How AI Can Turn Customer Data Into Real Action

With the amount of data businesses amass today, understanding customers to personalize their shopping experience is more possible than ever before. And that’s good news for retailers because customers expect that personalization. In fact, more than three-quarters of shoppers become frustrated when companies don’t deliver personalized interactions.

The translation of data into understanding into personalization often seems simple but is really quite complicated. For example, imagine you have an online shopping enthusiast interested in sustainable fashion brands — or so the historical data says.   In her last purchase, she bought three items — all from brands labeled as sustainable, all made with eco-friendly fabrics.

Based on this data, you might assume they always look for sustainable clothing. So now you ensure the ads, email promotions and product recommendations she sees are all made to fit her sustainable lifestyle. You’re delivering a personalized experience based on what the data says were her past purchases.

Here’s the issue: The last purchase was actually a gift for her eco-conscious sister. And currently, she’s shopping around the site for herself, looking for a dress to wear to a work event. However, she’s bogged down by ads, emails, SMS and banner offers for sustainable shoes and jackets, even though those products aren’t related to her current interests. She can’t seem to escape your sustainable recommendations.

In the absence of real-time data — data that would have shown you this shopper is no longer engaging with sustainable fashion content — you’ve sent a customer on a pretty frustrating journey. Frustration quickly leads to lost customers and lost revenue. One consumer survey found that 86% of consumers will leave a brand they were once loyal to after only two to three bad customer experiences.

The pitfalls of relying on the past

The personalization that drives better customer experiences falls short when it relies more on who customers were than who they are right now. Pitfalls come into play when relying on the last or most recent data point. Customers have high expectations for today’s shopping experiences. They move quickly throughout their journey and want to shop with brands that can keep up.

Historical data alone misses the mark when it tries to capture that ever-changing nature. Even data that was relevant hours or minutes ago can do a disservice in painting the picture of who a shopper is right now.

Gifting, as in the example of our not-so-eco-conscious shopper, is an obvious case here, but even seemingly small events like changes in the   weather or the location in which a customer opens their email can completely change what a shopper is looking for or what content they’re willing to engage with. Real-time — as in, right now real-time — makes a massive difference in the customer experience and marketers' success.

Striking a balance

That’s not to say historical data serves no purpose. It uncovers long-term trends, helping retailers spot patterns and make informed decisions based on past purchases. It allows marketers to take attributes like loyalty status into account. Over time, it can show them important preferences, like the time and channel in which customers want to receive campaigns.

But historical data is just one lens — blending it with real-time insights is key to capturing the evolving dynamics of consumer behavior. It's all about striking the right balance between the old and the new because,  together, they create the complete picture of the customer that every data-driven marketer strives to obtain.

 The combination of real-time and historical data makes the difference in knowing your customer and personalizing their experience. That complete, constantly evolving understanding enables them to build stronger customer relationships by delivering consistent value and relevance. When you always know who a customer is, you can always show them what they want.

Customer understanding, amplified with AI

It’s that bridge to actually showing them what they want that requires one more critical component, though. The technology that no retailer or marketer can escape these days is AI.

It is not a far bridge to cross from the conversation of data to one of AI. Today more than ever, those two go hand in hand. Data nourishes AI, but without AI, data is just noise.

Getting the full understanding of your customer through both historical and real-time data is critical, but AI is how that insight turns into action.

For example:

  • Data will shed light on which customers frequently make returns. AI will segment those customers and exclude them from certain discounts and promotions.
  • Data will show you that customer X prefers email campaigns sent in the morning, customer Y prefers SMS campaigns sent in the afternoon and customer X prefers in-app notifications in the evening.  AI is what will ensure your campaigns are automatically deployed to each of them in the right channel, at the right time.
  • Data will uncover the customers who open every one of your newsletters as well as the customers who rarely click on them. AI will adjust the frequency accordingly, reducing the number of emails going to your unengaged customers and ensuring your avid readers get every communication.
  • Data will tell you that a customer you segmented as ‘eco-conscious’ is now engaging with content and products that don’t fall into that category. AI will re-segment them accordingly.

Data gives you a rich understanding of who your customer is — past and present. With AI, you use that understanding to create an incredible experience that acknowledges both.

A limitless view of the customer

It’s not enough to know who your customer was at a certain point in their journey. Customers are constantly evolving, their needs and desires shifting as they see more of what a brand has to offer.

It’s by joining historical and real-time data that retailers can create a limitless view of their customers — a view that fuels endlessly personalized experiences, always putting the right product in front of the right customer at the right time.

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