System and method of preparing and processing data for trade promotion

• US 6,871,183 B2
• Filed: 01/24/2001
• Issued: 03/22/2005
• Est. Priority Date: 01/24/2001
• Status: Expired due to Term

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture (Accenture PLC)
• Inventors: Liu, Jie, Zaks, Alex, Gilday, Scott R., Orler, Vic, Singer, Gary E.
• Primary Examiner(s): Jeanty, Romain
• Application Number:
  US09/768,144
• Publication Number:
  US 20020198759A1
• Time in Patent Office: 1,518 Days
• Field of Search: 705/1, 705/7, 705/8, 705/10, 705/14, 705/28
• US Class Current: 705/14.41
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0243 Comparative campaigns

Abstract

A system and method for the preparation of data into an organized and effective methodology provides users with a data modeling tool. Data is confirmed and baseline calculations, event extraction, key performance indicator calculations and the production of event outputs are performed. Analytical tools can then be used to visualize patterns, trends and relationships in the data.

Claims

1. A computer-based method of processing data from disparate data sources into a combined data file for use in a system for conducting data processing for trade promotion event effectiveness analysis, the method comprising the acts of:

inputting into a computer consumption data, shipment data and event cost data, wherein the consumption data is extracted from a database provided by one or more syndicated consumer data reporting agency, the shipment data is provided by a user'"'"'s internal systems, and the event cost data is provided by the user'"'"'s organization;

transforming by said computer consumption data by checking and verifying the consumption data for uniqueness and consistency from one time period to another, computing and adding to the inputted consumption data, baseline consumption data for each event time period and identifying time periods and related consumption data associated with a trade promotion event,

aggregating by said computer the consumption data, shipment data and event cost data into a single data file; and

using the single data file to analyze effectiveness of trade promotional events.

2. The method of claim 1, further comprising:

verifying account and product hierarchies in the consumption data to ensure unique account identifiers and product identity and characteristics, respectively; and

recording the consumption data such that for the account a UPC exists for an observation, and such that observation characteristics are recorded uniformly.

3. The method of claim 2, further comprising:

rolling the consumption data into a sales summary report for viewing by a user.

4. The method of claim 3, wherein the sales summary report includes aggregated sales by account, by products and by account/product combinations.

5. The method of claim 1, further comprising:

mapping the shipment data in order to match shipment accounts to consumption data, and to match shipment SKUs to promoted groups; and

checking the shipment data to verify account, promoted group and week-level uniqueness.

6. The method of claim 1, further comprising:

verifying the uniqueness of a account, promoted group, start and a week and duration-level of the event cost data; and

removing events with inconsistent information.

7. The method of claim 1 wherein the syndicated consumer data reporting agency is Information Resources, Inc.™

(IRI).

8. The method of claim 1 wherein the syndicated consumer data reporting agency is AC Nielsen™

.

9. The method of claim 1 comprising the additional acts of

calculating at least one of key performance indicators (KPIs) for post-event analysis using the data in the single data file;

and

providing reports of the KPIs for products related to selected promotional events.

10. The method of claim 9 wherein the KPIs include data representing core measures which comprise at least one of spending efficiency, percent lift, incremental revenue and incremental profit.

11. The method of claim 10 wherein the KPIs further include data representing supporting measures which comprise at least one of the following:

cost per incremental unit, profit per incremental unit, weighted weeks of support, pass-through promotion dollars, retailer margin, total revenue, total profit, dollar market share, forward buy activity, forward buy cost, and any relevant measure requested by the user.

12. The method of claim 11 wherein the KPIs for products related to selected promotional events are compared to planned data for corresponding products and related promotional events to evaluate actual performance and to construct revised plans for future promotional events.

13. The method of claim 12 wherein the planned data is benchmark KPI data comprising at least one of data representing all events, and data representing a selected group of events.

14. The method of claim 1 wherein the consumption data is obtained directly from retail sales units in lieu of being obtained from a syndicated consumer data reporting agency.

15. A computer-based method of aggregating data to create a computer data file for trade promotion event effectiveness analysis, the method comprising the acts of:

inputting into a computer consumption data, shipment data and event cost data into files in the computer, wherein the consumption data is provided by one or more syndicated consumer data reporting agency, the shipment data is provided by a user'"'"'s internal systems, and the event cost data is provided by the user'"'"'s organization;

matching using said computer consumer account identification data in the consumption file with shipment account identification data in the shipment file and matching standard product identification data in the shipment file to product identification data in the consumption file;

outputting a consolidated historical trade promotion event data file based on matched data records in each of the consumption, shipment and event cost data files; and

using the consolidated historical trade promotion event data file to analyze effectiveness of trade promotional events;

whereby the consolidated historical trade promotion event data file is more efficient and more correct than raw data files for performing event in said computer analysis.

16. The method of claim 15, further comprising the acts of:

using the consumption data file to compute for each event week during a promotion event, a corresponding consumption data element designated a baseline consumption data element which represents base non-promoted consumption data and using the baseline consumption data elements to compute baseline consumption data elements for all time periods;

monitoring below baseline sales performance of data for promoted product category sales during a specified period following an event, by comparing the promoted product category sales data for each time period with a corresponding baseline consumption data element for a corresponding time period, whereby pantry loading can be detected;

monitoring below baseline sales performance of non-promoted products during the promoted product event, by comparing sales of the non-promoted products for each relevant time period with a corresponding baseline consumption data element for a corresponding time period, whereby Cannibalization can be detected; and

adjusting consumption data for sales in relevant periods to compensate for pantry loading and Cannibalization in the relevant periods whereby a more correct impact of trade promotion on sales for the relevant periods can be determined.

17. The method of claim 16 comprising the additional acts of

calculating key performance indicators (KPIs) for post-event analysis using the data in the consolidated historical trade promotion event data file;

and

providing reports of the KPIs for products related to selected promotional events.

18. The method of claim 17 wherein the KPIs include data representing core measures which comprise spending efficiency, percent lift, incremental revenue and incremental profit.

19. The method of claim 18 wherein the KPIs further include data representing supporting measures which comprise at least one of the following:

cost per incremental unit, profit per incremental unit, weighted weeks of support, pass-through promotion dollars, retailer margin, total revenue, total profit, dollar market share, forward buy activity, forward buy cost, and any relevant measure requested by the user.

20. The method of claim 19 wherein the KPIs for products related to selected promotional events are compared to planned data for corresponding products and related promotional events to evaluate actual performance and to construct revised plans for future promotional events.

21. The method of claim 20 wherein the planned data is benchmark KPI data comprising at least one of data representing all events, and data representing a selected group of events.

22. The method of claim 15 wherein the syndicated consumer data reporting agency is Information Resources, Inc.™

(IRI).

23. The method of claim 15 wherein the syndicated consumer data reporting agency is AC Nielsen™

.

24. The method of claim 15 wherein the consumption data is obtained directly from retail sales units in lieu of being obtained from a syndicated consumer data reporting agency.

25. A system for processing data from disparate data sources into a combined data file for use in a system for trade promotion event effectiveness analysis, comprising:

a database collecting consumption data, shipment data and event cost data wherein the consumption data is provided by one or more syndicated consumer data reporting agency, the shipment data is provided by a user'"'"'s internal systems, and the event cost data is provided by the user'"'"'s organization; and

a processor transforming the consumption data by checking and verifying the consumption data for uniqueness and consistency from one time period to another, computing and adding to the inputted consumption data baseline consumption data for each event time period and identifying time periods and related consumption data associated with a trade promotion event, and aggregating the transformed consumption data, shipment data and event cost data into a single data file, and using the single data file to analyze effectiveness of trade promotional events.

26. The system of claim 25 wherein the syndicated consumer data reporting agency is Information Resources, Inc.™

(IRI).

27. The system of claim 25 wherein the syndicated consumer data reporting agency is AC Nielsen™

.

28. The system of claim 25 wherein the consumption data is obtained directly from retail sales units in lieu of being obtained from a syndicated consumer data reporting agency.

29. A system for aggregating data from disparate data sources to create a data file for efficient analysis of trade promotion event effectiveness, comprising:

a database storing consumption data, shipment data and event cost data wherein the consumption data is provided by one or more syndicated consumer data reporting agency, the shipment data is provided by a user'"'"'s internal systems, and the event cost data is provided by the user'"'"'s sales organization; and

a processor matching consumer account identification data in the consumption data with shipment account identification data in the shipment data and matching standard product identification data in the shipment data to product identification data in the consumption data and matching trade promotion data in the event cost data to related time periods in the consumption data, and outputting a consolidated historical trade promotion event data file based on matched data records in each of the consumption, shipment and event cost data, and using the consolidated historical trade promotion event data file to analyze effectiveness of trade promotional events;

whereby the consolidated historical trade promotion event data file is more efficient and more correct than raw data files for performing event based analysis.

30. The system of claim 29 wherein the syndicated consumer data reporting agency is Information Resources, Inc.™

(IRI).

31. The system of claim 29 wherein the syndicated consumer data reporting agency is AC Nielsen™

.

32. The system of claim 29 comprising the additional acts of

calculating key performance indicators (KPIs) for post-event analysis using the data in the consolidated historical trade promotion event data file;

and

providing reports of the KPIs for products related to selected promotional events.

33. The system of claim 32 wherein the KPIs include data representing core measures which comprise spending efficiency, percent lift, incremental revenue and incremental profit.

34. The system of claim 33 wherein the KPIs further include data representing supporting measures which comprise at least one of the following:

cost per incremental unit, profit per incremental unit, weighted weeks of support, pass-through promotion dollars, retailer margin, total revenue, total profit, dollar market share, forward buy activity, forward buy cost, and any relevant measure requested by the user.

35. The system of claim 34 wherein the KPIs for products related to selected promotional events are compared to planned data for corresponding products and related promotional events to evaluate actual performance and to construct revised plans for future promotional events.

36. The system of claim 35 wherein the planned data is benchmark KPI data comprising at least one of data representing all events, and data representing a selected group of events.

37. The system of claim 29 herein the consumption data is obtained directly from retail sales units in lieu of being obtained from a syndicated consumer data reporting agency.