TWM631597U - Computing system for analyzing consumption intention - Google Patents

Abstract

A computing system for analyzing consumption intention, comprising: a user data processing module, a judgment computing module for offline bases and website access, and a judgment computing module for cross-site access. The user data processing module filters out the website visit data cluster and the offline base visit data cluster from the original data. The offline base and website access judgment calculation module is connected to the user data processing module, and the offline base and website access judgment calculation module obtains the offline base and website according to the website access data cluster and the offline base access data cluster. Visit the data cluster and obtain the information on the overlapping rate of visits to the offline base of the business store's website. The judging operation module for cross-website access is connected to the user data processing module to filter out single-website access data clusters about the set target website and obtain cross-website access overlap rate information.

Description

Computing system for analyzing consumption intentions

This creation is related to a computing system, especially a computing system for analyzing consumption intentions.

At present, surveys about consumers' preferences or consumption intentions are mostly accomplished by filling out questionnaires. The questionnaire directly lists a series of questions about the products and information of the surveyor, so that the respondents can answer one by one according to the listed questions. In other words, the answers given by the respondents are guided and designed, and they respond to the answer options pre-planned by the investigator. Even if the respondent's answer is not "true or false" or "choice" but a text description, the text description is produced by the respondent's strong suggestion and guidance from the questionnaire questions.

Therefore, the results obtained by the existing consumption intention survey methods can only reflect the biased ideas guided and implied by the investigators, and cannot obtain objective and fair consumption survey results. In addition, the answers filled in by the respondents will also be affected by the current environmental conditions (for example, on the side of a busy street, or in a quiet room with air conditioning), resulting in great contrast, and it is difficult to obtain the accuracy of the answer level.

Therefore, the purpose of this creation is to provide a computing system for analyzing consumption intention, which can accurately predict the consumption intention of consumers by analyzing the behavior of many individuals, so as to facilitate the layout of subsequent advertising and marketing.

The technical means adopted in this creation to solve the problems of the prior art is to provide a computing system for analyzing consumption intention, including: a user data processing module, which has a first computing processing device and a first memory, and includes a data acquisition unit and a data acquisition unit. A statistical unit, the data obtaining unit obtains raw data from a data storage server through the first arithmetic processing device and the first memory, the raw data contains a plurality of pieces of user data, wherein each piece of the user data corresponds to An individual consumer, and each piece of user data includes: website visit history and/or offline location visit history, the website visit history is the visit history of the online website visited by the individual consumer, the offline location visit history is the visit history of the offline sites visited by the individual consumer, and the data statistics unit obtains website visit data clusters by filtering the plurality of pieces of the user data through the first computing processing device and the first memory respectively and offline site access data cluster, the website access data cluster is the cluster of each user data with the website access history, and the offline site access data cluster is each user with the offline site access history. Clustering of data; the judgment operation module of offline base and website access is connected to the user data processing module, and the judgment calculation module of offline base and website access is provided with a second calculation processing device and a second memory. According to the website visit data cluster and the offline base visit data cluster, the offline base website double-visit data cluster is obtained, and the offline base website double-visit data cluster is the website visit history and visit to the online website of the business store. The cluster of each user data in the offline site visit history of the business store, and the offline site and website access judgment computing module is based on the offline site and website double access data cluster corresponding to the individual consumer relative to each other. The overlap ratio of the individual consumer corresponding to the website visit data cluster to obtain the website offline base visit of the business store The overlap rate information is used as an online-offline overlap analysis index; and a cross-site access judgment operation module is connected to the user data processing module, and the cross-site access judgment operation module has a third operation processing device and a first The three memories are filtered according to the website visit data cluster to obtain a single website visit data cluster for each target website set, and each single website visit data cluster has the website visit associated with each target website The historical single target website user data cluster, and the judgment operation module for cross-website access further obtains that the single target website user data cluster contains both access to the single target website and other user data clusters of the target website. The number of cross-site user data clusters, and the number of all user data clusters visiting a single target website is obtained, and the ratio of the cross-site user data cluster number to the number of all user data clusters is taken as the cross-site number of user data clusters. Site visit overlap rate information as an indicator for cross-site overlap analysis.

In an embodiment of the present invention, a computing system for analyzing consumption intention is provided, wherein the user data processing module further includes: a time period setting unit connected to the data statistics unit, and the time period setting unit is used for setting a data screening period, According to the data screening period, the data statistic unit selects from the plurality of pieces of the user data to obtain the website visit data cluster with the website visit history within the data screening period, and the website visit data cluster with The offline site access data cluster of the offline site access history.

An embodiment of the present invention provides a computing system for analyzing consumption intention, further comprising: a judgment computing module for actively browsing websites, connected to the user data processing module, and the judging computing module for actively browsing websites according to the The website visit data is clustered to analyze the ranking of the respective visit footprints of the plurality of rating websites set, so as to obtain the website popularity information, which is used as the popular website footprint analysis index, wherein the visit footprint is related to the individual rating. The total number of the individual consumers corresponding to each user data of the website visiting history of the website.

An embodiment of the present invention provides a computing system for analyzing consumption intention, wherein the user data processing module is connected to a data storage server, the data acquisition unit receives the original data from the data storage server, and the The real identity of the individual consumer is de-identified in the data storage server to obtain the individual identification code corresponding to the user data.

In an embodiment of the present invention, a computing system for analyzing consumption intention is provided, wherein the user data processing module has a website visit data preprocessing unit, and the website visit data preprocessing unit is based on the domain name format from the original Data analysis obtains the website visit history in each user data.

An embodiment of the present invention provides a computing system for analyzing consumption intention, wherein the user data processing module has an offline site access data preprocessing unit, and the offline site access data preprocessing unit is based on a preset address A data format is obtained from the raw data analysis to obtain the offline base visit history of each user data, and the preset address data format includes: Arabic numerals, commas and decimal points.

An embodiment of the present invention provides a computing system for analyzing consumption intention, wherein the offline site visit history of the user profile includes: latitude and longitude data of the offline site visited by the individual consumer.

The following technical effects can be obtained through the technical means adopted by the computing system for analyzing consumption intentions of this creation. Accurately predict consumers' preferences or consumption intentions to facilitate the layout of subsequent advertising and marketing.

100: Computing Systems for Analyzing Consumption Intentions

1: User data processing module

10: User Profile

10A: Website Visit History

10B: Offline base visit history

10D: Individual identification code

11: Data acquisition unit

12: Data Statistics Unit

13: Time period setting unit

14: Website visit data preprocessing unit

15: Offline base access data preprocessing unit

18G: Website Access Data Cluster

19G: Offline base access data cluster

2: Judgment calculation module for offline bases and website access

20G: Data cluster for dual access to offline sites and websites

3: Judgment operation module for cross-site access

31G: Single website access data cluster

32G: A single target website and a cluster of user data from other target websites

33G: Cross-site user profile clustering

4: The judgment operation module for actively browsing the website

B: Data storage server

R: original data

T1: Online-to-Offline Overlap Analysis Indicator

T2: Cross-site overlap analysis metrics

T3: Popular Website Footprint Analysis Metrics

[Fig. 1] is a block diagram showing the computing system for analyzing consumption intention according to an embodiment of the present invention; [Fig. 2] is a content schematic diagram showing the original data of the computing system for analyzing consumption intention according to an embodiment of the present invention ; [Fig. 3] is a schematic diagram showing that the computing system for analyzing consumption intention according to the present creative embodiment filters out the offline site website dual-visit data clusters from the original data; [Fig. 4] is a schematic diagram showing the computing system according to the present creative embodiment. The user data processing module of the system filters out the website visit data cluster and the offline base visit data cluster from the original data; A schematic diagram of cross-site visit overlap information derived from single-site visit data clustering and cross-site user data clustering.

Embodiments of the present invention will be described below with reference to FIGS. 1 to 5 . This description is not intended to limit the implementation of the present creation, but is one of the embodiments of the present creation.

As shown in FIG. 1, a computing system 100 for analyzing consumption intention according to an embodiment of the present creation includes: a user data processing module 1, an offline base and website access judgment computing module 2, a cross-site access The judgment calculation module 3 and the judgment calculation module 4 for actively browsing the website. Thereby, the computing system 100 for analyzing consumption intention of the present creation can analyze the behavioral characteristics of many consumers (for example, the visit data of individual consumers to some merchants' online websites and/or offline locations) to accurately analyze the behavioral characteristics of consumers. It can predict consumers' preferences or consumption intentions, which is conducive to the layout of subsequent advertising and marketing.

As shown in FIG. 1 to FIG. 3 , the user data processing module 1 has a first arithmetic processing device and a first memory, and includes a data acquisition unit 11 and a data statistics unit 12 . The data obtaining unit 11 obtains raw data R from a data storage server B through the first arithmetic processing device and the first memory, wherein the raw data R includes a plurality of user data 10 . Each piece of user data 10 corresponds to an individual consumer, and each piece of user data 10 includes a website visit history 10A and/or an offline site visit history 10B. Further, the website visit history 10A is the visit history of the online website visited by the individual consumer. The offline site visit history 10B is the visit history of offline sites visited by the individual consumer.

In the specific embodiment of the present creation, as shown in FIG. 3 , the data statistics unit 12 obtains website visit data from the plurality of pieces of the user data 10 through the first arithmetic processing device and the first memory, respectively. Cluster 18G and offline base access data cluster 19G. The website visit data cluster 18G is a cluster of each user data 10 having the website visit history 10A. The offline site access data cluster 19G is a cluster of each user data 10 having the offline site access history 10B.

Specifically, as shown in FIG. 3 , in the computing system 100 according to an embodiment of the present invention, the user data processing module 1 further includes: a time period setting unit 13 connected to the data statistics unit 12 . The time period setting unit 13 is used for setting a data screening time period, and the data statistics unit 12 respectively filters out a plurality of pieces of the user data 10 according to the data screening time period to obtain the website access history 10A within the data screening time period. The website access data cluster 18G, and the offline site access data cluster 19G having the offline site access history 10B within the data screening period. For example, through the setting of the data screening time period by the time period setting unit 13, the data statistics unit 12 can filter out the daily website visit data cluster 18G and the daily offline site visit data cluster 19G, so as to For the subsequent analysis of individual behavior data.

Specifically, as shown in FIGS. 1 to 3, according to the computing system 100 of an embodiment of the present invention, the user data processing module 1 is connected to the data storage server B, and the data acquisition unit 11 automatically The data storage server B receives the raw data R. In addition, the individual identification code 10D corresponding to the user data 10 is obtained by de-identifying the real identity of the individual consumer in the data storage server B through de-identification processing. That is to say, this creation is to process and analyze the original data R under the premise of compliance with the Personal Data Protection Law, without revealing the true identity of the individual. The data storage server B can be obtained from a telecom data provider, but not limited thereto.

In the specific embodiment of the present invention, as shown in FIG. 1 and FIG. 4 , according to the computing system 100 of an embodiment of the present invention, the user data processing module 1 has a website visit data preprocessing unit 14 . The website visit data preprocessing unit 14 analyzes and obtains the website visit history 10A in each user data 10 from the original data R according to the domain name format.

In addition, as shown in FIG. 1 and FIG. 4 , in the computing system 100 according to an embodiment of the present invention, the user data processing module 1 has an offline site access data preprocessing unit 15 . The offline site access data preprocessing unit 15 analyzes and obtains the offline site access history 10B of each user data 10 from the raw data R according to a preset address data format. The preset address data format includes: : Arabic numerals, commas, and decimal points.

In detail, according to the computing system 100 of an embodiment of the present invention, the offline site visit history 10B of the user profile 10 includes: latitude and longitude data of the offline site visited by the individual consumer.

As shown in FIG. 1 and FIG. 3 , the judgment computing module 2 of the offline base and website access is connected to the user data processing module 1 . The offline site and website access judgment computing module 2 has a second computing processing device and a second memory to access the data cluster 18G and the offline site according to the site Accessing the data cluster 19G results in a double-access data cluster 20G for the offline base website. The offline site website dual visit data cluster 20G is a cluster of each user data 10 having the website visit history 10A of the online website of the business store and the offline site visit history 10B of the business store.

In detail, as shown in FIG. 1 and FIG. 3 , the offline site and website access judgment calculation module 2 is based on the offline site double access data cluster 20G corresponding to the individual consumer relative to the website. The overlapping ratio of the individual consumer corresponding to the access data cluster 18G is used to obtain the information of the overlapping ratio of visits to the offline site of the business store's website, which is used as the online-to-offline overlapping analysis index T1. That is to say, this creation uses the behavioral characteristics of the individual consumer to visit the online website and offline location of the business store at the same time (indicating that the individual consumer has a strong interest in the products of the business store), and judges that the individual consumer is interested in the store's products. The preferences or consumption intentions of the business store.

As shown in FIG. 1 and FIG. 5 , the judgment computing module 3 for cross-site access is connected to the user data processing module 1 . The cross-website access judgment computing module 3 has a third computing processing device and a third memory, and filters out a single website access data cluster 31G for each target website set according to the website access data cluster 18G. Each of the single website visit data clusters 31G is a single target website user data cluster having the website visit history 10A associated with each of the target website.

And, as shown in FIG. 1 and FIG. 5, the judgment operation module 3 for cross-website access further obtains that the user data cluster of the single target website contains user data for visiting the single target website and other target websites. By clustering 32G, the number of cross-site user data clusters 33G is obtained, and the number of all user data clusters accessing a single target website is obtained. Thereby, the cross-website access judgment calculation module 3 takes the ratio of the cross-website user data cluster number 33G to the total user data cluster number as the cross-website access overlap rate information, and serves as the cross-website overlap analysis index T2. In other words, the cross-site user data cluster 33G shows "Visit multiple online sites for commodity consumption to obtain relevant Consumers who are “information about products” (that is, consumers who have a positive willingness to consume). In addition, the cross-site overlap analysis indicator T2, in addition to analyzing consumers with positive consumption intentions, can also summarize the competition and cooperation relationship between brands (that is, which brands overlap with each other) based on the analysis of the overlap rate relationship between different brand websites. more consumers), and then assist brand owners to analyze their competitors and consumer markets, and formulate market strategies.

As shown in FIG. 1 , in the computing system 100 according to an embodiment of the present invention, the judging computing module 4 for actively browsing the website is connected to the user data processing module 1 . The judging operation module 4 for actively browsing the website analyzes the ranking of the respective visit footprints of the plurality of rating websites according to the website visit data cluster 18G, so as to obtain the website popularity information, which is used as the popular website footprint analysis index T3 . Specifically, the visit footprint is the total number of the individual consumers corresponding to each user profile 10 having the website visit history 10A associated with the individual rating website. That is to say, by analyzing the rankings of the visiting footprints among a plurality of the evaluation websites, the present creation learns the evaluation websites visited by the consumers, so as to determine the evaluation websites (displayed on the display) of the consumers. information on product sales) preferences, or consumption intentions. The computing system 100 analyzes and eliminates the ranking of the visiting footprints among the plurality of evaluation websites, in addition to knowing consumers' preferences and intentions for commodity sales, it can also analyze consumption through the types of websites consumers prefer to browse. Interest behaviors of consumers, such as news, financial management, automobiles, maintenance, etc. (The reason is that popular websites are not only commodity websites, but may also be different communities, videos, news media, shopping, etc. that consumers like to browse. kind of website).

It can be seen from the above that the computing system 100 for analyzing consumption intentions of the present creation includes the judging computing module 2 for accessing the offline base and website, the judging computing module 3 for cross-site access, and the judging computing module for actively browsing the website. Group 4, and obtain the online-to-offline overlap analysis index T1 respectively (indicating that the individual consumer visits the online website and offline base of the business store at the same time, and the business of the business store is product), the cross-site overlap analysis indicator T2 (indicating that a consumer individual visits multiple online websites for a specific store or product, compares and searches to obtain information about the product), and the popular website footprint analysis indicator T3 (from high to low, it indicates the online websites visited by many individual consumers to learn about product information); in addition, it can also indicate, from high to low, the group of consumers who are interested in a specific product, as well as the groups of consumers who usually prefer to browse. various online site rankings). In this way, this creation analyzes the behavioral characteristics of many consumers through the online-to-offline overlap analysis index T1, the cross-site overlap analysis index T2, and the popular website footprint analysis index T3, so as to accurately predict consumers preferences, or consumption intentions, which is conducive to the layout of subsequent advertising and marketing.

The above descriptions and descriptions are only the descriptions of the preferred embodiments of the present invention. Those with ordinary knowledge in this technology can make other modifications according to the scope of the patent application defined below and the above descriptions, but these modifications should still be It is the new spirit of this creation and within the scope of rights of this creation.

100: Computing Systems for Analyzing Consumption Intentions

1: User data processing module

2: Judgment calculation module for offline bases and website access

3: Judgment operation module for cross-site access

4: The judgment operation module for actively browsing the website

B: Data storage server

T1: Online-to-Offline Overlap Analysis Indicator

T2: Cross-site overlap analysis metrics

T3: Popular Website Footprint Analysis Metrics

Claims (7)

A computing system for analyzing consumption intention, comprising: a user data processing module, having a first computing processing device and a first memory, and including a data obtaining unit and a data statistics unit, the data obtaining unit is connected with the first computing processing device through the first computing device. The first memory obtains original data from a data storage server, and the original data contains multiple pieces of user data, wherein each piece of user data corresponds to an individual consumer, and each piece of user data includes: website visit history and/or offline site visit history, the website visit history is the visit history of the online website visited by the individual consumer, and the offline site visit history is the visit history of the offline site visited by the individual consumer, The data statistics unit obtains a website visit data cluster and an offline base visit data cluster by filtering the plurality of pieces of the user data through the first computing processing device and the first memory, respectively, and the website visit data cluster has The cluster of each user data of the website access history, the offline site access data cluster is the cluster of each user data with the offline site access history; the offline site and website access judgment calculation module, Connected to the user data processing module, the offline site and website access judgment computing module has a second computing processing device and a second memory and is obtained according to the website access data cluster and the offline site access data cluster The double-visit data cluster of the offline base website, the offline base website double-visit data cluster is the data of each user that has the website visit history of visiting the online website of the business store and the offline base visit history of the business store cluster, and the judgment calculation module of the offline base and website access is based on the overlap ratio of the consumer individual corresponding to the offline base website dual-visit data cluster relative to the consumer individual corresponding to the website access data cluster , in order to obtain the information on the overlapping rate of visits to the offline site of the business store's website, which can be used as an online-to-offline overlapping analysis indicator; and The judgment computing module for cross-site access is connected to the user data processing module, and the judgment computing module for cross-site access has a third computing processing device and a third memory, which are respectively screened and obtained according to the website access data cluster. Regarding the set single website visit data cluster for each target website, each single website visit data cluster is a single target website user data cluster having the website visit history associated with each target website, and the cross website visit The judging operation module further obtains that the user data cluster of the single target website contains both the user data clusters that visit the single target website and other target websites, and obtains the number of cross-website user data clusters, and obtains the number of user data clusters that visit the single target website. The number of all user data clusters of the target website, whereby the ratio of the number of cross-site user data clusters to the number of all user data clusters is used as the cross-site visit overlap rate information, which is used as the cross-site overlap analysis indicator. The computing system according to claim 1, wherein the user data processing module further comprises: a time period setting unit connected to the data statistics unit, the time period setting unit is used for setting a data screening period, and the data statistics unit is based on the data The website visit data cluster with the website visit history within the data screening period and the offline site visit history within the data screening period are obtained by filtering the plurality of pieces of the user data during the screening period. Offline base access data cluster. The computing system according to claim 1, further comprising: a judging computing module for actively browsing the website, connected to the user data processing module, the judging computing module for actively browsing the website analyzes the setting according to the website visit data cluster The ranking of the respective visit footprints of a plurality of rating websites, so as to obtain website popularity information as an indicator for the analysis of popular website footprints, wherein the visit footprint is the number of visits to the website associated with the individual rating website. The total number of individual consumers corresponding to the user profile. The computing system of claim 1, wherein the user data processing module is connected to a data storage server and the data acquisition unit receives the original data from the data storage server, and the The real identity of the individual consumer is de-identified in the data storage server to obtain the individual identification code corresponding to the user data. The computing system according to claim 1, wherein the user data processing module has a website visit data preprocessing unit, and the website visit data preprocessing unit analyzes the original data according to the domain name format to obtain each user transaction The website visit history in the profile. The computing system according to claim 1, wherein the user data processing module has an offline site access data preprocessing unit, and the offline site access data preprocessing unit analyzes the original data according to a preset address data format Obtain the visit history of the offline base for each user data, and the preset address data format includes: Arabic numerals, commas and decimal points. The computing system according to claim 1, wherein the offline site visit history of the user profile includes: latitude and longitude data of the offline site visited by the individual consumer.

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