TAM SAM SOM Examples (Worked for Real Startups)

A complete TAM SAM SOM example shows three things: how many customers exist, what each pays per year, and how that total narrows to a slice you can win. Below are three worked walkthroughs — a vertical SaaS, a marketplace, and a D2C brand — built entirely from invented, illustrative numbers.

Quick Answer: Build each example bottom-up — TAM = potential customers × annual price, or GMV × take rate for a marketplace. Then narrow: SAM removes customers you cannot serve (wrong geography, wrong fit), and SOM is the share you can realistically win in about three years. Every figure below is invented to teach the method, not a real market size.

How These Three TAM SAM SOM Examples Were Chosen

The three examples were chosen to cover different revenue mechanics, because how you size a market depends on how it makes money. A per-seat SaaS, a transaction-based marketplace, and a subscription D2C brand each build TAM from a different unit — and each narrows to SAM and SOM for different reasons.

Three terms, defined precisely:

• TAM (total addressable market) is the annual revenue if every possible customer in the category bought at your price — the ceiling.
• SAM (serviceable addressable market) is the part of TAM your product and business model can actually serve today.
• SOM (serviceable obtainable market) is the realistic share of SAM you can capture in a defined near-term window.

Every number below is invented. These are teaching figures with round, made-up inputs — not researched market data, and you should never quote one as a real market size. What is real is the method, which follows the same startup market sizing logic you would apply to your own idea.

What a complete example must include. Each walkthrough names the customer unit, assumes a count, sets an annual price, multiplies to a TAM, then states the filters that cut TAM to SAM and SAM to SOM. If any of those steps is missing, the number is decoration.

Example 1: TAM SAM SOM for a Vertical SaaS (Dental Scheduling Tool)

For a vertical SaaS, TAM is the number of target accounts multiplied by annual contract value (ACV), segmented by account size. Take a hypothetical scheduling tool for independent dental practices — call it DentalDesk — billed per practice per year.

Define the customer unit first. The unit is one dental practice you invoice, not an individual dentist or patient. DentalDesk assumes two segments that pay different prices, so each is counted and priced on its own.

The bottom-up TAM stacks the two priced segments (all figures assumed for illustration):

TAM ≈ $140M (illustrative). Multiply each segment's count by its ACV and add them: a made-up ceiling assuming all 50,000 practices buy at list price. It is not a forecast.

SAM removes practices DentalDesk cannot serve. Suppose it launches in one country and integrates with only two of the practice-management systems dentists use. Cutting other geographies and incompatible software might leave about half the practices — an illustrative SAM near $70M. Each cut has a named reason, not a round-percentage guess.

SOM is the share DentalDesk can win soon. Given a small sales team and a realistic win rate, capturing 4% of that SAM over three years gives an illustrative SOM around $2.8M — how the company actually starts, through named channels. The build behind these steps is spelled out in how to calculate a bottom-up TAM.

Example 2: TAM SAM SOM for a Two-Sided Marketplace (Music-Lesson Platform)

For a marketplace, TAM is gross transaction volume (GMV) multiplied by your take rate, because you earn a cut of each transaction rather than a subscription. Take a hypothetical platform connecting students with private music teachers — call it LessonLoop — earning a commission on every lesson booked.

The customer unit is a transaction, not an account. LessonLoop's revenue depends on how much money flows across the platform, so the build starts from total lesson spend and applies the commission.

The illustrative build, one assumption at a time:

• Paying students: 800,000 who buy private lessons in a year (assumed).
• Annual lesson spend per student: $1,000 (assumed), giving a GMV pool of $800M.
• Take rate: 12%, so TAM ≈ $96M (illustrative).

SAM removes GMV LessonLoop cannot capture. Only students who book and pay online, in the metros the platform covers at launch, count toward SAM — perhaps 40% of the pool, an illustrative SAM near $38M. Cash lessons and uncovered cities are real revenue, just not addressable yet.

SOM is the near-term obtainable slice. Winning 5% of that SAM over three years against incumbents and direct booking gives an illustrative SOM around $1.9M. Notice that the take-rate model makes TAM far smaller than raw GMV — sizing a marketplace on GMV alone overstates the business.

Example 3: TAM SAM SOM for a D2C Subscription Brand (Senior-Dog Supplements)

For a direct-to-consumer brand, TAM is the number of target households multiplied by annual subscription revenue per household — a figure that looks enormous until SAM and SOM cut it down. Take a hypothetical monthly supplement for senior dogs — call it GreyPaws.

The customer unit is a subscribing household. GreyPaws assumes one plan at a flat monthly price, so annual revenue per customer (ACV) is simply twelve months of the subscription.

The illustrative build:

• Target households: 3,000,000 with a senior dog that already buy supplements online (assumed).
• Annual revenue per household: $180 ($15/month × 12).
• TAM = 3,000,000 × $180 ≈ $540M (illustrative).

SAM removes households GreyPaws cannot reach or convert. Restrict to households in shipping zones that buy pet products by subscription, and the serviceable slice might be 35% — an illustrative SAM near $190M. The TAM looked huge; the first honest cut nearly triples the distance to it.

SOM reflects a crowded, CAC-heavy channel. D2C acquisition is expensive and competitive, so winning 2% of SAM over three years is already ambitious — an illustrative SOM around $3.8M. The lesson: a nine-figure D2C TAM says little on its own, because the obtainable number is what an operator plans around.

The Three Examples Side by Side: Customer Unit, Revenue Driver, and Narrowing Logic

The three walkthroughs use the same skeleton but different mechanics. The table below contrasts what drives each number and why the illustrative TAM lands where it does (all totals are the invented figures from above).

Takeaway: The revenue driver — not the raw audience size — determines how you build TAM, and every credible example loses most of its TAM on the way to a SOM measured in low single-digit millions. A model where SOM is a large fraction of TAM is usually hiding an unstated constraint.

What Separates a Credible TAM SAM SOM Example From a Hopeful One

A credible example sources or names every input and narrows TAM with stated filters; a hopeful one starts from a giant headline number and multiplies by a round percentage. The difference shows up in a few tells.

• Named counts, not round guesses. "40,000 solo practices from a licensing registry" beats "let's say a million dentists."
• Annual prices, not monthly. Revenue per customer must be annual (ACV); a monthly price slipped into an annual TAM understates the market twelvefold.
• Filters with reasons. Each cut from TAM to SAM names what it removes — a geography, a missing integration, offline demand — instead of applying a percentage chosen because it felt realistic.
• A SOM tied to channels and win rates, not "1% of a huge market." The obtainable number describes how you actually start selling.
• Numbers labeled as assumptions. A credible model flags every estimate as a hypothesis to be refined, rather than presenting a guess as a fact.

The hopeful version — "the market is $50B, we'll take 1%" — inverts the whole exercise by starting from the answer it wants. Pressure-testing each assumption the way Edmired encourages, and following the full narrowing method in how to calculate TAM, SAM, and SOM, is what turns an example into a defensible model.

Key Takeaways

• A complete TAM SAM SOM example names the customer unit, the count, and the price, then narrows the total with stated filters — miss any step and the number is decoration.
• TAM is built bottom-up: customers × annual price, or GMV × take rate for a marketplace; the mechanic follows the money, not the audience headcount.
• SAM removes what you cannot serve today — wrong geography, incompatible product, offline demand — and every cut needs a named reason, not a round percentage.
• SOM is the share you can realistically win in about three years through named channels and a real win rate; a forecast lives here, not in TAM.
• Illustrative TAMs vary by revenue model: a seat-based SaaS, a take-rate marketplace, and a household-scale D2C brand produce very different ceilings from similar-sized audiences.
• A big TAM narrows hard — in each example the obtainable slice was low single-digit millions, so a large TAM alone proves little about the business.
• Every worked figure is a hypothesis, not a fact: label assumptions as assumptions, and sharpen them as real deals and conversion data arrive.

Frequently Asked Questions

What Is a Good TAM SAM SOM Example for a Startup?

A good example builds TAM bottom-up — potential customers times annual price — then narrows it with named filters to SAM and SOM. The vertical SaaS walkthrough above (accounts × ACV, segmented, cut to one geography and a realistic win rate) is the cleanest template, because every input is countable and every cut has a stated reason a reviewer can challenge.

How Do You Show TAM Narrowing to SAM and SOM?

Show it as three explicit steps, each with a reason. TAM is every possible customer at your price; SAM subtracts customers you cannot serve — wrong region, incompatible product, offline demand — with a filter you can name; SOM applies your realistic win rate and channel capacity over a set window. State each percentage cut and why, never a round number chosen because it felt right.

Should TAM SAM SOM Examples Use Real Market Numbers?

Use real sources for your own model, but teaching examples like these deliberately use invented figures so the method stays clear and no one mistakes a demonstration for a market claim. When sizing your actual startup, source counts from registries and associations and prices from your own deals — and label every remaining estimate as an assumption.