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Big data analytics help marketers assess customers' lifetime values

For marketers looking to leverage data quality into actionable knowledge that can help inform future sales initiatives, it's important to take a long view. Successful use of big data analytics in marketing isn't just about finding short-term quick fixes to meet sales goals next week - it also entails making long-term projections about the potential revenues to be gleaned from lifelong connections with loyal customers.

1to1 Magazine recently wrote about the importance of lifetime value, citing the book "Return on Customer" by Don Peppers and Martha Rogers, founding partners of Peppers and Rogers Group. According to the two authors, LTV is the net value of all future cash flow a company can expect to generate from a customer. It's difficult to pinpoint a dollar amount for this property exactly, but using accurate data on consumers and their spending trends, companies can find a good approximation.

"It doesn't matter that the extra business a customer might give a company won't happen for a few months or a few years," Peppers and Rogers wrote. "The customers' intent has changed already, and so the customer's LTV went up immediately, in the same way a share price would go up immediately if the company were suddenly expecting better profits sometime in the future."

Harvard Business School stresses the importance of keeping a running tab on each customer's lifetime value, using a mathematical formula to calculate the worth of a customer relationship and how it develops over time. It's important not only to have a good LTV tool, but to tweak it over time as you uncover new data that informs your opinions.

Compiling accurate data on consumers is a valuable pursuit that will help marketers improve their sales numbers, both immediately and in the years ahead. The effects of data quality are far-reaching.

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