exterior shot of a diner, with patrons and waiter visible
PHOTO: Ezra Jeffrey-Comeau

“Good morning. You need to sit at that table over there today.” This is pretty much the first thing my wife and I hear on entering our favorite local diner on a Saturday morning. Seth, one of the waiters, likes to make sure we sit at a table in whatever section he’s covering that morning.

We’ve become creatures of habit about our Saturday breakfast (something we swore we’d never do) and it’s mainly Seth’s fault. Over the last few years, he’s got to know us pretty well. For instance, we don’t have to order our breakfast. By the time we’ve settled ourselves at the table, our coffees are there. He just checks we want our “regular” and the order is put in. One week recently, when he wasn’t working due to a family emergency, we had to actually tell the waitress what we wanted and it took a few seconds for us to remember our exact order. Conversely, we’ve come to know him pretty well too, and if the diner is quiet we often have some pretty deep discussions around a range of subjects.

Want to Excel at Data-Driven Customer Experience? Watch Seth

Seth has a sociology and psychology degree, and it seems to make him an excellent waiter. He is very good at reading his customers. Often his section of the diner is full of his regular customers while others remain sparsely populated.

On a typical Saturday, he greets us by name, asks how our week has gone and asks after the kids and grandkids, and if we want our regular order. And that’s it. It’s a level of personalized engagement that shows knowledge of who his customers are, and he already knows what we want from a product standpoint. But Seth leaves the decision on an additional level of engagement up to us.

I think we can learn a lot about handling data-driven customer experience from watching expert customer service professionals like Seth. Just because we know a lot about a person doesn’t mean that we need to use that in every engagement.

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When Is Personalization too Personal?

Over the last few years, I’ve been involved with several digital transformation projects where the concept of personalization and the use of big data to drive that effort has been discussed. It usually isn’t long into those conversations before the phrase "creep factor" comes up. What level of personalization do we use to avoid creeping out the customer? It’s an interesting question and not just one of ethics, but also the aforementioned sociology and psychology.

On one such project with a major pharma company I was shown just what level of data, in an abstract way, they had on a typical patient using one of their products. It was a staggering amount of information. Data about us is being captured all the time — not just when we fill in a form, but through our devices as well as with things like voice and face recognition, location tracking, financial transactions and more. All of this information can be used to build accurate profiles about us as consumers, allow companies to know our preferences, and even predict our behavior to increasing levels of accuracy.

All of that can be used to build highly tailored customer experiences, but this is where the creep factor once again raises its head. How much data do we use, and how much do we feed back to the customer to give them that feeling of welcome without making them feel that we know too much? The research that was done as part of the pharma project showed their customers were OK with about five data points, as long as they were relevant to the exchange underway. I like to think of those five data points as lining up with Seth’s greeting:

  • Knows our name.
  • Knows our regular order.
  • Asks after the kids.
  • Asks after the grandkids.
  • Asks how our week has been.

The first two are perhaps the minimum he needs for the transaction, the later three help him judge our mood based on the responses, while at the same time gives him an opportunity to show empathy and build on the engagement.

Related Article: Marketers, Data Collection and the E-Word: Ethics

The Data You'll Need for Exceptional CX

I like to split the data needed for driving exceptional customer experiences into two distinct categories:

Explicit data

This is the information asked for during the early stages of the engagement that a customer is willing to share in exchange for better customer experience (i.e., name, email, address, maybe areas of interest). These explicit data collection points need to be as concise as possible and above all relevant to the task at hand. Don’t ask questions just for the sake of collecting data. 

I recently encountered a website that asked me about the age of my daughters, a question that was completely irrelevant to the exchange and definitely veering into creepy territory. I backed away and didn’t complete the purchase. It’s usually OK to return explicit data that a customer has supplied back to them as part of the personalized engagement. This is also the point where we should leave it up to the customer if they want to engage further and supply more information. Ask questions in a friendly empathic tone, (How’s your week been?) and you may get more information — but don’t push for it.

Implicit data

This is the stuff we know about customers because of systems, and transaction histories. This is the data that needs to be used in a subtle way. As I discussed in a recent column I like the fact that certain websites will present information to me based on where I am, the time of day, the weather, etc. But I don’t want them to be shouting that they have been tracking me. Implicit data can be used to develop and deliver some exceptional customer services (regular order today?), but it needs to be handled with subtlety. 

Just because we know a lot about people doesn’t mean we have to show we do, or use all of that data in every exchange. Maybe all that’s needed to engage the customer is a simple “Good morning.” Be like Seth.