Systems and methods relating to predictive analytics using multidimensional event representation in customer journeys

A system comprising:

• a processor;
• and
• a memory storing instructions which, when executed by the processor, cause the processor to perform the steps of:
• generating, via a training data process, training data samples from respective journey data samples, each of the journey data samples comprising a customer journey as represented by data describing:
• a sequence of events;
• for each of the events, values associated with respective event attributes of a list of event attributes;
• and
• a journey outcome;
• wherein the training data process comprises generating a vector embedding for each of the events included within a given one of the journey data samples that captures the value for each of the event attributes by:
• dividing the event attributes of the list of event attributes into a low cardinality group and a high cardinality group, wherein the dividing is done according to whether the values included within the training data samples for a given event attribute has a cardinality above or below a predefined cardinality threshold;
• for each of the low cardinality groups, categorically encoding the values included within the low cardinality group according to a total number of unique values appearing therein;
• for each of the high cardinality groups:
• clustering the values included within the high cardinality group to create a plurality of cluster groups;
• and
• categorically encoding the values included within the high cardinality group according to the plurality of cluster groups in which the value resides;
• deeming that each of the training data samples includes:
• a sequence of the vector embeddings generated from the sequence of events included in an associated one of the journey samples;
• and
• the journey outcome of the associated one of the journey samples;
• training a machine learning model using the generated training data samples, wherein:
• an input of the machine learning model, for each training data sample, comprises the sequence of the vector embeddings;
• and
• an output of the machine learning model, for each training data sample, comprises the associated journey outcome.