As major digital retailers such as Walmart and Amazon continue to grow, retail media networks (RMN) have become a core component of the media mix for enterprise brands. Currently, retail media accounts for around 10% of global ad spending and is projected to surge in the future.
With RMNs, marketers can gain access to a retailer’s first-party data, such as purchases made at the point of sale, and fine-tune their targeting capabilities with personalized messaging. Moreover, accessing first-party data from retailers is a privacy compliant replacement for cookies.
Thus, to maximize the opportunity that RMNs provide, a predictive tool like Neon can aid enterprises in better streamlining their ad investments. As brands race to keep up with the evolution of RMN, the need for tactical and actionable insights is a constant priority.
Customers are buying products and interacting with brands via numerous retail channels. The data they create in this process can unlock promising opportunities for media investment and digital strategy.
For many marketers, acquiring the resources to activate RMN at scale is daunting. Brands that are new to the space or lack resources to activate RMN campaigns are partnering with teams that can provide the necessary assets.
A key challenge with RMNs lies in activating shopper data and allocating campaign budgets since these networks tend to be more expensive than a DSP, IRI or other ad tech entities. Besides, they all have different metrics and KPIs that make direct comparison cumbersome. An automated tool with predictive capabilities can indeed aid businesses in planning their marketing budgets and making informed media choices.