Retail Utilizes New Technology to Add Personalization

Machine learning and AI, paired with ample customer data, will give retailers the foundation to revitalize their strategy.

Personalization, how to make sense of the massive amounts of customer data and how to best leverage artificial intelligence and machine learning tools were among the top topics on the minds of attendees and presenters at the National Retail Federation’s Shop.org 2018 conference Sept. 12–14 in Las Vegas. NRF’s innovation-focused event brought technology leaders together with retailers to discuss the future of the segment, which all agree is robust.

As retailers continue to streamline customer experiences and disrupt old shopping routines with tech-heavy strategies, several themes developed at the show. Here’s a snapshot of the top takeaways.

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Poor Personalization Means Poor Performance

About 50 percent of U.S. consumers switched the companies they buy from last year because of poor customer experience, according to a report from Accenture Strategy Global Consumer Pulse Research, contributing to a $756 billion loss in retail and brand sales in the U.S.

The survey goes on to note that 44 percent of U.S. consumers say they are frustrated when companies fail to provide relevant personalized experiences. Certona’s Meyar Sheik told attendees that while 4 percent of retailers rank their personalization initiatives and omnichannel maturity level as high, “a shocking 54 percent rate it as low.”

Sheik detailed several advanced tactics for personalizing the customer experience, including optimized Google paid search campaigns, personalized email and retargeting campaigns, and segment- and persona-based experiences.

Retailers that worked to dynamically personalize landing pages with photographs and product offerings based on geographic customer data saw significant increases in click-through rates, he said. Different retail subsegments saw varying degrees of success, so it’s important to remember that what works well in one retail segment won’t necessary translate to all.

Beyond what should or could happen within a personalized experience, retailers at the show also discovered that 33 percent of customers expect personalized recommendations. As retail competitors grow more familiar with the available tools, that number will continue to grow.

Data’s Success Depends on How It’s Stored, Processed and Analyzed

Diginomica’s Jon Reed attended the show and observed, “Retailers struggling with data is not the same as a retail apocalypse.” 

CVS Pharmacy President Kevin Hourican offered perhaps a more poignant assessment: “Retail isn’t dead. Bad retail is dead.”

He went on to outline how the retail giant has mined data inputs — the shopper’s journey, purchase histories, mobile engagement — to completely reimagine its customer experience. 

Rethinking the tech stack, getting a handle on shadow IT and ensuring IT operational goals align with business objectives are the first steps in getting a handle on the onslaught of data retailers face. 

Machine Learning Is a Powerful Customer Service Tool

Machine learning technology, as it rapidly evolves, can ensure the streamlined, on-point personalized experience that customers have come to expect — and personalization is really nothing more than “translating information into assistance,” said Google’s Managing Director of Retail Kiran Mani, speaking to Shop.org 2018 attendees.

Retailers are applying machine learning in several ways, he said, including one that may be considered primary among all retail segments’ goals: giving everyone in the company the information they need to work with any customer.

“The fundamentals of retail have not changed,” Mani said. “Machine learning just gives you so many more possibilities to get to your goal in more ways than ever before.”

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Sep 27 2018

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