Market Sizing and Forecasting: Estimating Revenue Potential
Evaluating the revenue potential of a new product concept requires sizing the target market and modeling adoption over time. Robust forecasting reveals whether your idea can scale into a sizable opportunity.
This guide explores core market sizing and forecasting frameworks – including the TAM SAM SOM model, persona modeling, and diffusion modeling. We’ll also cover building forecast scenarios and leading indicators to validate projections.
Let’s size markets strategically to reveal real revenue possibilities.
Why Market Sizing Matters Early On
It’s tempting to believe any compelling product concept represents a billion-dollar opportunity. But without rigorously sizing and modeling the opportunity, you risk overestimating potential:
- Wasting years pursuing ideas scaling to just niche markets
- Misallocating resources better applied to higher potential products
- Making erred financial projections used securing investments
- Failing to anticipate and react to emerging competitors
- Overbuilding capacity and infrastructure for actual demand
- Setting unrealistic growth expectations stunting morale
Robust modeling instead provides:
- Data-driven evaluation of revenue potential and product/market fit
- Understanding of optimal customer personas and segments to target
- Identification of niche competitors dominating pieces of the market
- Sensitivity analysis around scenarios for uncertainty
- Validation of assumptions and strategy through measurement against projections
With projections grounded in empirical analysis, you pursue ideas poised for growth, not mirages.
The TAM SAM SOM Framework
A structured approach to sizing opportunities and modeling adoption is the TAM SAM SOM framework:
Total Addressable Market (TAM)
The total market demand for solutions in your space – i.e. US fitness app spend. Indicates maximum revenue if you served every user.
Serviceable Addressable Market (SAM)
Subset of TAM realistically accessible based on geographic, demographic and other constraints. Narrows the universe.
Serviceable Obtainable Market (SOM)
Of the SAM, the portion achievable for your specific business within time period based on competitive factors and realities. Your target segment.
TAM establishes the broad opportunity. SAM narrows potential market scope for your positioning. SOM models realistic adoption.
Top-Down and Bottom-Up Sizing
Two core approaches exist for modeling TAM and SAM opportunity size:
Top-Down Market Sizing
Uses large datasets and statistical modeling to estimate market size. For example:
- US population x % smart device usage x % willing to pay for apps = TAM
- TAM x % in target demographics, locations = SAM
Works best when established datasets exist related to your industry. Requires assumptions.
Bottom-Up Market Sizing
Models your market by estimating and summing adoption across granular segments:
- Profile target personas – students, travelers, desk workers, etc
- Model personas – 5M US college students x 40% use fitness apps x $60 annual spend
- Size additional micro-segments and sum adoption
Bottom-upBuild up market from discrete adoption groups. Requires data on each. Combined together they offer more realistic market sizing than relying solely on high-level statistical approximations.
Forecasting SOM Adoption and Growth
With the SAM quantified, modeling adoption forecasts SOM opportunity. Methods include:
Charts adoption S-curve over product lifecycle from early adopters through majority based on innovation characteristics and buyer groups. Useful for new categories.
Estimate addressable market for each identified personas. Model lifecycle from awareness->consideration->conversion. Meta-analysis blends models.
Model adoption mathematically with key factors like:
- Market penetration %
- Market share % vs competitors
- Churn and retention rates
Estimates trial and repeat purchase rates mathematically based on coefficients for innovation and imitation. Predicts inflection point.
Models changes in adoption rates based on enhanced marketing, distribution, pricing, etc. Sensitizes forecasts.
Matching techniques to available data results in pragmatic forecasts.
Building Market Forecast Scenarios
Given inherent uncertainty, build adoption scenarios based on variations in key assumptions:
Favorable assumptions like faster growth, higher adoption, weaker competition. The best case reasonably achievable.
Model adoption using your most likely assumptions on market dynamics. The primary forecast.
Pessimistic assumptions like slower growth or more competition. Worst realistically foreseeable case.
Stress testing across multiple scenarios anticipates variability and prepares contingency plans if realities match the less desired forecasts.
Validating Market Sizing Forecasts
Reality-check projections with leading indicators as you go:
Customer Pipeline Stage Progression
If opportunity forecasts 100k customers, model if early stage pipeline is realistically advancing towards the projected rates.
Early Adopter Willingness to Pay
Survey targeted segments on willingness to pay. Does value match required pricing for profitability at projected adoption?
Total Early Customer Acquisition Cost Assumptions
Do projected CAC costs to acquire customers match what’s realistic for unit economics to work with forecast adoption?
Market Survey Data Points
Check if broader market surveys support your estimated segment sizes, willing-to-pay models, and feature preferences.
Competitor Analytics Proxy
If competitors exist, check whether their observable growth and adoption proxies support your forecast scale.
Continually validate projections match early tangible indicators of real market potential. Adjust forecasts quickly if diverging.
Avoiding Common Forecasting Pitfalls
Some common mistakes distort forecasts:
- Overweighting existing competitors – Incumbents constrain imaginations. But disruptive innovation can unlock untapped demand.
- Undermodeling external forces – Failing to anticipate rising competitor threat once an opportunity shows promise.
- Optimism bias – Allowing desired potential to cloud objectively sizing opportunity hurdles.
- Generalizing anecdotes – Letting a few passionate users obscure broader market reality. Verify anecdotes.
- Motive bias – Tailoring models to justify desired strategies rather than objectively following where data leads.
- Lacking analog proxies – In new categories with no benchmarks, creatively identify analogous examples indicating possible scale.
Adopt a mindset of pragmatism, not optimism or pessimism. Let data guide projections.
Key Takeaways for Market Sizing and Forecasting
Here are best practices for modeling real revenue potential:
- Frame opportunity size using the TAM – SAM – SOM model
- Build market projections bottom-up from real buyer groups and personas vs. just high-level statistical data
- Model adoption over time using diffusion frameworks and growth factors
- Construct forecast scenarios – upside, downside, expected – to stress test assumptions
- Check early empirical indicators frequently to validate projections match reality
- Avoid biases, over-weighting existing paradigms, or generalizing anecdotes
- Iteratively refine forecasts as new customer insights emerge
While sizing markets requires forecasts, ground projections in observed data plus sensitivity analysis.
Size markets diligently upfront to pursue opportunities poised for growth, not mirages. But refine projections as real data appears once executing.
With a structured approach, you gain clarity on possible trajectories – both upside and downside cases – then rally your team to make the upside a reality through strategic execution.
- 1 Market Sizing and Forecasting: Estimating Revenue Potential
- 1.1 Why Market Sizing Matters Early On
- 1.2 The TAM SAM SOM Framework
- 1.3 Top-Down and Bottom-Up Sizing
- 1.4 Forecasting SOM Adoption and Growth
- 1.5 Building Market Forecast Scenarios
- 1.6 Validating Market Sizing Forecasts
- 1.7 Avoiding Common Forecasting Pitfalls
- 1.8 Key Takeaways for Market Sizing and Forecasting