Challenges that brands face in obtaining and interpreting data from different retail media networks
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Limited and Biased Data:
Each retail platform operates as a “walled garden,” holding onto its data and sharing only selective information. For instance, if a customer is identified as a “new buyer” on Amazon, it doesn’t mean they’re new to the product or brand – they may just be new to Amazon. This makes the data obtained from each platform potentially biased and not representative of the overall consumer behavior.
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Inability to Connect Data Across Platforms:
There’s no current method to seamlessly connect or integrate data from various platforms. As a result, brands can’t develop a complete understanding of their customers’ multi-channel behavior.
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Difficulty in Identifying Unique Buyers:
As customers aren’t strictly loyal to one platform, they may appear as buyers on several platforms. This makes it hard for brands to differentiate between truly new customers and existing ones who are just shopping on a new platform.
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Inefficient Targeting and Frequency Control:
Without a unified view of customer behavior across platforms, brands may end up targeting the same customer multiple times across different channels. This leads to inefficient use of marketing budget and could also lead to audience fatigue.
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Difficulty in Avoiding Audience Overlap
If brands use multiple platforms with the same target audiences, they might be over-targeting the same individuals, resulting in a potential waste of resources.
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Struggle to Find Incremental Value:
Brands find it challenging to identify platforms that will help them reach new customers who aren’t available on their current platforms.
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Testing and Iterating Strategies:
Given these challenges, brands must experiment with different strategies and continually iterate based on their results. This process can be time-consuming and complex, with no guaranteed formula for success.
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Balancing Art and Science in Advertising:
Brands need to master both the ‘science’ of leveraging data for modeled audiences, and the ‘art’ of understanding the right creative context for their advertising. This requires an understanding of both data and human behavior, which can be tricky.