empathy at scale
AI - Artificial Intelligence

What Happens when You Can Empathize with Your Customers at Scale?

A conversation with Robin Collyer of Pegasystems

I had seen the headline announcing that Uber would begin deactivating riders if their rating dropped beyond a certain level. You can pull that idea apart, but somewhere along the way you have to acknowledge that the customer isn’t always right and that smarter companies need to find a way to have actual conversations with their customers that go beyond simple star ratings.

So, I was intrigued when I saw the announcement from Pegasystems that they had developed a technology that would allow organizations to have empathetic conversations with their customers – at scale. What follows is an excerpt from a lively conversation I had with Robin Collyer, who is Senior Director of Marketing & Decisioning Making at Pegasystems.

RC:  I read your piece in The Wise Marketer on getting to know your customer, and it’s a great primer to what we do as a company. “Know me – as an individual – not a segment”.  We’re propelled by that kind of thinking.  It brings to mind a brilliant advert from a Danish network – something about “Don’t put me in a box”.  It’s worth a watch. (Editor’s note: it’s definitely worth a watch. You can see it here.)

TWM:  There’s real value in an organization having some empathy with the customer. We’ve seen some pretty spectacular examples with chatbots that could mimic empathy in uncanny ways. That’s one way of achieving empathy at scale. But the Uber example brings up something else altogether – maybe the customer isn’t always right?

Maybe there are circumstances where some sort of genuine interplay would be best.

There are any number of scenarios that prove the customer isn’t always right. The customer can’t and shouldn’t always be able to get whatever they want. Companies wouldn’t be in business for long if they did that.

RC: There’s got to be an understanding of each individual situation and what’s appropriate for it. The ability of the (PegaSystems) technology to bring context to the conversation and determine next best action – not based on some sort of batching or segmentation but based on hearing – listening to what is being said – and how it’s being said. There are so many other factors to consider beyond the simple words being spoken. Tone of voice. Frequency of call. Significance of the issue at hand, etc.

But from a business perspective, the technology needs to be able to check off all the necessary boxes from an operational, administrative and ROI standpoint as well.

Balancing the business objectives on one side and the customer’s needs on the other. The platform listens and advises based on the rule-set that we have given it. What are your brand values? What kind of brand do you want to be? Those questions and the answers to them set the tone and direction for how the system processes the conversations and processes recommendations & next steps. In other words, this goes beyond simply answering the question at hand.

Essentially what the system is doing – what the AI is doing – is arbitrating between the goals of each party. So first, the platform listens. Secondly, it makes recommendations based on those inputs. And third, it learns. Through machine learning the AI is constantly refining its decision-making algorithms to better reflect the brand values and also to better help customers navigate their particular issues.

“Customer engagement could be so much more …”

TWM:  I’m fascinated by the idea that you’re essentially creating empathy at scale. But it must be an enormously complex undertaking to onboard a new client. I’m thinking about the diverse customer mindsets of various verticals. An airline customer will have an entirely different set of concerns than a banking customer, than a grocery customer, etc. What goes in to creating those virtual mindsets?

RC: The first thing to say is that we’re enabling that engagement not really creating it. We have to start the conversation by asking brands what are the drivers they use to frame those conversations? What are their brand values?

A traditional approach might have looked something like “I’ve got a proposition, go find me an audience for that proposition.” Now what the Pegasystems “brain” is doing is essentially allowing organizations to formulate their proposition, bring it to market to validate it, refine it, and then bring it to market at scale. That, in a sense is how this works. Not so much as a hard-and-fast batch of rules, more as a framework for learning how the customer and the brand can most efficiently interact. So, Pegasystems enables that at scale

We advocate starting at what we call a “micro-journey” – that PegaSystems and our client can take together to prove value. It might look like a cross-sell or upsell and then once its proven out, can be promulgated across the broader organization -through the customer service side, through the sales side, in-store or on the phone. Essentially the brain enables us to create conversations with learned empathy, and then scale that knowledge and tone across the broader organization.

TWM: Robin, thanks for your time. I love your company’s approach to bringing a level of humanity to scalable customer conversations. Best of luck and hope we can talk more about what Pega is up to in the near future. Cheers.

Mike Giambattista is Editor in Chief at The Wise Marketer and is a Certified Loyalty Marketing Professional (CLMP).

What Happens when You Can Empathize with Your Customers at Scale?
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