Unlocking the Power of Retail Media Networks with Shopper Insights.

“Data is the new oil,” as coined by Clive Humby, highlights how data, much like oil, has become a valuable resource that fuels modern economies.

According to Harvard Business Review, by 2025, global data creation is projected to reach 175 zettabytes, driven largely by consumers’ increasing digital interactions. For retailers and brands, shopper data has emerged as one of the most powerful tools to drive growth, optimise marketing strategies, and personalise customer experiences. However, as consumer expectations evolve, simply collecting data is no longer enough. Brands must dig deeper into shopper insights to truly understand their customers and deliver meaningful, relevant experiences.

With shopper behaviour shifting rapidly across global markets, brands face a critical challenge: how can they harness the massive volumes of data to stay ahead of the competition? As the future cookieless world looms, the answer lies in effectively leveraging first-party data, adopting advanced segmentation techniques, and embracing retail media networks as pivotal drivers of brand success.

The Rise of Retail Media Networks

Retail media networks (RMNs) have quickly become one of the most influential channels for brand visibility and customer engagement. What began as simple online ad placements on retail websites has evolved into a sophisticated ecosystem where retailers sell products and act as media platforms. As consumer behaviour shifts toward e-commerce, the value of these networks has skyrocketed, turning traditional retailers into major advertising players.

Key global players like Amazon, Walmart, and Alibaba have set the standard for retail media, leveraging their vast amounts of first-party data to offer brands highly targeted advertising opportunities. For instance, Amazon generated over $37 billion in ad revenue in 2022, making it one of the largest players in the digital ad market. Walmart’s retail media network, Walmart Connect, has also experienced rapid growth as brands flock to capitalise on insights derived from online and in-store consumer purchase behaviour.

Globally, retail media spending is surging. In the U.S. alone, omnichannel retail media ad spending will hit $129.93 billion in 2028, according to e-Marketer’s forecast, up from $54.85 billion this year. Markets like China are also experiencing significant growth, with Alibaba and JD.com leading the charge. This explosive growth is driven by RMNs’ unique ability to provide advertisers with direct access to consumer shopping data, enabling them to reach customers at critical moments in their shopping journey.

To remain competitive, brands must recognise the power of RMNs and understand how to leverage them effectively to boost brand visibility, engage consumers, and drive ROI.

Unlocking the Power of Shopper Data

In a cookieless future, first-party data is the cornerstone of deeper consumer insights for retail media networks. Unlike third-party data aggregated from external sources, first-party data is collected directly from customers through interactions with a brand’s channels, such as websites, apps, and in-store visits. This data is incredibly valuable because it provides a direct window into consumer behaviour, allowing retailers to tailor their marketing efforts with precision and relevance.

Retailers are key to these insights because they are at the forefront of consumer interactions. By tracking every touchpoint — from product searches and purchases to app usage and loyalty program engagement — retailers can develop a comprehensive understanding of what drives their customers’ decisions. This depth of insight allows for more personalised and effective marketing campaigns and better overall customer experiences.

However, collecting data is only the beginning. Brands must harness advanced analytics and AI-driven tools to unlock shopper data’s potential fully. These technologies can process massive volumes of raw data, identifying patterns, trends, and behaviours that would be impossible to detect manually. For instance, AI can analyze purchase history, browsing behaviour, and demographic data to predict future purchasing decisions, enabling brands to tailor their messaging and offers to individual consumers.

Types of Shopper Data

Purchase Behavior: Data on what customers buy, how often, and what quantities (from online and offline sales).
Search Patterns: Insights into what customers search for on retailer websites or apps, revealing their interests and needs.
Demographic Data: Information such as age, gender, location, and income level helps in segmenting and targeting customers effectively.
Engagement Data: Metrics on how customers interact with a brand’s digital properties, such as time spent on site, clicks, and video views.
Loyalty Program Data: Insights from customer participation in loyalty programs, including rewards earned, redemption habits, and repeat purchase behaviour.
Feedback and Reviews: Qualitative data from customer opinions and reviews can be invaluable for product development and customer service improvements.

Advanced Segmentation for Targeted Campaigns

Advanced segmentation techniques are essential for creating targeted campaigns that resonate with individual consumers. Shopper data offers deep insights, allowing brands to expand beyond broad demographic categories and, more precisely, segment their audience. By leveraging detailed behavioural, demographic, and psychographic data, brands can create highly personalised marketing strategies that speak directly to the needs and preferences of specific consumer groups.

Advanced segmentation involves breaking down your audience into smaller, more defined groups based on shared characteristics. Techniques such as clustering algorithms and machine learning can identify these subgroups, allowing marketers to create targeted messages and offers more likely to convert.

Examples of Advanced Segmentation Techniques

Behavioural Segmentation: Segmenting customers based on interactions with the brand, such as browsing habits, purchase history, and engagement levels. For example, targeting frequent buyers who haven’t made a purchase recently with re-engagement campaigns.
Predictive Segmentation: Using machine learning to predict which customers are most likely to convert or churn, allowing for proactive engagement strategies that retain or drive them toward specific products.
Life-Stage Segmentation: Segmenting consumers based on their life stage, such as new parents or retirees, and tailoring messaging to their needs and priorities.

Brands like Nike and Sephora have successfully used data-driven segmentation to enhance their marketing efforts. Nike leverages purchase data and engagement metrics to create personalised campaigns, while Sephora uses loyalty program data to offer tailored beauty recommendations and early access to new products.

Global Market Research Insights

Segmentation strategies vary across regions. In Western markets like the US and Europe, segmentation often focuses on lifestyle, preferences, and online behaviour, emphasising personalisation. In contrast, Asian markets, particularly China and Japan, emphasise social commerce and community-driven purchasing behaviour, requiring brands to target consumers based on participation in online communities or social platforms. Regional preferences and language also significantly affect segmentation in markets like India, where consumer behaviour varies significantly across different states.

Bridging the Gap: Global Retail Media Trends

Retail media rapidly evolves globally, but regional differences shape how brands and retailers approach this burgeoning space. The retail media landscape in Western markets differs significantly from that in Asia, driven by unique consumer behaviours, technological advancements, and market dynamics.

Western Markets: Data-Driven Growth

Retail media has seen significant growth in Western markets like the US and Europe, driven by e-commerce reliance and data-driven marketing strategies. Retailers like Amazon, Walmart, and Target have built sophisticated retail media networks that leverage first-party data to deliver highly targeted advertising opportunities to brands.

Asian Markets: Social Commerce and Mobile-First

In contrast, Asian markets like China, Japan, and India are leading in integrating retail media with social commerce and mobile-first strategies. According to eMarketer, Ecommerce channels will account for nearly 90% of retail media ad spending in China, or $49.49 billion as of June 2024, with platforms like Alibaba’s Tmall and JD.com capitalizing on community-driven shopping and mobile commerce. Mobile shopping and digital loyalty programs are key drivers of retail media growth in Japan and India.

Successful retail media strategies differ by region. Alibaba’s Tmall, Walmart Connect in the US, and Rakuten in Japan are prime examples of how retail media networks drive growth and engagement by leveraging regional preferences and technological advancements.

Measuring Success: ROI and Campaign Optimization

To ensure success in retail media, brands must track and measure their campaigns’ performance. This involves monitoring key metrics and optimizing campaigns based on data-driven insights.

KPIs for Measuring Success

Return on Investment: ROI Measures campaign profitability by comparing revenue generated against campaign costs.
Conversion Rates: Tracks the percentage of users who take a desired action, such as making a purchase, after interacting with an ad.
Customer Lifetime Value: CLV measures the total value a customer brings to a brand throughout their relationship.
Click-Through Rate (CTR): CTR tracks how often users click on an ad after seeing it.
Cost Per Acquisition: CPA calculates the cost of acquiring a new customer through a specific campaign.

Using tools like Google Analytics, Adobe Analytics, and retail-specific dashboards from Amazon Advertising and Walmart Connect, brands can track these KPIs, monitor performance in real-time, and adjust campaigns to maximise results.

The Future of Retail Media: What’s Next?

Emerging trends like AI-driven personalisation, the integration of social commerce, and the development of seamless omnichannel experiences are shaping the future of retail media. Brands investing in these areas will be well-positioned to capitalise on new opportunities and navigate future challenges.

AI-Driven Personalisation: AI enables hyper-personalisation at scale, analyzing real-time shopper data to deliver highly tailored content and offers.

Integration with Social Commerce: Social platforms like Instagram and TikTok are becoming powerful retail media channels, enabling consumers to discover, engage with, and purchase products directly within these platforms.

Omnichannel Experiences: Retail media networks increasingly facilitate omnichannel experiences to bridge the gap between online and offline shopping, ensuring consistent messaging across all touchpoints.

Future Challenges and Opportunities for Brands

While the future of retail media presents exciting opportunities, brands must navigate the growing complexity of data privacy regulations and manage multiple retail media networks across different regions. As consumers demand more control over their data and regulations like GDPR and CCPA become more stringent, balancing personalisation with privacy will be crucial. Brands investing in AI-driven personalisation, integrating social commerce into their strategies, and creating seamless omnichannel experiences will be well-positioned to thrive in this evolving landscape.

For brands, the key to success in the future of retail media will be leveraging the power of data while respecting privacy. Those who can navigate this balance will set the standard in the next generation of retail media.

By focusing on data-driven insights, regional customisation, and privacy-first approaches, brands can lead the charge in the rapidly evolving retail media landscape.