Another product, dubbed aCerno, has been in stealth mode for nearly four years. It collects anonymous information from an association of over 375 major multichannel retailers' Web sites (that aren't identified to one another), representing 140 million shoppers. The information is completely private and tagged only with an ID, with no cross-reference to personal information. ACerno compiles this anonymous data using cookies. The concept borrows heavily from the blind cooperative databases catalogers have been using for over a decade. In fact, aCerno is a wholly owned subsidiary of I-Behavior, one of the major co-ops.
ACerno's analytics provide two exclusive tracks of predictive information:
- Who customers are. Knowing what people shop for correlates strongly to who they are. For example, someone who buys a dress and a crib is almost certainly a woman with a baby.
- What customers will buy next. Large populations with similar purchase behavior patterns can be discovered and sold.
ACerno clients' best prospects are identified with modeling and profiling techniques, finding users who look most similar to their best customers. Once these high-value prospects are recognized, aCerno uses its massive advertising network to deliver targeted advertising messages directly to them, creating brand consideration or incremental transactions, which can be purchased on a CPA (define) basis
The company's extensive network of high-quality Web sites, publishers, and portals is targeted exclusively at the cookie level with banner ads and rich media to achieve maximum reach within the target audience. The network reaches over 95 percent of the Internet population with more than 80 percent of the impressions served into sites on comScore's Top 500.
This is predictive analysis; scoring million of cookies against hundreds of variables to create models. The concept was so successful to catalog mailers that it accounted for the vast majority of their prospecting efforts and budgets, ultimately replacing the use of individually rented mailing lists. Going forward, the same may hold true for retailers that can use custom models of their customers. These can change per promo or over time, and by using the profiles as selection criteria for branding efforts or to drive traffic to a store.