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Every business is unique in many ways. Sources, type, use cases to name a few. In our approach, Advertisers & Publishers, for a stronger revenue growth & control over processes, need to build data-first capabilities, ex: single view of the customer across martech & adtech data. Given challenges of forthcoming cookie-less world, having a sound Data Strategy is even more important as third party DMPs become unviable. Formulating an apt data strategy is a complex process. It encompasses, aligning data sources, insights and activation channels. Here is a high-level Data Strategy formulation approach.
Most of the campaign goals today are AI /ML driven in adtech. ai-ml is as good as the data it gets. A good data strategy makes all the difference in output of ai-ml. Some of the techniques used are Feature Engineering, Clustering, Classifications among others. You can consider the tools to solve problems of automation & efficiency for all types of campaign goals such as branding, cpm, cpc, cpa & kpi.
Data Strategy constitutes of defining and attaining a goal that is data-driven. Typically a data strategy formulation starts from the below three tangents:
We work with out-of-the box data strategy templates for rapid deployment. This provides a baseline for the marketer. We also go out to understand if a new data strategy is required.
