Understanding Customer Growth for the first time
Updated: Jul 17, 2020
Overall estimates of market size may provide some broad indication of the potential size of an opportunity but are poor at providing any real insights into customer adoption. We need to understand how customer growth actually happens and what real customer numbers might look like.
Orthodox thinking about how customer numbers grow has been shaped strongly by the work of Rogers(1) in the 1960s, which postulated that cumulative customer growth can be defined by the mathematics of the diffusion process. Although this approach has been slightly modified by marketeers over the subsequent decades, we believe it still suffers from three main problems:
growth is assumed to be continuous, when the reality is that this diffusion-driven growth actually shows three discontinuities or chasms
none of the empirical data from our 10-year research programme supports the elegant (and maybe desirable!) idea of five different customer behavioural types (innovators, early adopters, early majority, late majority, laggards
and the estimated ‘S’ growth curves present a significantly over-optimistic view of early growth
The Triple Chasm Model(2), based on a significant data set from more than 3,000 real-world companies shows that cumulative customer growth can be predicted using diffusion theory, with three discontinuities or Chasms where the type of customer changes in a significant way: a very small number of early customers called Proto-customers are engaged when crossing Chasm I; crossing Chasm II depends on engaging with Charter customers; crossing Chasm III leads to significant growth, where Mainstream customers come into play, engaging on full commercial terms.
Critically, the Triple Chasm Model shows that the normalised customer growth for all products over a wide range of market spaces displays the same kind of behaviour; and normalising the time taken to reach these customers, enables all cumulative customer growth to be mapped by a single growth curve. This shows the following: around Chasm I, the number of customers is about 0.1 % of the total number of customers at around 10% of the total time; around Chasm II, the equivalent numbers are 1% of customers at the 30% point in time; and at Chasm III, before mainstream customers kick in, companies will have about 10% of total customers but will be half-way along on their journey.
Applying this powerful insight requires a much clearer understanding of the maximum number of customers, abbreviated to ‘C-max’, which any product can reach, and the associated time, ‘T-max’, taken to reach this point. Estimating the values of C-max and T-max for your product are critical for two important reasons:
this can provide a much more realistic measure of the size of the market than top-down approaches based on the so-called ‘addressable market’;
they also help you to better understand the length and complexity of the journey you are embarked on.
Getting to C-max - Estimating your maximum number of customers
The approach to estimating C-max relies on three key steps:
understanding the market space, potential customers, and number of users
estimating numbers of potential customers based on understanding the specific opportunity and the customer type
and using the expected ratios from the Triple Chasm Model, to verify where you are on the journey
Estimating time to reach your maximum number of customers, T-max
Estimating the time it will take to reach this maximum customer number also relies on three steps:
using your insights to understand how long it will take;
using the data on T-max gathered during the data-driven research exercise which resulted in the Triple Chasm Model (which revealed systematic variations based on market spaces);
and using the expected ratios from the Triple Chasm Model to verify where you are on the journey.
This approach to estimating C-max and T-max can lead to significant strategic decisions that change the product focus in order to reach a larger or different set of target customers, for example targeting consumers rather than business customers.
Companies should review the implications of these findings, when estimating the maximum customer potential for their products and services, and the time it takes to hit these numbers.
1. Rogers & Everett (1962), Diffusion of Innovations, Glencoe: Free Press, New York
2. Phadke, U.P & Vyakarnam, S. (2017), Camels, Tigers & Unicorns, WSP, UK