If you like recommendations, you may also like…

While at Resource Interactive, I took training in and worked on several products with the ATG product suite. ATG is at the core an e-commerce solutions provider, with a stated goal as follows:

Our goal: to power the world’s most engaging and rewarding online shopping experiences. We are our customers’ first stop in providing the solutions, services, and ongoing guidance needed to power a more relevant, personal e-commerce Web site; continually attract and captivate new prospects; convert them to buyers; and ensure their satisfaction so they become loyal, repeat, profitable customers.” (emphasis mine)

How can software ensure something is engaging, rewarding and personal? Well, by offering a more personalized experience, I suppose. When I was enrolled in ATG training last Spring, we spent a fair amount of time learning how to display relevant content based on explicit user-specified preferences and more subtle user behavior on the site. Increasingly, we come to expect that our experience on a web site will precisely meet our own needs. And indeed, with the plethora of resources online competing for our attention, web visitors have the luxury of looking elsewhere if a site is frustrating or in any way lacking.

Amazon.com and Pandora are two well-known services that make recommendations based on the users’ previous interactions with the site. In some way, this is the equivalent of the waitress remembering our “usual” and letting us know about something new on the menu they think we’ll enjoy; we are forming a relationship, and there are inherent benefits thereto.

Amazon.com_ Why is this recommended for you?

Pandora Recommendations

Both of these services offer suggestions based on what we’ve done. But as the web becomes increasingly social, the notion of leveraging the wisdom of the crowds and the power of personal influence may come into play. You’ve likely seen this before, but did you pay attention? Again, Amazon lets you know what others purchased. Does this help sway your decision, or introduce other options?

Amazon - What did others buy

Facebook SuggestionFacebook taps more directly into your network. Friend suggestions may be based on your networks, your employment or school history, shared friends.. or other attributes. Recently several people have noted they are being suggested friends who share the same name as their existing friends. Unlike the Amazon and Pandora examples earlier, there is no link to see why these items are being suggested: Facebook is blatant in their name-dropping. The entire basis of their social ads (in all the incarnations, from beacon to sponsored ads with photos to friend recommendations) is to serve up “tailored ads” based on “actions your friends have taken on the site”. Whereas ATG stuck to providing an enriched experience based on an individual’s behavior, this extends to their trust network as well.

David Berkowitz unwittingly endorsing BlockbusterRecommendations are intended to offer a more engaging, personalized experience. Or to increase interaction and sell product or services. Recommendations may be subtle (showing contextually-relevant content), or explicit (“you may also like”). The rationale behind the suggestions may be disclosed or suppressed from the user, and lastly the recommendations may be made based on system rules, the behavior of the user or the behavior of the user’s network.

Are there certain situations where these different types of recommendations work well? Obviously for explicit recommendations based on the behaviors of friends, the site must be aware of such a network. But beyond those logistics, there must be merit to such a recommendation. I’m not necessarily going to select a paint color, a washer and dryer or a home based on the preferences or buying habits of my network. But offering me a complementary color palette based on a color I select provides a useful service. In contrast, the recommendation of something social like an event is likely to be much more effective if it comes from someone within a trusted network.

How do you think these different types of recommendations affect your purchase or engagement decisions? Are any more appealing than others?

One thought on “If you like recommendations, you may also like…

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