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How Retailers Will Leverage AI to Improve Profits in 2025

Contributed article by Mridula Rahmsdorf, CRO at IKASI  

Meeting aggressive revenue growth goals and addressing declining customer loyalty has led many retailers to look for ways to scale and improve user engagement at scale. Getting to know customers from their previous purchases and interests can help retailers drive loyalty; however the issue with most consumer-based marketing efforts is that practitioners don't necessarily think about each of their customers on an individualized level. This is ironic considering customers are the lifeblood for retailers and determining their lifetime value (LTV) involves more than actuarial exercises with generalized formulas and grouping. Understanding how much to invest in each individual customer is a constant challenge, which may be why it has led many retailers to adopt artificial intelligence (AI) to revolutionize how they conduct experiments and uncover untapped revenue.  

Improving Profits and Determining (Individual) Investment Strategies

Evaluating each customer and understanding how much to spend to get more revenue/trips out of each person is not something most retailers have been able to consider, until now. Using causal AI - a form of artificial intelligence designed to identify and understand the cause and effect of relationships across data - and AI-powered database marketing, retailers are making a significant impact on driving not only customer engagement but also profits and losses by driving more relevant loyalty/rewards and staying ahead of consumer fluctuations.

With causal AI, marketers can figure out the right size investment for each customer. By testing simultaneous models rather than relying on A/B testing methods, they can determine if the customer would pay more if they varied the price, and to what thresholds or if they charged less, would they come in more frequently? There's no amount of back testing that could ever generate these answers. Casual AI extends autonomous experiments at a massive scale so retailers can continuously explore and revisit new strategies to attain correct-sized investments for each customer to increase the frequency of visits and customer lifetime value and encourage more spend per visit. It also enables scenario experimentation in a low-risk way so retailers can actually see what is working for each individual and adjust quickly if needed.

Personalized pricing helps retailers gain a competitive advantage as they now realize they can no longer survive by simply meeting the expectations of their customers. Amazon experiments with pricing all the time, and they do it on an individual customer basis. Until now, this approach was only done by people with large data science teams. However, AI is changing the paradigm so that every company - even those that don't have a cadre of data scientists – can create experiments and offers down to the individual level. Leveraging AI to support hyper-personalization, retailers can identify individual preferences and incentives in real time so they can engage with customers in highly personal ways and foster loyalty. Those who have adopted these strategies are winning, as people tend to be slow to demonstrate loyalty to companies, especially in competitive markets. 

By understanding individual preferences on a mass scale, retailers can tailor experiences by understanding individual behaviors and pinpoint specific customer preferences and predicting behaviors. When armed with the ability to understand customers as individuals, retailers can better leverage what they know to drive engagement and apply AI to deliver the right message at just the right time. Companies that rely on AI to deliver “just the right experience” to “just the right customer” at ”just the right moment” consistently enjoy revenues that are 40 percent higher than those that don’t.

The more successful a business is at making its customers feel exceptional, the better it performs in areas like customer satisfaction, loyalty, retention, sales, ROI, and revenue growth. AI is proving to be key to helping retailers uncover revenue that may be hiding in their data and develop offers that optimize the right size investment for each individual. And we all know, customers reward businesses that deliver a truly personalized experience and make them feel special.

About the Author 

Mridula Rahmsdorf is the CRO at IKASI, a provider of patented, autonomous, causal AI solutions that help organizations optimize pricing, promotions and investment that are customized for each individual. For more information, visit https://ikasi.ai or follow her on LinkedIn.