Cohort Analysis
Traditionally, marketing teams spend money on subjective topics and strategies causing the outcome of marketing campaigns to be rarely entirely predictable or guaranteed. However, it is possible to provide better tools and a more accurate understanding of the consumers and their tastes.
The different losses possibly used for those neural networks, as well as the type of data feedback (implicit or explicit), impact the nature of those embeddings and the information that they contain. Hence, the cohort or similarities between two embeddings differ from one use case to the other and the cohorts or clusters observed.
The embeddings generated with Crossing Minds algorithms are a combination of content extraction, semantic graph embedding, and most importantly deep collaborative filtering. This approach allows embeddings to contain non-linearly computable relationships and information extracted from a set of user-item feedback.
Because of our improved embeddings, marketing teams can perform segmentation and audience targeting which provides a more accurate idea of the outcome or necessary strategy adjustments regarding specific campaigns, resulting in better customer understanding, better marketing insights and reduced media costs.
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