Personalisation of the retail returns experience: a new form of competitive advantage?

UK retailers are losing billions of pounds a year from managing reverse logistics costs for returned items across their physical and digital channels. Because of the multiple touch points involved margin can often deteriorate to a point where writing off the item as a loss is a better outcome than resell.

A key area of risk is online women’s fashion retail where customers may order multiple sizes or variations of the same item and then return those that don’t meet requirements. It’s estimated on average a returned clothing item costs a retailer an additional £15 to process back through its supply chain regardless of channel – extra cost that significantly reduces margin at full price (and much worse when further price discounting is applied).

But could personalisation (the application of big data analytics to pro-actively meet an individual customer’s changing needs) deliver a better outcome for both customer and retailer? Could such an approach incentivise a customer to self-manage the reverse logistics process or even be persuaded to keep the unwanted item (so reducing, or even eliminating, the additional £15 cost)?

For example, rather than a customer filling out a paper form using a nondescript reason code for a return, he or she could use a loyalty card smartphone app that captures their reasons as spoken voice text. Not only would this be more convenient (and user friendly) than form filling, it also provides the retailer with richer data about a customer’s preferences to enable better targeted personalised offerings in the future.

Secondly, the app could lever cloud big data analytics to make an on the spot personalised counter offer to the customer alongside the standard return. This could draw from the customer’s buying history and social media behaviour. The counter offer could ask the customer to give the item to charity in exchange for a future discount (so eliminating the return cost and refund while driving future sales and positive brand reputation). Alternatively it may make a third party offer for a ‘no return’ outcome (so driving cross- or up-sell opportunities with little cost impact).

Fundamentally, the counter offer approach is primarily driven by the need to preserve and, ideally, grow a retailer’s margin – the economic case. In addition, by gathering better data about an individual enables greater personalisation to build and retain their loyalty and reduce the volume of unwanted items (for example, future purchasing of clothing items online may include specific recommendations for an individual customer about size and colour based on this gathered insight). The app could also utilise a retailer’s existing core systems (e.g. databases) and new digital technology (such as cloud analytics or machine learning) together successfully – the opportunity to use the best of both worlds to create disruptive competitive advantage.

If you would like more information about how Sopra Steria can help your organisation benefit from cloud analytics please contact the Sopra Steria Digital Practice.

Published by

Mark Howard

Mark advises on user centric digital service design & transformation with a focus on tangible business benefits realisation. His passion is helping organisations to transform how their people use digital ways of working to interact with users to deliver an seamless, integrated customer experience. Mark’s recent private sector experience includes delivery of a mobile application prototype for a European business services company and advising a major UK high street retailer about using enterprise social media tools to drive competitiveness. Mark has 10+ years consultancy experience advising blue chip Telco, FMCG, Retail and Pharma clients as well as UK Central Government. Mark holds an MBA from Open University. www.linkedin.com/in/mark-howard-mba/

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