Understanding the causes, likelihood and impact of returns helps to drive profitability
The need: Across the retail sector, one third of all products ordered online are returned, and the rate of returns is rising faster than the rate of sales. How can retailers combat this dangerous trend?
The solution: Clear Returns has developed a predictive analytics service that reveals new insights into why returns happen, what impact they have on the business, and what actions can be taken to minimise them.
The benefit: By predicting and avoiding returns, Clear Returns’ clients are not only able to cut logistics costs, reduce inventory levels and boost profitability, but also to offer a more proactive and personal service – ensuring that returns don’t lead to customer churn.
- Clear Returns Intelligence Platform
- IBM® SPSS® Modeler
- IBM Digital Analytics
- IBM Sterling Order Management
- IBM WebSphere® Commerce
Retail business models traditionally focus on sales as a key metric – a model which works well when customers can try products out in-store before deciding whether they want to buy them.
However, with the rise of e-commerce, this model needs to change. Up to one third of all orders placed online are returned. As a result, measuring business performance based purely on sales can be highly misleading. In some cases, the cost of processing a return may completely 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 loyalty – and therefore on customer lifetime value.
Clear Returns was founded to help retailers understand how and why returns happen, and what they can do to minimise them. By harnessing predictive analytics technologies from the IBM® Watson™ Foundations portfolio, the company is helping transform the way retailers look at the fundamental mechanics of their industry.
“Today, the true point of sale isn’t the retailer’s website – it’s the customer’s bedroom, 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,” says Vicky Brock, CEO of Clear Returns.
Revealing a new perspective on online sales
Clear Returns has developed an end-to-end service that integrates and models data from retailers’ sales, order management, warehousing and in-store systems. It then analyses everything that happens to each order after the initial sales transaction – a blind-spot in most retailers’ operations – and empowers the Clear Returns team to advise their clients. Stephen Budd, Chief Product Manager at Clear Returns, comments: “From a technical perspective, it’s a big data problem. Not only is the volume and velocity of orders often very high, but the information is also very varied – held in multiple formats and fragmented across multiple systems and lines of business.” Clear Returns uses IBM-approved integration techniques to collect, clean and normalise this data from clients’ retail systems, including IBM Sterling Order Management, IBM WebSphere® Commerce and IBM Digital Analytics, as well as other, non-IBM systems. In combination with its own proprietary Clear Returns Intelligence Platform, it then uses tools such as IBM SPSS® Modeler to model and analyse the data. When the predictive analytics software detects a problem with a customer, order or product, it automatically alerts Clear Returns’ data scientists, who review and validate the alert and provide specific recommendations that clients can immediately act upon.
Identifying toxic products
Vicky Brock, CEO of Clear Returns, explains how the company’s predictive analytics approach can unlock insights that traditional retail analytics would simply overlook: “There are products that may not be very high sellers, and may not even make the top 100 of a company’s most-returned items, but can still cause big problems. We call these ‘toxic products’. For example, if you have a set of products that are often bought together, but one item in the set is poor-quality, customers will tend to return the entire order. “For example, one of our clients was selling a bath-mat as part of a bathroom set. 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 them to take the product out of their online catalogue, and sell it in-store only – which solved the problem.”
Getting proactive about customer service
Stephen Budd adds: “Another area where we really help our customers is in matching the response of their customer service teams to the seriousness of the issues raised by a return. “For example, 80 percent of first-time customers who return their order will never buy from that company again. When we see a customer has returned their first order, we can 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 to how customers feel about a retailer, and can ultimately boost loyalty.”
“With new insight into how and why returns happen, our clients can increase revenue by focusing on high-value customers, and reduce costs by optimising their product catalogue and reducing the return rate.” – Vicky Brock, CEO, Clear Returns
Feeding returns analysis into marketing strategies
Clear Returns can help optimise marketing strategies to focus on customers who offer the greatest lifetime value – not just those who place the most or biggest orders. Stephen Budd comments: “If you are launching a new newsletter or direct mail campaign, our analyses can help to segment the customer-base much more effectively and reach an audience of high-value customers. And that means customers who not only buy your products, but also keep what they buy.” Vicky Brock concludes: “The point of everything we do is to help retailers make decisions that target maximum profitability, not just maximum revenue. With new insight into how and why returns happen, our clients can attack the problem from both sides: they can increase revenue by focusing on high-value customers, and reduce costs by optimising their product catalogue and reducing the return rate.”
About Clear Returns
Through sophisticated data analysis and a combination of product and customer modelling, Clear Returns identify the causes of returns and, more significantly, the customers most impacted. This helps retailers minimise the negative impact of returns on the business and their customers, without hiring an expensive data science team.
To learn more about how IBM can help you transform your business, please contact your IBM sales representative or IBM Business Partner. Visit us at: ibm.com/software/analytics/retail