The Role of Retail Analytics in Customer Experience


As we move into a post-cookie world, here’s how data and retail analytics will change how brands and customers interact in 2023.

The beauty of being utterly connected to a brand, whether via in-person shopping experiences, Instagram advertising, or online customer service, is that every moment can be personalized and tailored to an individual customer.

For example, when you visit Sephora, you’re immediately able to have a truly individualized buying experience. You’re greeted with complimentary customer service offering free product testing and in-store tablets that grant access to your personal account with preferences and purchase history. Then at checkout, you can use points to access free products and even take home bonus items. Finally, after you check out, you’ll receive emails with instructions on how to use the products you bought.

The beauty of the marketing strategy—no pun intended—was in how it leverages data to build unique, omnichannel customer experiences. With a store that has hundreds upon thousands of options, Sephora offers its customers a targeted approach to shopping that taps into their preferences, history, and tastes.

This type of comprehensive customer experience is the foundation of forward-looking brands. And without data, brands are flying blind! As we move into a post-cookie world, here’s how data will change how brands and customers interact in 2023.

1. Predictive Analytics Will Power Personalization

According to a McKinsey study, 78% of consumers said that personalized communication was more likely to make them purchase again in the future. But, as we move beyond third-party cookies, a new phase of personalization built on zero-party and contextual data is in full swing.

At the most simple layer of personalization, a brand can use transaction data and predictive analytics to proactively highlight relevant products when a customer is most likely to want them. For example, for household items that are repurchased several times throughout the year, like water filters or coffee pods, a brand with a strong sense of their customer’s purchasing behavior can push marketing communications at the exact time they are likely to make a purchasing decision.

This and other data from sources across the organization—when compiled—create a unique “customer yearbook” or history at-a-glance of a single customer’s engagement with the brand over time. Such data allows for regular, personalized 1:1 marketing and advertising.

For example, REI has consistently been on the cutting edge of using contextual data to highlight relevant products. In one campaign, they looked at weather and geolocation data to surface ads for ski or rain gear depending on whether it was snowing or raining.

Even without third-party cookies, there will always be insights from predictive analytics using privacy-compliant data. Personalization isn’t dead; it’s just being rethought.

See also: The Personalization Paradox and How to Solve It

2. Retail Media Networks: Automation, Collaboration, and Fragmentation?

Retail media networks (RMN) have the potential to fill the void left by cookies. According to the World Advertising Research Center, retail media ad spend is forecast to reach $121.9B globally in 2023, up 10.1% from last year, making it the fourth largest advertising medium.

RMNs are essentially advertising networks built within a retailer’s marketplace. Think of Amazon, where sponsored products now routinely sit above organic results. In fact, Amazon’s ads division was one of its fastest-growing business units in 2022, representing $31.16B in revenue.

RMNs are lauded because they give brands and agencies access to first-party data at the point of sale, where customers are actively engaged in shopping and are the most receptive to relevant ads.

This type of advertising is also easily automated. Many small Amazon sellers quickly calculate their bids for high-value keywords using simple Excel formulas, while larger ones are embracing AI-driven advertising.

Equally exciting are the possibilities with Amazon Marketing Cloud’s “Clean Rooms,” essentially privacy-compliant data collaboration zones where brands and agencies can anonymously compare their customer data with Amazon’s to find novel insights.

As every retailer builds out their own ad network, the space will get more fragmented, and with that fragmentation comes data problems. Now more than ever, brands will need to invest in analytics to ensure that they can track and compare KPIs across different ad channels.

3. Experiential Shopping: Physical Insights Are Going Digital

As retail influencer Steve Dennis famously said in 2018 (and pretty consistently ever after), “Physical retail isn’t dead. Boring retail is.” We’ve seen that play out in the last 18 months as in-store shoppers have come roaring back.

But physical retail isn’t the same. It’s smarter, more digitally oriented. And it’s giving brands more data and customers more unique experiences.

With many in-store shoppers, grocery is leading the charge. In fact, according to Prosper Insight & Analytics’ “Prosper Wisdom Suite,” an insights-on-demand platform that includes zero-party data from a monthly representative survey, 77.5% of Americans have shopped in-person for groceries in the last 30 days, the highest of any category.

And so, it follows that grocers are the keenest on getting in-store data. In February, Wegmans announced it was testing out smart carts at two of its locations. This has the potential to reshape stores, quite literally. Using computer vision mounted on the carts, Wegmans can map customer routes to optimize their layouts. The carts also allow for contactless payment, improving customer experience and the ease with which they can checkout.

While grocery is certainly the leader, other industries are following suit, especially those with many in-store shoppers who like apparel and beauty. For instance, many brands have opted to build small pop-up installations within partnering stores or even as mobile pop-up trucks. They’re doing more than just gathering data—they’re using data to deliver unique, customer-centric experiences tailored to their target shopper.

Go Where the Data Takes You

During the pandemic, retail brands saw the importance of eCommerce. Then they learned the hard lessons of supply chain management as ships sat for weeks in ports across the world. As shoppers returned to physical stores in 2022, they saw the importance of omnichannel.

But through it all, brands were leveraging data like never before to make hard decisions. Demand is difficult to predict in this strange economy, but as retail media networks explode in popularity and traditional marketing techniques are being reinvented, the brands that use data to meet their customers wherever they will be the ones that win.

Vamsi Valluri

About Vamsi Valluri

Vamsi Valluri leads the retail industry practice at LatentView, overseeing client engagements, developing solution accelerators, and shaping thought leadership. Prior to LatentView, he led data science and analytics at Foot Locker, partnering with executive leadership and evangelizing and developing novel data solutions. He is a strategist with over 13 years of experience across sectors, functions, and cultures. He is passionate about augmenting the art and science of retail with customer data insights. Vamsi has leadership experience in customer insights and retail and omni-channel analytics. Vamsi has previously worked at Mu Sigma and the Hinduja Group and consulted with the Asian Development Bank, Manila, and the United Nations, Yangon. He holds an MA from the Fletcher School of Law and Diplomacy and an MBA from XLRI Jamshedpur. He lives in New York City with his wife, Divya, and daughter, Mira. An avid reader, his other interests include cooking, running, and traveling.

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