Spend a few minutes Googling about online clothing sales and you’ll discover a general consensus that something like 40 percent of fashion items bought online are returned. Fully 30 percent of returns are caused by first-time, dissatisfied customers.
This may simply be the cost of doing business.
But what if you use the analytics generally employed to get people to buy more, to get people to buy less instead? Cutting the cost of processing the original sale and the return handling could be astronomical.
Vicky started her working life in direct marketing, including working on Tesco Clubcard. After creating HP’s initial pan-EMEA analytics program, she co-founded and ran a digital insight agency and became the first Google Analytics authorized trainer outside the U.S. With all of that, plus four years on the board of directors of the Digital Analytics Association, Vicky was very familiar with the wide variety of tools and techniques employed to get people to buy more.
When she realized that online shopping returns were costing retailers literally billions, she decided to apply big data and predictive analytics to the world of returns intelligence.
“We don’t have a list of do’s and don’ts,” says Vicky. “While we could make a list of general rules-of-thumb, we’re much more focused on discovering which products, processes, marketing campaigns, and suppliers are most likely to cause returns.”
The most common factors are of the obvious-in-hindsight variety: poor quality products, a mismatch between product description and product reality, and less-than-informative illustrations.
Clear Returns uncovered faulty sizing and construction issues for one retailer amounted to £3.6 million in returns annually and £1 million in returns was generated annually by a mismatch in content and customer expectation. Damaged products only accounted for £145,000 per year.
Catching these issues up front spells serious bottom-line savings. Just identifying the top 10 percent of problem products can cut returns costs by 40 to 50 percent.
Clear Returns’ suite of analytics products can flag problem items and, when they are added to a shopper’s basket, communicate with online merchandising systems.
But the most intriguing factor for me is the analysis of customer behavior. Clear Returns’ predictive analytics algorithms analyze customer behavior and assign a Propensity to Return score. The company can classify return personas into groups such as the over-shopper who buys in multiple sizes and colors just to try them on at home, the cheeky shopper who returns an expensive item after wearing it once to that big party, and the buy-switch-and-return shopper who sends back an inferior product with switched labels.
“Every online retailer has an enormous amount of data,” says Vicky, “but they are not sifting through it like we can. We can identify neighborhoods where delivered goods are being stolen. We can identify ‘bad customers’ who return at a higher than normal rate – including ones who buy the same things and consistently return them, and with 95 percent accuracy so good customers aren’t penalized. So this drops the cost of shopping and returning, and ensures that the last item of its size or color remains available so that good customers also aren’t faced with stock-outs.”
Right now, Clear Returns is focused on the fashion industry due to “eye-watering” return rates. “Our next target markets will be jewelry and sporting goods. Those have obvious similarities to fashion, but Vicky is confident that the factors they analyze will apply to a wide variety of product categories.
What comes next? After winning an assortment of entrepreneurial contests with attached monetary awards, Clear Returns is out to scale the company by raising £1 million in investment. Vicky clearly has reason to expect significant returns.
Images via Shutterstock.
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