Loyalty and Bonding
One of the consistent themes of t
he technology market is the incessant march from one paradigm to another and tracking customer attitudes about vendors serves up a great example.
When we began tracking customer attitudes seriously in the last decade, we were looking at satisfaction. It was based on the assumption that a satisfied customer was some kind of nirvana. The customer asked for something and the vendor provided it whether product, service, information, or something else. What possible other configuration could a customer have other than satisfaction or its opposite? That question was usually derived from HIPPOs, the Highest Paid Person’s Opinion. But what did they know? Their frame of reference was person-to-person retail.
Indeed customer satisfaction was the standard for a long time, especially in retail where we dealt primarily with delivering the exact product a customer asked for. But things change, arms races take hold and a newer style of customer emerged that vendors wanted to cultivate, the loyal customer, one that will come back and buy something else.
Loyalty is good but it is hard to measure in a passive but satisfied customer or a hostile and simmering one, so it involves things like attitude, which can also be traced to satisfaction. Thus a satisfied customer could or could not be loyal depending on almost anything and so the Net Promoter Score (NPS) was designed by Fred Reichheld to help us measure whether or not customers were so loyal they’d recommend us.
Unfortunately, the NPS is forward looking. As buddy Paul Greenberg rightly points out, it doesn’t say if a customer has (past tense) recommended a vendor, product, or brand only that if the occasion permitted that the customer would (see, conditional future tense). Not good enough.
Today many vendors need a stable of customers who actively sing their praises in social environments like communities because so much influencing happens before a vendor can get into a sales cycle with a potential customer. These customers are labeled advocates because they presumably advocate for the vendor. But advocacy is a higher hurdle than either satisfaction or loyalty and it requires a greater commitment called bonding.
Bonding is the new black. It shoots past loyalty into a higher orbit that can only be reached if a customer has successfully engaged with a vendor and found it worthy. I like to point out that bonding is not a one-time thing, that customers have micro-decision points throughout their lifecycles. At each decision point, which I call a moment of truth, a customer makes a decision—what might even be a sub-conscious decision—like “This vendor rocks!” or “Who is this turkey and why did I buy that thing?” Add up the positives and subtract the negatives and you will know if you have bonded. But the rating scale is highly variable. Positive experiences (customers experience moments of truth) may not weigh as heavily as negative ones for the simple reason that the negatives have a tendency to derail the train while positive experiences simply get the train to the next station, no muss, no fuss.
Speaking of trains, Tolstoy knew this and a whole aphorism has grown up around this inequality called the Anna Karenina Principle. You could look it up if you wanted to.
So it’s bonding that counts because it drives advocacy and in contrast loyalty looks confused and so turn of the century. Nonetheless, there is a large and thriving loyalty industry that I think could use an injection of Anna Karenina and moments of truth. Where loyalty tends to be well intentioned but vague and dispersed, bonding and advocacy offer a direct path to a predictable and repeatable result.
Moments of truth and the Anna Karenina Principle form the basis of Customer Science, which you can read about here. It is a social science that gets to the heart of what motivates customers to act—the familiar structures of a relationship with, say, a vendor, or the impulse to take agency and find something better.
Loyalty still has its uses and it is a good term but with bonding and everything that underlies it, we at last have an approach that can deliver more predictable results.
Time to Drill Deeper
No, this is not an article about fracking, drilling for gas and oil in shale. This is about drilling down into big data. We’ve been using the term for a long time and it provides a useful metaphor for data analysis. But we’ve conditioned ourselves to think of drilling down only to a superficial degree and that needs a rethink.
When data wasn’t big and analytics relied on less robust hardware we were only able to scratch the surface of our data, a practice that survives to this day. Scratching often means looking for insights only at the end of business processes. So for example, we look for signs of churn next week or the next best offer today, or to forecast the next sales deadline. All of this is valuable but not enough. If we’re doing our jobs right, we should be using powerful analytics to perform root cause analysis to better forecast events so that we can either avoid them entirely or further enhance our likelihood of success.
What if you could go further into your data so that rather than simply discovering someone or some business that was about to leave your service (churn, non-renewal) you could find those moments of incipient danger and correct a problem at the source? You can but it requires change, not more hardware or better software though those things are always welcome, but a different way of framing the challenge in front of you.
Too often we make assumptions about some aspect of business and then collect and analyze data about it. That’s a good approach as long as the assumptions are valid and accurate but too often they are not. When we assume something we are building an ad hoc model of what we believe reality is and that’s a good thing. Modeling is the heart of all kinds of progress in any number of fields of human endeavor but it’s not something we do particularly well in business with some exceptions.
As Nate Silver writes in The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t, “We need to stop and admit it: we have a prediction problem. We love to predict things — and we aren’t very good at it.” You might recall that Silver called 49 out of the 50 states correctly in the 2012 presidential election. This man does not have a prediction problem.
Retailers might be an exception; they model heavily and they do a good job. They collect customer and store data so that they can model the ways they set up stores and plan the assortments that they stock. Those models mirror very closely the clientele and traffic for an individual store. When it comes to online business and B2B business we aren’t there yet because it’s both a different and a harder challenge.
Finding a solution in the online world starts with figuring out your model before you make any assumptions and before you implement something. (This is not your business model but your approach to customers, which is part of the business model.) It’s surprisingly easy to do if you take a two-step approach to analytics.
Step one, build a realistic model of your business by asking your customers. I call this discovering your moments-of-truth and I write about it in my new book, Solve for the Customer, which will be available shortly. As you know if you read this space often, a moment-of-truth is simply any time your customers expect you to live up to a promise whether that’s a product, company, or brand promise irrespective of whether the promise is expressed or implied.
Knowing your moments-of-truth you can build customer facing processes in Step Two. Your processes and supporting software will meet customers where they live, so to speak. The best way to do this is with journey mapping software because it lets you examine all of the contingencies and define sub-processes as appropriate. It’s also the logical place to define metrics that will tell you if you are meeting your goals in your moments-of-truth.
For example, customer onboarding is a good example of a moment-of-truth and there are many analytics vendors that focus on customer health as a function of how quickly customers get down your learning curve. People at Scout Analytics, for instance, tell me that there is a direct correlation between customer longevity and how fast they onboard. Knowing this, smart vendors deploy customer success managers to ensure that onboarding is swift and trouble free.
You can identify moments-of-truth like this throughout your customer lifecycle and often those moments do not automatically require expensive human intervention. But having a moments-of-truth approach plus good analytics, rather than assumptions, enables a vendor to deploy resources where they’ll be most beneficial to both the customer and to the vendor.
None of this is hard. In fact once you change your frame of reference (a.k.a. your paradigm) from ad hoc assumptions to dedicated and conscientious modeling, it flows. When we move from random approaches to modeling, which incorporates a bit of statistics (and that is what analytics is about to a great degree) we pass from a framework of art to one of science. That’s what’s happening right now in many areas of front office business and it’s why I’m saying we’ve arrived at a new science, Customer Science.
