Among retailers, tech companies and moderators at Shoptalk in Las Vegas in 2024, artificial intelligence (AI) persisted as a common thread throughout Monday’s programming. At one of the last panels of the day, which focused on AI and personalization, the conversation turned to one of the fundamental layers of AI strategy: data best practices.

At the panel, speakers from DoorDash, Kendra Scott and Alimentation Couche-Tard swapped stories about what they have achieved. In doing so, they offered tips for setting teams up for success when merchants begin leveraging data to tackle personalization.

Data best practices for setting a foundation

Just because data is already there does not necessarily mean that a company is ready to begin using that data to begin a personalization project, suggested Kamanasish Kundu, vice president, head of digital and ecommerce, for Kendra Scott. Speaking to the audience, the panelists each shared their own priorities and choices to consider carefully early on.

“When I joined Kendra Scott in January 2023 last year, I think one of the biggest challenges was data,” Kundu said. “We didn’t have enough confidence in terms of the data integrity.”

For Kundu, that presented a problem for the jewelry seller.

“Typically, that’s the foundation of any testing and personalization program,” Kundu explained. “So fast forward six months, we completed our data cleansing and brought in the experts. And we created a full–funnel view, identified or pinpointed all the friction points in the journey, and that became the foundation of our testing and personalization program.”

Kendra Scott is No. 670 in Top 1000 Database, where Digital Commerce 360 categorizes it as a jewelry retailer. The database ranks North America’s leading online retailers by their annual web sales.

Protecting data

Meanwhile, Kevin Lewis, chief growth officer at Alimentation Couche-Tard, the parent company of Circle K, gave some advice for retaining ownership of company data, even when a partner or vendor presents a tempting offer.

“There’s gonna be a time when someone is going to say, ‘Hey, give us your consumer data and we’re happy to pay for stuff,’” Lewis said. “For those of you who may not be of the size and scale, that’s a tempting trade-off: ‘Help us fund the future by selling the consumer stuff.’ I will tell you, I think that’s a false trade-off.”

What Lewis proposed instead was valuing that data as part of what makes a company competitive and safeguarding it accordingly.

“I’m pretty clear that anybody can build a store and anybody can load it with the stuff that we sell,” he stated. “But at the end of the day, there’s probably two things that will make us unique and differentiated. One, I believe, is our culture, and we’re not going to sell that. Two — over time — is going to be the data we have that allows us to do things that no one else can.”

The consequence, he posed, could be giving another company what it needs down the road to replicate what someone else has accomplished.

Data best practices for creating value

Jessica Lachs, vice president of analytics and data science at DoorDash, echoed Lewis’s sentiments. She noted her own perspective on demonstrating data’s value by prioritizing business impact.

“Data is at the core of everything we do at DoorDash and is why I love my job so much,” Lachs said. “We measure everything we possibly can, and I think that that is one way you can make a really great case for investing in the data, which is to show that when you do, you see real business impact that you can quantify and that can help you make some of these trade-offs.”

At DoorDash, that may involve A/B tests, switchback tests or what Lachs calls “quasi-experimental methods to be able to size the impact of product features.” DoorDash handles hundreds of thousands of SKUs on its app.

Ultimately, she considers this approach a differentiator for the company.

“I think that that’s something that is really unique to us and something that is a competitive advantage for us,” Lachs said.

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