Like retail As the industry becomes increasingly reliant and focused on data and artificial intelligence (AI), it is critical that retailers clearly understand how first-party data analytics can be crystallized into insights into customer behavior and, in turn, tangible competitive advantage. advantages.
To that end, consider the chart below, called The Data and AI Maturity Curve.
The data + AI maturity curve. Image credits: Zitcha/Databricks
This is a simplified view of how a retailer’s data and AI capabilities (plotted on the x-axis) directly correlate to the competitive advantage of its retail media network (plotted on the y-axis). A holistic strategic approach that follows this curve will allow retailers to move toward incremental steps of improvement, moving ever closer to the vaunted “predictive analytics” that will enable them to anticipate customer needs and deliver refined, personalized experiences.
However, all of this is much easier said than done, and some steps are more important than others when it comes to smart targeting. Let’s look at three important points on the road to predictive analytics in the context of retail media.
Clean, accepted data
The “tip” of this curve for retailers looking to harness the power of data and AI starts with a holistic view of clean and accepted data for all customer interactions and media placements, whether physical or digital, owned or leased. This data is critical to understanding the opportunity, managing profitability, and accurately measuring campaign performance.
As technology formalizes retail media as a category, the opportunity to lead on metric integrity and data quality is significant. Understanding unique customer numbers across physical and digital touchpoints is also important, as duplicating customer numbers to increase media network value is a risk to both trust and long-term budget growth.
Let’s look at three important points on the road to predictive analytics in the context of retail media.
The data is ideally fed into a behavioral data platform (BDP) and stored in a secure, cloud-based data lake. Data from SaaS systems updates BDP via a server-to-server connector. The data is then modeled and enriched by BDP, where each customer interaction is combined into one holistic view of the customer.
This provides a single profile with an event history of thousands of records per customer. While certainly an important step, this is really the ground floor when it comes to media targeting. once this foundation is established, maturity can begin.
Predictability/complexity. Image credits: Zitcha/Snowplow
Contextual targeting
The first level of true media targeting capability is delivering a message to the surface—the specific platform or device facing the target audience—based on its context. This is the most basic form of targeting and a crucial foundation for all other targeting capabilities. The role of data at this stage is to predict placement reach by placement type and location, which is critical for retailers to manage their media network and optimize profitability. Message relevance and brand safety also depend on this capability.