Predictive segmentation of customers

One or more non-transitory computer storage media storing instructions that are operable, when executed by one or more computers, to cause the one or more computers to perform operations comprising:

• accessing a customer record for each of a plurality of customers, wherein each customer record comprises a plurality of types of data, and wherein the plurality of types of data comprise at least resource consumption data;
• determining, for each of the plurality of customers and based on the customer records, a customer adoption status;
• generating an initial pattern set by traversing a decision tree, wherein each node of the decision tree defines a threshold data value or a range of data values for one of the plurality of types of data in the customer records;
• pruning the initial pattern set by removing patterns that are either ineffective or duplicative;
• assigning remaining patterns that have not been removed by the pruning to segments using an algorithm that iteratively maximizes a minimum effectiveness of the segments, wherein the effectiveness of a segment is a measure of a number of customer records that match the patterns assigned to the segment that have a positive adoption status, and wherein a customer record that matches multiple of the remaining patterns is assigned a weight that indicates a fractional coverage of each of the multiple patterns;
• and
• using the segments for targeted customer engagement, by selectively formulating and transmitting advertisements to only the customers matching the patterns of a corresponding segment, in order to increase marketing effectiveness.

A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computer to perform operations comprising:

• accessing a customer record for each of a plurality of customers, wherein each customer record comprises a plurality of types of data, and wherein the plurality of types of data comprise at least resource consumption data;
• determining, for each of the plurality of customers and based on the customer records, a customer adoption status;
• generating an initial pattern set by traversing a decision tree, wherein each node of the decision tree defines a threshold data value or a range of data values for one of the plurality of types of data in the customer records;
• pruning the initial pattern set by removing patterns that are either ineffective or duplicative;
• assigning remaining patterns that have not been removed by the pruning to segments using an algorithm that iteratively maximizes a minimum effectiveness of the segments, wherein the effectiveness of a segment is a measure of a number of customer records that match the patterns assigned to the segment that have a positive adoption status, and wherein a customer record that matches multiple of the remaining patterns is assigned a weight that indicates a fractional coverage of each of the multiple patterns;
• and
• using the segments for targeted customer engagement, by selectively formulating and transmitting advertisements to only the customers matching the patterns of a corresponding segment, in order to increase marketing effectiveness.