While it's relatively straightforward to create a working prototype of one's product idea, it's another matter of business calculus to size and qualify an opportunity where one has a product or solution that potential buyers need but won't necessarily buy.
This is acutely felt in the world of disruptive technologies, particularly solutions based on Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT). A solution may be perfectly developed but prove to be commercially elusive in the event that an audience for it exists but is unfounded or adoption presents unique hurdles. Market sizing can help developers, founders, and technology teams investigate such questions and save them from chasing unviable opportunities.
Let's explore the approaches to estimating the market size for a disruptive technology application and ways to validate such data.
1. Define the Market You Are Estimating
Before even getting one's hands dirty estimating anything, the team should first agree on what the technology being considered solves or addresses.
IoT is an umbrella term for a variety of connected sensors, management, tracking of devices and assets, monitoring of industrial equipment, and much more, making the total addressable market (TAM) of IoT highly abstracted and not necessarily reflective of a specific product opportunity. Before any research, ask the next 4 questions:
- What problem does the product solve?
- Who is likely to suffer the problem?
- In what industries and geographies is the pain real?
- With whom are the customers likely to have existing relationships?
Having a precise definition helps make the calculations meaningful and not pulled in wholly unrelated directions by large numbers.
2. Use the Top-Down Approach
The top-down approach entails identifying a base TAM which might relate to the industry or sector and then narrowing the scope down to the one the technology is targeting.
Say one is making AI-driven inspection quality control application. One first identifies and analyzes the TAM in the industry space which includes manufacturing software with automated inspection processes.
Next, they narrow down the TAM to the segment or sub segment targeted and consider how many such units are eligible to purchase or adopt the technology.
This approach is appropriate and effective when industry research findings or reports are available. However, the size of the TAM is by no means an indicator that companies there will be buying the proposed solution.
3. Build from Potential Customers Up
Bottom up analysis considers revenue potential by identifying target customers. Here's an example. If an IoT platform is targeting industrial monitoring, Research indicates that 300 factories fit the target buyer persona, The product sells at $10,000 an account with an expectation for a contract or annual fee;
So the calculation is relatively simple:
Potential customers: 300
Revenue per customer: $10,000
Potential annual revenue:
300 × $10,000 = $3,000,000
This suggests a hypothetical three million dollars per year revenue opportunity for the identified 300 companies. However, if the customer acquisition opportunity is conservatively at 5%, here's where that would come into play:
Potential customers: 300
Expected acquisition rate: 5%
Customers acquired:
300 × 0.05 = 15
Revenue opportunity:
15 × $10,000 = $150,000
These are illustrative calculations intended for use in understanding the relative value of a sales opportunity in absolute dollar terms from revenue. Of course, this does not factor multiple aspects including cost to acquire a customer and ongoing costs to retain them, competition, selling motion, and contract value in consideration of implementation requirements among other variables. The point is that revenue opportunity and potential and market opportunity are not one and the same.
4. Research to Validate Assumptions
A market sizing approach is only as good as the assumptions upon which it is made, but assumptions are always suspect. There are two research approaches for technology teams to combine their efforts in identifying buyers, researching competition, and establishing the relevance of the offering to prospective audiences.
Primary research
When carrying out primary research, it entails collecting information from potential buyers (decision makers) and members of the industry in which the technology is targeting.
Examples of primary research include;
Interviews with decision makers
Surveys on pain points and other attributes
Conversations with industry experts
Proof-of-concept testing and feedback
Pricing, contract value, and implementation requirement reviews
For example, the team developing predictive maintenance software can speak to factory managers about how problems are currently identified when they arise; the cost of equipment downtimes; what they expect an improved solution to offer and how such is likely to help their business; and various other details about their purchasing considerations, behaviors, and decision-making timelines.
This enables the team to investigate the opportunity objectively based on what the potential buyer is likely value and interested purchasing as opposed to the solutions' own notions.
Secondary research
Secondary research entails examining publicly available reports, analyses, and any other sources of data relevant to the opportunity at hand. Industry reports, competitor website reviews, government publications and databases, research reports on similar technologies and markets, and research studies in the space can be useful in gaining an understanding.
Since different studies may define and measure markets differently, ensure to consider the criteria one one used when comparing the data before synthesis.
Combining the approaches can help teams reach a consensus on what constitutes buyers and the degree and level of competition in the industry they are targeting.
5. Consider Use Cases
It can be helpful to identify different use cases for AI, ML, and IoT because each can open up a range of opportunities for value creation depending on who one is targeting.
Technology Use case (solution) Customer problem
AI Document processing Repetitive manual work
Machine learning Demand forecasting Uncertain future demand
IoT Equipment monitoring Inscrutable operations
AIoT Sensor based anomaly detection Unknown abnormality patterns
With this, it's evident even just from the names that these solutions are intended purposes. Each of these has varied buyers, price ranges, levels and nature of competition, and implementation requirements prior to adoption. In the case of adopting industrial monitoring solutions (say IoT), the equipment will have to have sensors; it'll need to be implemented in a manner that can support its effective use; and before analytics around operations can be valuable, data must first be correctly interpreted.
Market research should examine the total technology solution and its implementation requirements, not just an abstract approach.
6. Address Adoption Barriers
There is no guarantee that just because potential buyers identify a need, it means they are willing to buy what one has to sell. Adoption barriers are those reasons that prospective buyers have for hesitating to buy or adopt a new solution. Some typical barriers include but are not limited to:
- Cost prohibitions
- System incompatibility
- Information degradation
- Cybersecurity concerns
Lack of technical know how concerns in respect of poor returns or investment. For example, an industrial factory interested in adopting AI based equipment monitoring solutions may not have equipped its equipment with the right kinds of sensors.
Adoption requires a significant capital outlay before the solution even becomes useful to them.
When forecasting adoption, try to think of low, medium, and high estimates. For each scenario, the assumptions that make it possible should be identified and explored. Each one should inform the understanding of how one is to move forward and plan one's resources, staffing, sales, and other motions to achieve the desired effect.
7. Know TAM, SAM, and SOM
There are three standard ways in which TAM, SAM, and SOM are calculated to understand the market in which one is selling and one's position and prospects in it to develop a strategy.
TAM SAM SOM
The total opportunity for a given solution in a defined market The portion of TAM one can address within one's product or geographic scope The portions of SAM one can achieve realistically in a given period
For example, a technology company targeting the industrial space as an initial launch market for its AI-based software solutions in equipment monitoring would define its TAM in reference to how many manufacturing companies would benefit from such a product. It would then define its SAM around which of these manufacturing companies have the interest and geographies that match what it is targeting. And finally, the SOM is determined when the company explores realistic chances of reaching a prospect in respect to implementation requirements in competition for a potential contract.
Avoid estimating SOM as a percentage of TAM. Always use SAM as a guide and establish realistic assumptions of buyer eligibility, price points, purchasing tendencies, and competition to determine the estimate.
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8. Things to Watch Out For
One doesn't have to go very far online to see people making very impressive looking spreadsheets with one primary figure TAM. Here are some common pitfalls to avoid at all costs.
Overly broad TAM figures. The TAM of all forms of technology may not reflect the opportunity of a niche solution.
Blind assumptions about universal adoption among all eligible prospects. Prospects are unlikely to all be buyers, even one the solution fits their needs.
Failure to consider existing alternatives that could prove more appealing. Customers tend to look elsewhere if existing relationships offer more than the prospect technology can offer.
Double counting revenue. More than one vendor selling equipment to a prospective client.
Taking TAM, SAM, SOM, forecasts, and other assumptions as certainties in the absence of investigation as research. As discussed earlier, these figures are always based on a bunch of assumptions.
Failure to consider implementation cost barriers for customers. Prospective adopters may hesitate to adopt solutions as solutions such if the required infrastructure is costly to implement.
Confusing market sizes of opportunity with company level revenues. A company has little chance at capturing TAM even if it is a fit for its needs.
Understanding these limitations should help in making informed assumptions when embarking on a market-sizing task for an emerging technology opportunity.
A Checklist for Technology Founders
Before considering a potential market opportunity for making a decision relating to product innovation or making an investment, ask yourself the following questions;
Have one accurately defined the target customer and the specific problem one is targeting?
Can one clearly identify potential buyers as opposed customers?
Has one validated the problem the proposed technology would solve?
Are ones assumptions about pricing and potential customers reasonable?
Has one considered what competition and alternatives exist?
Has one accounted for implementation costs and other adoption barriers?
Would another person, given one's provided sources and assumptions, be able to perform the calculations and arrive at the same results?
If one cannot answer most of these questions confidently, one should be cautious about jumping to conclusions based on a single market-sizing initiative.
Closing Remarks
There is a combination of research, calculations, and validation that go into estimating the market size for disruptive technologies. A top-down approach provides a high-level view and understanding of market TAM, while bottom-up analysis links revenue and potential customers to the opportunity in terms of value. Customer research supports and validates assumptions and is essential to qualifying opportunities. Analysis of the use case and consideration of the target market TAM, SAM, and SOM help separate market theory from practice. It is this last part that differentiates market potential from a realistic opportunity for developing a technology business. What matters to developers interested in disruptive technologies like AI, ML, and IoT is asking the right questions around who has a problem and how many are there, and whether they would adopt a potential solution offered.
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