• by Laurie Sullivan , Staff Writer @lauriesullivan, June 2, 2010

Akamai launched an Online Shopping Data Visualization tool Wednesday to help marketers identify retail purchase trends. Mike Afergan, head of Akamai's Advertising Decision Solutions group, says the tool helps marketers target ads to consumers in conjunction with another Akamai tool, Shopographics, which offers online advertisers a way to find target markets by using shopping data to define audience segments.

The company launches DV with four categories: home goods, apparel/accessories, mass merchants, and all. The options show trend data for unique visitors, transactions and sales of what consumers seek on retail Web sites.

Akamai processes 800 billion connect requests daily through its platform, working with more than 3,000 online properties.

The data for DV comes from "co-op members" -- retail stores, product manufacturer, travel sites, telecom sites and others. The data lets marketers compare performance from Jan. 1, 2009, to the present, making it possible to contrast year-over-year performance.

When connected to Shopograhics, the DV platform turns into part of a behavioral targeting tool. "Advertisers are often times asked to guess how to reach their audiences," Afergan says. "We have seen that the concept of Shopographics, the process of identifying things consumers have historically purchased, provides better targeting for ads."

Afergan says Shopographics data is more accurate than demographics, psychographics, and geographics combined because how consumers spend money is the best method to define their interests -- more so than what they read or click on online.

DV, which can support data from Shopographics, acts as an index that compares daily unique visitors, transactions and sales within the four categories against an "average" day. The tools show that Memorial Day 2010 was significantly higher for sales and transactions than the same day in 2009 across the entire co-op, for example. The uptick indicates improved online retail health. The data represents what people browse for on retail Web sites, place in shopping carts, and purchase.

"We can see people are buying wedding dresses or golf clubs, rather than guessing," Afergan says.