U.S. patent application number 10/935728 was filed with the patent office on 2006-03-09 for system and method for dynamic price setting and facilitation of commercial transactions.
Invention is credited to William Addington, Steve Litzow, Rebel Rice.
Application Number | 20060053132 10/935728 |
Document ID | / |
Family ID | 35997422 |
Filed Date | 2006-03-09 |
United States Patent
Application |
20060053132 |
Kind Code |
A1 |
Litzow; Steve ; et
al. |
March 9, 2006 |
System and method for dynamic price setting and facilitation of
commercial transactions
Abstract
The present invention provides methods and systems for defining
commercial transaction components; defining rules for mapping
customer transactions into individual components; market
segmentation in light of these individual definitions and bundling
individual components of an offer into optimized packages for
presentation and sale. A data processing system in accordance with
one embodiment of the present invention, examines the commercial
behavior of enrolled customers, breaks each of the constituent
transactions into purchases of atom-level components; catalogues
those components; extracts demographic information from said
transactions and other sources; facilitates demographic studies of
groups of such customers; optimizes offerings to such groups; and
facilitates the consummation of those offers of sale. The
processing system may also facilitate customers fiscal management
through the communication of data necessary to practice the instant
invention.
Inventors: |
Litzow; Steve; (Mercer
Island, WA) ; Rice; Rebel; (San Francisco, CA)
; Addington; William; (Houston, TX) |
Correspondence
Address: |
BLACK LOWE & GRAHAM PLLC;Suite 4800
701 Fifth Avenue
Seattle
WA
98104
US
|
Family ID: |
35997422 |
Appl. No.: |
10/935728 |
Filed: |
September 7, 2004 |
Current U.S.
Class: |
1/1 ;
707/999.101 |
Current CPC
Class: |
G06Q 30/02 20130101 |
Class at
Publication: |
707/101 |
International
Class: |
G06F 7/00 20060101
G06F007/00 |
Claims
1. A computer system for providing interactive formulation of
product definitions between the system and a product vendor
comprising: a. at least one extensible transaction database for
storing flexible representations of product definitions; b. at
least one system controller for selectively retrieving and
comparing the vendor-entered information with the stored flexible
representation of the product definition; and, c. a plurality of
interactive scripts, wherein said interactive scripts comprise
presenting to the product vendor the option of selecting for entry
either the retrieved data, the vendor- entered information or an
option to further refine the vendor-entered information, and
wherein selection of one of the presented options comprises the
selecting one of said plurality of interactive scripts and
executing the one of said plurality of interactive scripts.
2. The computer system of claim 1 further comprising: a. at least
one extensible vendor database for storing flexible representations
of vendor definitions; b. a associating means for creating
associations between at least one of the flexible representations
of product definitions store in the transaction database and at
least one of the flexible representations of vendor definitions
stored in the vendor database; and, c. at least one editor means
for entering or changing flexible representations of vendor
definitions.
3. A method for compiling a customer database wherein information
predicting customer behavior can be collected, comprising: a.
providing a customer database comprising names and purchasing
habits of a multiplicity of customers; b. providing a transaction
database comprising definitions of a multiplicity of products; the
appropriate sales unit for each of the multiplicity of items, and,
a unique variable identifier associated with each product; c.
receiving a multiplicity of customer's bills from a multiplicity of
vendors; itself comprising: i. splitting each bill into component
line item transactions; ii. associating each line item with the
corresponding variable identifier from the transaction database;
iii. determining the amount of each product purchased in terms of
the sales unit; iv. determining the date of the line item
transaction; v. associating the line item transaction with the
customer.
4. The method for compiling a customer database of claim 3, wherein
step a. additionally comprises i. enrolling a customer, itself
comprising: (A) submitting a questionnaire comprising requests for
identification and demographic data; (B) reviewing customer's
responses to the questionnaire; (C) inputting customer's responses
to the questionnaire; and, (D) associating the inputted responses
with a unique variable identifier; ii. augmenting the customer
data, itself comprising: (A) requesting the customer's consumer
credit report from a credit reporting agency; (B) receiving the
consumer credit report; (C) inputting the contents of the consumer
credit report; and, iii. associating the inputted contents with the
unique variable identifier;
5. The method for compiling a customer database of claim 3,
additionally comprising: d. determining a cycle length for periodic
purchases; e. determining demographic factors desirable for
predicting purchase behavior; and, f. analyzing the compiled data
associated with a customer for the presence of each of the
demographic factors.
6. The method of compiling a customer database of claim 4,
additionally comprising: g. identifying specific demographic
factors lacking from the compiled data; h. formulating questions
related to the lacking demographic factors; i. transmitting the
questions to the customer; and j. associating the customer
responses with the unique variable identifier.
7. A method for facilitating electronic commerce between a
multiplicity of customers and a multiplicity of vendors,
comprising: a. creating a customer database, comprising: i.
enrolling a multiplicity of customers into a closed market; ii.
inputting a unique variable identifier to each of the multiplicity
of customers; and, iii. creating an initial record containing the
identity and demographic information for each of the multiplicity
of customers; b. creating a transaction database; comprising: i.
creating a record corresponding to each of a multiplicity of
products; ii. identifying a sales unit quantity appropriate to the
product; iii. inputting a unique variable identifier to the
product; and iv. sorting and categorizing the products according to
categories comprising function, use and composition; c. creating a
vendor database, comprising: i. creating a record corresponding to
each of a multiplicity of vendors; ii. inputting a unique variable
identifier to each of the multiplicity of vendors; d. recording all
purchases; comprising: i. identifying each customer who effected
the purchase being recorded by each customer's unique variable
identifier stored in the customer database; ii. identifying the
vendor, from the vendor database, from whom the customer effected
the purchase by the vendor's unique variable identifier; iii.
splitting each purchase into component line items; iv. determining
the number of sales units purchased in each component line item; v.
identifying each component line item by its unique variable
identifier inputted in the transaction database; vi. identifying a
date for each purchase; and, vii. inputting a relational instance
in the customer database, comprising: (A) the customer's unique
variable identifier; (B) the line item's unique variable
identifier, stored in the transaction database; (C) the multiple of
sales units; (D) the vendor's unique variable identifier, stored in
the vendor database; and, (E) the date of purchase.
8. The method for facilitating commerce in claim 7, further
comprising the steps of: e. receiving a vendor's offer to sell a
product to the multiplicity of customers; f. sorting the
demographic data and purchase history for each customer and, in
turn, sorting the multiplicity of customers by a multiplicity of
segments, each segment representing a group of one or more
demographic characteristics or purchases; g. calculating a
statistical score representing "market penetration" of the vendor's
offer for each of the multiplicity of segments based upon
demographic characteristics and purchase history; and, h. sorting
the multiplicity of segments by statistical score.
9. The method for facilitating commerce in claim 8, further
comprising the steps of: i. selecting a threshold score for likely
customer acceptance of the vendor's offer; and, j. communicating
the vendor's offer to those customers with a statistical score
greater than the threshold score.
10. The method for facilitating commerce in claim 9, further
comprising the steps of: i. selecting a threshold score for likely
customer acceptance of the vendor's offer; j. calculating the
profit the vendor is likely to realize k. calculating the number of
units sold the vendor is likely to realize by communicating the
vendor's offer to those customers with a statistical score greater
than the threshold score; l. calculating the market share the
vendor is likely to realize by communicating the vendor's offer to
those customers with a statistical score greater than the threshold
score;
11. The method for facilitating commerce in claim 10, further
comprising the steps of: m. selecting values reflecting the
acceptable profit, the acceptable number of units sold and the
acceptable market share the vendor will realize from the vendor's
offer; n. adjusting the terms of the vendor's offer in light of the
profit, number of units sold, and market share so calculated; o.
repeating steps e through h, and k through n until the profit,
number of units sold and the market share most closely meet the
selected values.
Description
RELATED APPLICATIONS
[0001] The present application claims the benefit of U.S. patent
application Ser. No. 09/714,853 filed Nov. 15, 2000, which claims
priority to U.S. Provisional Patent Applications Ser. Nos.
60/180,363 filed Feb. 4, 2000 and 60/203,183 filed May 8, 2000, all
of which are assigned to the assignee of the present patent
application and incorporated herein by reference.
FIELD OF THE INVENTION
[0002] This invention relates generally to a method and system for
more efficiently matching desired goods and services and offer
terms for those goods and services to willing consumers to
facilitate commercial transactions.
BACKGROUND OF THE INVENTION
[0003] Traditional Marketing
[0004] The marketing of goods and services to consumers has always
been more art than science, and the science aspect has been rather
inexact. The inexactness of the science derives primarily from the
fact that vendors are unable to obtain, at least without
prohibitive cost, sufficiently accurate information concerning
consumer's actual preferences, either individually, or in the
aggregate. The vendor's strategy for ascertaining aggregated
consumer preference data is, presently, to concentrate on segments
of the consumer market, but they are only able to isolate or define
these segments at a prohibitively high cost.
[0005] The vendor's study of segments of the consumer market allows
a vendor to study the behavior of the whole of the market by
studying the behavior of each of a number of smaller groups of
consumers in the market (market segments) and aggregating the
behavior over all of the groups. The key to such segmentation,
however, is the selection of the factors used to determine which
consumer is in which group. Each factor selected must be related to
the predicted behavior in order for the segmentation to be
effective.
[0006] Generally, the factors the vendor selects to define a market
segment are based upon demographic data which is costly to compile.
The principal methods of compiling demographic data have been
surveys and census data. Because census data is typically derived
by extensive survey, the methods are essentially survey-based.
[0007] The survey method of obtaining market segment data typically
consists of posing a number of questions of two types. The first
type of question is for ascertaining the demographics of the
surveyed consumer. The second type of question is for ascertaining
the consumer's purchasing behavior with regard to the relevant
product. Once the survey is complete, the vendor is able to define
a proposed segment of the market based upon the consumers' answers
to the two types of questions and then validate that segment by
testing its response to product offerings. For instance, the
vendor's market researchers might study all of the consumers who
are male, married, of European ancestry, and in the group from 40
to 45 years of age, in order to determine their motor oil
purchasing habits. These purchasing habits are then associated with
those demographic factors, are studied for degree of correlation.
If the degree of correlation is sufficiently high, a cause and
effect relation between the factors and the admitted behavior is
presumed.
[0008] This presumption is then extrapolated from the survey
sample, to the entire demographic. In other words, the survey
assumes that behavior of a small group of consumers that the vendor
randomly selects and who share a demographic factor is the same as
the whole group of consumers sharing that same demographic factor.
In practice, these assumptions generally prove to be reliable
enough to justify the cost of the survey, but all too often, just
barely justified. Knowing, for instance, that most members of a
constructed demographic group, (or "market segment") report the
purchase of a particular product will not justify a vendor in
presuming the same purchase by each member of that group.
[0009] Another method of studying segments of a market is the focus
group. In this method, the vendor selects a number of demographic
factors to define a group. The vendor then recruits consumer
members of this demographic group and presents them with the
relevant product. Their reactions to the relevant product are again
assumed to be the reactions of the whole of the market segment that
share these demographic factors.
[0010] Each method relies upon constructing a model of the segment
of the marketplace, and from that model extrapolating the behavior
of the market. If either the model is flawed or the group's
behavior does not accurately reflect the market place, the
resulting impressions of the marketplace are misleading.
[0011] Even once they have targeted an audience; vendors then must
spend a tremendous amount of money to deploy the marketing plan.
Specifically, even vendors who are equipped with reliable
demographic factors likely to yield a sale, the vendor then must
identify and locate which consumers share these demographic factors
and thus belong within that segment. For example, if a bicycle
manufacturer has learned that 25-45 year old, college educated,
white males are more likely to buy mountain bikes costing over $900
than any other market segment, it is not possible to immediately
offer such bikes to all those, and only those in that segment,
because it is not possible to accurately and precisely identify
them. Thus, defining a segment to target is one thing; hitting the
target is quite another. A well-known example of this problem is
direct mail from vendors to "potential" customers which is so
routinely discarded that it is known as "junk mail. For the
foregoing reasons, while current marketing does work, and products
are sold, the process is extremely inefficient.
[0012] Most in marketing would admit to the existence of a sort of
"Uncertainty Principle;" that the very act of observation will
shift the outcome. Even when queried for information about their
spending habits, consumers may consciously or subconsciously report
information they believe the surveyor wishes to hear or information
that makes them appear more appealing than would their purely
truthful answers. For instance, few consumers would readily admit
to purchasing large quantities of fatty foods. Yet, actual sales
studies suggest otherwise. Even more importantly, most consumers
believe they have certain preferences or spending habits but they
are simply honestly mistaken. This kind of mistake is prevalent
with recurring, but small and variable expenses such as groceries
and phone charges.
[0013] Another problem with traditional market research is that the
information gathered has a limited shelf life. Consumers often
present a moving target to vendors. What is in vogue one day may
well evaporate the next. Thus, even if consumers could perfectly
self-report in a vendor's study, the results of that study provide
only a "snapshot" valid only at the moment taken. Therefore, even
the time required to compile the results of such a snapshot tends
to diminish its value.
[0014] If the uncertainty in targeting a potential consumer could
be eliminated by better knowledge of each consumer's actual buying
habits, the known marketing techniques of market segmentation would
assure more successful marketing. Rather than conducting surveys,
or trying to guess the buying patterns of consumers or to trust
their responses to surveys, vendors require a "window" into the
actual buying habits of their market. In a well-observed market,
such a window would be both accurate and dynamic thereby overcoming
the principal shortcomings of current market study.
[0015] In summary, the principal short comings of market
segmentation studies as currently practiced generally arise from
three fundamental defects: 1) the approximation of the whole by
smaller defined groups, sometimes referred to as sampling or
extrapolation error; 2) lag time; and 3) the definition of the
group and its behavior based solely upon the consumer's self
reporting. Due to inconsistencies in and unreliability of
self-reporting, the data is less scientific than that allowed by
other "direct observation" disciplines. These fundamental problems
are part of what the present invention solves.
Intermediary Marketing
[0016] What has been lacking is a promontory from which to view
actual, objective, nearly contemporaneous, individual consumer
purchase activity. If the actual purchasing practices and
transactions of their actual and potential customers were known to
vendors, vendors would be able to more accurately ascertaining
segment or segments of the market to which the vendor's products
would appeal, and to target their offers precisely to that segment.
The buying habits and actual transaction data of all potential
customer segments are nowhere comprehensively, aggregated or
compiled in an accessible form, by either consumers or vendors.
And, because individual vendors are generally unwilling and unable
(due to differences in compiling such data and other reasons) to
share what data they have as to the behavior of particular
consumers, a more precise study is not likely to arise from vendor
records.
[0017] Because neither end of the sale/purchase transaction will
serve as a source of reliable market information, the answer must
rest in the middle, i.e. with an intermediary. The only place to
interpose such an intermediary is between the unreliable reporting
of consumers and unavailable and incomplete data compiled by
vendors. Between vendors and consumers there exists a well spring
of purchase information contained in consumer bills.
[0018] The vast majority of consumer purchases leave a "paper
trail" reflecting the exchange of goods or services for money.
While cash sales do exist, the predominant method of purchase is,
of necessity, some form of billing relationship. Especially in the
fields of periodically purchased products such as telephone
services or insurance, the billing component of the relationship
tells a great deal about the actual purchasing habits of the
customer.
[0019] Consider the traditional bill-paying model. Periodically,
generally monthly, a consumer will pay bills to various utilities,
vendors, credit card companies, and, perhaps, a mortgage holder.
These payments are in response to bills mailed to the consumer. In
this series of transactions, there exists a great deal of
information that would be invaluable to the various vendors as to
the consumer's preferences, and more importantly, willingness to
spend money for various features and unwillingness to do so for
others.
[0020] Should an intermediary be placed between the customer and
the vendors when the vendors distribute their bills, the customer
purchases of goods and services could be carefully tracked and
stored for further analysis. As consumers are typically creatures
of habit, what they do with their funds one month (at least in
terms of categories of recurring expenses) they will likely do the
next month and the next month.
[0021] The instant invention relates to the utilization of an
intermediary between the consumer and the vendors to "read" the
consumers' bills. An advantage of such an intermediary is that all
of the consumers' transactions are "seen" by the intermediary.
These transactions, whether by direct billing or by credit card,
accurately and objectively convey the purchase habits of the
consumers. The actual executed transactions by consumers reveal
objective, and thus very valuable information about consumers, both
individually, and in the aggregate.
[0022] If, either by means of electronic transfers of funds to pay
monthly bills or by cataloguing the contents of bills, the
intermediary would have access to the consumer's purchase patterns
and habits data, and if that data were analyzed, vendors would be
able to target their marketing much more effectively. Specifically,
vendors' market segmentation would be derived from actual
transaction data rather than subjective preferences prone to
sampling and perception error. Vendors would be able to more
precisely and accurately segment the market. In this way, consumers
would be presented offers and terms with a much higher probability
of being acceptable. Overall, the marketing goods and services with
consumers would be more efficient.
[0023] Moreover, in the present invention, purchase habit
information derived from transaction data can then be correlated
with those same consumers' demographic information. Such
demographic information can be obtained directly from the consumer,
by traditional methods, or, to a limited but substantial extent,
derived from the transaction data itself.
[0024] Regardless of how the demographic data is obtained, it is
correlated against actual, objective transaction data. Thus, at its
heart, the invention enables vendors to know exactly who, in terms
of demographics, is buying what, and on what terms. Equipped with
this knowledge, the vendors can sell and the purchasers buy goods
taking advantage of the lower prices that result from increased
efficiency in the process.
[0025] The efficiencies and advantages are not only for the benefit
of the vendors, but also for the benefit of consumers.
Specifically, as the marketing process becomes more accurate and
precise in targeting willing consumers via the present invention,
consumers in turn will tend to receive only those offers which have
a high likelihood of meeting their needs. Hence, there will be less
"junk mail," in whatever medium. Second, as mentioned, prices will
be lower. Third, transaction "search" costs (that is, the time and
hassle involved in "shopping" for more appropriate offers) will be
substantially reduced. Fourth, the invention will permit the
vendors to offer goods and services upon terms which, due to the
described inefficiencies, were previously not available on the
market at all. Put differently, the process known in economics as
"price searching" will be simultaneously less expensive and more
accurate, enabling vendors to create offers hitherto unavailable.
Fifth, the present invention effects these advantages without
involving more work for the consumers. Consumers simply must pay
their bills as per usual, and that very process becomes the primary
source of the raw data from which the present invention derives its
improved information, and consequent efficiencies. Sixth, the
invention ultimately reduces consumers' expenses, not only by
reducing prices, but by making it more likely that the consumer is
purchasing those combinations of goods and services and terms which
are most appropriate, that is, most closely track, that consumers'
demands.
[0026] In effect, the invention eliminates, or at least reduces,
paying for goods or services that the consumers neither want nor
need, but have hitherto been required to purchase as part of a
package with goods and services the consumer does want and/or
need.
[0027] The banking industry has presented one example of a limited
intermediary. Consumers, as taught in Motoyama, U.S. Pat. No.
5,913,202, purchase mortgages and investment instruments from a
plurality of banks. In order to interact with the intermediary, the
consumer must register and in the act of registering, provide the
intermediary with information as to subjective preferences in
banking services and in financial products. A banking intermediary,
which acts as a clearinghouse for these services or financial
products, compiles offers that meet the consumers stated
preferences and presents them to the consumer.
[0028] Motoyama falls short of the advantages of the instant
invention. First, the products and services the Motoyama invention
presents to the consumer are selected on the basis of easily
distinguishable attributes. For instance, interest rates, term,
principal, etc., on loan instruments are numerically described
attributes and hence easily categorized and compared. Throughout
practice, the invention gains no greater insight into the consumer
than the consumer himself was willing or able to describe in
enrolling. Motoyama also fails to teach collecting information from
one's household bills and using that information to find the most
suitable product or offer terms. The instant invention is distinct
from Motoyama in that the instant invention matches customers to
products by utilizing and analyzing the consumer's own purchase
history.
[0029] Peckover, U.S. Pat. No. 6,119,101, also recognizes the
potential of a system of matching consumers with vendors.
Specifically, Peckover teaches a system for electronic commerce
having personal agents (computer programs with the ability to
perform tasks) that represent consumers and providers in a virtual
marketplace, such as is presented on the Internet. The consumer
sends the specifications of the product desired out into the
virtual marketplace. These consumer personal agents create decision
agents that shop for products and return the results to the
consumer. The consumer software agent works as a sophisticated
search engine that further assists consumers in comparing and
ranking the found products. Among the shortcomings of Peckover is
its inability of deriving and/or validating the consumers'
preferences from their purchase history.
[0030] In contrast, the instant invention allows far greater
precision in both the search for and in the recording of the
purchasing of goods. The instant invention has the ability to
create a complete marketplace and in doing so, catalogues and
describes with a precision that is not available with the system of
agents Peckover describes. With each purchase, the customer of the
instant invention reveals more of his likes and dislikes. The
Peckover invention has no ability to deduce and/or project the
needs of the customer from the customers actual transactions.
[0031] Instead, Peckover relies only on the history of customers'
searches. By tracking and reviewing customers' search history, the
Peckover invention is able to inform vendors of the wants of
consumers. Because it is neither comprehensive, i.e. contains all
of consumer's purchase patterns, nor does it catalogue the terms of
the purchases, there is little data to extend the information
beyond that garnered by following a shopper as that shopper window
shops. The instant invention, on the other hand, learns about the
customer's likes and dislikes by watching all of the purchase
decisions, and the terms of the actual transactions. Furthermore,
unlike Peckover, the instant invention actively compiles
demographic information about the customer and constantly
correlates it against the pattern of purchases contained in its
customer database. Because each of these purchases is defined by
the identity of the product purchased, tiny distinctions between
competing and virtually identical products reveal the customer's
likes and dislikes right down to "label affinity." After compiling
such data and examining that data in light of the customer
demographics, the instant invention can predict behavior for groups
of consumers.
SUMMARY OF THE INVENTION
[0032] The object of this invention is to interpose a Data
Processing System ("DPS") between the consumer and vendors, and in
light of the information garnered by direct observation of consumer
buying habits, unite vendors and likely consumers for the sale of
goods and services. The DPS may stand-alone or might be hosted by a
consumer bank, a credit card issuer, a debit card bank or an
intermediary electronic bill paying service with access to some or
all of the customer's account or transaction data. The only
requirement is that the DPS is positioned, directly or indirectly,
to analyze some or all of the customer's bills or bill payment
transaction data.
[0033] The instant invention can be practiced in a "stand alone"
mode as described. A more advantageous mode of practice of the
invention occurs when the entity in control of the DPS "partners"
with a portal or data aggregator at which a consumer's bill paying
activity transaction data is aggregated in electronic form. Such
portals or data aggregators are sometimes referred to as Consumer
Service Organizations (or "CSO") One example of a CSO is a consumer
banking institution.
[0034] Such a partnering would allow electronic bill paying once
the entity presents the bills to the consumer. In such an
embodiment, the customer would receive the bills from the various
vendors through, or in connection with, the CSO. (In some cases the
CSO itself could be in control of the DPS, rather than in
commercial partnership with a separate DPS entity). Upon reviewing
such bills, the customer would designate an account from which a
bill is to be paid and direct payment. After that payment, the CSO
would then pass the payment transaction data to the entity in
control of the DPS. The entity in control of the DPS would then, on
behalf of the consumers and vendors, present and pay bills in an
integrated environment. Alternately, the DPS would simply have
electronic access to the stream of transaction data generated by
the electronic bill presentment and payment mechanism.
[0035] While such integration is not a necessary feature of the
invention, when practiced with the invention, it greatly enhances
its efficiency and assures greater customer acceptance of the
invention. Additional benefits of a transaction database tied to a
bill paying and/or presentment engine or related process (a CSO, or
other such data aggregator, such as, for example, for electronic
bill presentment and payment: cyberbills.com, paymybills,
yodlee.com) include the customer's ability to budget with a
precision that has not been previously available. Because each
expense is broken down into its component parts, consumers may
budget by exactly the number of units required. Such a system is
more completely described in our other application Ser. No. XXXXXX,
"ELECTRONIC COMMERCIAL BILL PAYING SYSTEM"
[0036] By allowing the DPS to serve as the customer's post box for
bills, or to read the transaction data stream from the electronic
bill payment process, the records the DPS compiles would yield
great insight into consumer's true buying habits. Augmenting the
traditional methods of developing demographic data (surveying,
census data, and data compiled by consumer credit reporting
agencies), the analysis of bills ascertains with precision and
accuracy, the buying habits of a particular customer and, in turn,
for a complete market of customers. Each bill contains details
specific to the usage of a product by a specific consumer. The
inventive system, then, studies the line item details in the bills
in order to determine the extent to which the consumer purchases a
particular product within a given time period, and upon what
terms.
[0037] To effect the analysis of these purchases, the DPS develops
a comprehensive and precise catalogue of goods and services, a
Transaction Database. This database "learns" new offers by
comparing them to existing line items it "knows" and when such a
good or service is distinct from those known, the system assigns a
statistical "name" to the good or service, such "name" to reflect
similarity, where such exists to "known" goods or services.
[0038] Controlling software or controllers exist within the DPS
which sort and categorize these goods and services such that like
goods and services are associated with like. Definitions of new
line item products are compared and contrasted with those existing
within the database, and as a result of that comparison, both the
existing and the new product definition might be refined. For
instance, the existing definition of a 35 mm still camera might be
modified when the same manufacturer offers the still camera with an
auto-focus feature. As a result, the transaction database catalogue
contains distinct but largely similar definitions of both the
auto-focus and the non-auto-focus instances of the product.
[0039] Before goods or services transactions are entered into the
database, each goods or service line item is dissected into all of
its constituent or elemental parts. As with, for example,
insurance, various coverages are offered at particular prices. The
whole product consists of a bundle of the coverages. For the
purposes of the DPS, these coverages must each be separately
defined. The bundle of these coverages is then reconstructed into
the product offering. The idea is to arrive at basic component
definitions derived from commercial compound transactions.
[0040] The analysis engine dissects or reduces a purchase into its
most "elemental" components in order to capture the total economic
effect of the purchase. Additionally, the analysis engine weighs
the elements of each purchase in order to optimize potential
purchases. What would save the consumer money?
[0041] Vendor's rules and, in some cases, the law, require
additional compounding or "bundling" of these purchases. The
components of a purchase may include fixed recurring costs, fixed
one time costs, variable costs, and incentives.
[0042] For instance, federal, state, and local taxes attach to most
sales and are a mandatory part of the bundle, in spite of the fact
that these taxes add no value to the bundle of rights purchased.
Similarly, a telephone bill may include, not only the fixed price
of monthly service, but also such charges as a one-time "hook-up"
charge and various set up fees that are necessary adjuncts to
providing services on a monthly basis. In sum, the contract to
provide a service is, generally, a contract to provide a group of
products.
[0043] In the absurd extreme, failure to recognize this fact of
bundling would allow two long distance telephone service offers to
stand as equals even if one required a million dollar "set-up" fee.
Such offers would not be equal in the eyes of the purchasing
consumer. In analysis of a number of purchase candidates for a
particular consumer, the analysis engine breaks the potential
purchase into discrete and comparable elements.
[0044] For a product or a service, the elements of the purchase may
be identical between several vendors. In the simplest instance,
where two competitors sell the same television under the same tax
and delivery rules, the less expensive set should represent the
better purchase for the consumer.
[0045] All economic factors yield to such analysis. If, for
instance, the sales tax that attaches to a purchase from one vendor
does not attach to that of another vendor, the second vendor's
offering should be the better purchase in the eyes of the consumer;
the analysis engines seeks to similarly score the purchase.
[0046] Non-economic factors play a part in shopping as well. When
the consumer seeks to deal with a burst pipe, a three-week delivery
lag would disqualify even the cheapest of mops, or more likely,
restoration contractors. The analysis engine must score such
non-economic factors as delivery time to mirror consumer shopping.
As the example suggests, non-economic factors weight the purchase
price rather than to add or subtract from it. The price of the
product is multiplied by a factor corresponding to the speed with
which the vendor can meet the need of the consumer. The weighting
need not be "straight line." In the earlier example, having the mop
or restoration contractor services within hours of the burst makes
the mop or services very valuable; having it on the second rather
than the third day after the burst should decrease the relative
value.
[0047] From product to product and factor to factor, the weighing
protocol will vary. Vast data available due to the many consumers
and the many bills for each consumer, will allow the analysis
engine to develop comprehensive weighting factors and scales for
every product within the DPS and associate those factors in the
analysis. In an example of a cellular telephone, the analysis
engine would consider the fixed, monthly, costs and multiply them
by a factor derived from coverage ratings (perhaps those from J. D.
Powers or other such rating sources), thereby scoring the competing
services, for comparison.
[0048] Known sources and weighting for rating families of products
exist. Journals publish such ratings regularly; Consumer Reports
rates numerous products as do journals unique to fields such as PC
Magazine for computing and Runners' World for running shoes. Where
a truly unique product comes into existence, studies by known
methods described above as marketing techniques can be used to
derive the weighting scales.
[0049] As the experience base grows, the analysis engine continues
to check the weighting scales against the experience base in order
to refine the weighting. The factors should predict consumer
choices and where the analysis engine finds a variance, it
increments the weighting factor and re-runs the analysis. In time,
this loop should assure further and further refinement until the
analysis engine can accurately predict each transaction.
[0050] Once these bundles are analyzed; the system catalogues the
data derived from these bills in the Customer Database, then
"recompiles" or returns the read and analyzed bills, and transmits
them in electronic form to the customer. Simultaneously, the fact
of the purchase is stored in the Customer Database. As the amount
of information in the Customer, Vendor and Transaction Databases
grows, the demographic information about each consumer becomes more
comprehensive. As the data set becomes more comprehensive, the
quality of the inferences to be drawn from the data increases. Each
bill conveys and refines the picture of the customer. Each
elemental transaction provides clues as to who purchases an
individual good or service.
[0051] When compared against known demographic data for the
customer (derived, as earlier stated, by customer interviews and
third party reporting agencies, or even from analysis of
pre-existing transaction data), each purchase leaves a benchmark
for gauging actual, and projecting future market penetration. For
instance, when a middle-aged male head of household, earning in
excess of one-hundred-thousand-dollars, and of European descent
purchased Valvoline(tm) motor oil, recording the event refines the
database.
[0052] Aggregate this single record of purchase, across the many
purchases in a billing cycle and then across the many purchasing
customers, and the inventive system has created an observable, and
analyzable micro-economy.
[0053] That is, that "micro-economy" in fact previously existed
within the larger economy, but it was previously not isolated, and
hence not meaningfully observable or amenable to analysis. The DPS
analyzes these purchases over time. The system contains a library
of known forecasting methods and by iterative analysis of the data,
determines which method produces the most accurate forecast for a
given consumer by both applying the history of the consumers past
behavior and analyzing the habits of groups of demographically
similar consumers. This forecasting method is then used to predict
consumer behavior for the next time period. Risk analysis is
applied to the forecast to minimize the economic effect if the
forecast varies from the actual usage.
[0054] Once the catalogue of products and services (Transaction
Database) is functionally operable, having a minimum amount of data
necessary for analysis, the system is able to predict the behavior
of the market defined by customers contained within the Customer
Database. At this phase the inventive function includes a method
and system for providing an offer, which of all available offers,
is the best available offer for a given consumer. Offers are
collected from one or more vendors and stored in an offer
repository (Vendor Database). The system analyzes these offers in
light of the predicted behavior of its customers. In each instance,
the offer is ranked according the particular market segment which
has shown the greatest market penetration in their past
behavior.
[0055] Each component of the offer is then analyzed by iterations
to optimize market segmentation. To achieve optimization, the
system deems several known demographic factors to be relevant of
those known factors. The market is segmented by such factors and
market penetration is gauged for each segment. The results are
recorded. The system then varies the factors and, again, gauges the
impact. Once that segmentation of the market that produces the
greatest market penetration in several of the segments is found,
those demographic factors that produced such penetration are deemed
relevant.
[0056] Now the offer is transmitted to all customers likely to
purchase that good or service within each segment. Along with the
bare offer, the system sends an projection (typically annualized)
of the cost of such a good or service based upon the consumer's own
prior use. Additionally, the system attaches those mandatory
components appropriate for "real world" comparison. In alternate
embodiments, the system can send several competing offers with the
same annualized projection. The consumer then selects the offer
that represents the best value.
[0057] Segments are created and allocated dynamically as new offers
are received. The computing capability available at moderate
prices, allows the constant computation and re-computation of
offers. Offers that previously met no market need are reanalyzed as
offers are added to and deleted from the system.
[0058] Just as the system refines its own knowledge of the
customer's likes and dislikes through market segment iteration, the
informed selections drive the vendors of such offers to optimize
those offers in terms of either economies or goods or services
offered. In order to be competitive in this DPS-defined market, the
offer has to be adequately competitive to make market penetration.
The cost of designing offers in this environment would be
substantially lower than the costs of production, design,
advertising, offer management, etc. of a traditional offer sent to
the real, but diffuse world.
[0059] Towards that same end, the system allows the vendor to
construct a bundle of goods or services in a manner to assure
greater market economies. For instance, should a vendor elect to
sacrifice margin in order to secure market share, that vendor can
project market penetration with greater accuracy with the
assistance of the DPS data engine. In light of the projection, the
vendor can continue to optimize the offer, without risk, in order
to assure greatest effect.
[0060] A second incentive exists for optimizing offers. The system
allows increased consumer mobility. By facilitating the consumer's
immediate and automatic move from one periodic provider of goods or
services to another, the vendor that does not change to meet the
desires of the market risks nearly immediate market isolation.
Thus, the system envisions a very mobile market.
[0061] The mobility of the market is maximized if the transaction
cost, especially search cost, for moving from one vendor to another
is minimized. To accomplish this capability, the system is designed
to allow the customer to define the factors for optimization and
then to back away from the system. This "set and forget" mode
allows the system to optimize in the background, with no
transaction costs in terms of consumer time or effort spent in
shopping for periodic providers. The effect is complete, efficient
and transparent coverage for the customer with a minimum of
customer effort.
[0062] In accordance with other aspects of the invention, vendors
will present offers only to likely consumers, thereby maximizing
the efficiency of the marketing process. Expenses associated with
advertising, marketing and storefront warehousing and supply are
eliminated. Focus groups are not necessary and the vendors are not
required to risk losses due to test marketing.
[0063] In accordance with still further aspects of the invention,
vendors will mold offers to the demographics of available consumers
and by that means, more realistically project margins on sales of
goods or services as the same are affected by volume.
[0064] In accordance with yet other aspects of the invention,
vendors will be able to offer services that would be otherwise
unprofitable due to the marketing necessary to reach otherwise
scattered likely consumers, thereby increasing the vendor's
competitive lines of goods or services. Thus, if an obscure market
for a unique good should surface from analysis of purchasing
habits, the vendor may consolidate that market, offer the obscure
good and capitalize on that demand without the great expenditure of
marketing to geographically or demographically diverse
consumers.
[0065] In accordance with still another aspect of the invention,
consumer's individual purchase patterns are not disclosed to
vendors, rather the consumption behavior of a universe of consumers
is presented, thereby allowing maximal market penetration without
compromise of private patterns of behavior by individual
consumers.
BRIEF DESCRIPTION OF THE DRAWINGS
[0066] The preferred embodiment of the present invention is
described in detail below with reference to the following
drawings.
[0067] FIG. 1 illustrates the presently preferred of several
embodiments of the invention, here being marketed with a natural
partner, banking services;
[0068] FIG. 2 illustrates the hardware necessary to enable this
preferred embodiment through the use of a communication
network;
[0069] FIG. 3 illustrates the components and interconnections of
the invention comprises;
[0070] FIG. 4 illustrates the customer's enrollment in the system
and analysis that constantly refines the contents of the Customer
Database;
[0071] FIG. 5 illustrates one hypothetical customer's aggregated
payment obligations for one billing cycle, typically, but not
necessarily, one month;
[0072] FIG. 6 illustrates the hypothetical customer's telephone
bill in two cycles and the several line items the bill
comprises;
[0073] FIG. 7 illustrates the system's method of analyzing and
cataloguing the bills as they are submitted;
[0074] FIG. 8 illustrates an overall schematic of the database
engine for producing best offers;
[0075] FIG. 9 illustrates the detailed flowchart of the process by
which a customer "shops" for a product using the system;
[0076] FIG. 10 illustrates the system's method for targeted
marketing of an offer by a vendor;
[0077] FIG. 11 illustrates the system's method of optimizing a
vendor's offer in light of market segmentation study; and,
[0078] FIG. 12 illustrates the consumer's method of automatically
starting and canceling periodic products for efficient and seamless
coverage.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
[0079] For the purpose of this application, the term software is
deemed to include instructions as to use.
[0080] According to the principles of this invention, certain
limitations imposed by conventional pricing systems are eliminated
allowing for a more fluid responsive micro-economy. A market,
comprising customers of the inventive system (also referred to as
Data Processing System or "DPS"), is observed closely in all of its
purchasing activity. As a result of the knowledge garnered in that
observation, the customers can purchase and the vendors can sell
goods that comply with the customer's needs with great efficiency.
Efficiencies in sales are realized in many ways, including the
extremely competitive pricing strategies that vendors tailor to
this market.
[0081] Referring to FIG. 1, while the DPS might be used in
partnership with a credit card issuer, a consumer debit card
account, a credit union, an electronic bill-paying service, or
other similar service, such a partnership is not necessary. The
invention can stand alone without any diminution of its function.
This FIGURE illustrates the partnering of the inventive system 100
with a consumer bank 200 and linked on the bank's Web page 210.
Much as some customers of credit unions are also granted access to
discount warehouse retailers of goods, customers of banks or other
institutions might be granted enrollment in the DPS as an incentive
for doing business with the bank. Because of the savings produced
by the efficient market defined by membership in the DPS, customers
might also pay for the privilege of enrollment. In either regard,
customers require some access to the system to fully participate in
the market.
[0082] Customers 10 gain access to the services of the inventive
system by means of the Communications Network 25 portrayed in FIG.
2. Also shown are communicative links to vendors 300 and such
third-party information services as Consumer Credit Reporting
services and other sources of third-party demographic information
which optionally may supplement that provided by the customer upon
enrollment.
[0083] FIG. 2 illustrates the hardware connections that provide the
context of environment for the preferred embodiment of the
invention. They presently preferred means of communication would be
a communications network 25 such as the Internet. As is discussed
below, however, the point to point communications necessary to
practice the invention can occur by any communications network, as
will various WAN and LAN technologies including wired and wireless
technologies or any combination of all of the communication means.
Indeed, because no node of the network requires real-time access,
the principal Communication Network 25 can be either by telephone
operators in verbal communication over telephone lines or, as is
discussed below, communication and bill forwarding through courier
or U.S. Postal Service. In short, it is more important that
communication occur than that the communication be immediate. Speed
is not the essence of the communication but such speed as the
Internet supports enhances the "mobility" of the market defined by
the invention.
[0084] Also portrayed in FIG. 2 are the outside sources of
information necessary for maximum utilization of invention, along
with the DPS 100. These are the customers 10; the vendors 301, 302,
and 303; and the third party credit reporting service 50. Also
shown in this embodiment is the host bank 200. In this diagram, the
outside sources of information are electronically connected to the
DPS 100 by means of a communication network, preferably the
Internet.
[0085] In FIG. 3, preferable operational components of the DPS 100
are described. These include relational databases 110, 120, and
130. In the Customer Database 110, the DPS stores Customer Data
111, i.e., all information from whatever source that might be
useful in demographic studies as well as all information personal
to the Customer. The Customer Database 110 will include all credit
history, all purchase history, responses to surveys (if any)*, and
the initial listing application information, including the
associations between vendor and customer for the production of
bills. The Customer Database is also the repository for any
obligations owing on bills from those vendors. Note that survey
information, while sometimes advantageous, is not essential to the
invention. Indeed, the entire contents of the Customer Database 110
(other than mere identification of customers) can be comprised of
data derived; directly or indirectly from the Transaction
Database
[0086] The Transaction Database 130 is the dictionary to the
system, containing, as it does, the elemental definitions of the
goods and services described in the system. While the Vendor
Database 120 will contain the definitions of bundles of products,
it will only do so as series of these definitions. The definitions
of goods will not contain prices, but will define the salable unit.
Additionally, the transaction database will contain such tags as
are necessary to indicate definitions of persons projected to have
an affinity to the product in question. These definitions are
dynamic but are stored here with the product.
[0087] The Vendor Database 120 carries all Vendor Data 121. Beyond
identification data, the Vendor Database 120 will carry
associations of vendors with defined products; prices in any
current offerings along with bundling rules associated with said
offering; and any conditions upon the delivery of relevant
product.
[0088] Finally, the Customer Service Database 190 stores all
requests for service on existing transactions. Service, in this
sense, means returns, stop payments and other adjustments on
accounts. The Customer Service Agency 191 handles the calls for
service and the results are stored on the database 190. While not
necessary for the practice of the invention, its inclusion here is
to indicate the inventor's belief that returns may tell as much
about the customer as the initial purchase.
[0089] Another alternate embodiment allows, not only the receipt of
bills but also their payment. Once the consumer has redirected
billings to the DPS, the consumer must also arrange payment of
bills through transfer of consumer's funds held at various banking
institutions on consumer's request to do so, in effect a draft
drawn on the individual banking institution, by consumer's
direction through the DPS 140. Upon presentation to the customer,
the customer has the opportunity to direct payment by means of any
of several known Electronic Banking means, a known practice that
enhances the utility of the instant invention. Payment may be
either to the DPS for payment to the individual vendors or directly
to the individual vendors. Wire transfers common in the banking
industry facilitate payment.
[0090] FIG. 4, a flowchart, describes customer enrollment in the
DPS and the on-going refinement of the Customer Database
information stored in the Customer's name. At step 111, the
customer initiates the process necessary to enroll. This process
may be a part of enrollment at a bank, or credit union, or it may
be a stand-alone step. At this step 111, the customer not only
gives a list of regular vendors to the DPS, but also directs the
vendors on that list to send their bills, not to the customer but
to the DPS. These data are compiled to create a record at Step
112.
[0091] Over time and as the system functions, the recording of
various transactions and optionally, the customer's own response to
questions posed, refines this record. Optionally, the invention is
the data within the system may augment the Customer Database, with
data from questionnaires posed to the customer on a regular or
occasional basis. After enrollment at step 113, the DPS examines
the information garnered against its own standards for operable
completeness. In the event that the information is either
incomplete or contains apparently inconsistent information 114, the
DPS will request and receive supplemental information from a third
party, such as, for example, credit reporting agencies 115. In
light of the additional information garnered, the information is
again compiled and tested for completeness. If it is still
incomplete after all available supplemental third party information
has been received, the enrolling customer may be contacted for
clarification.
[0092] Once the customer is deemed acceptable, based upon the
information garnered, the customer joins the population of the
"closed market" defined as those customers in the DPS Customer
Database. The DPS examines each customer as member of this "closed
market" for demographic information that will allow the DPS to
conduct market segmentation study. If, for example, in the course
of the analysis 116 of demographic data, the Matching Engine
determines a demographic factor for segmentation of markets is
necessary, and, it further determines, that information with regard
to that factor does not exist for a particular customer, the
Matching Engine software will compose a question or series of
questions or data queries to determine the existence or
non-existence of this factor for that customer. Upon receiving the
response to that question, the software will store the information
as part of the Customer Profile 117. Similarly, if the information
is of a type that is best garnered from third-party credit
reporting services 50, the engine will compose an inquiry and
transmit the same 115. The responses will be recorded at 112.
[0093] At such time as the DPS receives information about a
transaction 118, it will record the same 119 by recording an
association to a specific code or series of codes in the
Transaction Database 130. The specific means of doing so is set out
at FIG. 7.
[0094] FIG. 5 is a hypothetical bill to the customer, the contents
of which is received at, or entered into, the DPS. Such bills are
presently preferred source of transaction data for populating the
DPS Databases, because, in accordance with the invention, added
transaction data assures the precision of the marketing efforts and
for that reason, the information harvested from real bills,
electronic or otherwise, is ideal. Drawing from two bills to a
hypothetical customer, May 350 and June 360, the DPS will derive
some facts about the bundling of services and about the
customer.
[0095] Cable Television
[0096] For both the thirty-one days of May 351 and the thirty days
of June 361, the bill from TCI is in the amount of $37.50. Thus,
the system knows that the cable services are sold at a fixed
monthly rate. Additionally, the price of the services, likely
itemized would yield the precise nature of the product bundle.
[0097] Energy Consumption
[0098] Comparing the two months, the consumer paid $32.19 less to
Puget Sound Energy in June than in May 352, 362. Statistically,
June is a warmer month than May. Yet, the energy bill went down. A
consumer living in the Pacific Northwest will have drastically
different power requirements than a similar consumer in the
southeast, northeast, or the southwest. The customer's address
pinpoints the climate for the DPS. Prevailing warmer temperatures
in June may cause the consumption of energy to go down over
May.
[0099] Puget Sound Energy, in our example, happens to provide
electricity as well as natural gas. The distinction between a
coarse analysis of amounts paid and the finer analysis of line
items is evident; that distinction underscores the importance of a
line item analysis.
[0100] Satellite Communications
[0101] The consumer seems to have a fixed DirecTV (satellite
television) bill 353, 363.
[0102] Consumer Credit Cards
[0103] The consumer seems to be paying a revolving credit card off
with Discover Financial Services 354, 364. Each monthly payment is
in the amount of $250.00. Generally, the amount due on a credit
card would be reflective of purchases and unlikely to total in
round figures. Evidently, the customer is using the credit card as
a financing device. An opportunity exists for substituting a bank
for the credit card as financing device. Hence, the analysis of
credit card carrier as finance company or vendor will allow the
shopping for credit cards. Discover, like almost all credit card
issuing companies, has various financial packages that it presents
to its consumers. In fact, each package has several variables
including introductory interest rate, regular interest rate, annual
fee, grace period, etc.
[0104] A credit card company is both a vendor and, from the
standpoint of the DPS, a messenger. On the itemized bill,
transactions from numerous vendors would indicate the purchase of
several products. Even the selection of vendors yields information
as to the demographics and buying patterns of the customer.
[0105] Insurance Payments
[0106] The consumer pays a fixed monthly insurance premium with
State Farm Insurance 355, 365. The consumer makes a payment to an
insurance company of a fixed amount. It is essential to be able to
review the line items in order to determine the bundle of coverage
purchased.
[0107] Long Distance Telephone
[0108] The consumer seems to have made more long distance telephone
calls with the long distance provider 356, 366. This will be the
exemplar for the individual line item analysis. For such analysis
we turn to FIG. 6.
[0109] This FIG. 6 contains the typical information found in a long
distance telephone bill. The level of detail found on this bill
demonstrates the nature of product bundling by retailers. Some
typical examples of this bundling are evident in the several rates
that exist; one such example is at 356.10, in contrast to either
356.20, or 356.30. Due to the distinct times or zones in which the
calls are made, the rates for each call are different. Nonetheless,
the rules for determining the rates are definite and reproducible.
At the DPS level, study of telephone calls for an individual
consumer or across the several consumers will quickly yield a
mapping of rates and conditions. Similar rate differences exist for
international calls.
[0110] Also evident is regulatory bundling at 356.60 and 356.70.
City 356.60, state, and federal taxes are generally reckoned on a
percentage of call volume basis. On the other hand, 911 fees 356.70
are generally charged on a monthly rather than on a per volume
basis. Thus, the total of non-service fees, 356.80, is complexly
variable.
[0111] Acquiring data from the bills, in paper form, for analysis
is presently accomplished by the use of any of three current
technologies depicted in FIG. 7: Manual Data Entry 119.21; Optical
Character Recognition 119.22; or, "Screen Scraping 119.23. It is
envisioned that as the invention gains adherents, the various
vendors will have a standardized means of sending all such bills,
either by specialized software 119.25 designed by the DPS or by
means of Extensible Markup Language (XML) 119.24 an existing
standard for the interchange of such information. Additional
options became available via true electronic bill presentation and
payment. Until such time as the software means is generally
available:
[0112] Manual Data Entry 119.21
[0113] Manual data entry is by far the most labor intensive, most
prone to errors, and least desirable. However, until automated
information interchange mechanisms have 100% penetration, there
will always be some need for manually entering data. In effect, a
data entry operator must translate all of an individual paper
bill's contents into electronic data and that data is, in turn,
entered as the consumer's bill.
[0114] Optical Character Recognition 119.22
[0115] In this method a paper bill is entered into a scanning
system. An optical character recognition (OCR) system will scan the
page for pertinent transaction information from the vendor, then
convert the information to machine readable form, and, then,
automatically enter it as the consumer's bill. As with manual data
entry, this system is less than desirable because bills can be
mutilated, printing can be too light, or other such defects that
would cause the OCR system to produce incorrect results.
[0116] Screen Scraping 119.23
[0117] Modem electronic banking systems use CRT in character or
graphical presentations. The DPS can simulate the actions of a
consumer over a computer network. Once the DPS has identified
itself to an online bill (complete with details), a scanning system
can be employed to lift the information off the screen-hence the
term screen scraping. Like OCR, screen scraping is a little prone
to error mostly because the online bill may change its form from
time to time for aesthetic purposes, or simply because information
needs change.
[0118] On the vendor's billing cycle, a bill is sent by any of the
means set forth above to the DPS. Either the vendor or the DPS will
encode the bill, line item by line item into a standardized list of
the constituent parts 119.30; standardized, that is, to reflect
definitions contained in the Transaction Database 130. Here is an
essential step to ensure sufficient granularity of information. It
is the object of this standardization to allow the bill to present
a good or service in terms of basic units regardless of the
identity of the vendor, so that aggregation and/or comparison
access vendors is possible. Goods and services are treated as
fungible commodities under each definition in the DPS's Transaction
Database 119.40. Thus, for example, if a kilowatt hour of
electricity at a given time of day in a given season is assigned
the definitional designation of 1200 090 111 (much as inventory
items in a store receive UPC coding), then, no matter the
particular vendor supplying the kilowatt hour, it is encoded on the
bill as "n units of 1200 090 111@$2.11 per unit." Precision in
pricing, i.e. decimal places describing the price, will extend as
far as necessary to accurately rate the good. Similar definitions
work for all goods and services. For example, United States to
Tokyo telephone services between hours of 09:00 to 11:00 GMT for
"m" seconds might bear a code 3600 313 007@$0.099876 per unit. Any
taxes or other charges are coded similarly as products and
referenced with an association in the Vendors Database making them
part of a mandatory bundling. Similarly, if there is a dividend or
giveaway, that dividend is coded for its product identity and then
bundled with the product under the rules in the vendor
database.
[0119] In one embodiment, the DPS then re-assembles the bills in
line item form and presents them to the consumer 119.50. These
bills are descriptive using both the vendor's narrative description
and the DPS coding of the bill. Similar coding and presentation is
currently used in the medical community in response to the demands
of the health insurance community to describe various medical
procedures and the provision of supplies. An operation will have a
standardized code and a verbose name. Both are presented on the
bill to the consumer. In an alternate embodiment, the bills which
are read by the system are not changed in the least, but are
presented to the customer or consumer as always. But, in that
embodiment, the data which appears on the bills is obtained from
the bills or the vendor at any point after the transactions
reflected in the bills occur.
[0120] Thus, simultaneous with the presentation of the bill to the
consumer, or at any point after the actual underlying transactions
occur, the consumer's buying habits, as reflected by the bill, are
compiled and entered in each of the three databases according to
the nature of the data. The transaction is sent to the Customer
Database to describe, further, the customer's buying habits. Each
of these sales is anchored by the date of the transaction and
because of the standardization of the definition of the
transaction, a picture of the consumer as consumer begins to
emerge. That picture is stored on the consumer's individual file in
the Consumer Database 110.
[0121] The Vendor Database 120 contains associations that indicate
the vendor's current offerings. So, for example, if a rate plan for
long distance is considered, the software will attempt to match the
plan to a known plan. If the plan is not found, a new definition of
a plan is entered. Soon, the billing plans at the DPS should
perfectly mimic those at the vendor.
[0122] The Transaction Database 130 is continually being refined by
the offerings by various vendors. If, for example, the consumer
purchased a newly offered package of cable channels, that package
may not, at the time of purchase, be included in the Transaction
Database 130. At either the vendor's notification or upon the DPS's
failure to find an adequate definition for the good or service, the
DPS institutes a new designation for the new service. In this
manner, the database 130 is always up to date and
comprehensive.
[0123] One principal advantage of the invention is to make the
process of preparing and presenting offers to the customers
efficient and tailored to the needs and desires of the customers in
the database. That ability allows the vendors to forgo both
marketing and advertising in the traditional sense. Rather, an
offering is tailored to particular customers in the database and
transmitted in a targeted presentation to those customers. By
virtue of the customer's past buying habits, the offers can be
presented in a side-by-side comparison. The resulting efficiencies
should appear in the form of lower costs of sales, and, thus, lower
prices to consumers. FIG. 8 illustrates the workings of this
process. Importantly, these advantages are achieved without any
additional effort on the consumer's part.
[0124] In FIG. 8, a vendor will place an offer in the Vendor
Database 120 by communicating the same to the DPS. The event,
placing the offer, will trigger the Matching Engine 150 to begin
its process of, first, categorizing and normalizing the offer, much
as described above for billings; second, assembling from the
database a list of customers who currently, or who, by demographic
study (as is further described in FIG. 10), are likely to purchase
the product. For each such customer, the Matching Engine analyzes
the offer in light of the customer's prior use of it or of a
similar product or in light of need for such product demonstrated
by demographics or the constellation of other purchases stored in
the Customer Database 110. Such analysis will further narrow the
likely customers for whom this offering is a "good deal." After the
winnowing process, the DPS sends the "recommended" offer, along
with reports of analysis as to annualized costs and comparative
data, to the remaining designated customers. What the customer
receives is likely a very attractive offer 160. This offer process
is very likely to result in a sale without advertising or marketing
to create the exposure of the offer to likely consumers. Again, the
probability (that the offer will be accepted) is higher because the
data, upon which the sorting analysis is based, is data from actual
transactions of the targeted consumers. Thus, the vendor/offeror
can objectively demonstrate an objective improvement over the
consumers present expenditure.
[0125] The invention provides two-way matching. In the first
application, the invention allows customer "shopping." In FIG. 9,
the process for this "shopping" is set out. A customer signs on to
a shopping screen, indicating his interest in making a purchase
151.00. In an interactive interview with the customer on the
screen, the definition of the sought product is narrowed by
questions and the customer's answers thereto 151.10. Once an
operative definition of the sought product emerges, the DPS refers
to its product definitions in the Transaction Database and checks
for a match 151.20. If no such product exists in the database, an
exact definition of the specifications sought by the customer is
compiled from the product code 151.21. By virtue of the product
definition, the DPS locates similar products 151.22. While these
are, by Step 151.20, not complying products, the DPS presumes that
the products cluster in the marketplace, i.e. that a vendor of
similar products is the most likely vendor of the sought product.
The DPS frames the request for offer in "verbose" language and
transmits the same to vendor without identifying the customer
151.23. If the vendor does offer a complying product or wishes to
compose a complying offer, they forward that offer to the DPS both
as an offer in the database 151.23 and a specific notice to the DPS
for forwarding to consumer with reference to the request
151.24.
[0126] In the event that the product does exist in the database,
all such offers are collected 151.30. Whether in response to the
customer request or whether it resided in the database, the several
offers are compiled and annualized in light of the patterns
established in the Customer Database 151.40. The offers are ranked
with regard to such parameters as the customer has previously
defined. In default of such parameters, the least expensive product
over the annual cycle will be the leading offer. The DPS will
present the several offers to the customer for purchase 151.60. If
none of the offers interests the customer, the customer may choose
151.61 either to abandon the search or to redefine the parameters
of the search 151.61, and begin again.
[0127] If one of the selected products interests the customer, in
alternate embodiments of the invention, the DPS determines if the
product is a periodic product, at step 151.70, such as insurance or
telephones services, which is to say does the product actually
represent a series of periodic purchases. Drawing on the
information stored in the Customer database, the DPS will enroll
the customer for purchase of the new periodic product, confirm the
purchase and then cancel the customer's current product at Step
151.90. The process produces both efficient provision of product,
i.e. no overlapping product, and complete coverage, i.e. no gap in
coverage, especially for such products as insurance or heating oil.
The process is facilitated by the information contained in the
Customer Database (See FIG. 12). Alternatively, a procedure such as
that described in Boesche, U.S. Pat. No. 6,092,053, can provide an
alternate means of "automatic enrollment."
[0128] In the case of non-periodic product, the customer simply
places an order 151.80. In either event, the system facilitates
order placement for the customer as it does locating the conforming
product.
[0129] In FIG. 10, the principal advantage to this "closed market"
defined by the Customer Database, is the vendor's ability to tailor
their offers of particular products to the demographics of this
market. Because so much is known of the individual customers, the
DPS can perform a statistical analysis and artificial intelligence
to the process of market segmentation. Such segmentation study
relates to the finding of potential customers for an optimized
offer. The object of this process is to break the market into
segments 152.10 defined by specific demographic factors (e.g. age,
sex, income) and/or by preferences revealed by transaction data.
Once a market segment is defined, the product is tested for its
appeal to that market segment 152.20. Strictly defined, that appeal
is judged in terms of purchases recorded in the Customer Database
of similar goods. The more similar the good, the higher the score.
Other known statistical and analytical methods exist to refine this
score. The inventive focus of this is invention is to garner the
data and present it to the DPS for study by these known methods,
but the statistical methods, themselves, are not claimed.
[0130] Once the segments are scored for market penetration, those
segments that score highly are grouped and examined for demographic
factors common to many segments 152.30. The study may be done in
the negative as well, i.e. the factors common to the lowest scoring
segments are determined to likely be absent from the optimally
configured segments. In either regard, the grouping of segments
according to score and demographics is used to discern factors that
define likely customers to purchase the product in question.
[0131] Even in light of a principal factor, secondary factors may
also be of interest. The study iterates until all factors of
interest are defined 152.40. From the data thus collected, a list
of factors should result and compiling the same in terms of rank is
a known process 152.50. Segments of the market are then optimized
for a particular offer 152.60. The DPS determines a threshold score
below which the offer is deemed irrelevant to a particular
customer. The relevant customers are listed and ranked 152.80. The
offer is then formulated as in FIG. 9 as though the customer had
been shopping for the same at Step 151.40, et seq. in Step
152.90.
[0132] Note that in some embodiments, the transaction or bill data
can operate as the only source of data for market segmentation and
offer optimization. In other words, no information extrinsic to the
presented bills is necessary for the practice of the invention,
and, in a certain sense, it is in this embodiment that the
invention is the most powerful. For example, from a single bill and
nothing more, the DPS can ascertain the consumer, the transaction,
and the terms of the transaction. Even this information can be
sufficient to populate the DPS databases, and provide the raw data
necessary for the vendor, via the DPS, to segment a market and
optimize offers. The reason for this is that extrinsic demographic
information is fundamentally only an imperfect or crude proxy for
the actual preferences and likely purchases of a consumer group,
which can in many cases be more directly discerned with this
invention.
[0133] For example, a high-end bicycle vendor wants to know who
will buy a mountain bike costing over $900. The vendor wants to
know that because the vendor only wants to offer such bikes to such
persons. Traditionally, because the vendor doesn't know who such
persons are, the vendor learns by survey for example, that 75% of
such bikes are purchased by white, middle income males aged 25-45,
so the vendor tries to target those persons, in the hope that some
of them want to buy a bike in that price range. Thus, the
demographic information (white, middle income etc.), is merely a
means to an end, with no independent significance. The end is who
will buy those $900 bikes. Aside from who will buy the bikes, the
vendor doesn't really care about the traditional demographics. In
contrast, this invention provides what in many cases will be a more
meaningful means to the same end; actual purchases. For example, by
using the invention, the vendor will have access to persons who
have in fact purchased mountain bikes, or at least bikes (depending
on the level of detail in the bills), costing over $900. The vendor
would not necessarily know, or care, whether the person was white,
or male, or old or young, or anything of the sort. All the vendor
would really care to know was that the person purchased a bike in
that price range. In many cases, past purchases is a more reliable
indicator of future purchases than traditional demographic factors
such as age, race, income, etc.
[0134] Note also, as mentioned above, that the invention is not
restricted to analyzing a single variable, but can correlate
multiple variables, regardless of whether extrinsic demographic
data is available. Thus, for example, assume a vendor wants sell
ski equipment. Traditionally, again, the vendor may target a
certain demographic, which has demonstrated an imperfect, but at
least positive correlation to ski equipment purchases. However,
with the present invention, the vendor might be able to easily
present offers to only those consumers who, in the past 3 years,
have not only bought over $500 of ski equipment, but also either
live in areas where it snows, or who have bought plane tickets to
ski resorts, or who rent a ski chalet, or who are paying a mortgage
on a cabin near a ski area. All of that information can be gleaned
solely from the bills that are already being paid. Thus, again,
merely from the bills and nothing else, substantially more accurate
market segmentation and targeting can be accomplished, and at much
lower cost. Of course, the invention can be practiced with any
amount of additional extrinsic data, demographic or otherwise, but
the point is that no extrinsic data is necessary. In a sense, as
far as vendors are concerned, consumers are primarily defined by
where, when and how they spend their money. Methods of simple or
complex artificial intelligence can be applied to further analyze
the spending patterns of particular consumers, and so draw
increasingly useful inferences. For example, it may be possible to
deduce from a customer's health care expenses that they have
recently had a new child. New children typically trigger a
reasonably predictable chain of expenses for years after. This in
itself is useful market intelligence. However, that information can
be correlated against several other kinds of expenses discerned
from the bills to develop extremely precise market segmentation and
optimally targeted offers.
[0135] An additional refinement of the process is portrayed in FIG.
11. As in FIG. 10 the vendor conceives of a product for offering
153.10. Similarly, as well, the vendor must described that offer
with terms defined in the Transaction Database 153.20. In Step
153.30, the vendor replicates the steps 152.10 through 152.80. When
completed, rather than to immediately offer the product to the
customers, the DPS then, with the predicted market penetration
"costs out" the offer 153.40. Presuming the predicted sales,
standard methods of delivery and other relevant contingencies, the
vendor projects all of the costs of making the offer. Due to the
efficiency of the "closed market" the vendor may find otherwise
hidden economies, such as those of scale or production for a set
run of product 153.50. Indeed, based upon those projections, the
vendor can realize particular specificity in its contracts with
third party suppliers that may, itself, create economies in the
offering.
[0136] Realizing these efficiencies, the vendor may choose 153.60
to present the offer as then currently constructed or, in search of
greater market share, modify the offer to reflect these economies
and test the newly reconstructed offer 153.70. In either regard,
after iteration, the offer is deemed optimized. Based upon the
segmentation data gathered at Step 153.30, the DPS selects likely
purchasers from the Customer Database 110 at Step 153.80. As in
Step 152.90, the DPS presents the offer to likely customers
153.90.
[0137] FIG. 12, describes a two-sided process between the customer
and DPS to optimize the purchase of certain periodic products. In
such a scenario, the customer indicates the preference for
automated purchase of a particular product. Through an interactive
interview process, the customer's needs are ascertained, then
coupled with the customer's own prior use patterns, a qualifying
offer is defined and mapped onto the Customer Database 110 under
the customer's identity 154.30. Along with the definition are
stored the customer's authorization to and rules for purchase of
the periodic product.
[0138] At Step 154.40 the Matching Engine 150 constructs bundles of
services from the Transaction Database 130 and test them for
pricing in the Vendor Database 120. The resulting bundles are rated
for compliance with the customer's rules and if better than the
customer's current periodic product offer, the Matching Engine
sends an order to the vendors of the optimal bundle 154.50. Once
confirmed 154.55, the prior purchasing, if any 154.60, is cancelled
154.70. The result should be optimal coverage. Once the rules are
defined at Step 154.20, the purchase take place entirely in the
background with reports to the customer only upon request or upon
change of vendors. Thus, the customer receives optimal pricing for
coverage of periodic needs, without the requirement of price
shopping.
[0139] As this period product purchasing occurs, the vendor's own
optimizing of offers should result in significant and regular
changes in service in order to capture the best prices. Likewise,
the sellers gain greater exposure for products without the
otherwise required "front-end" advertising and marketing.
Efficiencies realized by means of this invention should be mutually
beneficial to vendors and customers.
[0140] While the preferred embodiment of the invention has been
illustrated and described, many changes can be made without
departing from the spirit and scope of the invention. Accordingly,
the scope of the invention is not limited by the disclosure of the
preferred embodiment. Instead, the invention should be determined
entirely by reference to the claims that follow.
* * * * *