Abstract: Purchase Media Metrics (PMM) Platform empowers advertisers to plan their cohort-targeting campaigns in a privacy-safe manner using unique viewer-purchaser graph that connects media views with brand and category purchases. At the same time, the PMM Platform helps publishers/networks identify the best advertisers for their inventories and package and prioritize their media offerings. By further enriching the viewer-purchaser graph with campaign exposure data, the PMM Platform enhances advertisers' campaign measurement and optimization capabilities.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to generate characteristic metrics. An example apparatus includes a characteristics identifier to identify characteristics corresponding to purchase data, a characteristic selector to select one of the characteristics, a likelihood calculator to calculate a likelihood value of a first level of the selected one of the characteristics, and an importance metric calculator to reduce discretionary input of an analyst by calculating an importance metric based on a ratio of (a) the likelihood value of the first level and (b) a maximum likelihood value corresponding to the first level of the selected one of the characteristics.

Abstract: Methods and apparatus to identify retail pricing strategies are disclosed herein. An example apparatus for identifying a pricing strategy employed by a store includes a calculator to calculate a first pricing strategy variable for the store based on sales data of the store. The example apparatus includes an index creator to index the first pricing strategy variable against aggregated data for a plurality of stores to generate a pricing index. The example apparatus includes a pricing strategy identifier to identify a pricing strategy for the store based on the pricing index.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to generate characteristic metrics. An example apparatus includes a characteristics identifier to identify characteristics corresponding to purchase data, a characteristic selector to select one of the characteristics, a likelihood calculator to calculate a likelihood value of a first level of the selected one of the characteristics, and an importance metric calculator to reduce discretionary input of an analyst by calculating an importance metric based on a ratio of (a) the likelihood value of the first level and (b) a maximum likelihood value corresponding to the first level of the selected one of the characteristics.

Abstract: Methods, apparatus, systems and articles of manufacture to identify local trade areas are disclosed. An example method includes selecting, with a processor, census block groups (CBGs) associated with a retailer location, identifying, with the processor, a plurality of stores within the selected CBGs and associated all commodities volume (ACV) values for respective ones of the plurality of stores, calculating, with the processor, similarity index values associated with respective pairs of the plurality of stores, generating, with the processor, local trade areas (LTAs) of subgroups of the plurality of stores based on a comparison of the similarity index values to a similarity threshold value, and when a respective one of the LTAs includes a violation of a releasability criterion, preventing, with the processor, erroneous disclosure of market share information by re-distributing the stores within the respective one of the LTAs to a geographically adjacent LTA.

Abstract: Methods, apparatus, systems and articles of manufacture to identify local trade areas are disclosed. An example method includes selecting, with a processor, census block groups (CBGs) associated with a retailer location, identifying, with the processor, a plurality of stores within the selected CBGs and associated all commodities volume (ACV) values for respective ones of the plurality of stores, calculating, with the processor, similarity index values associated with respective pairs of the plurality of stores, generating, with the processor, local trade areas (LTAs) of subgroups of the plurality of stores based on a comparison of the similarity index values to a similarity threshold value, and when a respective one of the LTAs includes a violation of a releasability criterion, preventing, with the processor, erroneous disclosure of market share information by re-distributing the stores within the respective one of the LTAs to a geographically adjacent LTA.

Abstract: Methods and apparatus to identify retail pricing strategies are disclosed herein. An example apparatus for identifying a pricing strategy employed by a store includes a calculator to calculate a first pricing strategy variable for the store based on sales data of the store. The example apparatus includes an index creator to index the first pricing strategy variable against aggregated data for a plurality of stores to generate a pricing index. The example apparatus includes a pricing strategy identifier to identify a pricing strategy for the store based on the pricing index.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve Bayesian posterior generation efficiency. An example apparatus to improve posterior calculation efficiency includes a logit model engine to generate a logit model associated with prior data, the logit model engine to assign initial logit coefficient values to products of interest for respective segments of interest, a penalty engine to improve posterior calculation efficiency by generating penalty modifiers, the penalty modifiers to balance modification of the initial logit coefficient values without merging the prior data with store conditions, and an analysis engine to calculate posterior output values of the prior data by evaluating the initial logit coefficient values with the penalty modifiers via a maximum likelihood estimation, the posterior output values indicative of modifications to the initial logit coefficient values caused by empirical store data sales activity.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to determine choice probability of new products. An example disclosed apparatus to predict choice probability for a new product includes a log likelihood engine to calculate market level characteristic utilities associated with non-panelist aggregate sales data of market available products, the market available products having a same category type as the new product, a choice probability engine to calculate household level choice probabilities based on household utilities for respective ones of characteristics associated with the category type, the log likelihood engine to anchor the household level choice probabilities to the market level characteristic utilities with a penalty function, and a scoring engine to predict a choice probability for the new product in a household of interest based on the anchored household level choice probabilities.

Abstract: Methods, apparatus, systems and articles of manufacture to identify local trade areas are disclosed. An example method includes selecting, with a processor, census block groups (CBGs) associated with a retailer location, identifying, with the processor, a plurality of stores within the selected CBGs and associated all commodities volume (ACV) values for respective ones of the plurality of stores, calculating, with the processor, similarity index values associated with respective pairs of the plurality of stores, generating, with the processor, local trade areas (LTAs) of subgroups of the plurality of stores based on a comparison of the similarity index values to a similarity threshold value, and when a respective one of the LTAs includes a violation of a releasability criterion, preventing, with the processor, erroneous disclosure of market share information by re-distributing the stores within the respective one of the LTAs to a geographically adjacent LTA.

Abstract: Methods and apparatus are disclosed to select retail store sites. An example method includes generating a list of first descriptor types associated with a plurality of existing store locations, calculating, with a processor, a set of analog principal components factors (PCFs) for corresponding ones of the plurality of existing store locations, calculating a set of candidate PCFs for corresponding ones of a plurality of candidate locations, calculating respective similarity values based on the PCFs associated with respective pairs of the plurality of existing store locations and the plurality of candidate locations, for corresponding ones of the plurality of candidate locations, calculating a sum of second descriptor types associated with the existing store locations based on the respective similarity value, and predicting the performance of the candidate store locations based on a ratio of a sum of second descriptor types and a sum of the similarity values for the corresponding existing store location.

Abstract: Methods and apparatus to monitor in-store media and consumer traffic related to retail environments are disclosed herein. In a disclosed example method to monitor a monitored establishment, a survey is presented to an auditor and a user-provided survey response is received indicative of a condition in a monitored establishment that affects an accuracy of shopper count information provided by the auditor. Instructions are displayed to the auditor to sequentially move to a plurality of predetermined locations in the monitored establishment and collect a shopper count corresponding to each of the predetermined locations. The example method also involves receiving and storing each of the plurality of shopper counts.

Abstract: Methods and apparatus are disclosed to dynamically estimate consumer segment sales with point-of-sale data. An example method includes generating a dataset of observed category panelist trips for a segment of interest, identifying a first signal variable associated with non-panelist data for a time period of interest, calculating a trip likelihood for the segment of interest based on the first signal variable, and estimating a decomposition of purchases by segment based on the trip likelihood and the non-panelist data.