Some retailers launched online deals well in advance of Thanksgiving, Black Friday and Cyber Monday.
Online product recommendations must not just be relevant to the consumer, they also must help the retailer achieve business objectives.
E-retailers are better than ever at identifying the type of customer visiting an e-commerce site by browsing and purchasing behavior. And, by comparing her behavior with that of many others, that means they can serve up ever-more-specific, personalized recommendations of products likely to prompt her to click and buy.
But what happens when presenting the products she is most likely to respond to conflicts with the retailer’s business objectives of the moment? While it’s hard to argue that higher conversion rates are anything but good for an e-commerce site, profitability demands that e-retailers take into account other factors as well-for instance, the need to move 200 yellow sweaters that remain after the identical sweater in pink has sold out, or the desire to boost margins by selling more of the products the retailer purchased at favorable prices.
Personalized content can be pressed into service toward these goals, but retailers must carefully balance how they tweak personalization-and relevance-to accommodate business objectives.
“It’s a very interesting tension that exists between trying to feed customers what will optimally convert the highest versus the other things that are in your interest,” says Eyal Gutentag, principal at BestBuyEyeglasses.com.
And that balancing act extends beyond the recommendations e-retailers present on their sites to every form of marketing, down to the language of search engine ads.
Gutentag has first-hand experience with adjusting recommendations to meet business goals. BestBuyEyeglasses.com uses the product recommendation engine of vendor Richrelevance, which allows the retailer to determine which items are most likely to appeal to a customer, and then adjusts how those recommendations are presented according to business objectives.
Recently, for example, it has successfully used that method to increase average order value. In automating product recommendations based on customer data, it steps slightly outside what the data would suggest showing to a shopper viewing a “customers who viewed this also viewed this” display to favor higher-margin products that still fall within the criteria of what’s relevant to that customer.
During a recent test over four to six weeks the retailer increased sales of one of its higher-margin products-Dolce & Gabbana frames-by 21% using this strategy, Gutentag says.
To remain effective, the tweaked product recommendations must still be personalized to a shopper, based on what the retailer knows about her behavior and that of customers like her. Gutentag says that over-representing how frequently Dolce & Gabbana frames appeared among product recommendations by 5% to 10% was enough to drive the sales increase.
“Say you had a product on which you’d built up a significant amount of inventory. You might look at over-representing it not by 5% to 10%, but 25% to 30% in the recommendations. But you have to be careful because if you skew too far in that direction, you lose relevance and you’ve shot yourself in the foot. It’s a fine balancing act,” he says.
The increase in the number of D&G; frames is automated, a business rule that rests on top of the product recommendations generated by the behavior of a substantial amount of traffic-the site receives hundreds of thousands of visits a month, providing statistically meaningful numbers for the Richrelevance engine to mine.
Gutentag adds this strategy has produced such positive results that the site may allocate more real estate to it in the future. Richrelevance offers pay-for-performance pricing based on sales driven by its recommendations, according to the vendor.
Many personalization products now make it possible to override the automated recommendations the systems produce, says Sucharita Mulpuru, principal analyst for retail e-business at Forrester Research Inc.
“Most of the personalization vendors have recognized what a good tool needs to be able to resolve, which is, what happens if the merchandiser wants to promote something, or doesn’t want to promote something, that is counter to what the algorithm says,” she says.
But merchants are still figuring out what works and how far they can go against what technology suggests. While BestBuyEyeglasses.com found that stepping outside the engine’s automated recommendations by no more than 10% was effective, there are no hard and fast rules.
“It varies from site to site. I think the bar now is just to be able to populate pages with relevant cross-sells that aren’t going to annoy vendor partners or present something to your customers that is going to make them suspicious because it’s something completely incongruent,” Mulpuru says.
While she says the personalization technology available has come a long way from the days of generic product recommendations, she adds, “we are still at the point where even automated cross-sells and upsells haven’t penetrated every sector of e-commerce.”
In personalizing product recommendations for its customers, Indigo Books and Music Inc., Canada’s largest multichannel seller of books, DVDs and CDs, looks for a balance between presenting the products its data suggests will be most interesting to customers and what it can deliver profitably.
“We do try to start with what is relevant and important to the customer-that’s the first filter we put on. The next is an inventory filter, and we can even get as sophisticated as to filter through certain price points or profit margins as well,” says Deirdre Horgan, executive vice president of marketing.
Horgan says the highly subjective judgments consumers make about products like books and music ruled out an out-of-the-box solution for personalization technology. Instead, the company built its own product recommendation engine and works with the computer science faculty of the University of Toronto on its ongoing development.
“Because someone bought one fiction book, it doesn’t necessarily mean they will like another genre of fiction as well,” Horgan explains. “You need to get granular with the content of the product so you can make recommendations that much more meaningful.”
To that end, Horgan says Indigo’s developers are looking for technology that would map the content of books so that the retailer can make more precise suggestions.
Indigo also is improving its ability to present personalized product recommendations with a preference center just added to its site. Customers can register the kinds of recommendations they are looking for-for instance, titles for children under age eight. “It takes some of the guesswork out of it for us.” Horgan says.