To navigate today's digital landscape, marketers must deliver tangible business results amidst heightened competition and an increasingly complex data privacy landscape. This requires a deep understanding of advertising data, the utilization of first-party data, optimized use of marketing platforms and identification of growth opportunities. And just as marketers are looking to understand how AI and machine learning fit into their digital advertising and data strategies, it’s no surprise that Google has innovated a game-changing solution that leverages machine learning to help optimize the already complicated consumer journey.
Modeled Value-Based Bidding (mVBB) enables precise audience targeting and media optimization through highly customized machine learning models. Relying primarily on advertisers’ first-party data, mVBB derives more value from traditional value-based bid strategies by drawing insights for bid optimizations in real time.
Modeled Value-Based Bidding addresses these challenges for marketers:
- Third-party cookie deprecation and tightening privacy regulations pose significant headwinds for brands looking to connect with consumers.
- With first-party data sources and data volumes growing at breakneck speeds, many marketers are overwhelmed by managing data manually.
- Companies that have large data sets can’t manage manual bid strategies with one person or even a team.
- More and more advertisers are looking to understand how AI and machine learning can fit into their digital advertising and data strategies to help drive efficiencies.
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