Internet marketers have long grappled with two seemingly opposite challenges: how to personalize online content for visitors while preserving their privacy.
Many have given up, relying instead on newsletters, RSS alerts and social network tools. Others monitor and manually update their sites—at great cost to their profit margins. But there’s another way.
In response, technology providers have introduced Web-based self-optimizing behavioral targeting and personalization products. These help firms of all sizes increase conversions, average order value and revenue—without getting too intrusive.
Instead of requiring demographic or classification information, these dynamic systems automatically generate representations of visitors and products solely from current user interaction on the Web site.
Self-optimizing tools leverages the traffic patters of visitors to provide the most relevant Web experience possible without the need for any personal information, third-party cookies or special interaction with the individual. Doing so allows retailers to transparently target each visitor individually, without compromising their privacy.
This process is the most effective way to deliver relevant content because consumers’ present behaviors on the site are the most accurate representation of their interests and needs at that time, not the demographic information that they filled out months ago.
As a result, self-optimizing behavioral targeting solutions make it possible to enhance a site’s product cross-sell/up sell and content personalization capabilities while preserving the visitor’s right to privacy.
These types of solutions also provide an enhanced Web experience for the customer by not requiring them to fill out annoying pop-up surveys or lengthy, intrusive online registration profiles. Additionally, the more advanced self-optimizing systems do not track visitors’ behavior from site to site.
Case in point: a multichannel retailer implemented a third-party, self-optimizing behavior targeting and personalization solution just prior to the 2007 holiday season. Since then, the company’s e-commerce site has witnessed a more than 15% increase in converted online visitors and a more than 18% rise in direct revenue contributions from automated product recommendations and merchandising.
On top of that, the systems provides higher click-through rates on content as well as increased conversions, site stickiness and repeat purchases while eliminating the tedious, labor-intensive task of manually generating numerous relevant product recommendations and personalized promotions to online visitors.
Other retailers have claimed that such systems account for 20% of their online revenues, with the average order value from visitors who acted upon the platform’s automated personalized recommendations being over 60% greater than those who didn’t. Such testimonials speak clearly to the value that self-optimizing behavioral targeting and personalization solutions bring to an organization’s bottom line.
Self-optimizing behavioral targeting and personalization solutions also provide significant intelligence-gathering capabilities to any organizations over other personalization methods, such as shopping basket analysis, expert systems, content analysis and collaborative filtering algorithms.
By actually profiling the entire individual clickstream behavioral pattern, self-optimizing systems focus on the person-to-content affinities and are not just limited to content-to-content affinity modeling often represented by: people who bought this item also bought these items.
Self-optimizing solutions are completely data driven as well, and thus able to process large and diverse transaction histories; even across product categories, cultures and languages. Furthermore, these types of systems do not rely upon any knowledge of the content other than the user’s interactions with it.
This greatly simplifies integration and unified content profiles across all types, from text to multi-media. Self-optimizing behavioral targeting and personalization solutions can scale to large user bases and catalogs while providing acceptable runtime performance.
Because of their automated profiling, content neutrality and adaptive content indexing, these types of systems can be embedded into almost any environment that can capture online behavior; including e-commerce, search, content, e-mail, mobile and streaming media.
Moreover, self-optimizing solutions allows like-minded visitors to be dynamically grouped together for predictive purposes, providing additional functionality to such applications as community radio, movie guides, gift registries and social shopping.
These types of systems can also be combined with other enterprise data to produce even broader predictive models of customer behavior. Doing so extends their benefits to off-line campaigns, such as direct mail, telemarketing, media advertising, and customer loyalty marketing campaigns.
Merchants that can understand their site visitors’ behaviors without invading their personal privacy and are able to instantly deliver what they need and what they like while building long-lasting, and profitable, relationships.
Meyar Sheik is CEO and co-founder of Certona Corporation, which creates Web 2.0 optimization and personalization platforms.