In the early years of recommendation software, making a recommendation to a consumer on what they might like or want to try next scored an online retailer high marks. Today, recommendation engines are capable of playing a much larger role in the relationship between ecommerce businesses and consumers than simply supplying a relevant recommendation. In this case, the inclusion of contextual search snippets with each recommendation is imperative.
What are contextual search snippets?
Just like they sound, contextual search snippets are product descriptions (in phrases, bulleted lists or a variation of both) used to inform and visually reveal the relevance of each recommendation to an individual’s search. For example, a search for “e-reader” on Amazon.com returns the following top two recommendations:
- All-New Kindle Paperwhite, 6" High-Resolution Display (300 ppi) with Built-in Light, Wi-Fi - Includes Special Offer
- Certified Refurbished Kindle with Special Offers (Current Generation)
Though best practices for contextual search snippets for ecommerce design are still evolving and will differ among retail segments, recent research shows that providing contextual search snippets as part of your recommendation strategy provides benefits to both the consumer and the retailer that can ultimately contribute to a stronger brand and increased profitability:
Contextual search snippets help users make informed decisions, faster.
Results related to product recommendation for retail customers, especially when the number of recommendations returned increases, can overwhelm. At the end of the day, all the user wants to do is find what they are looking for and compare the result to what they have in mind and/or what they have already found online (be that by price, product specifications, color, whatever). Often, a search result, though obvious in relevancy returns so many options, a user struggles an unnecessary number of clicks to compare each (known in the web world as pogo-sticking). For example, a search of a popular outdoor site for “goretex pants” returns 54 items. Though each of the items is indeed a pair of pants—the majority of them with GORE-tex in the description—how these pants relate to what the user is actually searching for is unknown.
Contextual search snippets convey consideration for a consumer’s time and unique search perspective.
Though once a user clicks on a pair of pants, more helpful detail is given (see below), the user may have become frustrated or overwhelmed by the search before they even get to the product they might have purchased:
Sitka's Men's Stormfront Pants are designed to comfortably fit over base layers. With a GORE-TEX® laminate and durable water-repellent finish, the Stormfront Pants excel in cold, wet weather. Three-layer hard shell is adjustable to comfortably fit over multiple layers. Articulated seat and knees allow for a full range of motion. Full side zips for easy on and off. Internal belt system. Zippered hand and thigh pockets. Polyester weave construction. Imported.
For example, someone looking for lightweight, warmer weather pants could skip clicking on this recommendation had they been given those details in a contextual search snippet upfront. Likewise, someone who hadn’t thought about the fact they might need pants that fit over multiple layers would appreciate the added information up front and could incorporate those terms into future searches.
Contextual search snippets keep consumers engaged longer.
Personalization engines are brilliant at making connections. But at the end of the day, they are machines. Online retailers have to be sensitive to the fact that the moment a consumer senses the coldness of a machine over the sense of personalization and relationship they are designed to create—engagement suffers. Without contextual search snippets, a search result may feel, to the user, disconnected from his/her reason for searching. That disconnect can drive feelings of mistrust for the brand (do they really know what they are doing?) and the 1-1 relationship consumers assign so much value to these days (do they really know who I am?).
Contextual search snippets are an affordable personalization alternative to introducing costly predictive analytics applications.
Though we are all learning the rules and benefits of ecommerce personalization at roughly the same rate, most ecommerce sites don’t have the kind of upfront capital necessary to employ a predictive analytics engine to further personalize the online shopping experience. And, for many, that kind of investment is simply unnecessary when they have the capability of improving personalization in less-costly ways like providing contextual search snippets.
To quote ecommerce experts studying contextual search usability methods, “if you’re Amazon and can afford to develop a highly advanced predictive solution, by all means go for it. But for the rest of us, contextual list item information represents an entry into the game of seamless user personalization, reaping 80% of the results at 20% of the cost.”
If you’d like more information on how Strands can help you optimizee your ecommerce recommendation strategy: