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WO2011109219A1 - Procédé informatisé pour l'amélioration de ventes de produits ciblés - Google Patents

Procédé informatisé pour l'amélioration de ventes de produits ciblés Download PDF

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Publication number
WO2011109219A1
WO2011109219A1 PCT/US2011/026064 US2011026064W WO2011109219A1 WO 2011109219 A1 WO2011109219 A1 WO 2011109219A1 US 2011026064 W US2011026064 W US 2011026064W WO 2011109219 A1 WO2011109219 A1 WO 2011109219A1
Authority
WO
WIPO (PCT)
Prior art keywords
product
purchase
products
customers
percentiles
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2011/026064
Other languages
English (en)
Inventor
Joseph Milana
Bo Zhang
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Opera Solutions LLC
Original Assignee
Opera Solutions LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US12/715,248 external-priority patent/US20110213651A1/en
Priority claimed from US12/715,302 external-priority patent/US20110213661A1/en
Application filed by Opera Solutions LLC filed Critical Opera Solutions LLC
Priority to EP11751093.3A priority Critical patent/EP2543013A4/fr
Publication of WO2011109219A1 publication Critical patent/WO2011109219A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the present invention relates to a computer-implemented marketing method for enhancing the sales of products and/or services and, more particularly, to a method that recommends customers likely to purchase specified products and/or services based on previous purchases of customers.
  • the present invention overcomes the limitations of the prior art by providing a marketing method that processes existing customer data to provide specific customer recommendations for a product.
  • Each customer recommendation is for a particular product, hereinafter “the target product”, and identifies recommended one or more customers, hereinafter “the target customers”, likely to purchase the target product.
  • the present invention uses a mathematical model, e.g., neural network model, that considers customers' prior purchases of products including those products that are similar or related to the target product.
  • the output of the mathematical model is used to select and optionally rank the target customers from a pool of customers.
  • the present invention includes the method of training the mathematical model, which may include collecting data, including the frequency of prior product purchases by customers, then normalizing that data across the customer population.
  • the normalized data may be input into the mathematical model, which may be trained to learn customers' likes and dislikes based on those prior purchases.
  • the mathematical model may attempt to determine estimated purchase tendencies of products (for which there is a known tendency) based on purchase tendencies of related products.
  • the model may then test the estimated tendency against the known tendency and adjust accordingly.
  • the present invention uses the concept of a tiered hierarchy to classify products.
  • the tiered hierarchy is used both in training the model and when using the model to identify target customers.
  • the disclosed technique is particularly suitable for use in "door-to-door" product sales.
  • Fig. 1 is a representative block diagram of sequential steps carried out in accordance with an embodiment of the present invention
  • Fig. 2 is an exemplary table showing customers, products purchased and purchase frequencies
  • Fig. 3 is an exemplary table showing purchase percentiles corresponding to purchase frequency for one product illustrated in Fig. 2;
  • Fig. 4 is an exemplary table showing purchase frequencies and purchase percentiles for a product and its hierarchy based on Figs. 2 and 3;
  • Fig. 5 is an exemplary table showing purchase frequencies and purchase percentiles for model training based on Figs. 2-4;
  • Fig. 6 is an exemplary block diagram of computer apparatus useable to carry out the methodology of the present invention.
  • the term "products” shall hereinafter mean products and/or services.
  • the notion of "sales” includes any form of product transfer from a product seller or offeror to a product purchaser or offeree wherein the purchaser acquires the right to use or consume a product in exchange for some consideration given to the seller by the purchaser. This benefit to the seller is generally in the form of money, but can take other non-monetary forms.
  • the notion of sales and purchases includes, includes all forms of product transfers including but not limited to all forms of sales, leases, barters, or the like.
  • each customer in the customer database may be a target customer.
  • an analysis of the data, for example, of each customer is performed to determine a listing of one or more customers who may purchase a target product, and optionally the likelihoods of those customers for purchasing such product. It should, of course, be understood that the present invention is not limited to providing a recommendation for a single product and, instead, can provide a recommendation for any desired subset of products in the database.
  • Fig. 1 depicts a block diagram of a representative method 10 in accordance with the invention.
  • the method 10 accesses the database and for each product, determines respective purchase frequencies, which is the number of occasions corresponding customers have purchased that product in a predetermined time period.
  • Products may be identified by name, stock-keeping- unit codes (SKUs) or other identifiers.
  • SKUs stock-keeping- unit codes
  • a time period for this step may be measured in days, weeks, months or years.
  • results of step 100 show customer "Allison” purchased a "peach frozen yogurt” product on four occasions in the past year.
  • the counting operation of step 100 may be repeated for each customer who purchased the product in the time period, or other operations or algorithms for counting product purchase frequency may be used in accordance with the invention.
  • customer "Bob” purchased "peach frozen yogurt” on two occasions in the past year.
  • the quantity of the product purchased may be incorporated in the analysis.
  • step 100 it may be advantageous to exclude from the results of step 100 those, customers who have purchased on fewer than a predetermined number of occasions in the time period, for example, on less than three (3) occasions in one year.
  • step 105 the distribution of purchase percentiles based on purchase frequency are calculated for a product.
  • This process is known as "normalization” and may be performed using known statistical analytical methods.
  • a lookup table is generated showing purchase frequency and corresponding purchase percentiles for a product.
  • Fig. 3 shows such a lookup table with purchase frequency and corresponding percentiles for "peach frozen yogurt" product.
  • the present invention may advantageously employ a hierarchical structure to organize products.
  • a four-tiered structure may be as follows, in order from highest/broadest to lowest/narrowest: category, family, sub-family, and product (SKU).
  • the product "peach frozen yogurt” may be in subfamily “frozen yogurt", family "half-gallons sq/round”, and category “ice cream”. It will be understood that other structures may be used including those with greater or fewer tiers.
  • Purchase frequency lookup tables for sub-families, families, and categories of products may be generated in a similar manner as above.
  • Steps 100 and 105 may be implemented or repeated for all products in a category with purchase frequency information, and resultant corresponding purchase percentiles for products, sub-families, families, and categories stored in, for example, the previously discussed database.
  • Fig. 4 is a table showing the purchase frequency and corresponding purchase percentile for customer "Allison" for the product "peach frozen yogurt” and its sub-family, family, and category for that product.
  • Allison purchased the product "peach frozen yogurt” on four occasions in the past year.
  • Fig. 4 shows that data for the sub-family, family, and category.
  • Allison purchased the product "blueberry frozen yogurt” on one occasion in the past year.
  • the purchase frequency tables for sub-family, family, and category are not shown.
  • a model is trained using products, hierarchy, and purchase percentiles.
  • One type of model suitable for use with the present invention is a feedforward artificial neural network, trained using back-propagation. Other types of statistical models may be used, such as simple linear regression or Support Vector Machines.
  • the neural network may be trained by causing the neural network to estimate the purchase percentile for a chosen product for a customer based on modified purchase percentiles for that product's sub-family, family, and category. As shown in Fig. 5 and described in more detail below, the chosen product's sub-family, family, and category purchase frequency may be modified by subtracting the purchase frequency of the chosen product. Continuing the example, as shown in Fig.
  • the neural network may be trained by causing the neural network to determine the purchase percentile for "peach frozen yogurt" for customer Allison whose modified purchase percentiles in that product's sub-family (“frozen yogurt") are 10%, family ("half gallons sq/round”) 15% and category ("ice cream”) 5%.
  • the product selected for training may be chosen specifically or randomly.
  • the neural network may generate an output by calculating predicted purchase percentiles for products that customers have never purchased or have not purchased in a time period. This may involve scoring using the trained model.
  • the method 10 determines, for a given product, one or more customers likely to purchase the product.
  • One way to make such determination is to identify customers with the highest purchase frequency or highest predicted purchase frequency for this product. If a customer has purchased this product in the time period, then the method may advantageously use the purchase frequency for that customer and product in this determination and may not use the neural network prediction.
  • the method is determining customers likely to purchase "Peach Frozen Yogurt" product, then the method 10 will use Allison's 50% purchase percentile shown in Fig. 4, in that analysis because Allison has purchased that product in the time period.
  • the method may use the neural network predicted purchase percentile, and correspondingly convert that predicted purchase percentile to purchase frequency using a table such as shown in Fig. 3.
  • the method 10 may select target customers for products.
  • a list of targeted products which may be geographically specific, choose for each customer that targeted product with the highest percentile (or alternatively purchase frequency); then balance supply constraints of the targeted product by excluding from the target list those products that have gone past their specified constraints.
  • a supply constraint is a quantity of a product available in a geographic region.
  • the method 10 may limit the number of target customers selected in a region based on the quantity of available product in that region.
  • the method 10 may make two to three times as many offers as quantity of product available to account for the fact that every recommendation may not lead to a purchase.
  • the method 10 may perform a product level optimization choosing the customers with the highest purchase frequency by solving a linear programming problem, which may be a lambda-assignment problem.
  • the method 10 may seek to maximize the expected percentile summed across all products offered given capacity constraints of each product, which may typically be multiplied by a factor of 2 or 3 to account for the fact that not all recommendations may be actually converted to sales.
  • the method may calculate an expected conversion rate for each customer and then multiply each predicted percentile for the customer with the expected conversion rate and seek to optimize this combined quantity. This may require having an accumulated history of responses to recommendations to build the response model, which may be present.
  • the product's purchase frequency may be replaced by the corresponding purchase frequency of the product's sub-family, family or category.
  • the method 10 may identify the most likely customers overall or may identify the most likely customers within a subset of customers. Subsets may be determined by location of customers, sales information for customers, or targeting customers based on purchases of higher profit margin products.
  • the invention of the present disclosure is readily useable to recommend new or existing products to those customers.
  • the method of the present invention is useable to recommend customers based on a determination that the customer is likely to purchase a new product.
  • a new product may be a product that has not yet been sold or offered for sale. Such a new product determination can be made based on a diversity of the customer's purchases, i.e., the number of different products purchased by the customer and the respective product quantities. For example, a customer who purchases a large quantity of a small selection of products may be less likely to purchase a new product than a customer who purchases a smaller quantity of a variety of products.
  • the customer's product diversity information, including product types and quantities may be input into the neural network to assist determining whether to offer the new product to the customer.
  • the customer's product diversity information may be used in connection with existing (non-new) products as well.
  • a new product determination may be based on the frequency in which a customer has bought from the new product's sub-family, family, and category.
  • Computer system 400 includes processor 410, memory 420, storage device 430 and input/output devices 440. Some or all of the components 410, 420, 430 and 440 may be interconnected by a system bus 950.
  • Processor 410 may be single or multi-threaded and may have one or more cores.
  • Processor 410 executes instructions which in the disclosed embodiments of the present invention are the steps described and shown in Figs 1 and 3. These instructions are stored in memory 420 or in storage device 430. Information may be received and output using one or input/output devices 440.
  • Memory 420 may store information and may be a computer-readable medium, such as volatile or non-volatile memory.
  • Storage device 430 may provide storage for system 400 including for the example, the previously described database, and may be a computer-readable medium.
  • storage device 430 may be a flash memory device, a floppy disk drive, a hard disk device, and optical disk device, or a tape device.
  • Input devices 440 may provide input/output operations for system 400.
  • Input/output devices 440 may include a keyboard, pointing device, and microphone.
  • Input/output devices 440 may further include a display unit for displaying graphical user interfaces, a speaker and a printer.
  • the computer system 400 may be implemented in a desktop computer, or in a laptop computer, or in a server.
  • the recommendations provided pursuant to the present invention can be provided on a computer display proximate to the computer system 900 or remote from such system and communicated wirelessly to a sales person's mobile communication device. In this manner, the recommendation can be personally presented to the target customer when such customer is visited by the seller's sales person. Alternatively, the recommendations for each target customer can be provided in mass to the seller for redistribution to the appropriate sales person that interacts with that target customer.
  • customer may be an individual, a group of individuals, such as a family a club ort social group or a company.

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  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Strategic Management (AREA)
  • Finance (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

La présente invention concerne un procédé informatisé permettant de fournir des recommandations de clients pour un produit. Pour chaque produit cible pour lequel une recommandation de clients est souhaitée, un ou des clients susceptibles d'acheter le produit cible est/sont identifié(s) à l'aide d'un modèle mathématique qui prend en considération des achats antérieurs des clients de produits qui sont similaires ou associés au produit cible.
PCT/US2011/026064 2010-03-01 2011-02-24 Procédé informatisé pour l'amélioration de ventes de produits ciblés Ceased WO2011109219A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP11751093.3A EP2543013A4 (fr) 2010-03-01 2011-02-24 Procédé informatisé pour l'amélioration de ventes de produits ciblés

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US12/715,248 US20110213651A1 (en) 2010-03-01 2010-03-01 Computer-Implemented Method For Enhancing Targeted Product Sales
US12/715,248 2010-03-01
US12/715,302 US20110213661A1 (en) 2010-03-01 2010-03-01 Computer-Implemented Method For Enhancing Product Sales
US12/715,302 2010-03-01

Publications (1)

Publication Number Publication Date
WO2011109219A1 true WO2011109219A1 (fr) 2011-09-09

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EP (1) EP2543013A4 (fr)
WO (1) WO2011109219A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10095671B2 (en) 2016-10-28 2018-10-09 Microsoft Technology Licensing, Llc Browser plug-in with content blocking and feedback capability
US11537495B2 (en) 2021-01-31 2022-12-27 Walmart Apollo, Llc Systems and methods for altering a graphical user interface
US11635878B2 (en) 2021-01-31 2023-04-25 Walmart Apollo, Llc Systems and methods for altering a graphical user interface

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US20020010622A1 (en) * 2000-07-18 2002-01-24 Fumino Okamoto System and method capable of appropriately managing customer information and computer-readable recording medium having customer information management program recorded therein
US20030217016A1 (en) * 2002-04-29 2003-11-20 Pericle Anthony J. Pricing model system and method
US20030225786A1 (en) * 1999-01-27 2003-12-04 Hall Douglas B. Method for simulation of human response to stimulus
US20070061190A1 (en) * 2004-09-02 2007-03-15 Keith Wardell Multichannel tiered profile marketing method and apparatus
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US6430539B1 (en) * 1999-05-06 2002-08-06 Hnc Software Predictive modeling of consumer financial behavior
WO2003081376A2 (fr) * 2002-03-20 2003-10-02 Catalina Marketing International Inc. Stimulations ciblees se basant sur un comportement predit

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US20030225786A1 (en) * 1999-01-27 2003-12-04 Hall Douglas B. Method for simulation of human response to stimulus
US20020010622A1 (en) * 2000-07-18 2002-01-24 Fumino Okamoto System and method capable of appropriately managing customer information and computer-readable recording medium having customer information management program recorded therein
US20030217016A1 (en) * 2002-04-29 2003-11-20 Pericle Anthony J. Pricing model system and method
US20070061190A1 (en) * 2004-09-02 2007-03-15 Keith Wardell Multichannel tiered profile marketing method and apparatus
US20100306820A1 (en) * 2006-10-16 2010-12-02 France Telecom Control of message to be transmitted from an emitter domain to a recipient domain

Non-Patent Citations (1)

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Title
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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10095671B2 (en) 2016-10-28 2018-10-09 Microsoft Technology Licensing, Llc Browser plug-in with content blocking and feedback capability
US10423710B2 (en) 2016-10-28 2019-09-24 Microsoft Technology Licensing, Llc Browser plug-in with document modification and feedback capability
US11537495B2 (en) 2021-01-31 2022-12-27 Walmart Apollo, Llc Systems and methods for altering a graphical user interface
US11635878B2 (en) 2021-01-31 2023-04-25 Walmart Apollo, Llc Systems and methods for altering a graphical user interface
US11836066B2 (en) 2021-01-31 2023-12-05 Walmart Apollo, Llc Systems and methods for altering a graphical user interface
US12079455B2 (en) 2021-01-31 2024-09-03 Walmart Apollo, Llc Systems and methods for altering a graphical user interface
US12373323B2 (en) 2021-01-31 2025-07-29 Walmart Apollo, Llc Systems and methods for altering a graphical user interface

Also Published As

Publication number Publication date
EP2543013A1 (fr) 2013-01-09
EP2543013A4 (fr) 2014-12-24

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