International Business Machines Corporation

Demandtec, Inc.

Acoustic, L.p.

Paritosh Desai

Suzanne Noel Valentine

Sean Kervin

Howard Yihzan Wu

Jason Lee Gunnink

Rob Parkin

Kamal Gajendran

Sean McCauley

Steve Colten

Krishna Venkatraman

Chi-Yi Kuan

Fedor N Dzegilenko

France Savard

David Davtian

Craig Silverman

Paul Algren

Phil Delurgio

Jonathan Dickinson

John Crowther

The present invention relates to a system and method for generating business decisions. Embodiments of this system and method receive customer transaction data and additional information (cumulatively referred to as 'modeling data'). This data is utilized to generate a product decision tree which models consumer purchasing decisions as a tree structure. The product decision tree may be utilized by the system to analyze demand for a given leaf (product) in association with other related products. In some embodiments, customers are segmented into groupings of customers who have similar attributes, including similar shopping behaviors. Customer insights are generated for the customer segments. The customer insights and the product decision tree are used to generate business plans, which may then be provided to a store for implementation. These plans may include a product assortment plan, an everyday pricing plan, a promotional plan, and a markdown plan.

Generating product decisions

The present invention relates to a system and method for customer segment analysis. The system receives customer transaction data for the generation of segments, including point of sales data and customer identification information. Errors in the data may be resolved. Then, individual customers may be segmented by statistically relevant groups. The segmentation of consumers may be accomplished by comparing data of known customers to known segments. Unknown customers, new customers and point of sales data which is missing customer data may also be segmented via statistical similarity to known segments. Using the customer segments, segment wide point of sale data may be generated. This data may then be aggregated by consumer groups. Consumer groups may include by household or other communal purchasing entity. The aggregated segment data may be validated and transformed for outputting to the optimization system. The price optimization system may the use the segment data for generation of preferred prices. Feedback from the optimization engine may be utilized by the customer segmentation system to generate customer segment specific promotional activity.

Analyzing customer segments

The present invention relates to a system and method for analyzing product roles. The system receives a listing of products for classification into roles. The system receives volume data for each item, as well as demand coefficient. Elasticity of the products may be determined from the demand coefficients. Product volumes and elasticities may then be compared against one another by graphing the product by its volume versus elasticity. From this comparison the products may be classified into one or more roles. These roles include image items, niche products, assortment completers, and profit drivers. The assortment completer role is populated with products which have high relative elasticity and low relative volume. Niche product role is populated with products which have low relative elasticity and low relative volume. The image item role is populated with products which have high relative elasticity and high relative volume. And lastly, the profit driver role is populated with products which have low relative elasticity and high relative volume. This comparison may also include generating an 'image value' for the product.

Product role analysis

The present invention relates to a system and method for customer retention. Historical transaction and customer data may be received from stores. Likewise, recent customer transaction data may be received from the stores. The transactions are linked to each customer. Attriters, historical customers who discontinued shopping, are identified. Next, risk factors for attrition may be identified by examining the attriters' transaction history for commonalities. From the risk factors a loss model may be generated. The loss model may be used, in conjunction with current transaction data, to generate the likelihood of loss for each of the current customers, which may then be reported. Retention measures may be generated for each customer by comparing the customer's transactions to the loss model and the risk factors. The retention measures may be outputted to the stores, and a price optimization system. Likewise, the retention measures may be validated by comparing actual customer loss to the loss model.

Predicting likelihood of customer attrition and retention measures

An apparatus for providing relative optimized pricing for markdown items is provided. A financial engine for determining revenue for determining sales costs is provided. An optimization engine receiving input from the financial engine and uses the determined sales costs to provide relative optimized pricing for markdown items and provides pricing and a promotion calendar for non-markdown items.

Providing markdown item pricing and promotion calendar

A system and method for merchandising decomposition analysis (MDA), useful with stores, is provided. Econometric models are generated for product sales and are used to set pricing. Price setting may include optimizing prices. Point of sales data is collected from the stores, and benefits forecasts are generated and displayed. Benefit forecasts includes generating compliance, generating optimization benefit and generating level benefits. Generating optimization benefit includes at least one of differential benefit analysis, authorized benefit analysis and testable benefit analysis. The differential benefit analysis includes old/new cost method, cost neutral method and ignore cost method. Benefit forecasts generally include determining pre- and post-optimization price of the products, determining pre- and post-optimization costs, generating pre- and post-optimization forecasted units, calculating forecasted pre- and post-optimization profit, generate raw profit benefit, and calibrate raw profit benefit.

System and method for forecasting price optimization benefits in retail stores utilizing back-casting and decomposition analysis

A system and method for price and promotion response analysis is provided. Such a system is useful for a business to analyze the forecasted lifts associated with changes in price and promotion activity. The system sets the configuration of the response report, which includes price change and promotion change intervals. Promotions include temporary price reductions, displays, ads and multiples. Modeling data is received for the products. Forecasts, both non-cannibalistic and cannibalistic, are generated for the sales of the products dependent upon the price change and promotion change intervals. Forecasts include at least one of product forecasts, demand group forecasts, line level forecasts and category level forecasts. Suspect forecasts below a minimum confidence may be flagged. Confidence matrices may be generated which reflect accuracy of the forecasts. The response report may be generated by collecting the forecasts and the confidence matrices according to the configuration of the response report.

Systems and methods for price and promotion response analysis

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Santa Clara, CA, United States of America

Paritosh Desai