Automobile rental exhibition period re-marketing evaluation method, device and equipment and storage medium

Abstract

The invention provides an automobile rental exhibition re-marketing evaluation method and device, equipment and a storage medium, and relates to the field of data processing. According to the method, the dimension evaluation data of the multiple dimensions of the clients in the preset period are collected, quantitative scoring is performed by using the preset scoring rule, comprehensive scoring calculation is performed in combination with the credit investigation coefficients of the clients and the weights of the dimensions, and finally, high-quality clients are quickly screened out according to the preset scoring threshold, so that accurate evaluation of stock clients is realized, and the accuracy of the stock clients is improved. The workload and the cost of manual screening are reduced, the risk caused by poor customer credit is also reduced, and the identification efficiency and the identification accuracy of the exhibition re-marketing business are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method, device, equipment and storage medium for evaluating car rental extension remarketing. Background Art

[0002] Auto financial leasing companies make profits by providing financial leasing services to lessees. At present, debt income is the main profit model of auto financial leasing companies, that is, auto financial leasing companies invest in purchasing leased objects and lease them to lessees for use. During the asset lease period, auto financial leasing companies, as lessors, have asset ownership, and lessees pay rent to lessors in installments. Therefore, the larger the customer base and the larger the leasing scale, the more income auto financial leasing companies will have. The auto financial leasing industry has been developing for many years and has now entered the era of "stock competition". The quality of stock customers is uneven. If only the operating mileage of the customer's leased vehicle is considered and the customer's credit information is ignored, there will be repayment risks for auto financial leasing companies if the customer's credit is overdue. Therefore, extending the lease term of the existing high-quality customers in the lease contract can increase the income of auto financial leasing companies under controllable risks.

[0003] Auto finance leasing companies have a large number of existing customers. If they manually screen customers one by one to extend the lease period, it will consume a lot of manpower and cost. In addition, there may be some low-quality customers with poor credit among the existing customers. Customers with poor credit may be overdue for repayment, which will cause certain risks to auto finance leasing companies. Therefore, it is particularly important for auto finance leasing companies to quickly and accurately identify existing high-quality customers and remarket to them for extensions.

[0004] Therefore, how to improve the recognition efficiency of extension remarketing business has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides a method, device, equipment and medium for evaluating the extension and remarketing of a car rental, aiming to improve the recognition efficiency of the extension and remarketing business.

[0006] In a first aspect, the present application provides a method for evaluating the remarketing of a car rental extension, the method comprising the following steps:

[0007] Obtaining dimension evaluation data of at least one dimension of a target customer within a preset period;

[0008] Scoring each of the dimensional evaluation data based on a preset scoring rule corresponding to each of the dimensional evaluation data to obtain a dimensional score corresponding to each of the dimensional evaluation data;

[0009] Based on the evaluation weights corresponding to the evaluation data of each dimension and the credit coefficient of the target customer, weighted calculation is performed on the scores of each dimension to obtain the customer evaluation score of the target customer;

[0010] Based on a preset scoring threshold and the customer evaluation score, an extended remarketing strategy for the target customer is determined.

[0011] In a second aspect, the present application further provides a car rental extension remarketing evaluation device, the car rental extension remarketing evaluation device comprising:

[0012] A data acquisition module, used to acquire dimension evaluation data of at least one dimension of a target customer within a preset period;

[0013] A dimension scoring obtaining module is used to score each dimension evaluation data based on a preset scoring rule corresponding to each dimension evaluation data, and obtain a dimension score corresponding to each dimension evaluation data;

[0014] A weighted calculation evaluation module, used to perform weighted calculation on the scores of each dimension based on the evaluation weight corresponding to the evaluation data of each dimension and the credit coefficient of the target customer, so as to obtain the customer evaluation score of the target customer;

[0015] A strategy determination module is used to determine the extended remarketing strategy for the target customer based on a preset scoring threshold and the customer evaluation score.

[0016] In a third aspect, the present application also provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the above-mentioned car rental extension remarketing evaluation method are implemented.

[0017] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the above-mentioned car rental extension remarketing evaluation method are implemented.

[0018] The present application provides a method, device, computer equipment and storage medium for evaluating the extension and remarketing of car rentals. The method of the present application includes obtaining dimension evaluation data of at least one dimension of a target customer within a preset period; scoring each dimension evaluation data based on the preset scoring rules corresponding to each dimension evaluation data to obtain the dimension score corresponding to each dimension evaluation data; weighted calculation of each dimension score based on the evaluation weight corresponding to each dimension evaluation data and the credit coefficient of the target customer to obtain the customer evaluation score of the target customer; and determining the extension and remarketing strategy of the target customer based on the preset scoring threshold and the customer evaluation score. Through the above method, the present application collects the dimension evaluation data of multiple dimensions of the customer within a preset period, uses the preset scoring rules for quantitative scoring, and then combines the customer credit coefficient and the weight of each dimension for comprehensive scoring calculation, and finally quickly screens out high-quality customers according to the preset scoring threshold, so as to achieve accurate evaluation of existing customers, which not only reduces the workload and cost of manual screening, but also reduces the risk caused by poor customer credit, and improves the recognition efficiency and accuracy of the extension and remarketing business. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A flowchart of a first embodiment of a car rental extension remarketing evaluation method provided by the present application;

[0021] Figure 2 A schematic diagram of a data acquisition platform provided in an embodiment of the present application;

[0022] Figure 3 A schematic diagram of a car rental extension remarketing evaluation process provided for this application;

[0023] Figure 4 It is a structural schematic diagram of a first embodiment of a car rental extension remarketing evaluation device provided by the present application;

[0024] Figure 5 It is a schematic block diagram of the structure of a computer device provided in an embodiment of the present application.

[0025] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0028] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0029] Please refer to Figure 1 , Figure 1 A flowchart of a first embodiment of a car rental extension remarketing evaluation method provided in this application.

[0030] like Figure 1 As shown, the car rental extension remarketing evaluation method includes steps S101 to S104.

[0031] S101, obtaining dimension evaluation data of at least one dimension of a target customer within a preset period;

[0032] In one embodiment, the dimensional evaluation data includes one or more of rental customer data, rental vehicle data, and rental vehicle operation data.

[0033] For example, it can be obtained from Figure 2 The data platforms shown, such as the rental customer ledger system, rental vehicle ledger system, vehicle operation system and personal credit system, obtain dimensional evaluation data of at least one dimension.

[0034] In one embodiment, the rental customer account system maintains the information of all rental customers of the automobile financial leasing company in the form of data records. Each data record contains fields such as customer ID (customer unique identifier), customer name, ID number, mobile phone number, etc. The data example is as follows:

[0035] Customer ID Customer Name ID number Phone number ... 1 Zhang San xxxxxxxxxxxxxxxxxx yyyyyyyyyyy ...

[0036] The rental vehicle ledger system maintains the information of all rental vehicles of the automobile financial leasing company in the form of data records. Each data record contains fields such as customer ID (customer unique identifier), frame number, vehicle age, etc. The data example is as follows:

[0037] Customer ID Frame number Vehicle age (years) ... 1 XXXXXXXXXXXXXXXXX 3 ...

[0038] The vehicle operation system maintains the operation data of all leased vehicles of the automobile financial leasing company in the form of data records. Each data record contains fields such as vehicle frame number, date, mileage, etc. The data example is as follows:

[0039] Frame number date Monthly mileage (km) ... XXXXXXXXXXXXXXXXX 2022-06 4000 ...

[0040] In one embodiment, an automobile financial leasing company may install a GPS device on the leased vehicle, and calculate the vehicle's itinerary (trip start time, trip end time, trip mileage, etc.) through the positioning information reported by the GPS device, and further calculate the vehicle's monthly mileage and other operating data.

[0041] In one embodiment, the personal credit reporting system, also known as the consumer credit information system, provides personal credit reports to consumer credit institutions.

[0042] In a specific embodiment, the rental customer data, rental vehicle data, and rental vehicle operation data may be obtained periodically (e.g., monthly):

[0043] Obtain customer information from the Qizu customer account system, including but not limited to customer ID (customer unique identifier), customer name, ID number, mobile phone number and other data;

[0044] Obtain the customer's rental vehicle information from the rental vehicle ledger system, including but not limited to the customer ID (customer unique identifier), vehicle frame number, rental start time, and vehicle age;

[0045] Obtain vehicle operation information from the vehicle operation system, including but not limited to vehicle frame number, date, and mileage.

[0046] S102, scoring each of the dimensional evaluation data based on a preset scoring rule corresponding to each of the dimensional evaluation data, to obtain a dimensional score corresponding to each of the dimensional evaluation data;

[0047] In one embodiment, based on preset scoring rules, a list of high-quality customers for extended remarketing is calculated and analyzed, including information such as customer names and mobile phone numbers.

[0048] Furthermore, historical vehicle evaluation data of at least one type of vehicle and vehicle value time series data of each vehicle within a preset time range are obtained; based on the time correspondence between the historical vehicle evaluation data and the vehicle value time series data, at least one data evaluation range corresponding to the dimensional evaluation data of each dimension is determined; based on at least one data evaluation range corresponding to the dimensional evaluation data of each dimension and the vehicle value time series data corresponding to each data evaluation range, the dimension score corresponding to each data evaluation range is set to generate the preset scoring rule.

[0049] In one embodiment, historical vehicle evaluation data and vehicle value time series data are obtained, which may include vehicle purchase price, maintenance cost, number of doors, number of passengers, luggage size, safety and other characteristics, as well as the market value of the vehicle at different time points.

[0050] Optionally, the collected data can be cleaned, missing values ​​and outliers can be processed, and non-numeric data can be converted to numerical data for analysis.

[0051] In one embodiment, features may be selected through statistical analysis or feature importance assessment using machine learning to determine which features are most important for vehicle value assessment as dimensions for data assessment, such as vehicle age, vehicle average monthly mileage, etc.

[0052] In one embodiment, a plurality of different data evaluation ranges may be set according to the impact of each feature dimension on the evaluation of vehicle value at different stages, and a suitable score may be configured for each data evaluation range.

[0053] Exemplary, extension remarketing evaluation dimensions and parameters: information on each dimension of extension remarketing scoring, including parameters such as dimension name, score of each dimension, and weight ratio. An example of parameter configuration is as follows:

[0054]

[0055]

[0056] In one embodiment, the scores of each rating dimension of the customer are calculated: the operation score of the customer is calculated using the acquired customer data, vehicle data and the configured relevant operation dimension parameters.

[0057] Furthermore, based on the rental vehicle data, the vehicle age and the average monthly mileage of the target vehicle are obtained; based on a preset vehicle age scoring rule, a first dimension score corresponding to the vehicle age of the target vehicle is determined; based on a preset average monthly mileage scoring rule, a second dimension score corresponding to the average monthly mileage of the target vehicle is determined.

[0058] In one embodiment, according to the preset scoring rules in the above table, the vehicle age is divided into a plurality of different vehicle age ranges, and different vehicle age ranges correspond to different evaluation scores. It can be understood that, since the technical condition of the vehicle will gradually age and wear out over time, the younger the vehicle age, the higher the vehicle value and the higher the corresponding vehicle score.

[0059] In one embodiment, the vehicle age of the target customer is evaluated according to the preset vehicle age range to determine its first dimension score. That is, the first dimension score is the evaluation score corresponding to the vehicle age range to which the vehicle age of the target customer belongs.

[0060] In one embodiment, the average monthly mileage is an important indicator for evaluating the vehicle's operational capacity, and is usually used to reflect the vehicle's usage frequency and operational intensity. The higher the vehicle's average monthly mileage, the higher the vehicle's utilization efficiency, indicating that the vehicle's operational capacity is stronger. Especially in industries such as logistics or taxis, the vehicle's average monthly mileage is often directly related to its operating income, so the vehicle's mileage is considered when evaluating the vehicle's operational capacity. In this case, the greater the vehicle's average monthly mileage, the higher the vehicle's utilization rate and operational efficiency, and thus a higher score may be obtained.

[0061] In one embodiment, the target customer's vehicle monthly mileage data is evaluated according to a preset monthly mileage range to determine its second dimension score. That is, the target customer's vehicle monthly mileage belongs to a monthly mileage range, and its second dimension score is the evaluation score corresponding to the monthly mileage range.

[0062] In one embodiment, personal credit data of the target customer is obtained; and the credit coefficient of the target customer is determined based on a preset credit scoring rule and the personal credit data.

[0063] In one embodiment, the personal credit data of the target customer is obtained from the personal credit system, which may include historical loan data, historical repayment records, overdue records, etc. Then, the personal credit coefficient of the target customer is determined according to the credit evaluation rules preset in the above list.

[0064] Understandably, the credit rating is an indicator used by financial institutions to assess a customer's credit risk, reflecting the likelihood that a customer will fulfill their financial obligations. In different financial scenarios, the credit rating may vary, but generally speaking, the calculation of the credit rating takes into account multiple factors such as the customer's credit history, repayment behavior, debt level, income status, etc.

[0065] For example, if the target customers have a record of overdue payments, it means that the target customers may not be able to repay on time and there is a risk of default. If the extension and remarketing business is implemented, there is a high possibility that it cannot be realized. Even if the extension and remarketing business is carried out, there is a great risk that the target customers will not be able to repay on time, which will cause losses to the financial institutions. If the target customers have no overdue records, it means that they repay on time, have good credit, and have the potential to carry out extension and remarketing business, which is convenient for increasing business volume with lower risks.

[0066] S103, based on the evaluation weights corresponding to the evaluation data of each dimension and the credit coefficient of the target customer, weighted calculation is performed on the scores of each dimension to obtain the customer evaluation score of the target customer;

[0067] Furthermore, based on a first evaluation weight corresponding to the vehicle age and a second evaluation weight corresponding to the vehicle average monthly mileage, a weighted calculation is performed on the first dimension score and the second dimension score to obtain a weighted evaluation score; based on the weighted evaluation score and the target customer's credit coefficient, the customer evaluation score of the target customer is calculated.

[0068] The customer evaluation score calculation formula is:

[0069] Customer evaluation score =

[0070] Credit coefficient × (first dimension score × first assessment weight + second dimension score × second assessment weight).

[0071] In one embodiment, the scores of each dimension are weighted and summed according to the preset evaluation weights of the evaluation data of each dimension, and then the credit coefficient and the weighted sum result are multiplied according to the credit coefficient of the target customer to obtain the customer evaluation score of the target customer.

[0072] For example, if the target customer has an overdue record, that is, the credit coefficient is 0, then the customer evaluation score of the target customer is 0. If the target customer has no overdue record, that is, the credit coefficient is 1, then the customer evaluation score of the target customer is the weighted sum score.

[0073] Optionally, the target customer's credit coefficient can also be other coefficient values. For example, even if the target customer has an overdue record, the credit coefficient can be gradually reduced according to the number of overdue records or overdue compensation measures, rather than being set to 0. For example, if there is only one overdue record in ten repayments and the repayment is completed within a certain period of time, the credit coefficient can be set to 0.9, and so on.

[0074] In a specific embodiment, the vehicle monthly mileage MonthMile may be the average of the vehicle monthly mileage from the start of the vehicle rental to the calculation date, and the customer evaluation score may be the credit factor multiplied by the sum of each scoring dimension, that is:

[0075] MonthMile = AVG (monthly mileage of vehicle)

[0076] Customer evaluation score = credit coefficient * ∑ (corresponding score of each dimension * weight)

[0077] For ease of understanding, here we take customer A as an example. If customer A's vehicle age Age = 3, vehicle average monthly mileage MonthMile = 4000, and there is no overdue record in the credit report, then customer A's customer assessment score is: 1*(90*30%+90*70%)=90.

[0078] S104: Determine an extended remarketing strategy for the target customer based on a preset scoring threshold and the customer evaluation score.

[0079] In one embodiment, if Figure 3 As shown, it is determined whether the total customer score (customer evaluation score) of the target customer meets the remarketing threshold score (i.e., the preset score threshold). If it does, proceed to the next step to perform the extended remarketing business for the target customer; otherwise, it ends.

[0080] Furthermore, when the customer evaluation score is greater than or equal to the preset scoring threshold, the target customer is marked as a first type of customer; customer information of the target customer is obtained; and based on the customer information, the extended remarketing strategy corresponding to the first type of customer is executed.

[0081] For example, assuming that the customer evaluation score of customer B is 90 points and the preset score threshold of the extension remarketing business is 80 points, the requirements for extension remarketing are met and customer B can be used as the target object of extension remarketing.

[0082] In one embodiment, customer information that meets the extension remarketing threshold can be added to a remarketing high-quality customer list, and high-quality customers in the remarketing high-quality customer list can be pushed to the customer service system to execute the extension remarketing business.

[0083] For example, the list data record contains fields such as customer name, mobile phone number, etc. The data example is as follows:

[0084] Customer Name Phone number ... Zhang San 18918819878 ...

[0085] In this embodiment, the automobile financial leasing company obtains its own customer data, leased vehicle data and vehicle operation data to perform scoring in various dimensions, and combines the customer's credit information to finally generate an extension remarketing customer list information, and pushes the extension remarketing customer list to the customer service system. The customer service system can perform customer extension remarketing based on the extension remarketing customer list to extend the lease period of high-quality customers' existing lease contracts.

[0086] In one embodiment, if the customer evaluation score is less than a preset scoring threshold, the target customer may be marked as a second type of customer, and no extended remarketing service is performed on this type of customer.

[0087] In this embodiment, by analyzing and scoring the lease data of the automobile financial leasing company and the credit information of the customer, high-quality remarketing customers are screened out, and the high-quality customers are pushed to the customer service system for customer remarketing. By presetting the extension remarketing evaluation dimension, the preset operation dimension and score weight parameter, credit coefficient and other configurations, the customer evaluation score is calculated, and then the high-quality remarketing customers are screened out according to the configured remarketing threshold score requirements, achieving the effect of quantifying high-quality customers.

[0088] In one embodiment, a data collection cycle may be preset to periodically collect dimensional evaluation data of vehicle rental customers, and the above-mentioned evaluation steps may be performed to evaluate each vehicle rental customer, screen high-quality customers, and expand the business scope.

[0089] In one embodiment, periodic screening of high-quality remarketing customers is timely, and can help automobile financial leasing companies screen out high-quality customers from a large number of existing customers, saving a lot of energy and manpower costs; at the same time, credit information is used to filter out low-quality customers, thereby increasing the profits of automobile financial leasing companies while controlling risks and achieving precision marketing.

[0090] The embodiment of the present application periodically obtains the data of the automobile financial leasing company's leased vehicles through preset operating dimensions and parameters, and finally selects high-quality remarketing customers based on the customer's credit information, generates a remarketing list, and pushes the high-quality customer list to the customer service system for customer remarketing. The system automatically selects high-quality remarketing customers from a large number of existing customers, and extends the lease period of high-quality customers in renting vehicles, which saves a lot of time and labor costs for automobile financial leasing companies under controllable risks, and increases the profits of automobile financial leasing companies.

[0091] The present embodiment provides a method for evaluating car rental extension remarketing. The present application method collects dimensional evaluation data of multiple dimensions of customers within a preset period, uses preset scoring rules to perform quantitative scoring, and then combines the customer's credit coefficient and the weight of each dimension to perform comprehensive scoring calculation. Finally, high-quality customers are quickly screened out according to the preset scoring threshold, and accurate evaluation of existing customers is achieved. This not only reduces the workload and cost of manual screening, but also reduces the risks caused by poor customer credit, and improves the recognition efficiency and accuracy of the extension remarketing business.

[0092] See also Figure 4 , Figure 4It is a structural schematic diagram of a first embodiment of a car rental extension remarketing evaluation device provided by the present application, and the car rental extension remarketing evaluation device is used to execute the aforementioned car rental extension remarketing evaluation method.

[0093] like Figure 4 As shown, the car rental extension remarketing evaluation device 200 includes: a data acquisition module 201, a dimension score acquisition module 202, a weighted calculation evaluation module 203 and a strategy determination module 204.

[0094] The data acquisition module 201 is used to acquire dimension evaluation data of at least one dimension of a target customer within a preset period;

[0095] A dimension scoring obtaining module 202 is used to score each dimension evaluation data based on a preset scoring rule corresponding to each dimension evaluation data, and obtain a dimension score corresponding to each dimension evaluation data;

[0096] A weighted calculation evaluation module 203 is used to perform weighted calculation on the scores of each dimension based on the evaluation weight corresponding to each dimension evaluation data and the credit coefficient of the target customer to obtain the customer evaluation score of the target customer;

[0097] The strategy determination module 204 is used to determine the extended remarketing strategy for the target customer based on a preset scoring threshold and the customer evaluation score.

[0098] In one embodiment, the dimensional evaluation data includes one or more of rental customer data, rental vehicle data, and rental vehicle operation data.

[0099] In one embodiment, the dimension score obtaining module 202 includes:

[0100] A dimension evaluation data acquisition unit, used to acquire the vehicle age and the average monthly mileage of the target vehicle based on the rental vehicle data;

[0101] A first dimension score determination unit, configured to determine a first dimension score corresponding to the vehicle age of the target vehicle based on a preset vehicle age scoring rule;

[0102] The second dimension score determination unit is used to determine the second dimension score corresponding to the vehicle monthly average mileage of the target vehicle based on a preset monthly average mileage scoring rule.

[0103] In one embodiment, the weighted calculation evaluation module 203 includes:

[0104] A weighted evaluation score obtaining unit, configured to perform weighted calculation on the first dimension score and the second dimension score based on a first evaluation weight corresponding to the vehicle age and a second evaluation weight corresponding to the vehicle average monthly mileage, to obtain a weighted evaluation score;

[0105] A customer evaluation score calculation unit, used to calculate the customer evaluation score of the target customer based on the weighted evaluation score and the credit coefficient of the target customer;

[0106] The customer evaluation score calculation formula is:

[0107] Customer evaluation score =

[0108] Credit coefficient × (first dimension score × first assessment weight + second dimension score × second assessment weight).

[0109] In one embodiment, the car rental extension remarketing evaluation device 200 further includes a credit coefficient determination module, including:

[0110] A personal credit data acquisition unit, used to acquire the personal credit data of the target customer;

[0111] The credit coefficient determination unit is used to determine the credit coefficient of the target customer based on a preset credit scoring rule and the personal credit data.

[0112] In one embodiment, the policy determination module 204 includes:

[0113] A first type customer marking unit, configured to mark the target customer as a first type customer when the customer evaluation score is greater than or equal to the preset scoring threshold;

[0114] A customer information acquisition unit, used to acquire customer information of the target customer;

[0115] The extended remarketing strategy execution unit is used to execute the extended remarketing strategy corresponding to the first type of customers based on the customer information.

[0116] In one embodiment, the car rental extension remarketing evaluation device 200 further includes a scoring rule formulation module, including:

[0117] A historical data acquisition unit, used to acquire historical vehicle evaluation data of at least one type of vehicle, and vehicle value time series data of each vehicle within a preset time range;

[0118] A data evaluation range division unit, configured to determine at least one data evaluation range corresponding to the dimension evaluation data of each dimension based on a time correspondence between the historical vehicle evaluation data and the vehicle value time series data;

[0119] A scoring rule generating unit is used to set the dimension score corresponding to each data evaluation range based on at least one data evaluation range corresponding to the dimension evaluation data of each dimension and the vehicle value time series data corresponding to each data evaluation range, and generate the preset scoring rule.

[0120] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described device and each module can refer to the corresponding process in the aforementioned car rental extension remarketing evaluation method embodiment, and will not be repeated here.

[0121] The apparatus provided in the above embodiment may be implemented in the form of a computer program. The computer program may be Figure 5 Runs on the computer device shown.

[0122] See also Figure 5 , Figure 5 1 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device may be a server.

[0123] See also Figure 5 The computer device includes a processor, a memory and a network interface connected through a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0124] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any one of the car rental extension remarketing evaluation methods.

[0125] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0126] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any one of the car rental extension remarketing evaluation methods.

[0127] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0128] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0129] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0130] Obtaining dimension evaluation data of at least one dimension of a target customer within a preset period;

[0131] Scoring each of the dimensional evaluation data based on a preset scoring rule corresponding to each of the dimensional evaluation data to obtain a dimensional score corresponding to each of the dimensional evaluation data;

[0132] Based on the evaluation weights corresponding to the evaluation data of each dimension and the credit coefficient of the target customer, weighted calculation is performed on the scores of each dimension to obtain the customer evaluation score of the target customer;

[0133] Based on a preset scoring threshold and the customer evaluation score, an extended remarketing strategy for the target customer is determined.

[0134] In one embodiment, the dimensional evaluation data includes one or more of rental customer data, rental vehicle data, and rental vehicle operation data.

[0135] In one embodiment, when the processor implements the preset scoring rules corresponding to each dimension evaluation data, scores each dimension evaluation data, and obtains the dimension score corresponding to each dimension evaluation data, it is used to implement:

[0136] Based on the rental vehicle data, the vehicle age and the average monthly mileage of the target vehicle are obtained;

[0137] Determine a first dimension score corresponding to the vehicle age of the target vehicle based on a preset vehicle age scoring rule;

[0138] Based on the preset average monthly mileage scoring rule, a second dimension score corresponding to the average monthly mileage of the target vehicle is determined.

[0139] In one embodiment, when the processor performs weighted calculation on the scores of each dimension based on the evaluation weights corresponding to the evaluation data of each dimension and the credit coefficient of the target customer to obtain the customer evaluation score of the target customer, it is used to implement:

[0140] Based on the first evaluation weight corresponding to the vehicle age and the second evaluation weight corresponding to the vehicle average monthly mileage, weighted calculation is performed on the first dimension score and the second dimension score to obtain a weighted evaluation score;

[0141] Calculating a customer evaluation score for the target customer based on the weighted evaluation score and the credit coefficient of the target customer;

[0142] The customer evaluation score calculation formula is:

[0143] Customer evaluation score =

[0144] Credit coefficient × (first dimension score × first assessment weight + second dimension score × second assessment weight).

[0145] In one embodiment, before implementing the weighted calculation of the scores of each dimension based on the evaluation weights corresponding to the evaluation data of each dimension and the credit coefficient of the target customer to obtain the customer evaluation score of the target customer, the processor is further used to implement:

[0146] Obtaining personal credit data of the target customer;

[0147] Based on the preset credit scoring rules and the personal credit data, the credit coefficient of the target customer is determined.

[0148] In one embodiment, when the processor implements the step of determining the extended remarketing strategy for the target customer based on the preset scoring threshold and the customer evaluation score, the processor is configured to implement:

[0149] When the customer evaluation score is greater than or equal to the preset scoring threshold, marking the target customer as a first type of customer;

[0150] Obtaining customer information of the target customer;

[0151] Based on the customer information, the extension remarketing strategy corresponding to the first type of customers is executed.

[0152] In one embodiment, before implementing the preset scoring rules corresponding to each dimension evaluation data, scoring each dimension evaluation data, and obtaining the dimension score corresponding to each dimension evaluation data, the processor is further used to implement:

[0153] Obtain historical vehicle valuation data for at least one type of vehicle and time series data on vehicle value for each vehicle within a preset time range;

[0154] Determine at least one data evaluation range corresponding to the dimension evaluation data of each dimension based on the time correspondence between the historical vehicle evaluation data and the vehicle value time series data;

[0155] Based on at least one data evaluation range corresponding to the dimensional evaluation data of each dimension, and the vehicle value time series data corresponding to each data evaluation range, the dimensional score corresponding to each data evaluation range is set to generate the preset scoring rule.

[0156] A computer-readable storage medium is also provided in an embodiment of the present application, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and the processor executes the program instructions to implement any one of the car rental extension remarketing evaluation methods provided in the embodiments of the present application.

[0157] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., equipped on the computer device.

[0158] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A car rental extension remarketing evaluation method, characterized in that: The method comprises: Obtaining dimension evaluation data of at least one dimension of a target customer within a preset period; Scoring each of the dimensional evaluation data based on a preset scoring rule corresponding to each of the dimensional evaluation data to obtain a dimensional score corresponding to each of the dimensional evaluation data; Based on the evaluation weights corresponding to the evaluation data of each dimension and the credit coefficient of the target customer, weighted calculation is performed on the scores of each dimension to obtain the customer evaluation score of the target customer; Based on a preset scoring threshold and the customer evaluation score, an extended remarketing strategy for the target customer is determined.

2. The car rental extension remarketing evaluation method according to claim 1, characterized in that: The dimensional evaluation data includes one or more of rental customer data, rental vehicle data, and rental vehicle operation data.

3. The car rental extension remarketing evaluation method according to claim 2, characterized in that: Scoring each dimension evaluation data based on a preset scoring rule corresponding to each dimension evaluation data to obtain a dimension score corresponding to each dimension evaluation data includes: Based on the rental vehicle data, the vehicle age and the average monthly mileage of the target vehicle are obtained; Determine a first dimension score corresponding to the vehicle age of the target vehicle based on a preset vehicle age scoring rule; Based on the preset average monthly mileage scoring rule, a second dimension score corresponding to the average monthly mileage of the target vehicle is determined.

4. The car rental extension remarketing evaluation method according to claim 3 is characterized in that: The step of performing weighted calculation on the scores of each dimension based on the evaluation weight corresponding to the evaluation data of each dimension and the credit coefficient of the target customer to obtain the customer evaluation score of the target customer includes: Based on the first evaluation weight corresponding to the vehicle age and the second evaluation weight corresponding to the vehicle average monthly mileage, weighted calculation is performed on the first dimension score and the second dimension score to obtain a weighted evaluation score; Calculating a customer evaluation score for the target customer based on the weighted evaluation score and the credit coefficient of the target customer; The customer evaluation score calculation formula is: Customer evaluation score = Credit coefficient × (first dimension score × first assessment weight + second dimension score × second assessment weight).

5. The car rental extension remarketing evaluation method according to claim 1, characterized in that: Before performing weighted calculation on the scores of each dimension based on the evaluation weights corresponding to the evaluation data of each dimension and the credit coefficient of the target customer to obtain the customer evaluation score of the target customer, the method further includes: Obtaining personal credit data of the target customer; Based on the preset credit scoring rules and the personal credit data, the credit coefficient of the target customer is determined.

6. The car rental extension remarketing evaluation method according to claim 1, characterized in that: The step of determining the extended remarketing strategy for the target customer based on the preset scoring threshold and the customer evaluation score includes: When the customer evaluation score is greater than or equal to the preset scoring threshold, marking the target customer as a first type of customer; Acquiring customer information of the target customer; Based on the customer information, the extension remarketing strategy corresponding to the first type of customers is executed.

7. The car rental extension remarketing evaluation method according to claim 1, characterized in that: Scoring each dimension evaluation data based on the preset scoring rules corresponding to each dimension evaluation data and obtaining the dimension score corresponding to each dimension evaluation data further includes: Obtain historical vehicle valuation data for at least one type of vehicle and time series data on vehicle value for each vehicle within a preset time range; Determine at least one data evaluation range corresponding to the dimension evaluation data of each dimension based on the time correspondence between the historical vehicle evaluation data and the vehicle value time series data; Based on at least one data evaluation range corresponding to the dimensional evaluation data of each dimension, and the vehicle value time series data corresponding to each data evaluation range, the dimensional score corresponding to each data evaluation range is set to generate the preset scoring rule.

8. A car rental extension remarketing evaluation device, characterized in that: The car rental extension remarketing evaluation device comprises: A data acquisition module, used to acquire dimension evaluation data of at least one dimension of a target customer within a preset period; A dimension scoring obtaining module is used to score each dimension evaluation data based on a preset scoring rule corresponding to each dimension evaluation data, and obtain a dimension score corresponding to each dimension evaluation data; A weighted calculation evaluation module, used to perform weighted calculation on the scores of each dimension based on the evaluation weight corresponding to the evaluation data of each dimension and the credit coefficient of the target customer, so as to obtain the customer evaluation score of the target customer; A strategy determination module is used to determine the extended remarketing strategy for the target customer based on a preset scoring threshold and the customer evaluation score.

9. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the car rental extension remarketing evaluation method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the car rental extension remarketing evaluation method according to any one of claims 1 to 7 are implemented.

Images