What makes shoppers buy? Price? Free shipping? A desperate need for that product at that moment? Convenience? Frankly, it''s difficult to know what motivates a shopper to make a purchase at any given moment. After-sale surveys, such as those conducted by BizRate are instructive but not conclusive. Intent-to-purchase surveys, such as those conducted by most of the other research firms are nearly useless on this score.

Personalization can increase a merchant''s odds of showing a shopper the right promotion (see Covert Personalization: Two Winning Models), however, I do realize that personalization is not even close to being mainstream. Many merchants are leery of implementing such a sophisticated technology. There are more than enough personalization horror stories (solutions purchased but never implemented) to give merchants cause for pause.

The Shopping Cart Abandonment Myth
Why do so many customers abandon carts? If they''re anything like me, they''re often just curiously poking around a site. Everyone laments the 0.7 percent, or 1 percent, or 2 percent conversion rate at merchant sites, but what should the conversion rate be? Let''s consider some (ostensible) merchant sites: homes, cars, clothing, home furnishings. How often does one click around on a realty site just because interest rates are low and one imagines upgrading to a nicer neighborhood? How about car shopping? Is it so difficult to imagine that most car shopping is, in fact, window shopping?

Perhaps it''s more useful to consider what discourages a shopper from buying. Of course, there''s the confusing checkout process (see Getting Back to the Basics of Checkout Protocol). Once you know you''re not driving customers away through a poorly designed interface, you have to consider some other factors that may make customers click away.

Too Many Choices
One major reason I personally leave a shopping site without buying - and this goes double for shopping bot sites, which I use when I don''t know which merchants sell the product I need - is that I''m overwhelmed with the choices. It''s easy enough to decide among three sweater sets based on price, color, fabric content, necklines, and button styles, but deciding among fifteen minivans or 17 printers that all meet my criteria? Forget it. I''ll simply decide not to decide. I''ll put off the decision to a day when my fear of buying the wrong product is offset by my need to own something that will get the job done (even if it turns out not to be the optimal product).

What can merchants do to help customers get a grip on enough buying parameters to feel comfortable making a purchase? Some sites, like BizRate, Gomez Advisors, and Zagat.com assign various rating systems to the merchants (or restaurants). You can see that Zagat, the restaurant guide, assigns scores to food, décor, service, and cost separately (on a scale of 1 to 30). Rating systems like this are very helpful, but when 30 options are returned, how does a consumer make sense of 20 for décor and 21 for service, versus 23 for décor and 18 for service?

Regression Analysis for Shoppers
A fascinating new service has cropped up from ProfSmart that allows a site to return a list of matching products with monetary values assigned to the best deals. What a concept! In the example in the screen capture, you can see that among the laptops returned for a search, the ones with gold backgrounds are bargain priced, relative to the competition. ProfSmart evaluates the criteria a shopper has indicated are important to him and then assigns a numerical value to each criterion (using regression analysis). For example, in this category, each gigabyte of hard drive adds $108.71 to the price, each MB of RAM adds $14.10 to the price, and having Windows98 adds $553.95 the value. Selecting different criteria in the first place would result in different "bargains."

The key to this solution is that it helps shoppers instantly narrow the choices in a quantifiable way. Notice that all options that match the criteria are returned, but those that are bargains relative to the competition are highlighted. Sometimes shoppers have other prejudices, such as "my wife will only let me buy IBM" that outweigh any quantifiable reason to buy something else. You clearly can''t address that kind of logic, but if a shopper can look at the list and know that he''s getting a quantifiable bargain, then you remove the reluctance to make a decision. Suddenly the various factors are all evaluated in terms of dollars. People like easy decisions. Profsmart helps them see a quantifiable reason to make a decision.

Note: Last week, I wrote about Merchant Bureau (see Balancing the Risk of Chargebacks), which was going to provide a chargeback database to its subscribers. Later in the week, I wrote about how a business should do competitive analysis before settling on a business model (see "Change Is Not Reform"). Yesterday I heard from Merchant Bureau: When they finally got around to doing competitive analysis, they found that someone else was already doing what they had planned to do and already had millions of chargebacks in their database. In the next week or two, I''ll write about that other company offering the chargeback database. Perhaps I should have written the two columns in the opposite order.

Alexis D. Gutzman is an E-commerce Technology Author and Consultant and author of The HTML 4 Bible, FrontPage 2000 Answers!, and ColdFusion 4 for Dummies. Her newest book, The E-commerce Arsenal: 12 Technologies You Need to Prevail in the Digital Arena is now available. She can be reached at agutzman@internet.com