Retailers Turn to Real-Time Data as 78% Anticipate Shifts in Consumer Behavior This Holiday Season – Friday, December 5, 2025

Retailers are increasingly relying on real-time data and AI to navigate the evolving landscape of consumer shopping behaviors this holiday season. The integration of these technologies is reshaping retail strategies, emphasizing the need for informed decision-making and enhanced customer experiences.

Who should care: VPs of retail, store operations leaders, eCommerce directors, customer experience teams, and retail technology decision-makers.

What happened?

The retail industry is undergoing a profound transformation as AI-driven shoppers and real-time data reshape holiday season strategies. This evolution is fueled by the urgent need to respond swiftly to shifting consumer demands and to optimize increasingly complex supply chains. AI technologies are enhancing customer experiences by enabling retailers to deliver personalized interactions at scale, while simultaneously revolutionizing supply chain management through agile, data-driven decision-making.

Central to this shift is the rise of decision marketing, where AI tools analyze real-time data to fine-tune marketing campaigns and tailor offers to individual consumers. This approach marks a significant departure from traditional retail practices, where decisions were often based on historical data and slower feedback loops. Today, success hinges on the ability to leverage AI and real-time insights to anticipate consumer needs and respond dynamically.

The retail media landscape, which underwent a seismic 'Big Bang' transformation 15 years ago with the rise of digital advertising, continues to evolve with AI now playing a pivotal role. Retailers are better equipped than ever to harness these technologies to create highly personalized shopping experiences, which are essential in a market crowded with competitors vying for consumer attention and loyalty.

Why now?

The accelerated adoption of AI and real-time data in retail is a direct response to the rapid pace of digital transformation intensified over the past 18 months. The pandemic acted as a catalyst, forcing retailers to innovate and adapt quickly to new consumer behaviors and expectations. As eCommerce growth continues unabated, the demand for seamless, personalized shopping experiences has become a critical differentiator.

This moment reflects a broader industry-wide shift toward data-driven decision-making, with retail at the forefront. The convergence of advanced AI capabilities and the availability of real-time data streams has created an environment where retailers must act swiftly or risk losing relevance.

So what?

The strategic implications for retailers are substantial. Effectively harnessing AI and real-time data can lead to optimized supply chains, more engaging and personalized customer journeys, and a stronger competitive position in the market. However, realizing these benefits requires significant investment in both technology and workforce training to ensure teams can fully leverage these tools.

Retailers that fail to adapt risk falling behind as consumer expectations continue to rise, potentially losing market share to more agile competitors. Embracing these technologies is no longer optional—it is essential for survival and growth in today’s retail environment.

What this means for you:

• For VPs of retail: Prioritize AI integration to enhance decision-making and elevate customer experience strategies.
• For eCommerce directors: Leverage real-time data to deliver personalized shopping experiences that drive sales and customer loyalty.
• For customer experience teams: Use AI proactively to anticipate consumer needs and tailor interactions more effectively.

Quick Hits

• Impact / Risk: Retailers who fail to integrate AI and real-time data risk falling short of consumer expectations and losing market share.
• Operational Implication: Investing in AI technologies and data analytics capabilities is critical to maintaining a competitive edge.
• Action This Week: Review current AI and data analytics strategies, identify technology gaps, and plan training sessions to enhance your team’s data utilization skills.