On Data Science Driven Customer Experience
Director of Consulting, Analytics and AI
OZ | Digital Consulting Wizards
A few years ago, when I was working at machine learning software company, I met with one of our clients, the lead data scientist at a top tier telecommunications firm. As she highlighted her customer retention modeling work, I was surprised at what I learned — she was developing very accurate models to predict how many customers would churn. The goal wasn’t to save them or put an offer in front of them, but rather to tell marketing how many new customers they needed to recruit in the future to make up for the expected deficit.
This was unsettling to me and a theme that I had heard too many times. She noted that by the time that a customer decides that they are going to leave and starts behaving in an identifiable way, it is often too late to do anything to save them. We explored a few additional signals in the data that we hoped would give us lead time to save the customer. Unfortunately, the result was the same — no special offer or better pricing would hold them for long, if they could get a more satisfying experience elsewhere.
I’ve seen this a few times in my career –
- I worked with an early day internet provider that was losing paid customers faster than they could replace them. No amount of churn modeling was going to fix the problem.
- I feared it recently when I interviewed to head up a customer retention program within company that is competing against new market entrants.
- I’ve worked with a few retailers and consumer products companies that had products that were all the rage until a new market entrant offered something easier, more desirable, prettier, or more economically dependable.
Today, so many companies are being displaced by new market entrants that offer compelling reasons to switch. I’ve used Data Science for years to discover that there is a problem and to pinpoint which customers are most impacted. I’ve also recognized that unless you change the way that you interact with these customers, or the offerings that you provide, data science alone, can’t solve the problem of growing and maintaining your customer base.
This is where Design Thinking and Data Science can work hand in hand to enhance the customer experience and identify opportunities for digital innovation. And why I joined OZ. Data Science can help organizations pinpoint potential problems and opportunities. Design Thinking can allow you to identify new methods and approaches that can enhance the Customer Experience. And then Data Science may become the enabler to provide a breakthrough offering that streamlines or personalizes your future customer interactions.