Two people call customer service at the same time to complain about the same thing. One waits a few seconds before a representative gets on the line. The other stays on hold. Why the difference?
There’s a good chance it has something to do with a rating known as a customer lifetime value, or CLV. That secret number is used by all manner of companies to measure the potential financial value of their customers.
Your score can determine the prices you pay, the products and ads you see and the perks you receive.
Credit-card companies use the scoring systems to decide what to offer customers who want to cancel their cards. Wireless carriers route high-value callers immediately to their most skilled agents. At some airlines, a high score increases the odds of a seat upgrade.
In the model, the scores ranged from $8.52 to $203.93.
Company will refrain from marketing to such customers and won’t be in a hurry to answer their messages.
Single man, 22
Lives in rural area
Shops rarely, mostly on weekdays
Usually buys at deep discounts
Returns merchandise excessively
Company will send an occasional discount.
Single woman, 31
Lives in suburbs
Shops a fair amount
Browses for discounts but often exceeds budget
Never returns merchandise
Company will invite such customers to VIP events and ensure their complaints get answered first.
Married woman, 41
Lives in big city
Shops regularly, mostly on weekends
Usually pays full price and rarely returns items
Buys and browses best-quality items
“There’s no free lunch,” says Sunil Gupta, a marketing professor at Harvard Business School who has researched models for calculating lifetime value. “The more profitable you are, the better service you will get.”
These days, companies are resorting to all sorts of data and scores to size up consumers and predict their behavior. Retailers use risk scores to try to limit merchandise returns and prevent e-commerce fraud. There are scores to measure the likelihood a person will become sick, cancel a subscription or bad-mouth a company.
Everyone with a bank account, cellphone or online shopping habit has at least one CLV score, more likely several. And most people have no inkling they even exist, let alone how they are used, what goes into them or how accurate they are. Unlike credit scores, CLVs aren’t available to consumers and aren’t monitored by any government agency.
“There needs to be a public conversation around the accuracy of the scores being used,” says Pam Dixon, executive director of the World Privacy Forum, a nonprofit digital-privacy research group. “You can essentially be accused of being cheap or a fraudster, and it may not even be true.”
To determine how the scores are compiled and how they are used, The Wall Street Journal interviewed data scientists who develop the models and employees of the software and analytics firms that help companies put them to use.
Most CLV score users contacted for this article declined to comment on how they score customers, citing competitive reasons. Many say the scores make them more comfortable offering costly services and products in the short term because they are confident they will pick up more business in the long term. Some say they aim to increase each customer’s lifetime value by encouraging repeat business.
In some respects, the scores are just a high-tech version of what shopkeepers have done for generations—make judgments on a customer’s value based on how they look or behave. As far back as 20 years ago, academics were publishing models to calculate the future value of customers.
Now there are hundreds of analytics firms that calculate customer lifetime value, each with its own approach. Some of them put a value on shoppers based simply on what they spend, while others use hundreds of data inputs, adding and deducting points for demographic information such as ZIP Codes or behavioral details such as the number of returns they make or when they shop.
“Not all customers deserve a company’s best efforts,” says Peter Fader, a marketing professor at the University of Pennsylvania’s Wharton School who helped popularize lifetime value scores. His scoring method is based on transaction history, which he says is all companies need to determine how customers will behave in the future. This year, he sold the firm he co-founded, Zodiac Inc., which performs such analysis, to
Alliance Data Systems
Epsilon, which sell information on such things as the number of bedrooms in a house and the type of credit card someone carries. Each piece of data is weighted based on past patterns and perceived level of predictability.
Marital status is often factored in, with some companies assuming that singles are better customers, and others, the opposite. Age also is a common input, potentially penalizing older people because of their shorter projected lifespans.