Artificial intelligence customer experience design: the frictionless theme park?

Many theme parks offer an additional paid service that provides a virtual queuing bot that gives the paying customer immediate access to a ride during an allocated time slot with minimum fuss. This can deliver a smoother customer experience while enabling the park operator further monetisation opportunities through differentiated ticket prices.

But such services are not perfect. For example, like real queues, virtual ones can still get filled up (so reducing availability of time slots), a customer can’t simply change their mind at the last minute and expect an alternative ride to be available at the same time and many of these systems don’t reflect other dynamic factors that could affect ride enjoyment like poor weather.

So how could Artificial Intelligence (AI) potentially address these challenges? Here are some ideas…

One opportunity is to apply retail thinking to personalise the end to end experience – via mobile, an AI could suggest rides to visit throughout the day based on a customer’s social media updates, current and expected volume/demand for an attraction and forecast weather. In “the background” (i.e. the Cloud), the AI is constantly analysing customer behaviour in the park to drive these suggestions to help manage the people flow through different areas and rides to minimise friction for all. This capability could also enable the operator to offer on the spot additional services (like offering the chance to immediately access any roller coaster ride for a small charge) to further delight and surprise a customer during their visit.

Conversely, such an application of AI may be counter to what an operator wants to offer – after all, exciting theme park experiences come from customers being spontaneous when choosing their next desired ride or attraction. Accepting such unlimited freedom is not possible – this still leaves the risk of friction (like boredom) when a customer is waiting for the next experience to become available. An AI could turn this “dead time” into an experience in itself – using it as an opportunity to send personalised media content and offers to a customer’s mobile or tablet to consume while queuing for a ride. Alternatively, the AI could create social events for people in the park to interact with each other like mobile gaming competitions or dating. Such services could also be linked to third party promotions to generate further revenue for the theme park operator.

These illustrative use case ideas are based on one key assumption – most customers visiting a theme park at the same time will follow the guidance or direction given by an AI consistently, even when it results in a lesser personal experience than intended (but results in all participants gaining mutual benefit). This notion that AI can effectively influence human behaviour at scale in one place (like a theme park) is a major challenge for Artificial Intelligence Customer Experience Design.

If you would like more information about how artificial intelligence can benefit your business, please leave a reply below or contact me by email.

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.

Teachable Brand AI – a new form of personalised retail customer experience?

Within the next five years, scalable artificial intelligence in the cloud – Brand AI – could potentially transform how retailers use personalisation to make every store visit a memorable, exclusive customer experience distinct from anything a competing digital disruptor could offer.

Arguably the success of this engagement approach is contingent upon a retailer’s ability to combine a range of data sources (such as social media behaviour, loyalty card history, product feedback) with its analytics capabilities to create personalised moments of delight in-store dynamically for an individual customer that drives their decision to purchase.

But could the truly disruptive approach be one where a customer is continually teaching the Brand AI directly about their wants or needs as part of their long-term personal relationship with a retailer?

Could this deliver new forms of customer intimacy online competitors can’t imitate? Here are some ideas…

  • Pre visit: Using an existing instant messaging app the customer likes (such as WhatsApp or Skype), he or she tells the Brand AI about their communication preferences (time, date, etc) and what content about a specific retailer’s products or services (such as promotions or new releases) they are interested in. This ongoing relationship can be changed any time by the customer and be pro-active or reactive – the customer may set the preference that the Brand AI only engages them when they are located within a mile of a retailer’s store or one week before a family member’s birthday, for example. Teachable Brand AI empowers the customer to be in complete control of their own personalised journey with a retailer’s brand.
  • In store: The Brand AI can communicate directly with in-store sales staff about a customer’s wants or needs that specific day to maximise the value of this human interaction, provide on-the-spot guidance and critical feedback about physical products their customer is browsing to drive a purchasing decision, or dynamically tailor/customise in-store digital experiences such as virtual reality or media walls to create genuine moments of customer delight. Teachable Brand AI has learned directly from the customer about what excites them and uses this deep insight to deliver a highly differentiated, in-store experience online competitors can’t imitate.
  • Post purchase: The customer can ask the Brand AI to register any warranties, guarantees or other after sales support or offers for their purchased good automatically. In addition, the customer can ask the Brand AI to arrange to return the good if unsatisfied or found faulty – to help ensure revenue retention a replacement or alternative is immediately suggested that can be exchanged at the customer’s own home or other convenient location. The customer can also share any feedback they want about their purchase at any time – Teachable Brand AI is driving customer retention and also gathering further data and insights to enable greater personalisation of the pre visit and in-store experience.

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

 

The ‘C’ word

Customer Experience (#CX), Customer Service, Customer Journeys, CRM, multi-channel – these are all complex, modern day challenges that current businesses face. There isn’t a single business today that doesn’t mention the ‘c’ word somewhere in their offices, in their stores, on their website or in their brochures – the customer is king, long live the king! And, throw in social media (word of mouth to the uninitiated), and we all know a company’s brand can be enhanced or destroyed based on the most trivial of vignettes or sound bites.

So, these are all modern day challenges in the 21st century – right? Well no ,you’re wrong! Every tradesman or business has had to deal with these same challenges since……the year dot actually! When the very first man (and it probably was a man!) decided to exchange something he had made, raised or grown for some form of currency, these same challenges existed except they didn’t have the same buzz words that we use in business today.

In 1996 Dr Christian Grönroos, a leading Finnish academic, published one of his seminal papers about a merchant in ancient China, which encapsulates all of the above.

In a village in ancient China there was a young rice merchant, Ming Hua. He was one of six rice merchants in that village. He was sitting in his store waiting for customers, but the business was not good.

One day Ming Hua realised that he had to think more about the villagers and their needs and desires, and not only distribute rice to those who came into his store. He understood that he had to provide the villagers with more value and not only with the same as the other merchants offered them. He decided to develop a record of his customers’ eating habits and ordering periods and to start to deliver rice to them.

To begin with Ming Hua started to walk around the village and knock on the doors of his customers’ houses asking how many members were there in the household, how many bowls of rice they cooked on any given day and how big the rice jar of the household was. Then he offered every customer free home delivery and to replenish the rice jar of the household automatically at regular intervals.

For example, in one household of four persons, on average every person would consume two bowls of rice a day, and therefore the household would need eight bowls of rice a day for their meals. From his records Ming Hua could see that the rice jar of that particular household contained rice for 60 bowls or approximately one bag of rice, and that a full jar would last for 15 days. Consequently, he offered to deliver a bag of rice free every 15 days to this house.

By establishing these records and developing these new services, Ming Hua managed to create more and deeper relationships with the villagers, first with his old customers, then with other villagers. Eventually he got more business to take care of and, therefore, had to employ more people: one person to keep records of customers, one to take care of bookkeeping, one to sell over the counter in the store, and two to take care of deliveries. Ming Hua spent his time visiting villagers and handling the contacts with his suppliers, a limited number of rice farmers whom he knew well. Meanwhile his business prospered.”

As you read through this story, I’m sure you will have recognized some familiar themes – the importance of having a direct dialogue with your customers, understanding your customers’ needs, delivering a service that meets “their” requirements, multi-channel, keeping records on your customer preferences, and delivering a differentiated and reliable service that ultimately provides value to your customers.

So, businesses today face the same challenges as businesses did in the past, which is somewhat reassuring given how easy it is to get lost amongst all the jargon of our day!