WO2013052081A2 - Système pour automatiser une décision d'achat de client - Google Patents

Système pour automatiser une décision d'achat de client Download PDF

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Publication number
WO2013052081A2
WO2013052081A2 PCT/US2012/000426 US2012000426W WO2013052081A2 WO 2013052081 A2 WO2013052081 A2 WO 2013052081A2 US 2012000426 W US2012000426 W US 2012000426W WO 2013052081 A2 WO2013052081 A2 WO 2013052081A2
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WO
WIPO (PCT)
Prior art keywords
user
store
shopper
list
items
Prior art date
Application number
PCT/US2012/000426
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English (en)
Other versions
WO2013052081A3 (fr
Inventor
Aaron L. Wadell
Kevin J. YOUNG
Original Assignee
Motyx Incorporated
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
Application filed by Motyx Incorporated filed Critical Motyx Incorporated
Priority to US13/661,075 priority Critical patent/US20140095285A1/en
Publication of WO2013052081A2 publication Critical patent/WO2013052081A2/fr
Publication of WO2013052081A3 publication Critical patent/WO2013052081A3/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06Q30/0241—Advertisements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]

Definitions

  • This disclosure relates to methods and systems for automating and streamlining consumer shopping purchases.
  • a shopper will analyze some of the available data to make a decision, but rarely do they have the time or ability to analyze all data to make an optimal decision. They may compare products on a store shelf, look at coupons and store circulars, they may request multiple quotes, or they may ask their friends, sales persons or read expert reviews. But few have the time or ability to Utilize all these options. As noted above, in some cases the shopper may not be qualified to understand or appreciate the product features critical to making a good purchase.
  • a shopper will visit one or more retail stores to meet their family's needs including traditional supermarkets, "dollar stores”, grocery stores, mass merchandisers, convenience or “C” stores, drugstores or so-called hybrid formats (e.g., Supercenters). shoppers may have a preferred retail store or may shop at multiple stores that the shopper views as substitutable. shoppers decide which store to shop at for a variety of reasons including:
  • Shoppers may choose a retail outlet based on their perception of the overall
  • Free-standing coupon inserts or "FSI's” provide the majority of coupon activity and total 291 billion coupons annually (KantarMedia.com, 2010, FSIs only) - or approximately 100 coupons per week per household. Another 41 billion coupons are delivered via direct mail and other in-store and on package means. In addition, multiple retailers deliver weekly circulars to shoppers in a market area that include 200-400 additional item level discounts and promotions. Retailers also provide pricing discounts and incentives via loyalty or frequent shopper cards. In-store, manufacturer sponsored temporary price reductions (TPRs) are tagged on-shelf where additional coupon machines may be present. Finally, once the shopper has purchased their groceries there are more coupons (17.3 billion in 2010) that are delivered after checkout that are triggered by the items purchased, such as Catalina Marketing coupons.
  • TPRs manufacturer sponsored temporary price reductions
  • a method comprises:
  • MD at least one comparison calculation among the baskets, the comparison calculation being performed at the category level or a lower level, identifying a combination of SKU's,
  • step (c) after step (b), receiving from at least one computer implemented direct marketing campaign management system, at least one additional retail incentive offer;
  • step (d) after step (c), transmitting to the MD an identification of a recommended store, the recommendation comprised of one of the at least one store, and a second list containing a set of respective SKU level item descriptors that best satisfy the comparison calculation for the basket, and results of the comparison calculation corresponding to the other Baskets for the other at least one store.
  • a method comprises:
  • a system comprises:
  • the programmed processor configured to communicate with a mobile device (MD), the MD configured with a shopper module that provides an interface to be used by a user for creating a shopping list to facilitate shopping in a retail store; and
  • the programmed processor configured to communicate with at least one direct marketing campaign management system, wherein the programmed processor is configured to receive promotional offers and advertisements for transmission to the MD while the shopper module is running, for presentation to the user on the MD.
  • FIG. 1 is a block diagram of a personal sales agent system.
  • FIG. 2 is a block diagram of the retailer component of the system of FIG. 1.
  • FIG. 3 is a block diagram of the product manufacturer marketer component of the system of FIG. 1
  • FIG. 4 is a diagram of the shopper databases of the system of FIG. 1.
  • FIG. 5 is a diagram of additional data sources used by the system of FIG. 1.
  • FIGS. 6 A and 6B are a flow chart of the method of recommending an optimally priced list of products for a shopper.
  • FIG. 7 is a block diagram showing the four main components of the system.
  • FIG. 8 is a flow chart of a method for computing the lowest cost for the one or more items.
  • FIGs. 9A and 9B are flow charts of the method performed by the shopper in a bricks-and-mortar store (FIG. 9A) and an e-store (FIG. 9B).
  • FIG. 10 is a table showing a computation of the dead net price by the system of
  • FIG. 11 is a block diagram of an embodiment of the system of FIG. 1 adapted for grocery stores.
  • FIG. 12 is a block diagram of the retailer component of the system of FIG. 1 1.
  • FIG. 13 is a block diagram of the product manufacturer marketer component of the system of FIG. 11.
  • FIG. 14 is a block diagram of the shopper preference database.
  • FIG. 15 is a flow chart of a method for computing the lowest cost for the one or more items.
  • FIG. 16 is a flow chart of the method performed by the shopper in a grocery store.
  • FIGS. 17A-and 17B show an example of a user interface display for developing the shopping list by traversing through different levels in the products database.
  • FIG. 17C shows an output displayed when the system has found a least cost store and basket.
  • FIG. 17D is a display of the mobile device after accepting the recommended store and list and indicating that shopper is about to go shopping where the coupons is printed and carried to the store on the shopping trip.
  • FIGS. 18A and 18B are messages displayed to the user upon checking in at a store other than the recommended store (FIG. 18A), or at the recommended store (FIG. 18B.)
  • FIG. 18C shows the display of a special offer to the user by way of the mobile device.
  • FIG. 18D is a display of a reminder to the user to enter a loyalty card number.
  • FIGS. 19A and 19B show the mobile device displaying the shopping list sorted by aisle (FIG. 19 A) or sorted by category (FIG. 19B).
  • FIG. 20 shows a screen displayed to the user for manual check-in at the store.
  • FIG. 21 shows an interface to permit the user to share information about good values with other users.
  • FIG. 22 shows a screen displayed to remind the shopper that one or more items have not been picked up when the user initiates checkout.
  • FIG. 23 shows a display of a bar code and frequent shopper number the shopper can use to scan in her store loyalty card number or present at checkout.
  • FIG. 24 shows a display of the store selection component.
  • FIGS. 25A-25C show the interface used to select items for the shopping list at a sufficiently low level that the candidate space includes substitutable goods.
  • FIGS. 26A-26B show the display of a promotional offer to the user, and the system response to acceptance of the offer by the user.
  • FIGS. 27A-27E show the display interface for sorting, viewing and selecting the promotional offers.
  • FIGS. 28A and 28B are diagrams of prior art methods for direct distribution of circulars and coupons.
  • FIGS. 29 A and 29B are block diagrams of a system for electronic delivery of e- circulars and e-coupons.
  • FIG. 30 is a block diagram of a web based campaign management system.
  • the disclosed system will enable the preparation of a shopping list or the evaluation of specific or individual purchase choices using a web portal, tablet and/or smart phone application.
  • the application recommends a list of items that best meet the shopper's requirements, based on price and relative performance - the "acceptable set", calculates the overall list price automatically and recommends a specific retail location for the shopper to secure the lowest overall price or cost inclusive of available promotions and coupons for the item(s) selected.
  • the application automates the way a shopper normally investigates and then makes purchase decisions thereby streamlining the purchase process.
  • the disclosed system will 1. Enable the Retailer to deliver marketing incentives based on conditional rules to a targeted shopper or shopper group that has decided to shop in the Retailers physical or online store, or another retail location, but has not yet made their purchase, thereby changing their retail location decision, 2. Enable the Retailer to deliver an e-circular that is targeted to the particular specials of an individual store and can also be targeted to a specific shopper or shopper group buying a specific item or list, and 3. Enable the delivery of targeted advertising via the shopper application at appropriate points during shopping preparation and execution. 4. Enable dynamic pricing for each customer, taking into account, e.g. customer profitability, price sensitivity, and other stores the customer is evaluating.
  • the disclosed system willl . Enable the delivery of electronic or e-Coupons and incentives to the shopper, 2. Enable the insertion of other special incentives, based on conditional rules, that are delivered after a shopper has decided what to buy - but before the purchase has been made allowing for strategic and targeted incentives, 3. Enable the delivery of targeted advertising via the shopper application at appropriate points during shopping preparation and execution process, and 4. Enable dynamic pricing for each customer, taking into account e.g., customer profitability, price sensitivity, and other stores the customer is evaluating..
  • Digital personal sales agents or "PSAs” are delivered via smartphones, tablets or other hand held devices and/or a computers and are herein used to analyze purchase choices and determine the optimal decision based on numerous factors including product performance and design criteria (eg. Amount of Random Access Memory (RAM) and computer processor clock speed in MHz) ), price, total cost of ownership, usage requirements, quality, and peer/expert reviews and other feedback.
  • product performance and design criteria eg. Amount of Random Access Memory (RAM) and computer processor clock speed in MHz
  • price total cost of ownership
  • usage requirements e.g. Amount of Random Access Memory (RAM) and computer processor clock speed in MHz)
  • the system may access substantial data processing and database repositories and algorithms, to recommend to a buyer or Shopper the best choice for an individual or a group of chosen items based on pre-defined Shopper preferences and or complex product
  • the PSA aggregates all existing coupons and other financial discounts available on the item(s) of interest and recommends the best retail location (if applicable) to purchase the item(s) at the best price.
  • “retail” broadly covers any sellers dealing directly with consumers and end users, and includes both traditional retail, wholesale stores or warehouses as well as newer "e- retailers and other web-based retailers (e.g., Amazon.com, Zappos.com, Dell.com).
  • the system allows retailers and brand marketers or manufacturers the opportunity to influence shopper choice just prior to actual purchase by providing targeted incentives based on the immediately planned purchase and/or shopping trip, shopper preferences, demographics, geographic or competitive inputs and other variables derived from the "smart" predictive systems and models contemplated.
  • the PSA also optimizes decision making on ancillary purchase decisions such as extended warranties, leasing/buying, financing options, etc.
  • the system comprises hardware, software, databases and servers, processors and communication networks both wired and wireless that provides a robust, extremely fast and highly reliable experience for target customers including consumers or Shoppers, Retailer marketing or staff personnel and brand Marketers/staff personnel and researchers and other professionals.
  • An embodiment comprises four inter-linked technology platforms targeting Shoppers, Retailers and brand Marketers.
  • the system includes a multi-level analytic system that elicits from the shopper how they intend to use a product and what specific performance variables are most important to the Shopper to establish purchase interest or utility for a given product offering.
  • the system recommends a sorted/prioritized list of items that best meet the shopper's requirements (Highest purchase interest or utility), the "acceptable set", calculate the overall dead-net price
  • the application in essence automates the way a shopper normally researchers and then makes purchase decisions thereby streamlining the purchase process.
  • the application functions as a Personal Sales Agent, or PSA, helping guide the Shopper in self- categorizing their intended product usage profile so that the system can establish appropriate preferences to aid in the down-selection of many feature rich and technical products into the "best" list of products that meet the Shopper's intended use.
  • PSA Personal Sales Agent
  • the system enables both retailers and brand marketers, herein "marketers” to deliver marketing incentives of various kinds including additional price-related incentives, directly to the Shopper during the preparation and execution of their purchase process. These incentives are included into the net purchase price and could be targeted by Retailers and Marketers based on a broad number of Shopper characteristics, preferences, behaviors, purchase history and the like.
  • the System further provides post and pre purchase analytics to help Marketers and Retails evaluate their
  • This system can influence Shoppers after they have indicated what they intend to purchase - but before the actual purchase has been made - in a streamlined highly functional application.
  • FIG. 1 is a block diagram of an exemplary system.
  • at least one database server provides access to the system databases, including retailer pricing/product database server 100, SKU item image database server 200, SKU/Item level retailer weekly circulars database server 300, S U/Item level coupon offers database server 400 and comprehensive SKU Features and technical specification database server 500. Data may also be received from other external data sources 501 , such as manual data entry, automated web capture, web crawlers, secret shoppers or the like.
  • the retailer pricing/product database server 100 includes: by-store, pricing zone, and individual item data for SKU #, Pricing, Brand/Mfg., List Price, Promoted Price, Description, and optionally other data. A plurality of retailers 1 .
  • N access the databases 100 and 300, and a plurality of product manufactures 100, 200, . . . , N00 access databases 200, 400 and 500.
  • the data transmissions between the various servers and various non-transitory computer readable storage medium may be via internet, wireless or other electronic or physical means.
  • FIG. 2 shows an example of the retailers in communication with retailer incentives database server 600, other external data sources 501 (shown in FIG. 1), such as manual data entry, automated web capture, or the like, retailer location based offers database server 700 , retailer advertising database server 800, and retailer logo and tag line database server 900 (which may be hosted in the same computer or a different computer from the servers of FIG. 1.
  • the server 600 contains conditional Retailer Promotional offers (Rls) insertion rules, to offer values, items for etc. offered to Shopper by Retailer within Basket Cost Minimization Algorithm impacting the Initial Lowest Cost (ILC) list or basket.
  • Rls conditional Retailer Promotional offers
  • Server 700 includes Marketing incentives presented to Shoppers when they enter or select a specific retailer for item purchase - similar to in-store "wall of values" or in-store circular offers.
  • Server 800 includes video, and still ads for display within Shopper smartphone and internet portal applications.
  • Server 900 includes Retailer logo and tag line files for insertion into Shopper smartphone application and Internet portal applications. logo and tag lines are also available to Retailer and Marketer portal applications.
  • FIG. 3 is a block diagram of an example of the product manufacturer interface.
  • Server 1000 provides conditional Promotional offers , insertion rules, offer values, items or the like offered to Shopper by Marketer within Value maximization/Price Minimization function impacting the Final Lowest Cost (FLC) for item (s).
  • Server 1 100 provides coupon incentives delivered online or other off-line efforts for use in calculating Initial Lowest Cost (ILC) list or basket.
  • ILC Initial Lowest Cost
  • Coupon incentives are delivered within Shopper smart phone and internet portal for use in calculating Initial Lowest Cost (ILC) for item (s)or basket .
  • Server 1200 Includes Marketer video, and still ads for display within Shopper smart phone and internet portal applications.
  • Server 1300 includes Marketer company logo, brand logo, tag line files for insertion into Shopper smart phone application and Internet portal applications. The logo and tag lines also available to Retailer and Marketer portal applications.
  • FIG. 4 show an example of the shopper preference databases 1400, shopper information database 1500 and shopper predictive purchase interest model (PPIM) database 1600.
  • Database 1400 includes information such as Location/Home Store; Other Favorite Stores - Store substitutes; Decision rules - How Shopper wants price minimization algorithm to perform relative to substitutes (e.g., lowest comparable price, price differential to choose brand substitutes, lowest price override etc.); Preferred brand(s) by category; Suitable substitutes by category (other brand, private label etc.). and Shopper ID code .
  • the database 1500 includes name, address, log-in name and ID, demographic information, psychographic and behavioral information, item purchase history; basket/ring purchase history; promotion response history; preferences information; pricing differential response history; store preference history.
  • Database 1600 includes Shopper ID code; Shopper basic category needs & usage (BCNU) prompt classification question responses (e.g., for determining the shopper's presets); Shopper Predictive Purchase Interest Model (PPIM) results from feature set prompt and feature driver weights; Shopper PPIM self-weighting inputs; Refined PPIM feature driver weights based on actual purchase.
  • the system uses learning for refining the PPIM feature driver weights.
  • the shopper preferences of the shopper database of product substitutes are initially set based on presets that are designed to represent different product preferences relating to various shopper affinity, demographic or psychographics.
  • presets are set for large families favoring larger sizes.
  • preset embodiment preferences are initially set to include a greater representation of premium and so- called super premium products.
  • initial preference presets are set to include those products more appropriate to smaller households.
  • Other preset embodiment can be developed for various groups including older shoppers, "foodies”, “scratch cookers,” “aggressive savings focused” etc.
  • FIG. 5 shows an example of additional databases accessed by the system as secondary sources.
  • Product level reviews database includes a database of product reviews from Shoppers and a Database of product reviews from industry experts and other expert reviewers.
  • Retailer reviews database 1800 includes Database of Retailer reviews by Shoppers, and Database of other Retailer information (# of complaints, # of units sold etc.). These databases may receive data from public sources available on the Internet, and/or proprietary sources, such as the registration information collected by the system, and information exchanges with partner systems, web sites, loyalty programs, manually input data, screen scraping, and the like.
  • FIGs. 6A and 6B are flow charts of an example of how Buyer value/preference information can be determined to streamline and automate purchase selection and decision process in complex goods.
  • the shopper picks a product category for purchase.
  • the shopper is prompted to answer by basic category needs and usage (BCNU) questions.
  • the results are stored in the shopper predictive purchase interest model (PPIM) database 604,
  • the BCNU algorithm develops the initial shopper segmentation and feature importance hypothesis.
  • the shopper is prompted with various feature-price pairs that cover choice set and is asked to indicate purchase interest
  • Shopper inputs information form the Predictive Purchase Interest Model (PPIM) and sets baseline variable weights. These results are stored in the data base 1600.
  • the shopper is prompted with questions seeking to refine PPIM by probing inconsistencies between BCNU hypothesis and PPIM view (if any).
  • the PPIM is applied to all product/ feature options to calculate "fit" or purchase interest score.
  • the shopper is presented with product choices with the highest PPIM score and, in some embodiments, additional subjective information, (e.g., reviews).
  • FIG. 7 is a block diagram of an example of a system 701. A shopper application
  • a retailer web portal campaign management display and input system provides the interface to retailers.
  • a marketer web portal campaign management display and input system 704 provides the market interface.
  • Analytics/research web portal display and input system 705 provides tools for the retailers and marketers.
  • the databases used by the system include the product database including
  • the analytic cost minimization module 701 receives all of the above inputs and determines Shopper Characteristics 1400, basic Category Needs & Usage (BCNU) 1500 Predictive
  • FIG. 8 is a flow chart for the method of Making a List and Identifying what and where to buy item spending the minimum amount within defined Shopper preferences.
  • the shopper logs on to the website or starts a streamlined version of the shopper app on the mobile device.
  • the shopper selects a category of interest (e.g., cameras).
  • the shopper responds to Questions to inform BCNU and Predictive Purchase Interest Model (PPIM).
  • PPIM Predictive Purchase Interest Model
  • the PPIM recommends a product list.
  • the system issues one or more non-on-list (NOL) coupon offers for consideration.
  • the shopper selects "Done" ending list creation and initiating cost minimization algorithm.
  • NOL non-on-list
  • the Minimization Algorithm Calculates DNP for each product recommended by PPIM at all alternate e-Retail/Retail locations.
  • the minimization function Identifies "Initial Lowest Cost” (ILC) item and basket option and Retail location providing.
  • RIs retailer incentives
  • step 820 the Minimization Algorithm incorporates RI's and recalculates basket cost, now the "Subsequent Lowest Cost" basket and Retail Location providing.
  • the ILC becomes the SLC.
  • SLC is displayed to Shopper along with Mis.
  • step 826 a determination is made whether there are any marketing incentives (Mis). If so, step 830 is performed. If not step 828 is performed. At step 830, the Minimization Algorithm recalculates basket or item cost including all Mi's - final SLC becomes "Final Lowest Cost (FLC) item(s) or list. At step 828 the SLC becomes the FLC. At step 832, the Lowest cost for item and Retail store (FLC) presented to Shopper for shopping trip/e-purchase.
  • FIG. 9A is a flow chart showing Retail store, and FIG. 9B shows the
  • the shopper uses FLC list to shop for item(s) in streamlined fashion.
  • the shopper takes the shopping list to the retail store on the smart phone or other mobile device.
  • Shopper is presented with a splash page of "In-Store Incentives" (ISIs) or Ad.
  • ISIs In-Store Incentives
  • Ad Ad
  • a determination is made whether ISI is accepted. If so, step 908 is performed, and the selected Items (if any) added to list and basket total.
  • the shopper picks up items on the list.
  • the shopper checks out, (optionally scanning his/her smart phone or other mobile device) to record and validate purchases, redeem e-coupons and create valid redemption record for coupon fulfillment
  • coupons and other promotional offers are validated and fulfilled by the displaying of a scanner bar, shopper number, frequent shopper card, radio signals, WIFI signal, blue tooth signal, near field communication, LED modulated light interacting with the scanner system or other electromagnetic, sound, visual means or other methods.
  • step 914 the Shopper goes to eRetailer and enters tracking code.
  • step 916 the Shopper is presented with a splash page of additional incentives, if any (ISIs) or Ad.
  • step 920 the selected Items (if any) added to list and basket total.
  • step 922 the shopper proceeds to checkout.
  • step 924 shopper checks out, entering tracking code to receive any additional incentives, ecoupons and records the purchase.
  • the mobile device determines whether all of the items on that list have been checked off.
  • FIG. 22 shows a warning issued if there is one or more item on the list that has not been checked off, and asking if the shopper wants to return to the list and continue shopping.
  • the mobile device displays a bar code and number representing the customer's loyalty card number to be scanned or read in by the cashier, as shown in FIG. 23.
  • the Shopper may use device-to-device communication methods other than barcode as enabled by system including: Bluetooth or cellular (eg CDMA) wireless transmission, near field communication, LED, modulated light, audio (sound wave) or other means.
  • the first component platform herein the "Shopper App” is web portal and Smartphone application for consumers or herein “Shoppers” that: [0131] Allows Shoppers to enter a specific product category of interest (eg. "Cameras”) forming the initial starting point for determining how a Shopper intends to use the item and what is important to the Shopper for establishing which specific products to research.
  • a specific product category of interest eg. "Cameras”
  • BCNU Basic Category Needs & Usage
  • PPIM Predictive Purchase Interest Model
  • Product options are ranked by the over "Fit" or purchase interest as determined within the PPIM model.
  • the product list includes links to peer and professional reviews, review key-word search database and other subjective or related information (reliability data).
  • recommendation list Recalculates value and re-presents when sliders were changed. Showing original rank next to items. Final slider settings are recorded in system for post purchase validation.
  • Displays recommend list of items for the shopper that includes a "check box” icon or other similar graphic device or icon for different choices.
  • Mi's are any form of promotional offer that is displayed to the shopper along with the final lowest cost (FLC) list.
  • Mi's can be planned and inserted into the shopper app by at least one web-based campaign management system. Mi's can be inserted by marketers, retailers, ad agencies or interested third parties.
  • the list will be a final list.
  • This list is herein called the Final Lowest Cost list (FLC)
  • the Shopper can then select an option or trigger that orders that basket list for more rapid product selection and purchase at the recommended store.
  • the list is ordered by item category.
  • the list is organized by location in the store.
  • the list is organized by store department.
  • the list is organized by shelf position.
  • the shopper Once the shopper has completed shopping, and items can be purchased either at a physical retail outlet or at an eRetailer.
  • the system will allow the shopper to use their smartphone application to present a scan-able bar code, blue-tooth signal or near field , Led signal, number communication (NFC) enabled signal to record the purchases and link electronic coupons, and shopper card incentives to the shoppers' identity, to reduce basket cost
  • the system will present the shopper with a purchase promotional code that will verify shopper identity, trigger incorporation of various shopper discounts and incentives (RIs and Mis) and record purchase in shopper information database and PPIM validation system.
  • the shopper App captures servers and databases, consumer demographic information, purchase information, preferences and decision rules and other information relating to purchase behavior.
  • the shopper App and supporting sytems is compatible with iPhone (Apple OS),
  • Android Android OS
  • RIMOS Blackberry devices
  • Microsoft OS and other suitable smartphone or mobile device operating systems, tablets and other mobile devices.
  • the Shopper responses are then used in an feature/price option by key algorithm to form an initial BCNU model Shopper segments and for that classifies the shopper into a shopper individual Shopper.
  • BCNU and PPIM inputs BCNU and PPIM inputs.
  • the PPIM then presents from 1- 10,000 product options ranked on how well
  • the system allows
  • the PPIM proprietary reviews, peer reviews, and algorithm identifies the items to present in reliability ratings.
  • Step 1 and Step 2 can be delivered segments) as a technology capability that can be
  • Manufacture Advertising an existing e-commerce site or physical and Promotion Engine that store location. serves up pre-determined offers to specific target Shoppers based on demographics, purchase history and purchase intent.
  • the PSA recommends the best venue to Database of Retail selects the purchase the item(s) taking into account information including item location to things like store preferences, retailer level detail, list prices, purchase the reputation, financial strength, personal promotional prices and item(s) purchase history, cost of travel, online location.
  • Alternative purchase venues may be time of day and day of provided that optimize economic utility in week, items selected, ways other than price - ie convenience, departure location, and past behavior, store loyalty programs, etc. store loyalty program
  • Promotion (Rls) Engine that serves up pre-determined offers to specific target Shoppers based on purchase intent, purchase history, and demographic information as they are making their decision regarding which store to shop at.
  • Shopper is The PSA presents the optimal location for Algorithm analyzes prices presented with purchase, and the final price net of all and promotions offered by the total "dead special offers and incentives.
  • retail location and net" cost to recommends the optimum purchase location for purchase based selected items on price, retail store and at the product preference and recommended previous shopping patterns.
  • retail location The system will learn over (virtual or time and optimize physical) recommendations based on actual Shopper buying patterns.
  • Shopper is Once the Shopper enters a store the store Geo Targeted Promotion offered can offer promotions specifically targeted Engine that serves up additional to that Shopper and presented on their retailer offers to select promotions smartphone.
  • Shoppers based on the once live in the Shoppers' demographics, store or on the A similar offer can be made by an e- purchase intent, purchase website commerce retailer once the Shopper visits history, and current location their business online.
  • the offer can be (both physical and virtual). presented via the Shopper's smartphone or Mis can also be presented directly on the website or once the cart is at this final pre-purchase displayed. stage.
  • This special offers including: Bluetooth or cellular (eg CDMA) purchase history can be wireless transmission, near field integrated and aggregated communication, LED, modulated light, with store loyalty program audio (sound wave) or other means. data.
  • Bluetooth or cellular (eg CDMA) purchase history can be wireless transmission, near field integrated and aggregated communication, LED, modulated light, with store loyalty program audio (sound wave) or other means. data.
  • the Shopper will Database of bar codes, utilize a special promotional code or tag special codes or tags that provided by the manufacturer and/or are assigned to Shoppers in retailer to redeem all relevant coupons and the PSA to identify each special offers. unique shopping experience and transmit promotional information at the point of purchase.
  • One detailed example of an embodiment is a system for automating and streamlining consumer grocery shopping purchase-decision process of product choice preference and lowest-price matching enabling targeted, immediately pre-purchase decision direct- marketing capability.
  • Some embodiments provide a system for delivering highly targeted and situation- ally relevant/aware promotional offers and advertising to a consumer shopping for groceries
  • a system in which smart phones or other hand held mobile devices and/or a computer are used to develop a grocery shopping list and streamline the shopping price for shoppers.
  • the system can access substantial data processing and database repositories, and calculate the lowest price for a group of chosen items both individually and together ("basket") thereby recommending to the shopper the specific items and retail location to purchase them at that results in spending the least amount of money.
  • the lowest price, or "Dead-net Price” is the price for a given item that takes into account the manufacturers item cost to the retailer, customer margin or pricing strategy, manufacturer incentives (TPR or temporary price reductions), other manufacture incentives, coupons of all types and other retailer incentives, sales and promotions.
  • dead-net price See Figure 10 for examples of dead net price.
  • Consumer dead-net price is the lowest price available to consumers on a individual item when all active promotional programs are taken into account. The following definitions are used:
  • the TPR and MIP may or may not be reflected in price or passed on by the retailer to the consumer.
  • the retailer markup may be negative for loss leaders, may vary by item, pricing zone, retailer strategy etc. - peculiar to chain, item and strategy.
  • the retailer gross margin and margin may be negative, or higher or lower versus typical margin. They could also include payment discounts/terms and returns & allowances payments or adjustments.
  • the dead-net price of that item represents the lowest price that would be charged to the shopper when all active marketing incentives or other price related incentives are taken into account along with the normal shelf price at a particular retailer. Even if all live marketing incentives are taken into account the price at one retailer may still be lower than another due to a lower mark-up (e.g., Every day low price (EDLP) strategy versus Hi-Low price strategy), more efficient operation, different cost of goods and other factors.
  • EDLP Every day low price
  • Hi-Low price strategy e.g., Hi-Low price strategy
  • Store #1 and Store #3 are two retailers pursuing an "ED LP" or "every day low price” pricing strategy.
  • Store # 2, 4 and 5 are all "HI/LO" or high-low pricing strategy customers.
  • Store 6 is an e-retailer. HI/Lo customers set the shelf price high and then discount or place the product on sale to attract shoppers.
  • Store #2 has just this kind of sale. Looking just at the shelf price - the shopper buying at Store 5 pays $1.10 MORE for the SAME product than if they had purchased the item at Store 1. While Store 6 has the highest price on this item, the shopper's overall basket may be lower, and a shopper's total list and by-item pricing are considered. Further as discussed below with respect to "cherry-picking", in some embodiments, the user is given the option to request a lowest price basket to split between/among two or more stores, so other items from store 6 may be relevant.
  • a shopper must go to each store and compare the prices for the desired items at the shelf, evaluating each item and each suitable substitute.
  • the shopper must sift through over 100 coupons delivered on Sunday via FSI, review the shopper's mail during the week for mailed coupons, review 200-400 items in weekly retailer circulars (for all the retailers in the immediate area).
  • the shopper would have to calculate the dead-net price for each item at each store. Once this is done the shopper would have to total up the various item options and their respective dead-net prices to determine a basket cost (for each combination) and then they can choose the lowest cost store.
  • TPRs temporary price reduction
  • TPRs are funds given to retailers by brand Marketers to promote specific brands or products. Typically, a brand Marketer gives the retailer a certain dollar amount per case or unit purchased by the retailer during a time period. Retailers "pass-through" the price reduction reducing the price of the targeted item. Often however, the retailers might purchase X weeks of inventory with the TPR but sell, say X-Y at the reduced price pocketing the TPR on the remaining inventory and increasing margin. TPRs, by their nature, are untargeted with all shoppers getting the discount when it is available.
  • the variables that are used as targeting variable can be but are not limited to, for example, demographic information, attitudinal information, behavioral
  • the system can be used while Shoppers are in the process of shopping and on mobile devices -the system has the unique ability to target shoppers as their purchase intent (Category, brand, product, flavor, size, etc.) is revealed - BUT BEFORE THE PURCHASE OCCURS.
  • the term "grocery” is not limited to items sold in traditional supermarkets (not just food, personal care items (e.g., deodorant), and household cleaning supplies (e.g.. dish soap), but extends to any other items that may be available in a supermarket, online auction or e-tailer (e.g., eBay), club store, convenience stores, dollar store, mass merchandiser, hybrids, e-store now or in the future.
  • this system can 1. Radically simplify and streamline the grocery shopping list preparation and the item selection and purchase process, 2. increase the visibility of the retailer and Manufacturer incentive programs, 3. Enable the shopper to interact with other shoppers and friends via social means, 4. Enable the shopper to track and monitor the spending, preferences and store-level price performance and5. Save shoppers money, 6. Save time, 7.
  • this system can: 1. Enable the retailer to deliver marketing incentives to a targeted shopper group that has decided to shop in the retailers store, or another retail location, but has not yet gone shopping, thereby changing or modifying the Shopper's decision, 2. Enable the retailer to deliver an e-circular to a shopper as they enter a specific grocery store or retail location that is targeted to the particular specials of an individual store and can be targeted to a specific shopper of shopping list, and 3. Enable the delivery of targeted advertising via the shopper application at appropriate points during shopping preparation and execution, 4. Signal personnel where a shopper is near 5. home, deliver and pickup.
  • this system can: 1. Enable the delivering of electronic or e-
  • Coupons to the shopper 2. Enable the insertion of special incentives that are delivered after a shopper has decided what to buy but before the purchase has been made - based on decision rules and /or conditional rules-allowing for strategic and targeted incentives, and 3. Enable the delivery of targeted advertising via the shopper application at appropriate points during shopping preparation and execution and 4. Enable completely targeted promotional support instead of the current mass untargeted approaches.
  • the system comprises hardware, software, databases and servers, processors and communication networks, wired and/or wireless, that provides a robust, extremely fast and highly reliable experience for target customers including consumers or shoppers, retailer marketing or staff personnel and brand Marketers/staff personnel and researchers and other professionals.
  • Some embodiments comprise four inter-linked technology platforms targeting shoppers, retailers and brand Marketers.
  • Basket is used to describe a group of products (1-X) that a shopper puts on a list and intends to purchase on a shopping trip or trips.
  • the items in the list may be specified with very specific detail, e.g., "Prego Spaghetti Sauce, 24 oz, Traditional” or more generally at the "category” level e.g., "Spaghetti Sauce.”
  • a shopper specifies an item on the list at the category level the shopper may have in mind very specific items (e.g., specific brand, flavor and size) that would be suitable to meet their needs, or the shopper may be indifferent between different brands of products within an acceptable range of quality and size.
  • the system allows shoppers to prepare a shopping list using a web portal or smart phone, tablet (or other mobile device), terminal or other input device.
  • the system could allow for the data entry of the basic list by filling out a form or making a paper list and then optically scanning the list into the device. The system them uses the scanned-in data in the manner described herein.
  • FIGS. 1 1-16 summarize the grocery shopping example.
  • FIG. 1 1 is a block diagram of an exemplary system.
  • at least one database server provides access to the system databases, including retailer pricing/product database server 100, S U item image database server 200, SKU/Item level retailer weekly circulars database server 300, and SKU/Item level coupon offers database server 400.
  • the retailer pricing/product database server 100 includes: by-store, pricing zone, and individual item data for SKU #, Pricing, quantity, flavor, brand/mfg., list price, promoted price, description, and optionally other data.
  • a plurality of retailers 1 . . . , N access the databases 100 and 300, and a plurality of product manufactures 100, 200, . . .
  • N00 access databases 200, and 400.
  • Data may also be received from other external data sources 501 , such as manual data entry, automated web capture, web crawlers, secret shoppers or the like.
  • the data transmissions between the various servers and various non- transitory computer readable storage medium may be via internet, wireless or other electronic or physical means.
  • FIG. 12 shows the retailers in communication with retailer incentives database server 600, retailer location based offers database server 700 , retailer advertising database server 800, and retailer logo and tag line database server 900 (which may be hosted in the same computer or a different computer from the servers of FIG. 1.
  • the server 600 contains
  • Server 700 includes marketing incentives presented to Shoppers when they enter or select a specific retailer for item purchase - similar to in-store "wall of values" or in-store circular offers.
  • Server 800 includes video interactive, and still ads for display within shopper smartphone and internet portal applications.
  • Server 900 includes Retailer logo and tag line files for insertion into Shopper smartphone application and Internet portal applications. logo and tag lines are also available to Retailer and Marketer portal applications. Data may also be received from other external data sources 101 , such as manual data entry, automated web capture, web crawlers, secret shoppers or the like.
  • FIG. 13 is a block diagram of the product manufacturer interface.
  • the product manufacturers 100, 200, N00 in communication with marketing incentives database server 1000, marketer coupon offers database server 1100, marketing advertising database server 1200, and marketer company and brand logo and tag line database server 1300.
  • Server 1000 provides conditional Promotional offers , insertion rules, offer values, items or the like offered to Shopper by Marketer within basket maximization cost minimization function impacting the Final Lowest Cost (FLC) list or basket.
  • Server 1100 provides coupon incentives delivered matching FSI or other off-line efforts for use in calculating Initial Lowest Cost (ILC) list or basket. Coupon incentives are delivered within Shopper smart phone and internet portal for use in calculating Initial Lowest Cost (ILC) for item (s)or basket.
  • ILC Initial Lowest Cost
  • Server 1200 Includes Marketer video, and still ads for display within Shopper smart phone and internet portal applications.
  • Server 1300 includes Marketer company logo, brand logo, tag line files for insertion into Shopper smart phone application and Internet portal applications. The logo and tag lines also available to Retailer and Marketer portal applications. Data may also be received from other external data sources 1301, such as manual data entry, automated web capture, web crawlers, secret shoppers or the like.
  • FIG. 14 show the shopper preference databases 1400, shopper information database 1500.
  • Database 1400 includes information such as Location/Home Store; Other Favorite Stores - Store substitutes; Decision rules (How Shopper wants price minimization algorithm to perform relative to substitutes, such as lowest comparable price per oz, price per wash, price differential to choose brand substitutes, lowest price override, or the like; preferred brand(s) by category; Suitable substitutes by category (other brand, private label etc.).preferences for size, flavor, color or the like.
  • the database 1500 includes name, address, log-in name and ID, demographic information, item purchase history; basket/ring purchase history; promotion response history; preferences information; pricing differential response history; and store preference history. [0185] FIG.
  • 15 is a flow chart for the method of making a list and identifying what and where to buy item(s) spending the minimum amount within defined Shopper preferences.
  • the shopper logs on to the website or starts the shopper app on the mobile device.
  • the shopper enters preferences data.
  • the shopper creates a shopping list.
  • the system issues one or more not-on-list (NOL) coupon offers for
  • the shopper selects "Done” ending list creation and initiating cost minimization algorithm.
  • the Minimization Algorithm Calculates DNP for each on the list, shopper preference identified item substitutes at all alternate retail locations.
  • the minimization function Identifies "Initial Lowest Cost” (ILC) list/basket and retail location providing.
  • RIs retailer incentives
  • the Algorithm incorporates RI's and recalculates basket cost, now the "Subsequent Lowest Cost" list and retail location providing.
  • the ILC becomes the SLC.
  • SLC is displayed to the shopper along with Mis.
  • a determination is made whether there are any marketing incentives (Mis). If so, step 830 is performed. If not step 828 is performed.
  • the Minimization Algorithm recalculates basket or item cost including all Mis; the final SLC becomes the "Final Lowest Cost (FLC) item(s) or list.
  • the SLC becomes the FLC.
  • the Lowest cost for item and retail store (FLC) are presented to shopper for the shopping trip.
  • FIG. 16 is a flow chart of an example of a Retail store, Shopper uses FLC list to shop for items in streamlined fashion.
  • the shopper takes the shopping list on smart phone to a retail store.
  • the shopper is presented with a splash page of "In-Store Incentives" (ISIs) or Ad .
  • ISIs In-Store Incentives
  • Ad Ad
  • the shopper picks up items on the list.
  • the shopper checks out, scanning smart phone or presents a frequent shopper card, frequent shopper number or other identifier to record and validate purchases, redeem e-coupons and create valid redemption record for coupon fulfillment.
  • the system described herein captures key shopper demographic and preference data including: decision heuristics, substitutable brands, preferred/substitutable stores or retail shopping locations, preferred sizes, flavors, packs, etc. This information comes both from internal sources upon registration and also from shared information from loyalty programs of the retailers participating in the program, as well as other external sources and co-developed sources, such as manually entered data, screen scraped data, or the like, . Additional information about the user is derived from data mining and analysis of the above data.
  • the system can use these data in calculating the dead-net price for an item and the
  • Final Lowest Cost representing the lowest overall item and basket cost to the shopper, simultaneously identifying the store to purchase the list of items for the FLC cost or price.
  • FLC Final Lowest Cost
  • the system allows retailers and Brand Marketers or Manufacturers the opportunity to influence shopper choice just prior to actual purchase by providing targeted incentives based on the immediately planned shopping trip list, preferences, demographics, geographic or competitive inputs, prior trip behavior, forecasted future behavior, competitive factors, weather, shopping location or other means.
  • the system includes at least one programmed processor that automatically: 1. Matches items on the shopper's list (General or specific) to specific substitute items at the targeted retail shopping store or stores, calculates the item and overall list basket price and dead-net prices for the items and basket and recommends a specific retail store for the shopper to achieve the lowest overall basket price or cost inclusive of available retail pricing, promotions and coupons for the items on the list and suitable, shopper defined substitutes.
  • shopper's list General or specific
  • the system includes at least one programmed processor that automatically: 1. Matches items on the shopper's list (General or specific) to specific substitute items at the targeted retail shopping store or stores, calculates the item and overall list basket price and dead-net prices for the items and basket and recommends a specific retail store for the shopper to achieve the lowest overall basket price or cost inclusive of available retail pricing, promotions and coupons for the items on the list and suitable, shopper defined substitutes.
  • FIGS. 17A - 17D show an example of this list process.
  • the user enters a plurality of items.
  • FIG. 17B when the user has selected a sub-classification sufficiently low-level for the products remaining to be substitutes, , but at a sufficiently high level that the system can select from several substitutable products to save the shopper more money, the mobile device displays that sub-classification with the ADD button.
  • the system executes the cost minimization and identifies a basket of products and a store. The results of the computation are shown in FIG. 17C. The total basket cost and percentage savings (relative to the same items at the most expensive store analyze) are shown.
  • each SKU # Details about each SKU # are displayed, with the number of units next to it.
  • An "Extra Savings" button next to one of the SKU #'s indicates an RI or MI that is available, and which the user can view and accept by selecting the extra savings button. For example, the system may suggest that the cost per jar is lower if the user buy's a third jar of spaghetti sauce.
  • the system optionally emails e-coupons to the user, instructs the user to print them and to check in upon entering the store (for additional offers).
  • FIGS. 18 A - 18D are wireframes showing an example of the store check in procedure.
  • the system recommends that the user go to the first store to save more money.
  • the system re-computes the user's basket at the second store.
  • the system prompts the user to perform a search using the system store search function, and to add the store to the user's list.
  • FIG. 18B shows the case in which the user checks in at the recommended store for the greatest savings.
  • a check in screen reminds the user to turn on location services, so that a GPS equipped mobile device precisely locates the user and automatically checks in the user upon arrival at the recommended store.
  • FIG. 18B also shows control buttons for home, specials & coupons, my stores and my profile. The specials and coupons can assist the user in manually finding any current specials.
  • the "My Stores" button allows the user to add or subtract up to a predetermined number (e.g., 4) of stores from which the user wants the app to recommend a store to find the lowest basket price on any given day. Selection of the "My- Profile” button takes the user to a screen for updating the user's registration information.
  • FIG. 24 shows an example of a display for selecting stores.
  • the stores are added by the user typing in the name of the store. Each store so entered appears on the display of the mobile device with a checkbox to allow the user to select the store as a candidate for the current shopping trip (from which the system will recommend a store).
  • the user enters a zip code and the mobile device locates stores within that region as well as displaying them on a map (for example, by checking a local white pages). If no store is found an appropriate message is conveyed to the user.
  • the mobile device app upon arrival, the mobile device app identifies when the user has reached the store, and displays an e-circular or additional in-store promotions. For example, in FIG.
  • the user is offered a special price on a baguette, which was not part of the user's list prior to entering the store.
  • the mobile device displays an ADD button which, if selected, adds the offered product to the user's list.
  • the mobile app checks whether the user's profile includes a loyalty card number for the store at which the user has checked in. If not, the mobile device prompts the user to enter the loyalty card number for greater savings.
  • FIG. 20 shows a manual check in screen displayed by a mobile device without a
  • Buttons are provided to enable the user to select: check in, not shopping yet, shopping at a different store, or skip check in. If the user skips check in, some offers may be unavailable to the user.
  • the system is able to match list descriptors to specific items and then provide the shopper with a recommendation of where to shop and what to buy (item level) reflecting actual retail pricing (shelf prices, promoted prices and combinations thereof).
  • the processor(s) running the shopping application program in essence automates the way a shopper selects a store and makes purchase decisions, in a way that was previously unachievable, thereby streamlining the grocery item purchase process.
  • the system enables both retail store marketers, herein “retailers” and brand marketers, herein “marketers” to deliver marketing incentives of various kinds including additional price-related incentives, directly to the shopper during the preparation and execution of their shopping list and shopping trip.
  • This capability is unique for the grocery industry.
  • These incentives are included into the basket cost and can be targeted by retailers and Marketers based on a broad number of shopper characteristics, preferences, behaviors, purchase history and the like.
  • the system further provides post and pre- shopping trip analytics to help Marketers and Retails evaluate their promotional efforts, better understand the shopper and profitably grow their respective businesses. [0198]
  • This system can influence shoppers after they have indicated what they intend to purchase on their shopping trip - but before the actual purchase has been made - in a streamlined highly functional application.
  • Some embodiments comprise a physical system architecture, software, internet web portals, a mobile device (e.g., smart phone) programmed with application(s) or "Apps" , databases, business systems and methods of capturing, analyzing information quickly to deliver in near-real time powerful analytic capabilities.
  • some embodiments includes a system for delivering electronic coupons, marketing incentives, and advertising to consumers ready to make a purchase at a retail store.
  • Some embodiments comprise a system of hardware, software, databases and servers, processors and communication networks both wired and wireless that provides a robust, extremely fast and highly reliable experience for target customers including consumers or shoppers, retailer marketing or staff personnel and brand marketers/staff personnel and researchers and other professionals.
  • An embodiment comprises four inter-linked technology platforms targeting shoppers, retailers and brand marketers.
  • the first component platform herein the "Shopper App” is a web portal and smartphone, tablet or other mobile access device application for consumers or herein “shoppers” that:
  • voice command voice recognition software
  • typing in a keyboard interface or smartphone touch pad/keyboard or via item scanning photo or handwriting recognition with the smart phone/device, accessing an item lookup database as the item is typed or filled-in.
  • [0202] (2) Includes an item level database stored in a non-transitory computer readable storage medium, with all (or a large number) of the unique items carried in grocery retail outlets in the targeted geography (e.g., United States, Canada, France, Germany, England, Japan, China, South Korea, Brazil, Argentina, Switzerland and Denmark - or other region sharing a brand and retail base).
  • the item database can have as many as -500,000 unique SKUs or such amount(s) as to cover more than 50% of the items most purchased and of concern to shoppers at a retail location.
  • the database size is smaller or larger
  • the database is highly structured with each item being dynamically categorized and tagged so as to enable rapid matching of category level descriptions to specific products.
  • one of the preferences specifies the way in which price is calculated and compared (on a SKU or unit/area/count/weight basis). That is, the user can specify whether the system finds the equivalent product having the lowest cost per package (usually a smaller package) or lowest amongst a single like size to minimize this trip's cost, or the lowest cost per unit of product, to minimize total long term costs.
  • the preference database is not updated on-the-fiy throughout the day, but is periodically updated (e.g., daily).
  • the system initializes each user to have a default set of preferences, which are subsequently updated. For example, the system may initially choose a set of default preferred products (e.g., the 3 or 4 top sellers) for each sub-classification. The system may recommend one of these products; if the shopper deselects that product, that product is dropped from the user's preferences or is reduced in rank.
  • a default set of preferences e.g., the 3 or 4 top sellers
  • the learning includes beginning with a large set of prospective recommended products, and reducing the candidate set for a given user based on that user's behavior.
  • the initial set may begin with the top market share products accounting for 80% of total sales in category. This is intended to eliminate the low quality products from the candidate set. Unless the user enters a specific product outside of the top 80%, the modification of the user's preferences results in subtracting products from the user's preferences most of the time.
  • the initial preferences and/or updates to the user's preferences may be varied based on the user's demographics. For example, the target size may be based on the size of the user's household, recognizing that large families are more likely to buy larger sizes). Similarly, if the user is older, or has a smaller family, the preference may be biased towards smaller sizes.
  • the system allows shoppers to save, re-use and delete lists, Track shopper's purchase history at category and S U level. Allows shopper to access frequently purchased items list (at a category to SKU Level) and add items easily to a current or new shopping list.
  • the system allows the user to designate any other registered user of the system as having rights to access the user's final shopping list using their smart phone or other mobile device.
  • RI's from one or more retailers are pre-programmed and are applied to the basket totals to calculate SLC basket.
  • retailers "bid” in a real-time or preprogrammed fashion for the shopper's basket such that the winning "bid” becomes the winning retail store that the algorithm will then present to the shopper.
  • the RI's are applied to the basket cost and then a second or more rounds of RIs are accepted until no more RIs are available to further reduce the basket cost.
  • a group of shoppers puts their baskets up for a bid wherein retailers can agree to sell the shopper or group of shoppers the item or items of the respective baskets at a specific price. Other embodiments do not permit counter RI's.
  • the icon when pressed will display Mis that may be accepted by the shopper and result in: the replacement of the item in shopping basket that was immediately adjacent to the icon, recalculation of basket cost and inclusion of additional incentives relevant to item and basket costing. Mis can also increase the amount of the items in the basket that the offer is associated with and can add another item to the list, a manufacturer may decide that, anytime the consumer is a large household, the basket is greater than a threshold value, and the customer lives in a particular zip code, the manufacturer wants want to offer $10 more off their basket.
  • the offers may be own-brand types of offers to encourage the shopper to buy more product, or competitive switching offers, to try to persuade the shopper to try a different brand instead of the brand on the user's list, These Mis and RJs are made after the ILC is calculated, and with a high degree of knowledge about what the consumer is about to do.
  • FIG. 25 A-25C show the operation of the "Keyword to Category to SKU Funnel
  • Logic which enables a user to enter a string or keyword, make a small number (e.g., 1 to 3) sub- set descriptor selections, and receive from the system a display of commercially equivalent products. It the remaining items in the sub-set are sufficiently similar to be considered substitutes, an ADD button (or similar selection device) is displayed next to each remaining item. If the user clicks the displayed ADD button for selecting a sub-set descriptor to the user's basket (without ADDing a specific SKU), the user has specified enough information for the system to make an automated minimum cost selection among equivalent products, and the system will recommend the lowest price product to satisfy the item on the list.
  • a small number e.g. 1 to 3
  • the system prefers that the shopper not be overly specific. That is, the system allows the user to specify a narrow descriptor sub-set having a small number of SKUs, or even to specify a single, specific SKU; but if the user does so, the system i ssues a message suggesting to the user that the system can provide greater savings if the user selects a higher level, classification (broader sub-set descriptor), and allows the system to recommend a minimum cost basket based on a lower cost substitute product, To effectively minimize cost, the system requests that the shopper selects a classification level (corresponding to a sub-descriptor) specific enough in defining a product so the shopper App can initially recommend and display a reasonable set of items (e.g., 20 to 30) from which the system or the user can select the one of the recommended items having the lowest cost.
  • a classification level corresponding to a sub-descriptor
  • the system does not know enough about what the shopper means or wants to make an effective recommendation or run the cost minimization algorithm.
  • the system can display these sub-set descriptors and allow the user to select the next lower level classification. Assume the user selects "A loaf of bread.”
  • the system can then give the user a sub-set choice between fresh baked bread (from the in-store bakery) or pre-packaged bread from an outside supplier. Assume the user selects "pre-packaged bread”.
  • the system can then display the next lower set of sub-set descriptors, which may be "white”, “whole- wheat,” “rye,” multi- grain, and " brioche,"
  • the sub-set descriptors are pre- determined so as to be mutually exclusive, and to be completely exhaustive of the available products at the stores on the user's store list. That is, at a given level, each sub-set descriptor or product that is within the classification at that level is contained within exactly one sub-set at the next lower classification level.
  • the system is able to quickly reduce the number of products from which the system will either seek to further reduce the number of recommended products from which to * make a recommendation, or from which the system will actually make the recommendation.
  • the shopper selects "whole-wheat"
  • the user has identified a sub-classification containing commercial substitutes.
  • There are now a suitably small set e.g., fewer than 20
  • brands, sizes and types of pre-packaged whole wheat bread from which the system can select the lowest price product.
  • the system displays an "ADD" button.
  • the system allows the user to manually select their favorite SKU by "whole wheat” display as brand choices.
  • the system If the user makes a manual selection, the user's preference for this type of whole wheat bread is recorded in the database. The system also reminds the user that more money can be saved if the user selects a sub-set descriptor instead of a specific SKU, and allows the system to make the selection. If the user does not make a manual selection of a specific SKU, or if the user returns to the "whole wheat bread" classification subset, the system selects the lowest cost loaf from the set. Specifically, the lowest cost pre-packaged whole-wheat bread.
  • This Keyword-Category-SKU Funnel allows the shopper Apps to start with a reasonable approach to selecting a specific item for consumers (as outlinedabove) and then quickly utilize "learning" to refine the approach over time.
  • the system has a database which is organized into several (e.g., 10-1 1) classification levels.
  • the shopper begins by typing in a string or keyword.
  • the shopper App of the system begins a keyword search of meta data, text, or of the available item database.
  • the database is organized so that at level 6, each of the classification sub-set descriptors defines a set of commercially equivalent, same or similar products.
  • the sub-set descriptors or SKUs at or below this level are displayed to the user with an ADD button, indicating that the remaining items within that sub-set are considered
  • the classifications above level 6 are arranged so that it is generally possible to traverse the index from a search term (e.g., bread) to the level containing commercial substitutes within three or some other small number (e.g., 2) sub-set descriptor selections.
  • a search term e.g., bread
  • the system allows the user to continue to drill down to level 0 or the lowest level, the greatest savings potential is provided when the user clicks the ADD button to add to his or her cart the sub-set descriptor for a broader sub-set.
  • SA search algorithm
  • the system causes the user's mobile device to display the three level-8 groups For example, the system can display, "soup,” “soup mix,” or "soup base.”
  • Level-8 group that corresponds with what the consumer wants (.e.g., soup)
  • the system will cause the MD to display the appropriate level 7 groups that go with the level 8 selection, (e.g., Ready-To-Eat, Condensed, Powdered, Mix).
  • Level 6 groups that correspond, (e.g, for a selection of " condensed," the level 6 groups of “chicken noodle", “beef, “chicken with rice”, etc. are displayed,) and ADD.
  • the level of specificity is low enough, e.g., the Level 6 descriptors are items considered commercial substitutes, for the system to quickly make a selection, or for the user to make a manual selection.
  • Level 6 group that corresponds e.g., chicken noodle
  • the sub-set descriptors may be "chicken noodle” and "homestyle chicken noodle”.
  • Level 5 group will display the Level 4 brands groups that correspond with Level 5 (e.g., Campbell's, Wegmans, Acme,Progresso).
  • the system allows the user to optionally continue to define further attributes that can further reduce the solution set.
  • the database is optimized so that the greatest savings can be realized if the user allows the system to select a product, once the user has made enough classification sub-set selections to reach a sub-classification in which all of the candidate items are commercial substitutes for one another, so that the system can select and recommend the lowest priced remaining candidate item.
  • the database is organized so that this level is reached by the time the user has drilled down to level 6 of the index. The consumer can continue to drill down to Level 1 or the lowest populated level.
  • the system permits the user to search on at least one string descriptor and find any product from the at least one preselected store in less than a predetermined number of inputs.
  • This predetermined number can be 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1.
  • FIG. 25 shows an example in which the user inputs the string "seventh”.
  • the mobile device displays several sub-classifications including the term, "seventh”.
  • the system displays the next level sub-classification descriptors, including "seventh generation household,” “seventh generation liquid,” “seventh generation dish,” “seventh generation trash,” and “seventh generation unscented,”
  • the sub-classification descriptors including that string are displayed, as shown in FIG. 25B.
  • the sub-classification has fewer than a predetermined number (e.g., fewer than 20)
  • all of the specific SKU #s are displayed with the ADD button.
  • the user has reached a smaller sub-set of substitutes than is desired for minimizing price.
  • the user has reached a lower level of the index (below level 6).
  • the system displays a reminder that the system can potentially save more money for the customer if the user specifies a category instead of an SKU#.
  • the mobile device suggests adding "dish soap” instead of "seventh generation dish liquid, lemongrass & Clementine zest.” If the user accepts this suggestion, then the item in the shopper's list is changed to "dish soap.”
  • Each successive selection acts as an additive (“Boolean”) filter to what is displayed. For example, assume there are 1500 items listed with “bread” in the description, with 3 level-8 groups of 500 products each. Once the Level 8 selection is made the search is now only considering the 500 products of the combined filter of "bread" and Level 8 group. [0239] In some embodiments, all the sub-set descriptors at a given classification level are mutually exclusive, and each remaining product at a classification level corresponds to exactly one of the sub-set classifications at that same level. In other embodiments, the database can be organized so that there is some small overlap between different sub-set classifications at that same level.
  • a rye-whole-wheat loaf may be classified under, rye bread, whole wheat bread and multi-grain bread.
  • the sub-set descriptors By defining the sub-set descriptors to minimize the number of such overlaps, the number of levels of selection through which the user drills down before a reaching a sub-classification containing only commercial substitutes is reduced.
  • a small number of miscellaneous items at a given classification level may not correspond to any of the sub-set descriptors in that level (in which case, selection of any of the sub-set descriptors at that level or a lower level will eliminate that small number of miscellaneous items from being considered in the final automated product selection.
  • an item may be eligible for selection by the system at level 6, if the user clicks the ADD button on a level 6 sub-set descriptor, but excluded if the user makes a selection at a lower level.
  • This rule permits the user to make fewer selections if the initial search by the user corresponds to a lower level (e.g., level 5 or 4) sub-classification and only a few SKUs correspond.
  • the algorithm will display the level 6 results with the ADD button. Once the user has clicked the "ADD" button, the user has provided sufficient information for the system to make an automated selection of a lowest price commercial substitute products at each store, corresponding to one of the items in the user's list. If the user has ADDed a sub-set identifier, the system adds the item (with whatever level of specificity the user has provided) to the user's basket for cost comparison between stores amongst similar items.
  • the rule of N does not continue beyond level 6.
  • the shopper can continue to drill down and specify additional sub-set descriptors, which act as filters, but the rule will no longer be used.
  • the algorithm will display Banana Bread-Loaf, Banana Bread-Mix, Banana Bread- Muffins .
  • the routine follows the steps outlined above but does NOT make the user click at Levels 10, 1. Rather, the system will display the lowest level (higher of 6 and 5) with ADD buttons immediately.
  • the algorithm will do an OR search of multiple keywords entered at levels 10 through 8 AND Level 6-3 . If there is a match at level 8 AND a match at Levels 6-3 the system will display subset of the identified level 8 categories which also match at level 6. If there is no match at levels 6-3 then the matching level 8 categories will be displayed; a standard (single keyword) drill down will start and the remaining rules of a simple search outlined herein will follow for the remainder of the search.
  • the system uses a price minimization algorithm, to find a local minimum price that satisfies the user's list.
  • the result is a referred to herein as a "minimum,” in that the total set of possible combinations is initially trimmed to reflect the user's selection of stores and preference settings. It is not necessarily the absolute minimum among every product sold at any store. Further, in some embodiments, the total set of products in the database which are considered as substitutes is trimmed to include a predetermined percentage (e.g., 80%) of the products of a given type.
  • stores may carry 30 different types (brands) of canned 10.5 oz cans of condensed chicken noodle soup. If 80% of all sales of canned 10.5 oz cans of condensed chicken noodle soup are made up of only the top ten sellers out of the 30 products, then the database can be trimmed to exclude the 20 products which make up the remaining 20% of total sales. Although one of these excluded products may have the absolute minimum price, if sales are used as a proxy for quality and preference, then the top selling products making up the 80% of total sales are viewed as having comparable, acceptable quality or as preferred by most shoppers. Thus, the top sellers are considered by the system as being substitutes for each other.
  • the minimum price determined by the system is not an absolute global minimum price, but a minimum price of a basket of products satisfying the user's preferences and meeting a level of market share or quality established by the administrator of the system database or users.
  • the user's preferences further allow the user to select a higher quality standard, so that only the products identified as "super-premium products" by the administrator are considered for this particular user, if there is a premium product version of one of the items on the user's list.
  • the system assigns an initial default set of preferences to each user.
  • the system provides the user with series of options for preferences, and allows a new user to self-identify his or her own preferences or affinity group(s). For example, in a grocery store setting, the system can allow the shopper to select a preference for super-premium food products. If the user selects this option, then the set of candidate products suggested to the user is reduced to exclude lower priced / lower quality items.
  • the system can allow the shopper to identify himself/herself as an extreme bargain hunter, in which case all products in the database (possibly including some products of lower quality and large sized units with lower price per ounce) are included in the initial candidate sets for that user. In this manner the system can establish a variety of product preference "presets" that represent different approaches to considering products as commercial substitutes and the shopper can select one of these presets as an initial starting point for their system preferences.
  • the system creates for each shopper a database of product substitutes (SdBPS) that will have the following initial presets- [0250] a.
  • SdBPS product substitutes
  • the system receives as inputs sales data for various products, which the system sorts by sales volume, and from which the system automatically identifies these top sellers, which are stored in the database. Alternatively, the System Administrator determines which product makes up the bulk of user demand.
  • the system correlates demographic information with size (e.g., households having four or more people most often choose the 32 oz. size, but households having 1-3 people most often choose the 16 oz. size). This information can be used in conjunction with the information obtained from the user during registration, to set the user's pre -defined size, via pre-sets or other setup capabilities.
  • Shoppers will have previously selected up to a predetermined number (for example, but not limited to, 4) of stores that the shopper considers acceptable substitutes from a shopper perspective. That is, these stores satisfy the shopper's criteria for convenience and perceived value. In addition the shopper will have created an initial shopping list of items at the categories and/or specific item level.
  • the system provides a real-time database of individual products including [0255] a. Categorization of each item at brand, sub-brand, size, flavor, pack and other descriptor levels (e.g., Organic, hormone free etc) -. The categorization according to levels will be based on a fixed or "dynamic" structure for different product categories, e.g., thereby effectively below 10 levels.
  • descriptor levels e.g., Organic, hormone free etc
  • the system will also keep a table or database of shelf prices and deadnet price (the price of an item incorporating all promotional discounts of any kind including coupons, temporary price reductions, shopper card discounts etc.)
  • the shopper selects one of the Level 7 descriptors. E.g., The shopper selects "Ready-to-Eat.”
  • Level 6 descriptor is sufficient for the App to make pricing choice decisions and so the App displays an "Add" button next to the flavors displayed in Level 6.
  • App displays "Chicken Noodle, Tomato, New England Clam Chowder.” The shopper selects "New England Clam Chowder”.
  • the system provides a warning message explaining to shoppers that more money can be saved by allowing the app to make tradeoffs at higher (less specific) levels within the database.
  • Pre-Algorithm List may be described by the Shopper in degrees of specificity ranging from broad to specific. All items on the list are described at a level that is at least as specific as to identify commercial substitutes within the product category. All items on the list are specified at most at the SKU level. For each item the MAP will look at related items (either related commercial substitutes, a more specific level of categorization but no more than SKU). For each item the MAP will compare the dead-net prices/unit of all substitutable items, if any, for each category, identifying the lowest priced item (price/unit) for the first store of the up to the predetermined number (e.g., 4) of stores selected above.
  • the predetermined number e.g. 4, 4
  • the MAP proceeds to make the same comparison for each item on the Pre- Algorithm list, until the lowest priced item is identified for each item (as now identified by the selected classification and sub-set descriptors) on the Pre-Algorithm List at the first store.
  • the app may first calculate the total basket cost at a retailer so that retailer coupons that are based on a specific basket cost target can be reflected prior to inclusion of other coupons or sales that might put the basket below the targeted amount.
  • the MAP also identifies the highest priced basket-store combination looking at the same size, category combinations identified for the lowest priced basket for each store. Thus, in this computation the same basket of products which is the lowest among all of the stores is then priced at all of the stores. The result is an "apples-to-apples" comparison for a basket of specific products (brands, sizes and flavors) at each of the selected stores.
  • the system displays the lowest priced Post- Algorithm List along with the name of the target store (i.e., the store having the lowest total price for the basket).
  • the savings level and total basket price for each store is also provided within the app for consumers tapping on display of "winning" store results.
  • buttons showing what types of discounts were incorporated into the final price are displayed next to the item (e.g., an icon for coupons, an icon for price reductions, and Icon for shopper card promotions)
  • SdBPS will cause a flag to be set for that size as a suitable substitute in the SdBPS.
  • the app will dynamically monitor size selection within a category and after a variable number of
  • Private Label Products Private label, store brand and generic products may be automatically selected as suitable substitutes within the SdBPS.
  • the system will determine in the initial presets which products are top sellers and suitably close in quality as to be considered suitable substitutes. Products that are of significantly low or lower quality than the top selling items in a category will not be flagged as substitutes.
  • Top selling is to mean those products and brands making up -80% of a category's sales and for which general consumer preference is positive.
  • the system and the App will seek to avoid recommending products that may be the cheapest - but have by individual consumer choice in the market - been deemed unacceptable or of significantly lower quality.
  • Top selling means those products and brands making up a predetermined percentage (e.g., -80%) of a category's sales. While these products may be flagged they will not likely be recommended by the MAP unless they are on sale and reach a unit cost that causes them to "win" in MAP - or the shopper changes preferences by using app or actively changing preference settings.
  • Organic/Free-Range/Wild/Hormone-Free products are automatically pre-selected as suitable substitutes within the SdBPS. Consumer generally recognize these items as being "better” but choose not to purchase them because of their higher price. Because of this, the system operates on the assumption that if these items are low enough in prices shoppers would be happy to have them appear on the post-algorithm list. The system will determine in the initial presets which products are top sellers and suitably close in quality as to be considered suitable substitutes. Products that are of significantly higher quality (and price) than the top selling items in a category are flagged as substitutes.
  • the system will also include certain descriptors in the database architecture - e.g., Level 0 of Table 1 so that these items can be added easily to a shopper's list and SdBPS.
  • a. Within the system setup screen on the app/website shoppers will be able to include or exclude organic/free-range/wild/hormone-free (or any) products from their SdBPS and the MAP.
  • the MAP can simply deselect the current or preset products in their SdBPS and select the Super Premium (or other) items.
  • the system will then use this new set for the MAP analysis. If there are sufficient oranic/free-range/ wild/hormone-free products in every category the system will make this a global preset; but if there are not sufficient oranic/free-range/wild/hormone-free products in every category the system will do this on a category-by-category or product-by-product basis.
  • the system allows the consumer to start from scratch in creating a personalized SdBPS, e.g., no presets, so the SdBPS is built by list creation. In this case, all items on the list are specific.
  • the system will capture nutritional and other product level descriptors that may include information relating to these attributes for each item in database.
  • FIG. 26A shows a screen that may be displayed if the user overrides the multiples approach, and includes all multiple promotional offers. Although the user's list requested 1 jar ($1.89), the offer provides six jars of the same product for a total of $6 (a 47% saving). Thus, if the shopper is willing to consider all multiple promotional offers, additional savings can be realized.
  • FIG. 26B shows the display after the user has tapped the "Accept" button. The quantity is automatically changed from "1 " to "6" and the extra savings indicator is shown. The estimated total savings and total cost are updated.
  • Bundle Packs Items are often packaged in bundles or with multiple units in a pack, e.g., toilet paper, 12 rolls. Bundle Packs are treated as suitable substitutes within the SdBPS as preset by the system. The system administrator will decide in advance what level of bundling is typical and appropriate and will cause these items to be flagged as suitable.
  • a global function will allow the user to override the Bundle Pack approach described above and include or exclude ALL Bundle Packs promotional offers in the MAP. If ALL bundle packs are included, then the quantity of an item bundled in one store is increased on the user's list, so that the same quantity is priced at all the stores.
  • a store "B" has fewer than the predetermined percentage (e.g., ⁇ 80%) of the items on the list, but except for these items Store B has the cheapest basket cost, and the savings is as least a preset opportunity cost (e.g., $10.00) then the system will present the Store B as the winner in the MAP and visually indicate that certain items are not available at Store B and indicate the single store of the stores that the MAP ran against that has the best price for these items (Split basket). This provide the user with an opportunity to realize savings by splitting the basket between two stores.
  • the predetermined percentage e.g., ⁇ 80%
  • Cherry Picking involves shopping multiple stores within the same week to maximize savings by purchasing predominantly sale items at two (or more) stores.
  • the system allows shoppers to set a minimum savings value (opportunity cost) that is used to judge whether Cherry picking/basket splitting is worthwhile and only pursue this strategy when the savings exceeds the opportunity cost. [0299] In some embodiments, the system allows the user to input a number of stores
  • the system exhaustively computes all the combinations in which any subset of the products is priced at one store and the remaining products are purchased at a second store.
  • the system can first do the normal (non-split) price computation at each store to compute the basket cost at each of 4 stores. Then the system assigns to each store the products for which that store has the best price, recompute sthe divided baskets at each store (taking out incentives that no longer apply when the total order is divided), and then compares the result to the single store cost.
  • the database includes a field for each product to identify whether the product is hypoallergenic or specially directed towards a segment of the population with low tolerance for a certain food (e.g., ciliac disease, diabetes, or the like). If a shopper is buying one of these products because of the absence of allergens and the system were to substitute something with an allergen not knowing the reason why the original product was chosen, the system would not provide the desired recommendations for the user. Some embodiments provide a warning or algorithm override for items that are known to be special diet in nature. Then the system notifies the shopper with a warning to be sure they specify specific items if an allergy concern is present.
  • a certain food e.g., ciliac disease, diabetes, or the like.
  • the system assembles a proprietary database of items that are "same” e.g. sauce varieties and sizes.
  • the size substitution algorithm only goes “1 up or 1 down” in size to avoid sizes that are inappropriate for small households. If the system collects individual household data the system can go 2 up/down for large households
  • user preference inputs can be provided for and engine to provide:
  • Driving, gas, parking, loading, checkout are all considered to be an incremental costs).
  • Driving time and cost are incorporated by looking at distance and shopper entered gas and time cost. Without shopper input, the App of the system uses average fuel price in the area of the shopper to determine cost of driving.
  • the system presents other retailer marketing incentives in a virtual "e-in-store circular", “e-circular” or “e-wall of values” format that may include a pop-up-screen on the smartphone, tablet, terminal, or other mobile device.
  • the e-circular is presented visually to the shopper when, and only when, they enter a specific retail location (location based service).
  • Service can be enabled by a variety of location based approaches including GPS, blue-tooth, FLC, cell tower triangulation or other methods.
  • the Mis and RIs may be keyed to seasons and basket contents in combination.
  • the retailer can have an RI within two weeks of
  • the system upon the user entering the store, if any of those four products is missing from the shopper's list, the system suggests to the user that he or she should purchase the missing item, and tell the user the sale price and aisle number. In another embodiment, the system only makes the suggestion of the user's list includes from 1 to 3 of the items (but not all four).
  • the system notifies the store when the shopper is within a pre-determined distance in travel time so that pre-picked grocery baskets can be brought to a pick-up location for the shopper's convenience.
  • Items may or may not change retail store recommendation once an item's cost is added to shopping basket total.
  • the Smartphone application presents a number scan-able bar code, blue-tooth signal, LED or near field communication (NFC) enabled signal to record the purchases and link electronic coupons, and shopper card incentives to the shoppers identity, to reduce basket cost appropriately and provide purchase record for validation.
  • NFC near field communication
  • the shopper App of the system captures consumer demographic information, purchase information, preferences and decision rules and other information relating to purchase behavior.
  • the shopper App and supporting systems are compatible with standard and widely used platforms, such as the iPhone (Apple OS), Android (Android OS) and Blackberry devices (RIMOS), Microsoft and related tablets, terminals and other mobile or stationary devices that can aptly deliver functionality.
  • iPhone Apple OS
  • Android Android
  • RIMOS Blackberry devices
  • the system allows the shopper to share shopping list and final purchase price with self-defined or other defined social networking peers, and allows the shopper to share store specials or other sales or products found at a retail location with friends or peers in a self-defined social network.
  • FIG. 21 shows an example of a screen in which the user has selected the "share" button, in response to which the mobile device displays a set of icons for selecting a sharing/communication method, such as Facebook, Twitter, short message service (SMS) or email.
  • a sharing/communication method such as Facebook, Twitter, short message service (SMS) or email.
  • the system provides links to permit the user to share and compare their savings with friends, to see who can save the most.
  • gaming techniques allow users to play shared games relating to shopping and saving using this system. Based on the user's behavior during the games, the system can extract additional information about the user's preferences. For example, in a game with options, the user's selection of options provides an indication of what the user likes, so that the system can use this information to populate/update the user's preferences.
  • the system provides the user tools for tracking how the user is doing against the user's own savings goals. The way the user uses these tools can further indicate the user's preferences.
  • the system collects point-of-sale information from the user transactions and stores this informational along with all demographic, behavioral, pricing basket items, and promotion information.
  • the system further comprises an analytic engine for evaluating promotional offers and tracking performance of the offers.
  • the system collects and updates items, item and store level pricing and promotional offers periodically. For example, the period can be monthly, every 3 weeks, every 2 weeks, every week, every day, or live updates.
  • the second component platform herein the "Retailer Portal,” is website based portal for grocery retailers or herein “retailers” that:
  • [0325] includes a web-based direct marketing campaign management system for grocery retailers that allows retailers to influence shoppers to shop at their store(s) by offering incentives to shopper and/or select target audiences.
  • the target audience(s) can be selected based on shopper characteristics.
  • shopper characteristics includes the following individual items or combinations thereof: demographic characteristics, psychographics or affinity groups, name, household location, past buying history, total basket price, specific item(s) in the basket, number of items in the basket, combinations of items in basket, time of year, date proximity to major holidays, date proximity to social events, date proximity to religious dates or holidays, date proximity to political events, family size, shopper preferences or decision heuristics, current basket of items, past basket items, estimated use-up rate for items, weather reorder recommendations, at-home inventor ⁇ ' for items, geo-location, location in store, and other factors.
  • RI incentives are presented at the point of store decision - when the shopper activates the shopper App algorithm to select the best store to shop at to obtain the lowest item and basket price amongst target store set. In some embodiments, RI's are presented post minimization, with Mi's.
  • the retailer portal also has a basic and advanced analytics module to analyze shopper behavior and promotional performance. The basic functionality is accessible through the standard retailer portal web site and campaign management system. Advanced analytic capabilities are offered via a fee based portal for Promotion and shopper Analytics and be available on a subscription, fee, consulting or other revenue generating basis.
  • the third component platform herein the "Marketer Portal” is website based portal for brand marketers, herein “marketers” that:
  • advertisement as the shopper is creating and or finalizing a grocery list prior to a shopping trip(s) or item purchase.
  • Figures 28A and 28B show traditional methods of distributing direct mail circular and coupons, respectively.
  • Traditional consumer promotions from both Retailers and Brand Marketers have been broadcast to large audiences with little or no customization.
  • Retailers distribute weekly circulars notifying all shoppers of the same specials available in their stores. These circulars can be tailored to a group of stores, but they are not tailored for each specific shopper.
  • Brand Marketers deliver a coupon or advertisement to an extremely large audience hoping to reach interested and relevant consumers.
  • Figures 29 A and 29B show an embodiment of a method of electronically delivering e-circulars and e-coupons through a processor or mobile device running the shopper app.
  • Promotions delivered through the shopping app are completely customized and delivered in real-time based on inputs received by the system. Rather than creating a circular for an entire store or set of stores, retailers can craft circulars for specific customers taking into account factors such as their purchase history, profitability, and intended purchases in their upcoming shopping trip. Pricing and promotions can be delivered dynamically and in real time in response to shopper actions, behaviors, preferences or competitive pricing available in the marketplace. Brand marketers can deliver custom e-Coupons, incentives, and advertising to specific shoppers based on data such as that shopper's past purchase history, their demographics, or their current purchase intent.
  • FIG. 30 is a block diagram of a web-based campaign management system.
  • the web-based campaign management system is used by retailers and brand marketers to manage the promotions delivered to consumers via the app.
  • Brand marketers and retailers can create incentives - marketing incentives 2902, coupons 2904, and advertising 2906 - that are delivered to shoppers dynamically and in real time based upon the shopper's current activity 1650, shopper information 1500, or shopping preferences 1400.
  • App usage populates the shopper activity 1650, shopper information 1500, and shopper preferences 1400 databases.
  • the web-based campaign management system takes real-time input from these databases and dynamically delivers customized promotions to specific targeted shoppers.
  • purchase incentives can include, but are not limited to, e-
  • Coupons electronic delivery of FSI coupons, price discounts in the form of reduced prices or TPRs, additional incentives, or incentives of any kind.
  • Mis or Marketer Incentives to be incorporated in the shopper App to calculate FLC as detailed above.
  • FIGS.26B, 26A There are many other incentive and advertising/messaging incentives that are well known to marketing professionals that may be included in the functionality of the Marketer Portal, retailer Portal and shopper App. This may also include incentives pre-final basket delivered electronically and POST final basket run that can change final choice or quantities in final basket.
  • the system provides targeted coupon and advertising/message delivery based on shopper Characteristics as defined above.
  • Advertising messages can be still, video, flash, popup, or any other type of message and delivery approach familiar to App and web-site developers.
  • FIGS. 27A-27E show an example of an interface for presenting coupons and offers to the user.
  • the user is presented with choice of viewing store specials or coupons.
  • the store specials are sorted by category, and the user can select any of the categories to view the store specials thereof.
  • the coupons are sorted by category, and the user can select any of the categories to view the coupons thereof.
  • FIG. 27D shows the listing of one category of specials in response to selection of that specials category.
  • Each product on special is displayed with price, expiration date, savings, and a selection checkbox.
  • FIG. 26E shows an additional sorting capability provided in some embodiments. Upon displaying the categories, the user can select a pop up menu for sorting the specials by savings percentage, or by popularity (as measured by the number of purchases by users of the system.
  • the marketer portal also has a basic and advanced analytics module to analyze shopper behavior and promotional performance.
  • the basic functionality will be accessible through the standard web site portal and campaign management system.
  • advanced capabilities can be fee based and accessible on subscription basis.
  • the system bases the driving distance on the location of the user's home GPS location to calculate drive costs. In other embodiments, the system computes driving distance and cost based on the current location of the user based on other methods (e.g., cell phone tower location).
  • the fourth component platform herein the "research portal,” is website based portal for marketers and retailers and other customers that:
  • the fourth component is alternatively a stand-alone portal and integrated as a section of the retailer can brand portals.
  • Periodcially captures e.g., on a daily basis, or more, or less frequently, as desired
  • item level shelf pricing for all items in all grocery stores (pricing zones) in a target geographical area (including individually or together the United States, Canada, France,
  • the system collects and updates items, item and store level pricing and promotional offers every month, every 3 weeks, every 2 weeks, every week, every day, or "Live” updates throughout the day.
  • Allows for institution of a pricing hierarchy with incentives based on degree of targeting, incentive amounts and a pricing algorithm may take into account market demand, exclusivity, category size, competitive substitutes, elasticity(s) and cross-price elasticity(s) and other factors relevant to maximizing revenue; allows pay-per-click or pay for performance pricing.
  • the system sorts the final list for the user by aisle when the user enters one of the stores (based on the layout of each individual store), letting shoppers know in which aisle to find their list items.
  • FIG. 19A is an example showing the shopping list sorted by aisle. Each item also has a checkbox the user can use to check off items as they are added to the shopper's physical shopping cart.
  • the screen has an "aisle" button, selection of which causes the mobile device to display a popup menu for selecting either sorting by aisle or by category.
  • the system allows the use of auto sort on the shopping list, matching shopping items with the ideal route through the store. That is, not only are the items sorted by aisle; but the aisles in the list are sorted in the same order as the store layout.
  • the items are sorted by category.
  • FIG. 19B shows an example of a list sorted by category.
  • the Phones LED flash/light provides a modulated signal that simulates the reflected light from a UPC bar being scanned by a POS system's laser.
  • the communication between the phone and the POS scanner allows for promotional offers and digital/mobile coupons to be "redeemed" within the stores standard paper coupon redemption systems.
  • a system for automating and streamlining consumer grocery shopping purchase processes and enabling targeted, immediately pre-purchase decision direct-marketing capability.
  • a system is provided for developing individual shopper preferences and matching with lowest available priced product basket that reflects preferences.
  • a system is provided for delivering highly targeted and situation-ally
  • the system provides behavioral insight driven promotional targeting in a mobile system.
  • a system for delivering location based promotional and advertising messaging in retail environment automated decision making mechanism for shoppers.
  • the system can find equivalent products and recommend better priced alternatives from within the set of equivalents.
  • the system enables the user to simply select items they intend to purchase by category and the search function identifies the items that would be acceptable to that shopper based on past behavior and the actions of similar shoppers.
  • the system operates by analyzing all possible combinations of store specific product prices, store specials, and coupons and automatically recommending the best store to shop and the best items to buy to maximize savings from within an acceptable set of product alternatives.
  • the system enables users to compete and track their savings progress from within a network of self-identified peers.
  • the system is provided for identifying the lowest priced basket of goods, within a system of retail outlets, that best meets shopper preferences.
  • the system provides both database system and architecture for rapid, mobile, in- process delivery of the above capabilities.
  • the methods and system described herein may be at least partially embodied in the form of computer-implemented processes and apparatus for practicing those processes.
  • the disclosed methods may also be at least partially embodied in the form of tangible, non-transient machine readable storage media encoded with computer program code.
  • the media may include, for example, RAMs, ROMs, CD-ROMs, DVD-ROMs, BD-ROMs, hard disk drives, flash memories, or any other non-transient machine-readable storage medium, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the method.
  • the methods may also be at least partially embodied in the form of a computer into which computer program code is loaded and/or executed, such that, the computer becomes a special purpose computer for practicing the methods.
  • the computer program code segments configure the processor to create specific logic circuits.
  • the methods may alternatively be at least partially embodied in a digital signal processor formed of application specific integrated circuits for performing the methods.

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Abstract

L'invention concerne un procédé qui consiste à : (a) recevoir, à partir d'un dispositif mobile (MD), une première liste identifiant un article et une liste de magasins identifiant un magasin, la première liste d'articles au niveau de chaque magasin représentant un panier ; (b) pour chaque magasin, réaliser dans un ordinateur à distance du MD un calcul de comparaison entre les paniers, le calcul étant réalisé au niveau de la catégorie ou au niveau inférieur, identifiant une combinaison de substituts commerciaux ou au moins de SKU, correspondant à des articles dans le panier, qui satisfait le mieux le calcul de comparaison pour chaque panier ; (c) après l'étape (b), recevoir, à partir d'un système de gestion de campagne de commercialisation directe mis en œuvre par ordinateur, une offre de prime de détail ; et (d) après l'étape (c), transmettre au MD une identification d'un magasin recommandé, ou une seconde liste contenant un ensemble de descripteurs de niveau de SKU respectifs qui satisfont le mieux le calcul de comparaison pour le panier, et des résultats correspondant aux paniers pour l'autre magasin.
PCT/US2012/000426 2011-10-03 2012-10-03 Système pour automatiser une décision d'achat de client WO2013052081A2 (fr)

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US201161542527P 2011-10-03 2011-10-03
US201161542519P 2011-10-03 2011-10-03
US61/542,519 2011-10-03
US61/542,527 2011-10-03

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TWI554957B (zh) * 2013-11-25 2016-10-21 雅虎股份有限公司 根據購買商品動態推薦商品組合之伺服器、網站以及方法
WO2017079208A1 (fr) * 2015-11-06 2017-05-11 Mastercard Internantional Incorporated Procédés et systèmes mis en œuvre par ordinateur pour identifier des produits achetés par des consommateurs individuels chez différents commerçants
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US9742853B2 (en) 2014-05-19 2017-08-22 The Michael Harrison Tretter Auerbach Trust Dynamic computer systems and uses thereof
US9881311B2 (en) 2014-04-30 2018-01-30 International Business Machines Corporation Determining a lowest price for a dynamically priced product
CN107886317A (zh) * 2016-09-30 2018-04-06 株式会社万代南梦宫娱乐 游戏系统、虚拟货币处理系统、处理方法及信息存储介质
CN109615450A (zh) * 2018-10-25 2019-04-12 口碑(上海)信息技术有限公司 业务信息的处理方法、装置及系统
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US11157868B2 (en) 2013-11-20 2021-10-26 Home Depot Product Authority, Llc Systems and methods for identifying substitute goods
CN113658597A (zh) * 2021-08-01 2021-11-16 杭州拼便宜网络科技有限公司 语音下单方法、装置、电子设备和计算机可读介质
CN113988970A (zh) * 2021-10-18 2022-01-28 笑拼购网络科技有限公司 一种购物推荐方法及网购平台
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US9652798B2 (en) 2013-10-09 2017-05-16 The Toronto-Dominion Bank Systems and methods for identifying product recommendations based on investment portfolio data
US11157868B2 (en) 2013-11-20 2021-10-26 Home Depot Product Authority, Llc Systems and methods for identifying substitute goods
TWI554957B (zh) * 2013-11-25 2016-10-21 雅虎股份有限公司 根據購買商品動態推薦商品組合之伺服器、網站以及方法
CH709062A1 (fr) * 2013-12-27 2015-06-30 Tc Logiciel S A R L Système et procédé mis en oœvre par ordinateur pour mettre en relation des acheteurs et des vendeurs de produits ou services.
US20150269645A1 (en) * 2014-03-18 2015-09-24 Xerox Corporation Method and apparatus for recommending a food item
US9940661B2 (en) * 2014-03-18 2018-04-10 Conduent Business Services, Llc Method and apparatus for recommending a food item
US9881311B2 (en) 2014-04-30 2018-01-30 International Business Machines Corporation Determining a lowest price for a dynamically priced product
US10305748B2 (en) 2014-05-19 2019-05-28 The Michael Harrison Tretter Auerbach Trust Dynamic computer systems and uses thereof
US11172026B2 (en) 2014-05-19 2021-11-09 Michael H. Auerbach Dynamic computer systems and uses thereof
US10666735B2 (en) 2014-05-19 2020-05-26 Auerbach Michael Harrison Tretter Dynamic computer systems and uses thereof
US9742853B2 (en) 2014-05-19 2017-08-22 The Michael Harrison Tretter Auerbach Trust Dynamic computer systems and uses thereof
CN115511484A (zh) * 2014-05-29 2022-12-23 苹果公司 用于支付的用户接口
US10475051B2 (en) * 2014-08-26 2019-11-12 Ncr Corporation Shopping pattern recognition
US20160063511A1 (en) * 2014-08-26 2016-03-03 Ncr Corporation Shopping pattern recognition
WO2016048914A1 (fr) * 2014-09-23 2016-03-31 Weinblatt Lee S Système et procédé de distribution d'incitations à l'achat
WO2017079208A1 (fr) * 2015-11-06 2017-05-11 Mastercard Internantional Incorporated Procédés et systèmes mis en œuvre par ordinateur pour identifier des produits achetés par des consommateurs individuels chez différents commerçants
CN107886317A (zh) * 2016-09-30 2018-04-06 株式会社万代南梦宫娱乐 游戏系统、虚拟货币处理系统、处理方法及信息存储介质
CN107886317B (zh) * 2016-09-30 2023-07-25 株式会社万代南梦宫娱乐 游戏系统、虚拟货币处理系统、处理方法及信息存储介质
CN109615450B (zh) * 2018-10-25 2021-08-27 口碑(上海)信息技术有限公司 业务信息的处理方法、装置及系统
CN109615450A (zh) * 2018-10-25 2019-04-12 口碑(上海)信息技术有限公司 业务信息的处理方法、装置及系统
US20220138793A1 (en) * 2020-11-03 2022-05-05 Quotient Technology Inc. Localized facility-specific presentation of digital temporary offer data
US11694219B2 (en) * 2020-11-03 2023-07-04 Quotient Technology Inc. Localized facility-specific presentation of digital temporary offer data
CN112464089A (zh) * 2020-11-25 2021-03-09 上海电力大学 一种共享物品推荐方法及存储介质
US12099586B2 (en) 2021-01-25 2024-09-24 Apple Inc. Implementation of biometric authentication
CN113658597A (zh) * 2021-08-01 2021-11-16 杭州拼便宜网络科技有限公司 语音下单方法、装置、电子设备和计算机可读介质
CN113658597B (zh) * 2021-08-01 2024-04-30 杭州拼便宜网络科技有限公司 语音下单方法、装置、电子设备和计算机可读介质
CN113988970A (zh) * 2021-10-18 2022-01-28 笑拼购网络科技有限公司 一种购物推荐方法及网购平台

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