Competing With Data & Analytics
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Traditional marketing practices are increasingly viewed with skepticism. In many organizations, marketers struggle to document the return on investment for marketing expenditures; as a result, the marketing function is poorly aligned with the strategic goals of the company, marketing has less influence in the boardroom — and the marketing budget allocation is viewed as a questionable cost rather than a worthy investment.
To negate such criticisms, marketers need to realign their role and redefine the scope of marketing so that it can directly relate to strategic outcomes for the company. One way to achieve this goal is through extensive application of predictive analytics, in both the formulation of marketing strategies and customer management. Along these lines, a study we conducted suggests that certain types of marketing efforts — those developed using analytics to identify customers’ lifetime value to a company — can create shareholder value and influence stock prices in a predictable fashion.
We began our research by analyzing more than five years of customer information from two Fortune 1000 companies, one selling to businesses and the other selling directly to consumers. The customer databases of both companies contained rich information related to customer transactions, marketing communications and customer-level characteristics that could potentially influence the customer lifetime value of each customer to the company. However, neither of the two companies had employed the CLV metric, which is defined as the discounted net present value of expected future cash flows from a customer.
V. Kumar and D. Shah, “Expanding the Role of Marketing: From Customer Equity to Market Capitalization,” Journal of Marketing 73, no. 6 (November 2009): 119-136.
As a result, one of our first steps with each of the two companies entailed developing a model to compute the lifetime value of that company’s customers, using as much customer-level information as possible from the company’s customer database. The lifetime value for each customer in this study was calculated as the net present value of the expected cash flows from that customer over the next three years. (Although the time horizon of three years may not reflect the “lifetime” duration of the customer, it captures the majority of the customer’s true lifetime value given the fact that the computation is based on the discounted cash flow approach.
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