Please read our key pointer summary on one of the most well written research paper (quantitative logic) written on valuing latest generation subscription businesses
As we see the growing popularity of subscription based businesses we need a certain predictability to gauge the valuation of those businesses. The predictability component of the subscription business can only be formed through certain disclosure norms. Data like Customer Churn, customer acquisition and retention techniques, etc, help in ascertaining value of the company.
Before delving deeply into the customer based valuation let’s first understand the basic ways of how valuation is done.
According to the standard corporate valuation theory, the value of a firm equals the value of the operating assets plus the non-operating assets (NOA), minus the net debt (ND) of the firm.
The value of a firm’s operating assets is equal to the sum of all future free cash flows the firm will generate, discounted at the weighted average cost of capital (WACC).
FCF is equal to the net operating profit after taxes (NOPAT) minus the difference between capital expenditures (CAPEX) and depreciation and amortization, minus the change in non-financial working capital.
All of the calculations given above represent DCF (Discounted Cash Flow).
The author gives a brilliant insight with respect to how to forecast revenue; he says that if we decompose the revenue into subsequent parts than separately modelling the constituent parts and then combine the forecasts of these components to arrive at the desired revenue forecasts.
He says that it is necessary to categorize revenue into number of customers and average revenue per customer as a start. Then after categorizing it, it makes further sense to divide the number of customers category into new customers acquired during the month and the number of customers acquired in previous months who still have a relationship with the firm.
Knowing the number of new customers acquired of each month is a critical input to any valuation exercise, especially for firms with high subscriber acquisition costs. It is important to note that such information will be overlooked if we simple apply a time series model to the revenue numbers.
Kim, Mahajan and Srivastava were the first to recognize the potential for using some of the models of customer behavior developed by Marketing Scientists to generate key inputs for estimating cash flows. They used the logistic internal-influence model for the diffusion of an innovation. The biggest limitation with this model is that it did not consider the reality of customer churn.
Nowadays, with the development of neuroscience and cognitive science we have started to understand the detailed nuances of the human brain. Psychologists have over the years backed on the theory of behaviorism and the studies that concern behaviorism have been used extensively in the fields of marketing.
When we develop a model after trying to input various data points, we are able to comprehend the data and understand the pattern there. We have seen the focus shifting from product based marketing strategies to relationship based marketing strategies.
When we talk of subscription based model it is very essential that there is some continuity and predictability in revenues. So, to keep this revenues going only customer acquisition is not enough. If all the energy and money will be spent to just acquire new customers than the business is not sustainable. So the focus has started to shift from not only acquiring new customers but retaining them. So the marketing strategies have become more personal and customized. It has become more relationship based so that the customer churn is avoided as much as possible.
The basic premise of this paper is that they are trying to gauge the value of the firm through new method of customer valuation. When we use internal-influence method of customer valuation; we are effectively saying that we are using data and expert knowledge in order to gauge certain behavioral patterns and after doing that trying to build the model that monetizes on that insight. With the help of the model we can calculate the value of a firm through using behavioural insights.
But by using the CLV (Customer Lifetime value), we don’t take into account the customer churn and keep the retention rate constant. Customer Lifetime Value is a model which combines the value generated from each customer assuming that he/she will stay with the firm till its dissolution. The CLTV method doesn’t take into account the capital structure and non operating assets.
Another important concept that they use in the valuation framework is Customer Equity. Customer equity is basically the total of the discounted lifetime values of the organization’s customer. In short, more loyal the customers; higher is the customer equity.
Estimating the retention rate becomes critical when you use the customer equity. If we combine parameters like retention, Average revenue from one consumer, customer churn, etc, we will be able to determine the value that a customer creates if he stays long enough.
There are basically four parameters that the author tries to touch upon while developing his model; customer acquisition, retention processes and ARPU.
In the retention processes model they are trying to figure out the active consumer every month. They will be able to do that through using a survival mode (A mode of probability). When you are able to figure out the active consumer per month with the help of survival model you are able to also identify the customers lost in the process.
Read Full: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2701093