Causal inference based incentive scheme determination method and apparatus, device and medium

Abstract

The embodiment of the application provides a method and device for determining an incentive scheme based on causal inference, equipment and a medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: obtaining customer basic information, historical incentive data and corresponding historical interaction behavior data of a target customer, and extracting multi-dimensional feature information of the target customer; performing deep clustering on the target customer according to the multi-dimensional feature information, obtaining a plurality of target customer groups, and determining a customer group feature vector of each target customer group; determining the interaction promotion gain of each target customer group under each incentive strategy based on the customer group feature vector through a trained causal inference model; performing multi-objective optimization according to the incentive cost of each incentive strategy and the corresponding interaction promotion gain, obtaining an optimal incentive strategy of each target customer group, and determining an incentive scheme of each target customer group according to the optimal incentive strategy. The embodiment of the application can improve the accuracy of issuing incentives to customers and the flow efficiency of an interaction platform.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method and apparatus, device, and medium for determining an incentive scheme based on causal inference. Background Art

[0002] Insurance companies typically develop customer-focused interaction platforms, using a layered approach of incentives from headquarters, agencies, and agents themselves to attract new customers from these platforms. Typically, within a given budget, various incentives are offered to agents' clients, hoping to maximize customer-agent interaction and drive more efficient sales. The form of incentives is typically determined by factors such as timing, quantity, and content.

[0003] In the past, incentives were largely determined manually by headquarters operations staff, lacking a comprehensive understanding of customer categorization and group management. The effectiveness of incentives was often assessed based on the final total, which affected the accuracy of incentive distribution. Furthermore, since each agent's client profile is unique, incentives lacking refined operations were detrimental to maximizing effectiveness and impacted the efficiency of attracting traffic to the interactive platform. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose a method and device, equipment and medium for determining an incentive scheme based on causal inference, aiming to improve the accuracy of incentives issued to customers and the efficiency of attracting traffic to the interactive platform.

[0005] To achieve the above objectives, a first aspect of an embodiment of the present application proposes a method for determining an incentive scheme based on causal inference, the method comprising:

[0006] Obtaining basic customer information, historical incentive data, and corresponding historical interactive behavior data of the target customer, and extracting multi-dimensional feature information of the target customer based on the basic customer information, the historical incentive data, and the historical interactive behavior data;

[0007] Performing deep clustering on the target customers according to the multi-dimensional feature information to obtain multiple target customer groups, and determining a customer group feature vector for each target customer group;

[0008] Determine the interaction enhancement gain of each target customer group under each incentive strategy based on the customer group feature vector and multiple preset incentive strategies through a pre-trained causal inference model;

[0009] According to the incentive cost of each incentive strategy and the corresponding interaction promotion gain, multi-objective optimization is performed to obtain an optimal incentive strategy of each target customer group, and then the incentive scheme of each target customer group is determined according to the optimal incentive strategy.

[0010] In some embodiments, the customer basic information, the historical incentive data and the corresponding historical interaction behavior data of the target customer are obtained, and multi-dimensional feature information of the target customer is extracted according to the customer basic information, the historical incentive data and the historical interaction behavior data, including:

[0011] The historical incentive data of the target customer in a preset first historical period and the historical interaction behavior data of the target customer in a preset second historical period are obtained through an interaction platform;

[0012] The historical incentive times and the historical incentive content are determined according to the historical incentive data, and the historical interaction behavior index is determined according to the historical interaction behavior data;

[0013] The customer basic information of the target customer is obtained, and a plurality of customer basic attributes are determined according to the customer basic information;

[0014] The multi-dimensional feature information is determined according to the customer basic attributes, the historical incentive times, the historical incentive content and the historical interaction behavior index;

[0015] The second historical period is the next period of the first historical period.

[0016] In some embodiments, the target customer is deeply clustered according to the multi-dimensional feature information to obtain a plurality of target customer groups, and a customer group feature vector of each target customer group is determined, including:

[0017] The multi-dimensional feature information is input into a pre-trained SCCL clustering model to obtain a customer category corresponding to each target customer;

[0018] The target customer is divided into a plurality of target customer groups according to the customer category;

[0019] The customer group feature vector is generated according to the customer basic information of each target customer in the target customer group.

[0020] In some embodiments, the SCCL clustering model is trained by the following steps:

[0021] A plurality of sample customer feature data are obtained, including sample customer basic attributes, sample incentive times, sample incentive content and sample interaction behavior index;

[0022] The sample customer feature data is data enhanced to obtain corresponding enhanced customer feature data, and then an enhanced data set is constructed according to the sample customer feature data and the enhanced customer feature data;

[0023] The SCCL clustering model is constructed, and the contrast loss function and the clustering loss function are determined;

[0024] The sample customer feature data and the corresponding enhanced customer feature data are used as a positive sample pair, and other data samples in the enhanced data set except the positive sample pair are used as negative samples. The positive sample pair and the negative sample are subjected to contrast learning and clustering learning, and the contrast loss value is determined by the contrast loss function, and the clustering loss value is determined by the clustering loss function;

[0025] The parameters of the SCCL clustering model are updated according to the contrast loss value and the clustering loss value to obtain the trained SCCL clustering model.

[0026] In some embodiments, the causal inference model is trained by the following steps:

[0027] A plurality of sample customer incentive data corresponding to each of the incentive strategies is obtained, the sample customer incentive data including a sample customer feature vector, incentive strategy encoding data, and corresponding sample interaction behavior indicators;

[0028] The incentive cost of each of the incentive strategies is determined, and a control group is constructed according to the sample customer incentive data corresponding to the incentive strategy with the minimum incentive cost, and a plurality of intervention groups are constructed according to the sample customer incentive data corresponding to each of the other incentive strategies;

[0029] Based on the X-Learner model, reinforcement learning and causal inference are performed according to the control group and the intervention groups to obtain the causal inference model.

[0030] In some embodiments, the interaction promotion gain of each of the target customer groups under each of the incentive strategies is determined based on the customer group feature vector and a plurality of preset incentive strategies by using the pre-trained causal inference model, including:

[0031] Each of the customer group feature vectors and the incentive strategy encoding corresponding to each of the incentive strategies are input into the causal inference model in pairs to obtain the interaction behavior indicator prediction value of each of the target customer groups under each of the incentive strategies;

[0032] The interaction behavior indicator prediction value of each of the target customer groups under the incentive strategy with the minimum incentive cost is determined as the interaction behavior indicator baseline value of each of the target customer groups;

[0033] The interaction promotion gain of each target customer group under each incentive strategy is determined according to a difference between the interaction behavior index prediction value and the interaction behavior index benchmark value.

[0034] In some embodiments, the multi-objective optimization is performed on the incentive cost of each incentive strategy and the corresponding interaction promotion gain, to obtain an optimal incentive strategy of each target customer group, including:

[0035] A target function related to the incentive cost and the interaction promotion gain is constructed, and an incentive cost constraint and a positive gain constraint are determined;

[0036] An initial population corresponding to each target customer group is constructed according to the incentive strategy and the corresponding interaction promotion gain, and a fitness function is determined according to the target function, the incentive cost constraint, and the positive gain constraint;

[0037] The initial population of each target customer group is optimized by using a genetic algorithm according to the fitness function, to obtain an optimal solution corresponding to each target customer group;

[0038] The optimal incentive strategy of each target customer group is determined according to the optimal solution.

[0039] To achieve the above object, a second aspect of the embodiment of the present application provides a device for determining an incentive scheme based on causal inference, which comprises:

[0040] A feature extraction module is configured to acquire customer basic information, historical incentive data, and corresponding historical interaction behavior data of a target customer, and extract multi-dimensional feature information of the target customer according to the customer basic information, the historical incentive data, and the historical interaction behavior data;

[0041] A customer clustering module is configured to perform deep clustering on the target customer according to the multi-dimensional feature information, to obtain a plurality of target customer groups, and determine a customer group feature vector of each target customer group;

[0042] An interaction promotion gain determination module is configured to determine an interaction promotion gain of each target customer group under each incentive strategy by using a pre-trained causal inference model based on the customer group feature vector and a plurality of preset incentive strategies;

[0043] An optimization module is configured to perform multi-objective optimization on the incentive cost of each incentive strategy and the corresponding interaction promotion gain, to obtain an optimal incentive strategy of each target customer group, and further determine an incentive scheme of each target customer group according to the optimal incentive strategy.

[0044] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory, a processor, a program stored on the memory and runnable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the method for determining an incentive scheme based on causal inference as described in the first aspect above is implemented.

[0045] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the incentive scheme determination method based on causal inference as described in the first aspect above.

[0046] The present application proposes a method, apparatus, device and medium for determining an incentive scheme based on causal inference, which obtains basic customer information, historical incentive data and corresponding historical interactive behavior data of target customers, and extracts multi-dimensional feature information of target customers based on the basic customer information, historical incentive data and historical interactive behavior data, deeply clusters the target customers based on the multi-dimensional feature information to obtain multiple target customer groups, and determines the customer group feature vectors of each target customer group. Through a pre-trained causal inference model, based on the customer group feature vectors and multiple preset incentive strategies, the interaction enhancement gain of each target customer group under each incentive strategy is determined, and multi-objective optimization is performed based on the incentive cost of each incentive strategy and the corresponding interaction enhancement gain to obtain the optimal incentive strategy for each target customer group, and then the incentive scheme for each target customer group is determined based on the optimal incentive strategy. The embodiment of the present application extracts multi-dimensional feature information of customers based on their basic information, historical incentive data, and historical interactive behavior data, performs deep clustering of customers based on the multi-dimensional feature information to obtain multiple customer groups, determines the interaction enhancement gain of each customer group under different incentive strategies through a causal inference model, and obtains the optimal incentive strategy for each customer group through multi-objective optimization, thereby determining the incentive plan for each customer group based on the optimal incentive strategy, thereby improving the accuracy of incentives issued to customers and the efficiency of attracting traffic to the interactive platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of a method for determining an incentive scheme based on causal inference provided in an embodiment of the present application;

[0048] Figure 2 yes Figure 1 A flow chart of step S101 in FIG.

[0049] Figure 3 yes Figure 1A flow chart of step S102 in FIG.

[0050] Figure 4 This is a flow chart for training the SCCL clustering model provided in an embodiment of the present application;

[0051] Figure 5 This is a flow chart of a causal inference model training method provided in an embodiment of the present application;

[0052] Figure 6 yes Figure 1 A flow chart of step S103 in FIG.

[0053] Figure 7 yes Figure 1 A flow chart of step S104 in FIG.

[0054] Figure 8 Schematic diagram of the structure of the device for determining an incentive scheme based on causal inference provided in an embodiment of the present application;

[0055] Figure 9 Schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application;

[0056] Figure 10 It is a structural diagram of the storage medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0058] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0060] First, let’s analyze some of the terms used in this application:

[0061] Artificial intelligence (AI): is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence; artificial intelligence is a branch of computer science, artificial intelligence tries to understand the essence of intelligence, and produce a new intelligent machine that can react in a similar way to human intelligence, the research in this field includes robots, language recognition, image recognition, natural language processing and expert systems, etc. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is also the theory, method, technology and application system of using digital computer or digital computer controlled machine to simulate, extend and expand human intelligence, perceive environment, acquire knowledge and use knowledge to obtain the best results.

[0062] Customer motivation: through the way of material reward, the behavior of improving certain operation / product data indicators can help insurance agents improve performance and achieve KPI; in addition, since the customer gets a certain incentive in the product, the sunk cost is increased, so the customer stickiness is also increased.

[0063] Deep clustering: clustering analysis is a key technology for mining the internal structure of data. In the era of big data, people are faced with data that usually has the characteristics of large scale, high dimension and complex structure. Direct application of traditional clustering algorithms often fails. Deep learning makes large-scale deep feature extraction possible due to its hierarchical nonlinear mapping capability, so clustering algorithms based on deep learning (deep clustering) have quickly become a research hotspot in the field of unsupervised learning.

[0064] Causal inference: causal inference is one of the core problems of statistics and data science. It is the process of drawing conclusions about causal relationships in the case of a phenomenon that has already occurred. It has wide application in biomedical, economic management and social science, and can reveal the causal relationship between variables and discover the deep reasons behind phenomena. Causal inference is also considered a paradigm revolution in the field of artificial intelligence, and is one of the research hotspots in this field in recent years.

[0065] Genetic Algorithm (GA): It was first proposed by John Holland of the United States in the 1970s. The algorithm is designed according to the evolution law of organisms in nature. It is a computational model that simulates the natural selection and genetic mechanism of Darwin's biological evolution process. It is a method of searching for optimal solutions by simulating the natural evolution process. The algorithm uses mathematical methods to simulate computer operations, and converts the problem solving process into processes such as chromosome gene crossing and mutation in biological evolution. In solving relatively complex combinatorial optimization problems, it can usually obtain better optimization results faster than some conventional optimization algorithms. Genetic algorithm has been widely used in combinatorial optimization, machine learning, signal processing, adaptive control and artificial life fields.

[0066] Insurance companies usually develop some interactive platforms for customers, and through the headquarters, institutions, agents themselves, etc. Incentives, agents can guide customers from the platform. Generally, under the given budget, some different forms of incentives are issued to the customers of the agents, hoping to maximize the interaction between the customers and the agents through incentives to promote more efficient business actions of the agents. The form of incentive is usually determined by time, quantity, content and other factors.

[0067] In the past, the incentive behavior was basically determined manually by the operation personnel of the headquarters, lacking classification understanding and group management of different customers. The effect of the incentive was also reviewed in terms of the final total amount, which affected the accuracy of the incentive issued to the customers. At the same time, since the customers of each agent are not the same, the lack of fine operation of the incentive activity is not conducive to the maximization of benefits, and also affects the efficiency of the interactive platform.

[0068] Therefore, the embodiment of the present application provides a kind of incentive scheme determination method and device based on causal inference, equipment and medium based on causal inference, to improve the accuracy of the incentive issued to the customers and the efficiency of the interactive platform. The embodiment of the present application introduces the incentive scheme of the interactive platform optimized by the causal inference model, which can actually improve the interests of the insurance company, the agent and the customer.

[0069] The incentive scheme determination method and device based on causal inference provided by the embodiment of the present application are specifically described by the following embodiments. First, the incentive scheme determination method based on causal inference in the embodiment of the present application is described.

[0070] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0071] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0072] The method for determining an incentive scheme based on causal inference provided in the embodiment of the present application relates to the field of artificial intelligence technology. The method for determining an incentive scheme based on causal inference provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the method for determining an incentive scheme based on causal inference, etc., but is not limited to the above forms.

[0073] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0074] It should be noted that in various specific embodiments of the present application, when relevant processing needs to be performed on data related to the identity or characteristics of the user, such as user information, user behavior data, user history data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of such data will comply with relevant laws, regulations, and standards. In addition, when the embodiments of the present application need to obtain sensitive personal information of the user, the separate permission or separate consent of the user will be obtained through a pop-up window or a jump to a confirmation page, and after obtaining the separate permission or separate consent of the user, the necessary user-related data for the normal operation of the embodiments of the present application will be obtained.

[0075] Figure 1 is an optional flowchart of the incentive scheme determination method based on causal inference provided by the embodiments of the present application, Figure 1 The method in can include but is not limited to steps S101-S104.

[0076] Step S101, obtaining customer basic information, historical incentive data, and corresponding historical interaction behavior data of a target customer, and extracting multi-dimensional feature information of the target customer according to the customer basic information, the historical incentive data, and the historical interaction behavior data;

[0077] Step S102, performing deep clustering on the target customer according to the multi-dimensional feature information to obtain a plurality of target customer groups, and determining a customer group feature vector of each target customer group;

[0078] Step S103, determining the interaction promotion gain of each target customer group under each incentive strategy based on the customer group feature vector and a plurality of preset incentive strategies through a pre-trained causal inference model;

[0079] Step S104, performing multi-objective optimization according to the incentive cost of each incentive strategy and the corresponding interaction promotion gain to obtain an optimal incentive strategy for each target customer group, and further determining an incentive scheme for each target customer group according to the optimal incentive strategy.

[0080] The steps S101-S104 shown in the embodiments of the present application extract multi-dimensional feature information of the customer according to the customer basic information, the historical incentive data, and the historical interaction behavior data of the customer, perform deep clustering on the customer according to the multi-dimensional feature information to obtain a plurality of customer groups, determine the interaction promotion gain of each customer group under different incentive strategies through a causal inference model, and obtain the optimal incentive strategy for each customer group through multi-objective optimization, so that the incentive scheme for each customer group can be determined according to the optimal incentive strategy, and the accuracy of issuing incentives to customers and the efficiency of attracting traffic to the interaction platform are improved.

[0081] Please refer to Figure 2 In some embodiments, step S101 may include but is not limited to steps S1011 to S1014:

[0082] Step S1011, obtaining the target customer's historical incentive data in a preset first historical period and historical interactive behavior data in a preset second historical period through the interactive platform;

[0083] Step S1012, determining the number of historical incentives and the content of historical incentives based on the historical incentive data, and determining the historical interactive behavior index based on the historical interactive behavior data;

[0084] Step S1013, obtaining basic customer information of the target customer, and determining multiple basic customer attributes based on the basic customer information;

[0085] Step S1014, determining multi-dimensional feature information based on the customer's basic attributes, historical incentive times, historical incentive content, and historical interactive behavior indicators;

[0086] Among them, the second historical period is the next period of the first historical period.

[0087] In some embodiments, customer characteristics are calculated offline based on customer data of an interactive platform: first, characteristic information of the customer is extracted from statistical data of multiple dimensions such as the incentives previously obtained by the customer on the interactive platform, basic information of the customer, and actual interactive behaviors generated by the customer. Such characteristic information includes age, gender, number of historical incentives, content of incentives, interactive behavior indicators, etc.

[0088] In some embodiments, basic customer information may include basic information, asset information, financial transaction information, fund flow information, payroll information, and mobile banking information. Basic information may include age, gender, location, and customer group. Customer groups may include retirees, fishing groups, and others. Asset information may include current deposits, time deposits, wealth management products, funds, precious metals, and loans. Financial transaction information may include the number and amount of credit card transactions, the number and amount of transfer and remittance transactions, the number and amount of fund, insurance, and wealth management transactions, and the amount of fund, insurance, and wealth management transactions. Fund flow information may include information on education, healthcare, and travel. Payroll information may include the amount of payroll payments and monthly fluctuations. Mobile banking information may include the number of days logged in, active columns, and active functions.

[0089] A customer profile can be constructed based on the customer's basic information, and the generated customer profile label can be used as multiple basic customer attributes of the customer.

[0090] See also Figure 3In some embodiments, step S102 may include but is not limited to steps S1021 to S1023:

[0091] Step S1021: Input the multi-dimensional feature information into the pre-trained SCCL clustering model to obtain the customer category corresponding to each target customer;

[0092] Step S1022, dividing the target customers into multiple target customer groups according to customer categories;

[0093] Step S1023 : generating a customer group feature vector according to the basic customer information of each target customer in the target customer group.

[0094] In some embodiments, target customers are deeply clustered based on the SCCL clustering model to divide target customer groups: the multidimensional feature information obtained by offline calculation is input into a pre-trained SCCL clustering model to obtain the customer category corresponding to each target customer, and the target customers are divided into multiple target customer groups according to the customer category; in addition, for each target customer group, a customer group feature vector is generated through the statistical characteristics of the basic customer information of each target customer in the group.

[0095] It should be noted that the statistical features here can be the mean, variance and other features of numerical data (such as age and deposits), or the probability features of numerical data in different numerical ranges, or the probability features of attribute data (such as gender and place of origin) under different labels, ultimately forming an overall customer group feature vector of the target object group.

[0096] SCCL is a clustering method that can simultaneously consider the relationship between samples and attributes for bidirectional optimization iteration. It can minimize the differences between clusters while maintaining the internal similarity of samples.

[0097] See also Figure 4 In some embodiments, the steps of training the SCCL clustering model may include but are not limited to steps S201 to S205:

[0098] Step S201, obtaining multiple sample customer feature data, the sample customer feature data including sample customer basic attributes, sample incentive times, sample incentive content, and sample interactive behavior indicators;

[0099] Step S202: performing data enhancement on each sample customer feature data to obtain corresponding enhanced customer feature data, and then constructing an enhanced data set based on the sample customer feature data and the enhanced customer feature data;

[0100] Step S203, constructing an SCCL clustering model and determining a contrast loss function and a clustering loss function;

[0101] Step S204, taking the sample customer feature data and the corresponding enhanced customer feature data as a positive sample pair, taking other data samples in the enhanced data set as negative samples, performing contrast learning and clustering learning on the positive sample pair and the negative samples, and determining a contrast loss value through a contrast loss function and a clustering loss value through a clustering loss function;

[0102] Step S205, updating the parameters of the SCCL clustering model according to the contrast loss value and the clustering loss value to obtain a trained SCCL clustering model.

[0103] In some embodiments, the overall framework of SCCL is composed of three parts, including a neural network, a contrast loss function and a clustering loss function, the neural network is used to map the input data to the representation space, and the contrast loss function and the clustering loss function are used to calculate the contrast loss value and the clustering loss value, respectively.

[0104] In some embodiments, the training data of the SCCL clustering model is composed of original data and enhanced data. For each sample customer feature data corresponding to one enhanced customer feature data is generated through data enhancement, and finally an enhanced data set

[0105] In some embodiments, one sample customer feature data and the corresponding enhanced customer feature data are selected as a positive sample pair from the enhanced data set, the remaining 2M-2 data samples are taken as negative samples, and after inputting into the SCCL clustering model, the contrast loss value is calculated through the contrast loss function to pull the distance between the positive sample pair and the distance between the positive sample pair and the negative sample, and the clustering loss value is calculated through the clustering loss function.

[0106] In some embodiments, the joint loss value of the SCCL clustering model is determined according to the contrast loss value and the clustering loss value, and the SCCL clustering model is optimized based on the joint loss value until the model converges or reaches a preset number of iterations, and a trained SCCL clustering model is obtained.

[0107] Please refer to Figure 5 In some embodiments, the step of training the causal inference model can include but is not limited to steps S301 to S303:

[0108] Step S301, obtaining a plurality of sample customer incentive data corresponding to each incentive strategy, the sample customer incentive data including a sample customer feature vector, incentive strategy encoding data and corresponding sample interaction behavior indicators;

[0109] Step S302: determining the incentive cost of each incentive strategy, constructing a control group based on the incentive data of sample customers corresponding to the incentive strategy with the lowest incentive cost, and constructing corresponding multiple intervention groups based on the incentive data of sample customers corresponding to other incentive strategies;

[0110] Step S303: Perform reinforcement learning and causal inference based on the X-Learner model according to the control group and the intervention group to obtain a causal inference model.

[0111] In some embodiments, the three-element vectors of time, quantity and incentive value are independently numbered using One-Hot encoding, and converted into binary form to represent different possible values, and then the incentive strategy encoding data is constructed using a time function.

[0112] In some embodiments, after determining the incentive strategy encoding data, a causal inference model is selected to estimate the causal effect. The embodiment of the present application uses the X-Learner model to model each incentive strategy, and uses the strategy with the smallest total incentive cost as the benchmark strategy to calculate and evaluate the improvement in the interactive effect of other incentive strategies, i.e., uplift. X-Learner is a model based on reinforcement learning and causal inference. It can estimate the influence of each strategy on the core indicators from the observed data and make decisions based on the estimated results.

[0113] In some embodiments, multiple sample customer incentive data under various incentive strategies are first determined, including sample customer feature vectors, incentive strategy encoding data, and corresponding sample interactive behavior indicators; then, the sample customer incentive data corresponding to the incentive strategy with the smallest incentive cost is used as a control group, and the remaining sample customer incentive data are divided into multiple intervention groups based on the similarities and differences in incentive strategies, and X-Learner models are established for the control group and the intervention group respectively; X-Learner first recognizes the possible information gap between the intervention group and the control group, and then estimates the missing effect of the intervention on the other group by using the data of each group, and then uses these estimates to predict the results to obtain a trained causal inference model.

[0114] See also Figure 6 In some embodiments, step S103 may include but is not limited to steps S1031 to S1033:

[0115] Step S1031: Input each customer group's feature vector and each incentive strategy's corresponding incentive strategy code into a causal inference model to obtain a predicted interactive behavior indicator value for each target customer group under each incentive strategy.

[0116] Step S1032, determining the interaction behavior index prediction value of each target customer group under the incentive strategy with the minimum incentive cost as the interaction behavior index benchmark value of each target customer group;

[0117] Step S1033, determining the interaction promotion gain of each target customer group under each incentive strategy according to the difference between the interaction behavior index prediction value and the interaction behavior index benchmark value.

[0118] In some embodiments, the customer group feature vector of the target customer group A and the incentive strategy encoding corresponding to the incentive strategy C are input into the causal inference model, and the model output of the interaction behavior index prediction value of the target customer group A under the incentive strategy C is obtained.

[0119] In some embodiments, the interaction behavior index prediction value of the target customer group A under the incentive strategy with the minimum incentive cost is taken as the interaction behavior index benchmark value of the target customer group A, and the interaction promotion gain of the target customer group A under the incentive strategy C is determined according to the difference between the interaction behavior index prediction value and the interaction behavior index benchmark value.

[0120] It should be noted that the interaction promotion gain corresponding to the incentive strategy with the minimum incentive cost is 0, and the interaction promotion gain corresponding to other incentive strategies can be positive or negative.

[0121] Please refer to Figure 7 In some embodiments, step S104 can include but is not limited to steps S1041 to S1044:

[0122] Step S1041, constructing a target function about the incentive cost and the interaction promotion gain, and determining the incentive cost constraint and the positive gain constraint;

[0123] Step S1042, constructing the initial population corresponding to each target customer group according to the incentive strategy and the corresponding interaction promotion gain, and determining the fitness function according to the target function, the incentive cost constraint and the positive gain constraint;

[0124] Step S1043, optimizing each initial population according to the fitness function by using the genetic algorithm to obtain the optimal solution corresponding to each target customer group;

[0125] Step S1044, determining the optimal incentive strategy of each target customer group according to the optimal solution.

[0126] In some embodiments, the optimal strategy is solved through multi-objective optimization: using the predicted uplift of the target customer group's core indicators under different incentive strategies (i.e., the interaction uplift gain), a genetic algorithm is used to perform multi-objective optimization, solving for the optimal incentive strategy that maximizes the difference between the benefit brought by the interaction uplift gain and the incentive cost. The objective constraints include the incentive cost constraint (which must not exceed an upper limit) and the need for the strategy to generate positive gains (the interaction uplift gain is not less than 0). This process aims to find the most appropriate, most effective, or well-balanced specific incentive solution that meets multiple needs.

[0127] In some embodiments, an objective function is constructed regarding the incentive cost and interaction improvement gain, where the objective function can be expressed as min(s*at), where s represents the interaction improvement gain, a represents the conversion rate of interaction and benefit, and t represents the incentive cost. The incentive cost constraint can be expressed as t≤t max , t max represents the upper limit of the incentive cost, and the positive gain constraint can be expressed as s≥0.

[0128] In some embodiments, the initial feasible solution of the objective function is determined based on the incentive strategy and the corresponding interaction improvement gain, the initial feasible solution is encoded, the fitness function is evaluated and selected, a certain number of primary populations are randomly generated in the feasible solution area, the individual fitness in the population is calculated, and selection, crossover, mutation and dimensionality increase are repeatedly performed until the convergence condition is reached, the operation is stopped, and the optimal solution is obtained.

[0129] In some embodiments, the optimal incentive strategy for each target customer group is determined based on the optimal solution. For each target customer in the target customer group, personalized adjustments can be made to the optimal incentive strategy to obtain a personalized incentive plan for each target customer.

[0130] It can be recognized that the embodiment of the present application extracts multi-dimensional feature information of customers based on their basic customer information, historical incentive data, and historical interactive behavior data, performs deep clustering of customers based on the multi-dimensional feature information to obtain multiple customer groups, determines the interaction enhancement gain of each customer group under different incentive strategies through a causal inference model, and obtains the optimal incentive strategy for each customer group through multi-objective optimization, so that the incentive plan for each customer group can be determined based on the optimal incentive strategy, thereby improving the accuracy of incentives issued to customers and the efficiency of attracting traffic to the interactive platform.

[0131] Compared with the prior art, the embodiments of the present application also have the following advantages:

[0132] 1) Data-Driven Decision-Making: Incorporating extensive customer profile data and incentive information into model calculations, this approach uses causal inference to build a strategy improvement model with higher predictive accuracy. Compared to previous incentive plans based on aggregated statistical results, this approach provides a more precise understanding of how each customer type responds to different forms of incentives.

[0133] 2) Personalized Marketing: Through deep clustering and segmentation, agents or institutions can gradually divide the large number of agents under their management into small-scale interactive subsystems, and design differentiated operations and activation measures based on user characteristics. This is conducive to achieving refined operations and personalized services.

[0134] 3) Improve customer engagement: By performing offline calculations of user characteristics and deep clustering using SCCL, we can better understand different customer types and design personalized incentive programs for them. By estimating the lift through causal models, we can select the most effective strategy to stimulate interaction between target users and agents, thereby increasing overall engagement.

[0135] See also Figure 8 The present application also provides an incentive scheme determination device based on causal inference, which can implement the above-mentioned incentive scheme determination based on causal inference. The incentive scheme determination device based on causal inference includes:

[0136] A feature extraction module is used to obtain the target customer's basic information, historical incentive data, and corresponding historical interactive behavior data, and extract the target customer's multi-dimensional feature information based on the basic information, historical incentive data, and historical interactive behavior data;

[0137] The customer clustering module is used to perform deep clustering of target customers based on multi-dimensional feature information, obtain multiple target customer groups, and determine the customer group feature vectors of each target customer group;

[0138] The interaction improvement gain determination module is used to determine the interaction improvement gain of each target customer group under each incentive strategy based on the customer group feature vector and multiple preset incentive strategies using a pre-trained causal inference model;

[0139] The optimization module is used to perform multi-objective optimization based on the incentive cost of each incentive strategy and the corresponding interaction improvement gain, obtain the optimal incentive strategy for each target customer group, and then determine the incentive plan for each target customer group based on the optimal incentive strategy.

[0140] The specific implementation of the device for determining an incentive scheme based on causal inference is basically the same as the specific implementation of the above-mentioned method for determining an incentive scheme based on causal inference, and will not be repeated here.

[0141] An embodiment of the present application further provides an electronic device comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory. When the program is executed by the processor, the aforementioned method for determining an incentive scheme based on causal inference is implemented. The electronic device can be any intelligent terminal, including a tablet computer and an in-vehicle computer.

[0142] See also Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0143] The processor 901 can be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0144] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called by the processor 901 to execute the incentive scheme determination method based on causal inference in the embodiments of this application.

[0145] Input / output interface 903, used to implement information input and output;

[0146] Communication interface 904, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0147] Bus 905 , which transmits information between various components of the device (e.g., processor 901 , memory 902 , input / output interface 903 , and communication interface 904 );

[0148] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0149] See also Figure 10The embodiment of the present application also provides a storage medium, which is a computer readable storage medium, and is used for computer readable storage. The storage medium stores one or more programs 1001, and the one or more programs 1001 can be executed by one or more processors to implement the above-mentioned incentive scheme determination method based on causal inference.

[0150] The memory, as a non-transitory computer readable storage medium, can be used to store non-transitory software programs and non-transitory computer executable programs. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged with respect to the processor, and these remote memories can be connected to the processor through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0151] The embodiment of the present application provides the method and device, equipment and medium based on causal inference of the incentive scheme determination, which obtains the customer basic information, the historical incentive data and the corresponding historical interactive behavior data of the target customer, extracts the multi-dimensional feature information of the target customer according to the customer basic information, the historical incentive data and the historical interactive behavior data, carries out deep clustering to the target customer according to the multi-dimensional feature information, obtains a plurality of target customer groups, and determines the customer group feature vector of each target customer group, through the pre-trained causal inference model, based on the customer group feature vector and the preset multiple incentive strategies, the interactive promotion gain of each target customer group under each incentive strategy is determined, the incentive cost of each incentive strategy and the corresponding interactive promotion gain are optimized, and the optimal incentive strategy of each target customer group is obtained, and then the incentive scheme of each target customer group is determined according to the optimal incentive strategy. The embodiment of the present application extracts the multi-dimensional feature information of the customer according to the customer basic information, the historical incentive data and the historical interactive behavior data of the customer, carries out deep clustering to the customer according to the multi-dimensional feature information, obtains a plurality of customer groups, determines the interactive promotion gain of each customer group under different incentive strategies through the causal inference model, and obtains the optimal incentive strategy of each customer group through multi-objective optimization, so that the incentive scheme of each customer group can be determined according to the optimal incentive strategy, and the accuracy of the incentive to the customer and the flow efficiency of the interactive platform are improved.

[0152] The embodiments described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0153] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0154] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0155] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0156] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0157] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0158] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0159] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0160] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0161] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0162] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A method for determining an incentive scheme based on causal inference, characterized in that: The method comprises: Obtaining basic customer information, historical incentive data, and corresponding historical interactive behavior data of the target customer, and extracting multi-dimensional feature information of the target customer based on the basic customer information, the historical incentive data, and the historical interactive behavior data; Performing deep clustering on the target customers according to the multi-dimensional feature information to obtain multiple target customer groups, and determining a customer group feature vector for each target customer group; Determine the interaction enhancement gain of each target customer group under each incentive strategy based on the customer group feature vector and multiple preset incentive strategies through a pre-trained causal inference model; Perform multi-objective optimization based on the incentive costs of each incentive strategy and the corresponding interaction improvement gains to obtain the optimal incentive strategy for each target customer group, and then determine the incentive plan for each target customer group based on the optimal incentive strategy; The acquiring of basic customer information, historical incentive data, and corresponding historical interactive behavior data of the target customer, and extracting multi-dimensional feature information of the target customer based on the basic customer information, the historical incentive data, and the historical interactive behavior data, includes: Acquire the target customer's historical incentive data in a preset first historical period and the target customer's historical interaction behavior data in a preset second historical period through the interactive platform; Determine the number of historical incentives and the content of historical incentives based on the historical incentive data, and determine the historical interactive behavior indicators based on the historical interactive behavior data; Obtaining basic customer information of the target customer, and determining a plurality of basic customer attributes based on the basic customer information; Determining the multi-dimensional feature information based on the basic attributes of the customer, the number of historical incentives, the content of the historical incentives, and the historical interactive behavior indicators; Wherein, the second historical period is the next period of the first historical period; The causal inference model is trained by the following steps: Acquire a plurality of sample customer incentive data corresponding to each of the incentive strategies, wherein the sample customer incentive data includes a sample customer feature vector, incentive strategy coding data, and a corresponding sample interactive behavior indicator; Determine the incentive cost of each incentive strategy, construct a control group based on the sample customer incentive data corresponding to the incentive strategy with the smallest incentive cost, and construct corresponding multiple intervention groups based on the sample customer incentive data corresponding to the other incentive strategies; According to the control group and the intervention group, reinforcement learning and causal inference are performed based on the X-Learner model to obtain the causal inference model.

2. The method for determining an incentive scheme based on causal inference according to claim 1, characterized in that: The deep clustering of the target customers according to the multi-dimensional feature information to obtain multiple target customer groups and determining the customer group feature vector of each target customer group includes: Inputting the multidimensional feature information into a pre-trained SCCL clustering model to obtain a customer category corresponding to each target customer; Dividing the target customers into multiple target customer groups according to the customer categories; The customer group feature vector is generated according to the basic customer information of each target customer in the target customer group.

3. The method for determining an incentive scheme based on causal inference according to claim 2, characterized in that: The SCCL clustering model is trained by the following steps: Acquire multiple sample customer feature data, wherein the sample customer feature data includes sample customer basic attributes, sample incentive times, sample incentive content, and sample interactive behavior indicators; Performing data enhancement on each of the sample customer feature data to obtain corresponding enhanced customer feature data, and then constructing an enhanced data set based on the sample customer feature data and the enhanced customer feature data; Construct the SCCL clustering model and determine the contrast loss function and clustering loss function; Taking the sample customer feature data and the corresponding enhanced customer feature data as a positive sample pair, taking other data samples in the enhanced data set except the positive sample pair as negative samples, performing contrastive learning and clustering learning on the positive sample pair and the negative samples, and determining a contrastive loss value using the contrastive loss function, and determining a clustering loss value using the clustering loss function; The parameters of the SCCL clustering model are updated according to the contrast loss value and the clustering loss value to obtain the trained SCCL clustering model.

4. The method for determining an incentive scheme based on causal inference according to claim 1, characterized in that: The method of determining the interaction enhancement gain of each target customer group under each incentive strategy using a pre-trained causal inference model based on the customer group feature vector and a plurality of preset incentive strategies includes: Inputting the characteristic vectors of each customer group and the incentive strategy codes corresponding to each incentive strategy into the causal inference model in pairs, and obtaining the predicted values ​​of the interactive behavior indicators of each target customer group under each incentive strategy; Determining the predicted interactive behavior index value of each target customer group under the incentive strategy with the minimum incentive cost as the interactive behavior index benchmark value of each target customer group; The interaction improvement gain of each target customer group under each incentive strategy is determined according to the difference between the predicted value of the interaction behavior indicator and the baseline value of the interaction behavior indicator.

5. The method for determining an incentive scheme based on causal inference according to any one of claims 1 to 4, characterized in that: The multi-objective optimization is performed based on the incentive cost of each incentive strategy and the corresponding interaction improvement gain to obtain the optimal incentive strategy for each target customer group, including: Construct an objective function about incentive cost and interaction improvement gain, and determine the incentive cost constraint and positive gain constraint; Constructing an initial population corresponding to each target customer group according to the incentive strategy and the corresponding interaction improvement gain, and determining a fitness function according to the objective function, the incentive cost constraint, and the positive gain constraint; Utilizing a genetic algorithm to optimize each of the initial populations according to the fitness function to obtain an optimal solution corresponding to each of the target customer groups; The optimal incentive strategy for each target customer group is determined based on the optimal solution.

6. A device for determining an incentive scheme based on causal inference, characterized in that: The device comprises: A feature extraction module is used to obtain basic customer information, historical incentive data, and corresponding historical interactive behavior data of a target customer, and extract multi-dimensional feature information of the target customer based on the basic customer information, the historical incentive data, and the historical interactive behavior data; A customer clustering module, configured to perform deep clustering of the target customers based on the multi-dimensional feature information to obtain multiple target customer groups, and determine a customer group feature vector for each target customer group; An interaction improvement gain determination module is used to determine the interaction improvement gain of each target customer group under each incentive strategy based on the customer group feature vector and multiple preset incentive strategies using a pre-trained causal inference model; An optimization module is used to perform multi-objective optimization based on the incentive cost of each incentive strategy and the corresponding interaction improvement gain to obtain the optimal incentive strategy for each target customer group, and then determine the incentive plan for each target customer group based on the optimal incentive strategy; The feature extraction module is specifically used for: Acquire the target customer's historical incentive data in a preset first historical period and the target customer's historical interaction behavior data in a preset second historical period through the interactive platform; Determine the number of historical incentives and the content of historical incentives based on the historical incentive data, and determine the historical interactive behavior indicators based on the historical interactive behavior data; Obtaining basic customer information of the target customer, and determining a plurality of basic customer attributes based on the basic customer information; Determining the multi-dimensional feature information based on the basic attributes of the customer, the number of historical incentives, the content of the historical incentives, and the historical interactive behavior indicators; Wherein, the second historical period is the next period of the first historical period; The causal inference model is trained by the following steps: Acquire a plurality of sample customer incentive data corresponding to each of the incentive strategies, wherein the sample customer incentive data includes a sample customer feature vector, incentive strategy coding data, and a corresponding sample interactive behavior indicator; Determine the incentive cost of each incentive strategy, construct a control group based on the sample customer incentive data corresponding to the incentive strategy with the smallest incentive cost, and construct corresponding multiple intervention groups based on the sample customer incentive data corresponding to the other incentive strategies; According to the control group and the intervention group, reinforcement learning and causal inference are performed based on the X-Learner model to obtain the causal inference model.

7. An electronic device, characterized in that: The electronic device includes a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the method for determining an incentive scheme based on causal inference as described in any one of claims 1 to 5 are realized.

8. A storage medium, which is a computer-readable storage medium and is used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the incentive scheme determination method based on causal inference as described in any one of claims 1 to 5.

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