Next gen personalisation: let customers play?

A key challenge of personalisation (the application of analytics by retailers to identify patterns of customer behaviour to create recipes such as targeted marketing or product recommendations) is how to gather the right data about an individual customer effectively in the first place.

Understanding what really makes an individual customer “tick” – what kind of personality he or she has (as defined by using Carl Jung’s Psychological Types that categorise how an individual perceives, interprets, thinks and feels about the world around them for example) can enable a retailer to create unique, specific recipes for that individual customer that competitors can’t imitate. For retailers carrying millions of products like Amazon, applying such deep, meaningful insight could help encourage a customer to explore a far greater range of its offerings and drive stronger loyalty.

Yet gathering data for these recipes is typically an implicit activity in the customer experience, produced by observing customer behaviour such as product browsing or buying history, social media feedback and often combined with other external factors like customer location, weather or time of day. Even the more disruptive forms of engagement – such as eBay’s “emotional recognition technology” prototype that observes a customer’s physical reaction to being shown different products to identify potential connections – is arguably a passive activity. Ideally to get a better understanding of someone’s personality involves proactively asking a series of self-reflective questions for an individual to answer privately – something totally unacceptable to the retail customer experience. So, could there be a different approach retailers could apply that gives similar valuable insights without the risk of appearing intrusive or insensitive?

One response is to let customers “play” with products before they buy them, including dynamically shaping this presales engagement based on observable behaviours. When someone buys a product online or even in-store a retailer has limited (if any) opportunities to observe how a customer reacts to it – an indicator that could help determine targeted recipes that excite, motive an individual’s exact personality. Encouraging customers to play, experiment with a product before purchase could however capture similar insight.

A non-grocer retailer may already have the tools in place to encourage such structured playtime with their products such as digital mirrors or augmented reality in store, or even have digital assets of product samples for 3D printing by a customer at home. Combined with capabilities like “emotional recognition technology”, these presale activities could become critical drivers for effective personalisation as well as offering exciting, differentiated customer experiences in their own right.

Such an approach is emerging from big retailers such as Toys R Us that is looking to transform itself into an experiential destination – the retail shopping experience as participatory theatre – so why can’t the grown-up customers have just as much fun?

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.

Ways to turn around poor customer experience using personalisation

Personalisation – the application of data analytics to identify patterns of customer behaviour to improve their engagement or retention – traditionally focuses on enhancing positive experiences such as browsing and buying. Yet could personalisation be used to turn around a seemingly unchangeable, negative customer experience that also benefits the retailer? Here are some ideas…

Personalising the complaints process to drive better customer engagement

Making personalisation effective is challenging – it requires a high level of data integrity and can be costly to implement. When it goes wrong it can irritate customers or worse, make them feel like a retailer doesn’t know them at all – for example, consider the negative impact on customer experience of repeatedly receiving the same unwanted product recommendations when shopping online.

Could this failure be caused by a retailer’s approach to personalisation that arguably only focuses on purchasing and other positive behaviour? Whereas what is also required is a complementary understanding of an individual’s dislikes and pet hates about its brand.

For instance, rather than using a generic approach a retailer could personalise its complaints process. This could involve asking the customer who is making the complaint specific, personalised questions about the totality of this experience with the retailer (e.g. pricing, quality, service) to better understand what this individual really feels about its brand – i.e. use this moment of catharsis to gain a deeper, more rounded understanding of a customer’s expectations. The retailer can then use these hard to reach insights to dynamically inform its future engagement with this individual – complaints as a source of brand loyalty and advocacy.

However, this disruptive approach arguably feels counter-intuitive and commercially risky; it will require new types of behaviour from (and greater trust between) customer and retailer alike to be successful.

Incentivising a customer to keep an unwanted item

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 retailer could lever cloud big data analytics to make an on the spot personalised counter offer to a customer alongside the standard return via a returns app. This could draw from the customer’s buying history and social media behaviour. The counter offer could ask the customer to keep the item in exchange for a future discount, special cross sell opportunity or third party offer (so eliminating the return cost and refund while driving future sales).

However, this disruptive approach to returns will need additional safeguards to mitigate risks of customer fraud or ‘gaming the system’ for unintended benefit.

Sharing insights with competitors to deliver unfulfilled customer orders same day

With the growing threat of digital disruptors like Amazon offering same day delivery on everything including groceries, customers are increasingly becoming more disappointed when other retailers can’t match such an experience. One example is Sainsbury’s acquisition of Argos was in part driven by a desire to access Argos’ supply chain capabilities that offer fast track delivery.

To combat this challenge, high street retailers could use a cloud-based platform to share local inventory information, fulfilling orders immediately for each other when the chosen retailer is out of stock – a faster, more convenient personalised customer experience than their online rivals. This approach to supply chain collaboration would also enable retailers to potentially increase the range of products they physically offer in store without needing to carry additional inventory.

However, for this form of coopetition to be successful it would need to have clear bottom line benefits for all participants given the risks to their market share involved.

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

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.