 # An equation for evaluating the adoption potential of a new product

There are some great frameworks to evaluate the diffusion of new innovations, but these tend to be qualitative and can therefore be difficult to evaluate multiple nuanced implementations against one another. Here is a “back of the envelope” quantitative approach to assessing whether a new solution might displace an incumbent one, when competing on “convenience”.

### Competing on Convenience

Increasingly new products are introduced as radically simpler options to an incumbent. When competing on this convenience-based value proposition, in order to gain initial adoption the user must be able to perceive (or expect) that the switching effort to the new solution will be worth it to them.

Said another way, the “expected lifetime effort” (ELE) of the proposed solution must be much less than that of the old for the solution to be adopted, which we can express as follows:

ELE(incumbent) >> ELE(proposed)

Let’s take a deeper look at each side of this “equation”.

### Evaluating the expected lifetime effort of the incumbent solution

First, we attempt to quantify the expected lifetime effort of an incumbent solution, which I’m calling ELE(incumbent), by graphing it as a function of time and effort as follows:

• Begin with the x-axis being the number of uses of a product over the effective lifetime
• Set the y-axis as the amount of effort expended by the user
• The initial effort, E(initial) for the incumbent solution is 0, since the upfront investment is already made (sunk) by the user
• Then plot the incremental effort of each trial, E(incremental). Here, I’m assuming since each use is independent the incremental effort is consistent each time, and therefore a flat line.
• Therefore the expected lifetime effort of the incumbent solution, is given by the area under this curve, which is: (n – 1/2) * E(incremental) ### Evaluating the expected lifetime effort of a proposed solution

Using the same axes as before, we can then plot the expected effort of the new solution similarly:

• E(initial) for the proposed solution has to account for the initial onboarding process (e.g. creating an account, inviting friends, etc.)
• E(incremental) is assumed to be much less than the incumbent solution (or else why would you introduce it?). As with the incumbent, we assume each trial as independent, so therefore plot a flat line.
• Once again, the expected lifetime effort is area under this curve, which in this case is = (n – 1/2) * E(incremental) + 1/2 * E(initial) ### Quantifying each side of the equation

With these formulae, the next step is to quantify each term. To do so, we’ll want to ensure that we’re trying to measure things with consistent units, so here are some rules of thumb for determining the values of E(incremental) and E(initial).

1. Start by enumerating the discrete number of steps in the process. For example, imagine you had an onboarding flow 5 form fields (first name, last name, email address, password, and credit card number) that comprise your E(initial) value.
2. For each step in the process assign a “multiplier” based on the difficulty for the user. It doesn’t truly matter your units, once they are consistent across the left and right-hand side. For this, I like to use a Fibonacci set, similar to the method used for assigning story points in an agile process.
3. Then sum the number of points for the initial and incremental effort
4. In our example above of the 5-step onboarding flow, I may assign values of 1+1+2+3+8=15 for the E(initial) value

### Evaluating each solution

With the values plugged into each side, both ELE(proposed) and ELE(incumbent) can be evaluated. Keep in mind that this model purely focuses on the “costs” to the user, and not benefits, but assuming those are equal we have a method to both analyze the likelihood a new technology, or solution, has at displacing an incumbent, and also help design the new solution, and see how shifting effort from upfront to incremental makes a difference (and vice versa).

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