A Profitero study showed Target’s online prices were 25% more expensive than Wal-Mart’s, which were just slightly more expensive than prices on Amazon.
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Djangos then focused on building a template for a streamlined shipping system to be used by all the partners. And in July, it launched an extranet that links inventory databases of all the partners to aggregate and display in-stock and out-of stock levels on Djangos.com in real time.
CEO Steve Furst estimates Djangos has spent more than $75,000 on analytics software, with the biggest chunk of that in the initial investment two and a half years ago. Djangos has since written applications internally that build on the original package.
Recently, NetIQ has supplemented its analytic software offering with an ASP model, but Furst says he’s satisfied with what he already has in place. “We haven’t investigated the ASP because we’re quite happy with the tracking we get,” he says.
Whether a retailer uses an ASP model or buys the software, however, one issue remains the same, and that is the difference between compiling data in a report and understanding the report. “Just because something is correlated doesn’t mean you can control causality by duplicating those conditions,” Berk says. “A lot comes down to who is issuing the report and what their training is.”
While that learning curve builds up, some of the simplest applications of analytics, for now, involve using them to identify barriers to the completion of defined tasks on the site. “Removing those barriers to commerce-related tasks is of great value,” Berk says. “We’ve seen retailers who’ve had pretty dramatic increases in their top line as a result.”