WO2014181557A1 - 市場調査・分析システム - Google Patents

市場調査・分析システム Download PDF

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Publication number
WO2014181557A1
WO2014181557A1 PCT/JP2014/051792 JP2014051792W WO2014181557A1 WO 2014181557 A1 WO2014181557 A1 WO 2014181557A1 JP 2014051792 W JP2014051792 W JP 2014051792W WO 2014181557 A1 WO2014181557 A1 WO 2014181557A1
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Prior art keywords
data
customer
market research
analysis system
waveform
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PCT/JP2014/051792
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English (en)
French (fr)
Japanese (ja)
Inventor
剛太郎 毛谷村
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カルチュア・コンビニエンス・クラブ株式会社
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Priority to KR1020147019843A priority Critical patent/KR101660445B1/ko
Priority to CN201480000684.3A priority patent/CN104285233A/zh
Publication of WO2014181557A1 publication Critical patent/WO2014181557A1/ja

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/02—Digital function generators
    • G06F1/022—Waveform generators, i.e. devices for generating periodical functions of time, e.g. direct digital synthesizers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0269—Targeted advertisements based on user profile or attribute

Definitions

  • the present invention relates to a market research / analysis system, particularly for calculating data for providing an advertisement in accordance with a customer's hobbies and intentions.
  • a recommendation system that extracts advertisements to be presented based on customer attributes and purchase history (Patent Documents 1, 3, and 4), and a service coupon issue system that extracts service coupons to be presented based on customer attributes and purchase history (Patent Document 2).
  • Patent Documents 1, 3, and 4 a recommendation system that extracts advertisements to be presented based on customer attributes and purchase history
  • Patent Document 2 a service coupon issue system that extracts service coupons to be presented based on customer attributes and purchase history
  • These market research and analysis systems and recommendation systems use member customer attributes and purchasing history as advertisement extraction profiles, and combine them with external factors such as weather and traffic information for specific advertisements. Member customers are extracted, and the criteria for extraction are fixedly set.
  • the present invention provides a system that enables reliable market research and analysis, and can provide the market research / analysis results in an easy-to-understand format to alliance companies that are users of the market research / analysis system. Objective.
  • the market research / analysis system of the present invention includes a waveform generation means for converting customer data accumulated in a database into a waveform, and a means for calculating a difference between at least two waveform points generated by the waveform generation means.
  • the waveform generation means generates a line graph.
  • the waveform generating means has means for calculating averaged customer data.
  • a means for selecting one or more destination data and a means for selecting one or more destination data are provided.
  • the waveform generation means generates a waveform of the source data and a waveform of the destination data, and calculates the degree of approximation of the waveform.
  • the means for calculating calculates the degree of approximation of the waveform of the destination data with respect to the source data.
  • a table creation means for displaying a list of approximations of the source data and the destination data is provided.
  • the destination data and destination data can be selected from at least one of the categories of customers, companies, stores, products, services, areas, and the like.
  • the actual data is discretized and predicted data is calculated by the prediction processing means based on the discretized actual data.
  • the forecast data calculated by the forecast processing means is used as customer data.
  • customer data is created by supplementing the missing items in the actual data with the forecast data calculated by the forecast processing means.
  • the prediction data is characterized by comprising a probability value or a prediction value obtained by threshold determination of the probability value.
  • -It is characterized by having a table creation means for displaying a list of customer data for customer IDs.
  • It has a recommendation means for associating a recommendation medium of a company, a store, a product / service or an area for which an arbitrary degree of approximation is calculated with respect to an arbitrary customer ID.
  • It has a recommendation means for associating a customer ID with an arbitrary degree of approximation with a recommendation medium corresponding to an arbitrary company ID, store ID, product / service ID or area ID.
  • the headquarters terminal is characterized by having source data and destination data selection means and market research / analysis result browsing means.
  • the market research / analysis system of the present invention enables highly accurate market research / analysis.
  • the market research / analysis system 1 of the present invention is a company that is connected to an operation company system 2 and an operation company system 2 consisting of a server and a database group under the jurisdiction of the operation company.
  • Store terminal 3 such as a POS terminal installed in the store, store terminal 3 such as a POS terminal connected to the operating company system 2 through the network 4 so as to be able to transmit information, a customer mobile terminal such as a mobile phone held by a customer, a smartphone, etc. 5, customer computer terminal 6 (hereinafter simply referred to as customer terminals 5 and 6), headquarters terminal 7 etc. installed in the company.
  • the market research / analysis system 1 of the present invention includes an actual data collection / accumulation unit that collects actual data as a previous stage of analysis, an estimation processing unit that calculates expected data from actual data, and actual data. And / or a waveform generating means for generating a waveform from customer data comprising predicted data, a synchronization rate calculating means for calculating a degree of approximation (hereinafter referred to as a synchronization rate) from the generated waveform, and a recommendation means added as necessary. Etc. are comprised.
  • This means is a means for collecting actual customer data and storing the collected actual data in a database as a pre-stage of market research and analysis.
  • the actual data collection and storage means constructed separately from the market research / analysis system of the present invention or constructed in a service point management system (not shown) forming part of the market research / analysis system of the present invention
  • customer ID For each identifier (hereinafter referred to as customer ID) given to the customer when the customer registers the system, the actual data is collected and stored in the database.
  • the customer ID is generally a number or symbol having an arbitrary number of digits different for each customer.
  • the customer ID is recorded on a service point card held by the customer, and the customer ID can be read and input by the store terminal 3 and the customer terminals 5 and 6.
  • the management company system 2 receives this, associates actual data for each customer ID, and stores them in the basic attribute database.
  • Examples of basic attribute data items include gender, age, address, residence characteristics, commuting destination area characteristics, and the like.
  • the store terminal 3 or the headquarter terminal 7 sends the history system data to the operating company system 2 together with the customer ID.
  • the management company system 2 receives this, and stores the actual data related to each item of the history data for each customer ID in the history database.
  • the history data items include a user company, a purchased product or a purchase service, a visit time or a visit time zone, a use store, and the like.
  • the customer transmits the research data together with the customer ID from the customer terminals 5 and 6 to the server of the operating company system 2, and the server receives the information and associates the actual data regarding each item of the research data with the customer ID.
  • Examples of research data items include member questionnaire items and orientation flags.
  • Examples of member questionnaire items include whether a customer who is a member of the system is married or unmarried, who is collected by answering the questionnaire, the presence of children, the state of residence, annual income, the presence of a driver's license, and the like.
  • the orientation flag item traditional orientation (individual preference) that classifies the degree of customer awareness, innovation orientation (innovation preference), luxury orientation (high quality) (Preferred orientation) and the like, and examples include setting a staged flag according to the degree of each orientation.
  • net behavior data indicating a usage behavior state of a customer to a communication network such as the Internet.
  • Items of the net behavior data include access time to a communication network, use medium, use site, and the like.
  • Examples of other items that can be added include service points given according to the purchase amount. Service points given when a customer makes a purchase are transmitted to the operating company system 2 from the store terminal 3 or the headquarters terminal 7 or the like, and the server receives this, and is accumulated by being associated with the customer ID. The service points acquired are added to the service points, and the service points are accumulated in the service point database as needed. Note that the above items are examples, and the actual data collected and accumulated is not limited to these items.
  • the forecast data is calculated from the actual data in the forecast process, and customer data as a basis for market research and analysis is created by the customer data creation process.
  • the prediction processing means uses the probabilistic reasoning algorithm from actual data stored in a basic attribute database, a research database, a history database, a point database, and other databases.
  • the forecast process for forecasting forecast data for each item is performed.
  • probabilistic reasoning algorithms include, but are not limited to, various methods such as Bayesian networks, neural networks, and random forests.
  • FIG. 5 shows a first embodiment of the prediction process.
  • FIG. 5 shows a complete table in which the prediction processing means performs the probability inference, and calculates the prediction data including the prediction value obtained by determining the probability value of the inference result with the threshold value.
  • the server of the management company system 2 calculates the probability of data for each item of the target customer from the actual data of the other customer item having actual data close to the target customer in the actual data of the basic item. Predictive processing is performed based on a probabilistic reasoning algorithm.
  • the server of operation company system 2 uses actual data of other items (for example, gender data, age data, address data, history data, etc.) From the actual data of the item “Separate Married / Unmarried” of another customer who has actual data close to customer A, the marriage probability value of customer A (for example, the marriage probability value 70%) is calculated.
  • the server of operating company system 2 uses actual data (for example, gender) of other items.
  • Probability value for purchasing the product “Saiten” from the actual data of the item “Saiden” of the product of other customer A who has actual data close to Customer A For example, a 10% probability of purchasing the product “garden” is calculated. Furthermore, due to the nature of the item, for items that should be represented as YES or NO, or 100% or 0% (eg, married or unmarried), YES if the probability value is greater than or equal to the threshold, If there is NO, similarly, a prediction process for determining an expected value of 100% if it is equal to or greater than the threshold and 0% if it is equal to or less than the threshold may be added. This threshold value is variable.
  • the threshold value is set to 50% with respect to the inference result of the marriage probability value (for example, the marriage probability value 70%) of the customer A
  • the expected value of “married” is calculated for the customer A.
  • the prediction process is performed only for the item for which the actual data is missing, or the actual data is missing after performing the prediction process for all the items.
  • the forecast data consisting of the actual data and the forecast value is used as the customer data.
  • the forecast processing is performed for all items and the forecast data is linked to all the items so that only the forecast data is obtained. You may make it calculate the customer data which consists of.
  • discretization processing is performed by various methods such as rounding or dividing the actual data numerical value to eliminate the outstanding data, and create discretized actual data Then, the discretized actual data may be predicted. That is, the actual data used as the basis of the prediction process may be collected raw actual data or discrete actual data.
  • FIG. 6 shows a second embodiment of the prediction process.
  • FIG. 6 shows a complete table in which the prediction processing means performs probability inference, the probability value of the inference result is directly used as prediction data, and the missing portion of the actual data is complemented with the prediction data including the probability value.
  • customer data composed of prediction data composed of actual data and probability values is calculated.
  • FIG. 7 shows a third embodiment of the prediction process.
  • FIG. 7 shows a complete table in which the prediction processing means performs probability inference, the probability value of the inference result is directly calculated as prediction data, and all items are filled with prediction data including probability values.
  • customer data composed of prediction data in which all items are made up of probability values is calculated.
  • the table creation means provided as necessary creates a complete table displayed in a state where customer data is linked to each item of the list. . This makes it possible to visually provide a complete table in which customer data is linked to a list and has no blank space.
  • the waveform generation means will be described with reference to FIGS.
  • the waveform generation means is means for plotting customer data on a graph and converting it into a waveform.
  • the waveform generation means When the operating company system 2 operates the waveform generation means, the waveform generation means performs plot processing for plotting customer data of each item corresponding to the category taken along the vertical axis (S1), and waveform generation processing to form a line graph (S2) is performed.
  • the horizontal axis represents items
  • the vertical axis represents customer data (%)
  • each item of customer A for example, annual income level, smoking rate, car
  • the property of customer A is represented by the waveform.
  • the waveform of customer B can also be represented.
  • the horizontal axis is the item
  • the vertical axis is the customer data (%)
  • the average value of the customer data of each item is calculated
  • the plot processing for plotting the averaged customer data is performed.
  • a waveform generation process for forming a line graph is performed. For example, as shown in FIG. 11, when generating a waveform of a product G, each item of customers having historical data on the purchase of the product G, where the horizontal axis is the item and the vertical axis is the customer data (%)
  • An average value of other customer data is calculated, and averaged customer data for each item of the product G (for example, annual income level, smoking rate, car ownership rate) is plotted, and this is converted into a waveform.
  • the plot processing of the item “smoking rate” of the product G calculates the average value of the smoking rate of customer data of all purchasers of the product G or specific purchasers belonging to a specific group such as gender, age group, This average value for “smoking rate” is plotted as customer data. Such processing is performed for all items, and a waveform is generated. As a result, the purchasers of the product G are represented by waveforms. As shown in FIG. 12, the plot processing of the item “luxury-oriented” in store A store and store B store the customer data of all users of store A store or specific users belonging to a specific layer such as gender and age group.
  • An average value of the degree of luxury orientation is calculated, and this average value is plotted as customer data for the item “luxury orientation”.
  • Such processing is performed for all items, and a waveform is generated.
  • waveform generation processing can be performed on analysis targets of various categories such as companies, areas, and ages as well as customers, products, and stores.
  • 10 to 12 show an example in which two waveforms are generated, one waveform may be generated, or three or more waveforms may be generated.
  • the line graph is suitable for calculating the synchronization rate described later, other types of graphs such as a curve graph, a function graph, a scatter diagram, an area graph, and a radar chart can be generated.
  • destination data and destination data can be selected in advance in order to compare a plurality of data.
  • the original data means the average value of the member customer group created based on the attribute, orientation, purchasing tendency, etc., which is a model for calculating the degree of approximation, that is, the synchronization rate
  • the destination data is the original data and The average value of a target person or a group of target persons created based on attributes, orientation, purchasing tendency, etc. for determining the synchronization rate.
  • customer A customer ID
  • customer B customer ID
  • the server of the operating company system 2 extracts customer data corresponding to the customer ID of customer A and the customer ID of customer B (S2), and customer data of customer A and customer B (for example, Then, plot processing (S3) for plotting annual income level rate, smoking rate, car ownership rate) and waveform generation processing (S4) for plotting each plot into a line graph are performed.
  • the server of the operating company system 2 stores the customer data of customer A and the customer who has purchased the product G.
  • the customer data averaged is calculated, and waveforms of customer A and product G are generated.
  • the server of the operating company system 2 stores the customer data averaged for the customers with store A's store history, The averaged customer data of customers with store B visit history is extracted, and the waveforms of store A and store B are generated.
  • Select destination data and destination data from the same category such as customer-to-customer, company-to-business, product-to-product, and area-to-area characteristics, such as customer-to-company, customer-to-product, and store-to-area characteristics. It is possible to select from different categories, and various categories such as customers, companies, stores, products / services, and area characteristics can be selected, and combinations of these categories are also arbitrary. By generating the waveform in this way, it is possible to visually confirm the feature to be compared.
  • the synchronization rate refers to the degree of approximation of at least two or more waveforms generated by the waveform generation means.
  • the synchronization rate is calculated by calculating a difference between at least two or more waveform points generated by the waveform generating unit and a line segment angle difference between at least two or more waveforms generated by the waveform generating unit.
  • the synchronization rate calculation means causes each of the two or more waveforms generated by the waveform generation means described above.
  • a difference in distance (probability difference) between points on the plot is calculated (S4).
  • the synchronization rate calculating means calculates the difference between each line of the line graph and the angle between the lines (difference between the line segment angles) (S5).
  • a threshold determination is made as to whether or not the calculated probability difference and line segment angle difference fall within the threshold (S6), and the ratio of variables falling within the threshold relative to the whole, that is, the point match rate and the waveform
  • the coincidence rate is calculated, and the sync rate is calculated by dividing the result obtained by adding the point coincidence rate and the waveform coincidence rate by 2 (S7).
  • the probability difference is a value indicating the distance between the corresponding plotted points by the difference of the probability values taken on the vertical axis, and referring to FIG.
  • the probability of item X 1 (for example, annual income level) of the item is X 1 A% (for example, 75%)
  • the probability of item X 1 (annual income level) of the item in destination data B is X 1 B% (for example, 73%)
  • Is plotted at the position of X 1 A% (75%)-X 1 B% (73%) probability difference Y 1 % (2%)
  • this calculation is calculated from X 1 to X n to calculate the repetition done probability difference Y 1 ⁇ Y n for the points corresponding to all of the items.
  • the point coincidence rate is calculated for all the calculation results.
  • the line segment angle difference is the line angle difference of the line graph.
  • a threshold value for example, ⁇ 3 °
  • the threshold value described above can be a variable condition.
  • the difference between the probability difference and the line segment angle is calculated from the distance between arbitrary points on the curve, the distance between arbitrary points on the tangent, and the angle of the tangent. It is also possible to calculate the difference between the probability difference and the line segment angle by differentiating the waveform. As described above, the difference between the probability difference and the line segment angle can be obtained by various calculation methods corresponding to each graph.
  • the management company system 2 may add a means for creating a list of synchronization rates of destination data with respect to destination data.
  • the synchronization rate of the store data B to the store L is represented with respect to the store A of the store data.
  • the synchronization rate between the categories such as customer-to-customer, company-to-business, store-to-store, etc.
  • the synchronization rate between different categories such as store, customer-to-product, customer-to-area, company-to-product, store-to-product, store-to-area, etc.
  • this system can perform survey analysis for various purposes such as analysis of product and store assortments and analysis of store openings.
  • the recommendation means is a means for providing a recommendation medium to a customer registered in the market research and analysis system 1.
  • the recommendation medium is displayed on a coupon ticket issued from the store terminal 3 or the like, an e-mail containing advertisements transmitted from the headquarters terminal 7 or the server 2 to the customer terminals 5 and 6, a member login screen on the website, etc. Examples include customized advertisements and direct mail sent to customers.
  • the recommendation means assigns the customer ID of the customer corresponding to an arbitrary synchronization rate to the destination data (for example, store A). Extract.
  • the recommendation means provides the recommended medium of the store A corresponding to the original data to the extracted customers by various output means.
  • the recommendation medium output means issues a coupon for store A to the store terminal 3 corresponding to the extracted customer's customer ID, and advertises store A to the e-mail address corresponding to the extracted customer's customer ID.
  • Send the content e-mail display the customized advertisement of store A on the member login screen corresponding to the extracted customer's customer ID, or store A directly the address corresponding to the extracted customer's customer ID
  • There are various output means such as address printing on mail.
  • the management company system 2 corresponds to the company ID of the company corresponding to the arbitrary synchronization rate and the arbitrary synchronization rate for the selected source data (for example, customer A).
  • the store ID of the store or the product / service ID corresponding to any sync rate is extracted.
  • the recommendation means provides a recommendation medium corresponding to the extracted company, store, or product / service to the customer ID of the customer A by various output means.
  • the output means of the recommendation medium is an e-mail address corresponding to the customer ID of the customer A who issues the coupon of the company, the store, or the product / service extracted to the store terminal 3 or the like corresponding to the customer ID of the customer A.
  • Customized advertisement of the extracted company, store, or product / service to the member login screen corresponding to the customer ID of customer A
  • Various output means such as displaying or printing the address on the direct mail of the company, the store, or the product / service from which the address corresponding to the customer ID of the customer A is extracted.
  • an alliance company is provided with a browser for issuing a login ID and password accessible to the operation company system 2 and displaying the market research / analysis results.
  • the server of the operating company system 2 is accessed with the login ID and the password, and the address and destination data can be selected and input, and the above-described survey / analysis system
  • the market research / analysis results (customer data table, waveform graph, synchronization rate list, etc.) obtained in 1 may be browsed.
  • the market research / analysis system of the present invention enables highly accurate market research / analysis.

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PCT/JP2014/051792 2013-05-07 2014-01-28 市場調査・分析システム WO2014181557A1 (ja)

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JP2017037577A (ja) * 2015-08-13 2017-02-16 カルチュア・コンビニエンス・クラブ株式会社 装置、方法、および、プログラム
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JP7283865B2 (ja) * 2018-03-27 2023-05-30 カルチュア・コンビニエンス・クラブ株式会社 顧客の属性情報に基づきレコメンドを行う装置、方法、およびプログラム
CN111902836A (zh) * 2018-03-27 2020-11-06 文化便利俱乐部株式会社 基于顾客属性信息进行建议的装置、方法、及程序
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KR20210109801A (ko) 2020-02-28 2021-09-07 주식회사 투플렌 물류정보를 이용한 판매 상품 컨설팅 시스템 및 방법
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