Retail media targeting the AI ​​maturity curve

If retail As the industry becomes increasingly reliant and increasingly focused on data and artificial intelligence (AI), it is essential that retailers understand exactly how to turn first-party data analytics into insights into customer behavior – and in turn a tangible competitive advantage.

To that end, consider the chart below, also known as the “Data & AI Maturity Curve”.

The data + AI maturity curve

The data + AI maturity curve. Image Credits: Zitcha/Databricks

This is a simplified representation of how a retailer’s data and AI capabilities (mapped on the x-axis) directly correlate with the competitive advantage of its retail media network (mapped on the y-axis). An overall strategic approach that follows this curve will see retailers take incremental steps toward sophistication, closer and closer to the vaunted “predictive analytics” that will enable them to anticipate customer needs and deliver finely tuned, personalized experiences to offer.

However, this is all much easier said than done, and some steps are more important than others when it comes to intelligent targeting. Let’s take a look at the three major milestones on the road to predictive analytics in the retail media context.

Clean, accepted data

The “rise” to this curve for any retailer seeking to harness the power of data and AI begins with a complete view of clean and accepted data across all customer interactions and media placements, whether physical or digital, owned or leased. This data is critical to understanding the opportunity, managing revenue, and accurately measuring campaign performance.

As technology formalizes retail media as a category, the opportunity to be at the forefront of metric integrity and data quality is significant. Understanding the unique number of customers along the journey across physical and digital touchpoints is also critical, as duplicating the number of customers to inflate the value of the media network risks both trust and long-term budget growth.

Let’s take a look at the three major milestones on the road to predictive analytics in the retail media context.

Data is ideally streamed to a behavioral data platform (BDP) and stored in a secure, cloud-hosted data lake. Data from SaaS systems updates the BDP through a server-to-server connector. Data is then modeled and enriched by the BDP, where every customer interaction is unified into one holistic view of the customer.

This produces a single profile with an event history containing thousands of records for each customer. While this is certainly a critical step, this is really ground floor when it comes to media targeting – once these foundations are in place, maturity can begin to build.


Predictability/complexity. Image Credits: Zitcha/Snowplow

Context targeting

The first level of true media targeting is delivering a message to a surface – a specific platform or device that an audience faces – based on context. This is the most fundamental form of targeting and a critical foundation for all other targeting capabilities. The role of data at this stage is to predict the inventory of available placements by placement type and location, which is essential for retailers to manage their media network and optimize revenue. Message relevance and brand safety also depend on this capability.

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