WO2022262001A1 - Système de gestion du marketing basé sur des mégadonnées internet - Google Patents

Système de gestion du marketing basé sur des mégadonnées internet Download PDF

Info

Publication number
WO2022262001A1
WO2022262001A1 PCT/CN2021/102092 CN2021102092W WO2022262001A1 WO 2022262001 A1 WO2022262001 A1 WO 2022262001A1 CN 2021102092 W CN2021102092 W CN 2021102092W WO 2022262001 A1 WO2022262001 A1 WO 2022262001A1
Authority
WO
WIPO (PCT)
Prior art keywords
value
module
consumer
members
unit
Prior art date
Application number
PCT/CN2021/102092
Other languages
English (en)
Chinese (zh)
Inventor
朱恒彬
马燕玲
Original Assignee
仙居县威马信息科技有限公司
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by 仙居县威马信息科技有限公司 filed Critical 仙居县威马信息科技有限公司
Publication of WO2022262001A1 publication Critical patent/WO2022262001A1/fr

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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2462—Approximate or statistical queries
    • 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
    • G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
    • G06Q30/0226—Incentive systems for frequent usage, e.g. frequent flyer miles programs or point systems

Definitions

  • the invention relates to the field of marketing technology, in particular to a marketing management system based on Internet big data.
  • 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.
  • the purpose of the present invention is to provide a marketing management system based on Internet big data.
  • 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 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 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
  • 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:
  • 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:
  • 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:
  • the maintenance unit is used to send short messages, voice calls and return visits to members.
  • 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 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.
  • 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;
  • Fig. 1 is a functional block diagram of a marketing management system based on Internet big data according to the present invention.
  • 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 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 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
  • 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:
  • 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:
  • 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:
  • 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 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;
  • 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;

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Finance (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Physics & Mathematics (AREA)
  • Marketing (AREA)
  • Economics (AREA)
  • General Business, Economics & Management (AREA)
  • Game Theory and Decision Science (AREA)
  • Data Mining & Analysis (AREA)
  • Probability & Statistics with Applications (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Computational Linguistics (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

La présente invention concerne un système de gestion de marketing basé sur les mégadonnées Internet, utilisé pour résoudre le problème de savoir comment sélectionner rationnellement les consommateurs, comment calculer avec précision les niveaux de membres, et comment retenir les membres réellement actifs et améliorer le service après-vente ; comprenant un module de collecte de données, un processeur, un module de stockage, un module d'analyse, un module de priorité, un module de marketing, une base de données de membres, un module de gestion, et un module de service après-vente ; sur la base des consommateurs filtrés avec une valeur de filtrage Mi d'une taille raisonnable, le système de gestion du marketing sélectionne des consommateurs préférés comme cible pour faciliter un meilleur marketing des produits ; en utilisant la formule (aa) pour acquérir un degré d'engagement des membres Di ; au moyen de la taille du degré d'engagement des membres Di, déterminer le degré d'engagement des membres ; en utilisant la formule Fi = MHi*w1+Di*w2 pour acquérir une valeur de niveau Fi ; au moyen de la taille de la valeur de niveau Fi, déterminer le niveau des membres pour ainsi allouer rationnellement les membres ; au moyen de la valeur de perte LSi, déterminer le degré de fidélisation du consommateur ; et ensuite nettoyer les membres pour ainsi retenir les vrais membres.
PCT/CN2021/102092 2021-06-15 2021-06-24 Système de gestion du marketing basé sur des mégadonnées internet WO2022262001A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110659142.9A CN113592525A (zh) 2021-06-15 2021-06-15 一种基于互联网大数据的营销管理系统
CN202110659142.9 2021-06-15

Publications (1)

Publication Number Publication Date
WO2022262001A1 true WO2022262001A1 (fr) 2022-12-22

Family

ID=78243738

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/102092 WO2022262001A1 (fr) 2021-06-15 2021-06-24 Système de gestion du marketing basé sur des mégadonnées internet

Country Status (2)

Country Link
CN (1) CN113592525A (fr)
WO (1) WO2022262001A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116089401A (zh) * 2023-02-17 2023-05-09 国网浙江省电力有限公司营销服务中心 用户数据管理方法及系统
CN117611349A (zh) * 2023-07-04 2024-02-27 交通运输部水运科学研究所 基于云平台的港区企业集群式安全责任险定价方法及系统

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101287970B1 (ko) * 2013-02-18 2013-07-19 주식회사 스마트싸다넷 지역 기반 통합 마케팅과 회원 관리시스템 및 관리방법
CN107833063A (zh) * 2017-11-30 2018-03-23 上海科瓴医疗科技有限公司 药店会员流失预警和智能干预系统及方法
CN109345306A (zh) * 2018-09-28 2019-02-15 中国平安财产保险股份有限公司 会员积分数据处理方法、装置、计算机设备及存储介质
CN110264238A (zh) * 2019-05-09 2019-09-20 浙江华坤道威数据科技有限公司 一种基于大数据的可视化营销管理系统
CN111861571A (zh) * 2020-07-24 2020-10-30 海南中金德航科技股份有限公司 会员管理系统
CN112150216A (zh) * 2020-10-21 2020-12-29 深圳市思迅软件股份有限公司 基于大数据的门店会员营销服务系统

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101464983A (zh) * 2007-12-18 2009-06-24 汤溪蔚 一种电子商务应用方法及其系统
CN110335093B (zh) * 2019-07-16 2022-11-25 湖南科技学院 一种基于云计算服务的会员管理系统
CN111047390A (zh) * 2019-11-08 2020-04-21 涿州市新合作网络科技有限公司 一种云供销综合性一体化o2o电商平台
CN110956508A (zh) * 2019-12-18 2020-04-03 杭州桐硕教育科技有限公司 大数据管理方法及大数据管理系统
CN112465520A (zh) * 2020-11-18 2021-03-09 马鞍山因特莱信息科技有限公司 一种基于人工智能和互联网的售后服务系统
CN112767005A (zh) * 2020-12-25 2021-05-07 南京欣网互联网络科技有限公司 一种基于数据库的会员合理分配权益管理系统

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101287970B1 (ko) * 2013-02-18 2013-07-19 주식회사 스마트싸다넷 지역 기반 통합 마케팅과 회원 관리시스템 및 관리방법
CN107833063A (zh) * 2017-11-30 2018-03-23 上海科瓴医疗科技有限公司 药店会员流失预警和智能干预系统及方法
CN109345306A (zh) * 2018-09-28 2019-02-15 中国平安财产保险股份有限公司 会员积分数据处理方法、装置、计算机设备及存储介质
CN110264238A (zh) * 2019-05-09 2019-09-20 浙江华坤道威数据科技有限公司 一种基于大数据的可视化营销管理系统
CN111861571A (zh) * 2020-07-24 2020-10-30 海南中金德航科技股份有限公司 会员管理系统
CN112150216A (zh) * 2020-10-21 2020-12-29 深圳市思迅软件股份有限公司 基于大数据的门店会员营销服务系统

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116089401A (zh) * 2023-02-17 2023-05-09 国网浙江省电力有限公司营销服务中心 用户数据管理方法及系统
CN116089401B (zh) * 2023-02-17 2023-09-05 国网浙江省电力有限公司营销服务中心 用户数据管理方法及系统
CN117611349A (zh) * 2023-07-04 2024-02-27 交通运输部水运科学研究所 基于云平台的港区企业集群式安全责任险定价方法及系统

Also Published As

Publication number Publication date
CN113592525A (zh) 2021-11-02

Similar Documents

Publication Publication Date Title
Rajgopal et al. The value relevance of network advantages: The case of e–commerce firms
US7664693B1 (en) Financial methodology for the examination and explanation of spread between analyst targets and market share prices
WO2022262001A1 (fr) Système de gestion du marketing basé sur des mégadonnées internet
KR et al. Intellectual Capital: Concept and its Measurement.
JP2009524894A (ja) インターネット広告メディア市場を運営するとともに、その市場で成立した取引に基づく広告を配信するシステム及び方法
Srinivasan et al. Branding and firm value
TW200903369A (en) A managing system for advertisement
Galambos et al. Making sense out of dollars: economic analysis for local government
Sibanda et al. Customer relationship management as a customer retention tool: A case study of OK Zimbabwe Limited
Cai et al. TikTok’s advertising policy in the Chinese market and impacts on China’s Internet economic advertising market
Nida et al. Analysis of the Impact of Tax Imposition on Electronic Transactions
Meidan Marketing strategies, organisation and performance control in insurance
Balocco et al. Lemonads: Impression quality in programmatic advertising
Bennett et al. Management of unprofitable donors by UK fundraising charities
Temponi et al. Factors impacting participation of Hispanic small businesses in government contracting in the USA
CN106557946A (zh) 适用于分散式多点营销网络的收益管理系统
Rudawska The influence of customer relationships on the market value of a bank–a methodological perspective
Sitorus et al. Business model improvement: A study case of channeling through e-commerce
CN109858824A (zh) 一种企业估值方法及系统
CN110442794A (zh) 一种分析师画像系统的构建方法
Tan et al. Globalization and trends in international marketing research in Asia
Li et al. Advertisements on Knowledge Payment Platforms: A Game Theoretic Analysis of Pricing Strategies
Hudson et al. Daily Hedonic Price Analysis in Cotton: An Alternative Approach for Providing Market Information
Pavlychenko TRENDS OF INTERNET MARKETING DEVELOPMENT
ZHANG TRAVEL E-COMMERCE MARKETING STRATEGY OF CTRIP IN TRAVEL ONLINE MARKETPLACE

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21945584

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21945584

Country of ref document: EP

Kind code of ref document: A1