Strategy Choice — generating, testing, and costing the move

Six moves for deciding what to do and proving it holds: generate real options, surface and test what must be true, war-game the move against a reacting opponent, set price, build the case, and allocate capital across the portfolio. Firm-specific frameworks are named in references/firm-lenses.md.

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Strategic Options (strategic-options)

A real option set and a conditional recommendation — not a single pre-baked answer dressed in alternatives.

Method: (1) Read the environment first — where profit pools sit and are migrating, and how strong/full the core is. This gates everything: chasing a shrinking pool or expanding from a weak core is the most common failure. (2) Generate options across the core and its adjacencies — Engine 1 (strengthen the core: pricing, cost, share, mix, loyalty — always ask if the core is at full potential before capital leaves it), adjacency (one step out, levering an existing strength via a repeatable model), Engine 2 (build the new: new models, platforms, M&A, category creation). Force at least two genuinely distinct moves per band. Give each a one-line theory of advantage ("why us, why durable"). (3) Treat options as staged commitments — separate the irreversible part from the reversible; identify cheap probes that buy information and the trigger that converts a probe to full commitment; value the optionality. (4) Set weighted criteria before scoring — gap to full potential (usually heaviest), feasibility, risk, strategic fit, advantage durability. (5) Score honestly (1–5, one-line rationale each, show weighted contributions; ties → prefer preserved optionality). (6) Reason to a conditional recommendation — the lead option, the immediate move (commit / probe / wait), the sequence with triggers, the kill-switch, and the specific conditions (swing assumption, threshold, signal) that would flip the call.

Output: environment read, option set (Engine 1 / adjacency / Engine 2, each with theory of advantage + staged structure), weighted scorecard, conditional recommendation with flip-conditions and kill-switch.

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Assumption Audit (assumption-audit)

The hidden load-bearing beliefs made explicit, the critical ones tested before the firm bets.

Method: (1) Reverse the logic — instead of "do I believe this," ask "what would have to be true" for the strategy to deliver; push across market, customer, loyalty, competitor, capability, cost, timing. (2) Build a falsifiable assumption register (rewrite vague beliefs so they could be shown false; note who holds each and its source). (3) Plot importance × confidence — the high-importance / low-confidence quadrant is where nearly all attention goes. (4) Grade the evidence A–F for each high-importance assumption (an assumption can be widely held and still earn an F — say so; watch confidence resting on consensus not data). (5) Design a kill-shot test for the riskiest one — built to disconfirm, with the killing result and decision rule defined before running it; state cost, time, rule. (6) Write a pre-mortem ("it's a year out, the strategy failed — why") and fold new assumptions back in. (7) Assign the decision (RAPID) and stage the commitment behind results-delivery gates so capital releases only as load-bearing assumptions confirm. (8) Verdict — robust / fragile / single-belief-contingent, and whether the fragility is acceptable for the size and reversibility of the bet.

Output: assumption register, importance × confidence map, evidence grades, the kill-shot test (cost/timeline/pre-committed rule), pre-mortem, RAPID roles + stage gates, the verdict with its "so what."

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War-Gaming (war-gaming)

A stress test of the strategy against intelligent, adaptive opposition — not a prediction.

Method: (1) Frame the decision question (one sharp question), the scenario (starting conditions, horizon, the move, the 3–4 external bets), the base-case decision absent the game, and the value at stake (gap to full potential). (2) Stand up the four-role game — Blue (you, played honestly with real constraints), Red (rivals, playing to win not to be reasonable), Yellow (customers/regulators/channel/suppliers/talent), Control (referee, injects shocks, enforces realism). (3) Run three turns forcing order effects — Turn 1 move (first-order reactions), Turn 2 counter (second-order: market structure, price levels, loyalty/switching, your economics), Turn 3 escalation/settle (third-order: where the system lands, who's structurally better/worse off, how much of the prize survives). (4) Read the signals — earliest observable indicators per path (a price filing, a hiring pattern, a channel shift, a loyalty-cohort move), ranked by how early and reliable. (5) Register failure modes and pre-commit response thresholds — for each way the strategy loses: the failure mode, its leading signal, the trigger threshold, the owner, the rehearsed response, classed anticipate / absorb / adapt. (6) Verdict — does the game confirm or overturn the base case, how much of the prize survives, what changes.

Output: scenario + decision question, role briefs, three-turn play log, ranked signal watch-list, trigger table (failure mode / signal / threshold / owner / response / class), verdict. A war game that changes nothing was theater — at least one finding must change the plan.

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Pricing Strategy (pricing-strategy)

Move pricing from cost-plus / competitor-match to value-based, segment-specific economics.

Method: (1) Anchor to the value the customer receives — establish each segment's reference price (next-best alternative), quantify differentiation value across the Elements of Value (functional: saves time / reduces cost / reduces risk; plus emotional and higher-order where they drive WTP and loyalty), compute value-based price = reference + positive differentiation − negative, and plot every segment on the price-to-value line (below = underpriced surplus left behind; above = churn-exposed). (2) Separate price by micro-segment (by use case, size, channel, urgency, switching cost) — average pricing overcharges the sensitive and undercharges the value-rich; quantify the pool lost to one-size-fits-all. (3) Probe willingness-to-pay (Van Westendorp band per segment; triangulate with win/loss, elasticity, discount patterns, who renews vs churns); flag evidenced vs assumed. (4) Model price-volume to the margin-maximizing zone — optimize contribution margin, show the breakeven volume tolerance per move, sanity-check against cost and loyalty position. (5) Design structure, fences, and discount discipline — the model that captures value (tiered / two-part / usage metric aligned to the benefit), legitimate fences against migration, and a mapped pocket-price waterfall (list → invoice → pocket) with the leaks named and guardrails set. (6) Sequence the plan — quick wins (close the worst discount leaks, reprice clearly underpriced segments), then structural (re-architect tiers/metrics/fences), then staged repricing (with enablement, protecting the loyal base).

Output: pricing diagnosis on the price-to-value line (pool at stake quantified), WTP/elasticity band per segment (evidence vs assumption), price-volume model with breakeven tolerance, structure/fences/discount recommendations with waterfall leaks, a sequenced action plan with owner-ready metrics.

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Business Case Builder (business-case-builder)

Transparent economics built from explicit drivers, framed against the gap to full potential.

Method: (1) Build a driver-based model — decompose into revenue drivers (market, growth, share, volume, price, attach, churn), cost drivers (variable per unit with scale effects, fixed, cost-to-serve), capital (capex, working capital, restructuring), timing (ramp, time to breakeven, phasing); each driver an assumption with value, unit, basis. Outputs must move when drivers move. (2) Compute NPV, IRR, payback and show the build — the period FCF bridge, discount factors, discounted FCF per period, terminal value — traceable to drivers, not a black box. (3) Frame against the gap to full potential — profit growth, position/quality (core, base, loyalty, repeatable model), cash (frees or consumes; can it self-fund). (4) Run sensitivities on the swing drivers — tornado on the 2–3 that move the answer, a coherent downside/base/upside set, each swing driver's breakeven. (5) State "what would have to be true" — the conditions for the case, rated by plausibility and control. (6) Treat staged investment as real options — stages with go/no-go gates, valuing the right to expand and the right to abandon cheaply. (7) Surface key risks + mitigations + early-warning indicators.

Output: driver table (value/unit/basis), FCF build + returns (shown not stated), full-potential view, sensitivity section (tornado + scenarios + breakevens), "what would have to be true," real-options structure, risks with mitigations. (For the live .xlsx, hand to financial-modeling.)

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Portfolio Review (portfolio-review)

Reallocate capital and attention across units on evidence, not inertia.

Method: (1) Sort each unit by role and economics — role (defended core / Engine 1, strong adjacency, Engine 2 bet, non-core) and economic strength (growth, ROIC vs cost of capital, competitive position, loyalty); size by revenue or capital employed. (2) Name the cash-flow logic explicitly — the core funds adjacencies and the few chosen Engine 2 bets; adjacencies self-fund as they mature; Engine 2 bets get concentrated (not sprinkled) investment; non-core is harvested/exited and its capital recycled. The discipline is routing cash to where it compounds and refusing to fund everything equally. (3) Overlay capital consumed vs returns to surface what the role-sort misses — cross-subsidies (a unit funded by a stronger sibling), starved winners (strong unit under-resourced by even spreading), value destroyers (capital in, sub-cost-of-capital return out). (4) Decide grow / hold / fix / exit per unit (fixes carry a deadline and a trigger). (5) Reallocate through a full-potential and sustainable-growth lens — move capital out of no-path-to-core pockets into compounders; test against the sustainable growth rate; show before-and-after split and net effect on portfolio returns and growth. Make the hard calls (exit a non-core unit, concentrate behind one Engine 2 bet, starve a pet project), don't defer them.

Output: units sorted by role × economics, explicit cash-flow logic, capital-vs-returns overlay (cross-subsidies + starved winners surfaced), a grow/hold/fix/exit call per unit, a before-and-after reallocation tested against sustainable growth.