CN117495430A - University student entrepreneur system and method based on data analysis - Google Patents

Abstract

The invention provides a university student entrepreneur system and a university student entrepreneur method based on data analysis, and relates to the technical field of data analysis. The invention comprises the following steps: collecting consumption information of customers and performing data analysis to obtain a data analysis result; generating a consumption constitution table according to the data analysis result; reading and classifying the consumption constitution table, analyzing commodity arrangement according to the reading and classifying result, providing supplier information, and providing a sales platform for the entrepreneur, wherein the sales platform comprises a sales data statistics interface; and acquiring sales data according to the sales data statistics interface and calculating to obtain profit data so as to remind a entrepreneur to adjust the entrepreneur policy in time. The invention can help the entrepreneurs of universities to select proper goods supply channels and proper commodities for sale through data analysis and intelligent platform construction, and can optimize the entrepreneur strategy according to the profit data, solve the problems faced by the entrepreneurs and improve the entrepreneur success rate.

Description

University student entrepreneur system and method based on data analysis

Technical Field

The invention relates to the technical field of data analysis, in particular to a university student entrepreneur system and method based on data analysis.

Background

In the current entrepreneur environment, many college students want to entrust by way of sales, but college student entrepreneurs face a series of problems including how to find popular goods for sales and how to better avoid goods accumulation; how to find a more suitable supply channel, and how to manually calculate the profit to cause the problem of funds loss, etc.

In view of the above problems, no effective technical solution is currently available.

Disclosure of Invention

The invention aims to provide a university student entrepreneur system and a method based on data analysis, which not only can help a university student entrepreneur to know the demands of customers and better find suitable commodities for sale, but also can provide a sales platform for the university student entrepreneur, and can help the university student entrepreneur to sell products better, improve marketing capability and increase income; the method and the device can also select proper goods supply channels for university entrepreneurs and provide accurate profit calculation service to provide reference for optimizing the following entrepreneur policy, so that the enthusiasm of the university entrepreneur is ensured.

In a first aspect, the present application provides a college student entrepreneur method based on data analysis, the method being applicable to a mobile terminal device, the method comprising:

s1, collecting consumption information of customers and performing data analysis to obtain a data analysis result;

s2, generating a consumption constitution table according to the data analysis result;

s3, reading and classifying the consumption constitution table, analyzing commodity arrangement according to the reading and classifying results, providing supplier information, and providing a sales platform for a creator, wherein the sales platform comprises a sales data statistics interface;

s4, acquiring sales data according to the sales data statistics interface and calculating to obtain profit data so as to remind entrepreneurs to adjust entrepreneur policies in time.

The beneficial effects of the technical scheme are as follows: the consumer information of all aspects of the customer is acquired and data analysis is carried out, so that a entrepreneur can be helped to better know the consumer demand of the customer, and the method has more pertinence to the products required to be sold by the entrepreneur; according to the result obtained by the data analysis, a consumption constitution table is generated, commodity arrangement and provider information are pushed to the entrepreneur through the consumption constitution table, and the entrepreneur can know products popular in the market and find a proper provider through automatically pushing out commodity arrangement and providing the provider information; providing a sales platform for the entrepreneurs so that the entrepreneurs have a platform to sell products and can better publicize the products; the method and the system also provide profit calculation service, so that the phenomenon that the creator manually calculates the profit to be wrong can be avoided; on the other hand, whether loss exists or not can be reflected through the profit data, and further the effects of avoiding goods accumulation and reminding entrepreneurs of timely adjusting entrepreneur strategies are achieved.

Preferably, step S1 includes:

performing data cleaning on the consumption information, including data deduplication, data standardization and data inspection;

classifying and reading the cleaned consumption information according to a clustering analysis method;

screening the classified and read consumption information according to a preset screening range to obtain a data analysis result;

the consumption information includes: basic information of customers, consumption behavior information and consumption preference information;

wherein, the basic information of the customer comprises the information of the sex, age, occupation and the like of the customer; the consumption behavior information of the customer comprises information such as purchase history, purchase quantity, purchase period, purchase price, purchase channel and the like of the commodity; the consumption preference information includes: information such as commodity category, purchase frequency, price sensitivity, payment method, etc.

Through the technical scheme, the consumption information can be subjected to data cleaning, and the operations of data deduplication, data standardization and data inspection are executed. The duplicate data is deleted, so that the accuracy and the reliability of the data are improved; the data is standardized, namely, data with larger phase difference is subjected to data standardization processing, such as purchase price and purchase frequency, so that information can be displayed more intuitively, and the data can be processed and analyzed conveniently; and the data inspection, namely the quality of the inspected data, carries out the mean value and interpolation random filling processing on the data with missing data or wrong format data, and improves the integrity and quality of the data.

Preferably, the classifying and reading the consumption information after washing according to a cluster analysis method includes:

the customers are divided into different consumer groups, and classified interpretation is carried out according to different consumption habits, preferences and behavior patterns, so that classified interpreted consumption information is obtained.

Preferably, the screening the consumption information after classification and interpretation according to the preset screening range, and obtaining the result of data analysis includes:

the preset screening range may be screening of a purchasing period range of the classified consumption information, including screening of a preset time period (e.g. 2-6 a.m.), so that cross-time data comparison is conveniently performed on the data, and besides the time period screening of the classified consumption information, screening of a lowest purchasing price, screening of a lowest purchasing quantity and the like can be performed on the classified consumption information so as to remove the data which does not meet the screening range in the classified consumption information.

Performing first screening on the classified and read consumption information according to a preset screening range to obtain first consumption data;

the first consumption data are data which are left after screening and deleting based on the purchasing channel of the customer as an index;

performing second screening on the first consumption data based on the consumption preference information of the customer as an index to obtain second consumption data;

through taking the consumption preference information of the customers as indexes for second screening, and through screening and analyzing different commodity categories, price sensitivity of the customers and purchase frequency of the customers, market dynamics and consumer demands can be better known, so that popular product ranking is more accurate, personalized and close to the market demands, and sales and marketing effects are improved.

And taking the first consumption data and the second consumption data as a result of data analysis.

The consumer information after cleaning is read and classified by adopting a cluster analysis method, customers can be divided into different consumer groups, and the consumer groups are divided according to the consumption habit, preference and behavior, so that a entrepreneur can better know the consumption behavior and preference of the customers, and a marketing strategy can be formulated better and personalized services can be provided.

And screening the classified and read consumption information according to a preset screening range, and removing the information which does not accord with the preset screening range, so that the critical data in the consumption information is rapidly locked, and the next processing is conveniently carried out through the critical data.

Preferably, the step S2 further includes:

sorting and summarizing the data analysis result, and carrying out data grouping and arrangement;

the consumption constitution table is generated according to the grouping and arrangement results.

Preferably, the step S3 includes:

reading and classifying the data of the consumption constitution table to obtain a read and classified result;

analyzing the result after interpretation and classification by adopting a correlation analysis method, for example, analyzing commodity ranks in the consumption constitution table, wherein the commodity is popular with customers, the correlation exists between data, the commodity appears frequently, the purchasing frequency of the commodity is highest, and the like, and providing supplier information;

the step S3 further includes:

acquiring identity information of a user during registration;

and when the identity information is a creator, providing a sales platform and provider information for a user according to the commodity arrangement.

Through the technical scheme, the consumption constitution table can be subjected to data interpretation and classification, the arrangement of commodity combinations is analyzed by adopting a correlation analysis method, and the information of suppliers is provided, so that a creator can better grasp market trend and supplier conditions, and purchasing and inventory management are optimized; by acquiring the identity information of the user, the customer and the entrepreneur are accurately distinguished, and different services are provided.

Preferably, the providing provider information includes:

automatically identifying commodity names of the commodity ranks, and judging whether the commodity names are contained in a preset information base or not;

if the preset information base does not contain the commodity name, searching the information of the suppliers on the network according to the commodity name to obtain a search result;

the preset information base comprises information such as commodity names, names of suppliers, telephones, addresses and the like.

Preferably, the searching for provider information on the network according to the commodity name includes:

automatically screening and removing the supplier information which does not accord with the evaluation index to obtain the supplier information which accords with the evaluation index, and obtaining the search result;

presenting the search results in a list mode;

the evaluation index includes one or more of a reputation and reputation index of the provider, an empirical capability index of the provider, a product quality index, a professional level index.

The beneficial effects of the technical scheme are as follows: by automatically identifying commodity names and judging whether commodities are included in the preset information base, time and effort of a creator for searching and screening commodity suppliers among a plurality of platforms can be saved; the provider information is screened through the evaluation index and is presented in a list form, so that the provider information of the leaning spectrum can be provided for the entrepreneur, a wider provider selection range can be provided for the entrepreneur, and the success rate of the entrepreneur is improved.

Preferably, the step S4 includes:

carrying out profit calculation on the sales data according to a preset calculation formula, and carrying out highlighting marking on the deficiency commodity so as to remind a entrepreneur to timely process the deficiency commodity and adjust a strategy;

the sales data includes product names, sales amounts, sales prices, sales amounts, product costs, and individual product profits and total profits.

The beneficial effects of the technical scheme are as follows: and the sales data is subjected to profit calculation according to a preset calculation formula, so that the accuracy of the profit data is improved, the error rate of the manual calculation of the profit data is reduced, and the profit data is used for highlighting the deficiency commodity, so that a creator can be helped to better manage the inventory and optimize the marketing strategy.

In a second aspect, to achieve the above object, the present application further provides a college student entrepreneur system based on data analysis, including:

and an acquisition and analysis module: the system comprises a data analysis module, a data analysis module and a data analysis module, wherein the data analysis module is used for collecting consumption information of customers and carrying out data analysis to obtain a data analysis result;

the generation module is used for: generating a consumption constitution table according to the result of the data analysis;

the analysis provides the module: the system comprises a consumer constitution table, a commodity counting interface and a commodity counting interface, wherein the consumer constitution table is used for reading and classifying the consumer constitution table, analyzing commodity ranking according to the result of reading and classifying and providing supplier information, and simultaneously providing a sales platform for a creator, and the sales platform comprises a sales data counting interface;

and a calculation reminding module: and the sales data statistics interface is used for acquiring sales data according to the sales data statistics interface and calculating to obtain profit data so as to remind a entrepreneur to adjust the entrepreneur policy in time.

According to the university student entrepreneur method based on data analysis, through data analysis and intelligent platform construction, accurate market information and sales data can be provided for a university student entrepreneur, the entrepreneur is helped to know market change, a sales platform and a commodity source channel of a leaning spectrum are provided, and an entrepreneur strategy is optimized according to profit data, so that the entrepreneur success rate and profit analysis capability are improved, and enthusiasm of the university student entrepreneur is guaranteed.

The technical scheme of the invention is further described in detail through the drawings and the embodiments.

Drawings

Fig. 1 is a flowchart of a college student entrepreneur method based on data analysis according to an embodiment of the present application.

Fig. 2 is a schematic structural diagram of a university student entrepreneur system based on data analysis according to an embodiment of the present application.

Description of the reference numerals: 201. acquiring an analysis module; 202. a generating module; 203. an analysis providing module; 204. and a calculation reminding module.

Detailed Description

Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.

In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

The following disclosure provides embodiments or examples for implementing different configurations of the present invention. In order to simplify the present disclosure, components and arrangements of specific examples are described below. They are, of course, merely examples and are not intended to limit the invention. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples, which are for the purpose of brevity and clarity, and which do not themselves indicate the relationship between the various embodiments and/or arrangements discussed. In addition, the present invention provides examples of various specific processes and materials, but one of ordinary skill in the art will recognize the application of other processes and/or the use of other materials.

Referring to fig. 1, a method for creating a business for universities based on data analysis according to an embodiment of the present application includes the following steps:

s1, collecting consumption information of customers and performing data analysis to obtain a data analysis result;

s2, generating a consumption constitution table according to the data analysis result;

s3, reading and classifying the consumption constitution table, analyzing commodity arrangement according to the reading and classifying results, providing supplier information, and providing a sales platform for a creator, wherein the sales platform comprises a sales data statistics interface;

s4, obtaining sales data according to the sales data statistics interface and calculating to obtain profit data so as to remind entrepreneurs of timely adjusting the entrepreneur policies.

The collecting the consumption information of the customer can be performed according to the identity information when the user registers and logs in, if the user registers and logs in, the user selects the customer, a questionnaire containing the consumption information needs to be filled in, wherein the consumption information comprises: basic information of customers, consumption behavior information and consumption preference information; wherein, the basic information comprises the information of the sex, age, occupation and the like of the customer; the consumption behavior information of the customer comprises information such as purchase history, purchase quantity, purchase period, purchase price, purchase channel and the like of the commodity; the consumption preference information includes: information such as commodity category, purchase frequency, price sensitivity, payment mode and the like, and entering a shopping platform page after filling is finished; if the identity information of the user when registering and logging in is the creator, the user can enter a sales platform page.

Further, in some of these embodiments, step S1 includes:

and cleaning the data of the acquired consumption information, wherein the data comprises the steps of data deduplication, data standardization, data inspection and the like.

Specifically, in some embodiments, taking the same customer as an example, after the customer fills out the questionnaire, the questionnaire is submitted repeatedly twice due to external factors, which may be that the network is blocked or that the mobile phone is blocked, the data of the consumption information needs to be deduplicated, that is, the repeated data is deleted, only one piece of data is left, and if the situation does not occur, the data standardization processing is performed on the consumption information.

Specifically, in some embodiments, the normalization processing is performed on the data, for example, two data with large differences are subjected to the logarithm processing, for example, in 3 users, the consumption information includes purchase amount (amount, unit is element) and purchase frequency (frequency, unit is secondary), the purchase amount of the user A, B, C is 100, 200, 300, and the purchase frequency is 1, 2, 3, respectively, and after the logarithm processing is performed on the two data, a new data set is obtained:

	purchase amount	Number of purchases
			User A	2.00	0.00
User B	3.00	0.69
			User C	4.00	1.09

The first logarithm amount 2.00 is the logarithm of 100 (the power 2 of 10 is equal to 100), the logarithm frequency 0.00 is the logarithm of 1 (the power 0 of 10 is equal to 1), and so on, through carrying out standardized processing on data, data with large differences in order of magnitude can be processed, so that the whole data is more compact and complete, and classification and interpretation are also convenient to carry out on the whole data.

Further, in some embodiments, step S1 further includes:

classifying and reading the cleaned consumption information according to a clustering analysis method;

and screening the consumption information after classification and interpretation according to a preset screening range to obtain a data analysis result.

The clustering analysis method is to divide customers into different consumer groups according to aspects such as consumption habit, preference, behavior, purchase amount, purchase frequency or purchase time period, for example, assume that 30 users fill out questionnaires, the users can be divided into different consumer groups by adopting the clustering analysis method, and the consumer groups can be students, workers, and other groups such as mom, each consumer group is a group of users, the first group can comprise 10 users, and the first group shows higher similarity in purchase amount, purchase frequency and the like; the second group may include 20 users who exhibit a higher similarity in terms of purchase period; and (3) reading the similarity obtained after classification, and presuming the reading result. For example, a first group of users may like to consume at a store of online lower entities; a second group of users may prefer to consume at night, and these interpreted results may help the entrepreneur to better understand the consumer's consumption behavior and preferences in order to better formulate marketing strategies and provide personalized services.

Specifically, the consumer information after classification and interpretation is screened according to a preset screening range, and the data analysis result is obtained by:

the preset screening range may be screening of a purchasing period range of the classified consumption information, including screening of a preset time period (2-6 a.m.), so that cross-time data comparison is conveniently performed on the data, and besides the time period screening of the classified consumption information, screening of a lowest purchasing price, screening of a lowest purchasing quantity and the like can be performed on the classified consumption information to remove data which does not meet the screening range in the classified consumption information.

Performing first screening on the classified and read consumption information according to a preset screening range to obtain first consumption data;

the first consumption data are data which are left after screening and deleting based on the purchasing channel of the customer as an index;

for example, the first screening is performed by using the purchasing channel of the customer as an index, and if the information that only online shopping or online shopping with a frequency significantly higher than that of offline shopping is used as a screening object, the consumption information that no online shopping is performed at all is removed, if one customer only performs offline shopping, the consumption data of the customer may not reflect the trend and the characteristics of online shopping, so that the customers who only perform offline shopping need to be removed, more accurate online shopping consumption data can be obtained, and the creator is helped to better know the consumption behavior and the preference of the target customer group, and obtain the first consumption data information.

Performing second screening on the consumption preference information of the customer based on the consumption preference information of the customer as an index to obtain second consumption data;

for example, the screening can be performed according to different commodity categories (such as food, clothing, electronic products, etc.), the screening can be performed according to price sensitivity of customers as an index, and the screening can be performed according to purchase frequency of customers, for example, the commodity category with higher purchase frequency of customers in different commodities is screened out; obtaining second consumption data; through screening of different commodity categories and analysis of purchase frequency, the most popular commodity category and trend in the current market can be found, so that corresponding hot commodities are pushed out or market trend is followed. For example, if the purchase frequency of a certain commodity category is high, the commodity category may be popular, and the creator may further learn about the specific products in the category and push out the corresponding popular products.

And taking the first consumption data and the second consumption data as the result of data analysis.

By the technical scheme, redundancy of a large amount of data is avoided, only important information is reserved for analysis by screening and removing, and the overall analysis efficiency is improved; in the second aspect, data errors are effectively avoided, and if the consumption information after classification and interpretation is directly used for analysis, errors may occur in analysis results. For example, some customers may make purchases multiple times in different channels, but such information may mislead the data analysis results due to repetition. Through the first filtering, the repeated information can be removed; in the third aspect, the one-sided screening is avoided, for example, instead of directly screening by using online and offline purchasing channels, more comprehensive consumption information data can be collected by considering the frequency of consumption on part of the user lines, and the sales range is enlarged, so that more accurate and comprehensive data analysis results are obtained.

Further, in some of these embodiments, step S2 includes:

sorting and summarizing the data analysis results, and carrying out data grouping and arrangement;

and generating a consumption constitution table according to the grouping and arrangement results.

The consumption constitution list comprises commodity names, purchase quantity, purchase time, purchase amount, consumption groups and the like, and the elements are grouped and arranged, for example, a group of users with consumption groups being baby can be grouped according to the commodity names (paper diapers), and then arranged from large to small according to the purchase quantity, so that a clear consumption constitution list can be obtained.

Further, in some of these embodiments, step S3 includes:

reading and classifying the data in the consumption constitution table to obtain a result after reading and classifying;

analyzing the result after interpretation and classification by adopting a correlation analysis method to obtain commodity ranking and providing supplier information;

for example, classification may be based on different trade names, such as snack foods, sporting goods, cosmetics, etc.; it can also be categorized by purchase amount: classification can be performed according to different purchase amounts; classified by time of purchase: the shopping peak time of the consumer can be deduced, and a classified result is obtained; reading the consumption component table by adopting a reading means of frequent item set analysis, for example, frequent item set analysis can be carried out on different commodity names in the consumption component table, and the association relationship between commodities can be obtained, for example, the 'sports equipment' and the 'running shoes' have higher association degree, because consumers who purchase the sports equipment are more prone to purchasing the running shoes; likewise, through frequent item set analysis, shopping preferences of consumer groups can be obtained, for example, "women" are more prone to purchasing "cosmetics", while "men" are more prone to purchasing "sports equipment", so that consumer groups with different sexes can be considered to have different shopping preferences, and the results after the consumer composition table is read, including commodity names, can be displayed in a picture or list mode.

Analyzing the result after interpretation and classification by adopting a correlation analysis method to obtain popular commodity ranking, wherein the analysis steps comprise:

1. determining common goods purchased in each consumer group; 2. sorting according to the purchase quantity of the common commodities purchased in each consumer group; 3. the ordered lists of each group of consumer groups are combined into a total ordered list.

Specifically, for example, the processed consumption constitution table has the following group of consumption information:

consumer group a purchased commodity 1, commodity 2 and commodity 3, wherein commodity 1 purchased the most, commodity 2 times, commodity 3 the least;

consumer group B purchases commodity 1, commodity 2, and commodity 4, wherein commodity 1 and commodity 2 have the same purchase quantity, and commodity 4 has the least purchase quantity;

consumer group C purchases commodity 2, commodity 3, and commodity 4, wherein commodity 2 purchases the most, commodity 3 times, commodity 4 is the least;

analysis of this shows that each consumer group purchased the commodity 2. Sorting according to the purchase quantity of each commodity to obtain the following sorting list of three commodities:

consumer group a: commodity 1> commodity 2> commodity 3

Consumer group B: commodity 1 = commodity 2> commodity 4

Consumer group C: commodity 2> commodity 3> commodity 4

Combining the three ordered lists into a total ordered list yields the following results:

wherein, the popularity is represented by a number > and commodity 2> commodity 1> commodity 3> commodity 4;

the technical scheme can be analyzed and modeled by using a machine learning or statistical method to realize popular commodity ranking.

And the commodity arrangement can be analyzed, meanwhile, the commodity can be recommended to the customer according to the result after the consumer formation table is read, and the sales platform and the supplier information are provided for the entrepreneur, and the entrepreneur can determine the product to be sold on the platform through the commodity arrangement and the commodity name in the result after the consumer formation table is read.

Through the technical scheme, the sales platform is provided for the entrepreneurs, the entrepreneurs are guaranteed to have sales channels, the entrepreneurs can know which products need to be sold through commodity arrangement, the problems that the entrepreneurs have no platform and do not know which products are popular are solved, but the problems that the entrepreneurs manually search and screen suppliers to have errors and cannot find the appropriate distribution channels by virtue still exist.

In this regard, further, in some embodiments, providing the vendor information comprises:

automatically identifying commodity names in commodity ranking, and judging whether the commodity names are contained in a preset information base or not;

if the preset information base contains commodity names, providing the supplier information in the preset information base for the user preferentially;

if the preset information base does not contain commodity names, searching the information of the suppliers on the network according to the commodity names to obtain search results;

preferably, the preset information base includes information such as commodity name, name of provider, telephone and address.

Automatically screening and removing the supplier information which does not accord with the evaluation index to obtain the supplier information which accords with the evaluation index, and obtaining a search result;

presenting the search results in a list mode;

the evaluation index may be a reputation and reputation index of a provider, an empirical capability index of the provider, a product quality index and a professional level index, for example, if one type of mug is arranged at the first place in commodity arrangement and a preset information base does not contain the commodity name of the mug, network searching is automatically performed on provider information, three providers sell the mug, the reputation and reputation of the provider are evaluated according to the reputation and reputation index of the provider, the reputation and reputation of the provider 1, the provider 2 and the reputation and reputation of the provider 3 are respectively 4.1, 4.5 and 4.8, and the reputation and reputation of the provider 3 are evaluated to be equal to or more than 4.6, and then the provider 3 is only left after screening and removing according to the index, and the provider with the highest reputation and reputation is preferentially selected, so that a supply channel of a reliable spectrum is provided for a creator.

Through screening of a preset information base and evaluation indexes, provider information meeting the conditions can be searched and screened out more accurately, the time for manual searching and screening is saved, the efficiency is improved, and the possibility of human errors is reduced.

Further, in some embodiments, the method further comprises:

in the sales platform provided for the entrepreneurs, the sales platform internally comprises a sales data statistics interface for acquiring sales data so as to perform statistics, analysis and reporting.

Further, in some of these embodiments, step S4 includes:

carrying out profit calculation on the sales data according to a preset calculation formula, and carrying out highlighting marking on the deficiency commodity, for example, carrying out shadow projection display on the deficiency commodity so as to remind a entrepreneur to timely process the deficiency commodity and adjust the entrepreneur strategy;

the sales data includes product names, sales amounts, sales prices, sales amounts, product costs, profitability of each product, and total profitability.

Wherein the calculation formula may be profit = sales-product cost;

sales = sales price × sales quantity

Cost = product cost number of sales

This formula can be used to calculate the profitability of a single product, and also to calculate the total profitability, i.e., the difference between the total revenue and total cost of sales for all products;

for example, whether a single product is profitable or not can be determined by judging whether the difference between the sales amount and the product cost is smaller than zero, if the difference between the sales amount and the product cost is smaller than zero, the profit loss is indicated, the loss product is shaded and displayed in a protruding mode, and the creator can quickly identify and adjust corresponding measures.

According to the technical scheme, the profit calculation is carried out on the sales data according to the preset calculation formula, the accuracy of the profit data is improved, the error rate of manual calculation of the profit data is reduced, and the profit data is used for highlighting and marking the deficiency commodity, so that a creator can be helped to better manage inventory and optimize marketing strategies.

In order to achieve the above objective, referring to fig. 2, fig. 2 is a schematic structural diagram of a university student entrepreneur system based on data analysis according to an embodiment of the present application, including:

acquisition analysis module 201: the system comprises a data analysis module, a data analysis module and a data analysis module, wherein the data analysis module is used for collecting consumption information of customers and carrying out data analysis to obtain a data analysis result;

the generation module 202: the consumption constitution table is used for generating a consumption constitution table according to the result of data analysis;

the analysis providing module 203: the system comprises a consumer composition table, a commodity counting interface and a commodity counting interface, wherein the consumer composition table is used for reading and classifying the consumer composition table, analyzing commodity ranking according to the reading and classifying result and providing supplier information, and providing a sales platform for a entrepreneur at the same time;

the computing alert module 204: and the system is used for acquiring sales data according to the sales data statistics interface and calculating to obtain profit data so as to remind a entrepreneur to adjust the entrepreneur policy in time.

The beneficial effects of the technical scheme are as follows: through data analysis and intelligent platform construction, accurate market information and sales data can be provided for university student entrepreneurs, the entrepreneurs are helped to know market changes, a sales platform and a goods source channel by means of a spectrum are provided, and the entrepreneur strategy is optimized according to the profit data, so that the entrepreneur success rate and the profit analysis capability are improved, and the enthusiasm of university student entrepreneur is ensured.

The foregoing is merely exemplary embodiments of the present application and is not intended to limit the scope of the present application, and various modifications and variations may be suggested to one skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims (10)

1. A college student entrepreneur method based on data analysis, the method comprising the steps of:

s1, collecting consumption information of customers and performing data analysis to obtain a data analysis result;

s2, generating a consumption constitution table according to the data analysis result;

s3, reading and classifying the consumption constitution table, analyzing commodity arrangement according to the reading and classifying results, providing supplier information, and providing a sales platform for a creator, wherein the sales platform comprises a sales data statistics interface;

s4, acquiring sales data according to the sales data statistics interface and calculating to obtain profit data so as to remind entrepreneurs to adjust entrepreneur policies in time.

2. The college student entrepreneur method based on data analysis as claimed in claim 1, wherein step S1 comprises:

performing data cleaning on the consumption information, including data deduplication, data standardization and data inspection;

classifying and reading the cleaned consumption information according to a clustering analysis method;

screening the classified and read consumption information according to a preset screening range to obtain a data analysis result;

the consumption information includes: basic information of customers, consumption behavior information and consumption preference information.

3. The data analysis-based college student business method of claim 2, wherein the classifying and reading the consumption information after washing according to the clustering analysis method comprises:

the customers are divided into different consumer groups, and classified interpretation is carried out according to different consumption habits, preferences and behavior patterns, so that classified interpreted consumption information is obtained.

4. The university student's startup method based on data analysis of claim 2, wherein the screening the consumption information after classification and interpretation according to a preset screening range to obtain a result of data analysis includes:

performing first screening on the classified and read consumption information according to a preset screening range to obtain first consumption data;

the first consumption data are data which are left after screening and deleting based on the purchasing channel of the customer as an index;

performing second screening on the first consumption data based on the consumption preference information of the customer as an index to obtain second consumption data;

and taking the first consumption data and the second consumption data as a result of data analysis.

5. The college student entrepreneur method based on data analysis as claimed in claim 1, wherein step S2 comprises:

sorting and summarizing the data analysis result, and carrying out data grouping and arrangement;

the consumption constitution table is generated according to the grouping and arrangement results.

6. The college student entrepreneur method based on data analysis as claimed in claim 1, wherein step S3 comprises:

reading and classifying the data of the consumption constitution table to obtain a read and classified result;

analyzing the result after interpretation and classification by adopting a correlation analysis method to obtain commodity ranking and providing supplier information;

the step S3 further includes:

acquiring identity information of a user during registration;

and when the identity information is a creator, providing a sales platform and provider information for a user according to the commodity arrangement.

7. The data analysis based college student business method of claim 6, wherein the providing provider information comprises:

automatically identifying commodity names of the commodity ranks, and judging whether the commodity names are contained in a preset information base or not;

if the preset information base does not contain the commodity name, searching the information of the suppliers on the network according to the commodity name to obtain a search result;

the preset information base comprises commodity names, names of suppliers, telephones and address information.

8. The data analysis-based college student startup method of claim 7, wherein the step of searching for provider information on the network according to the commodity name comprises:

automatically screening provider information according to the evaluation index to obtain the search result;

presenting the search results in a list mode;

the evaluation index includes one or more of a reputation and reputation index of the provider, an empirical capability index of the provider, a product quality index, a professional level index.

9. The college student entrepreneur method based on data analysis as claimed in claim 1, wherein step S4 comprises:

and calculating according to the sales data and a preset calculation formula to obtain profit data, wherein the profit data is used for highlighting and marking the deficiency commodity so as to remind a entrepreneur to timely process the deficiency commodity and adjust the entrepreneur strategy.

10. A college student entrepreneur system based on data analysis, the system comprising:

and an acquisition and analysis module: the system comprises a data analysis module, a data analysis module and a data analysis module, wherein the data analysis module is used for collecting consumption information of customers and carrying out data analysis to obtain a data analysis result;

the generation module is used for: generating a consumption constitution table according to the result of the data analysis;

the analysis provides the module: the system comprises a consumer constitution table, a commodity counting interface and a commodity counting interface, wherein the consumer constitution table is used for reading and classifying the consumer constitution table, analyzing commodity ranking according to the result of reading and classifying and providing supplier information, and simultaneously providing a sales platform for a creator, and the sales platform comprises a sales data counting interface;

and a calculation reminding module: and the sales data statistics interface is used for acquiring sales data according to the sales data statistics interface and calculating to obtain profit data so as to remind a entrepreneur to adjust the entrepreneur policy in time.