Deposit data analysis method and device, equipment, storage medium and product

Abstract

The invention discloses a deposit data analysis method and device, equipment, a storage medium and a product, and relates to the technical field of data analysis, and the deposit data analysis method comprises the steps: obtaining deposit data of a plurality of clients; on the basis of the deposit data and the clients in multiple dimensions, index combination data is constructed, and the index combination data is used for representing the deposit data in different dimensions; analyzing and processing the deposit data through a customer group rule corresponding to the index combination data to obtain a deposit trend result; and making a deposit marketing strategy corresponding to each customer according to the deposit trend result. According to the invention, the message hysteresis of deposit retention feedback is reduced.

Description

Technical Field

[0001] The present application relates to the field of data analysis technology, and in particular to a deposit data analysis method, device, equipment, storage medium and computer program product. Background Art

[0002] Banks' corporate deposits are facing the impact of peer competition and low-interest policies, which will cause serious pressure on bank deposits. A series of measures need to be implemented to retain and divert deposits. Most of the current deposit monitoring systems judge the customer's value based on the customer's current time and historical nodes to determine whether to retain the customer's deposits. This will lead to a lag in the feedback on customer deposit retention.

[0003] Therefore, how to reduce the message lag of deposit retention feedback is a problem that needs to be solved urgently.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a deposit data analysis method, device, equipment, storage medium and computer program product, aiming to solve the technical problem in the related technology of judging the customer value according to the customer's current time and historical nodes to determine whether to retain the customer's deposit, resulting in delayed feedback on deposit loss.

[0006] To achieve the above purpose, the present application proposes a deposit data analysis method, which includes:

[0007] Get deposit data of multiple customers;

[0008] Based on the deposit data and each of the customers in multiple dimensions, constructing indicator combination data, the indicator combination data is used to characterize the deposit data in different dimensions, the dimensions including scenario dimension, time dimension and value dimension;

[0009] Analyze and process the deposit data using the customer group rules corresponding to the indicator combination data to obtain a deposit trend result;

[0010] Deposit marketing strategies corresponding to each of the customers are formulated based on the deposit trend results.

[0011] In one embodiment, the step of constructing indicator combination data based on the deposit data and each of the customers in multiple dimensions includes:

[0012] Determine the data dimensions and data trends corresponding to each of the deposit data;

[0013] According to each of the customers in multiple dimensions, the data dimensions and the data trends are converted into data indicators to obtain indicator combination data.

[0014] In one embodiment, the step of converting the data dimensions and the data trends into data indicators according to the customers in multiple dimensions to obtain indicator combination data includes:

[0015] Cross-combining the data dimensions with the data trends to obtain customer group data;

[0016] According to the business scenarios corresponding to the customer group data, the customer group data are converted into statistical data indicators;

[0017] Based on the data indicators, indicator combination data is constructed.

[0018] In one embodiment, before the step of analyzing and processing the deposit data by using the customer group rule corresponding to the indicator combination data to obtain the deposit trend result, the step further includes:

[0019] According to the data indicators in the indicator combination data, an indicator threshold is set to obtain a first customer group rule;

[0020] The customer group characteristics of each of the customers are extracted, and based on the priority ranking of each of the customer group characteristics, the first customer group rule is adjusted to obtain the customer group rule corresponding to the indicator combination data.

[0021] In one embodiment, after the step of determining the customer group characteristics of each of the customers and determining the customer group rules based on the priority ranking of each of the customer group characteristics, the step further includes:

[0022] Randomly extract historical deposit data corresponding to different customer group characteristics;

[0023] Performing visual analysis on the historical deposit data to obtain visual analysis results;

[0024] The customer categories that do not conform to the preset customer clusters are extracted from the visual analysis results, and the customer cluster rules are optimized based on the customer categories.

[0025] In one embodiment, the step of analyzing and processing the deposit data by using the customer group rules corresponding to the indicator combination data to obtain the deposit trend result includes:

[0026] Determine the customer group priority of each deposit data;

[0027] For any of the deposit data, if it is determined that the customer group rule and any customer group determination rule have an intersection, then it is determined that the deposit data belongs to the customer group data corresponding to the maximum value of the customer group priority;

[0028] Identifying the deposit trend corresponding to the customer group data by using the customer group rule, and generating a deposit trend result;

[0029] For any of the deposit data, if it is determined that the deposit volatility corresponding to the customer group priority is greater than the preset volatility threshold, the indicator combination data is analyzed and processed by the full customer group processing method to obtain the deposit trend result.

[0030] In addition, to achieve the above purpose, the present application also proposes a deposit data analysis device, the deposit data analysis device comprising:

[0031] The acquisition module is used to obtain the deposit data of multiple customers;

[0032] A construction module, used to construct indicator combination data based on the deposit data and each of the customers in multiple dimensions, wherein the indicator combination data is used to characterize the deposit data in different dimensions, wherein the dimensions include a scenario dimension, a time dimension, and a value dimension;

[0033] An analysis and processing module is used to analyze and process the deposit data according to the customer group rules corresponding to the indicator combination data to obtain a deposit trend result;

[0034] A formulation module is used to formulate a deposit marketing strategy corresponding to each of the customers based on the deposit trend results.

[0035] In addition, to achieve the above-mentioned purpose, the present application also proposes a deposit data analysis device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the deposit data analysis method described above.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the deposit data analysis method described above are implemented.

[0037] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the deposit data analysis method described above are implemented.

[0038] The present application proposes a deposit data analysis method, device, equipment, storage medium and computer program product. The present application obtains deposit data of multiple customers, and constructs indicator combination data for characterizing different dimensions through the deposit data, so that the customer's deposit data can be comprehensively considered according to the scenario dimension, time dimension and value dimension, and the deposit data is analyzed and processed through the customer group rules corresponding to the indicator combination data to determine the deposit trend results. Then, according to the deposit trend results, a deposit marketing strategy corresponding to each customer is formulated, the deposit trend of each customer is predicted in advance, and the message lag of deposit loss feedback is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] Figure 1 A flowchart of the first embodiment of the deposit data analysis method of the present application is provided;

[0042] Figure 2 A flow chart of the second embodiment of the deposit data analysis method of the present application;

[0043] Figure 3 This is a schematic diagram of the overall implementation process involved in the deposit data analysis method for this application;

[0044] Figure 4 This is a schematic diagram of the module structure of the deposit data analysis device according to an embodiment of the present application;

[0045] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the deposit data analysis method in the embodiment of the present application.

[0046] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0048] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0049] The main solutions of the embodiments of this application are:

[0050] Get deposit data of multiple customers;

[0051] Based on the deposit data and each of the customers in multiple dimensions, constructing indicator combination data, the indicator combination data is used to characterize the deposit data in different dimensions, the dimensions including scenario dimension, time dimension and value dimension;

[0052] Analyze and process the deposit data using the customer group rules corresponding to the indicator combination data to obtain a deposit trend result;

[0053] Deposit marketing strategies corresponding to each of the customers are formulated based on the deposit trend results.

[0054] In the existing technology, bank deposits are under severe pressure due to competition from peers and the impact of low interest policies. A series of measures need to be taken to retain and divert deposits. Currently, there are the following problems in customer deposit monitoring:

[0055] 1. Lack of deposit monitoring system. Most of the customer value is judged based on the customer's current time and historical nodes, resulting in a lag in deposit loss feedback;

[0056] 2. Traditional rules are not sophisticated enough to judge customer deposit trends, they only consider a single dimension and cover a small number of customers;

[0057] 3. There is no rule verification mechanism, and the accuracy of the rules cannot be guaranteed.

[0058] The present application provides a deposit data analysis method, apparatus, device, storage medium and computer program product, which obtains deposit data of multiple customers and constructs deposit data for characterizing different dimensions through the deposit data, so that the customer's deposit data can be comprehensively considered according to the scenario dimension, time dimension and value dimension, and the indicator combination data is analyzed and processed through customer group rules to determine the deposit trend results, and then, according to the deposit trend results, a deposit marketing strategy corresponding to each customer is formulated, the deposit trend of each customer is predicted in advance, and the message lag of deposit loss feedback is reduced.

[0059] By judging customer deposit trends through multi-dimensional data, we can cover a wide range of customers.

[0060] Deposit trends are determined on indicator combination data through customer group rules to ensure the accuracy of rule determination.

[0061] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a deposit data analysis device, etc. The following takes the deposit data analysis device as an example to illustrate this embodiment and the following embodiments.

[0062] Based on this, the present application embodiment provides a deposit data analysis method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the deposit data analysis method of the present application.

[0063] In this embodiment, the deposit data analysis method includes steps S10 to S40:

[0064] Step S10, obtaining deposit data of multiple customers;

[0065] It should be noted that the deposit data refers to the amount of money deposited by the customer in the bank, the deposit currency type, and the depositor information, etc., without any specific limitation.

[0066] In addition, it should be noted that the deposit data collected in this application is multi-dimensional deposit data, which is used to analyze the data in various dimensions, so as to analyze and derive accurate data change trends. At the same time, the deposit data can be applied to a variety of scenarios, thereby improving the coverage of customer groups.

[0067] Step S20, constructing indicator combination data based on the deposit data and each of the customers in multiple dimensions, wherein the indicator combination data is used to characterize the deposit data in different dimensions, wherein the dimensions include a scenario dimension, a time dimension, and a value dimension;

[0068] It should be noted that, by extracting the data indicator data from the collected deposit data, the data indicator data is analyzed, combined, and other operations are performed to generate indicator combination data.

[0069] It should be noted that the indicator combination data is used to characterize deposit data of different dimensions, and is also used to characterize data indicators of each customer's deposit data, so as to reflect the value of the deposit data of various customers, so as to judge their subsequent deposit trend types. Among them, the deposit trend type can be a continuously rising type, a continuously declining type, or a stable and non-fluctuating trend type.

[0070] It should be noted that the scenario dimensions may include: value enhancement, deposit loss and suspected indicators, among which value enhancement may be a business scenario in which a customer is judged to have improved on the basis of its original value. For example, customer A's original deposit was 50,000. One month later, the customer's deposit became 100,000. The scenario dimension corresponding to customer A is value enhancement.

[0071] The business scenario corresponding to deposit loss is: if the customer's deposits will decrease in the historical time period or the predicted future time period, then it can be determined that the scenario applicable to the customer is a deposit loss scenario.

[0072] Suspected indicators indicate that the customer has experienced value enhancement or deposit loss, and they need to be further classified for analysis and judgment.

[0073] It should be noted that the time dimension takes into account the length of time it takes for a customer to open an account and the length of time it retains a deposit. Customers include new accounts, long-term depositors, short-term depositors, etc. The customer’s deposit retention status is determined from the time dimension.

[0074] It should be noted that the value dimension mainly considers low value, effective accounts, valuable accounts and high value. The value dimension can be determined by the customer's deposit amount and activity level. Customers with high deposit amounts and high activity levels are high-value customers, while customers with low deposit amounts and low activity levels are low-value customers. It is also possible to determine high-value customers or low-value customers solely by the deposit amount. For example, customers with high deposit amounts are high-value customers, and customers with low deposit amounts are low-value customers. Other determination methods are similar and will not be elaborated here.

[0075] In a feasible implementation manner, the step S20 of constructing the indicator combination data based on the deposit data and each of the customers in multiple dimensions may include steps S21 to S22:

[0076] Step S21, determining the data dimension and data trend corresponding to each of the deposit data;

[0077] It should be noted that the data dimensions are time dimension, scenario dimension and value dimension, and the data trend can be the growth rate of deposit data, the distribution coefficient of statistical indicators, etc.

[0078] Step S22: convert the data dimensions and the data trends into data indicators according to the customers in multiple dimensions to obtain indicator combination data.

[0079] It should be noted that data indicator data can be obtained through statistical methods. The indicator combination data includes data indicators, customer group characteristics and deposit data, etc. When the indicator combination data is subsequently analyzed, the data indicators are used to formulate customer group rules, and the data used for analysis are the customer group characteristics and deposit data of the customer, so as to analyze the customer type corresponding to the deposit data and the customer's deposit trend, and through the deposit trend, timely formulate corresponding deposit marketing strategies to retain customers.

[0080] In a feasible implementation manner, the step S23 of converting the data dimensions and the data trends into data indicators according to the customers in multiple dimensions to obtain indicator combination data includes:

[0081] Cross-combining the data dimensions with the data trends to obtain customer group data;

[0082] It should be noted that by cross-combining data dimensions and data trends, it is possible to determine the customer group to which the customer belongs, which may be a potential improvement customer group, a high-value customer group, etc.

[0083] It should be noted that through comprehensive analysis of data dimensions and data trends, the classified customer types can be made more accurate, avoiding inaccurate customer classification due to a single scenario.

[0084] According to the business scenarios corresponding to the customer group data, the customer group data are converted into statistical data indicators;

[0085] It should be noted that the business scenarios may be scenarios such as deposit loss or depositor potential improvement. After determining the business scenarios of deposit loss warning or deposit potential improvement, the customer data is converted into data indicators through statistical methods.

[0086] It should be noted that data indicators can be coefficient of variation, compound growth rate, skewness and kurtosis, etc.

[0087] Specifically, the specific meanings of various data indicators can be:

[0088] Coefficient of variation: standard deviation SD / mean Mean measures the degree of dispersion of the variable. The smaller the coefficient of variation, the more stable it is. It is used to determine whether the deposit remains stable.

[0089] Compound growth rate: It is the expected value obtained by describing the conversion of an investment return rate into a more stable investment return; (the average daily value of the current deposit in the remaining months / the average daily value of the deposit in the beginning of the period)^(1 / number of months)-1 is used to determine whether the deposit is growing steadily;

[0090] Skewness: also known as skewness coefficient, is a measure of the direction and degree of skewness of statistical data distribution. It is a numerical characteristic of the degree of asymmetry of statistical data distribution and is used to determine whether customer deposits have fallen sharply.

[0091] Kurtosis, also known as the peak coefficient, is an indicator that characterizes the height of the peak of the probability density distribution curve at the mean value, and is used to determine whether there is a quick in and out behavior in customer deposits.

[0092] Based on the data indicators, indicator combination data is constructed.

[0093] It should be noted that after the data indicators are counted, indicator combination data are generated through statistical combination.

[0094] Step S30, analyzing and processing the deposit data according to the customer group rules corresponding to the indicator combination data to obtain a deposit trend result;

[0095] It should be noted that the customer group rules are constructed through an indicator library composed of data indicators. The indicator library combines the customer's deposit behavior and can more accurately determine the customer's deposit habits or deposit methods. The deposit method can be current deposits, time deposits, deposit time, and deposit and withdrawal data during the deposit period.

[0096] In a feasible implementation manner, before the step S30 of analyzing and processing the deposit data by the customer group rule corresponding to the indicator combination data to obtain the deposit trend result, the step further includes:

[0097] Step A1, setting an indicator threshold according to the data indicators in the indicator combination data to obtain a first customer group rule;

[0098] It should be noted that the corresponding indicator threshold is set by the data indicators in the indicator combination data, and the first customer group rule is determined by the indicator threshold.

[0099] It should be noted that the first customer group rule is the judgment rule obtained after setting the indicator threshold. However, in the actual judgment process, it is also necessary to determine which customer group the customer belongs to, because the same customer may be classified into multiple customer groups, but the customer groups are required to be mutually exclusive, so the customer group priority needs to be set.

[0100] For example, the customer group to which a customer corresponds can be determined by setting a deposit threshold. When a certain threshold is exceeded, the customer group to which the customer belongs can be determined. The deposit threshold can be 50,000, 100,000, 500,000, etc., without specific limitation.

[0101] Step A2, extracting the customer group characteristics of each of the customers, and adjusting the first customer group rules based on the priority ranking of each of the customer group characteristics to obtain the customer group rules corresponding to the indicator combination data.

[0102] It should be noted that the method of extracting customer group characteristics may be to determine the customer group to which the customer belongs through the customer's deposit data, thereby determining its customer group characteristics.

[0103] It should be noted that customer group characteristics can be high-value customer groups, low-value customer groups, etc. Different customer groups correspond to different priorities. For example, the priority of high-value customer groups is greater than that of low-value customer groups. The customer's group is determined by judging the customer group priority corresponding to the customer.

[0104] Specifically, the priority rule is to first determine whether there is an intersection between this customer group rule and other rules. If there is no intersection, there will be no impact and it will be determined separately. If there is an intersection, it is necessary to determine which customer group this customer belongs to first. For example, customers with different scenarios will receive more attention. It is better for a customer to be judged as a continuously declining and lost customer than to be judged as a repeatedly fluctuating customer.

[0105] In a feasible implementation manner, after the step A2 of determining the customer group characteristics of each of the customers and determining the customer group rules based on the priority ranking of each of the customer group characteristics, the step further includes:

[0106] Randomly extract historical deposit data corresponding to different customer group characteristics;

[0107] It should be noted that when the number of customers counted is large enough, the customer group rules may not be applicable to all customers. There may be customer groups that cannot be applied. At this time, the customer group rules need to be optimized so that the customer group rules can be applied to each customer group.

[0108] It should be noted that for customer groups with different customer characteristics, feature extraction is performed by randomly sampling historical deposit data of different customers to ensure the diversity of customer deposit samples.

[0109] Performing visual analysis on the historical deposit data to obtain visual analysis results;

[0110] It should be noted that by visually analyzing the historical deposit data, a visual analysis result is obtained, wherein the visual analysis result can be a corresponding trend chart, which can be more intuitively presented to customers through visual display.

[0111] The customer categories that do not conform to the preset customer clusters are extracted from the visual analysis results, and the customer cluster rules are optimized based on the customer categories.

[0112] It should be noted that the customer group rules are optimized by extracting customer categories that do not conform to the preset customer groups in the visualization analysis results, and new sample data is added to the customer group rules, so that the optimized customer group rules can be used with more customer groups.

[0113] Step S40, formulating a deposit marketing strategy corresponding to each of the customers based on the deposit trend results.

[0114] It should be noted that the deposit trend results can divide the customer's corresponding customer base into 6 major customer categories and 11 minor customer categories, which can be used for deposit monitoring and to assist in scenario marketing. Deposit marketing strategies, such as potential improvement households can prompt account managers to market and thus promote value improvement and achieve value household assessment. Continuously declining value households can be used to recover customers before they are lost.

[0115] The present application proposes a deposit data analysis method, device, equipment, storage medium and computer program product. The present application obtains deposit data of multiple customers, and constructs indicator combination data for characterizing different dimensions through the deposit data, so that the customer's deposit data can be comprehensively considered according to the scenario dimension, time dimension and value dimension, and the deposit data is analyzed and processed through the customer group rules corresponding to the indicator combination data to determine the deposit trend results. Then, according to the deposit trend results, a deposit marketing strategy corresponding to each customer is formulated, the deposit trend of each customer is predicted in advance, and the message lag of deposit loss feedback is reduced.

[0116] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 The step S30 of analyzing and processing the deposit data by the customer group rules corresponding to the indicator combination data to obtain the deposit trend result includes steps S31 to S34:

[0117] Step S31, determining the customer group priority of each deposit data;

[0118] It should be noted that each customer corresponds to a deposit data, and its customer priority can also be determined.

[0119] Step S32, for any of the deposit data, if it is determined that the customer group rule and any customer group determination rule have an intersection, then determining that the deposit data belongs to the customer group data corresponding to the maximum value of the customer group priority;

[0120] It should be noted that for different customer groups, there are other customer group determination rules. For example, high-value customers and low-value customers correspond to a determination type respectively. For a customer, if there is no duplication in the customer group to which he belongs during the rule determination process, he can be determined individually according to the current customer group rules. If there is duplication, it is necessary to determine the customer's priority, that is, which customer group he belongs to.

[0121] For example, customer groups with operational value have a higher priority. For example, if the same customer is identified as a customer with continuously declining loss and a valid customer with a sharp drop in value in the past month, the sharp drop in deposits is more worthy of attention, so the customer should be identified as a valid customer with a sharp drop in value in the past month.

[0122] It should be noted that customer groups that have no intersection or ambiguity with other customer groups will be judged first. For example, existing low-value customers, whose deposits remain below 50,000, can be directly defined without the need to make trend judgments.

[0123] Step S33, identifying the deposit trend corresponding to the customer group data through the customer group rule, and generating a deposit trend result;

[0124] It should be noted that after determining which customer group a customer belongs to through customer group rules, the deposit trend corresponding to the customer group data can be identified, thereby generating the corresponding deposit trend results. After determining the customer's deposit trend results, the corresponding marketing strategy is executed on the customer to determine whether the customer's deposits need to be recovered.

[0125] Step S34, for any of the deposit data, if it is determined that the deposit volatility corresponding to the customer group priority is greater than the preset volatility threshold, the indicator combination data is analyzed and processed by the full customer group processing method to obtain the deposit trend result.

[0126] It should be noted that for customer groups that are difficult to identify with rules, such as repeatedly fluctuating customer groups, it is difficult to use rules to judge. The method of taking the difference set of the entire customer group is adopted to identify this part of the customer group, while also ensuring full coverage of customer rules.

[0127] In this embodiment, the customer group to which each customer belongs is determined by the customer group priority, and then the deposit trend corresponding to the customer group is determined, so that the account manager can influence according to the deposit trend and win back the customers before the valuable customers are lost.

[0128] For example, to help understand the implementation process of the deposit data analysis method obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Figure 3 , Figure 3 A schematic diagram of the implementation process of a deposit data analysis method is provided, specifically:

[0129] The extracted deposit data is divided into three dimensions: scenario, time and value. The customer's deposit trend is determined through deposit data of several dimensions. The specific implementation method is to define the customer's customer base through a combination of various dimensions, and then quantify the data indicators of the deposit data. Customer base rules are formulated by building an indicator library, and then customer base or value judgment is made through customer base rule information to generate the customer base to which each customer belongs and the customer's deposit trend. In addition, the customer base rules will be optimized regularly through sampling visualization and rule verification.

[0130] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the deposit data analysis method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0131] This application also provides a deposit data analysis device, please refer to Figure 4 , the deposit data analysis device comprises:

[0132] An acquisition module 10 is used to acquire deposit data of multiple customers;

[0133] A construction module 20, for constructing index combination data based on the deposit data and each of the customers in multiple dimensions, wherein the index combination data is used to characterize the deposit data in different dimensions, wherein the dimensions include a scenario dimension, a time dimension, and a value dimension;

[0134] An analysis and processing module 30 is used to analyze and process the deposit data according to the customer group rules corresponding to the indicator combination data to obtain a deposit trend result;

[0135] The formulation module 40 is used to formulate a deposit marketing strategy corresponding to each of the customers based on the deposit trend results.

[0136] Optionally, the building blocks include:

[0137] A determination unit, used to determine the data dimension and data trend corresponding to each of the deposit data;

[0138] A processing unit is constructed to convert the data dimensions and the data trends into data indicators according to the customers in multiple dimensions to obtain indicator combination data.

[0139] Optionally, the construction processing unit includes:

[0140] A combination processing subunit, used for cross-combining the data dimension with the data trend to obtain customer group data;

[0141] A conversion subunit, used to convert each customer group data into statistical data indicators according to the business scenarios corresponding to each customer group data;

[0142] The construction subunit is used to construct indicator combination data based on the data indicators.

[0143] Optionally, the deposit data analysis device includes:

[0144] A setting module, used to set an indicator threshold according to the data indicator in the indicator combination data to obtain a first customer group rule;

[0145] The extraction module is used to extract the customer group characteristics of each of the customers, and adjust the first customer group rules based on the priority ranking of each of the customer group characteristics to obtain the customer group rules corresponding to the indicator combination data.

[0146] Optionally, the deposit data analysis device further includes:

[0147] Random extraction module, used to randomly extract historical deposit data corresponding to different customer group characteristics;

[0148] A visual analysis module, used to perform visual analysis on the historical deposit data to obtain visual analysis results;

[0149] The optimization module is used to extract customer categories that do not conform to the preset customer clusters in the visual analysis results, and optimize the customer group rules based on the customer categories.

[0150] Optionally, the formulation module includes:

[0151] A first determining unit, used to determine the customer group priority of each deposit data;

[0152] A second determination unit is configured to determine, for any of the deposit data, that the deposit data belongs to the customer group data corresponding to the maximum value of the customer group priority if it is determined that the customer group rule and any customer group determination rule have an intersection;

[0153] A generating unit, configured to identify a deposit trend corresponding to the customer group data through the customer group rule, and generate a deposit trend result;

[0154] The analysis and processing unit is used to analyze and process the indicator combination data through the full customer group processing method for any of the deposit data to obtain a deposit trend result if it is determined that the deposit volatility corresponding to the customer group priority is greater than a preset volatility threshold.

[0155] The deposit data analysis device provided by the present application adopts the deposit data analysis method in the above embodiment, which can solve the technical problem of deposit data analysis. Compared with the prior art, the beneficial effects of the deposit data analysis device provided by the present application are the same as the beneficial effects of the deposit data analysis method provided by the above embodiment, and other technical features in the deposit data analysis device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0156] The present application provides a deposit data analysis device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the deposit data analysis method in the above-mentioned embodiment one.

[0157] Reference below Figure 5 , which shows a schematic diagram of the structure of a deposit data analysis device suitable for implementing the embodiment of the present application. The deposit data analysis device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The deposit data analysis device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0158] like Figure 5As shown, the deposit data analysis device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the deposit data analysis device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the deposit data analysis device to communicate with other devices wirelessly or by wire to exchange data. Although the deposit data analysis device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0159] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0160] The deposit data analysis device provided by the present application adopts the deposit data analysis method in the above embodiment, which can solve the technical problem of deposit data analysis. Compared with the prior art, the beneficial effects of the deposit data analysis device provided by the present application are the same as the beneficial effects of the deposit data analysis method provided by the above embodiment, and other technical features in the deposit data analysis device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0161] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0162] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0163] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the deposit data analysis method in the above-mentioned embodiment.

[0164] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0165] The computer-readable storage medium may be included in the deposit data analysis device; or may exist independently without being assembled into the deposit data analysis device.

[0166] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the deposit data analysis device, the deposit data analysis device:

[0167] Get deposit data of multiple customers;

[0168] Based on the deposit data and each of the customers in multiple dimensions, constructing indicator combination data, the indicator combination data is used to characterize the deposit data in different dimensions, the dimensions including scenario dimension, time dimension and value dimension;

[0169] Analyze and process the deposit data using the customer group rules corresponding to the indicator combination data to obtain a deposit trend result;

[0170] Deposit marketing strategies corresponding to each of the customers are formulated based on the deposit trend results.

[0171] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the client computer, partially on the client computer, as a separate software package, partially on the client computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the client computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0172] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0173] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0174] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned deposit data analysis method, and can solve the technical problems of deposit data analysis. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the deposit data analysis method provided in the above-mentioned embodiment, and will not be elaborated here.

[0175] The present application also provides a computer program product, including a computer program, which implements the steps of the deposit data analysis method as described above when executed by a processor.

[0176] The computer program product provided by this application can solve the technical problem of deposit data analysis. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the deposit data analysis method provided by the above embodiment, which will not be repeated here.

[0177] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A deposit data analysis method, characterized in that: The deposit data analysis method comprises: Get deposit data of multiple customers; Based on the deposit data and each of the customers in multiple dimensions, constructing indicator combination data, the indicator combination data is used to characterize the deposit data in different dimensions, the dimensions including scenario dimension, time dimension and value dimension; Analyze and process the deposit data using the customer group rules corresponding to the indicator combination data to obtain a deposit trend result; Deposit marketing strategies corresponding to each of the customers are formulated based on the deposit trend results.

2. The method according to claim 1, characterized in that The step of constructing indicator combination data based on the deposit data and each of the customers in multiple dimensions includes: Determine the data dimensions and data trends corresponding to each of the deposit data; According to each of the customers in multiple dimensions, the data dimensions and the data trends are converted into data indicators to obtain indicator combination data.

3. The method according to claim 2, characterized in that The step of converting the data dimensions and the data trends into data indicators according to the customers in multiple dimensions to obtain indicator combination data includes: Cross-combining the data dimensions with the data trends to obtain customer group data; According to the business scenarios corresponding to the customer group data, the customer group data are converted into statistical data indicators; Based on the data indicators, indicator combination data is constructed.

4. The method according to claim 1, characterized in that Before the step of analyzing and processing the deposit data by using the customer group rules corresponding to the indicator combination data to obtain the deposit trend result, the step further includes: According to the data indicators in the indicator combination data, an indicator threshold is set to obtain a first customer group rule; The customer group characteristics of each of the customers are extracted, and based on the priority ranking of each of the customer group characteristics, the first customer group rule is adjusted to obtain the customer group rule corresponding to the indicator combination data.

5. The method according to claim 4, characterized in that After the step of adjusting the first customer group rule based on the priority ranking of the customer group characteristics to obtain the customer group rule corresponding to the indicator combination data, the method further includes: Randomly extract historical deposit data corresponding to different customer group characteristics; Performing visual analysis on the historical deposit data to obtain visual analysis results; The customer categories that do not conform to the preset customer clusters are extracted from the visual analysis results, and the customer cluster rules are optimized based on the customer categories.

6. The method according to claim 1, characterized in that The step of analyzing and processing the deposit data by using the customer group rules corresponding to the indicator combination data to obtain the deposit trend result includes: Determine the customer group priority of each deposit data; For any of the deposit data, if it is determined that the customer group rule and any customer group determination rule have an intersection, then it is determined that the deposit data belongs to the customer group data corresponding to the maximum value of the customer group priority; Identifying the deposit trend corresponding to the customer group data by using the customer group rule, and generating a deposit trend result; For any of the deposit data, if it is determined that the deposit volatility corresponding to the customer group priority is greater than the preset volatility threshold, the indicator combination data is analyzed and processed by the full customer group processing method to obtain the deposit trend result.

7. A deposit data analysis device, characterized in that: The deposit data analysis device comprises: The acquisition module is used to obtain the deposit data of multiple customers; A construction module, used to construct indicator combination data based on the deposit data and each of the customers in multiple dimensions, wherein the indicator combination data is used to characterize the deposit data in different dimensions, wherein the dimensions include a scenario dimension, a time dimension, and a value dimension; An analysis and processing module is used to analyze and process the deposit data according to the customer group rules corresponding to the indicator combination data to obtain a deposit trend result; A formulation module is used to formulate a deposit marketing strategy corresponding to each of the customers based on the deposit trend results.

8. A deposit data analysis device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the deposit data analysis method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the deposit data analysis method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the deposit data analysis method according to any one of claims 1 to 6 are implemented.

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