Abstract: Methods, systems, and apparatus for calibrating data using relaxed benchmarks constraints are described. An example apparatus for generating a unique solution when calibrating data via a calibration model having relaxed benchmark constraints includes processor circuitry to execute computer-readable instructions. The computer-readable instructions are to execute the calibration model based on matrix data, a target loss function, a weight loss function, and a budget parameter. The computer-readable instructions are to determine calibrated weights resulting from execution of the calibration model. The computer-readable instructions are to incorporate a stability parameter into the calibration model in response to determining that the calibrated weights do not provide a unique solution for the executed calibration model. The stability parameter is to reduce an influence of the budget parameter on the calibration model to enable the generation of a unique solution.

Abstract: Methods, systems, and apparatus for calibrating data using relaxed benchmarks constraints are described. An example apparatus for generating a unique solution when calibrating data via a calibration model having relaxed benchmark constraints includes a calibration engine to execute the calibration model based on a target loss function, a weight loss function, and a budget parameter. The example apparatus further includes a calibrated weights determiner to determine calibrated weights resulting from execution of the calibration model. The example apparatus further includes a calibration model validator to incorporate a stability parameter into the calibration model in response to determining that the calibrated weights do not provide a unique solution for the executed calibration model. The stability parameter is to reduce an influence of the budget parameter on the calibration model to enable the generation of a unique solution.

Abstract: Methods, systems, and apparatus for calibrating data using relaxed benchmarks constraints are described. An example apparatus for generating a unique solution when calibrating data via a calibration model having relaxed benchmark constraints includes a calibration engine to execute the calibration model based on a target loss function, a weight loss function, and a budget parameter. The example apparatus further includes a calibrated weights determiner to determine calibrated weights resulting from execution of the calibration model. The example apparatus further includes a calibration model validator to incorporate a stability parameter into the calibration model in response to determining that the calibrated weights do not provide a unique solution for the executed calibration model. The stability parameter is to reduce an influence of the budget parameter on the calibration model to enable the generation of a unique solution.

Abstract: Methods, systems and apparatus for calibrating data using relaxed benchmarks constraints are described. An example apparatus for generating a unique solution when calibrating data via a calibration model having relaxed benchmark constraints includes a calibration engine to execute the calibration model based on a target loss function, a weight loss function, and a budget parameter. The example apparatus further includes a calibrated weights determiner to determine calibrated weights resulting from execution of the calibration model. The example apparatus further includes a calibration model validator to incorporate a stability parameter into the calibration model in response to determining that the calibrated weights do not provide a unique solution for the executed calibration model. The stability parameter is to reduce an influence of the budget parameter on the calibration model to enable the generation of a unique solution.

Abstract: Methods, systems, apparatus and articles of manufacture to generate projection factors are disclosed. An apparatus to reduce panel imbalance errors includes a data analyzer to identify a retailer in a geographic region indicative of shopping bias. The data analyzer also is to identify households of interest in the geographic region having combined panel data, the combined panel data representing core panelist data and auxiliary data. The apparatus also includes a modeling engine to calculate potential spending of each household at one or more stores of the retailer. The potential spending is based on observed spending. The apparatus also includes a projection engine to reduce panel imbalance errors by calculating projection weights for the combined panel based on (a) the potential spending at the one or more stores and (b) social or demographic representation data of the combined panel.

Abstract: Methods and apparatus are disclosed to align panelist data with retailer sales data. An example method includes calculating a panelist reporting period for a panelist dataset having a first number of occasions, the panelist dataset having a household resolution, applying trip weights to a retailer dataset, the retailer dataset including a second number of occasions during a market research reporting period, the retailer dataset having a trip resolution, and minimizing a gap between the household resolution and the trip resolution.