Patent Application
20120239523
The present invention relates in general to consumer purchasing and, more particularly, to a commerce system and method of acquiring product information to control consumer purchasing.
Economic and financial modeling and planning is commonly used to estimate or predict the performance and outcome of real systems, given specific sets of input data of interest. An economic-based system will have many variables and influences which determine its behavior. A model is a mathematical expression or representation which predicts the outcome or behavior of the system under a variety of conditions. In one sense, it is relatively easy to review historical data, understand its past performance, and state with relative certainty that past behavior of the system was indeed driven by the historical data. A more difficult task is to generate a mathematical model of the system which predicts how the system will behave with different sets of data and assumptions.
In its basic form, the economic model can be viewed as a predicted or anticipated outcome of a system defined by a mathematical expression and driven by a given set of input data and assumptions. The mathematical expression is formulated or derived from principles of probability and statistics, often by analyzing historical data and corresponding known outcomes, to achieve a best fit of the expected behavior of the system to other sets of data. In other words, the model should be able to predict the outcome or response of the system to a specific set of data being considered or proposed, within a level of confidence, or an acceptable level of uncertainty.
Economic modeling has many uses and applications. One area in which modeling has been applied is in the retail environment. Grocery stores, general merchandise stores, specialty shops, and other retail outlets face stiff competition for limited consumers and business. Most, if not all, retail stores expend great effort to maximize sales, revenue, and profit. Economic modeling can be an effective tool in helping store owners and managers to forecast and optimize business decisions. Yet, as an inherent reality of commercial transactions, the benefits bestowed on the retailer often come at a cost or disadvantage to the consumer. Maximizing sales and profits for a retailer does not necessarily expand competition and achieve the lowest price for the consumer.
On the other side of the transaction, the consumers are interested in quality, low prices, comparative product features, convenience, and receiving the most value for the money. Economic modeling can also be an effective tool in helping consumers achieve these goals. However, consumers have a distinct disadvantage in attempting to compile models for their benefit. Retailers have ready access to the historical transaction log (T-LOG) sales data, consumers do not. The advantage goes to the retailer. The lack of access to comprehensive, reliable, and objective product information essential to providing effective comparative shopping services restricts the consumer's ability to find the lowest prices, compare product features, and make the best purchase decisions.
For the consumer, some comparative product information can be gathered from various electronic and paper sources, such as online websites, paper catalogs, and media advertisements. However, such product information is sponsored by the retailer and slanted at best, typically limited to the specific retailer offering the product and presented in a manner favorable to the retailer. That is, the product information released by the retailer is subjective and incomplete, i.e., the consumer only sees what the retailer wants the consumer to see. For example, the pricing information may not provide a comparison with competitors for similar products. The product descriptions may not include all product features or attributes of interest to the consumer.
Alternatively, the consumer can visit all retailers offering a particular type of product and record the various prices, product descriptions, and retailer amenities to make a purchase decision. The brute force approach of one person physically traveling to or otherwise researching each retailer for all product information is impractical for most people. Many people do compare multiple retailers, e.g., when shopping online, particularly for high-ticket items. Yet, the time people are willing to spend reviewing product information decreases rapidly with price. Little time is spent reviewing commodity items. In any case, the consumer has limited time to do comparative shopping and mere searching does not constitute an optimization of the purchasing decision. Optimization requires access to data, i.e., comprehensive, reliable, efficient, and objective product information, so the consumer remains hampered in achieving a level playing field with the retailer.
A need exists to collect comprehensive, reliable, and objective product information for the benefit of the consumer. Accordingly, in one embodiment, the present invention is a method of controlling a commerce system including a plurality of retailers offering products for sale comprising the steps of collecting product information associated with the products, storing the product information in a database, providing a website for consumers to create a shopping list with weighted preferences for product attributes, optimizing the shopping list based on the product information in the database and the weighted preferences for the product attributes, providing the optimized shopping list to the consumer to assist with purchasing decisions, and controlling the purchasing decisions within the commerce system by enabling the consumers to select the products for purchase based on the optimized shopping list.
In another embodiment, the present invention is a method of controlling a commerce system including a plurality of retailers offering products for sale comprising the steps of collecting product information, storing the product information in a database, generating a shopping list with weighted preferences for product attributes, optimizing the shopping list based on the product information in the database and the weighted preferences for the product attributes, and utilizing the optimized shopping list to control purchasing decisions within the commerce system by enabling the consumers to select the products for purchase based on the optimized shopping list.
In another embodiment, the present invention is a method of controlling a commerce system including a plurality of retailers offering products for sale comprising collecting product information from the retailers by retrieving the product information from an electronic communication medium of the retailer using a consumer electronic communication device.
In another embodiment, the present invention is a method of controlling a commerce system including a plurality of retailers offering products for sale, comprising collecting product information from the retailers by confirming and updating the product information through electronic communication with the consumers while in a place of business of the retailer.
In another embodiment, the present invention is a computer program product usable with a programmable computer processor having a computer readable program code embodied in a computer usable medium for controlling a commerce system including a plurality of retailers offering products for sale comprising the steps of collecting product information, storing the product information in a database, generating a shopping list with weighted preferences for product attributes, optimizing the shopping list based on the product information in the database and the weighted preferences for the product attributes, and utilizing the optimized shopping list to control purchasing decisions within the commerce system by enabling the consumers to select the products for purchase based on the optimized shopping list.
a-12b illustrate confirmation request and product information updates on the consumer cell phone;
The present invention is described in one or more embodiments in the following description with reference to the figures, in which like numerals represent the same or similar elements. While the invention is described in terms of the best mode for achieving the invention's objectives, it will be appreciated by those skilled in the art that it is intended to cover alternatives, modifications, and equivalents as may be included within the spirit and scope of the invention as defined by the appended claims and their equivalents as supported by the following disclosure and drawings.
Economic and financial modeling and planning is an important business tool that allows companies to conduct business planning, forecast demand, and optimize prices and promotions to meet profit and/or revenue goals. Economic modeling is applicable to many businesses, such as manufacturing, distribution, wholesale, retail, medicine, chemicals, financial markets, investing, exchange rates, inflation rates, pricing of options, value of risk, research and development, and the like.
In the face of mounting competition and high expectations from investors, most, if not all, businesses must look for every advantage they can muster in maximizing market share and profits. The ability to forecast demand, in view of pricing and promotional alternatives, and to consider other factors which materially affect overall revenue and profitability is vital to the success of the bottom line, and the fundamental need to not only survive but to prosper and grow.
In particular, economic modeling is essential to businesses that face thin profit margins, such as general consumer merchandise and other retail outlets. Many businesses are interested in economic modeling and forecasting, particularly when the model provides a high degree of accuracy or confidence. Such information is a powerful tool and highly valuable to the business. While the present discussion will involve a retailer, it is understood that the system described herein is applicable to data analysis for other members in the chain of commerce, or other industries and businesses having similar goals, constraints, and needs.
A retailer routinely collects T-LOG sales data for most if not all products in the normal course of business. Using the T-LOG data, the system generates a demand model for one or more products at one or more stores. The model is based upon the T-LOG data for that product and includes a plurality of parameters. The values of the parameters define the demand model and can be used for making predictions about the future sales activity for the product. For example, the model for each product can be used to predict future demand or sales of the product at that store in response to a proposed price, associated promotions or advertising, as well as impacts from holidays and local seasonal variations. Promotion and advertising increase consumer awareness of the product.
An economic demand model analyzes historical retail T-LOG sales data to gain an understanding of retail demand as a function of factors such as price, promotion, time, consumer, seasonal trends, holidays, and other attributes of the transaction. The demand model can be used to forecast future demand by consumers as measured by unit sales. Unit sales are typically inversely related to price, i.e., the lower the price, the higher the sales. The quality of the demand model—and therefore the forecast quality—is directly affected by the quantity, composition, and accuracy of historical T-LOG sales data provided to the model.
The retailer makes business decisions based on forecasts. The retailer orders stock for replenishment purposes and selects items for promotion or price discount. To support good decisions, it is important to quantify the quality of each forecast. The retailer can then review any actions to be taken based on the accuracy of the forecasts on a case-by-case basis.
Referring to
Business plan 12 includes planning 12a, forecasting 12b, and optimization 12c steps and operations. Business plan 12 gives retailer 10 the ability to evaluate performance and trends, make strategic decisions, set pricing, order inventory, formulate and run promotions, hire employees, expand stores, add and remove product lines, organize product shelving and displays, select signage, and the like. Business plan 12 allows retailer 10 to analyze data, evaluate alternatives, run forecasts, and make decisions to control its operations. With input from the planning 12a, forecasting 12b, and optimization 12c steps and operations of business plan 12, retailer 10 undertakes various purchasing or replenishment operations 14. Retailer 10 can change business plan 12 as needed.
Retailer 10 routinely enters into sales transactions with customer or consumer 16. In fact, retailer 10 maintains and updates its business plan 12 to increase the number of transactions (and thus revenue and/or profit) between retailer 10 and consumer 16. Consumer 16 can be a specific individual, account, or business entity.
For each sale transaction entered into between retailer 10 and consumer 16, information describing the transaction is stored in T-LOG 20. When a consumer goes through the check-out at a grocery or any other retail store, each of the items to be purchased is scanned and data is collected and stored by a point-of-sale (POS) system, or other suitable data storage system, in T-LOG 20. The data includes the then current price, promotion, and merchandizing information associated with the product along with the units purchased, and the dollar sales. The date and time, and store and consumer information corresponding to that purchase are also recorded.
T-LOG 20 contains one or more line items for each retail transaction, such as those shown in Table 1. Each line item includes information or attributes relating to the transaction, such as store number, product number, time of transaction, transaction number, quantity, current price, profit, promotion number, and consumer category or type number. The store number identifies a specific store; product number identifies a product; time of transaction includes date and time of day; quantity is the number of units of the product; current price (in US dollars) can be the regular price, reduced price, or higher price in some circumstances; profit is the difference between current price and cost of selling the item; promotion number identifies any promotion associated with the product, e.g., flyer, ad, sale price, coupon, rebate, end-cap, etc.; consumer identifies the consumer by type, class, region, or individual, e.g., discount card holder, government sponsored or under-privileged, volume purchaser, corporate entity, preferred consumer, or special member. T-LOG 20 is accurate, observable, and granular product information based on actual retail transactions within the store. T-LOG 20 represents the known and observable results from the consumer buying decision or process. T-LOG 20 may contain thousands of transactions for retailer 10 per store per day, or millions of transactions per chain of stores per day.
The first line item shows that on day/time D1, store S1 had transaction T1 in which consumer C1 purchased one product P1 at $1.50. The next two line items also refer to transaction T1 and day/time D1, in which consumer C1 also purchased two products P2 at $0.80 each and three products P3 at price $3.00 each. In transaction T2 on day/time D1, consumer C2 has four products P4 at price $1.80 each and one product P5 at price $2.25. In transaction T3 on day/time D1, consumer C3 has ten products P6 at $2.65 each, in his or her basket. In transaction T1 on day/time D2 (different day and time) in store S1, consumer C4 purchased five products P1 at price $1.50 each. In store S2, transaction T1 with consumer C5 on day/time D3 (different day and time) involved one product P7 at price $5.00. In store S2, transaction T2 with consumer C6 on day/time D3 involved two products P1 at price $1.50 each and one product P8 at price $3.30.
Table 1 further shows that product P1 in transaction T1 had promotion PROM01. PROM01 can be any suitable product promotion such as a front-page featured item in a local advertising flyer. Product P2 in transaction T1 had promotion PROMO2 as an end-cap display in store S1. Product P3 in transaction T1 had promotion PROM03 as a reduced sale price. Product P4 in transaction T2 on day/time D1 had no promotional offering. Likewise, product P5 in transaction T2 had no promotional offering. Product P6 in transaction T3 on day/time D1 had promotion PROM04 as a volume discount for 10 or more items. Product P7 in transaction T1 on day/time D3 had promotion PROM05 as a $0.50 rebate. Product P8 in transaction T2 had no promotional offering. A promotion may also be classified as a combination of promotions, e.g., flyer with sale price, end-cap with rebate, or individualized offer as described below.
Retailer 10 may also provide additional information to T-LOG 20 such as promotional calendar and events, holidays, seasonality, store set-up, shelf location, end-cap displays, flyers, and advertisements. The information associated with a flyer distribution, e.g., publication medium, run dates, distribution, product location within flyer, and advertised prices, is stored within T-LOG 20.
Supply data 22 is also collected and recorded from manufacturers and distributors. Supply data 22 includes inventory or quantity of products available at each location in the chain of commerce, i.e., manufacturer, distributor, and retailer. Supply data 22 includes product on the store shelf and replenishment product in the retailer's storage area.
With T-LOG 20 and supply data 22 collected, various suitable methods or algorithms can be used to analyze the data and form demand model 24. Model 24 may use a combination of linear, nonlinear, deterministic, stochastic, static, or dynamic equations or models for analyzing T-LOG 20 or aggregated T-LOG data and supply data 22 and making predictions about consumer behavior to future transactions for a particular product at a particular store, or across entire product lines for all stores. Model 24 is defined by a plurality of parameters and can be used to generate unit sales forecasting, price optimization, promotion optimization, markdown/clearance optimization, assortment optimization, merchandize and assortment planning, seasonal and holiday variance, and replenishment optimization. Model 24 has a suitable output and reporting system that enables the output from the model to be retrieved and analyzed for updating business plan 12.
In
The purchasing decisions made by consumer 44 drives the manufacturing, distribution, and retail portions of commerce system 30. More purchasing decisions made by consumer 44 for retailer 40 leads to more merchandise movement for all members of commerce system 30. Manufacturer 32, distributor 36, and retailer 40 utilize demand model 48 (similar to model 24), via respective control systems 34, 38, and 42, to control and optimize the ordering, manufacturing, distribution, sale of the goods, and otherwise execute respective business plan 12 within commerce system 30 in accordance with the purchasing decisions made by consumer 44.
Manufacturer 32, distributor 36, and retailer 40 provide historical T-LOG 46 and supply data 50 to demand model 48 by electronic communication link, which in turn generates forecasts to predict the need for goods by each member and control its operations. In one embodiment, each member provides its own historical T-LOG data 46 and supply data 50 to demand model 48 to generate a forecast of demand specific to its business plan 12. Alternatively, all members can provide historical T-LOG data 46 and supply data 50 to demand model 48 to generate composite forecasts relevant to the overall flow of goods. For example, manufacturer 32 may consider a proposed price, rebate, promotion, seasonality, or other attribute for one or more goods that it produces. Demand model 48 generates the forecast of sales based on available supply and the proposed price, consumer, rebate, promotion, time, seasonality, or other attribute of the goods. The forecast is communicated to control system 34 by electronic communication link, which in turn controls the manufacturing process and delivery schedule of manufacturer 32 to send goods to distributor 36 based on the predicted demand ultimately determined by the consumer purchasing decisions. Likewise, distributor 36 or retailer 40 may consider a proposed price, rebate, promotion, or other attributes for one or more goods that it sells. Demand model 48 generates the forecast of demand based on the available supply and proposed price, consumer, rebate, promotion, time, seasonality, and/or other attribute of the goods. The forecast is communicated to control system 38 or control system 42 by electronic communication link, which in turn controls ordering, distribution, inventory, and delivery schedule for distributor 36 and retailer 40 to meet the predicted demand for goods in accordance with the forecast.
Each consumer goes through a product evaluation and purchasing decision process each time a particular product is selected for purchase. Some product evaluations and purchasing decision processes are simple and routine. For example, when consumer 62 is conducting weekly shopping in the grocery store, the consumer sees a needed item or item of interest, e.g., canned soup. Consumer 62 may have a preferred brand and flavor of canned soup. Consumer 62 selects the preferred brand and flavor sometimes without consideration of price, places the item in the basket, and moves on. The product evaluation and purchasing decision process can be almost automatic and instantaneous but nonetheless still occurs based on prior experiences and preferences. Consumer 62 may pause during the product evaluation and purchasing decision process and consider other canned soup options. Consumer 62 may want to try a different flavor or another brand offering a lower price. As the price of the product increases, the product evaluation and purchasing decision process usually becomes more involved. If consumer 62 is shopping for a major appliance, the product evaluation and purchasing decision process may include consideration of several manufacturers, visits to multiple retailers, review of features and warranty, talking to salespersons, reading consumer reviews, and comparing prices. In any case, understanding the consumer's approach to the product evaluation and purchasing decision process is part of an effective model or comparative shopping service. The model must assist the consumer in finding the optimal price and product attributes, e.g., brand, quality, quantity, size, features, ingredients, service, warranty, and convenience, that are important to the consumer and tip the purchasing decision toward selecting a particular product and retailer.
In
The personal recommendation engine 74 can be made available to consumers 62-64 via computer based online website or other electronic communication medium, e.g., wireless cell phone or other personal communication device.
The electronic communication network 80 further includes consumer service provider 72 with personal recommendation engine 74 in electronic communication with network 84 over communication channel or link 92. Communication channel 92 is bi-directional and transmits data between consumer service provider 72 and electronic communication network 84 in a hard-wired or wireless configuration.
Further detail of the computer systems used in electronic communication network 80 is shown in
Computer systems 100 and 114 can be physically located in any location with access to a modem or communication link to network 84. For example, computer 100 or 114 can be located in the consumer's home or business office. Consumer service provider 72 may use computer system 100 or 114 in its business office. Alternatively, computer 100 or 114 can be mobile and follow the user to any convenient location, e.g., remote offices, consumer locations, hotel rooms, residences, vehicles, public places, or other locales with electronic access to electronic communication network 84.
Each of the computers run application software and computer programs, which can be used to display user interface screens, execute the functionality, and provide the electronic communication features as described below. The application software includes an Internet browser, local email application, word processor, spreadsheet, and the like. In one embodiment, the screens and functionality come from the application software, i.e., the electronic communication runs directly on computer system 110 or 114. Alternatively, the screens and functions are provided remotely from one or more websites on servers within electronic communication network 84.
The software is originally provided on computer readable media, such as compact disks (CDs), external drive, or other mass storage medium. Alternatively, the software is downloaded from electronic links, such as the host or vendor website. The software is installed onto the computer system hard drive 104 and/or electronic memory 106, and is accessed and controlled by the computer's operating system. Software updates are also electronically available on mass storage medium or downloadable from the host or vendor website. The software, as provided on the computer readable media or downloaded from electronic links, represents a computer program product containing computer readable program code embodied in a computer program medium. Computers 100 and 114 run application software for executing instructions for communication between consumers 82 and 88 and consumer service provider 72, gathering product information, and generating consumer models or comparative shopping services. The application software is an integral part of the control of purchasing decisions within commerce system 60.
The electronic communication network 80 can be used for a variety of business, commercial, personal, educational, and government purposes or functions. For example, consumer 82 using computer 114 can communicate with consumer service provider 72 operating on computer 100, and consumer 88 using cellular telephone 116 can communicate with consumer service provider 72 operating on computer 100. The electronic communication network 80 is an integral part of a business, commercial, professional, educational, government, or social network involving the interaction of people, processes, and commerce.
To interact with consumer service provider 72, the consumer first creates an account and profile with the consumer service provider. The consumer can use some features offered by consumer service provider 72 without creating an account, but full access requires completion of a registration process. The consumer accesses website 120 operated by consumer service provider 72 on computer system 100 and provides data to complete the registration and activation process, as shown in
The consumer's profile is stored and maintained within consumer service provider 72. The consumer can access and update his or her profile or interact with personal recommendation engine 74 by entering login name 132 and password 134 in webpage 136, as shown in
One feature of personal recommendation engine 74 is webpage 138, as shown in
The consumer can also identify a specific preferred retailer based on convenience and personal experience. The consumer may assign value to shopping with a specific retailer because of specific products offered by that store, familiarity with the store layout, good consumer service experiences, or location that is convenient on the way home from work, picking up the children from school, or routine weekend errand route.
Personal recommendation engine 74 stores the shopping list and weighted product attributes of each specific consumer for future reference and updating. The individual products in the shopping list can be added or deleted and the weighted product attributes can be changed by the consumer. The shopping list entered into personal recommendation engine 74 is specific for each consumer and allows consumer service provider 72 to track specific products and preferred retailers selected by the consumer. Consumer service provider 72 can also present offers available to the consumer, as described below.
When the consumer is ready to go shopping, personal recommendation engine 74 executes a consumer model to optimize the shopping list and determine which products should be purchased from which retailers on which day to maximize the value to the consumer as defined by the consumer profile and list of products of interest with weighted attributes from webpage 138.
In order to generate the consumer model or comparative shopping service, personal recommendation engine 74 must have access to comprehensive, reliable, and objective retailer product information. The retailer product information is combined with the consumer's profile and list of products of interest or need with weighted attributes from webpage 138 to generate an optimized shopping list. In one approach, retailers 66-70 may grant access to T-LOG data for use by personal recommendation engine 74. As noted in the background, retailers may be reluctant to grant access to T-LOG data, particularly without quid pro quo. However, as personal recommendation engine 74 gains acceptance and the consumer relies on the optimized shopping list to make purchase decisions, retailer 66-70 will be motivated to participate. That is, retailers 66-70 will want to show up as the recommended source for as many products as possible on the optimized shopping list. Primarily, a particular retailer will be the optimized product source when the combination of price and product attributes offered by the retailer aligns with, or provides maximum value in accordance with, the consumer's profile and shopping list with weighted preferences.
One or more retailers 66-70 may decline to provide access to its T-LOG data for use with personal recommendation engine 74. In such cases, consumer service provider 72 can exercise a number of alternative data gathering approaches and sources. In one embodiment, consumer service provider 72 utilizes computer based webcrawlers or other searching software to access retailer websites for pricing and other product information. In
Consumer service provider 72 can also dispatch webcrawlers 150 and 152 from computers 154 and 156 used by consumers 62-64, or from consumer cell phone 116, or other electronic communication device, to access and request product information from retailer websites or portals 142-146 or other electronic communication medium or access point. During the registration process of
For example, the consumer logs into the website of consumer service provider 72 via webpage 136. Consumer service provider 72 initiates webcrawler 150 in the background of consumer computer 154 with a sufficiently low execution priority to avoid interference with other tasks running on the computer. The consumer can also define the time of day and percent or amount of personal computer resources allocated to the webcrawler. The consumer can also define which retailer websites and products, e.g. by specific retailer, market, or geographic region, that can be accessed by the webcrawler using the personal computer resources. Webcrawler 150 executes from consumer computer 154 and uses the consumer's login to gain access to retailer websites 142-146. Alternatively, webcrawler 150 resides permanently on consumer computer 154 and runs periodically. Webcrawler 150 identifies products available from each of retailer websites 142-146 and requests pricing and other product information for each of the identified products. Webcrawler 150 navigates and parses each page of retailer websites 142-146 to locate pricing and other product information. The parsing operation involves identifying and recording product description, UPC, price, ingredients, size, and other product information as recovered by webcrawler 150 from retailer websites 142-146. The product information from retailer websites 142-146 is sorted and stored in central database 148.
Likewise, webcrawler 152 uses consumer computer 156 and login to gain access to retailer websites 142-146. Webcrawler 152 identifies products available from each of retailer websites 142-146 and requests pricing and other product information for each of the identified products. Webcrawler 152 navigates and parses each page of retailer websites 142-146 to locate pricing and other product information. The parsing operation involves identifying and recording product description, UPC, price, ingredients, size, and other product information as recovered by webcrawler 152 from retailer websites 142-146. The product information from retailer websites 142-146 is sorted and stored in central database 148. The product information can be specific to the consumer's login. Retailers 66-70 are likely to accept product information requests from webcrawlers 150-152 because the requests originate from consumer computers 154-156 by way of the consumer login.
With the retailer product information collected and stored in central database 148, personal recommendation engine 74 generates an optimized shopping list 158, as shown in
Consumer 62 wants whole-grain bread with a high importance. Bread products that are whole grain are given a high score in accordance with the attribute weight and bread products that are not whole grain are given a low score. Consumer 62 wants fresh bread with a high importance. Bread products that are delivered within a short time of the consumer visit are given a high score in accordance with the attribute weight and bread products that are delivered a longer time before the consumer visit are given a low score. Consumer 62 wants a low price bread with a low importance. Bread products that are low cost relative to other similar bread products are given a high score in accordance with the attribute weight and bread products that are higher cost relative to other similar bread products are given a low score.
The weighted scores for each product attribute defined by the consumer are combined and a specific bread product from a specific retailer that comes closest to matching the consumer-defined weighted product attributes, i.e., the product with the highest score, is selected as the optimized product for the line item. In one embodiment, the weighted scores for the product attributes are summed. Personal recommendation engine 74 may determine that bread and milk should be purchased from retailer 66 on Monday to take advantage of the beginning of the week fresh product delivery and likelihood of plentiful stock. Paper towels should be purchased from retailer 68 before Wednesday based on current sale or promotional pricing. Toothpaste should be purchased from retailer 70 because retailers 66 and 68 do not carry the name brand preferred by the consumer. Retailers 66-70 are matched to the consumer's locale for convenience based on the profile information.
Retailers 66-70 can enhance their relative position and provide support for consumer service provider 72 by making T-LOG data available to consumer service provider 72. One way to get a high score when comparing retailer product attributes to the consumer-defined weighted product attributes is to ensure that personal recommendation engine 74 has access to the most accurate and up-to-date retailer product attributes via central database 148. Even though a given retailer may have a desirable product attribute, personal recommendation engine 74 cannot record a high score if it does not have complete information about the retailer's product attribute. By giving consumer service provider 72 direct access to T-LOG data, the retailer makes the product information readily available to personal recommendation engine 74 which will hopefully increase its score and provide more occurrences of the retailer as the recommended source for as many products as possible on the optimized shopping list. While the use of webcrawlers in
Retailers 66-70 can also enhance their relative position and provide support for consumer service provider 72 by offering discounts, special offers, or other rewards to consumers through personal recommendation engine 74. By utilizing personal recommendation engine 74, retailers 66-70 are not just randomly distributing a discount offer, e.g., as with mailbox flyers and coupons, with hope that a consumer might purchase a product from the retailer based on the discount. By teaming with consumer service provider 72, retailers 66-70 are reaching a targeted audience that has already acknowledged a need for the product by creating the shopping list via website 138. The discounted offer from retailers 66-70 can be customized for the consumer who is likely to buy or at least has expressed interest in the product. Retailers 66-70 will pay a premium to know that their advertising dollar is going directly to a likely-to-buy consumer who will also receive an objective and optimized recommendation to purchase from a trusted source, i.e., personal recommendation engine 74. Retailers 66-70 will have reached the consumer at or near the tipping point in the purchasing decision process. Consumer service provider 72 receives revenue or other compensation from retailers 66-70 by accepting special pricing for the retailers available through personal recommendation engine 74. Consumer service provider 72 may also receive access to T-LOG data from retailers 66-70 in general support of personal recommendation engine 74 or as part of its compensation.
The consumer patronizes retailers 66-70 with optimized shopping list 158 from personal recommendation engine 74 in hand and makes purchasing decisions based on the recommendations on the optimized shopping list. The consumers can rely on personal recommendation engine 74 as having produced a comprehensive, reliable, and objective shopping list in view of the consumer's profile and weighted product preferences, as well as retailer product information, that will yield the optimal purchasing decision to the benefit of the consumer. Personal recommendation engine 74 helps consumers quantify and develop confidence in making a good decision to purchase a particular product from a particular retailer. While the consumer makes the decision to place the product in the basket for purchase, he or she comes to rely upon or at least consider the recommendations from consumer service provider 72, i.e., optimized shopping list 158 contributes to the tipping point for consumers to make the purchasing decision. The consumer model generated by personal recommendation engine 74 thus in part controls many of the purchasing decisions and other aspects of commercial transactions within commerce system 60.
As another technique of collecting product information, consumer service provider 72 works with consumers 62-64 to gather product information directly from in-store activities. The optimized shopping list 158 can be downloaded onto the consumer's cell phone or other wireless communication device for easy reference while shopping, see cell phone 116 in
Consider an example of consumer 62 patronizing the store of retailer 66 with optimized shopping list 158.
Consumer service provider 72 checks central database 148 to determine if the price or other information related to product P1 needs to be updated. The product price is the most common attribute to change, although other product information, e.g., package size, ingredients, and features, may require confirmation from time to time. For example, the determination to confirm product pricing depends on the type of product, length of time since the last update, and market conditions. Some products are historically stable in price. Other products change regularly in price with manufacturing and distribution disruptions, currency fluctuations, weather, seasonality, or other market conditions. If the price of product P1 has not been updated for one or two weeks, then consumer service provider 72 requests a price confirmation. If the market for product P1 is dynamic, as noted by frequent price changes, then daily or bi-weekly updates may be indicated. If a product has been recently confirmed, then a confirmation request for that product is deferred for a period of time determined by the historically price stability of the product.
If the price of product P1 needs updating, consumer service provider 72 sends a request to consumer 62 to confirm or validate the price of product P1. The confirmation request appears as a popup window on display 160 of cell phone 116 with a confirmation request containing currently product information and pricing of product P1 according to central database 148, as shown in
If the confirmation request price on cell phone 116 is the different from the in-store price, i.e., there is a price discrepancy between retailer 66 and central database 148, then consumer 62 presses “update price” button 164. A price update window 166 is displayed on cell phone 116, as shown in
Consumer service provider 72 typically does not ask consumer 62 to confirm the price of every product on optimized shopping list 158. In one embodiment, consumer 62 may be asked to confirm a limited number of products, e.g. 10% of the items, on optimized shopping list 158. Alternatively, consumer 62 may be asked to confirm no more than a predetermined number of items during a period of time, e.g., no more than five confirmation requests during a given week. The confirmation of product information is distributed among a large number of people in the consumer community utilizing personal recommendation engine 74, i.e., other consumers are asked to confirm other products. The workload is uniformly and fairly distributed among the consumer community without undue inconvenience or burden to any one consumer.
Consumer service provider 72 can use price zones, assortment zones, promotion zones, and price families to minimize the number of price updates that need to be validated by consumer 62. A price zone is a group of retail stores that have the same regular price for a product. An assortment zone is a group of retail stores that have the same product assortment. A promotion zone is a group of retail stores that have the same promotion for a product. A price family is a group of products that have the same regular and promotional price.
Consumer service provider 72 may transmit a confirmation request to more than one consumer for a given product at a given retailer in a given period of time. Consumer 62 may make an error in the confirmation request, e.g., misinterpret the product information or make a data entry error on cell phone 116. For example, if a first consumer responds with a price change for product P1 at retailer 66, then a second consumer may receive a confirmation request for the same product P1 at retailer 66 in order to confirm the price change. Central database 148 can hold the price change as pending until verified by the second consumer. Once the second consumer confirms the price change, then the new price is recorded in central database 148 for use in optimizing shopping lists for other consumers. The redundancy of collecting the same price updates from multiple consumers negates or reduces human error in the confirmation process and ensures the accuracy of the product information in central database 148.
In another embodiment, the optimized shopping list 158 is tagged with specific confirmation requests at the time of download to cell phone 116. As the consumer checks off products that are tagged for confirmation, a confirmation request popup window similar to
The consumer may of course decline the confirmation request, e.g., if time does not permit for the additional task. The robustness and accuracy of the system is based on a multitude of consumers contributing to the product information updates so occasional omissions have negligible impact.
On the other hand, consumer 62 may choose to be proactive and confirm every product on optimized shopping list 158 in exchange for a reward for the extra effort, such as a special offer, price reduction on future purchases, or cash back reward. In another proactive example, consumer 62 can scan the UPC code of a product not on optimized shopping list 158 by taking a photo of the barcode and sending the photo to consumer service provider 72. The barcode is decoded to the specific product. If consumer service provider 72 determines that the product needs a price confirmation, then the price confirmation window is sent to consumer 62, similar to
The process of checking off each product from optimized shopping list 158 constitutes a check-in for that product in that consumer service provider 72 will receive confirmation that consumer 62 is on the premises of retailer 66 and making the purchasing decision for the product. The product check-in gives retailers valuable feedback as to time, location, and consumer demographics associated with purchasing decisions on a product-by-product basis. The product price confirmation and update, as described for
Many cell phones contain a global positioning system (GPS) capability. Consumer service provider 72 can be automatically notified by cell phone 116 that consumer 62 is presently on the premises of retailer 66 using GPS. Consumer service provider 72 can send needed price confirmation requests, again not necessarily the items on optimized shopping list 158, to consumer 62 while he or she is conveniently in the store. Consumer 62 has the option to respond to the price confirmation requests. Consumer 62 is incentivized to reply to the price confirmation request with special offers, price reductions on future purchases, or cash back reward.
The reward for contributions to the in-store product information confirmation can take the form of social status. Top contributors can be listed by name, with appropriate consumer permission, on webpage 138 for all registered consumers to see. People will recognize and appreciate that their friends and neighbors are doing their part and more for the benefit of the consumer community. Alternatively, consumer 62 can receive a message on cell phone 116 that another named consumer has already confirmed the price so they don't have to perform the task. Human nature appreciates name recognition.
The consumer can give feedback to consumer service provider 72 that optimized shopping list 158 was indeed used and helpful to make purchasing decisions. For example, consumer 62 can provide comments or testimonials, which are posted on the consumer service provider website. Consumers often place importance on the comments of other consumers, which build confidence and credibility in the benefits of personal recommendation engine 74 and optimized shopping list 158. Consumer service provider 72 can also send out questionnaires or surveys to the registered consumers asking confirmation of products actually purchased based on optimized shopping list 158, or inquiring as to the usefulness of personal recommendation engine 74, or soliciting recommendations for the consumer model or comparative shopping service.
Consumer 62 can also request comparative product pricing from cell phone 116 while on the premises of retailer 66. For example, consumer 62 can use cell phone 116 to take a photo of the bar code for a selected product, not necessarily on optimized shopping list 158. The photo is sent to consumer service provider 72 and decoded to a specific product, which is searched in central database 148. Consumer service provider 72 returns a list of retailers with the best pricing for the selected product. Consumer 62 can decide to purchase the selected product in retailer 66, or defer the purchase to a retailer with better pricing.
Consumer service provider 72 maintains a job manager 168 in
Job manager 168 also tracks retailer product information and prioritizes webcrawlers 140, 150, and 152 to queue up requests from consumer computers 154 and 156 to retailer websites 142-146. That is, not every webcrawler 140, 150, and 152 will search every retailer for every product. The workload is distributed by job manager 168 to avoid redundancy and minimize product information requests made to retailer websites 142-146. For example, job manager 168 may task webcrawler 150 to serially parse 100 products on retailer website 142. Job manager 168 also tasks webcrawler 152 to serially parse 100 products on retailer website 144. The workload is distributed among the numerous consumer computers to minimize impact on the retailers as well as the consumers. The product information retrieval jobs sent to retailer websites 142-146 can be queued sequentially or in parallel.
Retailers can also make effective use of consumer service provider 72. Many retailers will want to know what consumers are seeing and doing. By compiling a sample shopping list containing a strategic cross-section of products using personal recommendation engine 74, the retailer will how many times it is named as the optimized source or how many times the competitor is named as the optimized source on a product-by-product basis. With support of consumer service provider 72, the retailer can use the product information on central database 148 to run its own demand models or even demand models based on competitors' data in accordance with the description of
In the short term, the revenue model for consumer service provider 72 can involve dealing in competitive pricing data for retailers. In the longer term, consumer service provider 72 can offer targeted advertising through personal recommendation engine 74. Retailers 66-70 can reach a targeted audience that has already acknowledged a need for the product by creating the shopping list via website 138. The discounted offer from retailers 66-70 is customized for the consumer who is likely to buy or at least has expressed interest in the product. Retailers 66-70 will pay a premium to know that their advertising dollar is going directly to a likely-to-buy consumer who will also receive an objective and optimized recommendation to purchase from a trusted source, i.e., personal recommendation engine 74. Retailers 66-70 will have reached the consumer at or near the tipping point in the purchasing decision process. Consumer service provider 72 receives revenue or other compensation from retailers 66-70 by accepting special pricing for the retailers available through personal recommendation engine 74.
In summary, the consumer service provider in part controls the movement of goods between members of the commerce system. The personal recommendation engine offers consumers economic and financial modeling and planning, as well as comparative shopping services, to aid the consumer in making purchase decisions by optimizing the shopping list according to consumer-weighted preferences for product attributes. The optimized shopping list requires access to retailer product information. The consumer service provider uses a variety of techniques to gather product information from retailer websites and in-store product checks made by the consumer. The optimized shopping list helps the consumer to make the purchasing decision based on comprehensive, reliable, and objective retailer product information. The consumer makes purchases within the commerce system based on the optimized shopping list and product information compiled by the consumer service provider. By following the recommendations from the consumer service provider, the consumer can receive the most value for the money. Where retailers historically had an advantage over consumers with control of the T-LOG data and economic modeling to optimize profits, the consumer service provider has leveled the playing field by optimizing the purchasing decision within commerce system for the benefit of the consumer.
While one or more embodiments of the present invention have been illustrated in detail, the skilled artisan will appreciate that modifications and adaptations to those embodiments may be made without departing from the scope of the present invention as set forth in the following claims.
