Finding the right combination of statistical data and human intuition in cross-selling has for the most part eluded Internet retailers. Sure, collaborative filtering and data mining applications have made it possible to suggest accessories or companion products based on how frequently shoppers purchase those items in conjunction with another item, but there is no great mystery to the science behind the technology. Sales volume ultimately determines which products are offered and how high a product is ranked.
That`s not to say developing a cross-selling strategy around a rules-based mathematical model is a bad business choice. After all it has worked well for Amazon.com Inc. But it can be argued that books are an easier product to cross-sell in an online environment, especially for first-time customers for which there is no sales history. All that is really needed is a sense of the genre and the authors that appeal to the customer. Such data can easily be gleaned from the book the customer purchases. Someone who buys a novel by David Balducci, for example, is likely to enjoy other suspense thrillers by best selling authors.
The challenge to creating a more targeted cross-selling strategy in an online environment is much greater when it comes to products such as apparel, jewelry and furniture; that is, items that are driven more by taste than popularity.
The reason is simple: Rarely online is there is a sales associate who spends time with the customer learning about the customer`s wants, needs, likes and dislikes during the sales process to suggest an accompanying product. Whether the sales representative is servicing a first-time customer or repeat customer, such information is invaluable, because it enables the sales associate to suggest accessories more likely to appeal to the customer`s tastes.
A new generation
With this in mind, vendors of cross-selling software are developing a generation of applications that utilize data about a customer`s likely preferences, needs and wants to make the cross-sell a more effective proposition.
"Cross-selling applications are being geared to the principles of merchandising and that can pick up on variants of how people shop, rather than basing decisions on just mathematical data," says David Fry, CEO of Fry Inc., an Ann Arbor, Mich.-based web design, hosting and consulting company that helps Internet retailers develop branding and merchandising strategies. "It`s too easy for mathematics to lead to coincidental success in cross-selling."
The new generation of cross-selling applications infuses a larger dose of human intuition into the sales equation by not only tabulating historical sales data on a customer, but also including such data as the pages viewed, the types of products bought during a current shopping session and the price range of the products viewed and purchased. This type of information is then weighted based on information gleaned from customers with similar purchasing and behavior.
"Web analytics is no longer just looking at what people buy over time and using that data to create a list of the popular items bought," says John Squire, vice president of product management for San Mateo, Calif.-based Coremetrics Inc. "It includes weighting customer purchasing and behavior patterns to include more of a human element in the cross-selling process so that suggestions can be made in response to specific situations."
As part of its suite of web-based analytics applications, Coremetrics has developed Coremetrics 2005, a software platform that tracks all customer click data and stores it in a customer profile.
Within Coremetrics 2005, which is offered on an ASP basis, are reports that detail response rates to cross-sell promotions. Internet retailers can use the information not only to sharpen their cross-selling strategy, but also to measure response rates to suggested items, promotions, and even their overall merchandising strategy on a daily, weekly or monthly basis. Armed with this information, retailers can adjust their cross-selling strategy and product pairings sooner.
Taking the lost opportunities
Retailers can expect to generate a single digit lift in sales from implementing a cross-selling application, according to Bob Chatham, a principal analyst for Cambridge, Mass.-based Forrester Research Inc. That`s not a bad return considering that Coremetrics charges a one-time set up fee of $2,000 and $2,000 a month for its cross-selling application. In comparison, installing a proprietary application starts at about $500,000 and can easily exceed $1 million. Other providers of cross-sell applications include SPSS Inc., a Chicago-based analytics and data mining company.
"The goal is to avoid lost opportunities to cross-sell products by getting the right product in front of the customer, whether it is an add-on or a promotional item, by seeing what is working and what is not," Squire says.
As retailers view this information they can, in theory, suggest accessories more likely to appeal to an individual customer`s taste or a customer subset. They can also adjust how accessories are presented to the customer, such as a pop-up window or text directing them to another page or presenting the item during the checkout process to create an impulse buy.
"There can be a lot of permutations of price, presentation, and promotion when it comes to merchandising," says Chatham. "Rotating content through the web site and creating multiple test cells that run simultaneously can help retailers find improvements in sales in places that they might not have looked."
Retailers can track each test cell by attaching a cookie to the customer`s computer. Test cells can be set for first-time customers, repeat customers or customers with specific browsing or purchasing habits.
While such features look attractive on paper, what matters most is whether the application can actually increase sales. So far the indications are affirmative. Burnsville, Minn.-based Northern Tool & Equipment, for example, has increased the value of its average order by 5% in the more than 18 months it has been using Coremetrics 2005.
Northern Tool, which offers more than 10,000 SKUs, segments its product line into 50 categories and 450 sub-categories, making it difficult for the five-member support team to accurately track customer behavior patterns to suggest appealing accessories. As a result, its cross-sell suggestions were based on the popularity of the product. "Most of the decisions were based on logic," recalls Nate Miller, e-commerce marketing manager for Northern Tool. "If someone bought a chainsaw, we`d suggest safety glasses, chain oil or a replacement chain."