5 Data-Driven Strategies Retailers Might Have Overlooked for Holiday Success

With the holiday season well underway, retailers are grappling with rising customer expectations and a constantly evolving data landscape. As a result, many organizations struggle to optimize their seasonal marketing approach. From adapting to the decline of third-party cookies to embracing real-time analytics and artificial intelligence-driven insights, the following strategies illustrate how retailers can maximize this season’s results and future-proof their marketing plans.

1. Prioritize first-party data collection.

More consumers than ever are tuned into data privacy concerns, fueling a shift away from third-party cookies and emphasizing the importance of first-party data. However, few retailers are deploying their loyalty programs, onboarding journeys, and mobile applications in a way that allows them to build and refine a 360-degree profile of their customers. Brick-and-mortar stores are also missing opportunities to integrate in-store behavior with online interactions to provide a seamless omnichannel experience.

By collecting high-quality data with customers' consent, retailers can craft targeted campaigns that foster meaningful engagement and build loyalty. Retailers must clearly communicate how customer data will be used and ensure it’s securely stored in a centralized system, building trust and enabling deeper personalization.

2. Leverage generative AI to improve experiences.

Generative AI has revolutionized how retailers can tailor customer experiences, but many have yet to leverage it for product recommendations and shopping journeys. Retailers should look to examples like Walmart, which uses generative AI to improve its product catalog, helping customers find what they’re looking for and improving employee experience. Following suit, retailers can use AI to suggest items based on customer history, or employ it in loyalty programs to deliver targeted holiday offers. Predictive models can anticipate a customer’s next purchase, ensuring that the retailer’s suggestions align with individual preferences.

Offering timely discounts on AI-recommended products increases conversion rates and demonstrates clear value to the customer, boosting return on ad spend (ROAS).

3. Use predictive analytics to optimize inventory and pricing.

AI-powered predictive analytics can also forecast demand, align inventory with expected sales, and fine-tune pricing strategies to match real-time market trends. Retailers often face stock shortages or rushed pricing adjustments during the holidays, but predictive analytics offers a proactive solution.

Integrating data from historical sales and seasonal trends allows retailers to sidestep common pitfalls and boost margins. Prioritize metrics such as customer lifetime value (CLTV), churn rate, and ROAS to effectively guide decision-making during this high-stakes sales period.

4. Harness real-time data for agile campaign adjustments.

Retailers often neglect to refine marketing campaigns in the moment. Instead of relying on static reporting, instantaneous feedback allows businesses to pivot their strategies based on real-time data. Monitoring purchase trends across channels can help optimize ad spend or adjust offers to better resonate with customers.

Centralizing customer data in a cloud warehouse provides teams with a single source of truth, ensuring access to the most current information. Pairing this unified data with an AI-powered platform enables quick analysis and actionable campaign recommendations that continually optimize performance.

5. Unify cross-channel data to improve customer engagement.

Siloed data remains a major obstacle to retailers' delivery of cohesive messaging and seamless customer experiences. By combining data from in-store, online and mobile channels, businesses can craft a consistent journey for their customers.

Investing in data readiness ensures systems integrate seamlessly, creating a unified approach to engaging customers and planning for the future.

The Future of Data-Driven Retail Strategies

Adopting these data-driven strategies can help businesses transform missed opportunities into meaningful successes, enhancing holiday performance and year-round growth. Looking ahead to 2025, the importance of AI, real-time analytics, and unified data will only continue to rise, reshaping how retailers engage with customers to build lasting connections.

Chris O’Neill is the CEO of GrowthLoop, a composable customer data platform.

Jason Downie is the U.S. CEO of Making Science, an international digital acceleration company specializing in data, artificial intelligence, and proprietary ad tech technologies.