Programmatic merchandising system and method for increasing in-store transaction conversions via heuristic advertising
An automated advertising scheduling and distribution process reacts to the effectiveness of sales data. A hosted platform creates location-specific playlists based on key consumer variables that impact buying behavior, and dynamically performs data analytics. Utilizing a programmatic system and machine learning algorithmic methodology, the platform gathers data from the retailer's data warehouse and automatically pulls location-by-location sales data while simultaneously collecting playback data. If sales are not being affected on the particular item that is being promoted, then the platform may be configured to replace that message with a promotional message for another product with a higher likelihood of engagement and conversion. This virtual feedback loop ensures that the platform is optimizing the most effective series of promotional messages for any given location. The content management administrator accordingly delivers relevant advertising/messages to various display screens integrated into fuel pumps, through the store, and to retailer loyalty program applications.
1. A method for increasing in-store transaction conversions via heuristic targeted advertising, comprising:
associating between one or more purchased products or services at one or more client retail locations and an advertised product or service advertised at an exterior product terminal located at a first area of the client retail locations and sold at an interior transaction terminal located at a second area of the client retail locations, wherein the associating between one or more purchased products or services at one or more client retail locations and the advertised product or service is configured to be in part determined by, at a first server remote from the exterior product terminal, heuristically aggregating transactions based upon times following advertisement display on at least one exterior product terminal or the existence of at least one aggregated transaction with at least one sales transaction at the interior transaction terminal within a determined time period;
determining, at the first server, at least one static key consumer variable associated with a client retail location using at least in part the association between one or more purchased products or services at one or more client retail locations and an advertised product or service;
generating, at the first server, a first content set for advertising based on the at least one static key consumer variable;
iteratively refining, at a second server associated with the exterior product terminal, the first content set to create a second content set based on one or more variables selected from the at least one static key consumer variable, at least one dynamic key consumer variable and transactional data from the client retail location, wherein the first content set is received at the second server via an application program interface (API) associated with the first server and the transactional data is extracted via an application program interface (API) associated with a database of transactional data from the interior transaction terminal;
selecting, at the second server, an advertisement set from the second content set to display;
receiving customer-specific transactional data from interior transaction terminals and from exterior product terminals, wherein the customer-specific transactional data includes customer data;
activating a point of sale terminal based on customer selection at an exterior product terminal located at a first area of one of the client retail locations;
generating an advertisement set based on the customer data;
prioritizing advertisements of the advertisement set based on the customer data;
transmitting the advertisement set based on the customer data to one or more display devices linked to the customer activated point of sale terminal;
displaying at least part of the advertisement set based on the customer data on the one or more display devices;
analyzing advertisement performance based on correlations between transactional data and the selected advertisement set; and
adjusting an advertisement selection algorithm to improve and refine the content sets based on subsequent sales transactions and analyzed advertisement performance.
2. The method of claim 1 , further comprising:
probabilistically determining an advertisement's likelihood of generating a sales conversion with respect to static and dynamic key consumer variables;
prioritizing advertisements based upon successive probabilistic determinations; and
weighting time slots based upon the probabilistic determinations and advertisement priorities.
3. The method of claim 1 , further comprising:
including a unique display identifier with each advertisement;
requiring use of the unique display identifier at a point of sale terminal in order to receive a deal associated with the advertisement; and
associating particular advertisements with subsequent sales based on the unique display identifier.
4. The method of claim 1 , further comprising the steps of:
tracking advertisement performance based on correlations between advertisements and subsequent sales;
assigning each displayed advertisement a performance score associated with the selected client retail location;
prioritizing advertisements based upon the performance score;
ordering selected advertisements for display based on their performance scores; and
associating each displayed advertisement performance score with at least one key consumer variable and transactional data.
5. The method of claim 4 , further comprising:
extrapolating performance scores associated with a first client retail location and particular key consumer variables; and
utilizing the performance scores when selecting advertisements for a second client retail location based on at least one shared key consumer variable.
6. The method of claim 1 , further comprising:
identifying a specific customer from transactional data;
generating customer preferences based on transactional data;
updating a customer profile based on transactional data; and
automatically prioritizing relevant advertisements for display based on the customer profile.