What we talk about when we talk about Customer Experience (CX)
Customer Experience (CX) is a really huge field to navigate for brands, and finding ways to maximise it allows brands to better acquire new customers and retain existing ones. The challenge that digital teams face is creating an online experience that has the right balance of efficiency but also creates room for a personalised experience. This is a challenge that AI can help solve.An individual customer experience sits somewhere on a spectrum. At one end, customers just want to be left alone and find the answer themselves, while at the other are customers who need their hand held and want personal interactions. The challenge is that a customer can switch places along the spectrum depending on what they are looking to do. But also they can move along the spectrum in one website visit: starting off wanting to be left alone, get frustrated that they can’t do what they need to do, and suddenly need help. AI can be the bridging gap between the extreme ends of this spectrum. Modern generative artificial intelligence, built on large language models and tailored to the modern retail experience can transform the customer experience. By helping customers at the “Leave me alone” end when they get stuck, but also solving issues faster for the “Hold my hand” customers. More specifically, it can do this by:
Smoothing purchase journeys, allowing customers to easily find answers they need
Making intelligent product recommendations
Speeding up customer service interactions
Answering, but fully resolving customer service queries
Let’s explore some of these areas below.
Smoother purchase journeys
For customers who want to be left alone, one issue is helping them find the information that they need. However much a customer wants to be able to do their own research, it can be tedious sifting through information on a PDP (Product Details Page) to find a simple answer. Then, if a customer has an issue about shipping, or returns, then most likely they will have to navigate off the PDP to find an FAQ page that answers that particular question. Having to move off the PDP could derail the customer journey.If instead, the customer can stay on the page and ask a question directly to an AI-powered chatbot, this journey can be kept on track. This chatbot can use generative AI to find, and then summarise the information kept on the relevant FAQ page. For example:
Making intelligent product recommendations
Another more advanced use case that is possible is using AI to make product recommendations. Being able to process and “understand” the information in a product description, or some other information sheet, the AI can relay product features back to the customer.
Say a customer is looking at a range of products, but can’t work out which is the right one for their use. For Home and Garden retailers this could be a customer looking for the correct tool for a project, for electronics it could be understanding which computer screen meets their requirements, or for sportswear it could be which shoes suit flat feet most.
Taking this last example, using product knowledge – essentially the same content that an agent would be trained on – the AI could respond to this query with an appropriate answer and recommend the right shoe, if one existed.
Going a step further, an AI can even be trained to give more opinion-based recommendations. Using a customer’s order history, what other customers have ordered, or even following what human agents have recommended can allow AI to make recommendations on the right outfit to wear for an occasion, or to suggest complementary items.
Speeding up customer service interactions
Speed is one of the key components that underlies a good customer experience. If doing something takes too long then a customer will get frustrated and possibly leave a website and go elsewhere.
So, if a computer can do the same task as a human, then it can do it faster. Just as a calculator can perform arithmetic operations faster than a person, if a customer service AI can locate an order, it can do it quicker than a human.
For an agent to be able to locate an order with the order number, they need to look it up in an Order Management System (OMS), find which carrier the order is with, locate the tracking number or link, process the information and then relay that back to the customer.
An AI can be integrated with the OMS and carriers, and using the technology underlying AI (intent detection, summarising information) then this process can be massively accelerated.
For a customer this means the difference between waiting for an agent to come online, pick up this ticket, and then do the steps outlined above. Whereas with AI it can be a matter of seconds to get the same information.
Fully resolving customer service queries
This is the key point. Being able to answer questions accurately is a very good task for an AI to perform, but what will ultimately make the biggest difference to both the customer experience and the brand performance is resolving tickets. The examples given so far are all examples of where an AI can resolve queries. Either by giving the right, relevant information, providing credible recommendations, or successfully locating orders.With deep integrations into backend systems, AI can do a lot of what customer service agents can do. At DigitalGenius we have customers who are using our AI to:
Expedite late orders from the warehouse
Order replacement items for missing or damaged products
Generate returns labels Issue refunds early, when a return is in transit
Raise complaints with carriers
Proactively inform customers about late or missing shipments, and give them replacement or refund options
Refund shipping costs for late orders
All without a human getting involved in the action. These actions are performed faster than human agents and round the clock, with human-like responses that can be personalised based on who the customer is, meaning that some customers don’t even know they’re talking to a bot.
A transformed customer experience
At its heart if we look to improve the customer experience, what we want to do is to be able to make everything easier for the customer, and allow them to have a more joyful and less frustrating experience.
If customers complain about customer service saying they just want to speak to a human, what they mean is that the technology is not helping them. We’ve seen this before with “deflection” tactics where the answer is buried in an FAQ somewhere, or there is a poorly built chatbot, or automated phone service that takes them around in circles and eats up time.
But what if you could speak to a chatbot that could understand your problem and actually solve it, and could do it faster than a human? And crucially if it couldn’t help, it would pass you straight over to a human agent who could.
For those human agents, this solution means fewer tickets overall and fewer repetitive and boring tickets to handle. In exchange it means more interesting and challenging tickets where they can actually use their skills to provide a better experience.
That’s what this new generation of AI can achieve. Faster resolutions, more personalised responses, and happier customers and agents.
Book a demo with our team to find out more.
