Run these with a partner: one plays candidate and sees only the brief; the other plays interviewer and reveals each exhibit only once the candidate's structure earns it. All three companies are fictional, written for this site.
Before you open the first exhibit
What decision is actually being asked for, and by when?
Which two or three branches of your structure would you check first, and why those?
01 / Market entry
Candidate brief · Read aloud
QuickRide is a fictional ride-hailing app operating four-wheeler cabs in six metro cities. Short, congested trips in several Tier-2 cities make a low-cost two-wheeler taxi service look attractive, and leadership is considering launching in three of five candidate cities. Should QuickRide enter, and if so, where and how?
| City | Population | Avg trip length | Local competitor | Income level |
|---|---|---|---|---|
| City A | 18 lakh | 3.2 km | Weak — one small local player | Lower-middle |
| City B | 22 lakh | 4.1 km | None | Middle |
| City C | 14 lakh | 2.8 km | Strong — an established local app | Lower-middle |
Candidate task: use this to judge attractiveness and accessibility across the three cities — which looks winnable, and which looks like a hard fight for second place?
Average fare per trip
₹35
Platform take rate
20%
Driver net earning per trip
₹28
Trips per driver per day needed to break even on onboarding cost
≈ 12
City B is the strongest first move — no incumbent, the largest population and a favourable income profile — even though City A and City C have shorter trips that suit two-wheelers slightly better. Entering an uncontested city first lets QuickRide learn the operating model before it has to fight an entrenched local player in City C. A recommendation that leads with City C because it “looks most like a two-wheeler market” without weighing the competitive response is missing the accessible/winnable half of the framework.
02 / Growth
Candidate brief · Read aloud
PagesFirst is a fictional 40-store regional bookstore chain with flat same-store growth. Management has ₹3 crore to invest and is deciding between opening roughly seven new physical stores or building an online/omnichannel platform. Recommend a path.
| Metric | New physical store | Online platform |
|---|---|---|
| Upfront investment | ₹45 lakh per store | ₹3 crore platform build (one-time) |
| Payback period | ≈ 30 months per store | ≈ 20 months, if scale is reached |
| Gross margin | 38% | 44% (no store rent or staff) |
| Customer reach | Local catchment only | National, but crowded and discount-driven |
A recent customer survey: 58% discover new titles by browsing in-store, 27% through staff recommendations (also in-store), and only 15% through any online channel today. PagesFirst's core customer relationship is currently built almost entirely around the physical browsing experience.
The faster payback on the online platform is real, but Exhibit 2 shows why a pure online-first bet is risky: 85% of demand today is generated by the in-store experience the company would be de-emphasising. A stronger recommendation phases the investment — three or four new stores in proven catchments, funding a smaller online pilot alongside them, and using that pilot's actual conversion data (not the case exhibit's assumptions) before committing the full ₹3 crore either way.
03 / Pricing
Candidate brief · Read aloud
MetricLens is a fictional B2B analytics tool charging every customer the same flat monthly fee. Some customers use it constantly; others barely log in. Leadership is considering a usage-based, tiered model. Should they change, and how?
| Segment | Share of customers | Share of revenue | Willingness to pay more |
|---|---|---|---|
| Light users | 55% of customers | 12% of revenue | Low — mostly cost-sensitive |
| Medium users | 30% of customers | 33% of revenue | Moderate |
| Heavy users | 15% of customers | 55% of revenue | High — usage far exceeds flat fee value |
The heavy-user segment is only 15% of customers but 55% of revenue and clearly under-priced relative to the value they extract — that is where a usage-based tier should be introduced first. Light users are the majority of customers by count and price-sensitive; moving them to usage-based pricing risks churn for little revenue gain. The stronger recommendation keeps a flat, predictable entry tier for light and medium users, and introduces a usage-based premium tier targeted at the heavy segment, with existing heavy users grandfathered onto a transition price for a defined period to manage the change.
Continue practising