Customer outreach methods, devices, electronic equipment, and storage media based on real-time data warehouses

Abstract

This application discloses a customer outreach method, apparatus, electronic device, and storage medium based on a real-time data warehouse. Relating to the field of cloud computing, the method includes: acquiring transaction data, attribute data, and rule parameter data of initial objects using a real-time data warehouse; associating the transaction data, attribute data, and rule parameter data to generate target rules; filtering from multiple initial accounts corresponding to initial objects according to the target rules to obtain at least one target account corresponding to a target object, wherein the target object is a potential customer capable of supporting personal housing loans; generating a marketing outreach strategy based on the transaction data and attribute data, and executing the marketing outreach strategy on the terminal corresponding to the target account, wherein the marketing outreach strategy is used to provide personalized recommendations or marketing to the terminal corresponding to the target account. This application solves the technical problem in related technologies where the inability to reach customers in a timely manner leads to a low new customer acquisition rate.

Description

Technical Field

[0001] This application relates to the field of cloud computing, and more specifically, to a customer outreach method, apparatus, electronic device, and storage medium based on a real-time data warehouse. Background Technology

[0002] The current housing market situation is complex, making the acquisition of new mortgage customers a major concern in personal housing loan marketing. Currently, banks typically rely on offline promotional activities and online advertising to identify and reach potential mortgage customers. However, this approach requires significant upfront marketing costs and suffers from technical challenges due to the inability to reach customers promptly, resulting in low new customer acquisition rates.

[0003] There is currently no effective solution to the technical problem of low new customer acquisition rates caused by the inability to reach customers in a timely manner. Summary of the Invention

[0004] The main objective of this application is to provide a customer outreach method, apparatus, electronic device, and storage medium based on a real-time data warehouse, in order to solve the technical problem of low new customer acquisition rate caused by the inability to reach customers in a timely manner in related technologies.

[0005] To achieve the above objectives, according to one aspect of this application, a customer outreach method based on a real-time data warehouse is provided. The method includes: utilizing a real-time data warehouse of an initial object to acquire transaction data, attribute data, and rule parameter data of the initial object, wherein the real-time data warehouse stores different types of data of the initial object, the transaction data represents at least one transaction record performed by the initial object within a target time period, and the attribute data represents the personal characteristics and asset information of the initial object; associating the transaction data, attribute data, and rule parameter data to generate target rules; filtering from multiple initial accounts corresponding to initial objects according to the target rules to obtain at least one target account corresponding to a target object, wherein the target object is a potential customer capable of supporting personal housing loans; generating a marketing outreach strategy based on the transaction data and attribute data, and executing the marketing outreach strategy on the terminal corresponding to the target account, wherein the marketing outreach strategy is used to provide personalized recommendations or marketing to the terminal corresponding to the target account.

[0006] Optionally, the transaction data of the initial object is obtained using the real-time data warehouse of the initial object, including: obtaining the behavioral data of the initial object using the real-time data warehouse, wherein the behavioral data is used to characterize the transaction behavior generated by the initial object according to different demand information; and determining the transaction data of the initial object based on the behavioral data.

[0007] Optionally, the rule parameter data of the initial object is obtained by utilizing the real-time data warehouse of the initial object, including: using the real-time data warehouse of the initial object to determine multiple keywords based on transaction data and attribute data; determining the weights corresponding to the multiple keywords respectively; inputting the multiple keywords and multiple weights into the evaluation model for analysis to obtain the analysis results, wherein the evaluation model is established through multiple historical keywords and multiple historical weights; and determining the rule parameter data of the initial object based on the analysis results.

[0008] Optionally, based on the analysis results, the rule parameter data of the initial object is determined, including: based on the analysis results, determining the first evaluation value of multiple keywords respectively; sorting the multiple first evaluation values ​​according to the sorting rules to obtain the sorted multiple first evaluation values; and determining the rule parameter data of the initial object based on the sorted multiple first evaluation values.

[0009] Optionally, the transaction data, attribute data, and rule parameter data are correlated to generate target rules, including: determining customer characteristic rules in the target rules based on attribute data and rule parameter data, wherein the customer characteristic rules are rules in which the attribute data is greater than or equal to the attribute threshold data in the rule parameter data; and determining customer transaction rules in the target rules based on transaction data and rule parameter data, wherein the customer transaction rules are rules in which the transaction data is greater than or equal to the transaction threshold data in the rule parameter data.

[0010] Optionally, a marketing outreach strategy is generated based on transaction data and attribute data, including: determining the target audience's willingness data based on transaction data and attribute data, wherein the willingness data is used to characterize the target audience's willingness to pay for personal housing loans; evaluating the willingness data to obtain a second evaluation value; and determining the marketing outreach strategy based on the second evaluation value, a first threshold corresponding to the second evaluation value, and a second threshold corresponding to the second evaluation value, wherein the first threshold is less than the second threshold.

[0011] Optionally, a marketing outreach strategy is determined based on a second evaluation value, a first threshold corresponding to the second evaluation value, and a second threshold corresponding to the second evaluation value, including: determining a first marketing outreach strategy in response to the second evaluation value being greater than the first threshold and less than the second threshold, wherein the first marketing outreach strategy is used to reach customers by placing advertisements; and determining a second marketing outreach strategy in response to the second evaluation value being greater than or equal to the second threshold, wherein the second marketing outreach strategy is used to reach customers by arranging marketing management objects.

[0012] To achieve the above objectives, according to another aspect of this application, a customer outreach device based on a real-time data warehouse is provided. The device includes: an acquisition unit, configured to acquire transaction data, attribute data, and rule parameter data of the initial object using a real-time data warehouse, wherein the real-time data warehouse stores different types of data of the initial object, the transaction data represents at least one transaction record performed by the initial object within a target time period, and the attribute data represents the personal characteristics and asset information of the initial object; a generation unit, configured to associate the transaction data, attribute data, and rule parameter data to generate target rules; a filtering unit, configured to filter from multiple initial accounts corresponding to initial objects according to the target rules to obtain at least one target account corresponding to a target object, wherein the target object is a potential customer capable of supporting personal housing loans; and an execution unit, configured to generate a marketing outreach strategy based on the transaction data and attribute data, and execute the marketing outreach strategy on the terminal corresponding to the target account, wherein the marketing outreach strategy is used to provide personalized recommendations or marketing to the terminal corresponding to the target account.

[0013] Optionally, the acquisition unit may include: a first acquisition module, used to acquire behavioral data of the initial object using a real-time data warehouse, wherein the behavioral data is used to characterize the transaction behavior generated by the initial object according to different demand information; and a first determination module, used to determine the transaction data of the initial object based on the behavioral data.

[0014] Optionally, the acquisition unit may further include: a second determining module, used to determine multiple keywords based on transaction data and attribute data using the real-time data warehouse of the initial object; a third determining module, used to determine the weights corresponding to the multiple keywords respectively; an analysis module, used to input the multiple keywords and multiple weights into the evaluation model for analysis and obtain analysis results, wherein the evaluation model is established through multiple historical keywords and multiple historical weights; and a fourth determining module, used to determine the rule parameter data of the initial object based on the analysis results.

[0015] Optionally, the fourth determining module may include: a first determining submodule, used to determine the first evaluation value of multiple keywords based on the analysis results; an obtaining submodule, used to sort the multiple first evaluation values ​​according to the sorting rules to obtain the sorted multiple first evaluation values; and a second determining submodule, used to determine the rule parameter data of the initial object based on the sorted multiple first evaluation values.

[0016] Optionally, the generation unit may include: a fifth determining module, used to determine customer characteristic rules in the target rules based on attribute data and rule parameter data, wherein the customer characteristic rules are rules in which the attribute data is greater than or equal to the attribute threshold data in the rule parameter data; and a sixth determining module, used to determine customer transaction rules in the target rules based on transaction data and rule parameter data, wherein the customer transaction rules are rules in which the transaction data is greater than or equal to the transaction threshold data in the rule parameter data.

[0017] Optionally, the execution unit may include: a seventh determining module, used to determine the target object's willingness data based on transaction data and attribute data, wherein the willingness data is used to characterize the target object's willingness to pay for a personal housing loan; an evaluation module, used to evaluate the willingness data to obtain a second evaluation value; and an eighth determining module, used to determine a marketing outreach strategy based on the second evaluation value, a first threshold corresponding to the second evaluation value, and a second threshold corresponding to the second evaluation value, wherein the first threshold is less than the second threshold.

[0018] Optionally, the eighth determining module may include: a third determining submodule, used to determine the marketing reach strategy as a first marketing reach strategy in response to a second evaluation value being greater than a first threshold and less than a second threshold, wherein the first marketing reach strategy is used to reach customers by placing advertisements; and a fourth determining submodule, used to determine the marketing reach strategy as a second marketing reach strategy in response to a second evaluation value being greater than or equal to a second threshold, wherein the second marketing reach strategy is used to reach customers by arranging marketing management objects.

[0019] In this embodiment, the real-time data warehouse of the initial object can be used to obtain the initial object's transaction data, attribute data, and rule parameter data. Then, the transaction data, attribute data, and rule parameter data of the target object are correlated to generate target rules. Next, according to the target rules, at least one target account is obtained from the initial accounts corresponding to multiple initial objects. Finally, a marketing outreach strategy is generated based on the transaction data and attribute data, allowing the marketing outreach strategy to be executed on the terminals corresponding to the target account, achieving personalized recommendations or marketing to those terminals. Considering that after obtaining the transaction data, attribute data, and rule parameter data, they can be correlated to generate target rules, and then at least one target account can be obtained from the initial accounts according to the target rules, and after generating the marketing outreach strategy based on the transaction data and attribute data, the marketing outreach strategy can be executed on the terminals corresponding to the target account. This solves the technical problem of low new customer acquisition rates due to the inability to reach customers in a timely manner, achieving timely customer outreach and improving the technical effect of new customer acquisition rates. Attached Figure Description

[0020] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0021] Figure 1 A hardware block diagram of a computer terminal for implementing a touch-based approach based on a real-time data warehouse is shown.

[0022] Figure 2 This is a flowchart of a customer outreach method based on a real-time data warehouse according to an embodiment of this application;

[0023] Figure 3 This is a flowchart of a customer identification and outreach method based on real-time data warehouse technology provided in the embodiments of this application;

[0024] Figure 4 This is a schematic diagram of a customer-touch device based on a real-time data warehouse, provided according to an embodiment of this application.

[0025] Figure 5 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] According to an embodiment of this application, a method embodiment of a customer touch method based on a real-time data warehouse is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a touch-based approach based on a real-time data warehouse is shown. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0030] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0031] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the real-time data warehouse-based customer acquisition method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned real-time data warehouse-based customer acquisition method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0032] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0033] The display may be, for example, a touchscreen LCD display that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0034] Under the aforementioned operating environment, this application provides the following: Figure 2 The method of reaching customers is shown based on a real-time data warehouse. Figure 2 This is a flowchart of a customer outreach method based on a real-time data warehouse according to an embodiment of this application. The method mainly includes the following steps:

[0035] Step S201: Use the real-time data warehouse of the initial object to obtain the transaction data, attribute data and rule parameter data of the initial object.

[0036] In step S201 of this embodiment, the transaction data, attribute data, and rule parameter data of the initial object can be obtained using the real-time data warehouse of the initial object. The initial object can be an initial customer. The real-time data warehouse can be located in the cloud, which ensures rapid data processing.

[0037] Optionally, a real-time data warehouse is used to store different types of data for the initial object. Target object transaction data is used to characterize at least one transaction record of the initial object within a target time period, where the target time period is a preset time period, such as 30 seconds. It should be noted that if the target time period is very short, the transaction data can also be real-time transaction data, such as real-time transaction details data of card transactions.

[0038] Optionally, the target object attribute data is used to characterize the personal characteristics and asset information of the initial object, and may include: customer characteristic data and customer asset data. For example, customer characteristic data may be the customer's name, gender, age, contact information, hobbies, etc.; customer asset data may be account balance, investment portfolio, securities held, etc.

[0039] Optionally, the rule parameter data is constructed from transaction data and attribute data, and can be business rule parameters for credit applications, such as account balance thresholds, transaction detail data thresholds, etc.

[0040] It is understood that this is only a preferred implementation method for obtaining the transaction data, attribute data, and rule parameter data of the initial object, and no specific limitation is made on the process and method for obtaining the transaction data, attribute data, and rule parameter data of the initial object.

[0041] Step S202: Associate the transaction data, attribute data, and rule parameter data to generate the target rule.

[0042] In step S202 of this application embodiment, after obtaining transaction data, attribute data and rule parameter data, the transaction data, attribute data and rule parameter data can be associated to generate target rules.

[0043] For example, based on transaction details, customer characteristics, customer asset data, and rule parameters, relevant credit experts can formulate business rules and use these rules for further processing.

[0044] It should be noted that this is only a preferred implementation method for obtaining the target rule, and the process and method of obtaining the target rule are not specifically limited. As long as transaction data, attribute data and rule parameter data are associated, the process and method of generating the target rule are within the protection scope of this application, and will not be listed here.

[0045] Step S203: Filter from the initial accounts corresponding to multiple initial objects according to the target rules to obtain the target account corresponding to at least one target object.

[0046] In step S203 of this application embodiment, the initial accounts corresponding to multiple initial objects can be filtered according to the target rules in order to obtain the target account corresponding to at least one target object, wherein the target object is a potential customer who can support personal housing loans.

[0047] Optionally, the initial account can be a preset account, and the target account can also be a preset account, such as a transaction account or a mobile phone number. It should be noted that the initial account and target account are only provided as examples here, and no specific restrictions are placed on the account type or category set as the initial account and target account.

[0048] It is understood that this is only a preferred implementation method for obtaining the target account corresponding to at least one target object, and the process and method for obtaining the target account corresponding to at least one target object are not specifically limited.

[0049] Step S204: Based on transaction data and attribute data, generate a marketing outreach strategy and execute the marketing outreach strategy on the terminal corresponding to the target account.

[0050] In step S204 of this embodiment, marketers can formulate marketing outreach strategies based on the transaction data and attribute data obtained in the above steps, and then execute these strategies on the terminals corresponding to the target accounts. These marketing outreach strategies can be referred to as outreach recommendation strategies, or simply outreach strategies.

[0051] Optionally, marketing outreach strategies are used to provide personalized recommendations or marketing to the devices associated with target accounts, and can be referred to as marketing plans. The devices associated with target accounts can be used to represent the devices customers use for shopping or transactions, such as mobile phones, computers, and tablets.

[0052] For example, in customer marketing applications, marketers can plan marketing campaigns based on transaction details, customer characteristics, and customer asset data, and then execute these campaigns on mobile phones or computers corresponding to target accounts to reach customers.

[0053] It should be noted that this is only a preferred implementation method for executing marketing outreach strategies on the terminal corresponding to the target account. Any process and method that generates marketing outreach strategies based on transaction data and attribute data and executes marketing outreach strategies on the terminal corresponding to the target account is within the protection scope of this application and will not be listed here.

[0054] In this embodiment, the real-time data warehouse of the initial object can be used to obtain the initial object's transaction data, attribute data, and rule parameter data. Then, the transaction data, attribute data, and rule parameter data of the target object are correlated to generate target rules. Next, according to the target rules, at least one target account is obtained from the initial accounts corresponding to multiple initial objects. Finally, a marketing outreach strategy is generated based on the transaction data and attribute data, allowing the marketing outreach strategy to be executed on the terminals corresponding to the target account, achieving personalized recommendations or marketing to those terminals. Considering that after obtaining the transaction data, attribute data, and rule parameter data, they can be correlated to generate target rules, and then at least one target account can be obtained from the initial accounts according to the target rules, and after generating the marketing outreach strategy based on the transaction data and attribute data, the marketing outreach strategy can be executed on the terminals corresponding to the target account. This solves the technical problem of low new customer acquisition rates due to the inability to reach customers in a timely manner, achieving timely customer outreach and improving the technical effect of new customer acquisition rates.

[0055] In the customer acquisition method based on real-time data warehouse provided in this application embodiment, the transaction data of the initial object is obtained by utilizing the real-time data warehouse of the initial object, including: using the real-time data warehouse to obtain the behavioral data of the initial object, wherein the behavioral data is used to characterize the transaction behavior generated by the initial object according to different demand information; and determining the transaction data of the initial object based on the behavioral data.

[0056] In this embodiment, a real-time data warehouse can be used to obtain the behavioral data of the initial object, and then the transaction data of the initial object can be determined based on the behavioral data. The behavioral data can be called customer behavioral data, such as real-time event data like transaction details of credit cards and debit cards, and the behavioral data can be stored in a transaction database.

[0057] Optionally, by utilizing customer behavior data, customer transaction data can be determined to better understand customer consumption habits and preferences, thereby obtaining effective and accurate customer transaction data and achieving better identification of target customers.

[0058] In the customer outreach method based on a real-time data warehouse provided in this application embodiment, the rule parameter data of the initial object is obtained by utilizing the real-time data warehouse of the initial object, including: determining multiple keywords based on transaction data and attribute data using the real-time data warehouse of the initial object; determining the weights corresponding to the multiple keywords respectively; inputting the multiple keywords and multiple weights into an evaluation model for analysis to obtain analysis results, wherein the evaluation model is established through multiple historical keywords and multiple historical weights; and determining the rule parameter data of the initial object based on the analysis results.

[0059] In this embodiment, the real-time data warehouse of the initial object can be used first to determine multiple keywords based on transaction data and attribute data. Then, based on the multiple keywords, the weights corresponding to the multiple keywords can be determined. The multiple keywords and multiple weights are then input into the evaluation model for analysis to obtain analysis results. Finally, based on the analysis results obtained above, the purpose of determining the rule parameter data of the initial object can be achieved.

[0060] Optionally, keywords can include loan interest rates, loan amounts, repayment periods, and application requirements. The weight of multiple keywords can be assigned by marketers based on their importance; for example, loan interest rates could have a weight of 3, loan amounts 6, repayment periods 7, and application requirements 4. It should be noted that this is only an example illustrating keywords and their weights.

[0061] Optionally, after determining each keyword and its corresponding weight, the above data can be input into the evaluation model for analysis to obtain analysis results. Based on the analysis results, the rule parameter data for customers can be determined, making the rule parameter data more convincing. The target rules generated using the rule parameter data can quickly and accurately identify potential customers.

[0062] For example, based on the obtained transaction details, customer characteristic data, and customer asset data, multiple keywords are determined using regular expression matching. Then, based on these multiple keywords and their weights, an evaluation model is used to obtain the analysis results. Based on the analysis results, the goal of determining the rule parameter data is achieved.

[0063] In the customer outreach method based on a real-time data warehouse provided in this application embodiment, the rule parameter data of the initial object is determined based on the analysis results, including: determining the first evaluation value of multiple keywords based on the analysis results; sorting the multiple first evaluation values ​​according to the sorting rules to obtain the sorted multiple first evaluation values; and determining the rule parameter data of the initial object based on the sorted multiple first evaluation values.

[0064] In this embodiment, an evaluation model can be used to output first evaluation values ​​corresponding to multiple keywords. These first evaluation values ​​can then be sorted according to a sorting rule to obtain sorted first evaluation values. Based on these sorted first evaluation values, the rule parameter data for the initial object can be determined. The sorting rule can be a rule for sorting numbers from largest to smallest. For example, multiple first evaluation values ​​can be sorted in descending order.

[0065] Optionally, marketers can select the top target first evaluation values ​​from a sorted pool of first evaluation values ​​based on actual needs, and determine the initial object's rule parameter data based on the keywords corresponding to the target first evaluation values, making the rule parameter data more representative. The target values ​​can be preset values.

[0066] In the customer outreach method based on real-time data warehouse provided in this application embodiment, transaction data, attribute data, and rule parameter data are associated to generate target rules, including: determining customer characteristic rules in the target rules based on attribute data and rule parameter data, wherein the customer characteristic rules are rules where the attribute data is greater than or equal to the attribute threshold data in the rule parameter data; and determining customer transaction rules in the target rules based on transaction data and rule parameter data, wherein the customer transaction rules are rules where the transaction data is greater than or equal to the transaction threshold data in the rule parameter data.

[0067] In this embodiment, customer characteristic rules in the target rules can be determined based on attribute data and rule parameter data, and customer transaction rules in the target rules can be determined based on transaction data and rule parameter data. Here, customer characteristic rules can be referred to as rules revolving around customer characteristics. Customer transaction rules can be referred to as rules revolving around customer behavior, and can also be determined in the target rules based on behavior data and rule parameter data.

[0068] For example, rules based on customer characteristics could include specific thresholds that customers must meet, such as age, annual income, and asset status. These rules are based on customer profile data that doesn't change easily over time and can be pre-processed for later use. Furthermore, rules based on customer behavior could include whether they have recently made remittances to real estate or brokerage corporate accounts, and if so, the remittance amount must meet specific thresholds. These rules are based on transaction event data; the faster the transaction data is obtained, the faster the customer can be identified and identified, representing a bottleneck in the overall identification process.

[0069] In addition, different target rules are determined based on different data, and target customers are screened using different target rules to quickly obtain target customers with high overall evaluation, so that all target customers meet the target rules.

[0070] In the customer outreach method based on real-time data warehouse provided in this application embodiment, a marketing outreach strategy is generated based on transaction data and attribute data, including: determining the target object's willingness data based on transaction data and attribute data, wherein the willingness data is used to characterize the target object's willingness to pay personal housing loans; evaluating the willingness data to obtain a second evaluation value; and determining the marketing outreach strategy based on the second evaluation value, a first threshold corresponding to the second evaluation value, and a second threshold corresponding to the second evaluation value, wherein the first threshold is less than the second threshold.

[0071] In this embodiment, based on the obtained transaction data and attribute data, the target object's willingness data can be determined, and then the willingness data can be evaluated to obtain a second evaluation value. Based on the second evaluation value, a first threshold of the second evaluation value, and a second threshold of the second evaluation value, a marketing outreach strategy can be determined, wherein the first threshold can be a minimum value and the second threshold can be a maximum value.

[0072] Optionally, after obtaining the willingness data of potential customers, the willingness data can be evaluated to obtain a second evaluation value for assessing the willingness of potential customers. Then, based on different second evaluation values, marketing outreach strategies can be determined for different potential customers, so as to achieve the effect of using different marketing outreach strategies to meet the payment needs of customers with different willingness.

[0073] In the customer outreach method based on a real-time data warehouse provided in this application embodiment, a marketing outreach strategy is determined based on a second evaluation value, a first threshold corresponding to the second evaluation value, and a second threshold corresponding to the second evaluation value. This includes: determining a first marketing outreach strategy in response to the second evaluation value being greater than the first threshold and less than the second threshold, wherein the first marketing outreach strategy is used to reach customers through advertising; and determining a second marketing outreach strategy in response to the second evaluation value being greater than or equal to the second threshold, wherein the second marketing outreach strategy is used to reach customers by arranging marketing management objects.

[0074] In this embodiment, after obtaining the second evaluation value, the first threshold, and the second threshold, the second evaluation value can be compared with the first threshold and the second threshold respectively. If the second evaluation value is between the first threshold and the second threshold, it indicates that the target audience's willingness is medium, and the marketing touch strategy can be determined as the first marketing touch strategy. If the second evaluation value is greater than or equal to the second threshold, it indicates that the target audience's willingness is high, and the marketing touch strategy can be determined as the second marketing touch strategy.

[0075] For example, for high-intent, high-value customers, account managers need to be assigned to conduct one-on-one marketing. For medium-intent, medium-value customers, advertisements can be delivered through mobile application (APP) carousel, mini-program push, and unified outbound calls. Thus, different customer outreach methods can be implemented for customers with different intents, achieving fast and effective customer outreach while saving labor costs.

[0076] The customer outreach method based on a real-time data warehouse provided in this application first utilizes the real-time data warehouse of the initial object to obtain the initial object's transaction data, attribute data, and rule parameter data. Then, it associates the target object's transaction data, attribute data, and rule parameter data to generate target rules. Next, it filters from multiple initial accounts corresponding to the initial objects according to the target rules to obtain at least one target account corresponding to the target object. Finally, based on the transaction data and attribute data, it generates a marketing outreach strategy, which can then be executed on the terminal corresponding to the target account to achieve personalized recommendations or marketing to that terminal. Considering that after obtaining the transaction data, attribute data, and rule parameter data, they can be associated to generate target rules, and then filtered from multiple initial accounts according to the target rules to obtain at least one target account corresponding to the target object, and after generating the marketing outreach strategy based on the transaction data and attribute data, the marketing outreach strategy can be executed on the terminal corresponding to the target account. This solves the technical problem of low new customer acquisition rates due to the inability to reach customers in a timely manner, achieving the technical effect of timely customer outreach and improving the new customer acquisition rate.

[0077] The technical solutions of the embodiments of this application will be illustrated below with reference to preferred embodiments.

[0078] Personal housing loans are a crucial component of bank lending. Ensuring and steadily increasing the volume of personal housing loans plays a vital role in helping banks increase revenue, diversify risk, and enhance customer loyalty. Given the current complex and changing housing market, expanding the customer base for mortgage loans has become a primary focus in personal housing loan marketing.

[0079] Currently, banks typically identify and reach personal housing loan customers through offline promotional activities and online advertising. However, this approach is somewhat indiscriminate and has significant shortcomings in terms of marketing timeliness. For example, advertising cannot guarantee that ads will be delivered promptly when customers have a need; advertising at the wrong time can actually reduce customer brand awareness. Furthermore, the time losses at each stage of the personal housing loan marketing chain are not negligible, which can also lead to the failure to identify customers with mortgage needs in a timely manner, resulting in delayed customer outreach. Considering the high upfront marketing costs required by these methods and the technical issues leading to low new customer acquisition rates due to the inability to reach customers promptly, these approaches are not ideal.

[0080] To address the aforementioned issues, this application proposes a customer identification and outreach method based on real-time data warehouse technology. This method, by introducing real-time data warehouse technology, can identify potential customers for personal housing loans in real time and reach them in real time, thereby improving the timeliness, accuracy, and flexibility of the entire marketing process. It achieves the goal of maintaining the reach of the customer base and the accuracy of customer identification while simultaneously sensing and capturing customer needs in real time. This solves the technical problem of low new customer acquisition rates caused by the inability to reach customers in a timely manner, thus enabling timely customer outreach and improving the technical effect of new customer acquisition rates.

[0081] Figure 3 This is a flowchart of a customer identification and outreach method based on real-time data warehouse technology provided in the embodiments of this application, such as... Figure 3 As shown, the method mainly includes the following steps:

[0082] Step S301: Obtain real-time transaction details for card transactions.

[0083] Step S302: Credit business experts formulate business rules.

[0084] In this embodiment, credit experts use regular expression matching keywords or similar scoring card models to perform weighted calculations to formulate rules for identifying potential customers. From the perspective of the timeliness of data processing, the rules are divided into two categories: rules based on customer characteristics and rules based on customer behavior.

[0085] Optionally, rules based on customer characteristics can include specific thresholds that customer characteristics such as age, annual income, and asset status must meet. These rules are based on customer profile data that does not change over time and can be pre-processed for later use. Furthermore, rules based on customer behavior can include whether they have recently made remittances to real estate or brokerage corporate accounts, and if so, the remittance amount must meet specific thresholds. These rules are based on transaction event data; the faster the transaction data is obtained, the faster the customer can be identified and identified, which represents the time bottleneck in the entire identification process.

[0086] Step S303: Obtain business rule parameters according to business rules.

[0087] In this embodiment, business rule parameters are obtained according to business rules, and the business rule parameters and keywords can be stored in the credit application database so that credit business experts can adjust the rule content in a timely manner based on actual marketing results and market changes.

[0088] Step S304: Obtain customer characteristic data.

[0089] In this embodiment, customer characteristic data, such as age, gender, and asset status, can be acquired and uniformly processed by the bank's customer profiling application and stored in the data lake, so that various business applications can access it independently.

[0090] Optionally, customer behavior data can be acquired, such as real-time event data like transaction details from credit and debit cards, and this real-time event data circulates within the card application's transaction database through a processing platform (Kafka). Kafka is a distributed stream processing platform commonly used to build real-time data pipelines and streaming applications.

[0091] Step S305, Real-time data warehouse for credit applications.

[0092] In this embodiment, a real-time data warehouse (Flink SQL) job can be deployed in the real-time data warehouse of the credit application. This job subscribes to the transaction database in the card application as a data source, accesses the credit application database and the bank's data lake, obtains rule parameters and customer characteristic data, and uses these as dimension tables. When a transaction event occurs, the node receives the transaction details in real time and immediately triggers customer behavior rule verification. This transaction detail can be associated with the rule parameter dimension table and the customer characteristic dimension table, and regular expression matching and weighted scoring calculations are performed according to the rules. If the transaction ultimately meets the business rules, its rule score and transaction details data are processed, and the processed data is sent to the marketing application node, where the marketing application performs the corresponding data write operation in the database.

[0093] Step S306: Marketers develop marketing plans.

[0094] In this embodiment, within the customer marketing application, marketers plan marketing strategies based on customer transaction behavior data and the evaluation values ​​of that data. For example, for high-intent, high-value customers, account managers need to be assigned to conduct one-on-one marketing, while for medium-intent, medium-value customers, advertisements can be delivered through mobile application carousels, mini-program push notifications, and unified outbound calls.

[0095] Step S307: Based on the marketing plan, generate a marketing list for the customer's marketing applications.

[0096] Step S308: Utilize various marketing channels to reach customers.

[0097] In this embodiment, various marketing channel applications can obtain marketing plans and corresponding customer lists in real time through the deployed online interface, and implement corresponding outreach plans.

[0098] In this embodiment of the application, by introducing real-time data warehouse technology, the entire process of monitoring, identifying and reaching potential mortgage customers is made real-time. At the same time, it ensures accurate customer identification and optimizes the timeliness, accuracy and flexibility of the entire process of potential customer identification and marketing in personal mortgage business.

[0099] In this embodiment, considering that after obtaining transaction data, attribute data, and rule parameter data, these data can be correlated to generate target rules, and then multiple initial accounts can be filtered according to the target rules to obtain at least one target account corresponding to a target object, after generating a marketing outreach strategy based on transaction data and attribute data, the marketing outreach strategy can be executed on the terminal corresponding to the target account. This solves the technical problem of low new customer acquisition rate due to the inability to reach customers in a timely manner, and achieves the technical effect of timely outreach to improve the new customer acquisition rate.

[0100] This application also provides a customer outreach device based on a real-time data warehouse. It should be noted that this customer outreach device can be used to execute the customer outreach method based on a real-time data warehouse provided in this application. The following describes the customer outreach device based on a real-time data warehouse provided in this application.

[0101] According to an embodiment of this application, an apparatus for implementing the above-described customer outreach method based on a real-time data warehouse is also provided. Figure 4 This is a schematic diagram of a customer-facing device based on a real-time data warehouse, provided according to an embodiment of this application. Figure 4 As shown, the device includes: an acquisition unit 401, a generation unit 402, a filtering unit 403, and an execution unit 404.

[0102] The acquisition unit 401 is used to acquire the transaction data, attribute data and rule parameter data of the initial object using the real-time data warehouse of the initial object. The real-time data warehouse is used to store different types of data of the initial object. The transaction data is used to represent at least one transaction record of the initial object within the target time period. The attribute data is used to represent the personal characteristics and asset information of the initial object.

[0103] The generation unit 402 is used to associate transaction data, attribute data, and rule parameter data to generate target rules.

[0104] The filtering unit 403 is used to filter from the initial accounts corresponding to multiple initial objects according to the target rules to obtain at least one target account corresponding to a target object, wherein the target object is a potential customer who can support personal housing loans.

[0105] The execution unit 404 is used to generate marketing outreach strategies based on transaction data and attribute data, and to execute the marketing outreach strategies on the terminals corresponding to the target accounts. The marketing outreach strategies are used to make personalized recommendations or marketing to the terminals corresponding to the target accounts.

[0106] The customer outreach device based on a real-time data warehouse provided in this application embodiment acquires transaction data, attribute data, and rule parameter data of the initial object using the real-time data warehouse of the initial object. The real-time data warehouse stores different types of data for the initial object; transaction data represents at least one transaction record performed by the initial object within a target time period; and attribute data represents the initial object's personal characteristics and asset information. A generation unit associates the transaction data, attribute data, and rule parameter data to generate target rules. A filtering unit filters from the initial accounts corresponding to multiple initial objects according to the target rules to obtain at least one target account corresponding to a target object, where the target object is a potential customer capable of supporting personal housing loans. An execution unit generates a marketing outreach strategy based on the transaction data and attribute data and executes the marketing outreach strategy on the terminal corresponding to the target account. The marketing outreach strategy is used to provide personalized recommendations or marketing to the terminal corresponding to the target account, thus solving the technical problem in related technologies where the inability to reach customers in a timely manner leads to a low new customer acquisition rate. This achieves the technical effect of timely customer outreach to improve the new customer acquisition rate.

[0107] Optionally, in the customer acquisition device based on a real-time data warehouse provided in this application embodiment, the acquisition unit 401 may include: a first acquisition module, used to acquire behavioral data of an initial object using a real-time data warehouse, wherein the behavioral data is used to characterize the transaction behavior generated by the initial object according to different demand information; and a first determination module, used to determine the transaction data of the initial object based on the behavioral data.

[0108] Optionally, in the customer acquisition device based on a real-time data warehouse provided in this application embodiment, the acquisition unit 401 may further include: a second determining module, used to determine multiple keywords based on transaction data and attribute data using the real-time data warehouse of the initial object; a third determining module, used to determine the weights corresponding to the multiple keywords respectively; an analysis module, used to input the multiple keywords and multiple weights into an evaluation model for analysis to obtain analysis results, wherein the evaluation model is established through multiple historical keywords and multiple historical weights; and a fourth determining module, used to determine the rule parameter data of the initial object based on the analysis results.

[0109] Optionally, in the Touch Customer Device based on a real-time data warehouse provided in this application embodiment, the fourth determining module may include: a first determining submodule, used to determine the first evaluation value of multiple keywords based on the analysis results; an obtaining submodule, used to sort the multiple first evaluation values ​​according to the sorting rules to obtain the sorted multiple first evaluation values; and a second determining submodule, used to determine the rule parameter data of the initial object based on the sorted multiple first evaluation values.

[0110] Optionally, in the customer outreach device based on a real-time data warehouse provided in this application embodiment, the generation unit 402 may include: a fifth determining module, used to determine customer characteristic rules in the target rules based on attribute data and rule parameter data, wherein the customer characteristic rules are rules where the attribute data is greater than or equal to the attribute threshold data in the rule parameter data; and a sixth determining module, used to determine customer transaction rules in the target rules based on transaction data and rule parameter data, wherein the customer transaction rules are rules where the transaction data is greater than or equal to the transaction threshold data in the rule parameter data.

[0111] Optionally, in the customer outreach device based on a real-time data warehouse provided in this application embodiment, the execution unit 404 may include: a seventh determining module, used to determine the target object's willingness data based on transaction data and attribute data, wherein the willingness data is used to characterize the target object's willingness to pay for a personal housing loan; an evaluation module, used to evaluate the willingness data to obtain a second evaluation value; and an eighth determining module, used to determine a marketing outreach strategy based on the second evaluation value, a first threshold corresponding to the second evaluation value, and a second threshold corresponding to the second evaluation value, wherein the first threshold is less than the second threshold.

[0112] Optionally, in the customer outreach device based on a real-time data warehouse provided in this application embodiment, the eighth determining module may include: a third determining submodule, used to determine a marketing outreach strategy as a first marketing outreach strategy in response to a second evaluation value being greater than a first threshold and less than a second threshold, wherein the first marketing outreach strategy is used to reach customers by placing advertisements; and a fourth determining submodule, used to determine a marketing outreach strategy as a second marketing outreach strategy in response to a second evaluation value being greater than or equal to a second threshold, wherein the second marketing outreach strategy is used to reach customers by arranging marketing management objects.

[0113] It should be noted that the above-mentioned modules or units may be hardware or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above-mentioned modules may also be part of the device and may run in the computer terminal 10 provided in Embodiment 1.

[0114] Embodiments of this application may provide an electronic device. Figure 5 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 5 As shown, the electronic device may include: one or more ( Figure 5 (Only one is shown) processor 502, memory 504, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0115] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0116] The processor can invoke information and applications stored in the memory through the transmission device to perform the following steps: using the real-time data warehouse of the initial object to obtain the transaction data of the initial object, including: using the real-time data warehouse to obtain the behavioral data of the initial object, wherein the behavioral data is used to characterize the transaction behavior generated by the initial object according to different demand information; and determining the transaction data of the initial object based on the behavioral data.

[0117] The processor can also access information and applications stored in the memory via a transmission device to perform the following steps: using the real-time data warehouse of the initial object to obtain the rule parameter data of the initial object, including: using the real-time data warehouse of the initial object to determine multiple keywords based on transaction data and attribute data; determining the weights corresponding to the multiple keywords respectively; inputting the multiple keywords and multiple weights into the evaluation model for analysis to obtain the analysis results, wherein the evaluation model is established through multiple historical keywords and multiple historical weights; and determining the rule parameter data of the initial object based on the analysis results.

[0118] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: determining the rule parameter data of the initial object based on the analysis results, including: determining the first evaluation value of multiple keywords based on the analysis results; sorting the multiple first evaluation values ​​according to the sorting rules to obtain the sorted multiple first evaluation values; and determining the rule parameter data of the initial object based on the sorted multiple first evaluation values.

[0119] The processor can also invoke information and applications stored in the memory via a transmission device to perform the following steps: associating transaction data, attribute data, and rule parameter data to generate target rules, including: determining customer characteristic rules in the target rules based on attribute data and rule parameter data, wherein the customer characteristic rules are rules where the attribute data is greater than or equal to the attribute threshold data in the rule parameter data; and determining customer transaction rules in the target rules based on transaction data and rule parameter data, wherein the customer transaction rules are rules where the transaction data is greater than or equal to the transaction threshold data in the rule parameter data.

[0120] The processor can also invoke information and applications stored in the memory via a transmission device to perform the following steps: generating a marketing outreach strategy based on transaction data and attribute data, including: determining the target audience's willingness data based on the transaction data and attribute data, wherein the willingness data is used to characterize the target audience's willingness to pay for a personal housing loan; evaluating the willingness data to obtain a second evaluation value; and determining a marketing outreach strategy based on the second evaluation value, a first threshold corresponding to the second evaluation value, and a second threshold corresponding to the second evaluation value, wherein the first threshold is less than the second threshold.

[0121] The processor can also invoke information and applications stored in the memory via a transmission device to perform the following steps: determining a marketing reach strategy based on a second evaluation value, a first threshold corresponding to the second evaluation value, and a second threshold corresponding to the second evaluation value, including: determining a first marketing reach strategy in response to the second evaluation value being greater than the first threshold and less than the second threshold, wherein the first marketing reach strategy is used to reach customers by placing advertisements; and determining a second marketing reach strategy in response to the second evaluation value being greater than or equal to the second threshold, wherein the second marketing reach strategy is used to reach customers by arranging marketing management objects.

[0122] This application provides a solution for a customer outreach method based on a real-time data warehouse. First, the real-time data warehouse of the initial object is used to obtain the initial object's transaction data, attribute data, and rule parameter data. Then, the transaction data, attribute data, and rule parameter data of the target object are correlated to generate target rules. Next, according to the target rules, at least one target account is obtained from the initial accounts corresponding to multiple initial objects. Finally, a marketing outreach strategy is generated based on the transaction data and attribute data, allowing the strategy to be executed on the terminals corresponding to the target account, achieving personalized recommendations or marketing to those terminals. Because the transaction data, attribute data, and rule parameter data can be correlated to generate target rules, and then at least one target account can be obtained from multiple initial accounts according to these rules, and the marketing outreach strategy can be generated based on the transaction data and attribute data, the strategy can be executed on the terminals corresponding to the target account. This solves the technical problem of low new customer acquisition rates due to the inability to reach customers in a timely manner, achieving timely customer outreach and improving the new customer acquisition rate.

[0123] It should be noted that the information and data collected in this application (including but not limited to transaction data, attribute data, and rule parameter data) are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data all comply with relevant laws, regulations, and standards, and necessary confidentiality measures have been taken. This process does not violate public order and good morals, and corresponding access points are provided for users to choose whether to authorize or refuse. For example, interfaces are established between this system and relevant users or institutions, providing users with corresponding access points to choose whether to agree to or refuse automated decision-making results; if the user chooses to refuse, the process proceeds to the expert decision-making stage.

[0124] Those skilled in the art will understand that Figure 5The structure shown is for illustrative purposes only. Electronic devices can also be smartphones (such as Android phones, iOS phones, etc.), tablets, PDAs, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 5 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 5 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 5 The different configurations shown.

[0125] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0126] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the real-time data warehouse-based touch method provided in Embodiment 1.

[0127] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0128] This application also provides a computer program product that, when executed on a data processing device, is adapted to perform the steps of a customer reach method based on a real-time data warehouse.

[0129] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0130] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0131] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0132] The units described as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0134] If the integrated unit is implemented as 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 this application, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0135] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A customer outreach method based on a real-time data warehouse, characterized in that, include: Using the real-time data warehouse of the initial object, transaction data, attribute data, and rule parameter data of the initial object are obtained. The real-time data warehouse is used to store different types of data of the initial object. The transaction data is used to characterize at least one transaction record of the initial object within a target time period. The attribute data is used to characterize the personal characteristics and asset information of the initial object. The transaction data, attribute data, and rule parameter data are correlated to generate target rules; According to the target rules, the initial accounts corresponding to multiple initial objects are filtered to obtain at least one target account corresponding to a target object, wherein the target object is a potential customer who can support personal housing loans; Based on the transaction data and the attribute data, a marketing outreach strategy is generated, and the marketing outreach strategy is executed on the terminal corresponding to the target account. The marketing outreach strategy is used to make personalized recommendations or marketing to the terminal corresponding to the target account. The process of associating the transaction data, attribute data, and rule parameter data to generate target rules includes: determining customer characteristic rules in the target rules based on the attribute data and rule parameter data, wherein the customer characteristic rules are rules where the attribute data is greater than or equal to the attribute threshold data in the rule parameter data; and determining customer transaction rules in the target rules based on the transaction data and rule parameter data, wherein the customer transaction rules are rules where the transaction data is greater than or equal to the transaction threshold data in the rule parameter data. Based on the transaction data and the attribute data, a marketing outreach strategy is generated, comprising: determining the target object's willingness data based on the transaction data and the attribute data, wherein the willingness data is used to characterize the target object's willingness to pay the personal housing loan; evaluating the willingness data to obtain a second evaluation value; and determining the marketing outreach strategy based on the second evaluation value, a first threshold corresponding to the second evaluation value, and a second threshold corresponding to the second evaluation value, wherein the first threshold is less than the second threshold. Based on the second evaluation value, the first threshold corresponding to the second evaluation value, and the second threshold corresponding to the second evaluation value, the marketing reach strategy is determined, including: in response to the second evaluation value being greater than the first threshold and less than the second threshold, the marketing reach strategy is determined to be a first marketing reach strategy, wherein the first marketing reach strategy is used to reach customers by placing advertisements; in response to the second evaluation value being greater than or equal to the second threshold, the marketing reach strategy is determined to be a second marketing reach strategy, wherein the second marketing reach strategy is used to reach customers by arranging marketing management objects.

2. The method according to claim 1, characterized in that, Utilizing the real-time data warehouse of the initial object, obtain the transaction data of the initial object, including: Using the real-time data warehouse, behavioral data of the initial object is obtained, wherein the behavioral data is used to characterize the transaction behavior generated by the initial object according to different demand information; Based on the behavioral data, the transaction data of the initial object is determined.

3. The method according to claim 1, characterized in that, Using the real-time data warehouse of the initial object, the rule parameter data of the initial object is obtained, including: Using the real-time data warehouse of the initial object, multiple keywords are determined based on the transaction data and the attribute data; Based on the multiple keywords, determine the weights corresponding to the multiple keywords respectively; Multiple keywords and multiple weights are input into the evaluation model for analysis to obtain analysis results. The evaluation model is established using multiple historical keywords and multiple historical weights. Based on the analysis results, the rule parameter data of the initial object are determined.

4. The method according to claim 3, characterized in that, Based on the analysis results, the rule parameter data of the initial object is determined, including: Based on the analysis results, a first evaluation value for each of the keywords was determined. The multiple first evaluation values ​​are sorted according to the sorting rules to obtain the sorted multiple first evaluation values; Based on the sorted plurality of the first evaluation values, the rule parameter data of the initial object is determined.

5. A customer-facing device based on a real-time data warehouse, characterized in that, include: The acquisition unit is used to acquire the transaction data, attribute data, and rule parameter data of the initial object using the real-time data warehouse of the initial object. The real-time data warehouse is used to store different types of data of the initial object. The transaction data is used to characterize at least one transaction record of the initial object within a target time period. The attribute data is used to characterize the personal characteristics and asset information of the initial object. The generation unit is used to associate the transaction data, the attribute data, and the rule parameter data to generate target rules; The filtering unit is used to filter from the initial accounts corresponding to multiple initial objects according to the target rules to obtain at least one target account corresponding to a target object, wherein the target object is a potential customer who can support personal housing loans; An execution unit is configured to generate a marketing outreach strategy based on the transaction data and the attribute data, and execute the marketing outreach strategy on the terminal corresponding to the target account, wherein the marketing outreach strategy is used to make personalized recommendations or marketing to the terminal corresponding to the target account. The generation unit is further configured to: determine customer feature rules in the target rules based on the attribute data and the rule parameter data, wherein the customer feature rules are rules in which the attribute data is greater than or equal to the attribute threshold data in the rule parameter data; and determine customer transaction rules in the target rules based on the transaction data and the rule parameter data, wherein the customer transaction rules are rules in which the transaction data is greater than or equal to the transaction threshold data in the rule parameter data. The execution unit is further configured to determine the target object's willingness data based on the transaction data and the attribute data, wherein the willingness data is used to characterize the target object's willingness to pay the personal housing loan; evaluate the willingness data to obtain a second evaluation value; and determine the marketing outreach strategy based on the second evaluation value, a first threshold corresponding to the second evaluation value, and a second threshold corresponding to the second evaluation value, wherein the first threshold is less than the second threshold. The execution unit is further configured to, in response to the second evaluation value being greater than the first threshold and less than the second threshold, determine the marketing reach strategy as a first marketing reach strategy, wherein the first marketing reach strategy is used to reach customers by placing advertisements; and in response to the second evaluation value being greater than or equal to the second threshold, determine the marketing reach strategy as a second marketing reach strategy, wherein the second marketing reach strategy is used to reach customers by arranging marketing management objects.

6. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the computer-readable storage medium is located to perform the method of any one of claims 1 to 4.

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