fulfillment improves without shifting unreasonable travel or cancellation burden onto providers.

Go deeper: A global gain does not justify unexamined harm in a smaller city or underserved cohort.

Choose the local market before the growth lever

A city-wide booking total can conceal neighborhoods with no suitable supply. Define the user’s category, location and time requirement, then measure the share of eligible requests that result in a useful fulfilled match within that constraint. Decompose failed search, acceptance, cancellations and repeat use. More registrations do not solve a lack of available matching supply.

Test incremental value across both sides

Compare adding qualified supply, improving matching and generating demand based on the diagnosed bottleneck. A discount may move transactions from existing users rather than create durable value. Include courier or supplier payouts, support and refunds when assessing contribution. If treatment and control compete for the same inventory, account-level randomization may contaminate the comparison; discuss geographic or time-based designs and their limitations with the team. Follow repeat fulfillment after incentives end.

Connect the reading to this case

A marketplace needs evidence about both sides. Our local-liquidity case asks whether more activity creates useful fulfillment rather than simply moving existing demand.

Aakash Gupta: PM metrics interview questions

Which metrics would you choose to understand DoorDash marketplace health?

Attempt this second problem before comparing. Behavioral answers must use a true experience.

Compare with PMMockr’s worked solution

Original public teaching answer; hypothetical figures and fictional experience are not results to claim as your own.

I would clarify the business objective, market and user journey before naming metrics. Treat DoorDash as interview practice context. For this hypothetical food-delivery example, assume the proposed change is a promotional discovery tab; I am not asserting that the company offers this feature or uses these measures. The goal is incremental useful orders with sustainable economics and reliable delivery, rather than more tab visits.

Compare tab click-through and attributed orders with completed non-cancelled orders per eligible assigned customer over a defined period. I would choose the latter as the primary customer-level outcome for an experiment because customers might simply move an existing order into the promotional tab. Keep the denominator as all eligible assigned customers, not only tab visitors or coupon redeemers. Define an order once, reconcile cancellations and refunds, and look at new and returning customers separately. Pair the measure with contribution after discounts, delivery, refunds and support; neither gross order value nor attributed promotion revenue alone establishes incremental value.

Before interpreting changes, verify customer assignment, order tracking and the observation window. Review courier availability, merchant cancellations, delivery reliability, customer complaints and repeat ordering after the offer ends. If shared local supply creates spillovers, design the comparison with an analyst so the treatment does not silently worsen control service. I would expand only if the evidence supports incremental value and the operational requirements are met; pause or redesign if the apparent gain is cannibalization or depends on uneconomic discounts.

Pushback

Promotional-tab orders rise while total completed orders per assigned customer stay flat. Is that a win?

It may be channel substitution rather than incremental demand. I would inspect where the orders moved from and compare net contribution, repeat behavior and service quality. If the objective was incremental orders, I would not call the unchanged primary outcome a success.

Common mistake

Track tab clicks, coupon redemptions and promotional revenue.

Why this answer is stronger

Those measures describe the feature’s funnel but can hide cannibalization and cost. Practice defining a customer-level denominator and distinguishing attribution from incrementality before recommending a rollout.

From a weak answer to a defensible one

Weak: Spend more on acquisition because bookings are the North Star.

Stronger: I would first localize unfulfilled demand, inspect supply reliability and test a change that creates incremental useful matches without hiding subsidy costs.

Prepare your own answer

Choose one constrained neighborhood and explain a demand intervention, a supply intervention and the evidence that separates them.

Close the guide and answer aloud. After reviewing, identify one missing decision and retry on a different problem.

Watch one interview

Airbnb Product Manager Mock Interview: Increase Airbnb Bookings ↗

Exponent · Mock interview · YouTube

Watch with a task: Identify which side constrains bookings. Add cancellations, shared-inventory effects and post-incentive economics to your critique.

Practice with a PMMockr worked answer

Open the practice problem and attempt it before comparing.

Compare after your attempt

Read the original worked answer, follow-up and feedback

Compare problem framing, assumptions, rejected options, evidence and response to pushback. Explain what evidence would settle a disagreement.

Turn the reading into practice

Easy: outline your answer and explain every term you use.
Medium: compare two options, defend one and name a downside.
Hard: handle uncertainty, conflicting goals and a changed assumption.

Complete three questions at each level in your selected learning path where available. Review model solutions through the question’s solution action after meeting its access requirements.

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