A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
- accessing electronic interaction data associated with a first user, the electronic interaction data associated with an interaction by the first user with one or more products or services of an entity, the interaction data including itemized list of goods or services;
- anonymizing the electronic interaction data by associating interactions of the first user with hash identifiers such that the first user is anonymized in the electronic interaction data;
- accessing transaction log data for the first user, the transaction log data including historical transactions made by the first user using a transaction instrument, the transaction log data including purchases made by the first user using the transaction instrument with the entity, wherein the transaction log data includes the entity and time of the transaction, but does not include an itemized list of goods or services purchased;
- inferring an increased number of historical purchases for a particular item of the itemized list within the transaction log data by inputting the anonymized electronic interaction data that includes the itemized list of goods or service with the transaction log data that does not include the itemized list of goods or services purchased into a machine learning model, the machine learning model being trained to infer an increased number of historical purchases within the transaction log data based on the anonymized electronic interaction data and the transaction log data;
- accessing geolocation data of the first user that includes historical location data of the first user from a mobile device associated with the first user;
- identifying a merchant location associated with the electronic interaction data;
- determining a number of visitations by the first user to the merchant location based on the accessed geolocation data and the merchant location;
- for each of the visitations by the first user, determine a dwell time of the mobile device within the merchant location, the dwell time indicative of a length of time for the corresponding visitation;
- determining a projection factor for the first user based on (1) the increased number of historical purchases, (2) the number of visitations by the first user to the merchant location, and (3) the dwell times associated with the visitations, the projection factor corresponding to a multiplicative factor corresponding to the inferred increase in the number of historical purchases already made by the first user;
- generating a calibrated electronic interaction dataset by applying the multiplicative factor associated with the projection factor to the particular item in the electronic interaction data, the calibrated electronic interaction dataset including an inferred number of historical purchases absent in the electronic interaction data;
- and
- continuously:
- receiving updated electronic interaction data;
- and
- retraining the machine learning model based on the updated electronic interaction data to continuously reflect updated behavior of the first user.