Abstract: Timely projections of product sales for a reporting time period are obtained by combining actual sales data received from reporting stores and estimated sales data for non-reporting stores. The projections are adjusted to account for trailing data, which may be reported after the end of the subject time period.

Abstract: A method and system of predicting market information includes the steps of receiving first data, forecasting further data based on the first data, receiving second data and comparing the further data with the second data, and creating an adjustment factor to account for any difference between the further data and the second data.

Abstract: Systems and methods for product level projections of pharmacy prescriptions within product therapy classes are provided. Average wholesale dollar sales amounts are obtained or estimated at channel/outlet/product-therapy levels. All of the average wholesale dollar sales amounts are converted to prescription data at channel/outlet/product-therapy levels using a correlation function. The correlation function is derived by correlating samples of total prescription data where available with corresponding average wholesale dollar sales amounts.

Abstract: A method and system of predicting market information includes the steps of receiving first data, forecasting further data based on the first data, receiving second data and comparing the further data with the second data, and creating an adjustment factor to account for any difference between the further data and the second data.

Abstract: Timely projections of product sales for a reporting time period are obtained by combining actual sales data received from reporting stores and estimated sales data for non-reporting stores. The projections are adjusted to account for trailing data, which may be reported after the end of the subject time period.

Abstract: Projection factors are used to project product sales data from sample retail outlets to the universe of outlets. Weekly forecasts of market conditions and product demand are generated based on the projected product sales data. The projection factors and the weekly forecasts are updated during the course of the forecasted week, for example, daily, as data on actual product sales during the forecasted week is received.

Abstract: Timely projections of product sales for a reporting time period are obtained by combining actual sales data received from reporting stores and estimated sales data for non-reporting stores. The projections are adjusted to account for trailing data, which may be reported after the end of the subject time period.

Abstract: Systems and methods for product level projections of pharmacy prescriptions within product therapy classes are provided. Average wholesale dollar sales amounts are obtained or estimated at channel/outlet/product-therapy levels. All of the average wholesale dollar sales amounts are converted to prescription data at channel/outlet/product-therapy levels using a correlation function. The correlation function is derived by correlating samples of total prescription data where available with corresponding average wholesale dollar sales amounts.

Abstract: Timely projections of product sales for a reporting time period are obtained by combining actual sales data received from reporting stores and estimated sales data for non-reporting stores. The projections are adjusted to account for trailing data, which may be reported after the end of the subject time period.

Abstract: A method and system of predicting market information includes the steps of receiving first data, forecasting further data based on the first data, receiving second data and comparing the further data with the second data, and creating an adjustment factor to account for any difference between the further data and the second data.

Abstract: Timely projections of product sales for a reporting time period are obtained by combining actual sales data received from reporting stores and estimated sales data for non-reporting stores. The projections are adjusted to account for trailing data, which may be reported after the end of the subject time period.

Abstract: A method for predicting market information for a plurality of pharmaceutical outlets includes the steps of receiving first data representing purchases and sales of at least one pharmaceutical product from at least one pharmaceutical outlet over a time period in the past, calculating the amount of prescriptions that are not reported in a timely manner at a product-level, computing a product-level projection factor for the at least one pharmaceutical product and using the product-level projection factor to estimate the unreported amount of prescriptions.

Abstract: A method and system of predicting market information includes the steps of receiving first data, forecasting further data based on the first data, receiving second data and comparing the further data with the second data, and creating an adjustment factor to account for any difference between the further data and the second data.

Abstract: A method for predicting market information for a plurality of pharmaceutical outlets includes the steps of receiving first data representing purchases and sales of at least one pharmaceutical product from at least one pharmaceutical outlet over a time period in the past, calculating the amount of prescriptions that are not reported in a timely manner at a product-level, computing a product-level projection factor for the at least one pharmaceutical product and using the product-level projection factor to estimate the unreported amount of prescriptions.

Abstract: Timely projections of product sales for a reporting time period are obtained by combining actual sales data received from reporting stores and estimated sales data for non-reporting stores. The projections are adjusted to account for trailing data, which may be reported after the end of the subject time period.

Abstract: A method and system of predicting market information includes the steps of receiving first data, forecasting further data based on the first data, receiving second data and comparing the further data with the second data, and creating an adjustment factor to account for any difference between the further data and the second data.

Abstract: Systems and methods for product level projections of pharmacy prescriptions within product therapy classes are provided. Average wholesale dollar sales amounts are obtained or estimated at channel/outlet/product-therapy levels. All of the average wholesale dollar sales amounts are converted to prescription data at channel/outlet/product-therapy levels using a correlation function. The correlation function is derived by correlating samples of total prescription data where available with corresponding average wholesale dollar sales amounts.

Abstract: Systems and methods are provided for estimating non-sampled store activities by applying projection factors to sampled store activities taking into account restrictions on non-sampled store activities. The systems and methods adjust data projection factors for managed healthcare plans that have restrictions in non-sampled stores to prevent unrestricted activity under these plans at sample stores from being projected into non-sample stores where such projected activity would be restricted. Conventional sampled-to-non-sampled store projection factors leading to restricted plan activities are reallocated to non-restricted plan activities in the non-sample store based on the historical ratio of such activities observed in sample stores.

Abstract: Prescriptions are imputed or allocated to a non-reporting sample outlet in a universe of outlets that generally report prescription data to a database. Prescriptions are imputed or allocated based on the outlet's recent prescription or physician distribution. Prescription allocations to an outlet for a particular product in a product group are proportional to the ratio of the recent prescriptions for that particular product and for the product group.

Abstract: A method for estimating the sales of specialty products includes defining a first product specific universe using wholesale purchasing data to determine a product specific store size for a first group of retail outlets. A second product specific universe is defined using sampled retail sales data to determine a product specific store size for a second group of retail outlets, which were not represented in the first group of outlets. Geo-spatial projection is applied to the first and second product specific universes to determine product specific projection factors for retail outlets in the first and second universe. The product specific projection factors are then applied to sampled retail sales data for the product to estimate the sales of said specialty product in unsampled outlets. Preferably, the retail sales data includes prescriber information and the resulting estimates are attributable to individual prescribers authorizing the specialty product sale.