The future of customer service is scary and rewarding at the same time. It’s scary because the machine will know everything about you. It’s rewarding because shopping will become much easier as the machine makes more decisions about your life.
Big data and analytics platforms are merging with customer experience technology such as web content management systems (CMS) and customer relationship management (CRM) software. That means the machine is constantly learning and digesting information about you — and keeping it in a database that can be referenced for your benefit.
The goal, in the end, is to produce organizations that know so much about you and are so predictive that they can always make you happy.
“The big data trend is to try to become predictive enough so that you can please the customer every single time,” said Gary Ambrosino, the President and COO of TimeTrade, a customer-service software vendor that spoke with CMSWire.com last week.
For example, what if Best Buy knows you have been browsing TVs — including the makes and types — and contacted you to let you know about a special deal on the particular TV you’ve been looking at?
Of course, it’s still early days. As many of our marketing technologists know, not all of the pieces of the puzzle work together. The email systems are not always synched with CRM systems or web CMS platforms. But the overarching direction is hyper-personalized and predictive customer service.
I can give you one very low-tech example. I buy my dog food from Nature’s Select. My sales rep provides excellent customer service. He answers his mobile phone whenever I call. Better yet, he is way better at remembering when I need dog food than I am. Clearly he has good scheduling software that keeps track of how often we buy dog food, because I often get a call from him just days before I’m about to run out, in which he says, “Do you need dog food tomorrow?” When I check, indeed, I do.
The initial reaction to a hyper-personalized and predictive world is this: It can be kind of creepy. And is it an invasion of privacy? As Scott McNealy, the former founder and CEO of Sun Micosystems said in the 1999: “There is zero privacy anyway — get over it.”
Just What You Wanted
There are myriad examples, and many of them often come from the uber-hotel chains such as the Four Seasons or the Ritz-Carlton, two places I enjoy. In one story chronicled in the Huffington Post, a business executive explains how his kid’s lost toy giraffe was found and returned by the hotel. But not only was the stuffed giraffe returned after they had returned home from the trip: the Ritz-Carlton sent them a package with a hilarious photo montage of the stuffed giraffe enjoying the benefits of all the Ritz Carlton amenities, such as the spa. Do you think these folks will stay at a Ritz-Carlton again?
Such stories are legion. Check hotel reviews and you will frequently see the top hotels cited for their “personal touch.” For example, this review citing the Four Seasons Hong Kong for its “personal touch” explains how the guest received a birthday gift when he was checking out.
That is personalization, even though it’s not really predictive. But the more you know about a customer, the better you will be able to serve him.
The hotel examples are relatively simple cases of the hotel having some information in a database and then making human decisions about them. But what about when the machine can make all of the decisions?
The expanding field of predictive analytics aims to do just that. Think about the entire business being built around absorbing data and using that to figure out how, when and what people want.
For example, Eric Siegel, President of Prediction Impact, calls organization collaboration the foundation of good predictive analytics. Writing on his blog, he cites several goals to the process:
Predictive analytics is data mining technology that uses your customer data to automatically build a predictive model specialized for your business. This process learns from your organization’s collective experience by leveraging your existing logs of customer purchases, behavior and demographics.”
These are challenging to the goals, writes Siegel, because it requires lots of data and the prediction results may not even be strong enough to make a decision. On the bright side, however, Siegel said that many prediction analysis experiments yield unexpected results, where managers see the data proves the opposite of what they thought might happen.
Let’s look at yet another aspect of the complete customer service experience. There is the famous example of Target knowing when a customer was pregnant. But this example, which has now become a bit trite, is just one of many.
Google has gotten pretty good at knowing what you want and sends you ads to that effect.
And Amazon frequently knows what you want to buy. In the future, the company even wants to deliver what you want before you know it. Amazon has filed a patent for “anticipatory package shipping.”
Here’s another example: Match.com uses an algorithm powered by Synapse to predict human attraction. A product called Tinder, coupled with Facebook, can match you up with compatible users.
So, Match.com, Amazon, Google and Facebook want to know your life even better than you do. They want to get so good at it that they can find you and mate and deliver some products to your door (perhaps Champagne) before you even know it is going to happen. This is the future, whether you are ready for it or not.