Predicting Desires: Looking at Past Behaviors to See the Future

October 23, 2020

Imagine this.

Toyota emails a special offer on your next lease, just as your Camry hits a mileage milestone.

Kroger ships you the Barilla angel hair pasta, Breakstone’s low fat cottage cheese and Reese’s Peanut Butter Cups you want – before you realize your refrigerator and pantry are empty.

As the credits rolls on “The Crown,” a Netflix suggestion appears onscreen for the next thing you should watch – and it’s a terrific recommendation (Empire? Really?).

Here is a challenge for my Pure Romance Consultants: Take your top 20 loyal customers and create a spreadsheet with their past purchases and date of purchase. With this information, create a plan to reach out to each offering their favorite products to replenish their supplies in the time frame they need the product.

You don’t have to dream hard – as at least two of those tactics are already implemented.

Fortunately, predicting customers’ future needs has been made more simple with the help of smart data and predictive analysis. These strategies use existing data from the past to forecast potential future purchases. If the analysis is done in the right way, you can find answers to questions about how people act as individuals, what their values are and what phase of life they are in.

Customer loyalty proves to be the best predictive measure not only marketing, but for smart data. By reviewing previous data on loyal customers, businesses can use predictive analysis to watch for behaviors that indicate they are going to purchase the same potential item.

Technology has groomed us to expect instant gratification. The sooner an item can arrive, the more we’re willing to spend. As products become more readily available and technology develops, receiving an item in real time will be too late.

Comedian Ronny Chieng jokes that Amazon Prime Now is not fast enough. “We need Prime harder faster, stronger…let’s get Prime Before. Send it to me before I want it…

…in as many boxes as possible.”

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