Method and system for maximizing sales profits by automatic display promotion optimization

• US 20030220830A1
• Filed: 04/04/2002
• Published: 11/27/2003
• Est. Priority Date: 04/04/2002
• Status: Abandoned Application

• Current Assignee: Makor Issues & Rights Ltd.
• Original Assignee: David Myr
• Inventors: Myr, David
• Application Number:
  US10/115,698
• Publication Number:
  US 20030220830A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0245 Surveys
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0256 User search
• CPC Class Codes: G06Q 30/0277 Online advertisement

Abstract

The present invention is remotely controlled automatic optimization system for maximizing in-store net profits by customized script-generated clip promotions to thousands of individually networked retail display-nodes from central server (OptiRetailChain). The computer-based and machine-learning display system includes: an advertising optimization function for display nodes, point of sale (POS) data input, retail database mining engine (RDME), client access and management control module, and in-store networked electronic clip display apparatus. The optimization function obtains data from chain-store database, combines product bundling data, which describe the associative relationships of various product sales in stores with recorded times of sale, inventory costs, margin profits etc. Physical location of purchased products on the store floor-areas are correlated with relevant display-nodes to create optimal clip display program (playlist) configurations for that specific display location and time. Most preferred product advertising combinations will be displayed in the best time slots for each node automatically. OptiRetailChain uses two methods of promotion optimization: real time scheduling and longer-term statistical optimization. Utilizing the machine learning capabilities, actual video-clip playlists will be dynamically updated for every display-node and respond to daily sales fluctuations for that store display location. This enables the optimization system to effectively control and automatically feature target advertising to large number of display-nodes in supermarket chain networks optimizing advertising capital without necessitating outside intervention.

Claims

1. A system comprising an optimization server, a memory coupled to the CPU, an automatic electronic advertising optimization system executed by the server, self-learning advertising optimization system dynamically updating multitude of clip media playlists (display-schedules) to achieve an optimal advertising timetable in a large retail network requiring no additional input.

2. The system of claim 1 further comprising a graphical interface with a plurality of video-clips, catalogued according to subject and other statistics which provide basis for applying rules for advertising optimization system stored in the CPU server storage system.

3. The system of claim 1 further comprising at least one playlist for each remote display station in the retail network residing in the memory of the CPU and controlled by the central advertising optimization mechanism for modifying the base advertising schedule.

4. The system of claim 3 wherein each display station is associated with a list of products dynamically updated for each display node.

5. The system of claim 1 further comprising a database-mining engine residing in the memory of the CPU.

6. The system of claim 5 wherein the database-mining engine further comprises a plurality of Boolean filters used to search the plurality product sales records for each department contained in the database.

7. The system of claim 1 further comprising a data communicating mechanism capable of transmitting product data associated with each display station, associated product categories relevant to that station together with the store identification number and other relevant time and sales data and playlist status to the main optimization server.

8. The system of claim 5 wherein the database-mining engine filters the sales occurrence statistics for various product mixes listing the highest occurrence rating sequence for each display screen location.

9. The system of claim 5 wherein the database-mining engine filters the clip display history statistics for various product mixes.

10. The system of claim 5 wherein the database-mining engine filters historical sales data for various display stations according to date/time factors, with relevant promotion playlist data and relevant store data.

11. The system of claim 5 wherein the database-mining engine filters historical sales data for various display stations according to days, weeks and months according to sales and promotion playlist data for each store location.

12. The system of claim 1 wherein the optimization system automatically searches for best product pairing combinations based on the pair-items sales occurrence and based on the relevant time period for each display station in multiple store locations.

13. The system of claim 1 wherein the optimization system automatically searches possible display location for optimized promotion-bundle display based on relevant sales performance rules.

14. The system of claim 1 wherein the optimization system creates optimal timing sequence based on the clip availability and effectiveness (sales occurrence) for each display station.

15. The system of claim 1 wherein the optimization system creates optimal timing sequence based on custom pre-paid clip-blocks displays.

16. The system of claim 1 wherein the optimization system creates dynamic package-pricing advertising clips based on the promotion strategy requirements for each display station.

17. The system of claim 1 wherein the optimization system creates custom display clip-timetable combined with the optimized promotion strategy requirements for each display period.

19. Communication network connecting multiple retail nodes of claim 1 in the retail chain to the main CPU server automatically controlling and updating large clip playlist-files for dynamic clip display optimization within the retail chain.

20. The system of claim 1 wherein the optimization system searches and updates available clip storage dynamically for each of the multiple store node servers based on the suggested promotion clip sequence to enable continuous playlist display.

21. Intranet secure network communication system in all store locations connecting display node servers of claim 1 with each individual display node controlled remotely from central server.

22. Networked store display units of claim 1 comprising LCD single or doubled display units and a CPU unit capable of updating video display list and media video player rapidly displaying video clip sequences according the optimization script.

18. A method for updating the product Supply and Demand requirements forecasts based on statistical promotion influence-curves from clip promotion impact calculations.

23. A system for applying Department and Product Display Matching where several display options are available.

24. Using basket cost factor parameter for “

best matching”

screen and item promotion optimization.

25. A system for estimating clip promotion display influence curves by an estimation algorithm.

26. The system of claim 25 further comprising locally-weighted straight-line smoothers capable of dealing with relatively small samples of noisy data.

27. The system of claim 25 further comprising locally linear prediction function suitable for iterative linear optimization.

28. The system of claim 25 further comprising real time iterative optimization algorithm for calculation of optimal clip schedules.

29. The system of claim 25 further comprising method of incorporating constraints such as number of brand item clips and number of clips for given period at each display into the optimization program.

30. The system of claim 25 further comprising client manual input system including various Brand item promotion factors and adjusting automatically each individual display node'"'"'s playlist time-table.