In the last a few years, the Internet has turned commerce on its head. Suddenly, once sound retail practices, like the mass-merchandising of a handful of popular products (think Cabbage Patch Kids, Tickle Me Elmos, Nintendos or Air Jordans), no longer guarantee a big financial payoff in today’s retail environment.
The marketplace has become so segmented by niche consumers that best sellers only produce a fraction of the revenue they did previously, while niche or specialty goods can translate into big business for retailers who learn to tap that end of the market, commonly referred to as “the Long Tail.”
In catering to the Long Tail consumer, e-tailers have several advantages over traditional retailers, such as infinite shelf space and the ability to change or add to inventory more easily. This allows e-tailers to offer an unlimited selection of high-margin items at a fraction of the cost required of a brick-and-mortar retailer. Moreover, it’s now possible for online retailers to profit from the elusive Long Tail by applying social science to their e-commerce technology for the very first time.
Harnessing the Wisdom of Invisible Crowds
Matching millions of online shoppers with tens of thousands of products is easier said than done. To do this effectively, e-tailers must think beyond the old merchandising rules, which are too costly to develop and maintain, and are not resilient to the changing preferences of shoppers. Scientists have discovered that a random group of informed visitors can predict far better than any individual merchandiser what products people want. This is known as the “wisdom of crowds.”
The latest in online merchandizing is now based on what’s called “crowd sourcing,” or using the behavior of the invisible crowd of online shoppers to make products recommendations to one another on behalf of the retailer. Unlike specific product reviews, online retailers use the past context of shoppers who have been to the site to make product suggestions to the visitors that come after them.
In this model, retailers are leveraging the implicit, emergent behaviors of visitors who are anonymous and unknown to each other. By understanding these visitors and their intent, vendors can identify the thousands of micro-segments of shoppers who come to their Web sites and ultimately match each one with the best products available.
By observing which products truly give value to customers, e-tailers gain an in-depth understanding of community preferences and the thousands of micro-segments that emerge around products and categories without the risk of being misinformed by survey bias or misleading click-based data gathering systems. With this approach, the silent majority of site visitors are represented instead of being ignored. In addition, merchants can better understand how these communities self-organize into like-minded peer groups and better serve these micro market segments with more unique products, making the Long Tail even longer.
For example, an online store discovers that many of its shoppers purchasing kitchen appliances are also looking at flat-panel TVs. The retailer can conclude that these appliance shoppers would be likely to consider a small flat-panel TV when remodeling their kitchens. Online merchants are better equipped to tap into the Long Tail on their own Web sites because they can uncover the unique, previously hidden desires of their customer base.
The Importance of Contextual Recommendations
Tapping into the Long Tail isn’t just about adding more items to the menu. Research shows that shoppers feel overwhelmed and are less likely to make a purchase when confronted with too many decisions. The key is to target a smaller set of products to the right people at the right time.
One school of thought believes the problem can be solved with another round of personalization, profiling and behavioral targeting. The concept is to target products based on individual browsing history together with demographic information.
The latest social science research, however, has proven this thinking is flawed. As it turns out, individuals have thousands of profiles and past interests. A person can be a father, son, brother, golf lover, traveler, wine drinker, engineer and HR benefit seeker all at the same time. When taken out of context, our past behaviors poorly predict our future. On Amazon.com, cross-product recommendations have seen plenty of misfires outside of book suggestions that worked beautifully. The reason is simple: A book recommendation is within the context of the book. However, to always recommend diapers to an individual who’s made a past purchase of a baby gift will most likely miss the mark.
Human psychology has revealed something even more profound that we’re often not willing to admit — humans are like pack animals and our needs tend not vary too widely. Given a context, 95 percent of shoppers purchase the same types of products repeatedly. Context is a synonym for the micro Long Tail segments discussed earlier. By detecting like-minded peers, we effectively discover an unlimited number of buyer segments. Based on the common needs of like-minded peers, e-tailers can recommend products more precisely. The purchase rate goes up dramatically as a result.
Well-documented research has shown that contextual targeting (a shopper’s current context regardless of historical interests) gets 62 percent of the recommendations right while historical behavioral targeting gets it right only 18 percent of the time.
Because context is time sensitive, recommendations must also be timely, dynamic and taken from real-time feedback. For example, when Valentine’s Day approaches, the crowd begins surfacing gifts that are common to shoppers for that holiday. On February 15th; however, no one is buying chocolate hearts any longer. If the store is still recommending sweets over springtime patio furniture, it will lose business. The recommendation system must be able to detect real-time changes in season, consumer tastes and market trends to avoid falling out of sync with customers.
Soft Landing with Google Context
Finally, recall that your visitors are telling you exactly how to grab their attention on your site. Consider tapping into their experiences to deliver dynamic landing pages for each visitor coming from Google or other traffic referral sites. Rather than creating custom landing pages for every possible natural or paid keyword — an impossible feat, to say the least — merchants armed with an intimate knowledge of their site’s invisible crowd can use their collective wisdom to create dynamic recommendations on every page known to convert visitors who have come in through the same query. By doing so, merchants make their sites infinitely more “sticky” and increase sales.
For example, a customer may use the search terms “Fix a broken pipe” on Google and land on the Home Depot site. Her intent is not to look for broken pipes, of course. Showing her glue products together with PVC connectors and a do-it-yourself book will go a long way to serve her needs. By better understanding user context, merchants are able to connect customers to the specific products that like-minded peers found useful.
Today, online retailers must operate with a different set of rules than traditional retailers and embrace new techniques and technologies to increase revenues. By understanding Long Tail economics, improving product recommendations and harnessing the wisdom of crowds, e-tailers can stay competitive and increase visitor-to-buyer conversion rates by 50 percent and more.
Jack Jia is a founder and CEO of Baynote, a provider of content guidance software. Previously, he served as senior vice president and CTO of Interwoven, with executive responsibilities in engineering, products, marketing, strategy and vision. He is a frequent speaker at major conferences and has appeared on television programs in several countries.