February 21, 2024

as retail As the industry increasingly relies on and focuses on data and artificial intelligence (AI), retailers must understand exactly how first-party data analytics translate into insights into customer behavior that can lead to tangible competitive advantage.

To do this, consider the graph below, called the Data and AI Maturity Curve.

Data + AI Maturity Curve

Data + AI maturity curve. Image credits: Zitcha/data block

This is a simplified view of how a retailer’s data and AI capabilities (plotted on the x-axis) directly relate to its retail media network’s competitive advantage (plotted on the y-axis). A general strategic approach to following this curve will see retailers gradually mature, moving closer and closer to the vaunted “predictive analytics,” which will allow them to anticipate customer needs and deliver fine-tuned, personalized experiences.

However, all this is easier said than done, and some steps are more important than others when it comes to smart positioning. Let’s look at three of the most important milestones on the road to predictive analytics in retail media environments.

clean, acceptable data

For any retailer looking to harness the power of data and artificial intelligence, the “on-ramp” to this curve starts with a clean and A complete view of the data received. This data is critical to understanding opportunities, managing benefits and accurately measuring campaign performance.

As technology formalizes retail media as a category, the opportunity to lead in metrics completeness and data quality is significant. It is also important to understand the unique number of customers throughout the journey, across physical and digital touchpoints, as duplicating customer numbers to inflate the value of a media network is a risk to trust and budget growth in the long run.

Let’s look at three of the most important milestones on the road to predictive analytics in retail media environments.

Ideally, data is streamed to a Behavioral Data Platform (BDP) and stored in a secure cloud-hosted data lake. Data from the SaaS system updates the BDP through server-to-server connectors. The data is then modeled and enriched by BDP, where every customer interaction is unified into a single, holistic view of the customer.

This provides each customer with a single profile with an event history containing thousands of records. While this is undoubtedly a critical step, it is really the foundation of media positioning – and once this foundation is established, maturity can begin to build.


Predictability/complexity. Image credits: Zitcha/Snowplow

contextual targeting

The first level of true media targeting functionality is delivering the message to the surface according to its context – the specific platform or device for the target audience. This is the most basic form of aiming and an important foundation for all other aiming abilities. The role of data at this stage is to predict the inventory of available placements based on placement type and location, which is key for retailers to manage their media network and optimize revenue. Messaging relevance and brand safety also depend on this feature.