How AI and predictive analytics can aid retailers

Apr 13, 2023

3 minutes, 8 seconds

While e-commerce underwent a massive boom during the pandemic, consumers have returned to their old habits. Web traffic to e-commerce and direct-to-consumer sites is declining in the U.S. as in-store sales increase (Influencer Marketing Hub, 2022). Likewise, a survey from the National Retail Federation and IBM revealed that 45 percent of consumers prefer in-store, with 28 percent favoring online and 27 percent doing both when they shop (The Day, 2022).  

Evidently, the research points to a hybrid of in-store and online, and retailers need to respond with a robust omnichannel strategy. Of course, supporting omnichannel efforts without the appropriate technology is a recipe for inefficiency and potential failure. Retailers must adopt AI and predictive analytics to ensure the shopper’s experience flows effortlessly from one interaction to the next, in-store or otherwise.  

AI-powered customer engagement 

AI can serve many different purposes across the entire shopping experience – for both the shopper and the retail worker. For in-store experiences, retailers can use AI to enable seamless self-checkout; likewise, authentication technology can reduce shoplifting. Some futuristic examples of AI in retail include facial recognition payment methods and voice-enabled shopping assistants.

Moreover, with the rising desire among Gen Z and Millennials for “conversational commerce,” retailers can leverage AI to automate customer interactions over various channels, including text, voice, and social media (Fashion United, 2023). Advanced AI capabilities, such as sentiment and tonality analysis, on-demand language detection and translation, and speech-to-text and text-to-speech, are the fuel that will drive omnichannel communications, empowering customers to shop whenever and however they please.  

And by permitting shoppers to complete simple requests on their own through AI-enabled automation, like checking a package’s shipping status or item availability, retail staff can accelerate interactions, reduce agent staffing costs, decrease hold times, and boost customer loyalty.

Do you want to learn more about modernizing your contact center and accelerating customer engagement with generative AI and automation? Schedule an AI and automation Customer Interaction Intent Study with IntelePeer now.  

Support predictive analytics through AI 

Facilitating SMS or social messaging shopping will require AI-powered chatbots that make accurate inferences based on customer purchasing data and shopping history. Similarly, if multiple customers call about a particular item, but show up at the physical store only to discover the retailer is out of stock, they will undoubtedly become upset. The essence of predictive analytics is understanding one’s data and acting accordingly.

With AI, retail companies can enhance data mining processes, cutting costs and deadlines and helping them make informed and even real-time decisions to improve the customer experience significantly. Furthermore, predictive analytics and AI allow retail brands to maintain inventory and automatically send re-stocks requests when assets fall below a set threshold.

From a pricing perspective, retailers can use algorithms to analyze data from different sources, such as historical sales, competitor prices, stock levels, and special occasions, to determine the best pricing strategy for different products. For example, dynamic pricing, selling a product at varying price points to different customer groups, and cross selling a discounted item (e.g., buns) with a complimentary product (hot dogs) at full price (ITRex, 2021) are just two of the strategies that can be capitalized upon when a retailer has insight into available data.

Ideally, retail businesses should deploy a platform that can integrate with their existing reporting and business intelligence applications. Such a platform would enable retailers to achieve an even greater understanding of omnichannel customer interactions.

Empower your retail brand with IntelePeer 

If retail brands haven’t implemented AI and predictive analytics, they will fall behind the competition and fail to meet their customers’ high expectations for convenience, speed, and personalization.

As retail companies jostle for market share, IntelePeer’s platform, powered by automation, AI, and analytics, offers industry-leading time-to-value and rapid time to improve customer experiences through solutions that fit smoothly into existing software and infrastructure. Reach out today to discover how our AI and SmartAnalytics can help you make better and smarter business decisions.

Stevie Mulia

Stevie brings 23 years of product and software development experience to the IntelePeer team. With a background in telecommunications, FinTech, consumer electronics, and automotive, Stevie utilizes his vast experience leading cross-functional teams to build excellent products that solve critical customer needs. Outside work, Stevie enjoys traveling, photography, and spending time with family.

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