The customer comes first
Why the end goal of implementing AI should always be to improve the customer experience
As an expert in customer experience across a range of industries, Vinay Parmar, founder of consultancy, Dhruva Star, is a firm believer in the principle that AI should only be deployed if it is done so in the service of the customer.
“In this space, there’s lots of conversations about AI,” he says. “At every conference I’m going to, people are talking about it.
“But it’s all about understanding how each interaction affects customer satisfaction. Either they will increase trust, advocacy and loyalty, or you inadvertently cause distress, which reduces satisfaction, causes customer churn and pushes people away from your services and causes complaints. When you look at the whole tapestry across all of the interactions you have; all the way from brand awareness, right the way through, how customers feel about you is the key thing.
“It’s never about technology; it’s about the application of technology and the clarity of understanding why that technology is being used in the first place.”
Warm vs cold AI
Inspired by the work of author Terence Mauri, Parmar says there is an important difference between what he describes as ‘warm AI’ and ‘cold AI’. “Cold AI is driven by costs – how we can do everything cheaper. But if you’re implementing AI based on cost and cost alone, you’re going to fail because you’ll implement it for all of the wrong reasons, in all of the wrong ways and for all of the wrong things. Warm AI is where it’s people-centric or customer-centric, and that’s really about understanding what it is that we want to create – what is the experience we want our customers and employees to have and how can the technology enable it to happen?
“It always comes back to what’s the outcome you want to create in the organisation? And how does the technology enable it to happen? That’s a better starting point then saying, ‘I want to save 20% from our bottom line.’ You will still save money, but you’ll do it in a much more holistic way.”
Parmar is also a non-executive board member at housing association, Curo. He knows that housing providers have a different relationship with customers compared to other industries.
“What housing organisations particularly need to be aware of, because of the responsibility they have to their tenants, is the impact their customer experience has on tenants’ quality of life and mental wellbeing,” says Parmar. “Because if tenants think they’re not getting the service they expect every time they engage with their landlord, they will lose trust in them.
“They’ll think, ‘I’ve got no choice because I have to live in this house, but I don’t trust them to do the right thing for me.’ That can drive all manner of behaviours, so you need to build trust into the system.”
“Housing organisations need to be aware of the impact their customer experience has tenants’ quality of life.”
Vinay Parmar
Founder, Dhruva Star
“If you’re implementing AI based on cost and cost alone, you’re going to fail because you’ll implement it for all of the wrong reasons, in all of the wrong ways and for all of the wrong things.”
Bottom-up design
The important thing in designing customer experience is to start from the bottom up, or as Parmar puts it to “start with the customer first and work backwards with the design.” Applying this principle could even mean that organisations need to slow down their rush to use AI for things, such as customer service bots.
“The initial gusto has been rolled back,” says Parmar. “They’ve realised how hard it is and how much work is involved in getting their organisation AI-ready to be able to unleash a generative AI bot on customers with confidence.
“It’s more about co-piloting. How can generative AI be a co-pilot to empower the agent better, deliver a service or empower the engineer, who is out in the customer’s home, to deliver a better service?”
From reactive to proactive
In housing, Parmar thinks one of the other big cultural changes that AI can help bring about is to allow landlords to respond proactively instead of reactively. That proactivity can apply just as much to tenant engagement as to planning how to develop new housing.
“The data and analytics piece is huge and that’s where the biggest opportunity is,” he explains. “There’s analytical data we’re collecting all of the time from our interactions, so the question is how we are using it to model the future. ‘How do you use that to be able to say that? And based upon this data, these are the customers who are most likely to need us in the winter.’ By taking this approach, we can be more proactive about planning activity.”
Being able to build digital twins – or a digital sandbox environment – can also allow landlords to simulate certain outcomes, Parmar says, and this could be used to make better decisions around new build planning.
“From a new building perspective, if you’re looking at sites where you’re developing housing, we could almost simulate a model that said if there’s housing going in here, can we predict what the demand on services is going be like,” he explains.
“You can do it in the traditional town planning way, but I think AI could take you to another level, bringing those data points together in a really sophisticated way, which then helps you with forward planning and validating some of your hypotheses, rather than basing it on models created in Excel spreadsheets.”
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