Analytics drives down M&Co returns
M&Co has revealed £415,000 savings identified by using data analysis to remove the top 10% of frequently returned items from active promotion.
The UK fashion retailer’s success was announced yesterday as part of the results achieved by returns analysis and insights provider Clear Returns.
Clear Returns is using IBM Big Data and analytics technology to help retailers like M&Co understand how and why returns happen, and what they can do to predict and therefore minimise them. The company uses IBM predictive analytics technologies within its intelligence platform.
UK fashion retailer M&Co has adopted the predictive analytics approach and unlocked insights into how and why returns happen. As a result, M&Co reduced costs by optimising its product catalogue and reducing the return rate. The company identified £415,000 in savings by removing the top 10 percent of “toxic” frequently returned items from active promotion.
“We’re working with Clear Returns as it fits our innovative approach to ensuring that we deliver quality and service to our customers at an affordable price,” said Nichola Toner, M&Co head of ecommerce.
“Today, the true point of sale isn’t the retailer’s website, it’s the customer’s home, where they decide whether they actually want the products they ordered. So retailers need to get much more intelligent about the way they handle returns, and we see predictive analytics as a key tool in providing that intelligence,” said Vicky Brock, Clear Returns chief executive.
Clear Returns research shows that up to 80% of first-time customers who experience a return do not shop with that retailer again; 1% of serial returners can drive 10% of returns costs; and as few as 7% of products may be driving 50% of returns. In addition, products that are not high sellers, and may not even make the top 100 of a company’s most-returned items, can still cause big issues for retailers.
“We call these ‘toxic products,’” Brock explained. “For example, if you have a set of products that are bought together, but one item in the set is poor-quality, customers tend to return the entire order. One client was selling a bath mat as part of a bathroom set, foe example.
“Our analysis revealed that, if this bath mat was in a customer’s shopping basket, the chance of the whole basket being returned was three times higher than the average return rate. We advised to take the product out of the client’s online catalogue, and sell it in-store only, which solved the problem.”
Up to one third of all orders placed online are returned. In some cases, the cost of processing a return may wipe out any profit made on the sale. In others, the experience of purchasing a product that does not meet expectations can have a negative impact on customer loyalty and lifetime value.
Clear Returns models data from retailers’ sales, order management, warehousing and in-store systems. It analyses everything that happens to each order after the initial sales transaction to empower the Clear Returns team to advise their clients.
“Up to 80% of first-time customers who return their order will never buy from that company again,” said Stephen Budd, chief product manager at Clear Returns. “When we see that a customer has returned their first order, we recommend contacting that customer and offering them incentives to try a second time. This kind of personalised, real-time response can make a huge difference in how customers feel about a retailer, and can ultimately boost loyalty.”
Clear Returns uses IBM-approved integration techniques to collect, clean and normalise data from clients’ retail systems, including IBM SPSS Modeller, IBM Digital Analytics, IBM Sterling Order Management, IBM WebSphere Commerce as well as other, non-IBM systems.
“Retail consumers have become more empowered and informed about the choices available to them. This can make it more difficult for retailers to accurately predict demand, optimise inventory and drive maximum profitability,” commented Colin Linsky, global industry executive for IBM Retail.
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