MBA Opportunity Assessment Card Template (TAM, SAM, SOM)

Opportunity screening: TAM, SAM, SOM two ways, problem severity, alternatives, unit economics, weighted scoring, kill criteria, answer key

MBA और बिज़नेस स्कूलपूर्णCustomer development (Blank 2005); discovery-driven planning (McGrath and MacMillan 1995); customer value (Gupta, Lehmann and Stuart 2004)opportunity assessment card mathअपडेट किया गया 28 अक्टू॰ 2026

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उपयोग कैसे करें: Use it when you screen a venture idea, a beachhead segment or a corporate venture proposal. Size the market twice, top-down and bottom-up, and write the kill criteria before you collect evidence. The main pitfall is a top-down share of a huge market standing in for a bottom-up plan.

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Opportunity Assessment Concept Card: [Opportunity, venture or course case]

A concept card for screening a new venture or new-business opportunity before money and years are committed: market sizing as TAM, SAM and SOM computed both top-down and bottom-up, problem severity measured in the customer's money, competitive alternatives including "do nothing", unit economics, a weighted scoring model with sensitivity checks, and written kill criteria that say in advance what evidence would stop the project. It is for MBA students in the entrepreneurship and strategy courses, founders and corporate venture teams, and investors who screen deals. Worked example A assesses a fictional Corvane Robotics product line and is fully solved; Worked example B is half solved and Exercise C is left for you, with a separate answer key. Source basis: Blank (2005) on customer discovery, McGrath and MacMillan (1995) on discovery-driven planning, Ries (2011) on testing assumptions, and Gupta, Lehmann and Stuart (2004) on customer value, written up in original words. Replace every [bracketed] value and every row marked Example. This is a learning aid, not investment advice.

Document control

Field	Value
Opportunity	[Product, segment and geography in one line]
Assessed by	[Your name or team]
Evidence base	[Number of customer interviews, pilots, data sources, dates]
Decision this informs	[Go to pilot / build MVP / raise seed / stop]
Reviewed by	[Mentor, tutor or investment committee]
Version	[0.1]
Last updated	YYYY-MM-DD

When to use it
The idea in plain language
Formulas and notation
Assumptions and limits
How managers and investors use it
Worked example A (fully solved): Corvane Frost cold-storage picking
Kill criteria
Worked example B (half solved): Northfield gear rental subscription
Exercise C (unsolved): Tallow Creek scheduling software
Process diagram
Connections to other cards
Common mistakes and misconceptions
Self-test questions
My notes and next steps
Sources and further reading
Answer key
Revision history

When to use it

One-sentence rule: an opportunity is worth pursuing when a specific customer has a costly problem, will pay enough to solve it with your offer rather than the alternatives, can be reached at an acquisition cost the customer's lifetime value repays several times, and sits in a market large enough to matter; write down what would prove each of these wrong and test the riskiest first.

Use this card when	Use another tool when
Choosing between several venture ideas or beachhead segments	Writing the full plan for investors (business/business-plan, startup/pitch-outline)
Deciding whether to fund a pilot or MVP	Valuing a business with a track record (mba/mba-dcf-valuation-card)
Screening a deal or a corporate venture proposal	Positioning an existing product (mba/mba-stp-positioning-card)
Preparing for a case on market entry or new business	Analysing industry profitability in depth (mba/mba-five-forces-card)

Shane and Venkataraman (2000) defined the field of entrepreneurship around the discovery, evaluation and exploitation of opportunities. This card is a structured version of the "evaluation" step.

The idea in plain language

The assessment has five blocks. Each answers one question and each has a typical way of fooling yourself.

Market size (TAM, SAM, SOM). Total addressable market (TAM) is the annual revenue if every possible customer bought the category. Serviceable available market (SAM) is the part you can reach with your product, channels and geography today. Serviceable obtainable market (SOM) is what you can realistically win within a stated horizon. A top-down estimate starts from a published or estimated total and applies shares; a bottom-up estimate multiplies the number of customers by units per customer by price. Bottom-up is more credible to investors because every number can be tested in interviews. When the two differ by more than a factor of about two, one of them has a wrong assumption.
Problem severity. How much does the problem cost the customer, how often, and how urgently? Measure it in the customer's own money and time. A problem worth solving costs more than your price by a clear margin.
Competitive alternatives. The real competitor is often the current workaround: spreadsheets, manual labour, an incumbent supplier, or doing nothing. Price and differentiation are judged against these, not only against start-ups with similar products.
Unit economics. For one customer: gross profit per period, how long they stay, what it costs to acquire them. Lifetime value (LTV) over customer acquisition cost (CAC) and the CAC payback period summarise it (Gupta, Lehmann and Stuart 2004; mba/mba-customer-lifetime-value-card; finance/unit-economics-analysis).
Scoring and kill criteria. A weighted score makes the comparison of options explicit and debatable. Kill criteria, written in advance, turn the riskiest assumptions into tests with thresholds (McGrath and MacMillan 1995; Ries 2011).

Formulas and notation

TAM_{bottom\text{-}up} = N \times q \times p \qquad TAM_{top\text{-}down} = M \times s_1 \times s_2 \times \dots

SAM = TAM \times f_{reach} \qquad SOM_T = \sum_{t=1}^{T} \text{new customers}_t \times q \times p \quad \text{(no churn) or} \quad SAM \times \pi_T

LTV = m \cdot \frac{r}{1 + i - r} \qquad \text{LTV to CAC} = \frac{LTV}{CAC} \qquad \text{CAC payback (months)} = \frac{CAC}{m/12}

S_k = \sum_{c} w_c \, x_{kc} \qquad \sum_{c} w_c = 1

Symbol	Meaning	Unit
$N$	Number of potential customers (sites, firms, households)	Count
$q$	Units per customer (robots per site, seats per firm)	Count
$p$	Price per unit per year	Currency per year
$M$	Published or estimated total spend in a broader category	Currency per year
$s_1, s_2$	Shares of that spend that fall in your category and segment	Ratio
$f_{reach}$	Share of TAM your product, channels and geography can serve	Ratio
$\pi_T$	Penetration of SAM achievable by year $T$	Ratio
$m$	Annual gross profit per customer	Currency per year
$r$	Annual retention rate (1 minus churn)	Ratio
$i$	Annual discount rate	Rate
$CAC$	Fully loaded cost to acquire one customer	Currency
$w_c$ , $x_{kc}$	Weight of criterion $c$ , score of option $k$ on criterion $c$ (1 to 5)	Ratio, points
$S_k$	Weighted score of option $k$	Points

The LTV formula assumes constant margin and retention and that each year's margin is received at year end by the customers retained that year (Gupta, Lehmann and Stuart 2004). Other conventions (margin at the start of the year, monthly periods) give different numbers; state yours.

Assumptions and limits

Market sizes are hypotheses, not facts. Published market reports use different definitions; cite the source and year, and check the definition before applying shares.
SOM is a plan, not a forecast. It is only as good as the sales capacity, cycle length and win rate behind it.
Unit economics at small scale are noisy. Early CAC is often too low (warm leads, founder selling) or too high (no brand, no referrals). Treat the first cohort with caution.
Gross margin must match the business model. In a robots-as-a-service model the hardware sits on your balance sheet: gross margin after depreciation can look healthy while cash is consumed by fleet capital expenditure.
Scores hide judgement. A weighted score is a structured opinion. Its value is in making the weights and scores explicit and in testing whether the ranking survives other reasonable weights.
Severity is not willingness to pay. Customers may describe a painful problem and still not buy. Paid pilots and letters of intent are stronger evidence than interview enthusiasm (Blank 2005).
The assessment is a snapshot. Re-run it when evidence arrives; that is the point of discovery-driven planning.

How managers and investors use it

User	What they focus on	Typical threshold or question (illustrative, not a rule)
Founder choosing a beachhead	Severity, reachability, speed to first revenue	Can we name the first 20 customers?
Seed investor	Bottom-up market, evidence of pull, team fit	Could this be a large company if it works?
Growth investor	Unit economics by cohort, CAC payback, net revenue retention	Is growth efficient?
Corporate venture committee	Strategic fit, right to win, kill criteria and staging	What do we learn for how much money by when?
MBA case discussion	Logic and consistency of the numbers, the recommendation and its risks	Would you invest, and on what condition?

Commonly quoted practitioner rules of thumb, such as an LTV to CAC above about 3 or a CAC payback under about 12 to 18 months for business software, are conventions, not laws. Use them as prompts for questions, and compare with your own cost of capital and cash position.

Worked example A (fully solved): Corvane Frost cold-storage picking

Situation (fictional). Corvane Robotics, a growth-stage warehouse-robotics company, considers three new product lines: Frost, a picking robot for cold-storage (refrigerated and frozen) warehouses; a retail micro-fulfilment system for grocery back rooms; and a parcel sortation retrofit for existing hubs. Frost would be offered as robots-as-a-service (RaaS) at USD 42,000 per robot per year; a typical site uses 6 robots. All figures are fictional assumptions for the exercise.

A1. Market sizing, top-down and bottom-up

Example item	Assumption	Result	Excel formula
Top-down TAM	Global warehouse automation spend USD 28.0bn a year, cold-storage share 13%	USD 3.640bn	`=28*13%`
Bottom-up TAM	15,000 cold-storage sites worldwide x 6 robots x USD 42,000	USD 3.780bn	`=15000642000/1E9`
Gap, bottom-up vs top-down		+3.8%	`=3.78/3.64-1`
Top-down SAM	TAM x North America and Western Europe 45% x mid-size sites 62%	USD 1.016bn	`=3.6445%62%`
Bottom-up SAM	4,200 mid-size sites in those regions x 6 x USD 42,000	USD 1.058bn	`=4200642000/1E9`
SOM (penetration)	3% of SAM sites by year 5 = 126 sites	USD 31.75m ARR	`=ROUND(42003%,0)6*42000/1E6`
SOM (sales capacity)	Account executives 2, 4, 6, 8, 8; 4 new sites each a year; 112 sites by year 5	USD 28.22m ARR	`=SUM(2,4,6,8,8)46*42000/1E6`

The two TAM estimates agree within 4% and the two SAM estimates within 5%, which gives some confidence in the order of magnitude. The sales-capacity SOM (112 sites, 2.67% of SAM sites) is lower than the penetration SOM and is the one to plan on, because it is tied to hiring. Annual recurring revenue at each year end would be USD 2.02m, 6.05m, 12.10m, 20.16m and 28.22m (cumulative sites 8, 24, 48, 80 and 112).

A2. Problem severity, in the customer's money

Interviews with cold-storage operators (fictional) point to labour availability in sub-zero conditions, high staff turnover and injury risk. Per site and year:

Example item	Result	Excel formula
Labour saving: 6 full-time equivalents at USD 58,000 loaded cost	USD 348,000	`=6*58000`
Turnover and injury cost avoided	USD 45,000	Estimate from interviews
Total customer benefit	USD 393,000	`=348000+45000`
Frost fee: 6 robots x USD 42,000	USD 252,000	`=6*42000`
Net saving to the customer	USD 141,000	`=393000-252000`
Benefit to fee ratio	1.56	`=393000/252000`
Customer payback on a USD 60,000 integration cost	5.1 months	`=60000/(141000/12)`

A benefit of 1.56 times the fee is attractive but not overwhelming; the case depends on the robots really replacing one full-time equivalent each across shifts, which is the assumption to test in pilots.

A3. Competitive alternatives

Example alternative	What the customer does today	Frost's advantage	Frost's weakness
Do nothing (manual picking)	Hire, train and replace staff, pay cold-store premiums	Reliability in staff shortages, safety	Upfront change, integration effort
Fixed automation (automated storage and retrieval)	Large capital project	No capital outlay, faster to deploy, scalable	Lower throughput at very high volumes
General-purpose picking robots	Robots not rated for sub-zero work	Built for cold, sealed components	Smaller installed base, less proven
Temporary labour agencies	Flexible staff at a premium	Predictable cost	Agencies scale down more easily

A4. Unit economics per site

Example item	Result	Excel formula
Annual revenue per site	USD 252,000	`=6*42000`
Gross margin (after robot depreciation, field service, cloud)	55%	Assumption
Annual gross profit $m$	USD 138,600	`=252000*55%`
CAC per site (sales, pilot, integration support)	USD 180,000	Assumption
Retention $r$ / discount rate $i$	95% / 12%	Assumptions
LTV	USD 774,529	`=138600*95%/(1+12%-95%)`
LTV to CAC	4.30	`=774529/180000`
CAC payback	15.58 months	`=180000/(138600/12)`
Undiscounted LTV (margin / churn), for comparison	USD 2,772,000	`=138600/(1-95%)`
Fleet capital per new site (6 robots at USD 55,000)	USD 330,000	`=6*55000`

The discounted LTV to CAC of 4.30 clears a 3x convention; the undiscounted figure is 3.6 times larger, which shows how much a lazy LTV formula flatters a long-lived customer. The fleet capital of USD 330,000 per site is not in CAC or gross margin but must be financed before revenue arrives: Frost is capital-intensive, a point for mba/mba-venture-finance-cap-table-card and for any lease or debt financing of the fleet.

A5. Weighted scoring of the three options

Scores run from 1 (poor) to 5 (strong); risk is scored so that 5 means low risk.

Example criterion	Weight	Frost	Micro-fulfilment	Sortation retrofit
Market size and growth	20%	4	5	3
Problem severity	20%	5	3	3
Differentiation against alternatives	15%	4	2	3
Unit economics	20%	4	3	4
Right to win (team, technology, channel)	15%	4	2	3
Execution and regulatory risk (5 = low)	10%	3	3	4
Weighted score	100%	4.10	3.10	3.30

Excel: with weights in B2:B7 and Frost's scores in C2:C7, =SUMPRODUCT($B$2:$B$7, C2:C7) gives 4.10.

Sensitivity. With equal weights the scores are 4.00, 3.00 and 3.33; with a market-heavy set (market 35%, problem 10%, differentiation 15%, unit economics 20%, right to win 10%, risk 10%) they are 4.00, 3.45 and 3.30. Frost ranks first under all three; micro-fulfilment overtakes the sortation retrofit only when market size dominates, which says the micro-fulfilment case rests on market size alone.

Conclusion. Frost scores highest (4.10) and the ranking is robust to the weights. The recommendation is not "build Frost" but "fund a staged test of Frost against the kill criteria below", because the scores for severity and unit economics rest on interviews and assumptions, not on paid deployments.

Kill criteria

Example ID	Riskiest assumption	Test	Kill threshold	By	Owner
KC-01	Cold-storage operators rank labour as a top-3 problem	20 structured interviews (startup/customer-discovery)	Fewer than 10 of 20 rank it top-3	Month 2	[@owner]
KC-02	Customers will pay for a pilot	Paid pilot offers to 8 qualified operators	Fewer than 2 paid pilots signed	Month 4	[@owner]
KC-03	One robot replaces about one full-time equivalent across shifts	Two pilot sites, 8 weeks	Below 0.8 FTE per robot after tuning	Month 7	[@owner]
KC-04	Unit economics hold at pilot prices	Pilot cost and price data	LTV to CAC below 3.0 on pilot data	Month 8	[@owner]
KC-05	The market is large enough to matter	Bottom-up count of mid-size cold-storage sites verified with two data sources	SOM by year 5 below USD 20m ARR	Month 3	[@owner]

A failed criterion leads to one of three decisions agreed in advance: stop, pivot (change segment, product or model; startup/pivot-memo) or persevere with a documented reason. Ries (2011) calls the underlying beliefs "leap-of-faith assumptions"; McGrath and MacMillan (1995) recommend a reverse income statement and an assumptions checklist updated at each milestone.

Worked example B (half solved): Northfield gear rental subscription

Situation (fictional). Northfield Outdoor Co., a mid-size outdoor-gear retailer with stores and an online shop, considers a gear rental subscription at USD 29 a month in the three metro areas where it has stores.

Example item	Assumption	Result	Excel formula
Households in the three metro areas	2,400,000
Target households (camp or hike 3+ times a year)	18%	432,000	`=2400000*18%`
SAM households (within 30 minutes of a store)	40%	[ ]	`=432000*40%`
SOM subscribers (2.5% adoption by year 3)		[ ]	`=B4*2.5%`
Annual fee	USD 29 x 12	USD 348	`=29*12`
SOM revenue		[ ]	`=B5*348`
Gross margin / annual churn / CAC / discount rate	42% / 35% / USD 95 / 10%
Annual gross profit per subscriber		USD 146.16	`=348*42%`
LTV		[ ]	`=146.16*65%/(1+10%-65%)`
LTV to CAC		[ ]	`=B10/95`
CAC payback		7.80 months	`=95/(146.16/12)`

Questions: (1) Fill the gaps. (2) Score the option with the weights of Worked example A, using scores of 3, 2, 3, 2, 5, 4; what is the weighted score? (3) Which single assumption would you test first, and with what kill threshold? (4) Why does a fast CAC payback not rescue a low LTV to CAC here?

Exercise C (unsolved): Tallow Creek scheduling software

Situation (fictional). Tallow Creek Medical Center, a regional hospital, built an operating-theatre scheduling tool that cut idle theatre time. A spin-out would sell it to other hospitals as software at USD 85,000 a year per hospital. Assume 6,100 hospitals in the target regions, of which 55% are regional hospitals of 100 to 400 beds (the segment the product fits). Sales capacity: 1, 2, 3, 4 and 4 account executives in years 1 to 5, each winning 5 hospitals a year, no churn within the five years. Gross margin 78%, annual retention 92%, CAC USD 120,000, discount rate 12%.

Compute TAM and SAM (in hospitals and in USD) and the year-5 SOM in ARR.
What share of SAM hospitals does the year-5 SOM represent?
Compute annual gross profit per hospital, LTV, LTV to CAC and CAC payback in months.
Score the opportunity with the weights of Worked example A, using scores of 3, 4, 3, 4, 3, 2.
Write three kill criteria. At least one must address the long hospital buying cycle and one the risk that the tool's results depend on Tallow Creek's own processes.

Process diagram

The flow matches the sections of Worked example A: frame the customer and problem, size the market twice, check alternatives and unit economics, score, then test against the kill criteria before scaling.

Connections to other cards

Customer value in depth: mba/mba-customer-lifetime-value-card (cohorts, retention curves, margin definitions) and finance/unit-economics-analysis (the working document).
Segment and positioning: mba/mba-stp-positioning-card; price-setting: mba/mba-pricing-card.
Industry attractiveness and advantage: mba/mba-five-forces-card and mba/mba-competitive-advantage-card.
Funding the opportunity: mba/mba-venture-finance-cap-table-card (how much to raise, on what terms).
Staging and flexibility: mba/mba-decision-trees-card and mba/mba-options-real-options-card value the option to stop after a pilot.
Market sizing practice: mba/mba-market-sizing-drills.
Working templates: startup/customer-discovery for interviews, business/lean-canvas for a one-page model, business/feasibility-study and business/market-research-report for a fuller assessment, product/competitive-analysis for the alternatives.

Common mistakes and misconceptions

"If we get 1% of a huge market." A top-down share of a big number is not a plan. Build SOM from customers, capacity and win rates.
Calling TAM the whole economic category. TAM must be the spend your category of solution could capture, not the customer's entire budget.
Mixing units. Per robot and per site, per month and per year: put units on every column.
Measuring severity by enthusiasm. Count paid pilots, letters of intent and budget owners, not compliments.
Ignoring "do nothing". The status quo is free of switching costs and is the most common winner.
Using undiscounted LTV. Margin divided by churn ignores the time value of money and flatters long-lived customers.
Leaving capital out of unit economics. Hardware, inventory or fleet investment can make a high-margin model cash-hungry.
Fixing weights after scoring. Weights chosen once the scores are known can produce any ranking. Fix them first and run a sensitivity check.
Kill criteria without thresholds or dates. "We will see how pilots go" cannot fail, so it cannot inform a decision.
Treating the assessment as a one-off. The card should be re-run after each test.

Self-test questions

Define TAM, SAM and SOM in one sentence each.
Why do investors prefer a bottom-up market estimate? What does a large gap between top-down and bottom-up suggest?
A software product sells at USD 20,000 a year to 3,000 target firms; you can serve 40% of them today. Compute TAM and SAM.
Annual gross profit per customer USD 10,000, retention 80%, discount rate 10%. Compute LTV with the card's formula.
CAC is USD 15,000 and annual gross profit per customer is USD 10,000. What is the CAC payback in months?
Give two reasons a high LTV to CAC ratio can still describe a poor opportunity.
What is a kill criterion, and why should it be written before the test?
In Worked example A, why is the sales-capacity SOM the better planning number?
A weighted score ranks option X first only when one criterion carries 40% weight. What does that tell you?
Interleaving: which card or tool do you use? (a) Splitting ownership in a seed round. (b) Deciding whether to fund a pilot that can be stopped after six months. (c) Explaining why an industry earns low returns.

My notes and next steps

Item	My entry
Confidence sizing a market two ways (1 = lost, 5 = could teach it)	[ ]
My riskiest assumption in a real or case opportunity	[ ]
First test and kill threshold	[ ]
Mistakes logged in mba/mba-error-log	[Date and short description]
Next review date (spacing: +2 days, +1 week, +1 month)	YYYY-MM-DD
Next card to study	[mba/mba-venture-finance-cap-table-card / mba/mba-customer-lifetime-value-card]

Sources and further reading

Blank, S. G. (2005). The Four Steps to the Epiphany. Self-published (CafePress).
Ries, E. (2011). The Lean Startup. Crown Business.
McGrath, R. G., and MacMillan, I. C. (1995). Discovery-driven planning. Harvard Business Review, 73(4), 44-54.
Shane, S., and Venkataraman, S. (2000). The promise of entrepreneurship as a field of research. Academy of Management Review, 25(1), 217-226.
Gupta, S., Lehmann, D. R., and Stuart, J. A. (2004). Valuing customers. Journal of Marketing Research, 41(1), 7-18.
Osterwalder, A., and Pigneur, Y. (2010). Business Model Generation. Wiley.
Textbooks (by name only): Barringer, B. R., and Ireland, R. D., Entrepreneurship: Successfully Launching New Ventures; Aulet, B., Disciplined Entrepreneurship; Christensen, C. M., Hall, T., Dillon, K., and Duncan, D. S., Competing Against Luck.
Open resource: OpenStax, Entrepreneurship (CC BY-NC-SA 4.0), chapters on opportunity recognition and feasibility.

Answer key

Worked example B. (1) SAM 172,800 households; SOM 4,320 subscribers; SOM revenue USD 1.503m a year; LTV USD 211.12 per subscriber; LTV to CAC 2.22. (2) Weighted score 3.00 (0.60 + 0.40 + 0.45 + 0.40 + 0.75 + 0.40). (3) Churn: at 35% a year the subscriber stays under three years on average; test retention with a three-month paid trial cohort, kill threshold for example monthly churn above 3% (about 31% a year). Even at 31% the LTV to CAC is only about 2.6, so price, margin or CAC must also move for the case to clear 3x. Adoption is the second test. (4) Payback measures speed of recovery, not total value; with high churn and a modest margin each subscriber returns only about 2.2 times CAC in present value, below a 3x convention, and the SOM revenue of about USD 1.5m is small for Northfield.

Exercise C. (1) TAM 6,100 hospitals, USD 518.5m; SAM 3,355 hospitals, USD 285.2m; year-5 cumulative hospitals 70 (5 x (1+2+3+4+4)), SOM ARR USD 5.95m. (2) 2.09% of SAM hospitals. (3) Gross profit USD 66,300; LTV USD 304,980 (=66300*92%/(1+12%-92%)); LTV to CAC 2.54; CAC payback 21.72 months. (4) Weighted score 3.30. (5) Examples: KC-01 three of the first ten qualified hospitals sign within nine months, else stop; KC-02 idle theatre time falls by at least [x]% at two hospitals that are not Tallow Creek, else pivot to a services model; KC-03 implementation under 12 weeks per hospital, else rework the product before scaling sales.

Self-test.

TAM: annual revenue if every possible customer bought the category; SAM: the part reachable with your product, channels and geography; SOM: what you can realistically win in a stated horizon.
Each input (customers, units, price) can be checked in interviews and data. A large gap means at least one share, count or price is wrong, or the two estimates define the market differently.
TAM USD 60m (=3000*20000); SAM USD 24m.
$LTV = 10{,}000 \times 0.8/(1.1 - 0.8) = 26{,}667$ , about USD 26,667.
15,000 / (10,000 / 12) = 18 months.
Examples: the market is too small to build a meaningful business; large capital needs outside CAC; the ratio rests on an early, unrepresentative cohort; CAC excludes costs such as founder selling time.
A measurable test of a critical assumption with a threshold and date that triggers stop or pivot. Writing it first prevents fitting the criterion to the result.
It is tied to hiring, ramp and win rates the company controls and can track; the penetration figure is a top-down guess.
The ranking is fragile and depends on one judgement; discuss that weight explicitly and gather evidence on the criterion that drives it.
(a) mba/mba-venture-finance-cap-table-card; (b) a decision tree or real-options analysis (mba/mba-decision-trees-card); (c) mba/mba-five-forces-card.

Revision history

Version	Date	Author	Change summary
0.1	YYYY-MM-DD	[Your name]	First pass through the card
0.2	YYYY-MM-DD	[Your name]	Example: Replaced Frost assumptions with interview and pilot data, revised KC-03

Standard and licence note

Based on the opportunity evaluation and customer development literature listed above (Blank 2005; McGrath and MacMillan 1995; Ries 2011; Shane and Venkataraman 2000; Gupta, Lehmann and Stuart 2004). The text, examples, numbers and questions are original; no textbook text is copied or paraphrased, and Corvane Robotics, Northfield Outdoor Co. and Tallow Creek Medical Center are fictional. The template is independent and not affiliated with or endorsed by any business school, publisher or investor. Market data in your own assessment must carry its source and date. Template licence: MIT.