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Retail companies in today’s world are constantly embracing data and artificial intelligence (AI) to gain a competitive advantage in their respective industry. It is now more important than ever for retailers to understand how first-party data analysis can be transformed into insights on customer behavior to enhance their retail media network. As such, the “data + AI maturity” curve has emerged as a powerful tool to help retailers progress towards predictive analysis and personalized customer experiences.
The Data + AI Maturity Curve Explained
The data + AI maturity curve depicts how a retailer’s data and AI capabilities coordinate with their retail media network’s competitive advantage. The x-axis represents a retailer’s data and AI capabilities, while the y-axis represents the retailer’s competitive advantage. The curve suggests that retailers take small steps towards sophistication to reach predictive analysis, where they can accurately anticipate customer needs and provide customized experiences.
The Three Most Important Milestones
As retailers strive towards predictive analysis through data and AI, there are three crucial milestones they must reach:
Clean, Accepted Data
The first milestone is acquiring a comprehensive view of clean and accepted data across all touchpoints, be it digital or physical, owned or rented. This data is vital for managing yield, measuring campaign performance, and comprehending customer behavior.
Retailers should prioritize metric integrity and data quality while formalizing retail media as a category. It’s essential to count customers uniquely while tracking their journey across physical and digital touchpoints. A risk to both trust and budget growth in the long term arises when customer counts are duplicated to inflate the media network’s value.
Data is streamed to a behavioral data platform (BDP) and stored in a secure cloud-hosted data lake, where data from SaaS systems updates the BDP via a server-to-server connector. The BDP models and enriches the data, creating a single, holistic view of the customer with an event history with thousands of records for each customer.
Contextual Targeting
The second milestone is delivering a targeted message to a surface, i.e., a specific platform or device based on its context. This type of targeting is essential for all other targeting capabilities as it acts as a crucial basis. Retailers need to forecast the inventory of placements available by placement type and location to manage their media network and optimize yield. It also helps determine message relevance and brand safety.
Behavioral Targeting
The third milestone involves behavioral targeting. Retailers need to understand their customer’s behavior and use it to their advantage. Behavioral targeting involves classifying, predicting, and delivering messages based on various contexts. Retailers must consider customer interests and preferences, their purchase history, and other relevant data to deliver effective messages that encourage purchases.
Conclusion
The “data + AI maturity” curve can benefit retailers by helping them move towards predictive analysis and delivering personalized experiences. However, reaching this milestone involves a lot of effort and planning. Retailers need to break down their targets into small steps and work on milestones that will enable them to move forward. By doing so, they can gradually build up their retail media network and create a competitive advantage.
FAQs
What is the data + AI maturity curve?
The data + AI maturity curve represents how a retailer’s data and AI capabilities coordinate with their retail media network’s competitive advantage. The x-axis represents a retailer’s data and AI capabilities, while the y-axis represents the retailer’s competitive advantage.
What are the three milestones retailers must achieve?
The three milestones are clean, accepted data; contextual targeting; and behavioral targeting.
What is contextual targeting?
Contextual targeting involves delivering a message to a surface, i.e., a specific platform or device based on its context. Retailers need to forecast the inventory of placements available by placement type and location to manage their media network and optimize yield. It also helps determine message relevance and brand safety.
What is behavioral targeting?
Behavioral targeting involves using customers’ behavior to classify, predict, and deliver messages based on various contexts. Retailers must consider customer interests and preferences, their purchase history, and other relevant data to deliver effective messages that encourage purchases.
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