Initial Product Offering System and Method
A system and method for making an initial product offering of tangible products and services. A price for a product may be determined by calculating an aggregate customer history factor by aggregating customer history factors of potential purchasers in a buying group, calculating a cumulative demand for a product as a function of (i) expected purchase quantities of the product indicated by the potential purchasers in the buying group and (ii) the aggregate customer history factor, and setting a price of the product as a function of the cumulative demand for the buying group. The tangible products and services may be offered at the determined price.
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This application is a continuation of, and therefore claims priority to, prior U.S. patent application Ser. No. 11/515,113 filed on Sep. 1, 2006, which is a continuation-in-part of prior U.S. patent application Ser. No. 09/649,224 filed on Aug. 25, 2000, now U.S. Pat. No. 7,103,565 which claims benefit of prior U.S. Provisional Patent Application No. 60/150,993, filed on Aug. 27, 1999. The entire contents of these prior applications are incorporated herein by reference.
BACKGROUND OF THE INVENTION1. Field of the Invention
The invention relates generally to digital commerce and specifically to the pricing and sale of products and services through a network based product offering using demand packets.
2. Description of Related Art
Individual buyers of both consumer and business related products and services (identified hereinafter as “singular customer”) are at a disadvantage when making purchases because there is little negotiating leverage for a single sale. Large volume purchasers, on the other hand, have substantial leverage, such as when a retailer purchases goods from a wholesale supplier. There is a continuing need for a purchasing system that provides the leverage of large volume purchasing interest to singular customers, while disintermediating the sales chain of the product.
One approach to providing a solution for volume and pre-determined pricing curve based system for the aggregation of purchasing interest is outlined in U.S. Pat. No. 6,047,266 entitled “Demand Aggregation through Online Buying Groups.” This patent describes a method wherein an online buying group, referred to as a “co-op” is formed for the specific purpose of purchasing a particular product based on a predetermined pricing curve that is modified by the market data from the co-op. However, as significant disadvantages, (i) the seller has to disclose to the demand aggregation system its pricing curve which may be trade secret information instead of dynamically providing the pricing for the product, (ii) the system targets the co-op information to “a” particular vendor or manufacturer of the product, (iii) the system does not provide existing market-wide price transparency, and (iv) the system does not allow potential buyers to create their own said co-ops as the co-ops tend to be driven by the system and effectively by the pricing curve information provided by the vendor or the manufacturer.
Another approach to effectuating bilateral buyer-driven commerce through allowing prospective buyers to communicate a binding purchase offer globally to potential sellers, for sellers to conveniently search for potential buyer purchase offers, and for sellers to bind a buyer to its offer is outlined in U.S. Pat. No. 5,794,207, entitled “Method and Apparatus for a Cryptographically Assisted Commercial Network System Designed to Facilitate Buyer-Driven Conditional Purchase Offers.” This patent describes a method and system whereby buyers can negotiate a purchase price of a product or service with a seller through an online bid-offer system. However, as a significant disadvantage, the patent does not create buying groups that have the ability of large volume discounts.
There remains the need for a digital commerce system that allows singular customers to create their own demand or purchasing interest pools, and routes these packets of demand (“demand packets”) to a plurality of hosts comprising (i) multiple suppliers, vendors, manufacturers and distributors of the particular product or service, (ii) auction networks where these demand packets may represent both sell and buy-side entries, (iii) vertical exchanges where similar category of products and services are sold and brokered, and (iv) horizontal marketplaces where similar categories of products and services are sold and brokered. There also remains the need for this system to be available over a plurality of network access devices comprising mobile phones, mobile computers, personal computers, laptop computers, handheld computers, personal digital assistants, and handheld computers. The system should further provide optimal pricing for the products coupled with market-wide price transparency.
SUMMARY OF THE INVENTIONTo overcome the problems of conventional demand and supply aggregation systems, the principles of the present invention provide for a method and for determining a price for a product. The method may include calculating an aggregate customer history factor by aggregating customer history factors of potential purchasers in a buying group. The customer history factors may include at least one of the following customer related parameters: customer rating parameter, customer transaction parameter, customer demographics parameter, customer geographics parameter, customer psychographic parameter, and customer behavioral parameter. The method further calculates a cumulative demand for a product as a function of (i) expected purchase quantities of the product indicated by the potential purchasers in the buying group and (ii) the aggregate customer history factor. A price of the product may be set as a function of the cumulative demand for the buying group.
The system and method of the invention will be more readily understood and apparent from the following detailed description of the invention when read in conjunction with the accompanying drawings, and from the claims, which are appended at the end of the detailed description.
The foregoing will be appreciated more fully from the following further description thereof, with reference to the accompanying drawings, wherein:
To provide an overall understanding of the invention, certain illustrative embodiments will now be described, including demand packets and a system and method for group buying using same demand packets. However, it will be understood by those of ordinary skill in the art that the demand packet may be adapted to other forms, physical and virtual, provided they are capable of including the necessary demand packet parameters described below. It will also be understood that the methods and systems described herein can be suitably adapted to any other sales model where a customer can make an indication of interest while reconfirming based on trigger events, such as, for example, reconfirm automatically if the price of the product is in the given range. The terms “product” and “item” are used interchangeably herein to denote products of all kinds comprising products, services, consumer products, and solutions in all physical and abstract forms. The terms “customer”, “purchaser”, and “operator” are used interchangeably herein to denote a potential buyer, which places an indication of interest to purchase the given product.
Overview of System
As will be discussed in more detail below, the User can select a product offering through the network system server 145 and place an indication of interest. The network system server 145 collects additional indications of interest in the same product offering while pre-determined conditions are fulfilled (e.g., time frame for the product offering). After suspension of the collections of the indications of interest for the product offering, the network system server 145 transmits the indications of interest to the demand packet server 150. The demand packet server 150 processes the received information and communicates with the transaction server 155. Based on the indications of interest transmitted by the demand packet server 150 in the form of demand packets and certain pre-determined conditions of the product offering, the transaction server 155 presents to the User via the demand packet server 150 and the network system server 145, offers from one or more product fulfillment destinations for the specific product offering.
In the communicating relationship, the network system server 145 collects reconfirmation at the offer price for all the Users that had previously placed an indication of interest in the specific product offering. Upon receiving the reconfirmations, the system 100 negotiates the offer price with the plurality of fulfillment destinations that had presented the offer, selects the final offer price, the transaction server 155 reaffirms the offer price and notifies the User via the demand packet server 150 and the network system server 145, regarding parameters for consummating the purchase transaction (e.g., physical shipping of the product, electronic delivery of the service).
Software Databases
A product database 200 stores product parameters for a plurality of product offerings. As shown in
A conditions of indications of interest database 300 stores the conditions upon which the product offering shall be conducted for the product. As shown in
A demand packet database 400 stores the parameters that form a demand packet for each product offering. As shown in
A product pricing database 500 stores, for each product offering, the pricing date along with the final offer from the fulfillment destination on the pricing date. As shown in
A customer sales database 600 stores, for each customer, the details of interaction with the system 100 for specific product offerings. As shown in
A product network database 700 stores, for each product, the network of preferred and pre-determined fulfillment destinations for specific product offerings. As shown in
The Demand Packet
Hardware Servers
Method of Operation
An embodiment of the process for the system 100 described above will now be described in detail by reference to
Customer Process
Depending on the preferred and pre-determined fulfillment destinations for the demand packet, the demand packet may be routed to one or more of fulfillment processes including: a supplier process for a plurality of supplier destinations 1335, an auction process for a plurality of auction destinations 1340, a vertical exchange process for a plurality of vertical exchange destinations 1345, and a horizontal marketplace process for a plurality of horizontal marketplace destinations 1350.
In the supplier process 1335, the supplier server 1155 receives the demand packet 1335.05 and routes the demand packet to a plurality of supplier category fulfillment destinations 1335.10 prior to which the supplier server 1155 secures routing information from the product network database 700 and vice versa. Based on the information contained in the demand packet and the aggregated indications of interest obtained through the demand packet database 400, one or more supplier category fulfillment destinations announce the offers which includes the pricing 1335.15. The offers are entered into a product pricing database 500, and the pricing information is e-mailed to participating customers for reconfirmation 1335.20. In a finite time frame, the system 100 collects reconfirmations from the customers that had placed an indication of interest 1335.25, and this information is provided for in the demand packet database 400 and the customer sales database 600. It should be noted that one customer may, for one previously entered indication of interest, submit multiple reconfirmations to one or more of the offers received from the system 100. Upon transmitting the reconfirmations to all the supplier category fulfillment destinations that had made an offer based on the demand packet, the pricing is confirmed by the system 100, and, following that, the system 100 negotiates the best price with one or more supplier category fulfillment destinations. This set of final offers is compared against offers received from the auction process 1340, a vertical exchange process 1345, and a horizontal marketplace process 1350 to determine the final best price offer 1335.30, which if accepted by the system 100, will prompt the customers for entering credit information 1335.35 and, if required 1335.40, connect the customers to a credit processor 940 for credit solutions. As shown in step 1335.45, the system collects final shipping information and payment from the customer and stores it into the customer sales database 600. After shipping and payment information acquisition, the system 100 completes the purchase transaction and confirms via e-mail the closing of the purchase transaction with the customers 1335.50.
In the auction process 1340, the auction server 1160 receives the demand packet 1340.05 and routes the demand packet to a plurality of auction category fulfillment destinations 1340.10 prior to which the auction server 1160 secures routing information from the product network database 700 and vice versa. Based on the information contained in the demand packet and the aggregated indications of interest obtained through the demand packet database 400, one or more auction category fulfillment destinations announce the offers which includes the pricing 1340.15. The offers are entered into a product pricing database 500, and the pricing information is e-mailed to participating customers for reconfirmation 1340.20. In a finite time frame, the system 100 collects reconfirmations from the customers that had placed an indication of interest 1340.25, and this information is provided for in the demand packet database 400 and the customer sales database 600. It be noted that one customer may for one previously entered indication of interest submit multiple reconfirmations to one or more of the offers received from the system 100. Upon transmitting the reconfirmations to all the auction category fulfillment destinations that had made an offer based on the demand packet, the pricing is confirmed by the system 100, and following that the system 100 negotiates the best price with one or more auction category fulfillment destinations. This set of final offers are compared against offers received from the supplier process 1335, a vertical exchange process 1345, and a horizontal marketplace process 1350 to determine the final best price offer 1340.30, which, if accepted by the system 100, will prompt the customers for entering credit information 1340.35 and, if required, 1340.40 connect the customers to a credit processor 940 for credit solutions. As shown in step 1340.45, the system collects final shipping information and payment from the customer and stores it into the customer sales database 600. After shipping and payment information acquisition, the system 100 completes the purchase transaction and confirms via e-mail the closing of the purchase transaction with the customers 1340.50.
In the vertical exchange process 1345, the vertical exchange server 1165 receives the demand packet 1345.05 and routes the demand packet to a plurality of vertical exchange category fulfillment destinations 1345.10 prior to which the vertical exchange server 1165 secures routing information from the product network database 700 and vice versa. Based on the information contained in the demand packet and the aggregated indications of interest obtained through the demand packet database 400, one or more vertical exchange category fulfillment destinations announce the offers which includes the pricing 1345.15. The offers are entered into a product pricing database 500, and the pricing information is e-mailed to participating customers for reconfirmation 1345.20. In a finite time frame, the system 100 collects reconfirmations from the customers that had placed an indication of interest 1345.25, and this information is provided for in the demand packet database 400 and the customer sales database 600. It should be noted that one customer may, for one previously entered indication of interest, submit multiple reconfirmations to one or more of the offers received from the system 100. Upon transmitting the reconfirmations to all the vertical exchange category fulfillment destinations that had made an offer based on the demand packet, the pricing is confirmed by the system 100, and following that, the system 100 negotiates the best price with one or more vertical exchange category fulfillment destinations. This set of final offers is compared against offers received from the supplier process 1335, the auction process 1340, and the horizontal marketplace process 1350 to determine the final best price offer 1345.30 which, if accepted by the system 100, will prompt the customers for entering credit information 1345.35 and, if required, 1345.40 connect the customers to a credit processor 940 for credit solutions. As shown in step 1345.45, the system collects final shipping information and payment from the customer and stores it into the customer sales database 600. After shipping and payment information acquisition, the system 100 completes the purchase transaction and confirms via e-mail the closing of the purchase transaction with the customers 1345.50.
In the horizontal marketplace process 1350, the horizontal marketplace server 1170 receives the demand packet 1350.05 and routes the demand packet to a plurality of horizontal marketplace category fulfillment destinations 1350.10 prior to which the horizontal marketplace server 1170 secures routing information from the product network database 700 and vice versa. Based on the information contained in the demand packet and the aggregated indications of interest obtained through the demand packet database 400, one or more horizontal marketplace category fulfillment destinations announce the offers which includes the pricing 1350.15. The offers are entered into a product pricing database 500, and the pricing information is e-mailed to participating customers for reconfirmation 1350.20. In a finite time frame, the system 100 collects reconfirmations from the customers that had placed an indication of interest 1350.25, and this information is provided for in the demand packet database 400 and the customer sales database 600. It should be noted that one customer may, for one previously entered indication of interest, submit multiple reconfirmations to one or more of the offers received from the system 100. Upon transmitting the reconfirmations to all the horizontal marketplace category fulfillment destinations that had made an offer based on the demand packet, the pricing is confirmed by the system 100, and, following that, the system 100 negotiates the best price with one or more horizontal marketplace category fulfillment destinations. This set of final offers are compared against offers received from the supplier process 1335, the auction process 1340, and the vertical exchange process 1350 to determine the final best price offer 1350.30 which, if accepted by the system 100, will prompt the customers for entering credit information 1350.35 and, if required, 1350.40 connect the customers to a credit processor 940 for credit solutions. As shown in step 1350.45, the system collects final shipping information and payment from the customer and stores it into the customer sales database 600. After shipping and payment information acquisition, the system 100 completes the purchase transaction and confirms via e-mail the closing of the purchase transaction with the customers 1350.50.
The customer history factor can be determined in a number of ways. As previously described, a customer's actual buying history may be used for determining the customer history factor. As an example, if a customer submits an indication of interest indicating that he or she will purchase a product, but actually purchases the product 20% of the time, that person's actual buying history (i.e., customer history factor in one embodiment) is 20%. However, such a simplistic model for determining a customer history factor can be expanded to include an unlimited number of variables or parameters and calculated using a variety of mathematical functions and statistical models.
The customer related variables (CRV) may be used in determining a customer history factor:
-
- Rating variables: rating variables may include customer feedback score, customer rating, customer ranking, customer score, customer feedback rating, and so on. Feedback, such as feedback ratings, may be used to determine each member's feedback score. A positive rating may add a value, such as +1, to the customer's score, a negative rating may decrease a value, such as −1, from the customer's score, and a neutral rating may have no impact. The higher the feedback score, the more positive ratings the customer has received from members. In one embodiment, a member can increase or decrease another member's score by only ±1 no matter how many transactions they share.
- Customer transaction variables: customer transaction variables may include most recent purchases, highest monetary transactions, average monetary transactions, buy rate, sell rate, return rate, indication rate, indication-to-buy rate, transaction frequency, transaction initiation rate, transaction close rate, and so on.
- Demographic variables: demographic variables may include age, gender, race, education, occupation, income, religion, marital status, family size, number of children, home ownership status, socio-economic status, and so on.
- Geographic variables: geographic variables may include various classifications of geographic areas. For example, the classifications of geographic areas may include zip code, state, country, region, climate, population, and other geographical census data. In one embodiment, this information can come from national census data. Alternatively, maps, mapping databases, and other databases as understood in the art may be utilized to determine the geographic variables. A value may be assigned to the geographic variables depending on past, current, or future events affecting the location that a customer lives. For example, if a location is affected by a natural disaster, such as a hurricane or flood, the likelihood that the customer will purchase a product after submitting an indication of interest may be lower or higher depending on the particular product.
- Psychographic variables: psychographic variables may include life style, personality, values, attitudes, and so on. These variables may be useful in predicting whether a customer will purchase a product after submitting an indication of interest at a later date. For example, if a customer's life style includes business travel, then the customer history factor may be decreased because there is a potential that the customer will be traveling when the product begins to sell and the customer will be unable to purchase the product or forget about the product.
- Behavioral variables: behavioral variables may include product usage rate, brand loyalty, benefit sought, decision making units, ready-to-buy stage, consistent high-end product purchaser, and so on. A value may be assigned to the behavioral variables depending on a number of factors. For example, if a customer routinely purchases high-end products, then the customer may have a higher score for the high-end product purchaser variable than someone who does not routinely purchase high-end products.
Each of the customer related variables used for determining a customer history factor may be assigned a value. The values of the variables may be assigned numeric or alphanumeric values. The variables may be assigned values manually, semi-automatically, or automatically based on a variety of factors. The customer related variables may be used in mathematical functions in computing a customer history factor.
In addition to customer related variables, economic related variables (ERV) may be utilized in accordance with the principles of the present invention. Economic related variables may be related to macroeconomic or microeconomic factors. For example, macroeconomic variables may include household debt service burden, unemployment, consumer confidence index, producer price index, productivity report, retail sales index, durable goods orders, employment cost index, personal bankruptcy filings, inflation rate, GDP growth rate, S&P 500 stock market index, and so on. Microeconomic variables may be related to supply and demand related as to individual consumers and businesses in a local region, for example. Microeconomic variables may include supply of current certain products, current demand of certain products, local economy growth rate, consumer job status, consumer disposable income, and so forth. Applying microeconomic theory, if a particular region of the country is predicted to have a hurricane, certain products may be more important than others, such as flashlights, wood, and water. If a consumer has indicated that he or she wants to purchase a bedroom set, a value of a variable related to the purchase on a microeconomic level may be lowered.
A customer history factor may be computed in an unlimited number of ways using the consumer related variables and economic related variables. Some mathematical computations and modeling approaches are provided below. It should be understood that these computations and approaches are exemplary and that other computations and approaches may be used to compute the customer history factor in accordance with the principles of the present invention. Furthermore, the customer related variables and economic related variables may be used in the computations and models in any combination that is helpful in determining the customer history factor to provide a more accurate estimate of how many units of a product to produce based on the indications of interest received from a new product offering. Below are exemplary mathematical models and formulas that may be used to compute the customer history factor using the consumer and/or economic related variables:
Linear Approach:The linear models may be structured in the form of a cumulative model using customer and/or economic related variables or selective data models. Industry selective data models, such as recency, frequency, monetary (RFM) models and chi-squared automatic interaction detection (CHAID) analysis may be used wherever appropriate. The CHAID analysis can incorporate recency, frequency and monetary variables, but can also examine other variables to increase predictive power. One embodiment may compute a customer history (CHF) as:
CHF %=CRV %*ERV %
where CRV %=Σ{CRV(1) . . . CRV(n)}/n
-
- or
CRV %=Σ{CRV(1)/x . . . CRV(n)/z}
ERV %=Σ{ERV(1) . . . ERV(n)}/n
-
- or
ERV %=Σ{ERV(1)/x . . . ERV(n)/z}
Predictive neural network modeling is a very powerful predictive modeling technique. It is derived from nerve systems (e.g., human brains). The heart of the technique is a neural net (or network for short). A typical network includes layers of nodes and links between neighboring layers' nodes. The first layer is an input layer. Nodes of an input layer represent induction fields or values of nominal induction fields. The last layer is an output layer. Nodes of the output layer represent prediction values (or class names), i.e., values of a target field. The rest of layers are called hidden layers (or middle or internal layers). There is typically a single hidden layer, but there may be zero or more hidden layers. For example, the figure shown at the left-hand side contains a network that determines credit risk levels based on gender, age and salary. It includes an input layer of 15 nodes, one hidden layer of 15 nodes and an output layer of 3 nodes.
As understood in the art of neural networks, each link is assigned with a different weight. The weights provide for predictions from the neural network model, as understood in the art. As shown in
Binary categorical input data for neural networks can be handled by using 0/1 (off/on) inputs, but categorical variables with multiple classes (for example, marital status or the state in which a person resides) are awkward to handle. Classifying a result into multiple categories usually is done by setting arbitrary value thresholds for discriminating one category from another. It would be difficult to devise a neural network to classify the location of residence into the 50 U.S. states. Classification trees, on the other hand, handle this type of problem naturally. Neural networks, unfortunately, do not present an model that is readily understandable as compared to a decision tree, which is easy to identify initial variables that divide the data into two categories and then other variables split the resulting child groups.
While a neural network is one potential predictive model that may be utilized to predict whether a potential purchaser of a product who submits an indication of interest, it should be understood that other predictive and non-predictive logical and mathematical models as understood in the art may be utilized to determine whether a potential purchaser who submits an indication of interest will ultimately purchase a product. For example, decision trees, stochastic gradient boosting, linear regression and non-linear regression may be utilized. Each of these models may enable the various consumer and economic related variables to be processed in making a determination. The result of the determination may be a percentage that can be used to determine if or how many products should be produced for a single potential purchaser or a group of potential purchasers. For example, if a potential purchaser submits an indication of interest indicating that he or she (or a business) is interested in purchasing 100 items of a product and the predictive model predicts, based on consumer and economic related variables associated with the potential purchaser, that the likelihood of that potential purchaser is 25%, then the manufacturer can determine that 25 of the 100 products should be produced for that person, thereby producing a more accurate demand or supply to minimizing production and demand overrun.
The previous description is of example embodiments for implementing the principles of the present invention, and the scope of the invention should not necessarily be limited by this description. The scope of the present invention is instead defined by the following claims.
Claims
1. A method for determining a price for a product, said method comprising:
- calculating an aggregate customer history factor by aggregating customer history factors of potential purchasers in a buying group, the customer history factors including at least one of the following customer related parameters: customer rating parameter, customer transaction parameter, customer demographics parameter, customer geo graphics parameter, customer psychographic parameter, and customer behavioral parameter;
- calculating a cumulative demand for a product as a function of (i) expected purchase quantities of the product indicated by the potential purchasers in the buying group and (ii) the aggregate customer history factor; and
- setting a price of the product as a function of the cumulative demand for the buying group.
Type: Application
Filed: Feb 10, 2011
Publication Date: Jun 9, 2011
Applicant:
Inventor: Rahul R. Vaid (Dallas, TX)
Application Number: 13/024,757
International Classification: G06Q 30/00 (20060101);