Sample store forecasting process and system

• US 20060206365A1
• Filed: 12/02/2005
• Published: 09/14/2006
• Est. Priority Date: 01/22/2005
• Status: Active Grant

• Current Assignee: IQVIA, Inc. (IQVIA Holdings, Inc.)
• Original Assignee: IMS Software Services Ltd. (IQVIA Holdings, Inc.)
• Inventors: Copeland, Kennon, Wynne, Brian, Boardman, Chris
• Granted Patent: US 8,744,897 B2
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients

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.

Claims

1. A method of predicting market information executed on a processing arrangement comprising the steps of:

receiving first data;

forecasting further data based on the first data;

receiving second data;

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.

2. The method of claim 1 further comprising the step of predicting third data based on the adjustment factor and the further data.

14. The method of claim 1, further comprising the steps of:

receiving second data representing purchases and sales of products from the at least one pharmaceutical outlet; and

applying the product-level projection factor to the second data.

3. A system of predicting market information comprising a processing arrangement configured to perform the steps of:

receiving first data;

forecasting further data based on the first data;

receiving second data;

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.

4. The system of claim 3 further comprising predicting third data based on the adjustment factor and the further data.

5. A method for forecasting un-reported prescription/product transactions or transactions that are not timely reported in a subject time interval to a sample store or outlet in a universe of product stores, the universe of product stores comprising sample stores and non-sample stores in market channels such as retail, mail order, and long term care, the sample stores generally reporting prescription transaction data to a history database, the method comprising the steps of:

identifying new products that have been launched in a number of recent weeks based on analysis of prescription transactions stored in the database;

assigning products to product groups for each product group,

generating data files containing projected national prescription count information by product for each of the three channels namely retail, mail order, and long term care;

supplementing the data files with historical raw prescription data at the outlet/product level covering a prior number of weeks and also an estimate of national current week volume;

identifying outlets as normal volume or low volume outlets;

for normal outlets, using a 4 week average by product group as a forecast for the current week volume;

for low volume outlets, when the product is not new, using a moving four-week average of outlet/product raw prescription counts to forecast the current week volume based on outlet/product raw prescription counts for the prior number of weeks and projected national prescription counts for both the current week and the prior number of weeks; and

for low volume outlets, when the product is new, using a national ratio of product prescription counts to product group prescription counts applied at outlet level to forecast a new product volume for the current week.

6. The method of claim 5 wherein using a moving four-week average of outlet/product raw prescription counts to forecast the current week volume comprises using an Autoregressive Integrated Moving Average (ARIMA) model.

7. The method of claim 5 identifying new products that have been launched in a number of recent weeks comprises identifying new products that have been launched in a the last thirteen weeks and supplementing the data files with historical raw prescription data at the outlet/product level covering a prior number of weeks comprises raw prescription data for twenty five prior weeks.

8. The method of claim 5 wherein assigning products to product groups comprises identifying a number of leading products based on analysis of national prescription counts information and assigning such number of products to its own product group.

9. The method of claim 5 wherein assigning products to product groups comprises grouping products by therapy class.

10. A system for forecasting un-reported prescription/product transactions or transactions that are not timely reported in a subject time interval to a sample store or outlet in a universe of product stores, the universe of product stores comprising sample stores and non-sample stores in market channels such as retail, mail order, and long term care, the sample stores generally reporting prescription transaction data to a history database, the system comprising a processing arrangement configured to:

identify new products that have been launched in a number of recent weeks based on analysis of prescription transactions stored in the database;

assign products to product groups

for each product group,

generate data files containing projected national prescription count information by product for each of the three channels namely retail, mail order, and long term care;

supplement the data files with historical raw prescription data at the outlet/product level covering a prior number of weeks and also an estimate of national current week volume;

identify outlets as normal volume or low volume outlets;

for normal outlets, use a 4 week average by product group as a forecast for the current week volume;

for low volume outlets, when the product is not new, use a moving four-week average of outlet/product raw prescription counts to forecast the current week volume based on outlet/product raw prescription counts for the prior number of weeks and projected national prescription counts for both the current week and the prior number of weeks; and

for low volume outlets, when the product is new, use a national ratio of product prescription counts to product group prescription counts applied at outlet level to forecast new product volume for the current week.

11. The system of claim 10 further comprising an Autoregressive Integrated Moving Average (ARIMA) model.

12. A computer-readable medium for forecasting un-reported prescription/product transactions or transactions that are not timely reported in a subject time interval to a sample store or outlet in a universe of product stores, the universe of product stores comprising sample stores and non-sample stores in market channels such as retail, mail order, and long term care, the sample stores generally reporting prescription transaction data to a history database, the computer-readable medium having a set of instructions operable to direct a processing system to perform the steps of:

identifying new products that have been launched in a number of recent weeks based on analysis of prescription transactions stored in the database;

assigning products to product groups

for each product group,

generating data files containing projected national prescription count information by product for each of the three channels namely retail, mail order, and long term care;

supplementing the data files with historical raw prescription data at the outlet/product level covering a prior number of weeks and also an estimate of national current week volume;

identifying outlets as normal volume or low volume outlets;

for normal outlets, using a 4 week average by product group as a forecast for the current week volume;

for low volume outlets, when the product is not new, using a moving four-week average of outlet/product raw prescription counts to forecast the current week volume based on outlet/product raw prescription counts for the prior number of weeks and projected national prescription counts for both the current week and the prior number of weeks; and

for low volume outlets, when the product is new, using a national ratio of product prescription counts to product group prescription counts applied at outlet level to forecast a new product volume for the current week.

13. A method for predicting market information for a plurality of pharmaceutical outlets, the method being executed on a processing arrangement comprising 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; and

computing a product-level projection factor for the at least one pharmaceutical product.