Methods and apparatus to monitor in-store media and consumer traffic related to retail environments

An apparatus, comprising:

• a traffic count calibrator to generate a prediction model comprising a dependent canonical weight coefficient, an independent canonical weight coefficient, and a regression coefficient based on in-person-based shopper count data and sensor-based shopper count data collected in a first establishment to predict a shopper traffic count in a second establishment in which shopper counts are not collected, the in-person-based shopper count data collected by a person located in the first establishment, and the sensor-based shopper count data collected using an electronic detector in the first establishment,
• the dependent canonical weight coefficient to transform shopper count data to generate a shopper traffic variate,
• the independent canonical weight coefficient to transform sales data to generate a sales variate, and
• the regression coefficient to reduce an amount of error in a best-fit correlation between the shopper traffic variate and the sales variate;
• a traffic count generator to predict the shopper traffic count for the second establishment based on the prediction model and sales data of the second establishment;
• a database to store information media data indicative of one or more marketing campaigns;
• and
• a processor to determine an influential effect of the one or more marketing campaigns on the predicted shopper traffic count in the first establishment.

A tangible machine accessible memory comprising instructions stored thereon that, when executed cause a machine to at least:

• generate a dependent canonical weight coefficient, an independent canonical weight coefficient, and a regression coefficient based on in-person-based shopper count data collected by a person located in a second establishment, and sensor-based shopper count data collected using an electronic detector in the second establishment,
• the dependent canonical weight coefficient to transform shopper count data to generate a shopper traffic variate,
• the independent canonical weight coefficient to transform sales data to generate a sales variate, and
• the regression coefficient to reduce an amount of error in a best-fit correlation between the shopper traffic variate and the sales variate;
• use sales data of a first establishment in which shopper counts are not collected to predict a shopper traffic count based on the dependent canonical weight coefficient, the independent canonical weight coefficient, and the regression coefficient;
• receive information media data indicative of one or more marketing campaigns;
• and
• determine an influential effect of the one or more marketing campaigns on the predicted shopper traffic count in the first establishment.