Inferring consumer affinities based on shopping behaviors with unsupervised machine learning models

A non-transitory, machine-readable media storing instructions that when executed by one or more computers effectuate operations comprising:

• obtaining, with one or more computers, a first set of more than 10,000 consumer-behavior records, each consumer-behavior record indicating, for a respective consumer, at least two businesses patronized by the respective consumer;
• converting, with one or more computers, the first set of consumer-behavior records into respective consumer-behavior vectors in a first set of consumer-behavior vectors;
• determining, with one or more computers, more than 5 psychographic segments of consumers by training an unsupervised machine learning model with the first set of consumer-behavior vectors, wherein the first set of consumer-behavior vectors are not pre-labeled as members of any of the psychographic segments before determining the psychographic segments;
• obtaining, with one or more computers, a second set of consumer-behavior records after determining the psychographic segments of consumers;
• converting, with one or more computers, the second set of consumer-behavior records into respective consumer-behavior vectors in a second set of consumer-behavior vectors;
• classifying, with one or more computers, the second set of consumer-behavior vectors as each belonging to at least a respective one of psychographic segments with the trained machine learning model;
• determining, with one or more computers, based on the classification, for at least some of the second set of consumer behavior records, a property associated with a corresponding one of the psychographic segments and not associated with the respective consumer;
• and
• predicting, with one or more computers, based on the classification, for each of the second set of consumer behavior records, a likelihood of the respective consumer engaging in behavior associated with a corresponding one of the psychographic segments, wherein predicting a likelihood of the respective consumer engaging in behavior associated with a corresponding one of the psychographic segments comprises predicting a likelihood to patronize a retail store patronized by others in the same segment.

A non-transitory, machine-readable media storing instructions that when executed by one or more computers effectuate operations comprising:

• obtaining, with one or more computers, a first set of more than 10,000 consumer-behavior records, each consumer-behavior record indicating, for a respective consumer, at least two businesses patronized by the respective consumer;
• converting, with one or more computers, the first set of consumer-behavior records into respective consumer-behavior vectors in a first set of consumer-behavior vectors;
• determining, with one or more computers, more than 5 psychographic segments of consumers by training an unsupervised machine learning model with the first set of consumer-behavior vectors, wherein the first set of consumer-behavior vectors are not pre-labeled as members of any of the psychographic segments before determining the psychographic segments;
• obtaining, with one or more computers, a second set of consumer-behavior records after determining the psychographic segments of consumers;
• converting, with one or more computers, the second set of consumer-behavior records into respective consumer-behavior vectors in a second set of consumer-behavior vectors;
• classifying, with one or more computers, the second set of consumer-behavior vectors as each belonging to at least a respective one of psychographic segments with the trained machine learning model;
• determining, with one or more computers, based on the classification, for at least some of the second set of consumer behavior records, a property associated with a corresponding one of the psychographic segments and not associated with the respective consumer;
• and
• predicting, with one or more computers, based on the classification, for each of the second set of consumer behavior records, a likelihood of the respective consumer engaging in behavior associated with a corresponding one of the psychographic segments, wherein predicting a likelihood of the respective consumer engaging in behavior associated with a corresponding one of the psychographic segments comprises predicting a likelihood to transition from patronizing one plurality of retail businesses to patronizing another plurality of retail businesses transitioned to by others in the same segment.