2019 Marketing Predictive Customer Experience

[JasonBrown]

Predictive Analytics is such a buzzword isn’t it? Analysis plays an essential role in all aspects of business, there is no doubt about that. Yet here we are, trying to figure it all out. Do we have clean enough data to make good and accurate decisions? Do we have the structure to track the areas in which we need to? These techniques offer decision makers the data to grow product, sales, do upselling, improve optimization and production, and execute on revenue forecasting to the executives of a company.

The smartest companies master the art of knowing who their customer is. They focus on who they are, why do they like what they do, what do they like, who and what helped them? When we begin to understand our customers, the fundamentals of predictive become much easier to track. For example: If I’m an online retailer like Amazon. I might have enough data about you to know: that you like shoes that are a certain brand, I know you like them at a specific price range, I know you typically shop on the weekends, and I know your preferred method of communication is SMS. As a retailer I might be able to send you a 10% off coupon on a Saturday night via text. Even better if I have data behind the type of shoes you like I could even send you 10% on those exact shoes you looked at last weekend. The chances of you converting and buying those shoes are much higher. This is why predictive is such a powerful way of meeting the needs of the customer. So why is it a buzzword? Because it’s the “future” it’s where everyone is trying to get to, the goal, the standard, the moment when you can say, “Okay, we made it!” The real trick becomes do we have enough data, how do we collect all that data, and where do we store it?

Now when you put this in perspective with customers this becomes even more powerful. Back when I worked for a Manufacturing company, they used Big data and predictive data to determine when to hit the ground hard with Marketing. For example: the auto industry knows when you might have a lease that is up on your vehicle, so they’ll run targeting campaigns to just those people. Or maybe you are like me and have got those telemarketing calls about the factory warranty on your vehicle being up. These are all examples of how big data and predictive data is used. We knew their income size, their demographics, geo, etc. We knew it all and could very easily target a group of people with a special offer in those specific geographies. Imagine if you could prevent a car owner from ever leaving the brand they 1st purchased? Talk about lifetime-value. What if we could prevent your customers from leaving before they even knew they wanted to? Crazy enough, in this world, this is not out of the realm of possibility.