System For Individualized Customer Interaction

• US 20140114743A1
• Filed: 12/27/2013
• Published: 04/24/2014
• Est. Priority Date: 02/27/2004
• Status: Abandoned Application

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Fano, Andrew E., Cumby, Chad M., Ghani, Rayid, Krema, Marko
• Application Number:
  US14/142,160
• Publication Number:
  US 20140114743A1
• Time in Patent Office: Days
• US Class Current: 705/14.41
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 99/00 Subject matter not provided for in other groups of this subclass
• 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 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0207 Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0226 Incentive systems for frequent usage, e.g. frequent flyer miles programs or point systems
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0245 Surveys
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0273 Determination of fees for advertising
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 30/0633 Lists, e.g. purchase orders, compilation or processing
• CPC Class Codes: G06Q 50/12 Hotels or restaurants

Abstract

A method and system for using individualized customer models when operating a retail establishment is provided. The individualized customer models may be generated using statistical analysis of transaction data for the customer, thereby generating sub-models and attributes tailored to customer. The individualized customer models may be used in any aspect of a retail establishment'"'"'s operations, ranging from supply chain management issues, inventory control, promotion planning (such as selecting parameters for a promotion or simulating results of a promotion), to customer interaction (such as providing a shopping list or providing individualized promotions).

Claims

1. (canceled)

2. A computer-implemented method of evaluating performance of a shopping list predictor, the method comprising:

accessing a customer model that stores transaction data associated with a customer, wherein the transaction data describes previous purchases that were made by the customer;

determining, by one or more computers, at least one performance metric to be used for evaluating the shopping list predictor;

determining, from the transaction data, training data to be used for training the shopping list predictor and test data to be used for evaluating the performance of the shopping list predictor;

generating, by the one or more computers and based on the training data, a predicted shopping list using the shopping list predictor;

comparing, by the one or more computers, the predicted shopping list with the test data to determine a match between product categories that were predicted by the shopping list predictor with product categories that were actually purchased by the customer;

determining a value of a performance metric based on comparing the predicted shopping list with the test data; and

outputting, by the one or more computers, a measure of performance for the shopping list predictor based on the value of the performance metric.

3. The computer-implemented method of claim 2, wherein the at least one performance metric comprises at least one of a standard recall, a precision, an accuracy, or an f-measure.

4. The computer-implemented method of claim 2, wherein generating a predicted shopping list comprises predicting a probability that the customer will purchase a product from a particular product category.

5. The computer-implemented method of claim 2, wherein generating a predicted shopping list comprises:

determining, based on the training data, at least one attribute to be used in generating a predicted shopping list; and

generating, based on the determined at least one attributes, a prediction of a product category containing a product that the customer will likely acquire on a shopping trip.

6. The computer-implemented method of claim 5, wherein determining the at least one attribute comprises applying one or more rules to the training data to determine the at least one attribute.

7. The computer-implemented method of claim 5, wherein determining the at least one attribute comprises applying machine learning to the training data to determine the at least one attribute.

8. A computer-implemented method comprising:

accessing customer models that store transaction data associated with one or more customers;

determining, by one or more computers and based on the transaction data associated with the one or more customers, a predicted probability that customers will purchase products belonging to a product category during a predetermined period of time;

generating, by the one or more computers and based on the predicted probability, a prediction of an amount of products in the product category that will be purchased during the predetermined period of time; and

determining an amount by which an inventory of products in the product category can be reduced, based at least on the prediction of an amount of products in the product category that will be purchased during the predetermined period of time.

9. The computer-implemented method of claim 8, further comprising:

performing a promotion simulation based on the transaction data associated with the one or more customers; and

determining, based on results of the promotion simulation, an effect of promotions on the amount of products in the product category that will be purchased in the predetermined period of time.

10. The computer-implemented method of claim 9, wherein performing a promotion simulation comprises:

determining parameters of a promotion, the parameters including at least one of a discount for the promotion, customers targeted for the promotion, or customers not targeted for the promotion.

11. The computer-implemented method of claim 10, wherein performing a promotion simulation further comprises:

identifying one or more potential customers for whom a customer model is not available;

associating with the one or more potential customers an aggregate customer model that is statistically derived from existing customer models and;

performing the promotion simulation based on transaction data that includes transaction data obtained from aggregate customer models associated with the one or more potential customers.

12. The computer-implemented method of claim 8, wherein generating a prediction of an amount of products in a product category that will be purchased comprises generating a prediction of a number of items of a specific brand that will be purchased during the predetermined period of time.

13. The computer-implemented method of claim 8, further comprising determining a predicted change in revenue by removing or adding products from the product category.

14. A computer-implemented method of promotion planning, the method comprising:

determining, by one or more computers, a customer model comprising a plurality of promotion attributes comprising at least one of (i) a price sensitivity attribute indicative of sensitivity of a customer to a product price, or (ii) a brand loyalty attribute indicative of loyalty of the customer to a product brand, the promotion attributes being derived from customer data comprising transaction data associated with the customer;

predicting, by the one or more computers, a product of interest to the customer; and

generating, by the one or more computers, a promotional offer based on at least one of the promotion attributes and the product of interest to the customer.

15. The computer-implemented method of claim 14, further comprising:

determining the price sensitivity attribute based on a number of times the customer bought the product of interest in a particular store when the product of interest was priced at a particular price.

16. The computer-implemented method of claim 14, wherein the price sensitivity attribute is further indicative of sensitivity of the customer to prices of a plurality of products belonging to a product category that includes the product of interest.

17. The computer-implemented method of claim 14, wherein generating, by the one or more computers, a promotional offer based on at least one of the promotion attributes and the product of interest to the customer comprises:

accessing the price sensitivity attribute for the product of interest; and

determining, based on the price sensitivity attribute, an amount of discount to be offered for the product of interest as part of the promotional offer.

18. The computer-implemented method of claim 14, further comprising:

determining the brand loyalty attribute based on a brand loyalty score indicative of a propensity of the customer to buy a specific brand given the availability of that brand in a product category that includes the product of interest.

19. The computer-implemented method of claim 18, further comprising:

modifying the brand loyalty score based on a popularity of the specific brand to other customers or based on a price of the specific brand relative to other brands.

20. The computer-implemented method of claim 19, wherein modifying the brand loyalty score based on a popularity of the specific brand to other customers comprises:

reducing the brand loyalty score if the specific brand is determined to be popular among other customers, and increasing the brand loyalty score if the specific brand is determined to be unpopular among other customers.

21. The computer-implemented method of claim 19, wherein modifying the brand loyalty score based on a price of the specific brand relative to other brands comprises:

reducing the brand loyalty score if the specific brand is determined to be less expensive than the other brands, and increasing the brand loyalty score if the specific brand is determined to be more expensive than the other brands.