| Bucket | What goes inside | Your fintech version |
|---|---|---|
| 1 · Context | The business as snapshots, not trends: product segments and the size of each · geographic footprint and size per region · customer segments and size of each · main competitors and market share. | How does the product monetize (interest / interchange / fees / commission)? Which plans or tiers? Which markets? Which customer segments — new vs existing, retail vs premium? Who do we compete with and where do we sit? |
| 2 · Root Cause Analysis | Quantitative: profit tree to pinpoint the cause — historical trends by revenue stream and by cost line; comparison with competition (is it just us, or the whole market?). Qualitative: what drives each component — the "why" behind the number. |
Name the four words out loud: price · volume · fixed · variable. Then rates, not absolutes: take rate, loss rate, cost per transaction, step conversion. Then the "why": a pricing change, a campaign, a vendor swap, a mix shift. |
| 3 · Recovery Strategies | Direct — fix whatever broke on the revenue or cost side. Indirect — if the cause can't be fixed (a shrinking market, a macro shock), make up the loss elsewhere. |
2–3 solutions tied to the mechanism, each with sizing, trade-offs (PnL · UX · tech) and implementation. Label them immediate / structural / long-term, then say which one you'd start with. |
| 4 · Risks | Risks of your own proposals — customer adoption, regulatory, competitive response — plus what you'd do about them. | Churn from repricing, regulatory limits (best execution, affordability checks), vendor terms, cannibalisation. Never generic — always the risk of this recommendation. |
| Bucket | Branch | What it means — questions you're really asking |
|---|---|---|
| 1 · Competition | Market | The playing field: market size, growth or decline, trends and shocks. Is the whole market moving, or just us? (First test of "internal vs external".) |
| Competitors | Who else is in the game: market shares and their dynamics, new entrants, competitors' recent moves (price cuts, launches). Do they have the same problem we do? | |
| 2 · Company | Differentiators | What makes the client win: brand, technology, cost position, distribution, reputation. Has any advantage recently eroded? |
| Financials | The client's own numbers: revenue and cost trends, margins by segment/product/region. Where exactly in the P&L does the problem live? | |
| 3 · Customer | Preferences | Who buys and why: segments, needs, buying behavior, satisfaction, churn. Have preferences shifted (to a competitor, to a substitute, away from the category)? |
| Elasticity | Price sensitivity: how volume reacts to price changes, willingness to pay by segment. Can we raise price without losing the base — or did a past price move cause the problem? | |
| 4 · Product | Unit economics | One unit under the microscope: price − variable cost = contribution per unit, take rate, margin by SKU/plan. Which products actually make money and which quietly lose it? |
| Differentiators | The product vs alternatives: features, quality, value proposition, mix between products. Is the mix shifting toward lower-margin items? |
| Type | How it works | Example | MECE? |
|---|---|---|---|
| 1 · Equation | Buckets are the terms of a formula — everything that moves the metric lives in exactly one term. | Profit = Revenue (price × quantity) − Costs (fixed + variable). Your trading version: GP = volume × take rate − direct costs. | MECE by construction — the safest choice for profitability RCA. |
| 2 · Process | Buckets are the stages of a journey, start to finish — nothing happens outside the timeline. | Raw materials → transport → manufacturing → distribution → sale. Your version: visit → sign-up → first trade → repeat (the funnel). | MECE when it covers start→finish — best for funnel/KPI and ops cases. |
| 3 · Bucket (CPCC) | Category buckets: Customer · Product · Company · Competition (the 4-bucket table above). Not derived from a formula. | Broad "what's going on" prompts, growth cases, strategy questions. | Not strictly MECE — and that's fine: works for ~90% of for-profit cases if the sub-bullets are specific. |
| # | Min | Move | Say (your lines) |
|---|---|---|---|
| 1 | 0–1 | Restate & lock the metric. Define the metric, timeframe, units, target. | "So gross profit — revenue minus direct costs — dropped significantly last month. Two clarifications: are we talking absolute GP or margin? And is the goal to restore it to the previous level, or a specific target?" |
| 2 | 1–3 | Clarify with hypotheses. 3–4 questions max: business model, drivers, constraints. Every question carries a reason. | "How does this product make money — commission per trade, spread, subscriptions? …And on the cost side, what are the main direct costs — broker fees, market data, processing?" |
| 3 | 3–5 | One minute → equation structure + segment lenses. Announce the tree AND where you suspect the problem. | "May I take a minute to structure? …GP = revenue (trades × avg value × take rate) minus direct variable costs (broker fees, processing, data) minus direct fixed. I'll check which side moved first — and given the sudden one-month drop with no product changes, I suspect a cost or mix shock rather than churn. I'll also want cuts by trade size and user type." |
| 4 | 5–8 | Triage the top level in ONE move per branch. Kill the decoy out loud, explain the deprioritization. | "First split: what did revenue do over the month? …It grew 5%? Then revenue is healthy — the problem must be costs growing faster. I'm consciously deprioritizing the revenue branch and going into costs: fixed or variable?" |
| 5 | 8–15 | Drill with hypothesis-led requests. Cycle: hypothesis → data request → interpret aloud → next. If refused data — deduce. | "Variable costs up 10% MoM while volume grew 5% — cost per trade is rising, so it's a rate or mix issue, not scale. How is the broker fee structured — flat, tiered, per share? …If you can't share that, let me estimate: at 5¢ a share, a 150-share $1,500 trade costs $7.50 vs $0.75 for the same value under the percentage tier — a 10× unit-cost step." |
| 6 | 15–18 | Name the root cause as a mechanism, not a symptom, and size it. | "Root cause: our fee schedule has a step function at 100 shares, and the trade mix shifted heavily to large trades — +900% vs +10%. Unit cost jumps 10× above the step while our pricing doesn't pass it through. That mechanically compresses gross profit even as revenue grows." |
| 7 | 18–20 | Bridge to Part B with a recap. Announce the solution structure. | "To recap the diagnosis in one line: cost-side mix shock via the per-share fee tier. Let me propose 2–3 targeted fixes across pricing, the broker contract, and product — each with sizing, trade-offs and a KPI." |
| 8 | 20–30 | 2–3 solutions tied to the mechanism. Each: what → sizing → trade-offs (PnL·UX·tech) → owner/timeline → monitoring KPI. Prioritize short/mid/long. | "Short-term: reprice large trades — pass the per-share cost through (or cap free large orders in the plan tiers); rough sizing: if large trades are X% of volume… Mid-term: renegotiate the broker schedule or route orders to a volume-priced venue. Product-side: smart order handling — split/batch large orders below the step where compliant. I'd track GP per trade by tier weekly as the guardrail metric." |
| 9 | 30–35 | Unprompted synthesis close. 5 steps: Context → Findings → Recommendation → Risks → Next steps (~60–90 sec; see the card below). | "Let me summarize. We looked at why gross profit fell… What we found is… So I recommend… The main risks are…, which I'd mitigate by… As next steps I would…" |
| Step | What it is | On the trading case (one line each) |
|---|---|---|
| 1 · Context | Remind what problem we set out to solve — one sentence, reframes the whole conversation. | "We looked at why the stock trading product's gross profit dropped sharply last month." |
| 2 · Findings | The diagnosis: what the data showed, as a mechanism — the "why" chain, not a data dump. | "Revenue is healthy and growing 5%; the drop is entirely cost-side — the 100-share fee step combined with a 9× surge in large trades pushed unit costs ~10× up." |
| 3 · Recommendation | The decision, stated firmly — what to do, in priority order. | "Reprice large trades now, renegotiate or re-route broker execution next, and add tiered GP monitoring." |
| 4 · Risks | Concrete risks of your recommendation + mitigation. Never generic. | "Blunt repricing risks churning our most active traders — I'd bundle via plans and A/B the elasticity first; order-splitting needs a best-execution check." |
| 5 · Next steps | What happens Monday morning — actions, owners, the first metric to watch. | "Quantify the GP bridge by tier, launch the pricing experiment, open the broker conversation, ship the GP-per-trade dashboard." |
I: You're the PM for our stock trading product. Gross profit has dropped significantly over the last month. What would you do?
A: Let me play that back: gross profit — revenue minus direct costs — fell significantly within one month. Before I structure: is that absolute gross profit rather than margin? And is the goal to diagnose and restore it, or is there a target attached?
I: Absolute gross profit. Diagnose it and tell me what you'd do.
A: Two quick context questions. First, how does the product monetize — per-trade commission, spread, subscription, or a mix?
I: Assume a commission on each trade, a percentage of trade value.
A: And the main direct costs — I'd expect broker/execution fees, market data, payment processing. Is that the right picture?
I: Broadly yes. The biggest line is what we pay our executing broker.
A: Great. May I take a minute to structure? (structures) Since GP = revenue − direct costs, I'll first establish which side moved. Revenue = number of trades × average trade value × commission rate. Costs = variable per trade — mainly broker fees — plus direct fixed. A one-month sharp move with no mention of product changes makes me suspect a cost- or mix-side shock more than demand; I'll still check revenue first, in one question: what did revenue do over the month?
I: Revenue grew about 5%, same as the month before.
A: So revenue is healthy and growing — for GP to fall, costs must have grown materially faster. I'm deprioritizing the revenue branch. On costs: how did fixed and variable behave?
I: Fixed costs are flat. Variable costs grew 0%, then 2%, then 10% in the last three months.
A: Variable +10% against volume +5% — that means cost per trade is rising, so it's not pure scale; it's a rate or a mix problem inside variable costs. You said broker fees are the biggest line — how is the broker fee structured? Flat per trade, percentage, per share?
I: There's a schedule: trades under 100 shares cost us 0.05% of the dollar volume; trades of 100 shares or more cost 5 cents per share.
A: Interesting — that's a step function. Let me test what it does to unit cost. Take a $1,500 trade: under 100 shares it costs 0.05% ≈ $0.75. The same $1,500 as 150 shares at $10 costs 150 × 5¢ = $7.50. Ten times the unit cost for the same trade value. So if the trade mix shifted toward 100+-share trades, variable cost would explode while revenue — a % of value — wouldn't move. Do we have volume by trade size?
I: Small trades grew about 10% over the period. Large trades grew about 900%.
A: That's the root cause then, and it's mechanical: the mix shifted massively into the per-share tier, where unit economics are ~10× worse, and our commission — priced as % of value — doesn't pass that cost through. Gross profit compresses even as revenue grows. Quick sanity check on the "why now": did anything drive large-trade adoption — a feature, a campaign, market conditions?
I: Assume a viral trend — a few high-priced stocks got very popular with active traders. Why don't you move to what you'd do about it.
A: To recap the diagnosis in one line: a cost-side mix shock — the 100-share fee step multiplied by a 9× surge in large trades. I'll propose three fixes across pricing, the broker contract, and product, each with sizing, trade-offs and a KPI. First, short term — reprice the affected segment: introduce a per-share or tiered commission above the step, or include only N free large trades in Standard and shift heavy traders to a paid plan. If large trades are now, say, a third of volume at 10× unit cost, passing even half of that through restores most of the GP gap; I'd A/B it on new users first to measure churn elasticity. Trade-off: UX friction for our most active traders — mitigate with plan bundling rather than raw fees. Second, mid term — attack the cost itself: renegotiate the broker schedule with our new volume as leverage, or dual-route large orders to a venue priced on value; also smart order handling — where compliant, splitting a 150-share order into two sub-100 executions moves it back to the 0.05% tier. That needs a compliance/best-execution check — I'd flag it to legal before shipping. Third, monitoring: GP per trade by size tier as a weekly guardrail metric with an alert, so a mix shift never again reaches the P&L unseen. Owner: my squad; repricing ships within a sprint behind a flag; broker renegotiation is a quarter-long track.
I: Good. Can you wrap up?
A: Recommendation: fix the large-trade unit economics — reprice above the fee step now, renegotiate or re-route execution next, and add tiered GP monitoring. Reasons: revenue is growing and healthy; the entire GP decline is a variable-cost explosion from the 100-share fee step interacting with a 900% surge in large trades — unit cost is ~10× above the step while pricing doesn't reflect it. Risks: churn among high-value active traders if repricing is blunt — mitigate via plans and A/B elasticity testing — and broker-relationship terms limiting order splitting. Next steps: quantify the exact mix and GP bridge, run the pricing experiment, open the broker conversation, ship the monitoring dashboard. That closes the case for me — happy to go deeper on any branch.