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The Retail Revolution: 3 Ways AI Is Reshaping Customer Engagement

Until the third quarter of this year, it seemed like an economic recession had been looming – always just one month away – for over a year. At least, that’s what analysts and commentators had been predicting. However, buoyed by strong retail sales throughout the year, the US economy has come in for a so-called soft landing, and is headed into next year on the uplift of a very promising consumer spending level this holiday shopping season. 

Sturdy consumer demand should inspire retail executives worried about a sustained downturn as buyers hedged against rising inflation, interest rates, and economic uncertainty. It also raises the urgency for retailers to maximize their advertising potential to attract, convert, and retain customers when their wallets are open. 

In this way, Artificial Intelligence (AI) innovation has arrived at the exact right time. Right now, generative AI (GenAI) is reshaping custom engagement in retail by providing advanced personalization, targeted marketing, predictive analytics, dynamic pricing, interactive chatbots, and visual search capabilities. 

These advancements allow retailers to provide more relevant and engaging customer experiences that produce improved customer satisfaction, increased sales, and stronger brand loyalty.

Fortunately, this is more than just speculative or hypothetical. Here are three ways AI is actually impacting retail advertising right now. 

#1 Personalized Recommendations

Consumers can be turned off by generic advertising, but they respond to personalized product recommendations.

67% of consumers say they want personalized offers, while a separate report found that personalized emails receive a 139% increase in click rate (CTR). 

Adobe

Now, generative AI is making it possible to deliver personalized, 1:1 targeted content at scale. 

For example, a large consumer goods company has deployed AI algorithms that analyze vast amounts of consumer data, including purchase history, browsing behavior, and preferences, to deliver personalized product recommendations. 

This level of personalization enhances the customer experience, increases engagement, and drives higher conversion rates and sales.  

Some retailers are taking this a step further, experimenting with AI to create personalized products, with the cosmetics industry-leading innovation in this space.

#2 (More) Targeted Marketing Campaigns 

Marketing campaigns are most effective when they speak to a specific target audience. 

For instance, to make their marketing campaigns more impactful, a global provider of oral care products is using AI-powered algorithms that can identify specific customer segments based on various criteria, like age, location, interests, and online behavior. 

This allows the brand to create targeted marketing campaigns to reach the right audience with the right message. At the same time, AI can optimize ad placements across different channels and platforms to maximize the effectiveness of these campaigns, taking data-driven to the next level and empowering more target campaigns than was previously possible. 

#3 Visual Search and Image Recognition 

Retailers can leverage AI to enable customers to search for products using images rather than keywords. 

This technology allows the brand’s consumers to take pictures of products they like or find similar images online and receive recommendations or information about where to purchase them. Visual search capabilities provide new advertising opportunities for retailers to showcase their products.

A leading cosmetics company is deploying generative AI to significantly advance visual search and image recognition, helping customers find the products they want more efficiently and effectively than ever before. 

Proceed with Caution 

Despite all the excitement around AI, it has limitations, and necessary precautions are required.

Early adopters have experienced many false starts when experimenting with AI. Some of the more common miscues include:

  • Hallucinations: AI is far from perfect, and the engines can misinterpret certain inputs, resulting in inaccurate outputs or “hallucinations.”  When deploying AI in a production environment, the marketing team needs to deploy a continuous audit of AI output to catch these inaccuracies.
  • Data Security & Privacy: AI requires massive amounts of raw data to synthesize and create outputs that create business lift. Some companies have experienced growing pains with unexpected data breaches and consumer privacy actions, a common occurrence in this complex environment of big data ecosystems.
  • User Resistance & Trust: In most cases, consumers know they are dealing with a machine-generated transaction. The brand must be completely transparent to create the trust required for a fully digital interaction to be trusted. It’s important for marketers to strike the right balance between personalization and privacy and to communicate transparently with customers about the use of AI in marketing efforts.

When implemented thoughtfully and carefully, AI can radically reshape retailers’ customer engagement strategies. 

The specific results achieved will vary depending on the industry, business goals, and implementation strategies, but overall, AI has demonstrated its potential to enhance customer experiences, drive revenue growth, and improve marketing effectiveness.

Dennis DeGregor

Dennis DeGregor serves as Vice President, Global Experience Data Practice, at Verticurl, a WPP company and part of the Ogilvy Group. Dennis has an extensive client-side track record with Fortune 500 brands in enterprise CX transformation, data strategy, analytics, and leveraging technology for competitive business advantage. Dennis is known for building high-performance teams that accelerate clients’ end-to-end Experience Transformation initiatives via innovation in data strategy. He has written two books on the subject of enterprise data, strategic AI, and leveraging the global Internet for competitive advantage through data-driven CX transformation: HAILOs: Competing on AI in the Post-Google Era and The Customer-Transparent Enterprise.

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