The publisher is pairing with meal-delivery startup Chef’d to sell ingredients for recipes on its NYT Cooking site.
Even unsuccessful site searches might shed light on what shoppers want to find—if only the data could be captured and analyzed.
The term “dress up” means something quite specific to customers searching LillianVernon.com for one of the retailer’s most popular products, a trunk that holds scarves, hats and other costume pieces for children. But not long ago, Lillian Vernon’s site search engine wasn’t nearly so picky. A search on the term also delivered a barrage of other listings including one describing brass charger plates that “dress up” the table for the holidays.
On the flip side, a search on “jewelry box” delivered only a handful of results. Paul Goodman, vice president of Lillian Vernon Online, says the assortment could include 20 or more items that could be used as jewelry boxes. “But the language ‘great place to store jewelry’ wouldn’t be found under a ‘jewelry box’ search,” he says.
Too many, too few
Too many or too few results, relevant results mixed in with the irrelevant, no results at all: too often, this characterized the output of earlier generations of site search technology. More than frustrated shoppers and lost sales, ineffective site search also represented an untapped well of opportunity. For even unsuccessful searches might shed light on what shoppers want to find-if only the data could be captured and patterns analyzed.
CEO Steve Kusmer of search technology vendor Atomz Corp. started to realize that site search could be more than a set-it-and-forget it function in a previous job as head of electronic marketing at Macromedia. “People were searching our site and telling us in their own words what they were looking for, what their intentions were,” he says. “I thought, ‘What can we do with that?’”
As it turns out, plenty. Atomz is one of several search providers that now leverage the technology not only to improve search results, but also to do something more-improve on-site merchandising.
Newer functionalities underlying search products, such as natural language processing and guided navigation, are doing a better job of mapping computer logic to human thought. The new glamour technologies driving search results have high visibility among users, but site search has also benefited from smaller innovations behind the scenes, says Glenn Barnett of web technology consultants Molecular Inc. Take the fact that most search applications have increased their update frequency, for example.
More site search vendors have emerged to give e-retailers an alternative to the basic site search tools that arrive bundled into e-commerce platforms. Improved site search also has depended on a corresponding trend: the rise of web analytics.
Site search vendors are now integrating services with web analytics providers. Agreements between Atomz and WebSideStory Inc. and between Endeca Technologies Inc. and Coremetrics Inc., for example, have been announced this year. “Since users can now interact with search in more ways, it’s become harder to track what they are doing. So it’s critical for marketers to connect with something that is capable of crunching the numbers and giving a relevant business view of what their users are doing,” says Barnett.
New merchandising initiatives
Analytics not only capture data on site search behavior to point the way to new merchandising initiatives; they’re also delivering feedback on the performance of those initiatives in near real-time. In the future, say providers, analytics packages will not only collect data on site searches; but they will also feed back into and adjust search algorithms to populate search results as well.
“Say someone is searching for a product and they get a certain set of results. The third result is the one that gets the highest conversion rate. Why doesn’t that become the first result?” says Atomz’s Kusmer. “The future is having analytics automatically drive search relevancy.”
The next frontier
Several site search products are heading in that direction, already integrating limited analytic data into the presentation of search results. Like a number of other products, Qwiser, the search and navigation product of Celebros Inc., for example, automatically ranks listings within a set of search results based on products’ current best-seller status. Though compatible with outside analytics packages, Qwiser also has its own built-in analytics tool. Future releases will incorporate data such as personal or demographic information, into how it ranks search results on a site. “The next frontier for site search is combining search and navigation with analytics and merchandising rules,” says Michael Crandell, CEO. “That will create a self-tuning, automated system that constantly improves merchandising through the search results it delivers.”
For now, sites like LillianVernon.com are simply glad to fix their immediate search problems. Lillian Vernon last September licensed site search from vendor EasyAsk Inc. as a replacement for the site search function bundled into its IBM WebSphere e-commerce platform and saw a triple-digit increase in conversions off site search within weeks. Goodman won’t say what it cost Lillian Vernon, but he does say it paid for itself in improved sales before Christmas.
Natural language processing is at the core of a number of newer site search products. Its goal is to gain a deeper understanding of query intent. “We also use that information to be very directed about how we present the content we retrieve and how we leverage that content interaction to create opportunities to influence,” says Tony Frazier, senior vice president of marketing at iPhrase Technologies Inc., which provides natural language-based search technology both directly to its own customers as well as through OEM relationships with CRM technology vendors.
300% conversion boost
Here’s an example of how natural language processing works at iPhrase client eLuxury.com. Under earlier-generation search technologies, a search for “earrings under $200” would likely return results containing any of the three terms, reflecting a broad range of relevancy-including none. Natural language processing, however, parses the query-that is, breaks it into component parts to establish the meaning of each term individually and then in combination. That determines, for example, that “under $200” is a price constraint rather than two separate terms.
iPhrase’s natural language processing technology compares the individual and combined elements of the parsed query to what’s in the site’s searchable database, which includes category information, product information, brand names, longer merchandising descriptions, and more. “It takes what the user asks for and compares it against the different elements of back-end content,” says Frazier. That means results listings deliver more precise answers that get users to where they want to be faster, he says.