Avoiding the E-Tail Boomerang

Online retailers agree that it’s better to give than receive, yet they stand to receive more than most. Santa will barely have time to shuck his boots for his slippers before the merchandise misfits begin a return journey to the warehouse to wreak havoc on the season’s sales numbers.

“The reality is that returns are nearly always higher for e-commerce sales than for store sales, but that’s just the nature of the beast,” Sucharita Mulpuru, principal analyst of retail for Forrester Research, told the E-Commerce Times.

Year after year, e-tailers struggle to tame that beast. Efforts vary, and the results are mixed. Still, with online sales matching — and often exceeding — brick-and-mortar sales, no one minds if the beast remains a bit unruly.

Know Thy Customer

Many e-tailers tried the track-and-repeat method to slow the return rate, believing that the key to a satisfying purchase was bound to be linked to knowing what the customer bought in the past.

“Personalization is probably one of the most debated topics in e-commerce,” Randi Barshack, vice president of marketing atMercado Software, told the E-Commerce Times. “A lot of retailers tell us that for the most part, their customers don’t want to be put in a bucket — and find suggestions based on past purchases quite annoying.”

Too late, retailers came to understand that just because a buyer bought a thingamajig last year for Mr. Whatchamacallit, doesn’t necessarily mean that the buyer is into thingamajigs or even likes Mr. Whatchamacallit. This year’s list may call for a whizit for Ms. Whodathoughtit, the new boss, instead. In any case, it’s a safe bet that no one cherishes the thought of being labeled with Mr. Whatchamacallit’s and Ms. Whodathoughtit’s tastes for the next 50 purchases made online.

Rather than tracking and responding to individual buying trends, retailers are turning to a new tactic.

“We find more retailers are embracing segmentation or “persona”-lization, where their merchandising strategy reflects types of buyers or buyer segments — for example, a search for a kayak might indicate a shopper is an outdoor enthusiast, so the retailer has the opportunity to execute appropriate merchandising strategies based on this,” says Barshack.

Previews and Reviews Stick

The prevailing consensus among e-tailers is that it may be more important to give the shopper as many specifics as possible on the product than it is to collect specifics on the shopper.

“Usually, more robust information on product detail pages, color matching and live chat are all tools that can help consumers get more information about a product, which — if effective — can help to prevent returns,” says Mulpuru.

“In terms of conversion rate increases, retailers tell us they are getting between three and five times conversion rate increases from pages featuring detailed navigation refinements versus static product or category pages,” adds Barshack.

Giving customers access to other buyers’ reviews and comments also helps shoppers choose merchandise they are likely to find satisfying, says Bob Melcher, senior account executive,D&E Communications. Of course, that mostly helps when the reviews are good. Too many bad reviews may indicate a need to change out the merchandise.

“Sell merchandise that is exciting enough to not be returned,” Melcher told the E-Commerce Times.

Sift and Sort

“One of the ways online retailers reduce the number of returns is through really granular product selection features,” says Barshack. “By giving customers detailed refinement options, it’s like asking ‘What about this? Have you considered this color?’ or ‘Did you know this style comes in tall?’ This definitely leads to more-informed purchase decisions.”

“Another feature that does this is product comparison functionality,” adds Barshack. “Again, the more informed the customer is, the more confident he or she will be with the product choice.”

Algorithm King?

The buzz has it that Amazon’s fancy new algorithm can’t go wrong in pegging the customer. Does that mean the ultimate answer lies in a better algorithm?

“It’s not so much algorithms. It’s more about creating merchandising business rules,” says Barshack.

There’s even doubt that Amazon’s edge is strictly a matter of algorithm.

“Amazon’s isn’t the most sophisticated algorithm — it’s just what’s known as ‘collaborative filtering,'” says Mulpuru. “It’s product-to-product associations, but it does a pretty decent job, and it evidently drives lots of sales from their customers.”

There are some pretty slick algorithms in play out there, though.

“Netflix has a pretty good algorithm, and the trick is just lots and lots of data that they collect every day from customers,” adds Mulpuru.

Even so, not everyone believes the solution addresses the right problem.

“I think you are looking at the wrong end of the equation. Returns are a normal part of customer service, and you should be able to operate an e-business that accounts for them,” says Melcher.

If nothing else, an e-tailer can always pass the buck.

“When you have enough clout, make the manufacturer pay for returns, based on your return policy,” says Melcher.

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