Container asset precision marketing method and system

Abstract

The present application relates to a kind of container assets precision marketing method and system, the method is based on the original method acquisition basis data, increase the online order of customer in trade platform, online search data, and utilize the customer information data builds a bottom database;And according to customer data information attribute constructs a new unified customer label system, to realize the precision group of customer, and customer group and individual insight;In addition, through data interface, label and crowd package are provided to other data products to realize risk control management, marketing management and trade platform management and other customer label scene applications.The present application can help container leasing enterprise to comprehensively understand customer data through data analysis, while realizing the precision analysis of specific customer, to effectively propose marketing scheme and improve customer activity, at the same time, can assist container leasing enterprise to identify and control risk, formulate personalized marketing strategy suitable for different customer groups, realize precision marketing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of container management and informationization construction, and particularly relates to a container asset precise marketing method and system. BACKGROUND

[0002] The current society gradually enters the big data era, how to mine useful information for enterprise marketing from huge customer information data has become a problem that needs to be solved in the current container leasing industry, and the concept of container asset precise marketing arises at the historic moment. Container leasing enterprises need to master more comprehensive customer data, only by comprehensively understanding customers can more personalized marketing be realized, and for this reason, customer tags are applied to enterprise marketing. At present, in the industry, container leasing enterprises generally face the following problems when using and managing tags, 1. incomplete data collection and poor data quality; 2. incomplete customer tag system and insufficient control; 3. due to data loss and incomplete tag system, customer data analysis cannot be fully supported, it is difficult to design and build marketing scenarios that fit customer experience, and it is difficult to realize personalized precise marketing. SUMMARY

[0003] In order to solve the problems that container leasing enterprises face in the process of using and managing tags for container asset marketing, such as incomplete data collection, no unified customer tag system, and thus difficult data analysis and inaccurate customer insight, the present application provides a container asset precise marketing method and system, which adds online order and online search data of customers on the trade platform to the original collected basic data, and forms a unified customer tag system to assist enterprises in accurately positioning customer groups, relying on a relatively complete customer tag system to improve marketing scenario construction capability and realize true precise marketing.

[0004] The technical solution of the present application is as follows:

[0005] A container asset precise marketing method, comprising the following steps:

[0006] S1: bottom layer data collection step: obtaining customer information data meeting business needs from a data source and using the same to build a bottom layer database; the customer information data includes customer basic data, order data, trade platform online order data, and customer payment data;

[0007] S2: tag system construction step: establishing customer attributes, classifying customer information data in the bottom layer database of S1 step based on different customer attributes, and generating customer tags respectively corresponding to different customer attributes to form a unified customer tag system; the customer attributes include customer basic attributes, sell box transaction attributes, sell box preference attributes, trade platform attributes, risk control and financial attributes, and customer life cycle;

[0008] S3: The customer label management function implementation step: store the customer information data of S1 step and the customer label system of S2 step as customer management data in the label management database, and analyze the customer management data through customer segmentation, customer insight and customer label management, and form a corresponding label people package according to the segmentation label in the customer segmentation;

[0009] S31: The customer segmentation step: including creating segmentation and segmentation management, the creating segmentation includes filling in the segmentation name and description to form the segmentation label, and selecting or combining the attributes and event fields in the customer management data list according to the segmentation label to form a new segmentation list; the segmentation management is to display and modify the customer management data in the segmentation list, and the information displayed in the segmentation list also includes group name, number of customers in each group, group creator and group creation time;

[0010] S32: The customer insight step: comprehensively analyze the customer management data through customer individual insight and customer group insight, the customer individual insight is to generate the customer portrait of each customer based on the customer management data through conditional filtering, and the customer portrait can be displayed through the basic attribute subpage, the customer score subpage and the customer label subpage; the important top label displayed in the customer label subpage includes the sell box transaction attribute, the sell box preference attribute and the trade platform attribute; the customer group insight is based on the group created in the customer segmentation step S31, and the customer number and customer proportion of each label dimension under the analysis people group are displayed in the form of a chart, and the customer data can be analyzed by selecting a group or multiple groups; the label dimension includes purchase preference box type, preference box condition, preference port, customer life cycle, credit risk assessment, price acceptance, box picking days, box picking rate, payment days, order cancellation rate, industry and price;

[0011] S33: The customer label management step displays the customer number and proportion of each sub-dimension label under different label dimensions through a pie chart based on the customer management data, and the label dimension includes the customer's purchase preference box type and port;

[0012] Preferably, the corresponding label people package in S3 is provided to other data products through a data interface to realize risk control management application, marketing management application and trade platform application;

[0013] S1: the risk control management application step is: based on the label system to form 5 evaluation ranges, the evaluation ranges include customer background, order performance, box picking performance, payment performance and sales price factors; based on the evaluation range, the label system generates 10 score items for customer scoring, the proportion of each score item in the label system is evenly distributed, which is 10%; then generate customer credit rating according to the customer score to realize automatic credit, that is, automatically grant customer credit limit; the method of customer score is to generate customer total score by weighting the effective score and proportion of each score item; the 10 score items include five score items in the customer background, including establishment period, transaction history, income contribution ratio, transaction frequency and customer stickiness, order execution rate in order performance, box picking time limit in box picking performance, payment punctuality rate, average accounts receivable turnover days in payment performance, and premium index in sales price factors;

[0014] S2: the marketing management application step is: screening the positioning target customer group from the corresponding label of the people package in S3, and realizing different marketing management of different people packages by analyzing the customer management data in the people package; the customer management data in the people package includes purchase preference box type, box condition, customer price acceptance and time interval from the last order time to the current time;

[0015] S3: the trade platform application step is: based on customer label, different marketing measures are taken for different customers on the trade platform, the marketing measures include: according to customer preference, the recommended top is set, different coupons or different integral return mechanism are set for different customer labels, message notification is used to remind customers to pay as soon as possible or take corresponding measures such as credit rating adjustment and corresponding measures for customers who do not pay or delay payment, and marketing center is built; the marketing center includes customer insight, formulating marketing strategy scheme, post-event activity effect analysis, label rewriting activity improvement.

[0016] Preferably, the customer attribute corresponding label in the S2 step specifically includes:

[0017] The customer basic attribute corresponding label includes: customer name, customer industry, customer data creation time and creation day distance from today, customer classification, customer sales assistant, whether it is self yard, whether it is a distributor or a user; the customer classification includes rental box customer and sell box customer;

[0018] The sell box transaction attribute corresponding label includes: first order date and distance from today, last order date and distance from today, total order quantity, total transaction box quantity, total transaction amount, single box transaction amount, price acceptance, average box picking days, average box picking rate, average payment days, average order cancellation rate in each time period;

[0019] The sell box preference attribute corresponds to a label, including: historical and recent purchase preference box type, box condition, box grade and port in each time period in three scenarios of historical order, trade platform online order and trade platform search, and proportion of trade platform online order in total order, proportion of trade platform online transaction box quantity in total transaction box quantity;

[0020] The trade platform attribute corresponds to a label, including: customer user ID on the trade platform, whether registered on the trade platform and date, whether passed the trade platform audit and date, first active date and number of days from today, last active date and number of days from today, trade platform first order date and number of days from today, trade platform last order date and number of days from today, historical and recent login times in each time period, search times, add-to-cart times, order creation times, trade platform online order times, trade platform online transaction box quantity, trade platform online transaction amount, trade platform online single-box transaction amount, trade platform online order coupon or integral order discount proportion, trade platform online order coupon or integral amount subsidy rate, historical total acquired points, currently available points, historical used points, historical expired points, historical total acquired coupon quantity, currently available coupon quantity, historical used coupon quantity, historical expired coupon quantity, historical total acquired coupon face value, currently available coupon face value, historical used coupon face value, historical expired coupon face value;

[0021] The risk control and financial attribute corresponds to a label, including: payment type is cash payment or credit limit payment, credit limit, establishment period and score, transaction history and score, income contribution ratio and score, transaction frequency and score, customer stickiness and score, order execution rate and score, box pickup time and score, payment punctuality rate and score, average accounts receivable turnover days and score, premium index and score, etc.; wherein the order execution rate is the average order actual box pickup rate, the box pickup time is the average number of days required from order confirmation to box pickup, the payment punctuality rate is the proportion of the number of bills paid on time, the average accounts receivable turnover days is the average number of days from bill issuance to payment, and the premium index is the ratio of the customer's historical average sell box price to the company's guide price;

[0022] The customer life cycle corresponds to a label, including: potential customer, growing customer, mature customer, silent customer, lost customer.

[0023] Preferably, the method for selecting the customer lifecycle corresponding label is: checking whether the customer has placed an order in the past 6 months, if not, the customer is a lost customer; otherwise, checking whether the customer has placed an order in the past 3 months, if not, the customer is a silent customer; otherwise, checking whether the customer has placed an order in the past 1 month, if not, the customer is a potential customer; otherwise, checking the number of orders placed by the customer in the past 1 month, if only one order has been placed, the customer is a growth stage customer, if two or more orders have been placed, the customer is a mature stage customer.

[0024] Preferably, in the S31 step, editing groups, deleting groups, and updating group data operations can also be performed in the list; all customer information in the group can also be viewed by clicking on the number of customers in the list, including customer name and customer data with the group specified label, clicking on editing can modify group basic information and grouping rules, and clicking on updating can query data in real time and update customer data of the group.

[0025] Preferably, the customer group insight in the S32 step also includes statistics on the distribution of customer lifecycle, including displaying the number of customers of each lifecycle type and the trend of change in each month in the past 6 months from the time dimension, and setting a message reminder, according to the customer lifecycle, when the customer changes from a silent customer to a lost customer, a message reminder is displayed in the message reminder list, the message reminder list includes historical message reminder data in the past month.

[0026] Preferably, in the step of implementing the risk control management application, the method for scoring each evaluation range into 10 score items for customer scoring is: customer scoring and its judgment indicators specifically include: based on five types of evaluation ranges of customer background, order performance, box performance, payment performance, and sales price factors, 10 score items are used for customer scoring, each score item accounts for 10%, according to the effective score of each score item and the classification of the evaluation range it belongs to, the score of each range is displayed in the form of a radar chart, the total customer score is generated by weighting according to the effective score of each score item and the proportion, and the customer credit rating is generated according to the total customer score; wherein the score of each range is obtained by averaging the sum of the effective scores of the score items belonging to the range, the total customer score is obtained by weighting the sum of the effective scores of all score items according to the proportion, the customer credit rating is divided into five levels from 1 to 5 based on customer score, the higher the score, the higher the credit rating, and the higher the risk.

[0027] Preferably, in the risk control management application step, the method of generating a customer credit rating according to a customer score to realize automatic credit is: based on the customer credit rating, for the credit customers with payment type of credit limit payment and the cash customers with payment type of cash payment, respectively adopting the corresponding credit limit adjustment mechanism, giving the limit adjustment suggestion based on the existing credit limit of the customer, sending the adjustment information to the relevant business personnel, and after the audit is passed, the system automatically adjusts the limit.

[0028] Preferably, in the marketing management application step, the method of performing different marketing management on different customer groups is: based on the classification basis of customer life cycle, setting different business directions for customers in different life cycles, including: promoting the first transaction for potential customers; for the growing period customers who place orders for the first time and the orders are completed, promoting the repeat purchase; for the mature period customers who continuously place orders and the orders are completed, maintaining the customer relationship, keeping the customer stickiness and order frequency; for the silent customers and the lost customers, carrying out the touch and recall, encouraging them to place orders again, activating the silent customers and retaining the lost customers.

[0029] A container asset precise marketing system, comprising:

[0030] A bottom data collection module: customer information data collected from a data source of a company asset management system and a bottom database formed thereby; the customer information data includes customer basic data, order data, trade platform online order data, and customer payment data;

[0031] A label system construction module: including customer attributes and customer labels corresponding to the attributes generated by classifying the customer information data with different attributes, and a unified customer label system formed thereby; the customer attributes include: customer basic attributes, sell-box transaction attributes, sell-box preference attributes, trade platform attributes, risk control and financial attributes, and customer life cycle;

[0032] A customer label management function implementation module: including a label management database, a customer grouping unit, a customer insight unit, and a customer label management unit; the label management database includes customer management data composed of both the customer information data of the bottom data collection module and the customer label system of the label system construction module; the customer grouping unit includes a creation grouping module forming a grouping label and a grouping list, and a management grouping module managing the grouping through the grouping list, the creation grouping module further includes a customer group package formed according to the corresponding label of the grouping label; the customer insight unit includes a customer individual insight module generating a customer portrait and realizing customer individual insight based on the customer portrait, and a customer group insight module displaying and analyzing the number of customers and the proportion of customers in each label dimension of the analysis customer group in the form of a chart based on the grouping list; the customer label management unit includes a pie chart displaying the number of customers and the proportion of customers in each sub-dimension label under different label dimensions.

[0033] Scenario application module: including a data interface receiving the crowd package corresponding to the label in the creation clustering module, and a risk control management unit using the crowd package to perform customer credit rating and automatic credit granting on customer scores; a marketing management unit using the crowd package to develop marketing strategies for customer life cycle partitions, and a trade platform unit using the crowd package to take different marketing measures for different customers on the trade platform.

[0034] Preferably, the label corresponding to the customer attribute in the label system construction module specifically includes:

[0035] The label corresponding to the customer basic attribute includes: customer name, customer industry, customer profile creation time and number of days from today, customer classification, customer sales assistant, whether it is a self-owned yard, whether it is a distributor or a user; the customer classification includes a rent box customer and a sell box customer;

[0036] The label corresponding to the sell box transaction attribute includes: first order date and number of days from today, most recent order date and number of days from today, total order quantity, total transaction box quantity, total transaction amount, single box transaction amount, price acceptance, average box pickup days, average box pickup rate, average payment days, and average order cancellation rate in each time period;

[0037] The label corresponding to the sell box preference attribute includes: purchase preference box type, box condition, box grade, and port in each time period in the history and the most recent in the history of the three scenarios of historical orders, trade platform online orders, and trade platform searches, and the proportion of trade platform online orders in total orders, and the proportion of trade platform online transaction box quantity in total transaction box quantity;

[0038] The label corresponding to the trade platform attribute includes: customer user ID on the trade platform, whether it is registered on the trade platform and the date, whether it is audited on the trade platform and the date, first active date and number of days from today, most recent active date and number of days from today, trade platform first order date and number of days from today, trade platform most recent order date and number of days from today, login times, search times, add-to-cart times, order creation times, trade platform online order quantity, trade platform online transaction box quantity, trade platform online transaction amount, trade platform online single box transaction amount, trade platform online order coupon or integral order discount proportion, trade platform online order coupon or integral amount subsidy rate in each time period, and historical total acquired points, currently available points, historical used points, historical expired points, historical total acquired coupon quantity, currently available coupon quantity, historical used coupon quantity, historical expired coupon quantity, historical total acquired coupon face value, currently available coupon face value, historical used coupon face value, and historical expired coupon face value;

[0039] The risk control and financial attribute corresponding labels include: payment type is cash payment or credit limit payment, credit limit, establishment year and score, transaction history and score, income contribution ratio and score, transaction frequency and score, customer stickiness and score, order execution rate and score, box picking time limit and score, payment punctuality rate and score, average accounts receivable turnover days and score, premium index and score, etc.; wherein the order execution rate is the average order actual box picking ratio, the box picking time limit is the average number of days required from order confirmation to box picking, the payment punctuality rate is the proportion of the number of bills paid on time, the average accounts receivable turnover days are the average number of days from bill issuance to payment, and the premium index is the ratio of the average historical selling box price of the customer to the company's guide price;

[0040] The customer life cycle corresponding label includes: potential customer, growth period customer, mature period customer, silent customer and lost customer.

[0041] Preferably, the information displayed in the group list by the group management module further includes group name, number of customers in each group, group creator and group creation time.

[0042] Preferably, the risk control management unit includes a customer scoring module, a customer credit rating module and an automatic credit granting module, the customer scoring module scores the customer according to five types of evaluation ranges including customer background, order performance, box picking performance, payment performance and selling price factors, generates a total customer score according to the effective score and proportion of each score item, the customer credit rating module performs customer credit rating and judges the customer risk category according to the total customer score and customer credit rating rules, and the automatic credit granting module grants the customer a credit limit, i.e. automatic credit, according to the customer risk category.

[0043] Preferably, in the marketing management unit, the marketing strategies formulated according to the customer life cycle zones specifically include: promoting first transaction for potential customers; promoting repeat purchase for growth period customers who have placed orders and completed transactions; maintaining customer relationship, keeping customer stickiness and order frequency for mature period customers who have continuously placed orders and completed transactions; reaching out to and recalling silent customers and lost customers, encouraging them to place orders again, activating silent customers and retaining lost customers.

[0044] The technical effects of the present application are as follows:

[0045] The application provides a container asset precise marketing method. Specifically, in the S1 bottom layer data collection step, the application adds online order and online search data of customers on a trade platform to the original method based data, and builds a bottom layer database using the customer information data; in the S2 label system construction step, the application constructs a new unified customer label system according to customer data information attributes, and realizes precise group division of customers and customer group and individual insight in the S3 customer label management function implementation step; meanwhile, the application realizes risk control management, marketing management and trade platform management of the company to customers by using the crowd package in the S3 customer label management function implementation step, and can comprehensively and multidimensionally display customer data and give business customer data analysis support.

[0046] The application also provides a container asset precise marketing system corresponding to the container asset precise marketing method of the application, which can be understood as a system for realizing the container asset precise marketing method, and is essentially a background or server, including a bottom layer data collection module, a label system construction module, a customer label management function implementation module and a scene application module, which work cooperatively, can realize customer comprehensive insight and customer label management based on customer data and the label system, and are helpful to realize risk control management, marketing management and trade platform management. The container asset precise marketing system of the application is mainly applied to container leasing enterprises, and can assist the container leasing enterprises in identifying and controlling risks, formulating personalized marketing strategies and realizing precise marketing through deepening application of research results. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 It is a flowchart of a container asset precise marketing method.

[0048] Figure 2 It is a flowchart of six customer attributes and main label optimization of the application.

[0049] Figure 3 It is a flowchart of customer life cycle type judgment optimization of the application.

[0050] Figure 4 It is a flowchart of customer score and its judgment index optimization of the application.

[0051] Figure 5 It is a flowchart of customer score radar chart optimization of the application.

[0052] Figure 6 It is a flowchart of credit limit adjustment mechanism based on customer rating of the application.

[0053] Figure 7 It is a customer portrait page display generated in the customer insight step of the application.

[0054] Figure 8 is a customer insight step customer group insight page display of the application.

[0055] Figure 9 is a container asset precision marketing system. DETAILED DESCRIPTION

[0056] The application will be described below in conjunction with the drawings.

[0057] The application provides a container asset precision marketing method, and a flowchart of the method is shown in Figure 1 , comprising the following steps:

[0058] S1: bottom layer data collection step: obtaining customer information data meeting business needs from a data source and using the same to build a bottom layer database; the customer information data includes customer basic data, order data, trade platform online order data, and customer payment data;

[0059] S2: label system construction step: establishing customer attributes, and classifying the customer information data in the bottom layer database of step S1 based on different customer attributes and generating customer labels respectively corresponding to different customer attributes to form a unified customer label system; the customer attributes include customer basic attributes, sell box transaction attributes, sell box preference attributes, trade platform attributes, risk control and financial attributes, and customer life cycle;

[0060] S3: customer label management function implementation step: storing the customer information data of step S1 and the customer label system of step S2 as customer management data in a label management database, and analyzing the customer management data through customer segmentation, customer insight, and customer label management and forming a people package corresponding to the label according to the segmentation label in the customer segmentation;

[0061] S31: the customer segmentation step: including creating a segmentation and segmentation management, the creating a segmentation includes filling in a segmentation name and description to form a segmentation label, and selecting or combining attributes and event fields in the customer management data list to form a new segmentation list according to the segmentation label; the segmentation management is to display and modify the customer management data in the segmentation list, and the information displayed in the segmentation list by the segmentation management further includes a group name, a customer number of each group, a group creator, and a group creation time;

[0062] S32: the customer insight step: comprehensively analyzing the customer management data through customer individual insight and customer group insight, the customer individual insight is to generate a customer portrait of each customer based on the customer management data through conditional filtering, such as Figure 7As shown, the customer portrait can be displayed through the basic attribute subpage, customer score subpage, and customer label subpage; the important top labels displayed on the customer label subpage include sell box transaction attributes, sell box preference attributes, and trade platform attributes; as shown Figure 8 As shown, the customer group insight is a group created based on the S31 customer grouping step, and the customer quantity and customer proportion of each label dimension under the analysis population are displayed in the form of a chart. One group or multiple groups can be selected to analyze customer data; the label dimensions include purchase preference box type, preference box condition, preference port, customer life cycle, credit risk assessment, price acceptance, box pickup days, box pickup rate, payment days, order cancellation rate, industry, and price.

[0063] S33: The customer label management step displays the customer quantity and proportion of each sub-dimension label under different label dimensions through a pie chart based on the customer management data, and the label dimensions include the purchase preference box type and port of the customer.

[0064] Preferably, the data source can be the asset management system of the Floren Company.

[0065] Specifically, in the S2 step, six types of customer attributes and main labels are as shown Figure 2

[0066] The customer basic attribute is a classification of the customer according to the basic information data of the customer, including the following labels: customer name, customer industry, customer data creation time and creation day distance from today, customer classification as a rent box or sell box customer, customer sales assistant, whether it is a self-owned yard, whether it is a distributor or a user, etc.

[0067] The sell box transaction attribute is a classification of the customer according to the buy box transaction data of the customer, including the following labels: first order date and distance from today, most recent order date and distance from today, total order quantity, total transaction box quantity, total transaction amount, single box transaction amount, price acceptance, average box pickup days, average box pickup rate, average payment days, average order cancellation rate, etc.

[0068] Specifically, the customer order data observation period is divided into all history, year to date, month to date, past 90 days, past 180 days, and past 365 days.

[0069] ​Specifically, the total order quantity is the total quantity of orders of the customer in a certain time period; the total transaction container quantity is the sum of the container quantities of all orders of the customer in a certain time period; the total transaction amount is the sum of the transaction amounts of all orders of the customer in a certain time period, wherein the transaction amount of each order = SUM of all port container type pairs (the price of a container of a certain port container type pair in the current order * the container quantity of the container of the same port container type pair in the current order); the single container transaction amount = the total transaction amount of the customer in a certain time period / the total transaction container quantity of the customer in the same time period; the price acceptance = the total transaction amount calculated according to the price of the current order / the total transaction amount calculated according to the average price of all orders of all customers, wherein the total transaction amount calculated according to the price of the current order = [SUM of all port container type pairs (the price of a container of a certain port container type pair in the current order * the container quantity of the container of the same port container type pair in the current order)], the total transaction amount calculated according to the average price of all orders of all customers = [SUM of all port container type pairs (the average price of a container of the same port container type pair corresponding to the current order in all orders of all customers * the container quantity of the container of the same port container type pair in the current order)]; the average container pick-up day = the total container pick-up day of the customer in a certain time period / the total container pick-up quantity of the customer in the same time period, wherein the container pick-up day = the container pick-up date - the order placement date; the average container pick-up rate = the order container pick-up quantity of the customer in a certain time period / the total order container quantity of the customer in the same time period; the average payment day = the total payment day of the customer in a certain time period / the total container pick-up quantity of the customer in the same time period, wherein the payment day = the payment date - the order placement date; the order cancellation rate = the cancelled order quantity of the customer in a certain time period / the total order quantity of the customer in the same time period;

[0070] The container selling preference attribute is a classification of the customer according to the purchase preference data of the customer, including the following labels: the purchase preference container type, the purchase preference container condition, the purchase preference container grade and the port in the historical and recent time periods in the three scenarios of historical orders, trade platform online orders and trade platform searches, and the proportion of trade platform online orders in total orders and the proportion of trade platform online transaction container quantity in total transaction container quantity; specifically, the container condition is for the availability of the container, and the container grade is for the newness of the container; specifically, taking the historical all-order purchase preference container type as an example, the number of containers of each container type in the historical all orders of the customer and the proportion of the number of containers in all container numbers are calculated, and the container type with the largest number proportion is the historical order preference container type of the customer.

[0071] The trade platform attribute is a classification of the customer according to the data of the customer's registration and order on the trade platform, including the following labels: the customer's user ID on the trade platform, whether the customer registers the trade platform and the date, whether the customer passes the trade platform audit and the date, the first active date and the number of days from today, the last active date and the number of days from today, the first order date on the trade platform and the number of days from today, the last order date on the trade platform and the number of days from today, the number of logins in each time period, the number of searches, the number of adds, the number of order creations, the number of online orders on the trade platform, the number of online transaction boxes on the trade platform, the online transaction amount on the trade platform, the online single-box transaction amount on the trade platform, the order discount ratio using coupons or points on the trade platform, the coupon or points amount subsidy rate using coupons or points on the trade platform, the total number of points obtained in history, the number of points available for use, the number of points used in history, the number of expired points in history, the total number of coupons obtained in history, the number of coupons available for use, the number of coupons used in history, the number of expired coupons in history, the total face value of coupons obtained in history, the face value of coupons available for use, the face value of coupons used in history, and the face value of expired coupons in history, etc.

[0072] Specifically, taking the use of coupons as an example, the trade platform online order discount ratio using coupons = the number of orders using coupons in the trade platform online orders of the customer in a certain time period / the total number of trade platform online orders of the customer in the same time period; the trade platform online order coupon amount subsidy rate = the subsidy amount using coupons in the trade platform online orders of the customer in a certain time period / the total amount of trade platform online orders of the customer in the same time period;

[0073] The risk control and financial attribute is a classification of the customer according to the customer's various transaction data and payment methods, including the following labels: payment type is cash payment or credit limit payment, credit limit, establishment period and score, transaction history and score, income contribution ratio and score, transaction frequency and score, customer stickiness and score, order execution rate and score, order pickup time and score, payment on time rate and score, average accounts receivable turnover days and score, premium index and score, etc. Specifically, the order execution rate is the average order actual pickup rate, the order pickup time is the average number of days required from order confirmation to pickup, the payment on time rate is the proportion of the number of bills paid on time, the average accounts receivable turnover days is the average number of days from bill issuance to payment, and the premium index is the ratio of the customer's historical average sell box price to the company's guide price;

[0074] The customer life cycle is a classification of the customer according to the customer's order transaction volume in the past period, including the following five types of labels: potential customer, growth period customer, mature period customer, silent customer, and lost customer. Specifically, the customer life cycle judgment process is as follows Figure 3The customer is a lost customer if no order is placed in the past 6 months, a silent customer if no order is placed in the past 3 months, a potential customer if no order is placed in the past 1 month, a growing customer if only one order is placed in the past 1 month, and a mature customer if two or more orders are placed in the past 1 month.

[0075] Specifically, in the S31 step, the selection or combination of the label rules during the creation of the customer group includes: the region in the customer basic attribute is the Asia-Pacific region, the registration of the trade platform in the trade platform attribute is yes, the historical order number in the sell box transaction attribute is greater than 500, and the order number in the sell box transaction attribute in the past 180 days is equal to 0. After the rule is formulated and submitted, the customers meeting the conditions are queried from the database, i.e. the customers in the Asia-Pacific region who have registered the trade platform, have a historical order number exceeding 500, but have not placed an order in the past 180 days. All customer data of the group is displayed in a list form, including the customer name and the corresponding data of the specified label of the group.

[0076] Specifically, the customer insight step is a comprehensive analysis of customer data, including customer individual insight and customer group insight. Based on the customer data collected in the S1 step and the customer label system in the S2 step, the customer portrait of each customer is generated, and all labels of each customer are displayed in the customer dimension to realize customer individual insight. The customer portrait includes customer type, customer life cycle, credit risk assessment, all labels, and important labels on top. The important labels on top are sell box transaction attribute, sell box preference attribute, and trade platform attribute. The customer group insight is an aggregated display of the data of the built customer groups. Based on the groups created in the S31 customer grouping step, the number and proportion of customers in each label dimension of the analyzed population are displayed in the form of a chart. The customer data can be analyzed by selecting a group or multiple groups. The label dimensions include purchase preference box type, preference box condition, preference port, customer life cycle, credit risk assessment, price acceptance, box pickup days, box pickup rate, payment days, order cancellation rate, industry, and price. In addition, the customer group insight also includes customer life cycle distribution statistics, which displays the number of customers of each life cycle type and the change trend in each month in the past 6 months in the time dimension, facilitating the observation of the change trend of customers of each life cycle type. Message reminders are set when a customer changes from a silent customer to a lost customer, including a message reminder list and historical message reminder data in the past month.

[0077] The method can also provide the population package corresponding to the labels in S3 to other data products to realize risk control management application, marketing management application, and trade platform application through a data interface. Specifically, the method includes:

[0078] S1: the risk control management application step is: based on the label system to form an evaluation range to regenerate the scoring items corresponding to the evaluation range to score the customers; the evaluation range includes customer background, order performance, box performance, payment performance and sales price factors; the proportion of each scoring item in the label system is evenly distributed; and the customer credit rating is generated according to the customer score to realize automatic credit, that is, automatically granting the customer credit limit; the method for scoring the customer is to generate the total customer score by weighting the effective score and the proportion of each scoring item; the scoring items include five scoring items in the customer background, such as establishment period, transaction history, income contribution ratio, transaction frequency and customer stickiness, order execution rate in order performance, box time in box performance, payment punctuality rate in payment performance, average accounts receivable turnover days, and premium index in sales price factors;

[0079] S2: the marketing management application step is: based on the corresponding label of the people package to screen out the people package positioning target customer group, and by analyzing the customer management data in the people package, different marketing management is realized for different people packages; the customer management data in the people package includes purchase preference box type, box condition, customer price acceptance and time interval from the last order time of the customer to the current period;

[0080] S3: the trade platform application step is: based on the customer label, different marketing measures are taken for different customers on the trade platform, the marketing measures include: recommending and setting up according to customer preferences, setting different coupons or different integral return mechanisms for different customer labels, reminding customers to pay as soon as possible or taking credit rating adjustment and corresponding measures for customers who do not pay or delay payment of bills, and building a marketing center; the marketing center includes customer insight, development of marketing strategy, post-event activity effect analysis, and label rewriting activity improvement.

[0081] Specifically, in the risk control management application step, the customer score and its judgment index are as shown in Figure 4 Specifically, in the risk control management application step, the customer score and its judgment index are as shown in Figure 5 The total customer score is generated by weighting the effective score of all scoring items according to the proportion, and the customer credit rating is generated according to the total customer score. Based on the customer score, the customer credit rating is divided into five levels from 1 to 5, the higher the score, the higher the credit rating, and the higher the risk.

[0082] Specifically, 10 score items are seen in the risk control and financial attribute labels, each score item has a score range of 1-5, the establishment period is scored according to the customer registration establishment period; the transaction history is scored according to the number of years of business with the customer, the number of years = current time - the time of the customer's first order; the income contribution ratio is scored according to the proportion of the customer's transaction amount in the past 12 months in the total transaction amount, the total payment amount of the customer in the past 12 months / the total revenue of the container sold by all customers in the past 12 months; the transaction frequency is the number of months with transactions in the past 12 months of the customer, the transaction frequency = the number of months with transactions in the past 12 months of the customer / 12; the customer stickiness is the number of days from the last transaction date of the customer, the customer stickiness = current time - the time of the last order of the customer; the order execution rate is the average order actual container pickup ratio, the order execution rate = total container pickup amount / total order amount; the container pickup time efficiency is the average number of days required by the customer from order confirmation to container pickup, the average number of days required for container pickup = total container pickup days / total container amount, wherein the total container pickup days are divided into total days required for container pickup for containers that have been picked up and containers that have not been picked up, for containers that have been picked up, the number of days required for container pickup = container pickup time - order time, for containers that have not been picked up, the number of days required for container pickup = current time - order time; the payment punctuality rate is the proportion of the number of bills paid on time by the customer in the past 12 months, the payment punctuality rate = the number of bills paid on time / the total number of bills, wherein whether a bill is paid on time is determined by comparing the payment date and the bill due date; the average accounts receivable turnover days is the average number of days from the bill issue date to the payment of the customer in the past 12 months, the average accounts receivable turnover days = total accounts receivable turnover days / total bill amount, wherein the accounts receivable turnover days = payment date-bill issue date; the premium index is the ratio of the average container sale price of the customer in the past to the company's guide price, only a few key container types are counted.

[0083] Specifically, in the risk control management application step, the credit limit adjustment mechanism based on the customer rating is as shown in the following table: Figure 6 Specifically, the credit limit adjustment mechanism is as follows:

[0084] If the customer has maintained a credit rating of 1 for the past three months, for existing credit customers, the existing credit limit is recommended to be increased by 20%, and the condition is met: existing limit ≤ recommended adjustment limit ≤ 120% of the highest monthly transaction amount in the past 12 months; for cash customers, the credit limit is recommended according to the highest monthly amount of the customer in the past 12 months, and the condition is met: the highest monthly amount in the past 12 months ≤ limit recommendation ≤ $200,000.

[0085] If the customer maintains a credit rating of 2 or 1 in the past three months, for existing credit customers, it is recommended to increase the existing credit limit by 10%, and the condition is: the existing limit <= the recommended adjusted limit <= 110% of the highest transaction amount in the past 12 months; for cash customers, the credit limit is recommended based on the average transaction amount of the customer in the past 12 months, and the condition is: the average transaction amount in the past 12 months <= the recommended limit <= $100,000;

[0086] If the customer maintains a credit rating of 3 in the past three months, no adjustment is recommended;

[0087] If the customer's credit rating rises or falls to 4, the customer will be listed as a watch list category number, and the system will issue a prompt message to continuously observe the customer's performance. If the payment performance is good, the existing limit will be maintained to continue business;

[0088] If the customer's credit rating rises to 5, the customer will be listed as a watch list category number, and the system will issue a prompt message to immediately freeze the customer's existing credit limit and only maintain cash transactions.

[0089] Specifically, the marketing management application step, the marketing strategy for the customer life cycle partition specifically includes: based on the classification basis of the customer life cycle, different business directions are set for customers in different life cycles, potential customers are individually reached to promote the first transaction; for the growth period customers who have placed an order for the first time and the order has been completed, the repeat purchase is promoted; for the mature period customers who have placed an order and the order has been completed, the customer relationship is maintained, and the customer stickiness and order frequency are maintained; for the silent customers and the lost customers, reach and recall are carried out, and re-ordering is encouraged to activate the silent customers and retain the lost customers.

[0090] As shown in Figure 9 The present application also provides a container asset precision marketing system, which corresponds to the container asset precision marketing method described above. It can be understood as a system for implementing the container asset precision marketing method. The system is essentially a background or server, which includes the following modules:

[0091] Bottom layer data collection module: customer information data collected from the data source of the company asset management system and the bottom layer database formed thereby; the customer information data includes customer basic data, order data, trade platform online order data, and customer payment data;

[0092] A label system construction module includes customer attributes and corresponding customer labels generated by different attribute classifications of customer information data, and a unified customer label system formed thereby; the customer attributes include customer basic attributes, sell-box transaction attributes, sell-box preference attributes, trade platform attributes, risk control and financial attributes, and customer life cycle attributes;

[0093] A customer label management function implementation module includes a label management database, a customer grouping unit, a customer insight unit, and a customer label management unit; the label management database includes customer management data composed of both customer information data of the underlying data collection module and the customer label system of the label system construction module; the customer grouping unit includes a create grouping module that forms grouping labels and grouping lists, and a manage grouping module that manages groupings through grouping lists, the create grouping module further includes a crowd package corresponding to a label formed according to the grouping label; the customer insight unit includes a customer individual insight module that generates customer portraits and realizes customer individual insight based on the customer portraits, and a customer group insight module that displays the number of customers and the proportion of customers in each label dimension of an analysis crowd in the form of a chart based on the grouping list; the customer label management unit includes a pie chart that displays the number of customers and the proportion of customers in each sub-dimension label under different label dimensions;

[0094] A scenario application module includes a data interface that receives a crowd package corresponding to a label in the create grouping module, and a risk control management unit that uses the crowd package to perform customer credit rating and automatic credit granting through customer scoring; a marketing management unit that uses the crowd package to develop marketing strategies for customer life cycle partitions, and a trade platform unit that uses the crowd package to take different marketing measures for different customers on a trade platform.

[0095] Preferably, in the label system construction module, the six types of customer attributes and corresponding labels specifically include:

[0096] The customer basic attributes are a classification of customers according to basic information data of the customers, and include the following labels: customer name, customer industry, customer profile creation time and number of days from today, whether the customer is a sell-box or a rent-box customer, customer sales assistant, whether the customer has a self-owned yard, whether the customer is a distributor or a user, etc.;

[0097] The sell-box transaction attributes are a classification of customers according to buy-box transaction data of the customers, and include the following labels: first order date and number of days from today, most recent order date and number of days from today, total order quantity, total transaction box quantity, total transaction amount, single-box transaction amount, price acceptance, average pick-up day, average pick-up rate, average payment day, and average order cancellation rate in each time period, etc.

[0098] The selling box preference attribute is a classification of customers according to their purchase preference data, including the following labels: historical and recent purchase preference box type, box condition, box grade and port in the three scenarios of historical orders, trade platform online orders and trade platform searches, and the proportion of trade platform online orders in total orders and the proportion of trade platform online transaction box volume in total transaction box volume;

[0099] The trade platform attribute is a classification of customers according to their registration and order data on the trade platform, including the following labels: customer user ID on the trade platform, whether registered on the trade platform and the date, whether passed the trade platform audit and the date, first active date and number of days from today, last active date and number of days from today, trade platform first order date and number of days from today, trade platform last order date and number of days from today, historical and recent login times, search times, add-to-cart times, order creation times, trade platform online order times, trade platform online transaction box volume, trade platform online transaction amount, trade platform online single-box transaction amount, trade platform online order coupon or points order discount proportion, trade platform online order coupon or points amount subsidy rate, historical total points, current available points, historical used points, historical expired points, historical total coupon number, current available coupon number, historical used coupon number, historical expired coupon number, historical total coupon face value, current available coupon face value, historical used coupon face value, historical expired coupon face value, etc.

[0100] The risk control and financial attribute is a classification of customers according to their transaction data and payment methods, including the following labels: payment type as cash payment or credit limit payment, credit limit, establishment period and score, transaction history and score, income contribution ratio and score, transaction frequency and score, customer stickiness and score, order execution rate and score, box pickup time and score, payment punctuality rate and score, average accounts receivable turnover days and score, premium index and score, etc.; wherein the order execution rate is the average order actual pickup rate, the box pickup time is the average number of days required from order confirmation to box pickup, the payment punctuality rate is the proportion of bills paid on time, the average accounts receivable turnover days is the average number of days from bill issuance to payment, and the premium index is the ratio of the customer's historical average selling box price to the company's guide price;

[0101] The customer life cycle is a classification of customers according to the order transaction volume of the customers in the past period of time, including the following five types of labels: potential customers, growing customers, mature customers, silent customers and lost customers. The judgment process is as follows: check whether the customer has placed an order in the past 6 months, if not, the customer is a lost customer; otherwise, check whether the customer has placed an order in the past 3 months, if not, the customer is a silent customer; otherwise, check whether the customer has placed an order in the past 1 month, if not, the customer is a potential customer; otherwise, check the number of orders placed by the customer in the past 1 month, if only one order is placed, the customer is a growing customer, and if two or more orders are placed, the customer is a mature customer.

[0102] Preferably, in the customer grouping function module, the selection or combination of label rules during the creation of the customer grouping specifically includes: the region in the customer basic attribute is the Asia-Pacific region, the trade platform attribute whether the trade platform has been registered is yes, the historical order number in the sell box transaction attribute is greater than 500, and the order number in the sell box transaction attribute in the past 180 days is equal to 0. After the rule is formulated and submitted, the customers meeting the conditions are queried from the database, i.e. the customers in the Asia-Pacific region who have registered a trade platform, have a historical order number exceeding 500 but have not placed an order in the past 180 days. All customer data of the group is displayed in the form of a list, including the customer name and the corresponding data of the specified label of the group.

[0103] Preferably, in the risk control management application module, the customer score and its judgment index specifically include: customer scoring based on five types of evaluation ranges of customer background, order performance, box delivery performance, payment performance and sales price factors, 10 score items, each score item accounts for 10%, according to the effective score of each score item and the classification of the evaluation range, the score of each range is displayed in the form of a radar chart, the total score of the customer is generated by weighting according to the effective score of each score item and the proportion, and the credit rating of the customer is generated according to the total score of the customer. Among them, the score of each range is obtained by averaging the sum of the effective scores of the score items belonging to the range, the total score of the customer is obtained by weighting and summing the effective scores of all score items according to the proportion, the credit rating of the customer is divided into five levels from 1 to 5 based on the customer score, the higher the score, the higher the credit rating, and the higher the risk.

[0104] Preferably, in the risk control management application module, automatic credit specifically includes: based on the customer credit rating, the credit customers with payment type of credit limit payment and the cash customers with payment type of cash payment are respectively subjected to different credit limit adjustment mechanisms, the credit limit adjustment suggestion is given based on the existing credit limit of the customer, the adjustment information is sent to the relevant business personnel, and the credit limit is automatically adjusted after the audit is passed.

[0105] Preferably, the marketing management application module, the marketing strategy for customer life cycle partitioning specifically comprises: based on the classification basis of the customer life cycle in the tag system construction module, setting different business directions for customers in different life cycles, personalized touch for potential customers, promoting first transaction; for the growth period customers who place orders for the first time and complete the orders, pull repeat purchases; for mature customers who continue to place orders and complete the orders, maintain customer relationship, keep customer stickiness and order frequency; touch and recall for silent customers and lost customers, encourage them to place orders again, activate silent customers and retain lost customers.

[0106] The system PC end framework is based on the encapsulation of lightweight J2EE architecture of Spring Boot+Hibernate JPA, the front-end interface layer adopts Vue technology, and uses Ant design UI framework; the back-end adopts micro-service architecture, adopts RPC communication, uses Zookeeper as a registration center, uses Redis cache service, Nacos configuration center and introduces Docker container technology, and uses Tomcat as a method application server and is deployed on Linux. The front-end infrastructure adopts the MVVM design pattern and uses http for network communication. The MVVM mode is an event-driven programming method for simplifying the user interface.

[0107] The system PC end framework uses the current popular B / S structure and is specifically divided into three layers:

[0108] The front-end interface layer: the WEB front-end according to the data interaction standard required by ant design, or the front-end based on the antdesign UI development framework, and the user only needs a common web browser to operate.

[0109] The application server layer: using mature technology in the industry, using Tomcat7 as the method application server and deploying on Linux, for providing encapsulated application service support.

[0110] The database access layer: Hibernate JPA has Session mechanism and second-level cache in the tuning aspect, and can also optimize the design of SQL. Large databases such as Oracle, MySQL, SQL Server can be perfectly supported.

[0111] Spring is a lightweight Inversion of Control (IoC) and Aspect-Oriented (AOP) container framework. It has the following characteristics:

[0112] Lightweight - Spring is lightweight in terms of both size and overhead.

[0113] Inversion of Control - Spring facilitates loosely coupled architecture by a technique called Inversion of Control (IoC).

[0114] Aspect-Oriented - Spring provides rich support for Aspect-Oriented Programming, allowing cohesive development by separating application business logic from cross-cutting concerns like transaction management, etc.

[0115] Spring Boot is a new framework provided by the Pivotal team, which is designed to simplify the initial setup and development process of new Spring applications. The framework uses specific ways to configure, so that developers no longer need to define the boilerplate configuration.

[0116] Docker is an open-source application container engine that allows developers to package their applications and dependencies into a portable container that can be shipped to any popular Linux machine, as well as virtualization. Containers are completely sandboxed and will not have any borrowing between each other.

[0117] RPC (Remote Procedure Call) - a remote invocation procedure, which is a protocol that requests services from a remote computer program over a network without knowing the underlying network technology. RPC protocol assumes the existence of some transport protocol, such as TCP or UDP, to carry information data between communication programs. In the OSI network communication model, RPC crosses the transport layer and the application layer. RPC makes it easier to develop applications that include network-distributed multi-programs.

[0118] RPC uses the client / server model. The requesting program is a client and the service provider is a server. First, the client-side call process sends a call message with process parameters to the service process, and then waits for the reply message. On the server side, the process remains in a sleep state until the call message arrives. When a call message arrives, the server obtains the process parameters, calculates the result, sends the reply message, and then waits for the next call message. Finally, the client-side call process receives the reply message, obtains the process result, and then the call execution continues.

[0119] Nacos is a service infrastructure for building modern application architectures (such as microservices paradigm, cloud native paradigm) centered on "service". Support DNS-based and RPC-based service discovery (can be used as a springcloud registry), dynamic configuration service (can be used as a configuration center), dynamic DNS service. Commit to help discover, configure and manage microservices. Provides a simple and easy-to-use feature set to help achieve dynamic service discovery, service configuration management, service and traffic management. More agile and easy to build, deliver and manage microservice systems.

[0120] Data Transfer Object (DTO) is a software application method for transferring data between design patterns. Data transfer objects are often data access objects that retrieve data from a database. The difference between a data transfer object and a data interaction object or a data access object is that a data transfer object is an object that has no behavior other than storing and retrieving data (accessors and mutators).

[0121] Hibernate is an object-relational mapping solution in Java. Object-relational mapping or ORM framework is a technology that maps application data model objects to relational database tables. Hibernate not only focuses on mapping from Java classes to database tables, but also maps Java data types to SQL data types.

[0122] Web2.0 is a new type of Internet application relative to Web1.0. Web2.0 focuses more on user interaction, and implements the new generation of Internet mode with new theories and technologies such as xml and ajax. It can provide users with better interaction and user experience.

[0123] Using a reverse proxy server, requests can be evenly forwarded to multiple application servers, or cached data can be directly returned to the client. Such acceleration mode can improve access speed to a certain extent, so as to achieve the purpose of load balancing. Using reverse proxy, load balancing and proxy server caching technology can be combined to provide beneficial performance and stability, which is an effective guarantee for 7*24 service.

[0124] It should be noted that the above specific embodiments can enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail with reference to the drawings and examples, those skilled in the art should understand that the present invention can still be modified or replaced by equivalents, in short, all technical solutions and improvements that do not deviate from the spirit and scope of the present invention should be covered in the protection scope of the present invention.

Claims

1. A method for precise marketing of container assets, characterized by, Comprise the following steps: S1: bottom layer data collection step: obtain customer information data meeting business needs from a data source and use it to build a bottom layer database; the customer information data includes customer basic data, order data, trade platform online order data, customer payment data; S2: label system construction step: establish customer attributes, and classify customer information data in the bottom layer database of step S1 based on different customer attributes and generate customer labels corresponding to different customer attributes, forming a unified customer label system; the customer attributes include customer basic attributes, sell box transaction attributes, sell box preference attributes, trade platform attributes, risk control and financial attributes, and customer life cycle; S3: customer label management function implementation step: store the customer information data of step S1 and the customer label system of step S2 as customer management data in a label management database, and analyze the customer management data through customer segmentation, customer insight, and customer label management, and form a corresponding label people package according to the segmentation label in the customer segmentation; S31: the customer segmentation step: includes creating a segmentation and segmentation management, the creating segmentation includes filling in the segmentation name and description to form a segmentation label, and selecting or combining the attributes and event fields in the customer management data list according to the segmentation label to form a new segmentation list; the segmentation management is to display and modify the customer management data in the segmentation list, and the information displayed in the segmentation list by the segmentation management further includes group name, number of customers in each group, group creator, and group creation time; S32: the customer insight step: comprehensively analyze the customer management data through customer individual insight and customer group insight, the customer individual insight is to generate a customer portrait for each customer based on the customer management data through conditional filtering, and the customer portrait can be displayed through a basic attribute subpage, a customer score subpage, and a customer label subpage; the important top labels displayed on the customer label subpage include sell box transaction attributes, sell box preference attributes, and trade platform attributes; The customer group insight is based on the groups created in the customer segmentation step S31, and displays the number of customers and the customer proportion in each label dimension under the analysis people group in the form of a chart, and can select a group or multiple groups to analyze customer data; the label dimensions include purchase preference box type, preference box condition, preference port, customer life cycle, credit risk assessment, price acceptance, box pickup days, box pickup rate, payment days, order cancellation rate, industry, and price; S33: the customer label management step displays the number of customers and the proportion of each sub-dimension label under different label dimensions based on the customer management data through a pie chart, and the label dimensions include customer purchase preference box type and port.

2. The container asset precision marketing method according to claim 1, wherein, The corresponding label people package in S3 can also be provided to other data products through a data interface to realize risk control management application, marketing management application, and trade platform application, specifically including: S1: the risk control management application step is: based on the label system to form an evaluation range to regenerate the scoring items corresponding to the evaluation range to score the customer; the evaluation range includes customer background, order performance, box picking performance, payment performance and sales price factors; the proportion of each scoring item in the label system is evenly distributed; then generate customer credit rating according to the customer score to realize automatic credit, that is, automatically grant customer credit limit; the method of the customer score is to generate the total score of the customer according to the effective score and proportion of each scoring item; the scoring items include five scoring items in the customer background, including establishment period, transaction history, income contribution ratio, transaction frequency and customer stickiness, order execution rate in order performance, box picking time in box picking performance, payment punctuality rate, average accounts receivable turnover days in payment performance, and premium index in sales price factors; S2: the marketing management application step is: based on the corresponding label of the crowd package to screen out the target customer group, and by analyzing the customer management data in the crowd package, different marketing management is realized for different crowd packages; the customer management data in the crowd package includes purchase preference box type, box condition, customer price acceptance and time interval from the last order time to the current time; S3: the trade platform application step is: based on the customer label, different marketing measures are taken for different customers on the trade platform, the marketing measures include: according to customer preference to recommend top, set different coupons or different integral return mechanism for different customer labels, remind customers to pay as soon as possible or take credit rating adjustment and corresponding measures for customers who do not pay or delay payment, and build marketing center; the marketing center includes customer insight, formulating marketing strategy, post-event activity effect analysis, label rewriting activity improvement.

3. The container asset precision marketing method of claim 1, wherein, The customer attribute corresponding label in the S2 step specifically includes: The customer basic attribute corresponding label includes: customer name, customer industry, customer data creation time and creation day distance today, customer classification, customer sales assistant, whether it is a self-owned yard, whether it is a distributor or a user; the customer classification includes rental box customers and sell box customers; The sell box transaction attribute corresponding label includes: first order date and distance from today, last order date and distance from today, total order quantity, total transaction box quantity, total transaction amount, single box transaction amount, price acceptance, average box picking days, average box picking rate, average payment days, average order cancellation rate in each time period of history and recent; The sell box preference attribute corresponding label includes: purchase preference box type, box condition, box grade and port in three scenarios of history order, trade platform online order and trade platform search in each time period of history and recent, and the proportion of trade platform online order in total order, the proportion of trade platform online transaction box quantity in total transaction box quantity; The trade platform attribute corresponding label includes: customer ID in the trade platform, whether the customer registers the trade platform and the date, whether the customer passes the trade platform audit and the date, the first active date and the number of days from today, the last active date and the number of days from today, the trade platform first order date and the number of days from today, the trade platform last order date and the number of days from today, the login times in each time period, the search times, the add times, the order creation times, the trade platform online order quantity, the trade platform online transaction box quantity, the trade platform online transaction amount, the trade platform online single box transaction amount, the trade platform online order coupon or integral order discount ratio, the trade platform online order coupon or integral amount subsidy rate, the total historical acquisition points, the current available points, the historical used points, the historical expired points, the total historical acquisition coupon quantity, the current available coupon quantity, the historical used coupon quantity, the historical expired coupon quantity, the total historical acquisition coupon face value, the current available coupon face value, the historical used coupon face value, and the historical expired coupon face value; The risk control and financial attribute corresponding label includes: payment type, credit limit, establishment period score, transaction history score, income contribution ratio score, transaction frequency score, customer stickiness score, order execution rate score, order pickup time efficiency score, payment punctuality rate score, average accounts receivable turnover days score, and premium index score; wherein the order execution rate is the average order actual pickup rate, the order pickup time efficiency is the average number of days required from order confirmation to pickup, the payment punctuality rate is the proportion of bills paid on time, the average accounts receivable turnover days is the average number of days from bill issuance to payment, and the premium index is the ratio of the customer's historical average sell box price to the company's guide price; The customer life cycle corresponding label includes: potential customer, growing period customer, mature period customer, silent customer, and lost customer.

4. The container asset precision marketing method according to claim 3, wherein, The method for selecting the customer life cycle corresponding label is: checking whether the customer has placed an order in the past 6 months; if not, the customer is a lost customer; Otherwise, checking whether the customer has placed an order in the past 3 months; if not, the customer is a silent customer; Otherwise, checking whether the customer has placed an order in the past 1 month; if not, the customer is a potential customer; Otherwise, checking the number of orders placed by the customer in the past 1 month; if only one order has been placed, the customer is a growing period customer; if two or more orders have been placed, the customer is a mature period customer.

5. The container asset precision marketing method of claim 1, wherein, In the S31 step, group editing, group deletion, and group data updating operations can also be performed in the list; all customer information in the group can also be viewed by clicking the customer number in the list, and the customer information includes customer name and customer data with the group specified label; the group basic information and grouping rules can be modified by clicking edit; and the customer data of the group can be updated in real time by clicking update.

6. The container asset precision marketing method of claim 1, wherein, The customer group insight in the S32 step further includes customer life cycle distribution statistics: including the number of customers of each life cycle type and the trend of change in each month in the past 6 months from the time dimension, and setting a message reminder, according to the customer life cycle, when the customer changes from a silent customer to a lost customer, the message reminder is displayed in the message reminder list, and the message reminder list includes historical message reminder data in the past one month.

7. The container asset precision marketing method according to claim 2, wherein, The method for generating and evaluating the score corresponding to the evaluation range to score the customer in the S1 step is: the customer score and its judgment index specifically include: based on five evaluation ranges of customer background, order performance, box delivery performance, payment performance and sales price factors, the customer is scored in 10 score items, each score item accounts for 10%, according to the effective score of each score item and the classification of the evaluation range, the score of each range is displayed in the form of a radar chart, the total customer score is generated by weighting according to the effective score of each score item and the proportion, and the customer credit rating is generated according to the total customer score; wherein the score of each range is obtained by averaging the sum of the effective scores of the score items belonging to the range, the total customer score is obtained by weighting and summing the effective scores of all score items according to the proportion, the customer credit rating is divided into five levels from 1 to 5 based on the customer score, the higher the score, the higher the credit rating, and the higher the risk.

8. The container asset precision marketing method according to claim 2, wherein, The method for generating customer credit rating according to customer score to realize automatic credit in the S1 step is: based on the customer credit rating, the credit customers with payment type of credit limit payment and the cash customers with payment type of cash payment are respectively subjected to corresponding credit limit adjustment mechanism, the limit adjustment suggestion is given based on the existing credit limit of the customer, the adjustment information is sent to the relevant business personnel, and the limit is automatically adjusted after the audit is passed.

9. The container asset precision marketing method according to claim 2, wherein, The method for different marketing management of different customer groups is: based on the classification basis of customer life cycle in the S2 step of claim 1, different business directions are set for customers in different life cycles, including: promoting first transaction for potential customers; driving repeat purchase for growth period customers who have placed orders and completed transactions; maintaining customer relationship for mature period customers who have continuously placed orders and completed transactions, keeping customer stickiness and order frequency; reaching out to and recalling silent customers and lost customers, encouraging them to place orders again, activating silent customers and retaining lost customers.

10. A container asset precision marketing system, characterized by, It includes: A bottom data collection module: customer information data collected from a data source of a company asset management system and a bottom database formed by the customer information data; the customer information data includes customer basic data, order data, trade platform online order data, and customer payment data; A label system construction module: including customer attributes and customer labels corresponding to different attribute classifications generated by using customer information data, and a unified customer label system formed thereby; the customer attributes include: customer basic attributes, box selling transaction attributes, box selling preference attributes, trade platform attributes, risk control and financial attributes, and customer life cycle; The customer label management function implementation module comprises a label management database, a customer grouping unit, a customer insight unit and a customer label management unit; the label management database comprises customer management data composed of both customer information data of the underlying data collection module and a customer label system of the label system construction module; the customer grouping unit comprises a creation grouping module for forming a grouping label and a grouping list and a management grouping module for grouping management through the grouping list, the creation grouping module further comprises a corresponding label people package formed according to the grouping label; the customer insight unit comprises a customer individual insight module for generating a customer portrait and realizing customer individual insight based on the customer portrait, and a customer group insight module for displaying the number of customers and the proportion of customers of each label dimension under the analysis people group in the form of a chart; the customer label management unit comprises a pie chart for displaying the number of customers and the proportion of customers of each sub-dimension label under different label dimensions; The scenario application module comprises a data interface for receiving the people package corresponding to the label in the creation grouping module, and a risk control management unit for customer credit rating and automatic credit granting by scoring customers using the people package; a marketing management unit for formulating marketing strategies for customer life cycle partitions using the people package, and a trade platform unit for taking different marketing measures for different customers on a trade platform using the people package.

11. The container asset precision marketing system of claim 10, wherein, The label corresponding to the customer attribute in the label system construction module comprises: The label corresponding to the customer basic attribute comprises: customer name, customer industry, customer data creation time and distance from today, customer classification, customer sales assistant, whether it is a self-owned yard, whether it is a distributor or a user; the customer classification comprises a box renting customer and a box selling customer; The label corresponding to the box selling transaction attribute comprises: first order date and distance from today, most recent order date and distance from today, total order quantity, total transaction box quantity, total transaction amount, single box transaction amount, price acceptance, average box picking days, average box picking rate, average payment days, average order cancellation rate in each time period; The label corresponding to the box selling preference attribute comprises: purchase preference box type, box condition, box grade and port in each time period in three scenarios of historical order, trade platform online order and trade platform search, and the proportion of trade platform online order in total order, the proportion of trade platform online transaction box quantity in total transaction box quantity; The trade platform attribute corresponding label includes: a user ID of a customer on the trade platform, whether the customer is registered on the trade platform and a date, whether the customer is audited through the trade platform and a date, a first active date and a number of days from today, a last active date and a number of days from today, a first order date on the trade platform and a number of days from today, a last order date on the trade platform and a number of days from today, a number of login times in each time period, a number of search times, a number of add-to-cart times, a number of order creation times, a number of online order boxes on the trade platform, a number of online transaction boxes on the trade platform, an online transaction amount on the trade platform, an online single-box transaction amount on the trade platform, an online order coupon or integral order discount proportion on the trade platform, an online order coupon or integral amount subsidy rate on the trade platform, a total number of historical acquired points, a number of currently available points, a number of historical used points, a number of historical expired points, a total number of historical acquired coupon sheets, a number of currently available coupon sheets, a number of historical used coupon sheets, a number of historical expired coupon sheets, a total number of historical acquired coupon denominations, a currently available coupon denomination, a number of historical used coupon denominations, and a number of historical expired coupon denominations; The risk control and financial attribute corresponding label includes: a payment type being cash payment or credit limit payment, a credit limit, a period of establishment and a score, a transaction history and a score, a contribution ratio and a score, a transaction frequency and a score, a customer stickiness and a score, an order execution rate and a score, a box pickup time limit and a score, a payment punctuality rate and a score, an average accounts receivable turnover period and a score, and a premium index and a score; wherein the order execution rate is an average order actual box pickup ratio, the box pickup time limit is an average number of days required from order confirmation to box pickup, the payment punctuality rate is a proportion of the number of bills paid on time, the average accounts receivable turnover period is an average number of days from bill issuance to payment, and the premium index is a ratio of a customer's historical average selling box price to a company's guide price; The customer life cycle corresponding label includes: a potential customer, a growth period customer, a mature period customer, a silent customer, and a lost customer.

12. The container asset precision marketing system of claim 10, wherein, The information displayed in the group list by the group management module further includes a group name, a number of customers in each group, a group creator, and a group creation time.

13. The container asset precision marketing system of claim 10, wherein, The risk control management unit includes a customer scoring module, a customer credit rating module, and an automatic credit granting module. The customer scoring module scores a customer according to ten score items in five evaluation ranges, including customer background, order performance, box pickup performance, payment performance, and selling price factors, and generates a total customer score by weighting the effective score and proportion of each score item. The customer credit rating module performs customer credit rating and judges the customer risk category according to the total customer score and customer credit rating rules. The automatic credit granting module grants a customer credit limit, i.e., automatic credit, according to the customer risk category.

14. The container asset precision marketing system of claim 10, wherein, The marketing management unit is configured to formulate a marketing strategy for each customer life cycle stage, including promoting first transaction for potential customers, promoting repeat purchase for growth stage customers who have placed an order and completed the transaction, maintaining customer relationship for mature stage customers who have continuously placed orders and completed the transaction, keeping customer stickiness and order frequency, and reaching out to and recalling dormant customers and lost customers, encouraging them to place orders again, activating dormant customers and retaining lost customers.

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