Machine learning segmentation methods and systems

A system comprising:

• a processor;
• and
• a memory storing instructions that, when executed by the processor, configure the system to:
• receive, by a segmentation engine, data associated with each item of a plurality of items, each item associated with one or more attributes;
• engineer, by the segmentation engine, features associated with one or more signals, the one or more signals comprising either:
• i) one or more internal signals associated with the data;
• or ii) one or more internal signals associated with the data and one or more external signals;
• select, by the segmentation engine, a set of the engineered features;
• train, by the segmentation engine, a plurality of cluster-based machine learning models on the set of the engineered features;
• generate, by the segmentation engine, a plurality of segmentations of the plurality of items;
• select, by the segmentation engine, a first segmentation of the plurality of segmentations based on the one or more cluster-based metrics;
• visualize, by a visual user interface, the first segmentation of the plurality of segmentations;
• and
• amend, in response to user input received via the visual interface, one or more segments within the first segmentation of the plurality of segmentations;
• wherein amending comprises:
• retraining the plurality of cluster-based machine learning models;
• and
• selecting, by the segmentation engine, a second segmentation of the plurality of amended segmentations based on one or more cluster-based metrics.

A non-transitory computer-readable storage medium for segmenting a plurality of items into a plurality of segments, each segment consisting a subset of the plurality of items, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:

• receive, by a segmentation engine, data associated with each item of the plurality of items, each item associated with one or more attributes;
• engineer, by the segmentation engine, features associated with one or more signals, the one or more signals comprising either:
• i) one or more internal signals associated with the data;
• or ii) one or more internal signals associated with the data and one or more external signals;
• select, by the segmentation engine, a set of the engineered features;
• train, by the segmentation engine, a plurality of cluster-based machine learning models on the set of the engineered features;
• generate, by the segmentation engine, a plurality of segmentations of the plurality of items;
• select, by the segmentation engine, a first segmentation of the plurality of segmentations based on the one or more cluster-based metrics;
• visualize, by a visual user interface, the first segmentation of the plurality of segmentations;
• and
• amend, in response to user input received via the visual interface, one or more segments within the first segmentation;
• wherein amending comprises:
• retraining the plurality of cluster-based machine learning models;
• and
• selecting, by the segmentation engine, a second segmentation of the plurality of amended segmentations based on one or more cluster-based metrics.