CN115049442B - Data analysis method and application system - Google Patents
Data analysis method and application system Download PDFInfo
- Publication number
- CN115049442B CN115049442B CN202210964496.9A CN202210964496A CN115049442B CN 115049442 B CN115049442 B CN 115049442B CN 202210964496 A CN202210964496 A CN 202210964496A CN 115049442 B CN115049442 B CN 115049442B
- Authority
- CN
- China
- Prior art keywords
- commodity
- combination
- basic
- consumer
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Finance (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (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)
- Medical Treatment And Welfare Office Work (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention provides a data analysis method and an application system, and relates to the technical field of data analysis, wherein the application system is applied to the field of supermarket data analysis; the application system comprises a data classification module, an effectiveness assignment module, an association analysis module and a combined classification module; the data classification module is used for carrying out basic qualitative and quantitative classification on the data; the effectiveness assignment module is used for carrying out comprehensive analysis based on qualitative classification and quantitative results of data and obtaining effectiveness assignment results; the association analysis module is used for performing association analysis on the assigned heterogeneous data to obtain the association analysis results of the heterogeneous data.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a data analysis method and an application system.
Background
The data analysis means that a large amount of collected data are analyzed by using a proper statistical analysis method, and the data are summarized, understood and digested so as to maximally develop the functions of the data and play the roles of the data. Data analysis is the process of studying and summarizing data in detail in order to extract useful information and to form conclusions.
In the operation process of the existing supermarket, due to the fact that the positions of the supermarket are different, the types of consumer groups in the peripheral area of each supermarket are different, the selling directions and the types of commodities are different, however, in the existing analysis method applied to supermarket data, the data combinability analysis is insufficient, the combinability of the data is difficult to find through the existing analysis method, and therefore deviation is easy to occur in the combination analysis judgment of the final commodities and consumers, effective data support cannot be provided for the overall marketing analysis of the supermarket, and the final marketing effect of the supermarket is influenced.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a data analysis method and an application system, which can improve the comprehensiveness of comprehensive association analysis of data by carrying out careful combinative analysis on data of consumers and commodities, thereby providing effective data analysis support for marketing analysis of supermarkets and solving the problems that the existing data analysis mode is single and an effective analysis result is difficult to provide.
In order to achieve the purpose, the invention is realized by the following technical scheme: the invention provides a data application system, which is applied to the field of supermarket data analysis; the application system comprises a data classification module, an effectiveness assignment module, an association analysis module and a combined classification module;
the data classification module is used for carrying out basic qualitative and quantitative classification on the data;
the effectiveness assignment module is used for carrying out comprehensive analysis based on qualitative classification and quantitative results of data and obtaining effectiveness assignment results;
the correlation analysis module is used for performing correlation analysis on the assigned different types of data to obtain correlation analysis results of the different types of data;
and the combined classification module is used for obtaining a combined classification result based on the correlation analysis result.
Further, the data classification module is configured with a data classification policy, the data classification policy comprising: dividing data into consumer data, consumer behavior data and commodity data;
then dividing the consumer data into young men, young women, children and adolescents, middle-aged men, middle-aged women, old men and old women;
dividing the behavior data of the consumers into purchase duration, purchase quantity, purchase average total price and purchase frequency;
the commodity data is divided into commodity selection duration, commodity purchase times and commodity price.
Further, the validity assignment module includes a validity assignment policy, and the validity assignment policy includes:
substituting the purchase duration, the purchase quantity, the average purchase total price and the purchase frequency of each consumer into a consumer quantitative assignment formula to obtain a basic consumer coefficient assigned value;
according to the obtained basic consumer coefficient assigned values of different consumers, basic young male, young female, children and adolescents, middle-aged male, middle-aged female, old male and old female in the consumer data are respectively assigned with a basic young male coefficient, a basic young female coefficient, a basic child and adolescent coefficient, a basic middle-aged male coefficient, a basic middle-aged female coefficient, a basic old male coefficient and a basic old female coefficient; the assignment method of the basic coefficient of each type of consumer comprises the following steps: selecting basic consumer coefficient assigned values of the consumers of the type with the first type number to calculate an average value, and taking the average value calculated by each type as the basic coefficient of the type;
the commodity data is divided into commodity selection duration, commodity purchase times and commodity price, and the commodity selection duration, the commodity purchase times and the commodity price are substituted into a commodity quantitative assignment formula to obtain a basic commodity coefficient assigned value;
the consumer quantitative valuation formula is configured as:
(ii) a Wherein Pjy gives a value to the basic consumer coefficient, jz is the average total price for purchase, sgm is the number of purchases, lgm is the frequency of purchases, and Tgm is the duration of purchases;
the commodity quantitative assignment formula is configured as follows:
(ii) a Wherein Psp is a basic commodity coefficient assigned value, tsp is commodity selection duration, ssp is commodity purchase frequency, and Jsp is commodity price.
Further, the correlation analysis module comprises a consumer correlation analysis unit, a commodity correlation analysis unit and a comprehensive analysis unit;
the consumer association analysis unit is used for analyzing data of a plurality of consumers during shopping at the same time; the commodity association analysis unit is used for analyzing the data of combined purchase of different commodities; the comprehensive analysis unit is used for analyzing the comprehensive relevance between the consumers and the commodities.
Further, the consumer association analysis unit is configured with a consumer association analysis policy, which includes: setting a first correlation analysis duration, and acquiring the type of the appearing consumer combination in the first correlation analysis duration; then obtaining the times, the total price of multiple purchases and the total duration of multiple purchases when different combination types are combined and matched for shopping;
substituting the times of combining and matching shopping of different combination types, the total price of multiple times of purchasing and the total duration of multiple times of shopping into a consumer combination coefficient solving formula to solve the consumer combination coefficient;
substituting the consumer combination coefficient and the basic consumer coefficient assigned value of a single consumer in the consumer combination into a consumer combination calibration formula to obtain a consumer combination calibration coefficient;
sorting different consumer combinations from high to low according to the consumer combination calibration coefficient, and setting the consumer combination with the first consumer proportion as a basic consumer combination;
the consumer combination coefficient solving formula is configured as follows:
(ii) a The system comprises a data acquisition module, a data processing module and a data processing module, wherein Xyz is a consumer combination coefficient, cyz, jyd and Tyz are respectively the times of combination and collocation shopping, the total price of multiple times of purchase and the total duration of multiple times of shopping for different combination types; the consumer combination calibration formula is configured to:
(ii) a Xyj is the calibration coefficient of the consumer combination, and Pjy to Pjyi respectively give values to the basic consumer coefficients of different consumers in the consumer combination.
Further, the commodity association analysis unit is configured with a commodity association analysis policy, where the commodity association analysis policy includes: acquiring the type of each two commodity combinations appearing in the first correlation analysis duration; acquiring the times and the total price of the matched purchase of different types of commodity combinations;
substituting the times and total prices of different commodity combination types which are collocated and purchased into a commodity combination coefficient solving formula to solve a commodity combination coefficient;
substituting the commodity combination coefficient and the basic commodity coefficient assigned value of a single commodity into a commodity combination calibration formula to obtain a commodity combination calibration coefficient;
sorting the commodity combinations from high to low according to the commodity combination calibration coefficient, and setting the commodity combinations with the first commodity proportion in the front sorting as basic commodity combinations;
the commodity combination coefficient solving formula is configured as follows:
(ii) a Xsz is a commodity combination coefficient, and Cdz and Jspz are the times and total price of matched purchase of different commodity combination types respectively; the commodity combination calibration formula is configured to:
(ii) a Wherein Xjsz is a commodity combination calibration coefficient, and Psp1 and Psp2 are basic commodity coefficients of two commodities in a commodity combination.
Further, the comprehensive analysis unit is configured with a comprehensive analysis strategy, and the comprehensive analysis strategy comprises: acquiring the times of purchasing the basic commodity combination of the basic consumer combination, setting the times as the basic consumer commodity combination, and substituting the times of purchasing the basic consumer commodity combination into a comprehensive processing formula to obtain a comprehensive processing coefficient;
sorting the basic consumer commodity combinations from high to low according to the comprehensive processing coefficient, and setting the combination with the first comprehensive proportion in the front as the basic comprehensive combination;
the integrated processing formula is configured as:
(ii) a Wherein Xzh is the comprehensive processing coefficient, a is the comprehensive conversion base number, czh is the basic consumer goods combination numberAnd (4) counting.
Further, the combined classification module is configured with a combined classification policy, the combined classification policy comprising: and setting a basic consumer combination classification unit, a basic commodity combination classification unit and a basic comprehensive combination classification unit for the basic consumer combination, the basic commodity combination and the basic comprehensive combination respectively, and setting a basic consumer memory, a basic commodity memory and a basic comprehensive memory in the basic consumer combination classification unit, the basic commodity combination classification unit and the basic comprehensive combination classification unit to store data of the basic consumer combination, the basic commodity combination and the basic comprehensive combination.
An analysis method of a data application system, the analysis method comprising the steps of:
s10, firstly, carrying out basic qualitative and quantitative classification on data;
s20, carrying out comprehensive analysis based on qualitative classification and quantitative results of the data, and obtaining an effectiveness assignment result;
step S30, performing correlation analysis on the assigned different types of data to obtain correlation analysis results of the different types of data;
and S40, obtaining a combined classification result based on the correlation analysis result.
Further, the specific implementation method of step S10 includes the following steps:
step S101, dividing data into consumer data, consumer behavior data and commodity data;
step S102, dividing the consumer data into young men, young women, children and adolescents, middle men, middle women, old men and old women;
step S103, dividing the consumer behavior data into purchase duration, purchase quantity, purchase average total price and purchase frequency;
step S104, the commodity data is divided into commodity selection duration, commodity purchase times and commodity price.
Further, the specific implementation method of step S20 includes the following steps:
step S201, substituting the purchase duration, the purchase quantity, the average purchase total price and the purchase frequency of each consumer into a consumer quantitative assignment formula to obtain a basic consumer coefficient assigned value;
step S202, according to the obtained basic consumer coefficient assigned values of different consumers, a basic young male coefficient, a basic young female coefficient, a basic child and adolescent coefficient, a basic middle-aged male coefficient, a basic middle-aged female coefficient, a basic old male coefficient and a basic old female coefficient are respectively assigned to young males, females, children and adolescents, middle-aged males, middle-aged females, elderly males and elderly females in the consumer data;
step S203, the commodity data is divided into commodity selection duration, commodity purchase frequency and commodity price, and the commodity selection duration, the commodity purchase frequency and the commodity price are substituted into a commodity quantitative assignment formula to obtain a basic commodity coefficient assignment value.
Further, the specific implementation method of step S30 includes the following steps:
step S3011, setting a first correlation analysis duration, and acquiring types of the appearing consumer combinations in the first correlation analysis duration; then obtaining the times, the total price of multiple purchases and the total duration of multiple purchases when different combination types are combined and matched for shopping;
step S3012, substituting the times of shopping combined and matched with different combination types, the total price of multiple purchases and the total duration of multiple purchases into a consumer combination coefficient solving formula to solve a consumer combination coefficient;
step S3013, substituting the consumer combination coefficient and the basic consumer coefficient assigned value of a single consumer in the consumer combination into a consumer combination calibration formula to obtain a consumer combination calibration coefficient;
step S3014, according to the consumer combination calibration coefficient, different consumer combinations are ranked from high to low, and the consumer combination ranked in the first consumer proportion is set as a basic consumer combination;
step S3021, acquiring the type of each two commodity combinations appearing in the first correlation analysis duration; acquiring the times and the total price of the matched purchase of different types of commodity combinations;
step S3022, substituting the times and total prices of the collocated purchases of different commodity combinations into a commodity combination coefficient solving formula to solve a commodity combination coefficient;
step S3023, substituting the commodity combination coefficient and the basic commodity coefficient assigned value of a single commodity into a commodity combination calibration formula to obtain a commodity combination calibration coefficient;
step S3024, sorting the commodity combinations from high to low according to the commodity combination calibration coefficient, and setting the commodity combination with the first commodity proportion in the sorting as a basic commodity combination;
step S3031, acquiring the times of purchasing the basic commodity combination of the basic consumer combination, setting the times as the basic consumer commodity combination, and substituting the times of purchasing the basic commodity combination of the basic consumer into a comprehensive processing formula to obtain a comprehensive processing coefficient;
step S3032, the basic consumer commodity combinations are sorted from high to low according to the comprehensive processing coefficient, and the combination with the first comprehensive proportion in the front sorting is set as the basic comprehensive combination.
Further, the specific implementation method of step S40 includes the following steps:
step S401, a basic consumer combination classification unit, a basic commodity combination classification unit and a basic comprehensive combination classification unit are respectively set for the basic consumer combination, the basic commodity combination and the basic comprehensive combination, and a basic consumer memory, a basic commodity memory and a basic comprehensive memory are arranged in the basic consumer combination classification unit, the basic commodity combination classification unit and the basic comprehensive combination classification unit to store the data of the basic consumer combination, the basic commodity combination and the basic comprehensive combination
The invention has the beneficial effects that: according to the method, firstly, basic qualitative and quantitative classification is carried out on data, then comprehensive analysis is carried out on the qualitative classification and quantitative results based on the data, an effectiveness assignment result is obtained, association analysis is carried out on the different types of data after assignment, association analysis results of the different types of data are obtained, and finally a combined classification result is obtained based on the association analysis results, so that the association of data analysis of the supermarket can be improved, the effectiveness of the data analysis is improved, and a more real, comprehensive and effective data base can be provided for marketing and management of the supermarket;
according to the invention, the comprehensive analysis unit can process the obtained comprehensive processing coefficient, the commodity combinations of the basic consumers are sorted from high to low according to the comprehensive processing coefficient, the combination with the first comprehensive proportion in the front of the sorting is set as the basic comprehensive combination, and the association between the consumers with relatively high frequency and high total consumption and the commodities frequently purchased by the consumers can be obtained through the comprehensive processing coefficient, so that the accuracy of the analysis of commodity sales association is further improved.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a functional block diagram of a control system of the present invention;
FIG. 2 is a functional block diagram of the association analysis module of the present invention;
FIG. 3 is a flow chart of an analysis method of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further explained by combining the specific embodiments.
Example one
Referring to fig. 1, the present invention provides a data application system, which is mainly applied to the data analysis field of a supermarket, but the method of the present invention can also be used for reference in other similar fields, and therefore, the present invention is not limited to the application in the supermarket field, the present invention provides a specific embodiment, which is applied to the supermarket field, and the application system includes a data classification module, an effectiveness assignment module, an association analysis module, and a combination classification module; through careful combinative analysis on data of consumers and commodities, comprehensiveness of comprehensive contact analysis of the data can be improved, so that more effective data analysis support is provided for supermarket marketing, and the problems that an existing data analysis mode is single and effective analysis results are difficult to provide are solved.
The data classification module is used for carrying out basic qualitative and quantitative classification on the data; the data classification module is configured with a data classification policy, the data classification policy comprising: dividing data into consumer data, consumer behavior data and commodity data; then dividing the consumer data into young men, young women, children and adolescents, middle-aged men, middle-aged women, old men and old women; dividing the behavior data of the consumers into purchase duration, purchase quantity, purchase average total price and purchase frequency; the commodity data is divided into commodity selection duration, commodity purchase times and commodity price.
The effectiveness assignment module is used for carrying out comprehensive analysis based on qualitative classification and quantitative results of data and obtaining effectiveness assignment results; the effectiveness assignment module comprises effectiveness assignment strategies, and the effectiveness assignment strategies comprise:
substituting the purchase duration, the purchase quantity, the average purchase total price and the purchase frequency of each consumer into a consumer quantitative assignment formula to obtain a basic consumer coefficient assigned value; the consumer quantitative valuation formula is configured as:
(ii) a Wherein Pjy gives a value to the basic consumer coefficient, jz is the average total price for purchase, sgm is the number of purchases, lgm is the frequency of purchases, and Tgm is the duration of purchases; the shorter the purchase duration, the higher the average total price and the purchase frequency, and the smaller the purchase quantity, the higher the purchasing power representing the consumer, and the main type of the consumer group faced by the supermarket can be obtained by solving the value given to the basic consumer coefficient.
And respectively assigning a basic young male coefficient, a basic young female coefficient, a basic child and adolescent coefficient, a basic middle-aged male coefficient, a basic middle-aged female coefficient, a basic old male coefficient and a basic old female coefficient to young men, young women, children and adolescents, middle-aged men, middle-aged women, old men and old women in the consumer data according to the obtained basic consumer coefficient assignment values of different consumers. The assignment method of the basic coefficient of each type of consumer comprises the following steps: and selecting basic consumer coefficient assigned values of the consumers of the type with the first type number to calculate an average value, and taking the average value calculated by each type as the basic coefficient of the type.
The commodity data is divided into commodity selection duration, commodity purchase times and commodity price, and the commodity selection duration, the commodity purchase times and the commodity price are substituted into a commodity quantitative assignment formula to obtain a basic commodity coefficient assigned value; the commodity quantitative assignment formula is configured as follows:
(ii) a Wherein Psp is a basic commodity coefficient assigned value, tsp is commodity selection duration, ssp is commodity purchase frequency, and Jsp is commodity price. The commodity selection duration is obtained by monitoring the residence duration of a consumer in front of the commodity through a supermarket, and the residence duration is converted into the selection duration by default; the longer the selection of a good, the longer the length of time the consumer needs to stay in the supermarket, the higher the number of purchases of the good and the price of the good, the higher the ability of the good to create value.
Referring to fig. 2, the association analysis module is configured to perform association analysis on the assigned different types of data to obtain association analysis results of the different types of data; the correlation analysis module comprises a consumer correlation analysis unit, a commodity correlation analysis unit and a comprehensive analysis unit; the consumer association analysis unit is used for analyzing data when a plurality of consumers shop at the same time; the consumer association analysis unit is configured with a consumer association analysis policy, which includes: setting a first correlation analysis duration, and acquiring the type of the appearing consumer combination in the first correlation analysis duration; then obtaining the times, the total price of multiple purchases and the total duration of multiple purchases when different combination types are combined and matched for shopping;
substituting the times of combining and matching shopping of different combination types, the total price of multiple times of purchasing and the total duration of multiple times of shopping into a consumer combination coefficient solving formula to solve the consumer combination coefficient; the consumer combination coefficient solving formula is configured as follows:
(ii) a The system comprises a data acquisition module, a data processing module and a data processing module, wherein Xyz is a consumer combination coefficient, cyz, jyd and Tyz are respectively the times of combination and collocation shopping, the total price of multiple times of purchase and the total duration of multiple times of shopping for different combination types;
substituting the consumer combination coefficient and the basic consumer coefficient assigned value of a single consumer in the consumer combination into a consumer combination calibration formula to obtain a consumer combination calibration coefficient; the consumer combination calibration formula is configured to:
(ii) a Xyj is the calibration coefficient of the consumer combination, and Pjy to Pjyi respectively give values to the basic consumer coefficients of different consumers in the consumer combination.
And sorting different consumer combinations from high to low according to the consumer combination calibration coefficient, and setting the consumer combination with the ranking in the proportion of the first consumer as the basic consumer combination. Wherein the first consumer proportion is typically set between 5% and 15%.
The commodity association analysis unit is used for analyzing the data of combined purchase of different commodities; the commodity association analysis unit is configured with a commodity association analysis strategy, and the commodity association analysis strategy comprises: acquiring the type of each two commodity combinations appearing in the first correlation analysis duration; acquiring the times and the total price of the matched purchase of different types of commodity combinations;
substituting the times and total prices of different commodity combination types which are collocated and purchased into a commodity combination coefficient solving formula to solve a commodity combination coefficient; the commodity combination coefficient solving formula is configured as follows:
(ii) a Xsz is a commodity combination coefficient, and Cdz and Jspz are the times and total price of matched purchase of different commodity combination types respectively;
substituting the commodity combination coefficient and the basic commodity coefficient assigned value of a single commodity into a commodity combination calibration formula to obtain a commodity combination calibration coefficient; the commodity combination calibration formula is configured to:
(ii) a Wherein Xjsz is a commodity combination calibration coefficient, and Psp1 and Psp2 are base commodity coefficient assignment values for two commodities in a commodity combination; and sorting the commodity combinations from high to low according to the commodity combination calibration coefficient, and setting the commodity combination with the first commodity proportion in the front sorting as the basic commodity combination. Wherein the first commercial proportion is typically set between 10% and 15%.
The comprehensive analysis unit is used for analyzing the comprehensive relevance between the consumer and the commodity; the comprehensive analysis unit is configured with a comprehensive analysis strategy, and the comprehensive analysis strategy comprises: acquiring the times of purchasing the basic commodity combination of the basic consumer combination, setting the times as the basic consumer commodity combination, and substituting the times of purchasing the basic consumer commodity combination into a comprehensive processing formula to obtain a comprehensive processing coefficient; the integrated processing formula is configured as:
(ii) a Wherein Xzh is the comprehensive processing coefficient, a is the comprehensive conversion base number, and Czh is the number of times of combination of basic consumer goods; wherein the value of a is between 1 and 2.
The commodity combinations of the basic consumers are sorted from high to low according to the comprehensive processing coefficient, the combination with the first comprehensive proportion in the front of the sorting is set as the basic comprehensive combination, and the relation between the consumers with relatively high frequency and high total consumption and the commodities frequently bought by the consumers can be obtained through the comprehensive processing coefficient, so that the accuracy of analyzing the commodity sales relevance is further improved. The first overall ratio is usually set to between 10% and 20%.
The combined classification module is used for obtaining a combined classification result based on the relevance analysis result, and is configured with a combined classification strategy, wherein the combined classification strategy comprises: and setting a basic consumer combination classification unit, a basic commodity combination classification unit and a basic comprehensive combination classification unit for the basic consumer combination, the basic commodity combination and the basic comprehensive combination respectively, and setting a basic consumer memory, a basic commodity memory and a basic comprehensive memory in the basic consumer combination classification unit, the basic commodity combination classification unit and the basic comprehensive combination classification unit to store data of the basic consumer combination, the basic commodity combination and the basic comprehensive combination.
In the first embodiment, the units of the time length are uniformly set to be minutes, and the unit of the price is the unit of RMB.
Example two
Referring to fig. 3, the present invention further provides an analysis method of a data application system, where the analysis method includes the following steps:
s10, firstly, carrying out basic qualitative and quantitative classification on data;
s20, carrying out comprehensive analysis based on qualitative classification and quantitative results of the data, and obtaining an effectiveness assignment result;
step S30, performing correlation analysis on the assigned different types of data to obtain correlation analysis results of the different types of data;
s40, obtaining a combined classification result based on the correlation analysis result;
the specific implementation method of the step S10 includes the following steps:
step S101, dividing data into consumer data, consumer behavior data and commodity data;
step S102, dividing the consumer data into young men, young women, children and adolescents, middle men, middle women, old men and old women;
step S103, dividing the behavior data of the consumers into purchase duration, purchase quantity, average purchase total price and purchase frequency;
step S104, the commodity data is divided into commodity selection duration, commodity purchase times and commodity price.
The specific implementation method of the step S20 includes the following steps:
step S201, substituting the purchase duration, the purchase quantity, the average purchase total price and the purchase frequency of each consumer into a consumer quantitative assignment formula to obtain a basic consumer coefficient assigned value;
step S202, according to the obtained basic consumer coefficient assigned values of different consumers, a basic young male coefficient, a basic young female coefficient, a basic child and adolescent coefficient, a basic middle-aged male coefficient, a basic middle-aged female coefficient, a basic old male coefficient and a basic old female coefficient are respectively assigned to young males, females, children and adolescents, middle-aged males, middle-aged females, elderly males and elderly females in the consumer data;
and step S203, substituting the commodity data into a commodity quantitative assignment formula to obtain a basic commodity coefficient assignment value, wherein the commodity data are divided into commodity selection duration, commodity purchase times and commodity price.
The specific implementation method of step S30 includes the following steps:
step S3011, setting a first correlation analysis duration, and acquiring types of the appearing consumer combinations in the first correlation analysis duration; then obtaining the times, the total price of multiple purchases and the total duration of multiple purchases when different combination types are combined and matched for shopping;
step S3012, substituting the times of shopping combined and matched with different combination types, the total price of multiple purchases and the total duration of multiple purchases into a consumer combination coefficient solving formula to solve a consumer combination coefficient;
step S3013, substituting the consumer combination coefficient and the basic consumer coefficient assigned value of a single consumer in the consumer combination into a consumer combination calibration formula to obtain a consumer combination calibration coefficient;
step S3014, according to the consumer combination calibration coefficient, different consumer combinations are ranked from high to low, and the consumer combination ranked in the first consumer proportion is set as a basic consumer combination;
step S3021, acquiring the type of each two commodity combinations appearing in the first correlation analysis duration; acquiring the times and the total price of the matched purchase of different types of commodity combinations;
step S3022, substituting the times and total prices of the collocated purchases of different commodity combinations into a commodity combination coefficient calculation formula to calculate a commodity combination coefficient;
step S3023, substituting the commodity combination coefficient and the basic commodity coefficient assigned value of a single commodity into a commodity combination calibration formula to obtain a commodity combination calibration coefficient;
step S3024, sorting the commodity combinations from high to low according to the commodity combination calibration coefficients, and setting the commodity combinations with the first commodity proportion in the front sorting as basic commodity combinations;
step S3031, acquiring the times of purchasing the basic commodity combination of the basic consumer combination, setting the times as the basic consumer commodity combination, and substituting the times of purchasing the basic commodity combination of the basic consumer into a comprehensive processing formula to obtain a comprehensive processing coefficient;
step S3032, the basic consumer commodity combinations are sorted from high to low according to the comprehensive processing coefficient, and the combination with the first comprehensive proportion in the front sorting is set as the basic comprehensive combination.
The specific implementation method of the step S40 includes the following steps:
step S401, a basic consumer combination classification unit, a basic commodity combination classification unit and a basic comprehensive combination classification unit are respectively set for the basic consumer combination, the basic commodity combination and the basic comprehensive combination, and a basic consumer memory, a basic commodity memory and a basic comprehensive memory are arranged in the basic consumer combination classification unit, the basic commodity combination classification unit and the basic comprehensive combination classification unit to store data of the basic consumer combination, the basic commodity combination and the basic comprehensive combination.
In the second embodiment, the units of the time length are uniformly set to be minutes, and the unit of the price is the unit of RMB.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (7)
1. A data application system is characterized in that the application system is applied to the field of supermarket data analysis; the application system comprises a data classification module, an effectiveness assignment module, an association analysis module and a combined classification module;
the data classification module is used for carrying out basic qualitative and quantitative classification on the data;
the effectiveness assignment module is used for carrying out comprehensive analysis based on qualitative classification and quantitative results of data and obtaining effectiveness assignment results;
the correlation analysis module is used for performing correlation analysis on the assigned different types of data to obtain correlation analysis results of the different types of data; the correlation analysis module comprises a consumer correlation analysis unit, a commodity correlation analysis unit and a comprehensive analysis unit;
the consumer association analysis unit is used for analyzing data of a plurality of consumers during shopping at the same time; the commodity association analysis unit is used for analyzing the data of combined purchase of different commodities; the comprehensive analysis unit is used for analyzing the comprehensive relevance between the consumer and the commodity;
the consumer association analysis unit is configured with a consumer association analysis policy, which includes: setting a first correlation analysis duration, and acquiring the type of the appearing consumer combination in the first correlation analysis duration; then obtaining the times, the total price of multiple purchases and the total duration of multiple purchases when different combination types are combined and matched for shopping;
substituting times, total prices of multiple purchases and total duration of multiple purchases of different combination types into a consumer combination coefficient solving formula to solve a consumer combination coefficient; the consumer combination coefficient solving formula is configured as follows:
(ii) a The system comprises a data acquisition module, a data processing module and a data processing module, wherein Xyz is a consumer combination coefficient, cyz, jyd and Tyz are respectively the times of combination and collocation shopping, the total price of multiple times of purchase and the total duration of multiple times of shopping for different combination types;
substituting the consumer combination coefficient and the basic consumer coefficient assigned value of a single consumer in the consumer combination into a consumer combination calibration formula to obtain a consumer combination calibration coefficient; the consumer combination calibration formula is configured to:
(ii) a Xyj is a customer combination calibration coefficient, and Pjy to Pjyi respectively give values to basic customer coefficients of different customers in the customer combination;
sorting different consumer combinations from high to low according to the consumer combination calibration coefficient, and setting the consumer combination with the first consumer proportion as a basic consumer combination;
and the combined classification module is used for obtaining a combined classification result based on the correlation analysis result.
2. The data application system of claim 1, wherein the data classification module is configured with a data classification policy, the data classification policy comprising: firstly, dividing data into consumer data, consumer behavior data and commodity data;
dividing the consumer data into young men, young women, children and teenagers, middle men, middle women, old men and old women;
dividing the behavior data of the consumers into purchase duration, purchase quantity, purchase average total price and purchase frequency;
the commodity data is divided into commodity selection duration, commodity purchase times and commodity price.
3. The data application of claim 2, wherein the validity assignment module comprises a validity assignment policy, the validity assignment policy comprising:
substituting the purchase duration, the purchase quantity, the average purchase total price and the purchase frequency of each consumer into a consumer quantitative assignment formula to obtain a basic consumer coefficient assigned value;
according to the obtained basic consumer coefficient assigned values of different consumers, basic young male, young female, children and adolescents, middle-aged male, middle-aged female, old male and old female in the consumer data are respectively assigned with a basic young male coefficient, a basic young female coefficient, a basic child and adolescent coefficient, a basic middle-aged male coefficient, a basic middle-aged female coefficient, a basic old male coefficient and a basic old female coefficient; the assignment method of the basic coefficient of each type of consumer comprises the following steps: selecting basic consumer coefficient assigned values of the consumers of the type with the first type number to calculate an average value, and taking the average value calculated by each type as the basic coefficient of the type;
and substituting the commodity data into a commodity quantitative assignment formula to obtain a basic commodity coefficient assigned value, wherein the commodity data comprises commodity selection duration, commodity purchase times and commodity price.
4. The data application system of claim 3, wherein the commodity association analysis unit is configured with a commodity association analysis policy, and the commodity association analysis policy comprises: acquiring the type of each two commodity combinations appearing in the first correlation analysis duration; acquiring the times and the total price of the matched purchase of different types of commodity combinations;
substituting the times and total prices of different commodity combination types which are collocated and purchased into a commodity combination coefficient solving formula to solve a commodity combination coefficient;
substituting the commodity combination coefficient and the basic commodity coefficient assigned value of a single commodity into a commodity combination calibration formula to obtain a commodity combination calibration coefficient;
and sorting the commodity combinations from high to low according to the commodity combination calibration coefficient, and setting the commodity combination with the first commodity proportion in the front sorting as the basic commodity combination.
5. The data application system of claim 4, wherein the integrated analysis unit is configured with an integrated analysis policy, the integrated analysis policy comprising: acquiring the times of purchasing the basic commodity combination of the basic consumer combination, setting the times as the basic consumer commodity combination, and substituting the times of purchasing the basic consumer commodity combination into a comprehensive processing formula to obtain a comprehensive processing coefficient;
and sorting the basic consumer commodity combinations from high to low according to the comprehensive processing coefficient, and setting the combination with the first comprehensive proportion in the top as the basic comprehensive combination.
6. The data application system of claim 5, wherein the combined classification module is configured with a combined classification policy, the combined classification policy comprising: and setting a basic consumer combination classification unit, a basic commodity combination classification unit and a basic comprehensive combination classification unit for the basic consumer combination, the basic commodity combination and the basic comprehensive combination respectively, and setting a basic consumer memory, a basic commodity memory and a basic comprehensive memory in the basic consumer combination classification unit, the basic commodity combination classification unit and the basic comprehensive combination classification unit to store data of the basic consumer combination, the basic commodity combination and the basic comprehensive combination.
7. The method for analyzing a data application system according to any one of claims 1 to 6, wherein the method for analyzing comprises the steps of:
s10, firstly, carrying out basic qualitative and quantitative classification on data;
s20, carrying out comprehensive analysis based on qualitative classification and quantitative results of the data, and obtaining an effectiveness assignment result;
step S30, performing correlation analysis on the assigned different types of data to obtain correlation analysis results of the different types of data;
and S40, obtaining a combined classification result based on the correlation analysis result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210964496.9A CN115049442B (en) | 2022-08-12 | 2022-08-12 | Data analysis method and application system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210964496.9A CN115049442B (en) | 2022-08-12 | 2022-08-12 | Data analysis method and application system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115049442A CN115049442A (en) | 2022-09-13 |
| CN115049442B true CN115049442B (en) | 2022-10-28 |
Family
ID=83166908
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210964496.9A Active CN115049442B (en) | 2022-08-12 | 2022-08-12 | Data analysis method and application system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115049442B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110555712A (en) * | 2018-05-31 | 2019-12-10 | 北京京东尚科信息技术有限公司 | Commodity association degree determining method and device |
| CN112232888A (en) * | 2020-11-06 | 2021-01-15 | 深圳市护家科技有限公司 | Intelligent analysis system and method for consumer behaviors |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108288182A (en) * | 2018-02-12 | 2018-07-17 | 安徽千云度信息技术有限公司 | Supermarket's commodity management system based on big data and method |
| CN113344676A (en) * | 2021-06-29 | 2021-09-03 | 郑州铁路职业技术学院 | Electronic commerce big data information management system |
-
2022
- 2022-08-12 CN CN202210964496.9A patent/CN115049442B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110555712A (en) * | 2018-05-31 | 2019-12-10 | 北京京东尚科信息技术有限公司 | Commodity association degree determining method and device |
| CN112232888A (en) * | 2020-11-06 | 2021-01-15 | 深圳市护家科技有限公司 | Intelligent analysis system and method for consumer behaviors |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115049442A (en) | 2022-09-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Jiang et al. | Redesigning promotion strategy for e-commerce competitiveness through pricing and recommendation | |
| Kim et al. | Which is more important in Internet shopping, perceived price or trust? | |
| Greenstein-Messica et al. | Personal price aware multi-seller recommender system: Evidence from eBay | |
| US20080183552A1 (en) | Method for evaluating, analyzing, and benchmarking business sales performance | |
| JP2009169699A (en) | Sales information analysis device | |
| Silver | An evaluation of the use of hedonic regressions for basic components of consumer price indices | |
| TWI652639B (en) | Recommended system and method of product promotion combination | |
| KR20190086173A (en) | Sale product analysis and promotion system of on-line shopping mall | |
| JP5039579B2 (en) | Sales information analyzer | |
| CN116739652A (en) | Clothing e-commerce sales prediction modeling method | |
| Astuti et al. | Influence Analysis of Customer Ratings Reviews Online, Free Shipping Promotion and Discount Promotion on Purchasing Decisions in E-Commerce | |
| Farooq Baqal et al. | Analysis of factors influencing the decisions over purchasing second-hand products | |
| CN115049442B (en) | Data analysis method and application system | |
| Riady et al. | The Influences of Social Media Marketing Activities Towards Brand Loyalty | |
| Hsu et al. | Competitiveness and consumer preferences of US fruits in Taiwan | |
| Liu | Moderating influence of perceived risk on relationships between extrinsic cues and behavioral intentions | |
| Hasan et al. | A study of factors influencing consumer’s behaviour towards purchasing a smart phone in Raipur city | |
| CN117934048B (en) | E-commerce commodity sales analysis method and system | |
| Saadah et al. | The Role Of Artificial Intelligence (AI) In Digital Marketing: How Personalization Of Content Has Implications For Purchase Intention In Ecommerce | |
| Nanakali et al. | The impact of digital market upon accounting profit of physical market: An exploratory study of the opinions of a group of retailers in Erbil, Kurdistan region | |
| Jundrio et al. | The Influence of Website Quality, Electronic Word of Mouth, and People Lifestyle on Purchase Decisions on the Zalora Indonesia Marketplace | |
| Nurjanah | The Influence of Customer Service and Product Quality on Purchasing Decisions Motorcycle | |
| Saputra et al. | The Effect of Social Commerce Constructs on Purchase Intention in Social Commerce in Bandung City | |
| Pasaribu et al. | The Influence of Product Quality and Prices on Government Brand Products (Foodstation) on Purchasing Decisions at Jakmart Pasar Jaya Cikini | |
| Andina et al. | The effect of endorser credibility on purchasing decisions of neo coffee products (Study of the 2019 Palembang NCTzen Community Members Who Consumed NEO Coffee Products) |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |