December 26, 2000, 9:55 AM

Gotta Have It

(Page 3 of 3)

Creating an aura of personal service is critical if merchants expect to capture the most coveted of online impulse shoppers: the customer such as Himes who buys multiple times each month.

And that’s precisely the kind of shopper wants to attract. In addition to its product database created by San Francisco-based Fort Point Inc., is also carrying merchandise from such well-known companies as Elizabeth Arden and Calvin Klein and creating personalized beauty profiles for its frequent customers.

When a registered shopper clicks on the, the shopper is greeted by name and instantly surrounded with product choices and beauty advice tailored to their specific tastes and lifestyle. “Impulse buying is all about making sure that each and every spontaneous purchase is just right for that customer,” says Varsha Rao, co-founder. “Knowing who they are and what they like is the key to making an impulse buying strategy work.”

Getting to know all about you

It’s certainly more refined than looking into a crystal ball and guessing the future, but more Web retailers are relying on sophisticated personalization software integrated with the right databases to predict what items shoppers will buy impulsively.

Personalization software uses sophisticated mathematical models linked to an Internet retailer’s inventory, merchandising and transaction history databases to predict what products a shopper may buy on the spur-of-the-moment. Some personalization applications ask the shopper to fill out a series of electronic forms to create a customer profile. The next time the shopper visits the site and clicks on a particular merchandise category, the retailer’s “frequent customer program” calls up the profile and begins offering them products they’re likely to buy-perhaps a new CD from a favorite artist.

But this product recommendation technology, which uses collaborative filtering, goes far beyond just individual profiling. It also considers what item a shopper is looking at, searches through integrated databases for profiles of other customers who’ve bought that particular product, identifies other items those people purchased and then uses that information to make other suggestions to the original customer.

For example, it seems highly unlikely that a customer shopping online for a $25 blue oxford shirt will also wind up buying an $85 cigar humidor, but that’s precisely what’s happening on sites such as “The software analyzes hundreds of similar customers and their buying patterns and convinces the buyer to say to himself ‘Hey, how did they know I wanted one of those?” says Steve Larsen, vice president of Net Perceptions in Eden Prairie, Minn.

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