Machine Learning Embedding
If you don't start to focus on the uniqueness of your consumers and their taste, your competitor will. And then you'll have no consumer left to focus on.
Understanding your consumer like you never have.
Crossing Minds is the first and only company in the world to provide a full encoding of consumer’s behavior and taste, without ever putting their privacy at risk.
We are able to build a “digital DNA” through the embedding of information gathered through cross-platforms by creating an anonymous mathematical representation of a consumer that contains all their tastes and behaviors. This gives any company an obvious competitive advantage and can further be leveraged for numerous predictions and analyses such as recommendation engines, churn prediction and predictive lead scoring.
How to leverage Machine Learning Embeddings
When Machine learning and embeddings are combined it permits us the ability to more accurately determine which data qualifies as features. Correspondingly, Machine Learning Embedding serves three primary purposes:
It performs unsupervised algorithms such as finding nearest neighbors or clustering in the embedding space. For instance, embeddings automatically discover clusters of products or group consumers into cohorts.
It serves as input to a machine learning model for a supervised task, such as classification or recommendation. These can be used to personalize an online visitor experience based on their interests.
It supports the visualization of concepts and relations between categories. This is helpful to find hidden correlations between seemingly unrelated products or consumers.