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Method and system for using data analytics to translate customer buying patterns into increased sales Disclosure Number: IPCOM000239368D
Publication Date: 2014-Nov-03
Document File: 4 page(s) / 82K

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The Prior Art Database


Disclosed is an algorithm known as the Bid Interest Factor (BIF) that quantitatively translates the attractiveness of a customer to a store. The results can be applied to incent more purchases when customer is in store, increase revenue, and potentially balance store inventory.

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Method and system for using data analytics to translate customer buying patterns into increased sales

Disclosed here are a method and system that use data analytics to evaluate buying patterns to mutually improve sales, inventory control, and consumer shopping experience.

A consumer's buying history is accessible through various methods including online history, loyalty or frequent shopper cards, online coupons, credit card statements, and customer surveys and applications; however, the use of or leveraging of this history is very limited. Discounts and coupons are available for users of loyalty cards , but not in a

way that is customized to the individual consumer. In some cases, rewards are made available to consumers to incent increased purchases , but not in a way that leverages the specific consumer's buying history.

The novel contribution is a method to user data analytics to improve profitability , save time, and save money for the individual based on previous and future consumer needs . The method integrates a system of engagement , interfacing with the customer while in the store, to a system of record, which contains concrete data about the customer (e.g., spending patterns and demographics).

The basis of this method is a store bidding process that provides incentives to entice consumers to shop at a particular store. In the key embodiment, a consumer submits a shopping list to a single store or a group of stores to request a bid for the items . The store offers incentives to customers based on a plurality of factors including spending history, current purchase value, and other factors that identify the buyer as a desired customer.

These factors are inputs to an algorithm known as a Bid Interest Factor (BIF). The BIF is calculated based on the inputs for the specific customer , resulting in a numeric value,

which is then compared against a BIF table for the store to determine incentives for

which the purchase is eligible. The store may opt for inputs outside of those for the specific consumer, such as increased BIF for higher inventory items, or increased BIF for days or times of shopping. The method can also leverage social networks to identify buying patterns and improve communications.

The Bid Interest Factor (BIF) is an algorithm that utilizes information about the consumer's buying patterns and history as inputs to calculate a score, which then determines incentives for the consumer to reward loyalty and encourage further patronage. To initiate the BIF process, a consumer submits a bid into a store through a

web or social network application. The submitted bid includes all of the items they want to purchase in an upcoming store visit. Recognizing that the consumer might be exploring multiple opportunities, the store looks to incent the consumer to shop there .

The store's level of interest in enticing the consumer to shop depends on a plurality of factors, including current list, shopping history, and other factors...