“Personalization was really important in enabling Amazon to differentiate itself and grow in past ten years,” David Selinger, CEO and co-founder of RichRelevance. Selinger also was Amazon’s Manager, Consumer Behavior Research and helped build some of the site’s personalization features a number of years ago. “Personalization will be the differentiating factor in e-commerce and digital commerce going forward, especially for multichannel retailers and new entrants online.”
Amazon and Netflix represent the first wave of personalization. I believe that we are going to enter into the next wave of a more personalized e-commerce experience as retailers and e-commerce sites move towards mining data to improve sales and conversions.
It’s highly likely that you have helped boost Amazon and Netflix’s conversion rates on movies, books, or other products thanks to personalized suggestions of items that you may like based on your previous purchase data, other consumers’ purchase history and more.
In a previous blog post we mentionned a quote from eBay CEO who said that in the next 3 years there will be more changes to online shopping than in the last 20 years. eBay has also been personalizing the marketplace experience with recommendations of similarly viewed or bought items for some time, and is looking to expand personalization efforts with PayPal and the recent acquisition of Hunch. eBay will leverage data mining. For most retailers, the toughest hurdle is to have enough data on an individual to actually help personalize the experience. For the majority of buyers who purchase from a specific site once every few months, or even less frequently, a retailer may have no real sense of direction on how to present similar products.
Getting these data points is the biggest challenge that retailers face. But retailers do have significant data for the small amount of regular, routine customers for an e-commerce site, including clicks, purchase history, shopping cart information, shares and Likes, and more. Retailers face challenges on how to store and organize this data, and then turn this into personal recommendations.
A store owner or shop keeper would engage you in a conversation when walking into a store and looking for something open-ended, such as a birthday gift. One way to do this is to present a personalized item suggestion but ask the consumer (in a Pandora-like fashion) if the recommendation sucks and how they can make the shopper’s life better “People want to help the system and love to correct things,” according to DJ Patil of Greylock Partners. And similar to Pandora, people become more invested in a platform that knows their preferences and will be more likely to return.
Patil draws an interesting comparison with how grocery stores have been able to structure their layouts to provide serendipity and useful discovery. “When you go to the supermarket, the stores know you are definitely going to milk aisle, so they often put it in the back of the store, so you can find serendipitous stuff on the way. Online retailers need to replicate that on e-commerce sites.” In the end, the goal is to be able to deliver personalization without being predictable.
Social data (i.e. the Facebook Likes of products, what products people are recommending on Facebook or Twitter) is going to be a big part of personalization for retailers in the future. Already plenty of retailers are using Facebook social plugins and Connect integrations to leverage Facebook data to show visitors what friends bought or shared, what products relate to their Likes, and which friends they might want to invite. The problem with this data is that much of it is unstructured, and there is really no one who has effectively nailed social personalization in the commerce arena the way Amazon was able to do with data from purchase behavior.
David Selinger, CEO and co-founder of RichRelevance, thinks that mining social data for ecommerce may lose steam before it takes off, drawing the comparison to email. “In 2007, if you were to walk into VC’s office with an idea about ecommerce and email, you would have been sent out the door,” Selinger says. But he explains that while there is an inherent enterprise value in this social data, it will take a long time to take off, similar to the way it took awhile for personalized email and commerce models to enter the market. “When someone figures out how to do it and do it well, it will grow really quickly,” he maintains.
The challenge for retailers is making sense of the Facebook news feed — i.e. streamlining recommendations, attaching brands and tags to this data and then serving this to shoppers in a useful, personalized format. Basically, your social network can become your Consumer Reports.
The challenge for the data mining community, explains Patil, is actually figuring out the intent in much of the unstructured data that is posted about retail products and brands on Facebook. And it’s important to keep in mind that some of this data from Facebook users is private.
So consumers both on Facebook as well as on retail sites will have to be more willing to give up key data like purchase history, Likes and other social actions, and even location in order to get a more personalized shopping experience on retail sites.
The key will be getting consumers to understand that more data will improve their shopping experience, and making the choice of opting-in a no brainer.
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