To remain competitive and thrive in our rapidly evolving economic and technological environment, retailers should consider tapping into the power of AI to meet customer needs and improve the shopping experience this holiday season. Here’s how. 

What trends are most likely to affect holiday shopping in 2023? Inflation and recession concerns. The recent interest rate increase of .25% in July 2023 marked the 11th hike by the Fed since 2021—raising the interest rate to its highest level in 22 years. High interest rates, combined with inflation, may cause consumers to reduce their discretionary spending, focusing more on saving. Additionally, if the Fed continues to raise interest rates, the likelihood of a recession could increase.

You can prepare your business for the likelihood of increased consumer caution by leveraging your data to optimize the results of every touchpoint with your customer. You can further enhance the quality of their experience with you by supplementing existing data analytics and machine-learning practices with real-time trend analysis to employ micro-targeted AI-generated messaging that will convert and retain customers more effectively—even in uncertain economic conditions.

However, despite economic pressures, consumers are still consuming. Deloitte’s 2022 holiday retail survey noted that 77% of retail executives expect holiday sales to increase year-over-year, and 66% expect online holiday shopping traffic to have at least single-digit growth year-over-year. This challenging moment still contains the opportunity for retailers to thrive by applying emerging technology tools like AI. Will consumers return to physical stores and malls in large numbers, or will the nature of retailing continue to evolve? Either way, brands are changing their products and services to meet customers where they are—and they need to change how they manage their customer relationships for the same reasons.

Three steps marketers can take to test generative AI this holiday season

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Generative AI for retail report

Our Generative AI for Retail report lays out three steps to get your organization AI-ready.

Read the report for our full findings, but here are the top takeaways:

1. Get your data AI-ready.

Without clean or good data to work with, customer acquisition, engagement, and retention methods will fail to perform optimally. Prepare your data to fill key data gaps and help the AI tools deliver the right decisions and models. This in turn will help orchestration engines deliver the right experiences to the right person at the right time on the right channel.

2. Identify the right use cases and build your AI roadmap.

Generative AI and machine learning can potentially increase quality and efficiency at every stage of the customer journey. We’ve identified the following use cases as high-impact opportunities to improve agility, efficiency, and customer satisfaction this holiday season.

  • Advertising. Generative AI text and image tools can be used to create faster and stickier campaigns, decreasing time to market and increasing engagement to capitalize on emerging consumer demands.
  • Web Experience. AI can help you connect with customers on the website in a more targeted and personalized way by helping curate selections and guide customers to the answers they need—right when they need them.
  • In-store Experience. Integrating generative AI capabilities into your in-store experience can provide a cost-effective way to deploy interactive “virtual personal shopper” experiences.
  • Supply Chain. AI and machine learning tools can predict, prevent, and resolve supply chain challenges to improve speed and profitability at every stage, from production to delivery.
  • Customer Service. Generative AI listening tools, quality management services, and chatbot technologies can help you deliver more effective and efficient service experiences while saving on call center costs.

3. Test and learn.

One of the primary reasons generative AI is increasing in commercial popularity is that it can enhance an organization’s agility by rapidly prototyping and testing new advertising campaigns, service flows, and predictive insight tools. Small-scale experiments can be enacted frequently to determine which implementations will likely have the most significant positive impact on organizational profitability and efficiency. A test-and-learn methodology can quickly produce positive effects on:

  • Quality. Produce higher-quality marketing content without adding headcount.
  • Scalability. Personalize content efficiently for a wide range of audience segments and consumer needs.
  • Adaptability. Respond more quickly to changing customer sentiment and market trends.
  • Optimization. Continuously improve merchandise assortments, channel spend, and marketing performance.
  • Accuracy. Surface better analytics insights to predict future trends and prevent future challenges more effectively.

Happier holidays ahead

The fate of the 2023 holiday shopping season is mostly written, even if we have yet to read how it plays out. There are many steps retailers can take now to take advantage of generative AI’s potential, even in a few weeks’ time. But this year’s experiences will also serve as a helpful lens for the future. The ways consumers address their holiday desires amid these rapidly changing economic conditions will indicate the longer-term trends to come. Thanks to the growing power of data and artificial intelligence, retail organizations can move forward with more agility and resilience. They can test and learn rapidly, knowing that flexible delivery-model options will offer more control over their investment and level of commitment while they learn. What retailers test in the late autumn of 2023 may not change outcomes dramatically this season, but it can set the stage for more efficient and effective customer experiences for years to come.
Read the complete AI for Retail report

Trinadha Kandi is a managing director and the leader of the Advertising, Marketing & Commerce practice at Deloitte Digital, a global leader in digital transformation and innovation. He has over 20 years of experience delivering data-driven marketing solutions for clients across various industries, leveraging his expertise in marketing technology, data, and AI.

Mark Singer is the US chief marketing officer for Deloitte Digital and the head of the Deloitte Digital experience agency.

Sai Medi is a data science and ML specialist at Deloitte, working at the intersection of customer data and marketing. He supports clients in the domains of RCP, health care, and tech.

Eashan Bhattacharyya is an experienced machine learning engineer at Deloitte Digital who has demonstrated AI proficiency across diverse sectors, enhancing operations with data-driven solutions, analysis, and visualization in various industries, including marketing, advertising, TMT, and life science.