🚀 How to estimate market size

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3 tactics to estimate market size

⬆️ Use the bottoms-up approach

• Revenue = # of customers X average revenue per customer per year

• Calculate # of customers:

  • Focus on segments that contribute to revenue significantly (e.g. for DoorDash, employed Americans aged 25-40 who eat out at least once per week)

  • Estimate how many new customers can be acquired and retained in 5 years (7–10 years for long-term trends like electric vehicles)

  • Benchmark market share against existing dominant players and adjust expectations based on market fragmentation and distribution difficulty (e.g. capturing 10% of Fortune100 is more realistic than capturing 10% of 100,000 SMBs)

• Calculate average revenue per customer per year:

  • Focus the market sizing on the core revenue stream

  • Estimate transaction volume (eg. How many products will be consumed, How much data storage is required, How many seats per company, etc.)

  • Estimate pricing via 3 main approaches:

    • Value-based (generally the best approach and ideal for SaaS): Charge 10–30% of the value created

    • Competitor-based (common in consumer): Anchor to existing products; explain any premium or discount clearly

    • Cost-based (for ops-heavy or asset-heavy businesses): Add margin to cost of delivering the product

📶 Plan for incremental expansion across customer segments and products

• Serviceable Obtainable Market (SOM):

  • Realistically obtainable revenue within 5–10 years based on go-to-market strategy and sales capacity

  • # of acquired customers X average revenue per customer per year

• Serviceable Available Market (SAM): 

  • Revenue for initial product and initial target customer segment within current strategy for the next few years (e.g. specific consumer demographic, company size, geography, price).

  • # of target customers X average revenue per customer per year

• Total Addressable Market (TAM): 

  • Total potential revenue across all possible customer segments and products in the category

  • # of total customers X average revenue per customer per year

• There is no single definition of market size—the textbook definitions for TAM / SAM / SOM are vague

🌎 Focus only on geographies tied to the go-to-market plan

• Global market size is rarely relevant

• If US-focused, state US addressable market only; if multiple smaller countries, aggregate only those markets with a clear international plan

• Present revenue as annualized figures (e.g. $10M over two-year buying cycle = $5M per year)

2 traps to avoid

🚨Estimating using top down % market share approach

• % market share assumption is often unsubstantiated, resulting in meaningless and unconvincing estimate

• High market shares (like 10%) are anchored from dominant consumer-facing giants, not other fragmented categories 

• Typical tech IPOs capture only 0.1% to 2% of their addressable market

• Use top-down estimates to sanity check the magnitude of the bottom-up estimates

🚨Obsessing over TAM precision based on existing demand

• Excessive TAM precision at early stages can underestimate true potential (e.g. latent demand, network effects, and vertical dominance can massively expand TAM)

• Focus on a precise beachhead SOM instead of precise TAM in the early days

• Estimate using hypothetical demand (e.g. how average revenue per customer and total demand can grow in 5-10 years) instead of existing demand

1 tool to leverage

📖 Best practice on market sizing for venture

• Aim for beachhead SOM above a few hundred million and TAM above mid-single digit billions to be sufficient for a $100M ARR venture-scale success

• 93% of public software businesses TAMs are <$100B, 78% are <$50B, and >20% are $10B or lower; those with TAMs ≤$10B have average ARR ~$480M

• Leverage tools like IBISWorld for market size estimates and customer demographics reports, and cross-check with census data for precision

Bonus: 1 trend to spark startup ideas

📈 Rising tide of AI-driven cyberattacks demands a new era of defense

• AI will write nearly all software within 12 months, boosting code volume 10x but also multiplying vulnerabilities—AI-assisted code has 41% more bugs on average

• LLM agents are already able to exploit up to 10% of unknown (“zero-day”) security flaws and 13% of recently found (“one-day”) vulnerabilities

• Hackers always had an unfair advantage, but AI makes it even more uneven:

  • AI lets hackers launch attacks cheaply and often, and only need to succeed once, while companies have to defend perfectly every time

  • Failed AI attacks costs almost nothing, but successful hacks can be very profitable

  • SMBs can no longer hide under the radar as autonomous agents scan everything and make everyone a potential target

• High-growth opportunities:

  • AI-driven white-hat hacking platforms to proactively discover and fix security flaws

  • Autonomous vulnerability scanners that continuously monitor and patch in real-time 

  • Pre-deployment tools to test for vulnerabilities before launch