Everyone loves a bargain. But marketers who resort to discounting to boost share-of-wallet from Web site visitors may pay a particularly high price to instill a bad habit in the buyer and the seller.
The cost of keeping the discount machine running to satisfy customers’ insatiable drive for “a steal” is surely no bargain for merchants once the cost of promotions and the pressure on margins is factored in. Unfortunately, easing up on the “percent-off” throttle often means waving goodbye to customers as they bolt to another site in search of their next cheap thrill.
That’s no way to build loyalty or profits. But the temptation to discount grows as the size of the e-commerce marketplace increases. From being non-existent a few years ago, the value of retail e-commerce in 1998 was estimated at $7 billion. It is expected to jump to $42 billion by 2002. Business-to-business e-commerce will represent approximately 10 percent of all business transactions conducted by 2003, according to Forrester Research. And the numbers could go higher as more marketers try to take advantage of a good thing.
Clearly, savvy marketers must keep their discount banners in check until they take advantage of e-targeting to optimize their sales efforts without minimizing profits.
E-targeting and its foundation technologies provide the sophisticated e-commerce tools that can drastically increase a marketer’s likelihood of reaching potential customers and keeping purchases growing. New IT tools can help marketers package an offer to which a customer is more likely to respond, and produce behavioral insights into individual customers that are advantageous in a directing marketing strategy.
The issue now facing i-marketers is how to gain efficiencies and share-of-wallet through a better understanding of individual customers.
Tried-and-true technologies, such as sophisticated database querying, still hold a rich storehouse of insights into customer preferences. But one must be willing to dig into the data using standard modeling technologies to reveal the likelihood of future purchases.
Such database querying is the basis of collaborative filtering, a tool being used to great effect in increasing sales through cross-selling suggestions gleaned from database information.
Collaborative filtering’s ongoing value lies in its ability to analyze recent purchases or click streams, comparing a visitor to other customers who have established similar patterns. It then makes merchandise offers based on what those other customers have bought. Amazon.com has made its reputation, in part, providing this kind of suggestive selling. It’s a good system that builds a sense of loyalty, not to mention additional sales if one’s algorithms are in alignment.
Filtering and other database analyses are complemented by the ability to communicate immediately, often and inexpensively with customers via e-mail, e-newsletters, e-catalogs, etc. Those create ongoing contacts that reinforce relationships and minimize reliance on discounting strategies. They also can be employed in self-selection strategies directed at buyers. In those cases, more accurate e-targeting is made easier by allowing customers to be explicit about their own preferences so marketers can respond accordingly with the right products, price points, etc.
While traditional database querying technologies do an excellent job analyzing important historical information, they can’t provide real-time insights into buyer behaviors for more accurate indications of the likelihood of future purchases.
Fortunately, the torrent of e-commerce technologies is solving that with the introduction of solutions such as behavior mapping, a technology that can provide a quantum leap in understanding customers.
Behavior mapping finally allows e-marketers to understand and address buyers as the unique individuals they are. Older database/filtering techniques categorize a Web site visitor by his or her similarities to others who establish similar click streams. But those comparisons can be highly inaccurate because each person is unique.
Behavior mapping’s strength is its ability to assess in real-time a customer’s behavior and make offers on the basis of the customer’s past interactions coupled with their current click stream.
The result is a revenue-generating shift away from the fuzzier all-for-one filtering widely used today and toward the one-to-one marketing that behavior mapping makes possible. It does this by capturing the uniquely different paths of behavior each customer establishes and projects it onto a behavior map. The paths those consumers take reveal their specific needs and inclinations. And they help answer many vexing questions that have heretofore baffled marketers.
What is the likelihood of an immediate purchase? Are sales accelerating or decelerating? Is the customer more price-sensitive than interested in convenience? Do customer behavior paths portend a growing or shrinking share of wallet? Behavior mapping is just the latest tool that can help answer all those questions and more. More importantly, it can answer them accurately for a specific person rather than for some vague cohort that shares a common profile.
When marketers understand individual purchase patterns, they learn what circumstances, if any, are necessary to spur sales – hopefully, without having to rely on discounting as a financial prod. They also find out which offers may be exercises in futility before they waste a lot of time and money in the discovery process.
In either case, the growing portfolio of e-targeting technologies finally delivers the systems intelligence needed to generate more revenues, reduce marketing costs, protect margins and, most importantly, re-program discount-driven buyers determined to go for your jugular.
