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.

Convenience, Integration, Context: the retail store experience 2020?

As retailers exploit the opportunities offered by digital technology and ways of working to innovate their in-store customer experience; what might this look and feel like in 2020? Here are some ideas…

Convenience: The way customers physically purchase products will be as seamless as any digital channel experience and critically will encourage them not to use their smartphone (a distraction from the real world in-store environment that also brings competitors’ offerings within easy reach). In 2020, a customer can simply touch a button on the product itself or its point of sale display to purchase it instantly – like a biometric version of Amazon’s Dash Button technology that carries out the transaction authorised by the customer’s fingerprint. This simple, convenient process also means a customer doesn’t have the hassle of queuing up at a till – freeing up their time to explore the physical retail space further.

Integration:  Delivery of purchased in-store products will rival any experience an online retailer can offer. Content such as films, music, books are downloaded immediately to the customer’s device of choice. Large and small physical goods can be dispatched from a warehouse and delivered direct to a customer’s home the same day (a service available today that is rapidly growing in scale led by retailers such as Argos). And because items can be sourced direct from distribution; the Retailer can lever greater supply chain efficiencies (such as reduced in-store inventory costs) to drive competitive, dynamic pricing to continually challenge competitors.

High Street retailers are already using cloud-driven big data analytics to accelerate and rationalise their own supply chain operations (for example, Zara levers such capabilities to achieve product lead times as short as two weeks from catwalk design to store). Further application of this “tech company” approach to achieve deeper supply chain and channel management integration could enable a fully converged physical and digital retail experience in 2020 that constantly exceeds customer expectations.

Context: Relevance with be a key way the in-store experience differentiates itself from digital-only channels in 2020. Whereas online personalisation arguably means funnelling a customer to a specific area of interest, a High Street retailer can also use other dynamic data and insights to enrich and contextualise this experience to create unique moments of delight only possible in the physical store environment.

By a customer choosing to share data (such as transmitting their location via their mobile’s Bluetooth capability to in-store beacons), the retailer can identify what product ranges he or she is browsing to trigger nearby interactive display screens that present more in-depth information about those items, share social media content such as user reviews or allocate a sales person to provide advice. Because the customer is in the physical retail space they are in complete control of their personalised shopping experience – moving to areas of interest based on their emotional reaction to the world around them without the limitations or constraints of a smartphone user interface. Nike’s emerging Fuel Station interactive store concept, where customers can choose to engage in different contextual situations (such as having their running style analysed when using an in-store treadmill to identify the right running shoes to enhance their performance) is one example of the potential power of in-store contextualisation that can’t be replicated digitally.

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

Four forces disrupting retail today and ways to respond

Incumbents and new players alike are exploiting digital ways of working and technology to create new forms of competitive advantage. Here are some examples impacting retailers today…

  1. CX shifters positively change customer behaviour to drive wider engagement and sales – often through encouraging new forms of brand advocacy and self service that increase wider demand while lowering lower costs. Domino’s Australia applied this form of social commerce innovation to successfully create its Pizza Mogul campaign; where customers designed their own pizzas, self promoted their creations on social media and then shared in a percentage of any revenues generated from Domino’s selling them. An estimated 15% plus revenue growth for Domino’s Australia was attributed to this initiative by the end of last year.One approach for retailers with limited experience of such disruptive customer experience design is to engage tech start-ups to jointly create new forms of market engagement unique to its brand (with John Lewis’ JLAB initiative being one such example). Such an approach can accelerate the “ideation process” although scaling these opportunities to the level or volume required for market use can be challenging given start-ups may lack the experience or capabilities for such rapid industrialisation.
  2. Agile SCM operators combine big data and analytics with traditional just-in-time production and distribution methods to optimise their supply chains. Zara’s “rapid fashion, minimal inventory” approach levers such capabilities to control and drive synergies across its own supply chain that often results in lead times as little as two weeks from catwalk design to production to distribution of a garment to its physical and online retail stores. This disruptive approach has helped Zara become one of the world’s leading fashion retailers with a presence in over 80 countries.One competitive response to challenge Zara is to adopt “fully linked collaboration” supply chain management; where retailers and suppliers use cloud platform technology to openly share data and other capabilities to deliver goods and services together. However to be successful this may require radical new ways of working such as co-opetition between competitors for their shared mutual benefit.
  3. Instant branders use their high recognition, flexible brands to drive successful market penetration of their diversified products or services as sources of rapid growth. One such example is Amazon Web Services that not only powers Amazon’s retail business; it has also fast become the dominant provider for B2C cloud infrastructure services with a 25% worldwide market share reportedly gained over the last five years.Responding to these challengers can mean an organisation completely redefining itself as a “tech company” where its operating model is primarily focused on using technology driven innovation to differentiate and cost optimise its products or services.  Such an approach is core to the success of “Digital Disruptors” such as Uber and Airbnb where they established their own global enterprise platform brands to penetrate B2C markets in different geographies simultaneously. However, these new entrants did not need to transform their existing technology, processes or culture first to be successful unlike existing market players.
  4. Price racers lever their bulk buying power and other supply chain economies of scale to lower prices for targeted quality products to challenge established competitors. The discount supermarkets Aldi and Lidl have applied this model to grow faster than the “big four” UK supermarkets including opening more new stores than these established players combined during the last year.One way to challenge these discounters is to offer competitive prices with greater convenience – same day grocer delivery at home (a model currently being piloted in the UK market by AmazonFresh).  However, effectively delivering this complex supply chain and distribution of potentially hundreds of product lines from multiple suppliers is challenging; it took Ocado over a decade to turn a profit using a similar model. In addition, successfully replicating an individual customer’s preferences for picking fresh goods in-store arguably requires more sophisticated personalised experiences using emerging digital technologies such as big data analytics or even virtual reality.

What are your thoughts on these examples? Leave a reply below or contact me by email.

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

Brand AI: The invisible omni-channel for retailers?

Digital can pose a range of risks for a bricks and mortar (B&M) retailer including:

  • Declining market share as customer loyalty to its established, traditional brand is eroded away by disruptive new on-line entrants and more innovative high street competitors
  • Poor ROI from implementing new in-store digital technologies because they fail to create a superior personalised customer experience across its physical and online channels
  • The inability to deliver better inventory management using big data and analytics due to immature organisational capabilities in these areas across its supply chain

So how could scalable retail artificial intelligence in the cloud – Brand AI – potentially turn these challenges into unique opportunities for competitive advantage during the next five years? Here are some disruptive ideas…

Brand AI as a personal human relationship

A retailer could personify its brand as a virtual customer assistant accessible anywhere, anytime using voice and text commands from a mobile device. But unlike today’s arguably bland, soulless smartphone versions that focus on delivering simple functionality, Brand AI would have a unique, human character that reflects the retailer’s values to inform its interactions and maturing relationship with an individual customer. Intended to be more than another ‘digital novelty’, this disruptive form of customer engagement builds on and enhances a B&M’s traditional brand as a trusted long-term friend throughout the entire customer journey by offering compelling, timely presale insights, instant payment processing and effective after sales support and care.

Brand AI as an invisible omni-channel

A customer is empowered to select what personal data they choose to share (or keep private) with the Brand AI to enrich their relationship. Social, location, wearable or browsing and buying behaviour data from complementary or even competing retailers could, potentially, be shared via its cloud platform. The Brand AI can analyse this liquid big data using its machine-learning capabilities to create dynamic real-time personalised actionable insights seamlessly across a customer’s physical and digital experience – it is the heartbeat of the retailer’s invisible omni-channel offering.

Critically, Brand AI can transform every retail store visit into a memorable, exclusive customer experience distinct from anything a competing digital disruptor could offer. For example, the Brand AI can advise in-store sales staff in advance what specific products a customer wants or needs that particular day to help personalise this human interaction, provide on-the-spot guidance and critical feedback about products available immediately to drive a purchasing decision, or tailor in-store digital experiences such as virtual reality or media walls to create genuine moments of customer delight. In addition, the AI can capture the customer’s emotional and physical reactions via wearables to these experiences (such as a raised heartbeat when seeing a new product for the first time). Such insights can then be explored later by the customer (including socially with family and friends) using the AI on the retailer’s integrated digital channel to sustain their retention.

Brand AI as an operating model

A further opportunity for using Brand AI is its potential ability to streamline inventory management to improve the customer experience and reduce operating risk. Key processes such as store returns and transfers could benefit from such an approach – not only would the invisible omni-channel AI enable a customer to easily raise the need to return goods, it can also capture the specific reasons why this is happening (rather than this information having to be interpreted by different customer service staff using prescriptive reason codes, for example). Also because the Brand AI has an established personal relationship with the customer it can proactively order a replacement for home delivery or pick up (store or other convenient location) or suggest a suitable alternative product or other cross-sell opportunities to keep the customer satisfied and minimise revenue losses for the retailer.

Managers can also use the AI to help interrogate and identify trends from this complex dataset on returns and transfers. Inventory management reporting and insights are available on demand in a manager or team’s preferred format (such as data visualisation) to support stock purchasing decisions, resolution of supply chain performance issues or investigate region or store specific fraud and theft. And because these analytics are running in the cloud they can be aligned to existing organisational capabilities in this area.

The illustrative benefits for a bricks and mortar retailer using scalable artificial intelligence in the cloud (Brand AI) potentially during the next five years include:

  • Refreshes the competitive advantages of an established, traditional high street retail brand using new disruptive forms of marketing and customer advocacy
  • Materially de-risks strategic investment in new in-store digital technologies by explicitly linking these capabilities to an holistic, long-term customer experience
  • Can improve organisational agility using big data and analytic capabilities to improve existing business processes that directly benefit the retailer and its customers

If you would like more information about how big data and analytics can benefit your organisation please contact the Sopra Steria Digital Practice.

Liquid Big Data: the next digital disruption?

Liquid Big Data is when competitors use Cloud technology and ways of working to openly share and analyse large volumes of data together for their mutual benefit. Yet an organisation engaging in this form of co-opetition risks losing competitive advantage over its peers and increases the threat of new entrants stealing market share. But could the strategic value of such a move outweigh these risks? Here are some ideas…

The customer comes first

Using Liquid Big Data to join up the customer experience across not just an individual organisation’s sales channels but complementary and even competitor offerings would demonstrate its commitment to personalisation. Customers themselves are already using digital services (such as price comparison websites) to disrupt the silo experience of individual brands to personalise their customer experience – how can organisations gain shared competitive advantage by working together to supercharge this form of empowerment? This approach could help address falling demand in physical stores being experienced by the UK Retail sector for example.

Agile supply chain performance management

Liquid Big Data can drive greater collaboration between organisations and their large (and small) suppliers to help reduce the risk of producing unwanted stock or inventory and deliver better resolutions to other supply chain issues. For example, by sharing real-time sales and operating performance data enables them potentially to work closer together to deliver more accurate, timely forecasting of demand that improves their management of Lean or Agile-like approaches such as just-in-time manufacturing. In addition, it creates opportunities for both partners to adopt new ways of working to further strengthen the agility of their own supply chains.

Data as currency

Given the rise of digital currencies for business to customer transactions using the Cryptocurrency approach, could there be an opportunity to extend this model to enable the trading of Liquid Big Data between organisations instead of cash payments? These business to business transactions could be occurring at different speeds – for example, the instantaneous sharing of insights between organisations wanting to sell tailored complementary services to the same customer or one-off trading of large volume, complex service performance data between suppliers to help them build collaborative services for the same client.

Increased resilience against cybercrime

Every day, many organisations face the risk of hackers trying to disrupt their digital services or steal their large volumes of customer personal data. To mitigate such risks, organisations could collaborate to build and jointly manage secure Cloud services to protect these critical Big Data assets together. Although this approach does not mean these competitors are sharing data with each other, potentially it could enable the creation of a secure Liquid Big Data platform that could be sold as a service to other organisations for their mutual benefit.

Component full life view

Some organisations are trialling the use of IoT sensors in their goods or products to track their performance through the supply chain and customer experience. This approach could also be used to gather data on “long life components” used in consumer electronics, cars or aircraft. Such Liquid Big Data could then be shared with competitors to help validate sector-wide benchmarks for component longevity or be combined with other information (such as environmental factors) to identify other issues that affect their performance.

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