Online Retailers Follow "Chemical Trails"to Predict Buying Trends

By  Jack Jia, founder and CEO of Baynote
Leading industry analysts at Gartner and Forrester have both been giving a lot of attention to the concept of studying customer sentiment to make predictions on future consumer trends. Forrester calls this Customer Intelligence and Gartner has dubbed it pattern-based strategy. According to Gartner, a pattern-based strategy provides a framework to proactively seek, model and adapt to leading indicators, often-termed "weak" signals that form patterns in the marketplace. This is something that transactional-based systems, such as business intelligence (BI) and complex event processing (CEP), simply haven't been able to deliver.

For years BI, CEP and other related technologies have helped organizations become much more efficient by automating their interactions with customers. However, in the process of creating huge economies of scale, they forced companies to lose the "mom and pop" touch that consumers expect when they walk into a local hardware store or butcher. In failing to create digital mom and pop experiences, online retailers have placed unnecessary emphasis on promoting popular, lower margin products such as the latest iPhone or Harry Potter book, thereby losing out on profits to be gained from merchandising their niche, or long tail products.

At the same time, most retailers still look in the rearview mirror to determine which products are poised for financial success in the future and should therefore be prominently merchandized on their sites. This is largely because so-called "predictive" applications tend to prioritize the wrong set of indicators, often identifying consumer trends months, after the fact. However, the reality is that e-commerce transactions actually lag other more relevant indicators, such as online comparison shopping, by as much as 90 days.

Innovative retailers who understand this reality are now tapping into the collective intelligence of their Web site visitors to see early signals, spot trends and develop strategies around them before their competitors catch on. This holds particularly true for less popular but highly lucrative long-tail products.

For example, US-Appliance, a major e-commerce site for household appliances, tapped into the implicit behaviors of its Web site visitors to discover that they were engaging with colored washers and dryers. Even though the sales for these products were low, the retailer followed the advice of its customers and began promoting the washers/dryers as "most popular products" on the site. A few months later, these same products became best sellers. Nine months after that, Home Depot and Best Buy began promoting similar products in their stores.

Online Merchandising of Tomorrow: Taking Cues from the Ants
Steven Johnson's book Emergence described an amazing biological phenomenon - a colony of seemingly unintelligent ants can together form a highly intelligent society, as ants communicate with each other via chemical trails. This concept is known as emergent behavior in academic circles, and it is the social science foundation for the wisdom of human society.

Applying this concept to the online world, web users can become "visible" to each other if Web sites can observe behaviors and connect them with like-minded users by their invisible "chemical" trails. This guidance not only helps them make better decisions, it also connects them to web users with similar interests.

It is important to note that when applying emergent behavior to the web, the chemical trails must accurately reflect user interest, context and value. Just tracking clicks or page views is misleading, because these signals are often a function of site design rather than an indicator of whether the user actually likes the content. If there is a large, enticing product promotion on the home page, users might click it to check it out, momentarily deterring from their initial purpose, but then go back to their intended browsing. Companies should therefore not just focus on whether a consumer viewed or purchased a product, but whether they truly considered it, compared it, and actually liked it. This level of understanding enables retailers to pinpoint customer sentiment way ahead of the curve and develop forward looking online marketing and merchandising programs around their future hit products.

Jack Jia is founder and CEO of Baynote, a provider of on-demand product recommendation and social search technology.