OUr technology

Machine Learning Embedding

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. 

What is an Embedding

An embedding is a low-dimensional vector of continuous numbers learned to represent relevant information in a compact and convenient form. Embeddings are designed to reduce the dimensionality of both categorical and continuous variables and meaningfully represent categories or features in the transformed space. 

Embedding is the future when it comes to reducing the dimensionality of categorical and continuous variables. The challenge has always been deciding which clusters, classifications, and actions are significant and turning this data into features.

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:

Behavorial Clustering

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.

Personalization

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. 

Taste Visualization

It supports the visualization of concepts and relations between categories. This is helpful to find hidden correlations between seemingly unrelated products or consumers.