At-shelf brand strength tracking and decision analytics
View Patent ↗A method and system for analyzing product strength or brand strength by determining shopper decision behavior during a shopping trip. Specifically, shopper behavior can be analyzed to determine whether a shopper's decision to purchase an item occurred at-shelf or pre-shelf. Aggregating decision data across many shoppers over time can then be used to generate analytics regarding the strength of a product or brand. The analysis can then be used to make recommendations to manufacturers or retailers about how to strengthen the product or brand. A deployment of cameras and mobile signal sensors can be utilized to recognize shoppers and track their behavior. Demographics information can also be estimated about the tracked shoppers. The visual and mobile signal trajectories can be fused to form a single shopper trajectory, then associated with Point of Sale (PoS) data. This results in a dataset describing the shopping trip for each tracked shopper.
1. A method for analyzing product strength or brand strength by determining shopper decision behavior during a shopping trip, utilizing at least a camera, at least a mobile signal sensor, and at least a processor for performing the steps of:
a. detecting the presence of a shopper at a location using an At-Door Shopper Detector module,
b. tracking the movements of the shopper throughout the location using at least one camera, at least one mobile signal sensor, and a Multi-modal Shopper Tracker module, wherein the Multi-modal Shopper Tracker module further comprises
i. using a Vision tracker module to obtain a set of vision data from at a camera,
ii. detecting a shopper at a specific time and location,
iii. using a Mobile Tracker module to obtain a set of mobile data for the shopper using a mobile device,
iv. localizing the mobile device using the MAC address using a trilateration based method,
c. integrating a set of data from the Multi-modal Shopper Tracker module using a Multi-modal Shopper Data Associator, which comprises the following steps:
i. detecting the completion of at least one mobile trajectory,
ii. retrieving a set of shopper profile data from the in-store shopper database, wherein the shopper profile data contains at least one vision trajectory,
iii. performing matching between the at least one vision trajectory and the at least one mobile trajectory,
iv. fusing vision trajectories that are associated with the same target at a given time frame using measurement fusion,
v. combining the fused vision trajectories with the mobile trajectory to complete missing segments in the vision trajectories,
d. calculating at least one decision factor using the Shopper Decision Tracker module,
e. determining whether the shopper decision was made at-shelf or pre-shelf, based on the at least one decision factor, using a Decision Determination module, and
f. analyzing the shopper decision results, aggregated across a plurality of shoppers, to derive metrics representing the strength of a product or brand of products.
2. The method of claim 1 , wherein the Shopper Decision Tracker module uses one or more factors from a list comprising time based factors, category based factors, environmental factors, and behavior based factors.
3. The method of claim 2 , wherein the list of time based factors is comprised of time allocation, shopping time, navigation time, and navigational complexity, the list of category based factors is comprised of category size, category orientation, category complexity, fixture type, and share of category space, the list of environmental factors is comprised of frequently changing elements, aisle fixtures, and crowding, and the list of behavior based factors is comprised of stopping frequency, shopper velocity, shopper trajectory, and shopper repeat visits.
4. The method of claim 1 , wherein the Shopper Decision Tracker module utilizes at least one decision factor to populate a decision model used by the Decision Determination module.
5. The method of claim 4 , wherein the decision model is generated by the steps of:
a. performing an association analysis for identifying factors that have a bivariate association with one another using an Association Analysis module,
b. estimating the probability that, given a set of factors, that a particular outcome is present, using a Multiple Logistic Regression module, and
c. scoring the model for goodness of fit, applying necessary transformations based on the scoring, and refining the model using a Model Validation and Optimization module.
6. The method of claim 1 , wherein the derived metrics include:
a. a brand stability quotient comprising a ratio of a percentage of a first brand pre-shelf decisions divided by a percentage of the first brand total decisions, and
b. a category stability quotient comprising a ratio of a percentage of a first category pre-shelf decisions divided by a percentage of the category total decisions.
7. The method of claim 6 , wherein the derived metrics further include:
a. a brand stability index comprising a ratio of the brand stability quotient divided by the category stability quotient, and
b. an at-shelf win rate comprising a ratio of the percentage of the first brand pre-shelf decisions divided by a percentage of at-shelf decisions for the total category.
8. The method of claim 1 , wherein the analyzing further comprises using the derived metrics to generate an At-Shelf Brand Strength Scorecard.
9. The method of claim 8 , further comprising making a recommendation for improving the strength of a product or brand based on an interpretation of results provided by the At-Shelf Brand Strength Scorecard.
10. The method of claim 1 , wherein the steps are repeated for a plurality of shoppers at a single retail location or across multiple retail locations.
11. A system for analyzing product strength or brand strength by determining shopper decision behavior during a shopping trip, utilizing at least a camera, at least a mobile signal sensor, and at least a processor for performing the steps of:
a. detecting the presence of a shopper at a location using an At-Door Shopper Detector module,
b. tracking the movements of the shopper throughout the location using at least one camera, at least one mobile signal sensor, and a Multi-modal Shopper Tracker module, wherein the Multi-modal Shopper Tracker module further comprises
i. using a Vision tracker module to obtain a set of vision data from at a camera,
ii. detecting a shopper at a specific time and location,
iii. using a Mobile Tracker module to obtain a set of mobile data for the shopper using a mobile device,
iv. localizing the mobile device using the MAC address using a trilateration based method,
c. integrating a set of data from the Multi-modal Shopper Tracker module using a Multi-modal Shopper Data Associator which comprises the following steps:
i. detecting the completion of at least one mobile trajectory,
ii. retrieving a set of shopper profile data from the in-store shopper database, wherein the shopper profile data contains at least one vision trajectory,
iii. performing matching between the at least one vision trajectory and the at least one mobile trajectory,
iv. fusing vision trajectories that are associated with the same target at a given time frame using measurement fusion,
v. combining the fused vision trajectories with the mobile trajectory to complete missing segments in the vision trajectories,
d. calculating at least one decision factor using the Shopper Decision Tracker module,
e. determining whether the shopper decision was made at-shelf or pre-shelf, based on the at least one decision factor, using a Decision Determination module, and
f. analyzing the shopper decision results, aggregated across a plurality of shoppers, to derive metrics representing the strength of a product or brand of products.
12. The system of claim 11 , wherein the Shopper Decision Tracker module uses one or more factors from a list comprising time based factors, category based factors, environmental factors, and behavior based factors.
13. The system of claim 12 , wherein the list of time based factors is comprised of time allocation, shopping time, navigation time, and navigational complexity, the list of category based factors is comprised of category size, category orientation, category complexity, fixture type, and share of category space, the list of environmental factors is comprised of frequently changing elements, aisle fixtures, and crowding, and the list of behavior based factors is comprised of stopping frequency, shopper velocity, shopper trajectory, and shopper repeat visits.
14. The system of claim 11 , wherein the Shopper Decision Tracker module utilizes at least one decision factor to populate a decision model used by the Decision Determination module.
15. The system of claim 14 , wherein the decision model is generated by the steps of:
a. performing an association analysis for identifying factors that have a bivariate association with one another using an Association Analysis module,
b. estimating the probability that, given a set of factors, that a particular outcome is present, using a Multiple Logistic Regression module, and
c. scoring the model for goodness of fit, applying necessary transformations based on the scoring, and refining the model using a Model Validation and Optimization module.
16. The system of claim 11 , wherein the derived metrics include:
a. a brand stability quotient comprising a ratio of a percentage of a first brand pre-shelf decisions divided by a percentage of the first brand total decisions, and
b. a category stability quotient comprising a ratio of a percentage of a first category pre-shelf decisions divided by a percentage of the category total decisions.
17. The system of claim 16 , wherein the derived metrics further include:
a. a brand stability index comprising a ratio of the brand stability quotient divided by the category stability quotient, and
b. an at-shelf win rate comprising a ratio of the percentage of the first brand pre-shelf decisions divided by a percentage of at-shelf decisions for the total category.
18. The system of claim 11 , wherein the analyzing further comprises using the derived metrics to generate an At-Shelf Brand Strength Scorecard.
19. The system of claim 18 , further comprising making a recommendation for improving the strength of a product or brand based on an interpretation of results provided by the At-Shelf Brand Strength Scorecard.
20. The system of claim 11 , wherein the steps are repeated for a plurality of shoppers at a single retail location or across multiple retail locations.