I started out doing database and marketing analytics at IBM in 1994, where the mail-order marketer Fingerhut was one of our major clients. We helped the company create a customer-selection system called “mail-stream optimization” that tailored catalog mailings more directly to customer needs and interests. Direct mail companies—whether it’s Fingerhut or L.L. Bean or Williams Sonoma—have used predictive models to determine who’s going to buy for decades, but that could only be done using purchase history and publicly available demographics.
Now the world is evolving to be much more digital, and we have an increasingly omnichannel retail environment, where purchases in the store and online have come together with digital browse data to form a more complete view of the customer. A broader set of data enables better decisions. It’s easier in the digital world to capture traffic than it is in the store, so we’re now able to capture much more of what the customer is willing to share online—and to use that information to drive engagement.
“We were able to collect the information, create the insights, and build an application that made a difference in the customer’s experience in the stores—we’re using data, almost in real time, to personalize your experience.”
What that means is that you can create personalized experiences at a much larger scale than ever before. Everyone used to get the same newspaper. That’s not the case anymore; newspapers are printed by the area. Everyone used to see the same TV commercials, and now your local cable networks will show you advertising based on your demographics. Tons of research says that the consumer is looking for relevant experiences. It’s valuable to them, so the exchange of data is valuable on both sides.
I started at Target in 2002. Over my decade there, I helped build out an analytics team that could take the data and solve questions like, ‘What’s the best product we can recommend to you based on what’s in your basket?’ One application that resulted from this work was the ability to print customized coupons at the point of sale. That’s based on what’s on the belt, what you’re already going to buy. We were able to collect the information, create the insights, and build an application that made a difference in the customer’s experience in the stores—we’re using data, almost in real time, to personalize your experience. That’s one of the things you can do with grocery. The challenge here at Maurices is, how do we do similar or new analytics with fashion? How can we gain insights from product sales that we see to optimize the experience in the store or online?
When I arrived here in 2013, the company was just building its database. I came to Maurices because of the rich opportunity to build out a base and foundation of an omnichannel-data-driven approach to marketing in an ever-evolving world of omnichannel retail. Data will empower Maurices to make data-based decisions without the bias of a particular channel; let the customer’s data drive the experience, not the particular channel they are shopping.
Last October, we relaunched our loyalty program. Previously, we hadn’t been taking the information customers give us and leveraging it with purchase behavior to further personalize the experience at Maurices. We want to create the value beyond the promotion—you’re a friend of Maurices, and here’s what’s coming to you; here’s how we can help you with your fashion style; we can help you complete this look; we can tell you about upcoming fashion trends. What I love about our approach at Maurices is that we’re doing this through an opt-in loyalty program, which signals that the customer knows the value of participating and is willing to exchange data.
We’re seeing an uptick in email-address submission, and when our customers provide both a mailing address and an email address, we can leverage that for email communications. And mobile devices are really expanding email marketing. We know our customer is going to be much more engaged with us through email because of her mobile device—people are reading way more emails on their mobile devices than they used to on desktops.
We face the challenge of bridging the gap between entrepreneurs and data scientists. Buying product and running a business, using data to understand profits and loss, are all part of a different skill set than using analytics from a customer-insight perspective. You might know how to sell jeans, but you don’t necessarily see that jeans are one part of a consumer’s wardrobe, and you can use data to figure out how to take that information and use it to inform your jeans or denim buy.
How do we combine the data science and the business to move from the product-centric sales and performance metric world that most merchants live in to a customer performance world where lifetime value and filling out her wardrobe are more impactful metrics?
At the end of the day, I’m running a services team for the merchants and marketing, which puts the onus on an analytics team to understand the data and the business process—and be able to translate insight