WO2022262001A1 - Marketing management system based on internet big data - Google Patents

Abstract

Disclosed in the present invention is a marketing management system based on Internet big data, used for solving the problem of how to rationally screen consumers, how to accurately calculate member levels, and how to retain truly active members and improve after-sales service; comprising a data collection module, a processor, a storage module, an analysis module, a priority module, a marketing module, a member database, a management module, and an after-sales service module; on the basis of screened consumers with a screening value M_i of a reasonable size, the marketing management system selects preferred consumers as a target to facilitate better product marketing; using the formula (aa) to acquire a degree of member engagement D_i; by means of the size of the degree of member engagement D_i, determining the degree of engagement of the members; using the formula F_i = MH_i*w1+D_i*w2 to acquire a level value F_i; by means of the size of the level value F_i, determining the level of the members to thereby rationally allocate the members; by means of the loss value LS_i, determining the degree of consumer stickiness; and then cleaning up the members to thereby retain true members.

Description

A Marketing Management System Based on Internet Big Data technical field

The invention relates to the field of marketing technology, in particular to a marketing management system based on Internet big data.

Background technique

The big data marketing management system is a software system that combines the unique identity information code technology of products and market consumption management methods to realize consumer information collection and consumption behavior analysis. It is also an independent anti-counterfeiting extended value-added service software platform. The consumption management system can realize functions such as consumption information collection and analysis, consumption behavior analysis and management, membership points, gift exchange, member lottery draw, anti-counterfeiting inquiry, and logistics tracking. Various functions can be combined flexibly, it is a bridge for real-time communication between enterprises and consumers, and it is a powerful tool for enterprise marketing.

Contents of the invention

The purpose of the present invention is to provide a marketing management system based on Internet big data.

The technical problem to be solved by the present invention is:

(1) How to reasonably screen consumers so that merchants can do better marketing;

(2) How to accurately calculate the membership level, so as to allocate reasonable rights and interests;

(3) How to retain real and active members and improve after-sales service;

The purpose of the present invention can be achieved through the following technical solutions: a marketing management system based on Internet big data, including a data acquisition module, a processor, a storage module, an analysis module, a priority module, a marketing module, a membership database, a management module and after-sales service module;

The data collection module is used to collect consumer personal data and consumption data; the personal data includes name and contact information; the consumer data includes products, the price of the purchased product and the date of purchase; the data collection module will consume The consumer's personal data and consumption data are sent to the processor; the processor receives the consumer's personal data and consumption data sent by the data acquisition module and sends the data to the storage module for storage; the analysis module is used to analyze the consumer's personal data and consumption data Carry out analysis; Described analysis module analysis step is as follows:

Step 1: Set the collected consumers as X _i , (i=1...n); set the consumers to buy products as A _i , (i=1...n); and record the product label classification as B _i , (i=1...n); the price corresponding to the purchase price of product A _i is recorded as C _i , (i=1...n); the purchase date of product A _i is recorded as T _i , (i=1... ...n); the product label classification is recorded as B _i includes several products B _ij , (j=1...n);

Step 2: The product A _i purchased by the consumer is matched with the product B _ij ; specifically, when the product A ₁ purchased by the consumer is the same as B ₁₁ , the product label classification B1 is output;

Step 3: Divide the consumption time of consumers into three grades: TA at the beginning of the month, TB at the middle of the month, and TC at the end of the month; among them, TA at the beginning of the month is from the 1st to 10th; TB at the middle of the month is from the 11th to 20th; TC at the end of the month from the 21st to the 31st;

Step 4: Match the purchase date T _i with the consumer's consumption time; the specific performance is that when the purchase date is from the 1st to the 10th, it is the beginning of the month TA; the price corresponding to the price of the purchased product A _i is recorded as C _i Sum to get the purchase value M _TA at the beginning of the month; when the purchase date is from the 11th to the 20th, it is the mid-month TB; the price corresponding to the price of the purchased product A _i is recorded as C _i and summed to obtain the mid-month purchase value M _TB ; When the purchase date is from the 21st to the 31st, it is the TC at the end of the month; the price corresponding to the price of the purchased product A _i is recorded as C _i and summed to obtain the purchase value M _TC at the end of the month;

Step 5: Use the formula M _i =M _TA *J1+M _TB *J2+M _TC *J3 to obtain the screening value M _i of consumer X _i ; where J1, J2, and J3 are preset ratio fixed values;

Step 6: Screen consumers according to the size of the screening value M _i ; mark consumers X _i with M _i >k2 as preferred consumers; mark consumers Xi _with k1<M _i <k2 as optional consumers; Mark M _i <k1 as unselected consumers, where k1 and k2 are fixed values for preset screening;

The analysis module sends the product label classification of the preferred consumer and the optional consumer and the purchase value at the beginning of the month, the purchase value in the middle of the month and the purchase value at the end of the month to the preferred module; the classification of the product label and the purchase value at the beginning of the month, the purchase value in the middle of the month and the purchase at the end of the month The value is marked as purchase data; the preferred module receives and stores the preferred consumer and optional consumer purchase data sent by the analysis module; the marketing module is used to send preferred product information to preferred consumers and optional consumers and a member registration information form; the preferred product information is product information and preferential information classified by consumer product labels; the member registration information form is used for consumers to fill in registration information; the extraction module is used to extract and fill in the member registration information form The consumer and the purchase data corresponding to the consumer and send it to the member database; the member database receives and extracts the consumer who fills in the member registration information form and the purchase data corresponding to the consumer sent by the extraction module and marks it as member storage;

The management module is used for level calculation, distribution, management and maintenance of members in the member database; the management module includes an activity sending unit, a communication unit, a collection unit, a calculation unit, a distribution unit, a management unit and a maintenance unit; The activity sending unit is used to send preferential activities to members; the communication unit is used for communication between members and for communication between members and merchants; and the number of exchanges between members and merchants; the acquisition unit sends the collected time and frequency of member participation in preferential activities, the number of exchanges between members and the number of exchanges between members and merchants to the calculation unit; the calculation unit receives the time for members to participate in preferential activities sent by the acquisition unit , the number of times, the number of exchanges between members and the number of exchanges between members and merchants, and calculate them; the specific calculation steps are as follows:

S1: Set the start time of promotion launch and release as t _j ; the time for members to participate in promotional activities is recorded as t _i ;

S2: Use the formula t _ij = t _i -t _j to obtain the time t _ij for members to participate in preferential activities;

S3: Set the number of times members participate in preferential activities as P _j , and the number of book exchanges between members as P _b ; the number of times members communicate with merchants is recorded as P _c ;

S4: Use the formula

Get member participation D _i ; among them, u1, u2, u3 and u4 are preset fixed values;

S5: set the purchase value of the member as MH _i ; where; MH _i = M _TA + M _TB + M _TC ;

S6: Use the formula F _i =MH _i *w1+D _i *w2 to obtain the level value F _i ; wherein, w1 and w2 are fixed values compared to the preset ratio;

The calculation unit sends the grade value to the distribution unit; the distribution unit receives the grade value sent by the calculation unit and distributes it; the distribution process is as follows:

A: Set the membership level as G _i , (i=1...n); set the membership level as G _i and the range value is (h _i-1 , h _i ];

B: Match the level value F _i with the membership level G _i range value (h _i-1 , h _i ], the specific performance is as follows: when the level value h ₂ <F1≤h ₃ ; then output the membership level G ₃ ;

The management unit is used to collect the member's unconsumed product time, the number of non-participating activities, and calculate and clear the loss value. The specific calculation and clearing steps are as follows:

SS1; Set the time when the member has not consumed the product as Y _i , (i=1...n), and the number of times that the member has not participated in the activity is Z _i , (i=1...n);

SS2: Utilize the formula

Obtain the loss value LS _i ;

SS3: Set the default cleaning value to Q1;

SS4: compare the loss value LS _i with the cleaning value Q1; when LS _i -Q1>0; then clean up the member;

The maintenance unit is used to send short messages, voice calls and return visits to members.

Further, the after-sales service module is used for consumer and merchant visualization services; the after-sales service module includes a visual unit, a replacement refund unit, an evaluation collection unit and a gift compensation unit; the visual unit is used for consumers and merchants Merchant conducts after-sales exchange of products; the replacement and refund unit allows consumers to exchange and refund products with merchants; the evaluation collection unit collects consumer evaluation values for the product; the gift compensation unit is used to calculate the amount of additional compensation gifts and the gift corresponding to the amount; the specific calculation steps of the gift compensation unit are as follows:

Step 1: Set the evaluation value of the consumer as Ni; the price corresponding to the purchase price of product A _i is recorded as C _i ;

Step 2: Set default values for Ni and C _i ; set the default value of Ni as g1; set the default value of C _i as g2;

Step 3: Use the formula PF _i =N _i *g1+C _i *g2 to obtain the payout value PF _i ;

Step 4: Set the compensation gift as R _i , (i=1...n); the price range corresponding to the compensation gift R _i is (f _i-1 , f _i ];

Step 5: The compensation value PF _i is matched with the price range corresponding to the compensation gift R _i ; specifically, when f ₅ <PF ₁ ≤ f ₆ , then the compensation gift R ₆ is output.

Beneficial effects of the present invention:

(1) The present invention analyzes the consumer's personal data and consumption data through the analysis module; utilizes the formula M _i = M _TA * J1 + M _TB * J2 + M _TC * J3 to obtain the screening value M _{i of the consumer X i ;} Reasonably screen consumers according to the size of the screening value M _i , select the preferred consumers as the target, and facilitate better marketing of products;

(2) The present invention utilizes the formula

Obtain the degree of member participation D _i ; judge the degree of member participation by the degree of member participation D _i ; use the formula F _i = MH _i *w1+D _i *w2 to obtain the grade value F _i ; judge the member by the size of the grade value F _i level, so as to allocate members more reasonably;

(3) The present invention utilizes the formula

Obtain the loss value LS _i ; compare the loss value LS _i with the cleaning value Q1; when LS _i -Q1>0; clean up the member; judge the degree of stickiness of consumers by the loss value LS _i ; the more the loss value LS _i Larger, the lower the degree of cohesion, when the member's loss value LS _i is greater than the cleaning value Q1; the member is cleaned up; thereby retaining the real member;

(4) The present invention uses the formula PF _i =N _i *g1+C _i *g2 to obtain the compensation value PF _i ; the compensation value PF _i is matched with the price range corresponding to the compensation gift Ri; by reasonably calculating the additional compensation value PF _i , Choose the right gift, reduce the level of complaints about after-sales service, and improve consumer satisfaction.

Description of drawings

The present invention will be further described below in conjunction with the accompanying drawings.

Fig. 1 is a functional block diagram of a marketing management system based on Internet big data according to the present invention.

detailed description

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

Please refer to Fig. 1, the present invention is a marketing management system based on Internet big data, including data collection module, processor, storage module, analysis module, priority module, marketing module, membership database, management module and after-sales service module ;

The data collection module is used to collect personal data and consumption data of consumers; personal data includes name and contact information; consumer data includes products, price of purchased products and date of purchase; data collection module sends personal data and consumption data of consumers to Processor; the processor receives the consumer's personal data and consumption data sent by the data acquisition module and sends the data to the storage module for storage; the analysis module is used to analyze the consumer's personal data and consumption data; the analysis steps of the analysis module are as follows:

Step 1: Set the collected consumers as X _i , (i=1...n); set the consumers to buy products as A _i , (i=1...n); and record the product label classification as B _i , (i=1...n); the price corresponding to the purchase price of product A _i is recorded as C _i , (i=1...n); the purchase date of product A _i is recorded as T _i , (i=1... ...n); the product label classification is recorded as B _i includes several products B _ij , (j=1...n);

Step 2: The product A _i purchased by the consumer is matched with the product B _ij ; specifically, when the product A ₁ purchased by the consumer is the same as B ₁₁ , the product label classification B1 is output;

Step 3: Divide the consumption time of consumers into three grades: TA at the beginning of the month, TB at the middle of the month, and TC at the end of the month; among them, TA at the beginning of the month is from the 1st to 10th; TB at the middle of the month is from the 11th to 20th; TC at the end of the month from the 21st to the 31st;

Step 4: Match the purchase date T _i with the consumer's consumption time; the specific performance is that when the purchase date is from the 1st to the 10th, it is the beginning of the month TA; the price corresponding to the price of the purchased product A _i is recorded as C _i Sum to get the purchase value M _TA at the beginning of the month; when the purchase date is from the 11th to the 20th, it is the mid-month TB; the price corresponding to the price of the purchased product A _i is recorded as C _i and summed to obtain the mid-month purchase value M _TB ; When the purchase date is from the 21st to the 31st, it is the TC at the end of the month; the price corresponding to the price of the purchased product A _i is recorded as C _i and summed to obtain the purchase value M _TC at the end of the month;

Step 5: Use the formula M _i =M _TA *J1+M _TB *J2+M _TC *J3 to obtain the screening value M _i of consumer X _i ; where J1, J2, and J3 are preset ratio fixed values;

Step 6: Screen consumers according to the size of the screening value M _i ; mark consumers X _i with M _i >k2 as preferred consumers; mark consumers Xi _with k1<M _i <k2 as optional consumers; Mark M _i < k1 as unselected consumers, where k1 and k2 are preset screening fixed values; reasonably screen consumers according to the size of the screening value M _i , and select the preferred consumers as the target to facilitate better product marketing ;

The analysis module sends the product label classification of preferred consumers and optional consumers, as well as the purchase value at the beginning of the month, the purchase value of the middle month and the purchase value at the end of the month to the optimization module; the classification of product labels and the purchase value at the beginning of the month, the purchase value of the middle month and the purchase value of the month end It is purchase data; the optimization module receives and stores the purchase data of preferred consumers and optional consumers sent by the analysis module; the marketing module is used to send preferred product information and member registration information forms to preferred consumers and optional consumers; The preferred product information is product information and preferential information classified by consumer product labels; product information includes product functions and prices, preferential information includes product discount prices and gift giving, and the member registration information form is used for consumers to fill in registration information; the extraction module is used for Extract the consumers who filled out the member registration information form and the purchase data corresponding to the consumers and send them to the member database; the member database receives and extracts the consumers who fill out the member registration information form and the purchase data corresponding to the consumers sent by the extraction module and marks them as member storage;

The management module is used for level calculation, distribution, management and maintenance of members in the member database; the management module includes an activity sending unit, a communication unit, a collection unit, a calculation unit, a distribution unit, a management unit and a maintenance unit; the activity sending unit is used for Send preferential activities to members; the communication unit is used for communication between members and the communication between members and merchants; The collected time and times of members participating in preferential activities, the number of exchanges between members and the number of exchanges between members and merchants are sent to the calculation unit; The number of exchanges between members and merchants is calculated; the specific calculation steps are as follows:

S1: Set the start time of promotion launch and release as t _j ; the time for members to participate in promotional activities is recorded as t _i ;

S2: Use the formula t _ij = t _i -t _j to obtain the time t _ij for members to participate in preferential activities;

S3: Set the number of times members participate in preferential activities as P _j , and the number of book exchanges between members as P _b ; the number of times members communicate with merchants is recorded as P _c ;

S4: Use the formula

Obtain member participation D _i ; among them, u1, u2, u3 and u4 are preset fixed values; judge the degree of member participation by member participation D _i ;

S5: set the purchase value of the member as MH _i ; where; MH _i = M _TA + M _TB + M _TC ;

S6: Use the formula F _i =MH _i *w1+D _i *w2 to obtain the level value F _i ; among them, w1 and w2 are fixed values compared to the preset ratio; the level of the member is judged by the level value F _i , which is more reasonable assign members;

The calculation unit sends the level value to the allocation unit; the allocation unit receives the level value sent by the calculation unit and distributes it; the allocation process is as follows:

A: Set the membership level as G _i , (i=1...n); set the membership level as G _i and the range value is (h _i-1 , h _i ];

B: Match the level value F _i with the membership level G _i range value (h _i-1 , h _i ], the specific performance is as follows: when the level value h ₂ <F1≤h ₃ ; then output the membership level G ₃ ;

The management unit is used to collect the member's unconsumed product time, the number of non-participating activities, and calculate and clear the loss value. The specific calculation and clearing steps are as follows:

SS1; Set the time when the member has not consumed the product as Y _i , (i=1...n), and the number of times that the member has not participated in the activity is Z _i , (i=1...n);

SS2: Utilize the formula

Obtain the loss value LS _i ;

SS3: Set the default cleaning value to Q1;

SS4: The loss value LS _i is compared with the cleaning value Q1; when LS _i -Q1>0; the member is cleared; the degree of stickiness of consumers is judged by the loss value LS _i ; the greater the loss value LS _i , the greater the degree of stickiness The lower it is, when the member's loss value LS _i is greater than the cleaning value Q1; the member is cleaned up; thereby retaining the real member;

The maintenance unit is used to send short messages, voice calls and return visits to members.

The after-sales service module is used for visual service between consumers and merchants; the after-sales service module includes a visual unit, a replacement and refund unit, an evaluation collection unit and a gift compensation unit; the visual unit is used for after-sales communication between consumers and merchants ; Communicate between merchants and consumers through the visual unit, so as to better solve problems and increase the relationship between consumers and merchants; the replacement refund unit consumers and merchants perform product replacement and refund; the evaluation The acquisition unit collects the evaluation value of the product by the consumer; the gift compensation unit is used to calculate the additional compensation gift amount and the gift corresponding to the amount; the specific calculation steps of the gift compensation unit are as follows:

Step 1: Set the evaluation value of the consumer as Ni; the price corresponding to the price of the purchased product Ai is recorded as C _i ;

Step 2: Set default values for Ni and C _i ; set the default value of Ni as g1; set the default value of C _i as g2;

Step 3: Use the formula PF _i =N _i *g1+C _i *g2 to obtain the payout value PF _i ;

Step 4: Set the compensation gift as R _i , (i=1...n); the price range corresponding to the compensation gift R _i is (f _i-1 , f _i ];

Step 5: Match the compensation value PF _i with the price range corresponding to the compensation gift R _i ; specifically, when f ₅ <PF ₁ ≤ f ₆ ; then output the compensation gift R ₆ ; by reasonably calculating the additional compensation value PF _i , thus Improve consumer satisfaction;

Beneficial effects of the present invention:

(1) The present invention analyzes the consumer's personal data and consumption data through the analysis module; utilizes the formula M _i = M _TA * J1 + M _TB * J2 + M _TC * J3 to obtain the screening value M _{i of the consumer X i ;} Reasonably screen consumers according to the size of the screening value M _i , select the preferred consumers as the target, and facilitate better marketing of products;

(2) The present invention utilizes the formula

Obtain the degree of member participation D _i ; judge the degree of member participation by the degree of member participation D _i ; use the formula F _i = MH _i *w1+D _i *w2 to obtain the grade value F _i ; judge the member by the size of the grade value F _i level, so as to allocate members more reasonably;

(3) The present invention utilizes the formula

Obtain the loss value LS _i ; compare the loss value LS _i with the cleaning value Q1; when LS _i -Q1>0; clean up the member; judge the degree of stickiness of consumers by the loss value LS _i ; the more the loss value LS _i Larger, the lower the degree of cohesion, when the member's loss value LS _i is greater than the cleaning value Q1; the member is cleaned up; thereby retaining the real member;

(4) The present invention uses the formula PF _i =N _i *g1+C _i *g2 to obtain the compensation value PF _i ; the compensation value PF _i is matched with the price range corresponding to the compensation gift Ri; by reasonably calculating the additional compensation value PF _i , Choose the right gift, reduce the level of complaints about after-sales service, and improve consumer satisfaction.

The above content is only an example and description of the structure of the present invention. Those skilled in the art make various modifications or supplements to the described specific embodiments or replace them in similar ways, as long as they do not deviate from the structure of the invention or Anything beyond the scope defined in the claims shall belong to the protection scope of the present invention.

Claims (2)

1. A marketing management system based on Internet big data, characterized in that it includes a data acquisition module, a processor, a storage module, an analysis module, a priority module, a marketing module, a membership database, a management module and an after-sales service module;

   The data collection module is used to collect consumer personal data and consumption data; the personal data includes name and contact information; the consumer data includes products, the price of the purchased product and the date of purchase; the data collection module will consume The consumer's personal data and consumption data are sent to the processor; the processor receives the consumer's personal data and consumption data sent by the data acquisition module and sends the data to the storage module for storage; the analysis module is used to analyze the consumer's personal data and consumption data Carry out analysis; Described analysis module analysis step is as follows:

   Step 1: Set the collected consumers as X _i , (i=1...n); set the consumers to buy products as A _i , (i=1...n); and record the product label classification as B _i , (i=1...n); the price corresponding to the purchase price of product Ai is recorded as C _i , (i=1...n); the purchase date of product A _i is recorded as T _i , (i=1... n); the product label classification is recorded as including several products B _ij in B _i , (j=1...n);

   Step 2: The product A _i purchased by the consumer is matched with the product B _ij ; specifically, when the product A ₁ purchased by the consumer is the same as B ₁₁ , the product label classification B ₁ is output;

   Step 3: Divide the consumption time of consumers into three grades: TA at the beginning of the month, TB at the middle of the month, and TC at the end of the month; among them, TA at the beginning of the month is from the 1st to 10th; TB at the middle of the month is from the 11th to 20th; TC at the end of the month from the 21st to the 31st;

   Step 4: Match the purchase date T _i with the consumer's consumption time; the specific performance is that when the purchase date is from the 1st to the 10th, it is the beginning of the month TA; the price corresponding to the price of the purchased product A _i is recorded as C _i Sum to get the purchase value M _TA at the beginning of the month; when the purchase date is from the 11th to the 20th, it is the mid-month TB; the price corresponding to the price of the purchased product A _i is recorded as C _i and summed to obtain the mid-month purchase value M _TB ; When the purchase date is from the 21st to the 31st, it is the TC at the end of the month; the price corresponding to the price of the purchased product A _i is recorded as C _i and summed to obtain the purchase value M _TC at the end of the month;

   Step 5: Use the formula M _i =M _TA *J1+M _TB *J2+M _TC *J3 to obtain the screening value M _i of consumer X _i ; where J1, J2, and J3 are preset ratio fixed values;

   Step 6: Screen consumers according to the size of the screening value M _i ; mark consumers X _i with M _i >k2 as preferred consumers; mark consumers Xi _with k1<M _i <k2 as optional consumers; Mark M _i <k1 as unselected consumers, where k1 and k2 are fixed values for preset screening;

   The analysis module sends the product label classification of the preferred consumer and the optional consumer and the purchase value at the beginning of the month, the purchase value in the middle of the month and the purchase value at the end of the month to the preferred module; the classification of the product label and the purchase value at the beginning of the month, the purchase value in the middle of the month and the purchase at the end of the month The value is marked as purchase data; the preferred module receives and stores the preferred consumer and optional consumer purchase data sent by the analysis module; the marketing module is used to send preferred product information to preferred consumers and optional consumers and a member registration information form; the preferred product information is product information and preferential information classified by consumer product labels; the member registration information form is used for consumers to fill in registration information; the extraction module is used to extract and fill in the member registration information form The consumer and the purchase data corresponding to the consumer and send it to the member database; the member database receives and extracts the consumer who fills in the member registration information form and the purchase data corresponding to the consumer sent by the extraction module and marks it as member storage;

   The management module is used for level calculation, distribution, management and maintenance of members in the member database; the management module includes an activity sending unit, a communication unit, a collection unit, a calculation unit, a distribution unit, a management unit and a maintenance unit; The activity sending unit is used to send preferential activities to members; the communication unit is used for communication between members and for communication between members and merchants; and the number of exchanges between members and merchants; the acquisition unit sends the collected time and frequency of member participation in preferential activities, the number of exchanges between members and the number of exchanges between members and merchants to the calculation unit; the calculation unit receives the time for members to participate in preferential activities sent by the acquisition unit , the number of times, the number of exchanges between members and the number of exchanges between members and merchants, and calculate them; the specific calculation steps are as follows:

   S1: Set the start time of promotion launch and release as t _j ; the time for members to participate in promotional activities is recorded as t _i ;

   S2: Use the formula t _ij = t _i -t _j to obtain the time t _ij for members to participate in preferential activities;

   S3: Set the number of times members participate in preferential activities as P _j , and the number of book exchanges between members as P _b ; the number of times members communicate with merchants is recorded as P _c ;

   S4: Use the formula

   

   Get member participation D _i ; among them, u1, u2, u3 and u4 are preset fixed values;

   S5: set the purchase value of the member as MH _i ; where; MH _i = M _TA + M _TB + M _TC ;

   S6: Use the formula F _i =MH _i *w1+D _i *w2 to obtain the level value F _i ; wherein, w1 and w2 are fixed values compared to the preset ratio;

   The calculation unit sends the grade value to the distribution unit; the distribution unit receives the grade value sent by the calculation unit and distributes it; the distribution process is as follows:

   A: Set the membership level as G _i , (i=1...n); set the membership level as G _i and the range value is (h _i -1, h _i ];

   B: Match the level value F _i with the membership level G _i range value (h _i-1 , h _i ], the specific performance is as follows: when the level value h ₂ <F1≤h ₃ ; then output the membership level G ₃ ;

   The management unit is used to collect the member's unconsumed product time, the number of non-participating activities, and calculate and clear the loss value. The specific calculation and clearing steps are as follows:

   SS1; Set the time when the member has not consumed the product as Y _i , (i=1...n), and the number of times that the member has not participated in the activity is Z _i , (i=1...n);

   SS2: Utilize the formula

   

   Obtain the loss value LS _i ;

   SS3: Set the default cleaning value to Q1;

   SS4: compare the loss value LS _i with the cleaning value Q1; when LS _i -Q1>0; then clean up the member;

   The maintenance unit is used to send short messages, voice calls and return visits to members.
2. A kind of marketing management system based on Internet big data according to claim 1, it is characterized in that, described after-sales service module is used for consumer and merchant visual service; Described after-sales service module comprises visual unit, replacement refund unit , an evaluation collection unit and a gift compensation unit; the visual unit is used for after-sales exchanges between consumers and merchants; the replacement and refund unit allows consumers to exchange and refund products with merchants; the evaluation collection unit collects consumer The evaluation value of the product; the gift compensation unit is used to calculate the additional compensation gift amount and the gift corresponding to the amount; the specific calculation steps of the gift compensation unit are as follows:

   Step 1: Set the evaluation value of the consumer as Ni; the price corresponding to the price of the purchased product Ai is recorded as C _i ;

   Step 2: Set default values for Ni and C _i ; set the default value of Ni as g1; set the default value of C _i as g2;

   Step 3: Use the formula PF _i =N _i *g1+C _i *g2 to obtain the payout value PF _i ;

   Step 4: Set the compensation gift as R _i , (i=1...n); the price range corresponding to the compensation gift R _i is (f _i-1 , f _i ];

   Step 5: The compensation value PF _i is matched with the price range corresponding to the compensation gift R _i ; specifically, when f ₅ <PF ₁ ≤ f ₆ , then the compensation gift R ₆ is output.

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