VISIT PREDICTION
Examples of the present disclosure describe systems and methods for visit prediction using machine learning (ML) attribution techniques. In aspects, data relating to users and their venue visits is collected and merged with data relating to various directed information impressions. Features of the merged data are identified for one or more time intervals and assigned values and/or labels. The identified features and corresponding values/labels may be used to train an ML model to provide a visit probability for each user represented in the merged data. Based on the visit probabilities provided by the ML model, the percentage increase (or “lift”) in venue visit rates attributable to the directed information impressions can be accurately estimated
1 . A system comprising:
one or more processors; and
memory coupled to at least one of the one or more processors, the memory comprising computer executable instructions that, when executed by the at least one processor, performs a method comprising:
receiving visit information associated with one or more users;
receiving impression information relating to directed information, wherein the impression information is associated with at least a portion of the one or more users;
merging the visit information and the impression information to create merged data, wherein the merged data comprises a set of features;
grouping the set of features into a set of groups;
assigning one or more feature values for the set of features;
assigning one or more group values for the set of groups; and
training a machine learning model using the merged data.
2 . The system of claim 1 , wherein the visit information comprises at least two of: user identification data, demographic data, or user visit behavior data.
3 . The system of claim 1 , wherein the impression information comprises at least two of:
directed information identification data, directed information exposure data, or user identification data.
4 . The system of claim 1 , wherein creating the merged data comprises matching a portion of the visit information to a portion of the impression information.
5 . The system of claim 1 , wherein grouping the set of features comprises organizing the set of groups according to at least one of user or day.
6 . The system of claim 5 , wherein the set of groups is assigned names according to at least one of the user or the day.
7 . The system of claim 1 , wherein assigning one or more feature values comprises calculating values representing causal impacts of impression information features on user visitation behavior.
8 . The system of claim 1 , wherein assigning one or more group values comprises determining a visit indication value indicating whether a user visited a location on a specified day.
9 . The system of claim 1 , wherein assigning one or more group values comprises determining an exposure indication value indicating whether a user has been exposed to the directed information.
10 . The system of claim 1 , wherein the machine learning model is a binary logistic regression model used to determine a probability the one or more users identified in the merged data visited a location on a specified date.
11 . A system comprising:
one or more processors; and
memory coupled to at least one of the one or more processors, the memory comprising computer executable instructions that, when executed by the at least one processor, performs a method comprising:
receiving visit information associated with one or more users, wherein the one or more users have been exposed to directed information;
receiving impression information relating to the directed information, wherein the impression information is associated with at least a portion of the one or more users;
idenitifying an attribution window associated with the directed information;
providing the visit information and the impression information within the attribution window to a machine learning model to calculate an expected visit rate for the one or more users;
determining an actual visit rate for the one or more users; and
evaluating the expected visit rate against the actual visit rate to calculate a visit lift rate.
12 . The system of claim 11 , wherein the visit information is collected from a contextual awareness engine that records user visitation patterns to locations.
13 . The system of claim 11 , wherein the attribution window defines a date of exposure to the directed information and a number of days subsequent to the date of exposure.
14 . The system of claim 11 , wherein the machine learning model is a binary logistic regression model.
15 . The system of claim 11 , wherein the expected visit rate represents a probability that the one or more users visited one or more locations on one or more days.
16 . The system of claim 11 , wherein the actual visit rate represents a number of visits that actually occurred by users during the attribution window.
17 . The system of claim 11 , wherein the visit lift rate represents a percentage increase in visit rate attributable to the directed information.
18 . The system of claim 11 , wherein calculating the visit lift rate comprises dividing the actual visit rate by the expected visit rate.
19 . The system of claim 11 , wherein the method further comprises:
performing one or more actions responsive to calculating the visit rate lift, wherein the one or more actions include automatically generating a report.
20 . A method comprising:
receiving visit information associated with one or more users, wherein the one or more users have been exposed to directed information;
receiving impression information relating to the directed information, wherein the impression information is associated with at least a portion of the one or more users;
idenitifying an attribution window associated with the directed information;
providing the visit information and the impression information within the attribution window to a machine learning model to calculate an expected visit rate for the one or more users;
determining an actual visit rate for users during the attribution window; and
calculate a visit lift rate using the expected visit rate and the actual visit rate, wherein the visit lift rate represents an increase in visit rate attributable to exposure to the directed information.