Classifications

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

Definitions

• Patent Document 2 JP-A-2000-259719
• Non-Patent Document 1 “The Indian Journal of Statistics 2000”, Vol. 62, Series B, Pt. 2, pp. 233—248
• the present invention (1)
• At least one type of consumer attribute information and at least one type of communication history information including at least one type of communication condition information and response result information are individually stored as a unit of a consumer or transaction consisting of multiple fields.
• Database that stores the records that have been searched and updated so that they can be searched and updated.
• First storage means that stores posting condition information for each communication driver so that they can be searched and updated
• Effective field pattern extraction means for extracting, as an effective field pattern, a combination of information of fields determined to be effective with respect to the response result from the dependency, and information on the submission condition for each communication driver stored in the first storage means.
• a campaign dynamic optimization system comprising a publication condition information updating means for updating the operable range so as to match information of each field of the effective field pattern.
• the invention according to (1) further comprising an information linking means for adding a new field to the record of the consumer database corresponding to the search element and additionally recording the derived standard external information. It is a dynamic optimization system.
• the present invention (3) The campaign dynamic optimization system according to any one of the inventions (1) and (2), wherein the consumer database is updated in real time or periodically.
• the present invention (4)
• a mass media information database in which mass media information relating to the amount of contact is stored in a searchable manner based on at least one kind of consumer attribute information of the consumer database; and at least a common feature of the consumer database and the mass media information database.
• the mass media information database is searched by using one kind of consumer attribute information as a search element, standard mass media information for each search element is derived, and the derived standard external information corresponds to the search element.
• Mass media information linking means for setting a new field in the record of the consumer database and additionally recording the new field, respectively.
• Power is a campaign dynamic optimization system of one invention.
• the present invention (5)
• the mass media information database is a campaign dynamic optimization system according to the present invention (4), wherein the data is updated in real time or periodically.
• the present invention (8)
• a consumer database transaction is searched for a consumer or transaction corresponding to the record that matches the valid field pattern, and a promising user or transaction related to the record is identified as a promising customer.
• Customer detection means
• Characteristic quantity of information of each field regarding the reference data group ( m: average value of each feature quantity, ⁇ : Statistical processing means for calculating a correlation coefficient (r) between the information of the respective fields normalized by the characteristic amounts, and deriving a correlation matrix (R). All combinations (y) of the possible values of each field are
• a predetermined number of patterns in which the value of the dependency (i.e., Mahalanobis distance) evaluated by the dependency evaluation means is equal to or greater than a predetermined value or ranked higher than the predetermined value is correlated with communication history information that is effective in the response result.
• Communication is a general term for acts such as advertising, publicity, and information provision that introduce a company brand to consumers. It is not limited to the so-called “mass media,” but it also includes individual advertising activities for specific consumers, such as sending e-mail to addressables.
• Conser attribute information is information on the gender, age, occupation, address, and! / Of the resident, who is the target person, and the inherent properties and characteristics of the resident or transaction.
• Field refers to the area allocated for specific information stored in a record, where one record contains multiple fields.
• FIG. 1 is a schematic diagram of a system configuration embodying the present invention.
• FIG. 6 is a diagram schematically showing a process of integrating data in a consumer database of the present invention.
• FIG. 10 is an explanatory diagram of an arrangement of data in a consumer database of the present invention.
• FIG. 11 is an explanatory diagram of a standardization method when using the Bayesian network of the present invention.
• FIG. 14 is a diagram showing an example of a data arrangement in a second storage means when using the MT system of the present invention.
• FIG. 15 is a diagram showing an example of stored contents of a consumer database of the present invention.
• FIG. 17 is a diagram showing an example of a restriction condition in link connection of the present invention.
• FIG. 18 is a diagram showing an example of a probability distribution at each node in FIG. 16 of the present invention.
• FIG. 19 is a diagram (part 1) illustrating an example of a conditional probability table at each node in FIG. 16 of the present invention.
• FIG. 18 is a diagram (part 2) illustrating an example of a conditional probability table at each node in FIG. 17 of the present invention.
• FIG. 18 is a diagram (part 3) illustrating an example of a conditional probability table at each node in FIG. 17 of the present invention.
• FIG. 23 is a diagram showing an example of a response probability ranking for each field pattern that is useful in the present invention.
• FIG. 25 is a diagram showing an example of stored contents of a communication contact database of the present invention.
• FIG. 26 is a diagram showing an example of a return on investment (ROI) ranking for each field pattern that works on the present invention.
• ROI return on investment
• FIG. 27 is a diagram showing an example of stored contents of a consumer database of the present invention.
• FIG. 30 is an explanatory diagram showing a process of processing a push-type advertisement using addressable media, which is useful in the present invention.
• FIG. 1 shows an outline of the entire system configuration including a campaign dynamic optimization system that works on the present invention.
• the central processing unit CPU is connected to a consumer database DB1 and a communication contact database DB2 via an external nosline B2.
• the central processing unit CPU is also connected to the first storage means and the second storage means via the internal bus line B1.
• the dedicated processing units include a standardization processing engine E1, an information linking engine E2, various dependency evaluation analysis engines E3, an effective field pattern extraction unit Ul, and a submission condition update processing unit U2.
• each client CL1 C Lm and each communication terminal Tel of each of the media M-1 to M-n is communicably connected to each client CL1 C Lm and each communication terminal Tel of each of the media M-1 to M-n through a network represented by the Internet.
• each client CL1-CLm can use various communication means such as telephone and direct mail, as well as e-mail through the network, to contact the consumers P1-P.
• Each media manager M-1—Mn assumes an environment in which information can be transmitted to consumers through their managed information media, such as newspapers and TV.
• step 1 of Fig. 2 The normalization process in step 1 of Fig. 2 is started for the consumer database that has been subjected to the preliminary process of connecting the external information. That is, in this step, the value is standardized to a value (typically 0 or 1) that can be used by the analysis engine. It should be noted that the value that can be handled is not limited to 0 or 1, and the value of the standard is selected according to the analysis engine used. For example, if an analysis engine using a Bayesian network is adopted, it is sufficient if the value can be distinguished into a finite number of categories, while if an analysis engine using an MT system is adopted, the number of continuous Can be used even if The data after the normalization processing is stored in the second storage means.
• a value typically 0 or 1
• the value that can be handled is not limited to 0 or 1
• the value of the standard is selected according to the analysis engine used. For example, if an analysis engine using a Bayesian network is adopted, it is sufficient if the value can be distinguished into
• the consumer database also stores communication history information, and here, media such as "DB for TV”, “DB for newspaper”, “DB for banner”, “DB for web”, and the like are stored.
• This example illustrates a method of storing the information on a consumer ID basis based on the consumer ID. current Considering the power of the method of tracking the communication histories of consumers, it is desirable to store each media as a unit in addition to the attribute information as shown in Fig. 4 and to recalculate for each consumer ID as preliminary processing.
• FIG. 8 schematically shows a process of connecting information in the external database with specific values.
• the common items of the external information database DB4 and the consumer database DM1 are “area” and “date”, so the area where the response was and the information power of date are also
• the weather information at the time is determined, and the weather information is added to each response history. That is, in the system, a field for storing the weather information is added for each type of response.
• the normalization process in step 1 of FIG. 2 differs between the case where the analysis engine using the Bayesian network is used and the case where the analysis engine using the ⁇ system is used.
• the column relating to “gender” may be set to 1 for a male and 0 for a female, so it is not necessary to divide the column for gender.
• the number of continuations such as “age” is stored
• FIG. 11 shows an example of the contents stored in the second storage means when the analysis engine using the Bayesian network is applied.
• a Bayesian network that combines communication chains constructed by applying structure learning means Is shown in FIG.
• junction tree algorithm is a class that combines a directed graph structure into an undirected graph and merges nodes. This solves the computational problem of belief propagation by converting the network structure into a multiplex tree.
• stochastic inference of Bayesian networks is not particularly limited to junction trees.
• the contents stored in the first storage means are updated to the first pattern in the ranking in FIG.
• the power of selecting one pattern at the top of the response probability ranking as an “effective field pattern” is not limited to this.
• Various selection methods are conceivable, such as a mode that considers the balance with the budget range and the posting slot, and a method that selects all of those with a predetermined response probability or higher.
• the response probability or the ranking thereof is used as it is, and the first storage unit is used.
• the effective field pattern stored in is obtained, but here, the effective field pattern is obtained with an effect in consideration of the investment amount.
• FIG. 25 shows an example of the stored contents of the communication contact database (DB3), in which the unit price of the advertisement cost for communicating with the consumers is stored in a database. Furthermore, the expected profit unit price when a response is obtained is also stored. For this profit unit price, another external database may be referred to.
• DB3 communication contact database
• Example 4 an effective field pattern can be obtained based on the Mahalanobis distance by applying an analysis engine using the MT system to the record group of FIG. 27 similar to the record group of FIG.
• FIG. 29 schematically shows the process. If such processing is performed as needed or periodically, it will be possible to optimize the ongoing media plan from time to time, and to select media and advertisement formats with high effectiveness.
• each explanatory variable (called a feature amount in the MT system) is used for evaluation with the case where it is not used for evaluation.
• the record that resulted in the response was regarded as an abnormal value for the group of records that resulted in the response, and the Mahalanobis distance for the abnormal value was calculated, and the SN ratio for each set of explanatory variables was calculated. It is desirable to determine the difference between the case where the explanatory variable is used and the case where the used force is not used, and evaluate using only the explanatory variable showing a large value.
• a field pattern considered to be highly effective against a response collected in the previous month for example, an analysis engine using a Bayesian network. If the response probability is estimated to be "60% or more" and the analysis engine using the MT system, the Mahalanobis distance is estimated to be "1.2 or more".
• the record is also searched in the consumer database, and the record is regarded as a “promising customer” and a push-type advertisement is developed using addressable media such as e-mail.
• non-addressable mass media information can be unifiedly handled by being linked to individual consumers in the same manner as addressable media information, and media, vehicles, creatives, and the like. It is now possible to evaluate different dimensions of advertising tools with the same index and handle them collectively.

Landscapes

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

Priority Applications (1)

Applications Claiming Priority (2)

Publications (1)

Family

ID=34736449

Family Applications (1)

Country Status (2)

Cited By (7)

Families Citing this family (1)

Citations (2)

Family Cites Families (1)

• 2004
  • 2004-12-24 WO PCT/JP2004/019319 patent/WO2005064511A1/ja not_active Ceased
  • 2004-12-24 JP JP2005516632A patent/JP4767017B2/ja not_active Expired - Fee Related
• 2011
  • 2011-04-04 JP JP2011082535A patent/JP2011134356A/ja active Pending

Patent Citations (2)

Non-Patent Citations (3)

Also Published As

Similar Documents

Legal Events