An investment thesis often depends on adoption in one form or another (e.g., of a new product, business model, technology, etc.) so we thought it helpful to lay out “best and worst” practices for assessing adoption potential.

First, common pitfalls.

Having reviewed hundreds of CIMs, VDDs, and industry reports addressing adoption, we find that the prevailing approaches are often over-simplified in one of these two forms:

• An S Curve presented on top of a chart of current penetration rates without sufficient research or analysis to identify, let alone support, wherein the S-Curve we are (and when to reasonably expect the next inflection point) for the product; and
• A representation and testimony of having an attractive value proposition. For example, a customer survey demonstrating the superior appeal of a new technology’s value for price and benefits indicating widespread adoption by itself

Accurately forecasting adoption requires understanding the purchasing process and hurdles – specifically, how it is driven and influenced by real individuals, operating in unique cultures, priorities, constraints, and roles.

Failure to fully grasp the purchasing environment and dynamics can lead to two major analytical errors: dramatically wrong addressable market sizes, and errors in forecasting adoption timing:

• Getting addressable market size wrong is often the result of focusing too much on the value proposition, and failing to understand the perceived risks that customers face when adopting new technologies. Dozens of factors contribute to a higher risk-based barrier: high cost of downtime, high re-training costs, uncertainty of return (i.e., relative to other options competing for resources), and even the end-users’ demographics. These create significant risk-aversion that may override any economic or other benefits.

• Getting timing wrong is often the result of conflating different types of decision-making events within the customer organizations: customers sometimes have radically different processes for changing technologies vs. reconsidering brands within a technology vs. replenishment. Conflating these can lead to miscalculating adoption rates and timing by an order of magnitude. This can cause cash flows from adoption to occur 3, 5 or even 10 years after they were projected, with significant impact on return.

Best practice starts with asking what the inhibitors are, particularly in terms of risk-aversion. Make sure to understand:

1. Who is the real decision maker?

2. What might that person(s) perceive the risk of taking on something new to be?

3. What are the consequences – focusing on both the personal as well as the organizational consequences – of getting it wrong?

4. How much work does the decision maker perceive to exist to fix the situation if the wrong choice is made?

5. What organizational inhibitors exist (e.g., competing priorities)?

Clear answers to these questions are the best guideposts to adoption. And, understanding the differences by segment can reveal the action-ability of each segment and therefore the best addressable market estimate. When combined with a strong understanding of the purchasing cycle, understanding the purchasing process can provide visibility into both timing and magnitude of adoption.

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