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Personalized Shopping Experience Connects Companies to Customers

March 13, 2019

In this article you will learn:

By Christian Selchau-Hansen, CEO

Let’s face it. Discounts really suck. A one-size fits all discount—20% off all sweaters—treats all customers the same and invariably results in offering some customers too much (and some customers too little) for the desired action. Because they are not targeted, relevant, or specific to any customer, they annoy customers, clogging inboxes with an irrelevant soup of numbers, percentages, dates, and codes. What’s more, they train customers to expect discounts, reducing your products’ value. And most importantly, they’re simply an inefficient way to grow.

But how do you make them better? That was the topic of my discussion at the 2019 ShopTalk conference in Las Vegas: Here are three insights that we’ve developed at Formation, while helping companies like Starbucks use AI to connect to millions of their customers.

Understand customer motivations

All shoppers are not created equal. Indeed, two shoppers for the same product may be driven by entirely different motivations. Take a sweater shopper—did she make her purchase because she was cold or because she loved the style? Because of an irresistible price or just for the joy of shopping? The customer’s underlying motivation will dictate how that customer will respond to a promotion. The chilly customer, for example, will probably be more excited by free same-day delivery than by the equivalent cash-value discount. A brand with insight into a customer’s motivations, therefore, can not only more effectively encourage the customer to act, it can do so more efficiently.

With machine learning, you can develop a much more comprehensive understanding based on customer interactions (or the lack thereof) combined with additional first-party, behavioral, and aggregated customer data. With the right data, experimentation, and machine learning approaches, it’s possible to create a rich picture of each customer’s motivational underpinnings—and do so quickly and at scale.

Create new behaviors with hyper-engaging experiences

The second application of AI is to leverage machine learning to build new behaviors, rather than just optimize simple transactions. Turning back to our hypothetical sweater shopper—getting her to buy an additional sweater isn’t going to drastically shift her lifetime value; however, encouraging her to start buying pants and shoes in addition to sweaters will.

This, of course, raises the question—which new behavior should one incentivize? Should the retailer encourage the sweater buyer to start buying jackets on the theory that she’s cold and will, therefore, be interested in other cold-weather products? Or does the retailer encourage the sweater buyer to start buying beauty products on the theory that they are low cost and high margin? A wrong choice might leave money on the table or, worse yet, alienate the customer.

As with customer motivations, by applying AI, it’s possible to match each customer to their optimal new behavior based on their motivation. However, in today’s world of constant distraction, you need to do more. Even good content can get lost in the noise. What you need is a highly engaging construct where you can apply all of the great AI-enabled insights and decisions.

This is where a Multi-Step Offer makes all the difference. Multi-Step Offers contain a series of micro-offers that nudge customers to engage in the behaviors required to reach their motivational goal. Each step is tailored to the individual, in order to maximize engagement across the customer lifecycle. Moreover, each step reacts to the previous steps, ensuring that the offers remain responsive to where the customer is in their journey.

Reinforce new behaviors with a personalized shopping experience

So you’ve used AI to identify the customer’s motivations and the best new behavior and to optimize a highly-engaging construct. But you're not done; now you need to keep reinforcing the relationship and new behavior.

To reinforce the behavior, we again apply AI. With reinforcement learning and a library of highly-engaging personalized shopping experiences, it’s possible to give customers a series of experiences aligned to their motivations that further deepen the value the customer gets from the interactions with your brand.

At the end of the day, marketing is about the customer. It’s about speaking to their values and their journey, not just tweaking price in the hopes of pushing the product du jour. Technology now allows us to create truly 1:1 relationships—relationships that deliver more value to the customer and the brand—so now is the time for us all to say, “death to discounts."

To learn more about our retail solutions and personalized shopping experience, contact us today.