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Keeping Up With Retail Consumers by Leveraging Data and AI

Brands that evolved by adapting to consumer behaviors left significant marks in the history of retail. Nike is one such brand. It doubled its revenue to USD 44.5 billion in ten years by focusing on its customers’ needs1. The sports and athleisure company’s success is often attributed to its innovative spirit, global reach, and use of data to enhance customer experience. Nike exemplifies the power of keeping up with the consumers.

Understanding the post-pandemic consumer

Fulfilling all consumer demands is not simple. Especially when geopolitical, health, and environmental issues disrupt supply chain movements, affecting the retailers’ inventory and production targets. After vaccinations started in the US, retail sales jumped leading the sector to gain 35% in market capitalization from February 2020 to April 20212. The boost in sales overwhelmed the already struggling retailers and their supply chain, driving the prices higher. As a result, inflation in the US rose to 8.6% in May 20223 , the highest in 40 years.

While inflation has still not affected retail spending, consumers have begun showing signs of pulling back4 . For example, shifting to more affordable brands is a tell-tale sign of a loss of loyalty toward their oft-used brands. Plus, a new Consumer Sentiment survey reported that 54% of its respondents had reduced their shopping frequency and only went for shopping trips when extremely necessary5. The post-vaccination increase of foot traffic in brick-and-mortar stores is expected to again decrease owing to the new realities.

The same survey also found that consumers were spending more on their essential needs, such as grocery and personal care, and less on other items, such as alcohol, cosmetics, fashion apparel and accessories, footwear, entertainment, and consumer electronics. In-store service with personnel interactions helped retailers, like Trader Joe’s garner better sales6. As consumers continue to change their behaviors according to the market and economy, it could become difficult and tiring for brands and retailers to keep pace. In such a scenario, sustaining business, retaining customers, and ensuring growth in the long-term require real-time alignment with the consumer pulse and innovative ways of adjusting to their behaviors.

Data and AI to the rescue

It is clear that technology is key to innovations as data reflects consumers’ behaviors. Add analytics and AI or machine learning to the data in real-time and you will see how adjusting to the consumer behaviors becomes simpler and faster. Following are key use cases demonstrating the applications of data and AI in retail –

  • Price optimization Leveraging mathematical and marketing analysis, businesses can ascertain customers’ reactions to different costs of products and services across channels. AI can help build an effective price adjustment strategy that can keep a check on the designated margin and ensure competitive pricing. Such a price optimization tool helped a fashion accessories retailer increase its absolute EBITDA by studying consumer behaviors with their products’ pricing and effectively bundling offerings to make these attractive to them7.
  • Hyper-personalization In the past few years, AI-powered recommendation engines using customers’ purchase data became a major marketing tactic for retailers as 63% of US marketers witnessed increased conversion rates from personalization8. Further advancements are now enabling retailers to hyper-personalize the consumers’ shopping experiences. Hyper-personalization uses deep analyses of multiple datasets, such as page views, browsing patterns, and significant online consumer behaviors. Machine learning algorithms study every minute shopper behavior to help the retailer achieve sales. For example, if a shopper abandons a purchase, the AI uses different ways, such as offering a discount or product alternatives to ensure the shopper completes their purchase.9
  • In-store customer analytics Retailers can identify critical sales conversion opportunities by tracking shopper behavior using heat sensors, intelligent displays, 3D modeling, and in-store analytics. These digital tools enable the staff to gain insights from the in-store traffic flow and allow them to optimize store layouts, staff movements, and shifts. Leveraging such customer analytics, Amazon is helping brands gain anonymized insights about their products’ performance in its Amazon Go and Fresh stores10.
  • Adaptive inventory management While struggling to come out of the out-of-stock blackhole that intensified at the beginning of the pandemic, retailers realized the importance of using technology to manage inventory. AI’s self-learning ability can be a game-changer, enabling retailers to optimize their inventory depending on multiple external and internal factors. AI can improve inventory planning by demand forecasting, calculate inventory arrival time by analyzing multiple supply chain-related inputs, and intelligently react to incoming demand by automatically balancing items with cost.
  • In-store and online AI-enabled innovations In recent years, retailers have adopted a plethora of digital innovations to enhance the customer experience both in-store and online. In brick-and-mortar stores, retailers, such as Amazon Go and Whole Foods are using computer vision, sensors, and deep learning to provide cashier-less checkouts11. Brands with omnichannel presence, such as Levis, Old Navy, and Gap, among others, have deployed AI-driven virtual fitting kiosks to reduce the shoppers’ time spent trying on each garment or fashion accessory12. And online retailers, such as Asos offer their shoppers a visual search tool to search through thousands of products13.

How can Xebia help

Xebia has had a competitive edge in building data and AI solutions since the early 2000s. Our team of experts design and implement reliable data-driven systems that continuously deliver insights. The data pipelines enable self-service reporting and visualization that can help retailers and brands gain relevant insights to take consumer-focused decisions and maximize revenue. We have helped multiple retailers and brands leverage the benefits of data and AI technologies to improve their understanding of their customers and adjust to their behaviors on time.

As we continue to move further away from the tremors of the pandemic, we cannot avoid the fact that the retail business is heavily dependent on consumers. Companies must embrace the power of data and AI to survive in such unprecedented situations as inflation and supply chain disturbances. Only then, a business can stay competitive and resilient to weather all future storms.















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