It’s no secret marketers have always tried to predict what people want, and then get them to buy it. Hello. Marketing 101.
This has become common practice among online retailers, you know, pushing customers toward other products they may or may not want. Both Amazon and Netflix, two of the best-known practitioners of targeted upselling, have long recommended products or movie titles to their clientele. They do so using a technique called collaborative filtering, basing suggestions on customers’ previous purchases and on how they rate products compared to other consumers.
In an article in today’s NYTimes, the search for a better understanding of buying habits continues with numerous companies selling algorithms that promise a retailer more of an edge. For instance, Barneys New York says it got at least a 10% increase in online revenue by using data mining software that finds links between certain online behavior and a greater propensity to buy.
Using a system developed by Proclivity Systems, Barneys used data about where and when a customer visited its site and other demographic information to determine whom it should focus its e-mail messages.
For instance, an e-mail message announcing sales might go to those Web site visitors who had purchased certain products or types of products in the past, but only when the items were on sale. In the simplest terms, if someone buys only when something is on sale, but never buys anything in December, then the e-mail sale flier might not be sent to that customer in December. “There is a digital trail of interest left by customers,” said Sheldon Gilbert, Proclivity’s chief executive and founder.
Barneys experienced at least a 10% increase in online revenue and found 20% more customers would purchase once sent the targeted e-mail messages. The company has saved money by not sending e-mail letters to customers unlikely to buy.
Read more "Guessing the Online Customer’s Next Want" hereSee the Top Ten Summer 2016 Trends for Women Over 40