Promotion processing system for identifying store location for an aisle violator sign and related methods

A promotion generation server comprising:

• a processor and an associated memory configured to
• obtain purchased product identifiers collected from a point-of-sale (POS) terminal at a store from products being purchased at the store,
• determine a store-transaction count based upon the collected purchased product identifiers,
• for each purchased product identifier, map the corresponding product to an aisle location within the store,
• determine a corresponding aisle-transaction count for the aisle location corresponding to each product based upon the mapped aisle locations,
• determine shopper paths of travel within the store based upon the store-transaction count and the aisle-transaction counts,
• identify at least one location within the store for an aisle violator sign for a promoted product located in one of the aisle locations based upon the shopper paths of travel and the aisle-transaction counts, the aisle violator sign comprising text and at least one machine-readable image,
• generate a digital promotion for the promoted product,
• cooperate with a mobile wireless communications device associated with a given user to operate a messenger bot implemented in the form of a two-way dialog and based upon the machine-readable image, and communicate the digital promotion to the given user via the messenger bot within the two-way dialog, the messenger bot being operated based upon artificial intelligence (AI) such that the messenger bot responds to input as if the two-way dialog were between two humans instead of a human and a machine,
• cooperate with the POS terminal to track redemption of the digital promotion, and
• use machine learning to learn combinations of text and machine-readable images that increase interest in the promoted product by accepting, as input to a machine learning algorithm, the tracked redemption, the aisle-transaction counts, the store transaction count, and the text and at least one machine-readable image of the aisle violator sign, and updating, as output from the machine learning algorithm, the text and the at least one machine-readable image.

A method of generating a promotion comprising:

• using a promotion generation server to
• obtain purchased product identifiers collected from a point-of-sale (POS) terminal at a store from products being purchased at the store,
• determine a store-transaction count based upon the collected purchased product identifiers,
• for each purchased product identifier, map the corresponding product to an aisle location within the store,
• determine a corresponding aisle-transaction count for the aisle location corresponding to each product based upon the mapped aisle locations,
• determine shopper paths of travel within the store based upon the store-transaction count and the aisle-transaction counts,
• identify at least one location within the store for an aisle violator sign for a promoted product located in one of the aisle locations based upon the shopper paths of travel and the aisle-transaction counts, the aisle violator sign comprising text and at least one machine-readable image,
• generate a digital promotion for the promoted product,
• cooperate with a mobile wireless communications device associated with a given user to operate a messenger bot implemented in the form of a two-way dialog and based upon the machine-readable image, and communicate the digital promotion to the given user via the messenger bot within the two-way dialog, the messenger bot being operated based upon artificial intelligence (AI) such that the messenger bot responds to input as if the two-way dialog were between two humans instead of a human and a machine,
• cooperate with the POS terminal to track redemption of the digital promotion, and
• use machine learning to learn combinations of text and machine-readable images that increase interest in the promoted product by accepting, as input to a machine learning algorithm, the tracked redemption, the aisle-transaction counts, the store transaction count, and the text and at least one machine-readable image of the aisle violator sign, and updating, as output from the machine learning algorithm, the text and the at least one machine-readable image.

A non-transitory computer readable medium for generating a promotion, the non-transitory computer readable medium comprising computer executable instructions that when executed by a processor of a promotion generation server, cause the processor to perform operations comprising:

• obtaining purchased product identifiers collected from a point-of-sale (POS) terminal at a store from products being purchased at the store;
• determining a store-transaction count based upon the collected purchased product identifiers;
• for each purchased product identifier, mapping the corresponding product to an aisle location within the store;
• determining a corresponding aisle-transaction count for the aisle location corresponding to each product based upon the mapped aisle locations;
• determining shopper paths of travel within the store based upon the store-transaction count and the aisle-transaction counts;
• identifying at least one location within the store for an aisle violator sign for a promoted product located in one of the aisle locations based upon the shopper paths of travel and the aisle-transaction counts, the aisle violator sign comprising text and at least one machine-readable image;
• generating a digital promotion for the promoted product;
• cooperating with a mobile wireless communications device to operate a messenger bot implemented in the form of a two-way dialog and based upon the machine-readable image, and communicate the digital promotion via the messenger bot within the two-way dialog, the messenger bot being operated based upon artificial intelligence (AI) such that the messenger bot responds to input as if the two-way dialog were between two humans instead of a human and a machine;
• cooperating with the POS terminal to track redemption of the digital promotion;
• and
• using machine learning to learn combinations of text and machine-readable images that increase interest in the promoted product by accepting, as input to a machine learning algorithm, the tracked redemption, the aisle-transaction counts, the store transaction count, and the text and at least one machine-readable image of the aisle violator sign, and updating, as output from the machine learning algorithm, the text and the at least one machine-readable image.