How to value a startup by looking at its customers.
Coming up with a fair valuation is always difficult, even more so for startups; customer-unit economics can help.
Customer-driven investment methodologies use customer metrics to assess a startup underlying value. Simply put, can you -the startup team- acquire customers profitably and retain them for many years, therefore having a longer-term profit potential larger than the revenue growth to date had implied?
Whether you have recurring (subscription-based) or discretionary revenue stream, customer-unit economics can shed light on your fair value by making projections from the bottom up, looking at how individual customer behaviour drives the top line.
Investors prefer businesses with recurring revenues as there is more certainty of income and the investment calculations are quite straight-forward (recurring revenue + new revenue — churn), giving a sense of the “true value” of the business. For startups with discretionary revenue, customer behaviour is not as easy to predict, at least until there is enough data for statistical analysis and by that time you might already have run out of cash! For early-stage start-ups with such discretionary revenue, the best path is to test the behaviour of their customer cohorts at all levels of the purchase funnel to get benchmarks and build a “proof of concept” of the path to profitability.
To get started, we need to build a customer behaviour model based on:
a customer acquisition model, forecasting he inflow of new customers and their cost of acquisition (CAC, aka CPA),
a customer retention model, forecasting how long customers will remain active,
the purchase model, forecasting how frequently customers will transact with your business, including what other product or service they might buy (cross-selling), and
the average spend per purchase.
Putting it all together enables us to infer an average LTV (Customer Lifetime Value, aka CLV) and make operating profitability forecasts based on the difference between the LTV and the CAC. This approach produces a more precise estimate of future revenue stream, and -importantly- helps understand some of the key commercial drivers. A good CRM system will make such analysis quite straight-forward, but a good old excel spreadsheet can do the trick as long as you keep track of all the data.
You can look at the data on a time-period basis for revenue forecasting, or on a cohort basis to see what product changes optimised income. By adding cohort analysis to the process, ie looking at changes in acquisition and retention based on changes in pricing or product features and services, we can model the best path to profitable growth, and get clear insights as to how to maximise customer value and ROI. From this, you can make a better range of estimates of what a company is worth, based on a range of scenarii.
Good examples of publicly available data that you can google are from Slack Technologies, Dropbox, Lyft or FarFetch. Conversely, a poorer example is Peloton Interactive, who provided some aggregate-level data for its IPO but used a methodology that did not account for the time value of money (ie discount back to present value).
The customer-driven approach is not new, but seems to be under-represented, most likely for 2 reasons:
it takes more work than a top-down approach
you need to validate the data and have a representative sample, which is not always the case for early-stage startups.
So make yourself a favour and include customer-unit economic analysis in your commercial diligence for your next investment; it will shed a new light on how you see the start-up prospects!