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04/07/2021

CSA Exclusive: Retailers see value in AI for customer relationships

Dan Berthiaume
Senior Editor, Technology
Dan Berthiaume profile picture
AI

New data indicates retailers are more likely to utilize artificial intelligence (AI) technology to engage customers than employees.

According to results of a January 2021 survey of 1,870 IT decision-makers released exclusively to Chain Store Age by multi-cloud technology services company Rackspace Technologies, almost half (48%) of retail respondents gained more traction than other industries in managing customer relationships using AI and machine learning (ML).

However, only one-third (33%) of retailers are interested in using AI/ML when it comes to understanding employee morale and engagement better. When asked how they plan to use AI/ML technology going forward, retail respondents were most likely to provide answers that were externally-focused:

  • Increase revenue (44%)
  • Competitive edge (41%)
  • Understand the customer better (39%)
  • Resource optimization (38%)
  • Innovation (36%) 
  • Predict business performance (34%)

 “It’s clear from the data that retailers have been quick to use AI and ML, Jeff DeVerter, Rackspace Technologies CTO, told Chain Store Age. “Especially in response to consumer patterns generated by the pandemic, retailers have turned to direct income-generating tools as opposed to internal ones.

“How AI and ML can be used by retailers is different for each organization. But the goal should be to better understand their customer and their patterns to be better able to predict their buying behaviors which will better enable them to optimize operational efficiencies.

“Retail executives should ask technology experts if their data strategy is centered around ‘productizing’ the data assets so they can be consumed by analysts throughout the organization.  Once that occurs, the data foundation is established and modernizing consumption can begin.

“Getting started in AI and ML isn’t easy. First, data engineering work has to occur - data needs to be clean and ordered. Then, ML models need to be built, tested, and refined.  All of this needs to happen before AI can be pointed at the models to start making predictions,” DeVerter concluded.