CN114445099A - Returned freight insurance pricing method, pricing system and construction method thereof - Google Patents
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Abstract
本发明属于退货运费险定价领域,具体地涉及一种退货运费险定价方法以及定价系统及其构建方法,包括:基于客户对当前购买商品的行为数据和购买数据,采用退货概率预测模型预测客户对该当前商品的退货概率;将退货概率带入利润最大化目标函数,计算退货运费险价格。其次,通过利用上述定价方法得到的最优价格作为价格软目标,利用上述行为数据和购买数据,将退货概率预测模型以退货运费险价格为预测目标,计算模型预测价格与价格软目标间的误差,优化模型参数,得到各客户的定价系统,用于对应客户的退货运费险定价。本方法充分利用电商企业拥有充足、大量的客户行为数据及历史数据,可一步式地获取最优定价决策,无需人工参与,计算效率高。
The invention belongs to the field of return freight insurance pricing, and in particular relates to a return freight insurance pricing method, a pricing system and a construction method thereof. The return probability of the current product; the return probability is brought into the profit maximization objective function to calculate the return freight insurance price. Secondly, by using the optimal price obtained by the above pricing method as the price soft target, using the above behavior data and purchase data, the return probability prediction model takes the return freight insurance price as the prediction target, and calculates the model. The error between the predicted price and the soft price target , optimize the model parameters, and obtain the pricing system of each customer, which is used for the corresponding customer's return shipping insurance pricing. This method makes full use of the sufficient and large amount of customer behavior data and historical data owned by e-commerce companies, and can obtain optimal pricing decisions in one step without manual participation, and has high computational efficiency.
Description
技术领域technical field
本发明属于退货运费险定价领域,更具体地,涉及一种退货运费险定价方法以及定价系统及其构建方法。The invention belongs to the field of return freight insurance pricing, and more particularly, relates to a return freight insurance pricing method, a pricing system and a construction method thereof.
背景技术Background technique
随着网络购物的日常化,电商企业的线上订单也日益增加,而由统计调查表明,平均三个订单中会产生一个退货订单。客户选择退货的原因有很多,商品质量问题、消费者冲动购买后的遗憾、商品与消费者预期不符等等。退货运费险是保险企业与电商企业为解决因退货产生的损失而提出的一种新型保险类型。消费者在线上购买产品、下订单时可自愿性的购买该退货运费保险,若购买了该保险,该笔订单出现任何原因的退货,退货运费将会由保险公司承担。部分平台的部分在线零售商会为消费者赠送退货运费险,以此来提高服务水平和增加销售量。With the dailyization of online shopping, the online orders of e-commerce companies are also increasing, and according to statistical surveys, an average of three orders will generate a return order. There are many reasons why customers choose to return products, such as product quality problems, regrets after consumers' impulse purchases, products that do not meet consumers' expectations, and so on. Return freight insurance is a new type of insurance proposed by insurance companies and e-commerce companies to solve the losses caused by returns. Consumers can voluntarily purchase the return shipping insurance when purchasing products online and placing an order. If this insurance is purchased, the return shipping will be borne by the insurance company if the order is returned for any reason. Some online retailers on some platforms will provide consumers with return shipping insurance to improve service levels and increase sales.
作为第三方提供退货运费险业务的保险公司需要进行风险评估和管理从而制定退货运费险的定价策略,包括保费和赔偿费用。而目前线上平台提供的运费险的定价方法比较单一,赔偿费用一般为8-12人民币,而保费则简单的根据消费者在前一段时间的退货率来确定。虽然现行的退货运费险定价方法在一定程度上有效,但并没有充分考虑电子商务的背景和其特性,它既不能满足消费者的需求(尤其是他们的心理需求),也不能使保险公司的利润最大化。Insurance companies that provide return shipping insurance as a third party need to conduct risk assessment and management to formulate pricing strategies for return shipping insurance, including premiums and compensation costs. At present, the pricing method of freight insurance provided by online platforms is relatively simple. The compensation fee is generally 8-12 RMB, and the insurance premium is simply determined according to the return rate of consumers in the previous period. Although the current return shipping insurance pricing method is effective to a certain extent, it does not fully consider the background and characteristics of e-commerce, it can neither meet the needs of consumers (especially their psychological needs), nor make insurance companies' Profit maximization.
发明内容SUMMARY OF THE INVENTION
本发明提供一种退货运费险定价方法以及定价系统及其构建方法,用以解决现有退货运费险定价不能有效兼顾消费者需求以及保险利润的技术问题。The present invention provides a return shipping insurance pricing method, a pricing system and a construction method thereof, which are used to solve the technical problem that the existing return shipping insurance pricing cannot effectively take into account the needs of consumers and insurance profits.
本发明解决上述技术问题的技术方案如下:一种退货运费险定价方法,包括:The technical solution of the present invention to solve the above-mentioned technical problems is as follows: a method for pricing return shipping insurance, comprising:
基于客户对当前购买商品的行为数据和购买数据,采用退货概率预测模型,预测客户对该当前商品的退货概率;将所述退货概率带入以利润最大化为目标的目标函数,计算退货运费险价格;Based on the customer's behavior data and purchase data of the currently purchased product, the return probability prediction model is used to predict the customer's return probability of the current product; the return probability is brought into the objective function with the goal of maximizing profit, and the return shipping insurance is calculated. price;
其中,所述行为数据包括由点击商品、收藏商品和加入购物车的行为所产生的当前商品信息和行为信息所构成的数据;所述购买数据包括客户在线上平台上支付的订单数量、订单金额、退货订单的数量、退货商品信息和退货金额。Wherein, the behavior data includes the data formed by the current commodity information and behavior information generated by the behaviors of clicking the commodity, collecting the commodity and adding to the shopping cart; the purchase data includes the order quantity and the order amount paid by the customer on the online platform , the number of returned orders, the returned item information, and the return amount.
本发明的有益效果是:本方法是一种基于大数据挖掘算法的定价方法,该方法充分利用电商企业拥有充足、大量的客户行为数据及历史数据,对其通过预测模型评估客户的退货概率,既满足消费者的需求,也能使保险公司的利润最大化,是一种新型有效的退货运费险定价方法。The beneficial effects of the invention are as follows: the method is a pricing method based on a big data mining algorithm, which makes full use of the sufficient and large amount of customer behavior data and historical data owned by e-commerce enterprises, and evaluates the customer's return probability through a prediction model. , which not only meets the needs of consumers, but also maximizes the profits of insurance companies. It is a new and effective pricing method for return shipping insurance.
上述技术方案的基础上,本发明还可以做如下改进。On the basis of the above technical solutions, the present invention can also be improved as follows.
进一步,所述退货概率预测模型的构建方法为:Further, the construction method of the return probability prediction model is:
采用多个客户的对各商品的所述行为数据和所述购买数据,构建训练集和预测集;并采用所述训练集训练多个模型,采用所述预测集选择预测精度最高的模型,作为退货概率预测模型;Use the behavior data and the purchase data of multiple customers for each commodity to construct a training set and a prediction set; and use the training set to train multiple models, and use the prediction set to select the model with the highest prediction accuracy, as Return probability prediction model;
所述多个模型包括:多层感知器、随机森林和逻辑回归。The plurality of models include: Multilayer Perceptron, Random Forest, and Logistic Regression.
本发明的进一步有益效果是:预先构建训练集和预测集,通过训练集训练多个模型并用预测集来选择预测性能较好的模型,作为退货概率预测模型,提高退货运费险定价的客观性。The further beneficial effects of the present invention are: pre-constructing a training set and a prediction set, training multiple models through the training set, and using the prediction set to select a model with better prediction performance as a return probability prediction model, thereby improving the objectivity of return shipping insurance pricing.
进一步,在采用所述行为数据和所述购买数据之前,对所述行为数据和所述购买数据进行清洗、去重、去空预处理。Further, before using the behavior data and the purchase data, the behavior data and the purchase data are cleaned, deduplicated, and devoid of preprocessing.
进一步,所述目标函数为:π=pD(p,r)-PrC;Further, the objective function is: π=pD(p, r )-Pr C;
其中,p表示商品的退货运费险价格,C为赔偿费用,Pr为所述退货概率预测模型得到的退货概率,D(p,r)=α-βp-γr代表当退货运费险价格为p下该商品评价指数为r时的客户购买意愿,其中α为基本购买概率,β和γ为敏感指数。Among them, p represents the return freight insurance price of the product, C is the compensation cost, P r is the return probability obtained by the return probability prediction model, D(p,r)=α-βp-γr represents when the return freight insurance price is p The customer's purchase intention when the product evaluation index is r, where α is the basic purchase probability, and β and γ are the sensitive indexes.
本发明还提供一种退货运费险定价系统的构建方法,包括:The present invention also provides a method for constructing a return shipping insurance pricing system, comprising:
获取目标客户所下订单对应的多条数据样本,每条数据样本包括目标客户对各对应商品的如上所述的一种退货运费险定价方法中所述的行为数据和购买数据;Acquiring a plurality of data samples corresponding to the orders placed by the target customers, each data sample including the behavior data and purchase data described in the above-mentioned one return shipping insurance pricing method for the corresponding commodities by the target customers;
基于每条数据样本,采用如上所述的一种退货运费险定价方法,得到该条数据样本对应的商品的退货运费险价格;Based on each data sample, a return freight insurance pricing method as described above is used to obtain the return freight insurance price of the commodity corresponding to the data sample;
将每条数据样本及其对应的退货运费险价格构成一条训练样本数据,得到训练样本集,并输入如上所述的一种退货运费险定价方法中所述的退货概率预测模型,以退货运费险价格作为预测目标,训练得到退货运费险定价模型,作为该目标客户的退货运费险定价系统。Each data sample and its corresponding return freight insurance price constitute a piece of training sample data to obtain a training sample set, and input the return probability prediction model described in the above-mentioned one return freight insurance pricing method, to return freight insurance The price is used as the prediction target, and the return freight insurance pricing model is obtained by training, which is used as the return freight insurance pricing system for the target customer.
本发明的有益效果是:通过利用上述定价方法得到的最优价格最为价格软目标,将所选取的数据挖掘模型以退货运费险价格为预测目标,通过计算模型预测的价格与价格软目标之间的误差,最小化误差函数,从而优化模型参数,得到最后的定价系统,提高定价系统构建效率,同时为每个客户制定一个定价系统,针对性强,定价合理。The beneficial effects of the invention are: the optimal price obtained by using the above pricing method is the softest price target, the selected data mining model takes the return shipping insurance price as the prediction target, and the price predicted by the calculation model is between the price and the soft price target. The error is minimized, the error function is minimized, the model parameters are optimized, the final pricing system is obtained, and the construction efficiency of the pricing system is improved. At the same time, a pricing system is formulated for each customer, which is highly targeted and reasonable in pricing.
本发明还提供一种退货运费险定价方法,获取目标客户当前商品的所述行为数据和所述购买数据,输入由如上所述的方法所得到目标客户的退货运费险定价系统,得到目标客户当前商品的退货运费险价格,完成目标客户当前商品的退货运费险定价。The present invention also provides a return shipping insurance pricing method, which acquires the behavior data and the purchase data of the current commodity of the target customer, inputs the return shipping insurance pricing system of the target customer obtained by the above method, and obtains the current product of the target customer. Return freight insurance price of the product, complete the return freight insurance pricing of the target customer's current product.
本发明的有益效果是:采用上述的各客户的定价系统,可以一步式地获取最优定价决策,无需人工参与,系统自动给出针对客户个体的最优退货运费险价格,计算效率高。The beneficial effects of the present invention are: using the above-mentioned pricing system for each customer, the optimal pricing decision can be obtained in one step without manual participation, the system automatically provides the optimal return shipping insurance price for individual customers, and the calculation efficiency is high.
本发明还提供一种计算机可读存储介质,其特征在于,所述计算机可读存储介质包括存储的计算机程序,其中,在所述计算机程序被处理器运行时控制所述存储介质所在设备执行如上所述的一种退货运费险定价方法、如上所述的一种退货运费险定价系统的构建方法和/或如上所述的一种退货运费险定价方法。The present invention also provides a computer-readable storage medium, characterized in that the computer-readable storage medium includes a stored computer program, wherein when the computer program is run by a processor, the device where the storage medium is located is controlled to execute the above The method for pricing return freight insurance, the construction method for the above-mentioned method for pricing return freight insurance, and/or the above-mentioned method for pricing return freight insurance.
附图说明Description of drawings
图1为本发明实施例提供的一种退货运费险定价方法流程框图;1 is a flowchart of a method for pricing return shipping insurance provided by an embodiment of the present invention;
图2为本发明实施例提供的一种退货运费险定价系统构建流程图;Fig. 2 is a flow chart of constructing a return shipping insurance pricing system provided by an embodiment of the present invention;
图3为本发明实施例提供的一种退货运费险定价系统构建的具体实施的流程图。FIG. 3 is a flowchart of a specific implementation of the construction of a return shipping insurance pricing system provided by an embodiment of the present invention.
具体实施方式Detailed ways
为了使本发明的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本发明进行进一步详细说明。应当理解,此处所描述的具体实施例仅仅用以解释本发明,并不用于限定本发明。此外,下面所描述的本发明各个实施方式中所涉及到的技术特征只要彼此之间未构成冲突就可以相互组合。In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.
实施例一Example 1
一种退货运费险定价方法,如图1所示,包括:A pricing method for return shipping insurance, as shown in Figure 1, includes:
基于客户对当前购买商品的行为数据和购买数据,采用退货概率预测模型,预测客户对该当前商品的退货概率;将退货概率带入以利润最大化为目标的目标函数,计算退货运费险价格。Based on the customer's behavior data and purchase data of the currently purchased product, the return probability prediction model is used to predict the customer's return probability of the current product; the return probability is brought into the objective function with the goal of maximizing profit, and the return freight insurance price is calculated.
其中,行为数据包括由点击商品、收藏商品和加入购物车的行为所产生的当前商品信息和行为信息所构成的数据;购买数据包括客户在线上平台上支付的订单数量、订单金额、退货订单的数量、退货商品信息和退货金额。Among them, the behavior data includes the data composed of the current product information and behavior information generated by the behavior of clicking the product, collecting the product and adding to the shopping cart; the purchase data includes the order quantity paid by the customer on the online platform, the order amount, and the return order. Quantity, returned item information, and return amount.
本方法是一种基于大数据挖掘算法的定价方法,该方法充分利用电商企业拥有充足、大量的客户行为数据及历史数据,对其通过预测模型评估客户的退货概率,既满足消费者的需求,也能使保险公司的利润最大化,是一种新型有效的退货运费险定价方法。This method is a pricing method based on big data mining algorithm. This method makes full use of the sufficient and large amount of customer behavior data and historical data owned by e-commerce companies, and evaluates the customer's return probability through a prediction model, which not only meets the needs of consumers It can also maximize the profits of insurance companies, and it is a new and effective pricing method for return shipping insurance.
优选的,上述退货概率预测模型的构建方法为:Preferably, the construction method of the above-mentioned return probability prediction model is:
采用多个客户的对各商品的行为数据和购买数据,构建训练集和预测集;并采用训练集训练多个模型,采用预测集选择预测精度最高的模型,作为退货概率预测模型;所述多个模型包括:多层感知器、随机森林和逻辑回归。Use the behavior data and purchase data of multiple customers for each commodity to construct a training set and a prediction set; and use the training set to train multiple models, and use the prediction set to select the model with the highest prediction accuracy as the return probability prediction model; The models include: Multilayer Perceptron, Random Forest, and Logistic Regression.
预先构建训练集和预测集,通过训练集训练多个模型并用预测集来选择预测性能较好的模型,作为退货概率预测模型,提高退货运费险定价的客观性。Build a training set and a prediction set in advance, train multiple models through the training set, and use the prediction set to select the model with better prediction performance as the return probability prediction model to improve the objectivity of return shipping insurance pricing.
优选的,在采用行为数据和购买数据之前,对行为数据和购买数据进行清洗、去重、去空预处理。Preferably, before the behavior data and the purchase data are used, the behavior data and the purchase data are cleaned, deduplicated, and empty preprocessed.
优选的,上述目标函数为:π=pD(p,r)-PrC;Preferably, the above objective function is: π=pD(p, r )-Pr C;
其中,p表示商品的退货运费险价格,C为赔偿费用,Pr为退货概率预测模型得到的退货概率,D(p,r)=α-βp-γr代表当退货运费险价格为p下该商品评价指数为r时的客户购买意愿,其中α为基本购买概率,β和γ为敏感指数。此处的商品评价指数可通过统计分析商品的评价信息得到,敏感指数可通过对客户历史数据通过统计分析得到。Among them, p represents the return freight insurance price of the product, C is the compensation cost, P r is the return probability obtained by the return probability prediction model, and D(p,r)=α-βp-γr represents when the return freight insurance price is p. The customer's purchase intention when the commodity evaluation index is r, where α is the basic purchase probability, and β and γ are the sensitive indexes. The commodity evaluation index here can be obtained through statistical analysis of commodity evaluation information, and the sensitivity index can be obtained through statistical analysis of customer historical data.
实施例二Embodiment 2
一种退货运费险定价系统的构建方法,包括:A construction method of a return shipping insurance pricing system, comprising:
获取目标客户所下订单对应的多条数据样本,每条数据样本包括目标客户对各对应商品的如实施例一所述的一种退货运费险定价方法中所述的行为数据和购买数据;Acquiring a plurality of data samples corresponding to the orders placed by the target customer, each data sample including the behavior data and purchase data described in the method for pricing return shipping insurance according to the first embodiment of the target customer for each corresponding commodity;
基于每条数据样本,采用如实施例一所述的一种退货运费险定价方法,得到该条数据样本对应的商品的退货运费险价格;Based on each data sample, a return shipping insurance pricing method as described in Embodiment 1 is used to obtain the return shipping insurance price of the commodity corresponding to the data sample;
将每条数据样本及其对应的退货运费险价格构成一条训练样本数据,得到训练样本集,并输入如实施例一所述的一种退货运费险定价方法中所述的退货概率预测模型,以退货运费险价格作为预测目标,训练得到退货运费险定价模型,作为该目标客户的退货运费险定价系统。Each data sample and its corresponding return shipping insurance price constitute a piece of training sample data to obtain a training sample set, and input the return probability prediction model described in the method for pricing return shipping insurance as described in Embodiment 1, to obtain a training sample set. The return freight insurance price is used as the prediction target, and the return freight insurance pricing model is obtained by training, which is used as the return freight insurance pricing system for the target customer.
通过利用实施例一的定价方法得到的最优价格最为价格软目标,将所选取的数据挖掘模型以退货运费险价格为预测目标,通过计算模型预测的价格与价格软目标之间的误差,最小化误差函数,从而优化模型参数,得到最后的定价系统。The optimal price obtained by using the pricing method of the first embodiment is the soft price target, the selected data mining model takes the return shipping insurance price as the prediction target, and the error between the price predicted by the model and the soft price target is the smallest. The error function is optimized to optimize the model parameters and obtain the final pricing system.
例如,图2给出了本实施例提出的一种基于大数据技术的退货运费险定价系统构建的流程示意图,如图2所示,包括如下步骤:For example, FIG. 2 shows a schematic flowchart of the construction of a return shipping insurance pricing system based on big data technology proposed in this embodiment, as shown in FIG. 2 , including the following steps:
步骤1,客户行为数据与历史数据提取及预处理。Step 1, customer behavior data and historical data extraction and preprocessing.
电商企业线上平台的网络日志中存储着大量的客户行为数据与历史数据,均为消费者在电商网站上相应的操作留下的数据。将这些数据从网络日志中提取出来进行预处理,包括对数据的清洗、去重、去空,并通过初步的统计去除非普适性数据、异常数据等。A large amount of customer behavior data and historical data are stored in the network logs of the online platforms of e-commerce enterprises, which are all data left by consumers' corresponding operations on the e-commerce website. These data are extracted from network logs for preprocessing, including data cleaning, deduplication, and emptying, and preliminary statistics to remove non-universal data and abnormal data.
步骤2,预测客户退货概率并构建定价模型,得到适用于客户的最优价格,将其作为训练定价系统的软目标。Step 2: Predict the customer's return probability and build a pricing model to obtain the optimal price applicable to the customer, which is used as a soft target for training the pricing system.
具体地,图3的上半部分描述了该步骤的具体实施的流程图。将与处理过的数据作为输入,选取一个预测模型,该预测模型可以是某一个数据挖掘算法,在此以多层感知器预测模型为例,该预测模型将消费者是否退货作为预测目标,预测消费者的退货概率,将预测得到的退货概率用于价格决策模型性,得到最优价格决策,将其称为价格软目标,将用于指导定价预测模型的训练。Specifically, the upper part of FIG. 3 describes the flow chart of the specific implementation of this step. Taking the processed data as input, select a prediction model, which can be a data mining algorithm. Here, take the multi-layer perceptron prediction model as an example. The consumer's return probability, the predicted return probability is used for the price decision model, and the optimal price decision is obtained, which is called the price soft target, and will be used to guide the training of the pricing prediction model.
该步骤中的价格决策模型的目标函数为保险公司每一单的利润函数,公式如下:The objective function of the price decision model in this step is the profit function of each order of the insurance company, and the formula is as follows:
π=pD(p,r)-PrCπ=pD(p, r )-Pr C
其中,p表示此单保险的保费,C为赔偿费用,Pr为前一步的预测模型中得到的客户退货概率,D(p,r)=α-βp-γr代表当退货运费险价格为p,该商品评价指数为r时的客户购买意愿,其中α为基本购买概率,β和γ为敏感指数。此处我们可设置客户的基本购买概率α=0.5,商品评价指数可通过计算商品的好评率得到,敏感指数可通过对客户历史数据通过统计分析,将其设置为[0,1]区间内的数值。Among them, p represents the premium of this single insurance, C is the compensation cost, P r is the customer return probability obtained in the prediction model of the previous step, D(p,r)=α-βp-γr represents when the return shipping insurance price is p , the customer's purchase intention when the product evaluation index is r, where α is the basic purchase probability, and β and γ are the sensitive indexes. Here we can set the customer's basic purchase probability α=0.5, the product evaluation index can be obtained by calculating the product's favorable rate, and the sensitivity index can be set as a value in the [0,1] interval through statistical analysis of the customer's historical data. numerical value.
通过该公式,以利润最大化为优化目标,得到最优的定价,并将其作为价格软目标运用于定价系统的指导训练。Through this formula, taking profit maximization as the optimization goal, the optimal pricing is obtained, and it is used as the price soft objective to guide the training of the pricing system.
步骤3,利用获得的价格软目标训练价格预测模型,构建定价系统。Step 3, using the obtained price soft target to train a price prediction model to build a pricing system.
具体地,图3的下半部分描述了该步骤的具体实施的流程图。将步骤2中选取的预测模型,以步骤1得到的数据作为输入,以退货运费险价格作为预测模版,并利用步骤2得到的价格软目标进行模型训练,通过计算模型预测的价格与价格软目标之间的交叉熵,最小化误差函数,从而优化模型参数,得到最后的定价系统,该系统可以将预处理后的客户行为数据与历史数据作为输入,然后直接输出最优定价决策。Specifically, the lower part of FIG. 3 describes the flow chart of the specific implementation of this step. Take the prediction model selected in step 2, take the data obtained in step 1 as input, take the return shipping insurance price as the prediction template, and use the price soft target obtained in step 2 for model training, and calculate the price and price soft target predicted by the model. The cross-entropy between the two can minimize the error function, so as to optimize the model parameters and obtain the final pricing system, which can take the preprocessed customer behavior data and historical data as input, and then directly output the optimal pricing decision.
因此,本方法是一种基于大数据挖掘算法的定价方法,并提出基于客户数据的一步式的定价系统。该方法充分利用电商企业拥有充足、大量的客户行为数据及历史数据,对其进行预处理后通过预测模型评估客户的退货概率,并以利润最大化为目标,将客户退货概率值带入目标函数,计算得到最优的退货运费险价格,最后将得到的最优价格作为价格软目标,指导训练所选取的预测模型以退货运费险价格为预测目标,优化模型参数,得到最后的定价系统。本发明实施例可以有效的利用客户行为数据与历史数据,从数据中挖掘有效信息,优化退货运费险价格,构建准确有效的定价系统。Therefore, this method is a pricing method based on big data mining algorithm, and proposes a one-step pricing system based on customer data. This method makes full use of the sufficient and large amount of customer behavior data and historical data owned by e-commerce companies, and evaluates the customer's return probability through a predictive model after preprocessing, and takes profit maximization as the goal, bringing the customer return probability value into the target. Function, calculate the optimal return freight insurance price, and finally take the obtained optimal price as the soft price target, guide the training selected prediction model to take the return freight insurance price as the prediction target, optimize the model parameters, and get the final pricing system. The embodiment of the present invention can effectively utilize customer behavior data and historical data, mine effective information from the data, optimize the return shipping insurance price, and build an accurate and effective pricing system.
实施例三Embodiment 3
一种退货运费险定价方法,获取目标客户当前商品的行为数据和购买数据,输入由如实施例二所述的方法所得到的目标客户的退货运费险定价系统中,得到目标客户当前商品的退货运费险价格,完成目标客户当前商品的退货运费险定价。A return freight insurance pricing method, obtaining the behavior data and purchase data of the current commodity of the target customer, inputting the return freight insurance pricing system of the target customer obtained by the method as described in the second embodiment, and obtaining the return of the current commodity of the target customer. Freight insurance price, complete the return freight insurance pricing of the target customer's current product.
实施例二的系统可以一步式地获取最优定价决策,无需人工参与,计算效率高,系统自动给出针对客户个性化的最优退货运费险价格。The system of the second embodiment can obtain the optimal pricing decision in one step, without manual participation, and has high calculation efficiency, and the system automatically provides the optimal return shipping insurance price personalized for the customer.
以上实施例设计的退货运费险的定价方法和系统可以有效的利用客户行为数据与历史数据,从数据中挖掘有效信息,并通过价格决策模型将其转化为最优的价格信息,以此来指导价格预测模型的训练,构建准确、有效的定价系统。该技术方案构建的定价系统可以将预处理过的客户行为数据与历史数据作为输入,从而得到针对每个客户个体的最优退货运费险定价,使得该项保险业务利润最大化。不同于现有技术,本文所提出的定价方法考虑了商品的评价指数对退货运费险购买的影响并从大数据中挖掘出客户的潜在退货意愿信息,制定对于客户个体个性化的定价方案。同时,本文构建的定价系统可以一步式地获取最优定价决策,无需人工参与,系统自动给出针对客户个体的最优退货运费险价格,计算效率高。The pricing method and system for return shipping insurance designed in the above embodiments can effectively use customer behavior data and historical data, mine effective information from the data, and convert it into optimal price information through a price decision model, so as to guide Training of price prediction models to build accurate and effective pricing systems. The pricing system constructed by the technical solution can take the preprocessed customer behavior data and historical data as input, so as to obtain the optimal return shipping insurance pricing for each individual customer, so as to maximize the profit of the insurance business. Different from the existing technology, the pricing method proposed in this paper considers the impact of the evaluation index of the product on the purchase of return shipping insurance, and mines the customer's potential return willingness information from big data, and formulates a personalized pricing plan for the customer. At the same time, the pricing system constructed in this paper can obtain the optimal pricing decision in one step, without manual participation, the system automatically gives the optimal return shipping insurance price for individual customers, and the calculation efficiency is high.
实施例四Embodiment 4
一种计算机可读存储介质,其特征在于,所述计算机可读存储介质包括存储的计算机程序,其中,在所述计算机程序被处理器运行时控制所述存储介质所在设备执行如实施例一所述的一种退货运费险定价方法、如实施例二所述的一种退货运费险定价系统的构建方法和/或如实施例三所述的一种退货运费险定价方法。A computer-readable storage medium, characterized in that the computer-readable storage medium includes a stored computer program, wherein when the computer program is run by a processor, the device where the storage medium is located is controlled to execute as described in the first embodiment. The method for pricing the return freight insurance described in the above, the construction method for the return freight insurance pricing system as described in the second embodiment, and/or the return freight insurance pricing method as described in the third embodiment.
相关技术方案同实施例一至实施例三,在此不再赘述。The related technical solutions are the same as those of the first embodiment to the third embodiment, and are not repeated here.
本领域的技术人员容易理解,以上所述仅为本发明的较佳实施例而已,并不用以限制本发明,凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等,均应包含在本发明的保护范围之内。Those skilled in the art can easily understand that the above are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention, etc., All should be included within the protection scope of the present invention.
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