CN112991071A - Financial product market promotion strategy optimization system and method based on big data - Google Patents
Financial product market promotion strategy optimization system and method based on big data Download PDFInfo
- Publication number
- CN112991071A CN112991071A CN202110463585.0A CN202110463585A CN112991071A CN 112991071 A CN112991071 A CN 112991071A CN 202110463585 A CN202110463585 A CN 202110463585A CN 112991071 A CN112991071 A CN 112991071A
- Authority
- CN
- China
- Prior art keywords
- financial product
- sold
- information data
- preset
- screening
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Asset management; Financial planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Economics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Human Resources & Organizations (AREA)
- Operations Research (AREA)
- Technology Law (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a financial product marketing strategy optimization system and method based on big data, and the system comprises: the financial product A to be sold directly adopts the marketing strategy of the sold financial product B to sell if the difference value between the first sale period and the second sale period is smaller than a preset period threshold value; and if the difference value between the first sales period and the second sales period is greater than a preset period threshold value, optimizing and adjusting the marketing strategy of the sold financial product B, providing a targeted marketing strategy reference for the financial product A to be sold, shortening the marketing strategy formulation period, improving the marketing efficiency, improving the marketing process, and reducing the time cost, the labor cost and the financial cost spent on marketing.
Description
Technical Field
The invention relates to the technical field of finance, in particular to a financial product market promotion strategy optimization system and method based on big data.
Background
Financial products refer to a variety of non-physical assets that have economic value and can be publicly traded or redeemed, also called securities, such as cash, money orders, stocks, futures, bonds, insurance policies, and the like.
We can purchase any goods, including financial products, in cash; we can go to the bank to accept the draft (into cash): we can buy and sell (trade) stocks, futures, etc. at will in the corresponding financial markets; bonds, policies, etc. we hold may be redeemed (turned into cash) by due.
With the development of internet technology, network resources related to financial products are gradually abundant, various financial products are more and more, financial institutions providing financial products are more and more difficult to make effective popularization schemes for financial products to be put into the market, only the financial products to be put into the market can be made into a general popularization strategy according to industry experience, then the financial products are put into the market, and after the financial products are popularized on the market for a period of time according to the made general popularization strategy, the current financial product popularization strategy is adjusted according to the popularization result fed back by the market.
Due to the rapid development of the big data era, the data volume required to be maintained or analyzed by a plurality of organizations and enterprises is huge continuously, the financial service industry needs to accurately and directionally analyze images of users, and the traditional backward data use cannot adapt to the current big data volume.
Moreover, most data of the financial institutions are stored in the database at present, and cannot be effectively utilized.
In the prior art, chinese patent application No. CN201910330994.6 (application date: 2019, 4/23) discloses an automatic financial policy generation method, apparatus, system and recording medium, wherein the method includes obtaining financial user data, the financial user data including a plurality of tags; receiving a configuration of financial user data to configure the financial user data into sample data; receiving a configuration of a financial policy goal, the goal being a target parameter to be achieved by a financial product employing the financial policy; receiving a configuration of an algorithm for a financial model; according to the configuration of the financial strategy target, the importance of each label and the influence degree of each label on the financial strategy target are automatically calculated by utilizing the configuration of the financial user data and the configuration of the algorithm of the financial model, so that an optimal financial strategy is obtained, the intellectualization and automation of data analysis are realized by utilizing an automatic decision analysis tool, an optimal strategy report which is beneficial to an analyst to use is generated, and the work efficiency of the analyst is improved.
Disclosure of Invention
In view of at least one of the above problems in the prior art, embodiments of the present invention provide a system and a method for optimizing a financial product marketing strategy based on big data, which can provide a marketing strategy for a financial product a to be marketed by a financial platform when the financial product a to be marketed is to be marketed according to the big data, the marketing strategy being a preset marketing target of the financial product a to be marketed as a similar sold financial product B of the financial product a to be marketed, and a second marketing period required by the marketing strategy, thereby providing a targeted marketing strategy reference for the financial product a to be marketed, shortening a marketing strategy formulation period of the financial product a to be marketed, improving the marketing efficiency of the financial product a to be marketed, improving the marketing process of the financial product a to be marketed, and reducing the time cost spent by the financial product a to be marketed in marketing, Human and financial costs.
In order to achieve the above purpose of the invention, the following technical scheme is adopted:
in a first aspect, the present application provides a financial product marketing strategy optimization system based on big data, including:
a first sale period calculation module, configured to calculate a first sale period required by the financial product a to be sold to complete a preset sale target according to the return on investment, the investment amount, the return on investment period, and the purchasing power index of the current market of the financial product a to be sold
；
A second sales cycle calculation module for obtaining the same investment according to the big data processing analysis moduleMarket promotion strategy for the same kind of sold financial product B of the financial product A to be sold and completing the preset sales target of the financial product A to be sold, of the return rate, the investment amount, the return on investment period and the purchasing power index of the current market, and a second sales period required by adopting the market promotion strategy
；
A sales cycle comparison module for calculating the first sales cycle
And the second sales cycle
The difference value of (a), wherein,
if the first sales period
And the second sales cycle
When the difference value is smaller than the preset period threshold value, the financial product A to be sold directly adopts the marketing strategy of the sold financial product B to sell;
if the first sales period
And the second sales cycle
When the difference value is greater than the preset period threshold value, performing optimization adjustment on the basis of the marketing strategy of the sold financial product B.
Further, the specific calculation steps of the preset period threshold value are as follows:
based on the second sales cycle
Ranking all financial products according to the number of times that all financial products of the same type as the sold financial product B are purchased to obtain the sold financial product B in the second sale period
The popularity ranking of all financial products in the bank;
based on the sold financial product B in the second sale period
Calculating the popularity ranking of all financial products in the second sale period of the sold financial product B
Inner heat coefficient:
;
wherein the content of the first and second substances,
in the second sale period for the sold financial product B
Internal heat coefficient;
in the second sale period for the sold financial product B
The popularity ranking of the financial products in the bank;
in the second sale period for the sold financial product B
The number of purchases made;
based on the sold financial product B in the second sale period
And calculating the preset period threshold value according to the internal heat coefficient:
;
wherein the content of the first and second substances,
representing the preset cycle threshold;
representing the first sales cycle
And the second sales cycle
A difference of (d);
indicating that the sold financial product B is in the second sale period
Internal heat coefficient;
the first sales cycle
And a second sales cycle
The calculating step of the difference value of (a) is:
wherein the content of the first and second substances,
representing the first sales cycle
And the second sales cycle
A difference of (d);
representing a first sales period required by the financial product A to be sold to complete a preset sales target;
indicating a second sales cycle required for a marketing strategy when the same kind of sold financial product B as the financial product A to be sold completes the preset sales target of the financial product A to be sold.
In a second aspect, the present application provides a method for optimizing a financial product marketing strategy based on big data, which utilizes the above system for optimizing a financial product marketing strategy based on big data, and includes the following steps:
according to the return on investment rate, the investment amount, the return on investment period and the purchasing power index of the current market of the financial product A to be sold, calculating a first sales period required by the financial product A to be sold to complete a preset sales target
；
Obtaining the same type of sold financial product B with the same return on investment rate, investment amount, return on investment period and market purchasing power index as the financial product A to be sold according to a big data processing and analyzing module to finish the pre-sale of the financial product A to be soldMarketing strategy when setting marketing target and second marketing period required by adopting marketing strategy
；
Calculating the first sales period
And the second sales cycle
The difference value of (a), wherein,
if the first sales period
And the second sales cycle
When the difference value is smaller than the preset period threshold value, the financial product A to be sold directly adopts the marketing strategy of the sold financial product B to sell;
if the first sales period
And the second sales cycle
When the difference value is greater than the preset period threshold value, performing optimization adjustment on the basis of the marketing strategy of the sold financial product B.
Further, the big data processing and analyzing module comprises: a command receiving module, a collection scheme making module, a collection scheme executing module, an information data storage module, a calculation screening module and an information data feedback module, wherein,
the command receiving module is used for receiving data of the return on investment rate, the investment amount, the return on investment period and the purchasing power index of the current market of the financial product A to be sold, which are sent by the upper computer;
the collection scheme making module is configured to make an information data collection scheme for the same kind of sold financial product B as the financial product a to be sold according to the data of the return on investment rate, the investment amount, the return on investment period, and the purchasing power index of the current market, which are sent by the upper computer and received by the command receiving module, where the information data of the sold financial product B includes: market promotion strategy data when the same type of sold financial product B as the financial product A to be sold completes the preset sales target of the financial product A to be sold and a second sales cycle required for adopting the market promotion strategy
Data;
the collection plan execution module is used for executing the plan formulated by the collection plan formulation module
The information data collection scheme of the sold financial product B is used for collecting the information data of the sold financial product B;
the information data storage module: for storing the information data of the sold financial product B collected by the collection scenario execution module;
the calculation screening module is used for screening and filtering the information data of the sold financial product B stored in the information data storage module, so that the accuracy of the collected information data of the sold financial product B is improved;
and the information data feedback module is used for feeding back the information data of the sold financial product B filtered by the calculation and screening module to the upper computer.
Further, the calculation screening module is configured to screen and filter the information data of the sold financial product B collected by the collection scheme execution module, which is stored in the information data storage module, specifically including:
calling a preset screening strategy to obtain initial screening and filtering words in the preset screening strategy; judging whether the initial screening filtering words of the preset screening strategy are related to the information data of the sold financial product B stored in the information data storage module; deleting the current initial screening filtering words from the preset screening strategy if the current initial screening filtering words of the preset screening strategy in the calculation screening module are not related to the current certain information data of the sold financial product B stored in the information data storage module;
if the current initial screening filtering words of the preset screening strategy in the calculation screening module are related to the current certain information data of the sold financial product B stored in the information data storage module, calculating the screening efficiency value of the current initial screening filtering words, further verifying and modifying the current initial screening filtering words according to the screening efficiency value, and finally selecting the optimal target preset screening strategy.
Further, the current initial screening and filtering words are further verified and modified according to the screening efficiency value, and an optimal target preset screening strategy is finally selected, wherein the method specifically comprises the following steps:
calculating the total screening efficiency value of the current screening and filtering words;
simplifying the total screening efficiency value of the screening and filtering words according to the total number of the information data of the sold financial product B stored in the information data storage module;
comparing the simplification processing result with a preset simplification processing threshold value;
if the simplification processing result is larger than or equal to the preset simplification processing threshold value, retaining the corresponding current screening and filtering words, and identifying the current screening and filtering words as the optimal screening and filtering words; finally, summarizing all the optimal screening and filtering words to form an optimal target preset screening strategy;
and if the simplification processing result is less than the preset simplification processing threshold value, deleting the corresponding current screening and filtering words.
Further, the calculation filtering module is configured to perform deduplication on the information data of the sold financial product B stored in the information data storage module before performing filtering on the information data of the sold financial product B stored in the information data storage module, and the specific steps are as follows:
establishing a preset deduplication strategy according to the information data of the plurality of sold financial products B stored in the information data storage module, and associating the information data of the plurality of sold financial products B stored in the information data storage module with the preset deduplication strategy one by one;
the preset duplication elimination strategy is provided with name parameters and content parameters corresponding to the information data of the sold financial product B stored in the information data storage module;
the information data of the sold financial product B stored in the information data storage module is compared with the duplication elimination strategy according to the name parameter and the content parameter, and the information data of the sold financial product B stored in the information data storage module is duplicated after comparison with the information data of the plurality of sold financial products B stored in the information data storage module corresponding to the same duplication elimination strategy, so that each duplication elimination strategy retains one information data of the sold financial product B.
Further, according to the name parameter and the content parameter, comparing the information data of the sold financial product B stored in the information data storage module with the duplication elimination strategy, specifically comprising the following steps:
filtering out information data of the sold financial product B which is stored in the information data storage module and does not correspond to the name in the information data of the sold financial product B according to the name parameter in the duplication removing strategy;
and filtering out the information data of the sold financial product B, which does not correspond to the content in the information data of the sold financial product B, stored in the information data storage module according to the content parameters in the duplication elimination strategy.
Further, the screening efficiency value of the current initial screening and filtering words is calculated, and the specific steps are as follows:
and if one piece of information data of the sold financial product B corresponds to one preset screening strategy, considering that the screening efficiency value of the preset screening strategy relative to the corresponding information data of the sold financial product B is a first numerical value.
Further, the specific calculation steps of the preset period threshold value are as follows:
based on the second sales cycle
Ranking all financial products according to the number of times that all financial products of the same type as the sold financial product B are purchased to obtain the sold financial product B in the second sale period
The popularity ranking of all financial products in the bank;
based on the sold financial product B in the second sale period
Calculating the popularity ranking of all financial products in the second sale period of the sold financial product B
Inner heat coefficient:
;
wherein the content of the first and second substances,
in the second sale period for the sold financial product B
Internal heat coefficient;
in the second sale period for the sold financial product B
The popularity ranking of the financial products in the bank;
in the second sale period for the sold financial product B
The number of purchases made;
based on the sold financial product B in the second sale period
And calculating the preset period threshold value according to the internal heat coefficient:
;
wherein the content of the first and second substances,
representing the preset cycle threshold;
representing the first sales cycle
And the second sales cycle
A difference of (d);
indicating that the sold financial product B is in the second sale period
Internal heat coefficient;
the first sales cycle
And a second sales cycle
The calculating step of the difference value of (a) is:
wherein the content of the first and second substances,
representing the first sales cycle
And the second sales cycle
A difference of (d);
representing a first sales period required by the financial product A to be sold to complete a preset sales target;
a second sales cycle required for representing a marketing strategy at the time of completing the preset sales target of the financial product A to be sold with a similar sold financial product B of the financial product A to be sold
By adopting the technical scheme, the invention has the following beneficial effects:
the financial product market promotion strategy optimization system and method based on big data provided by the application are based on the return on investment rate, the investment amount, the return on investment period and the sum of the return on investment of the financial product A to be soldThe purchasing power index of the current market calculates a first sales period required by the financial product A to be sold to complete a preset sales target
(ii) a Obtaining a marketing strategy when the same type of sold financial product B of the financial product A to be sold and the same type of sold financial product B of the financial product A to be sold have the same return on investment rate, investment amount, return on investment period and purchasing power index of the current market and complete the preset sales target of the financial product A to be sold according to a big data processing and analyzing module, and adopting a second sales period required by the marketing strategy
(ii) a Calculating the first sales period
And the second sales cycle
Wherein if said first sales cycle is not successful, said method further comprises
And the second sales cycle
When the difference value is smaller than the preset period threshold value, the financial product A to be sold directly adopts the marketing strategy of the sold financial product B to sell; if the first sales period
And the second sales cycle
Is greater than a preset period threshold, then optimization is performed on the basis of the marketing strategy of the sold financial product BThe adjustment is carried out by the following steps,
therefore, a market promotion strategy reference with pertinence is provided for the financial product A to be sold, the market promotion strategy formulation period of the financial product A to be sold is shortened, the market promotion efficiency of the financial product A to be sold is improved, the promotion flow of the financial product A to be sold is improved, and the time cost, the labor cost and the financial cost spent on market promotion of the financial product A to be sold are reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic structural diagram of an optimization system of a financial product marketing strategy based on big data according to the present invention.
FIG. 2 is a schematic flow chart of the method for optimizing the marketing strategy of the financial product based on big data according to the present invention.
Fig. 3 is a schematic structural diagram of a big data processing and analyzing module provided by the present invention.
Fig. 4 is a schematic flow chart illustrating further verification and modification of the current initial screening filtering words and finally selecting an optimal target preset screening strategy according to the present invention.
Fig. 5 is a schematic flow chart illustrating the process of removing duplicate information data of the sold financial product B stored in the information data storage module according to the present invention.
Reference numerals: a financial product marketing strategy optimization system 10 based on big data; a first sales cycle calculation module 11; a second sales cycle calculation module 12; a big data processing and analyzing module 13; sales cycle comparison module 14.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The first embodiment is as follows:
as shown in fig. 1, the present application provides a big data based financial product marketing strategy optimization system 10, comprising:
a first sale period calculation module 11, configured to calculate a first sale period required by the financial product a to be sold to complete a preset sale target according to the return on investment, the investment amount, the return on investment period, and the purchasing power index of the current market of the financial product a to be sold
；
Wherein a represents the investment amount of the financial product A to be sold; b represents the return on investment for the financial product A to be sold; c represents a return on investment period; d represents the purchasing power index of the current market for the fund a to be sold;
a second sales cycle calculation module 12, configured to obtain, according to the big data processing and analyzing module 13, a marketing strategy when the same type of sold financial product B of the financial product a to be sold completes the preset sales target of the financial product a to be sold and a second sales cycle required by the marketing strategy, where the same type of sold financial product B has the same return on investment rate, investment amount, return on investment cycle, and purchasing power index of the current market
；
A sales cycle comparison module 14 for calculating the first sales cycle
And the second sales cycle
The difference value of (a), wherein,
if the first sales period
And the second sales cycle
When the difference value is smaller than the preset period threshold value, the financial product A to be sold directly adopts the marketing strategy of the sold financial product B to sell;
if the first sales period
And the second sales cycle
When the difference value is greater than the preset period threshold value, performing optimization adjustment on the basis of the marketing strategy of the sold financial product B.
Specifically, the specific calculation step of the preset cycle threshold is as follows:
based on the second sales cycle
Ranking all financial products according to the number of times that all financial products of the same type as the sold financial product B are purchased to obtain the sold financial product B in the second sale period
The popular names of all financial products inSecondly;
based on the sold financial product B in the second sale period
Calculating the popularity ranking of all financial products in the second sale period of the sold financial product B
Inner heat coefficient:
;
wherein the content of the first and second substances,
in the second sale period for the sold financial product B
Internal heat coefficient;
in the second sale period for the sold financial product B
The popularity ranking of the financial products in the bank;
in the second sale period for the sold financial product B
The number of purchases made;
based on the sold financial product B in the second sale period
And calculating the preset period threshold value according to the internal heat coefficient:
;
wherein the content of the first and second substances,
representing the preset cycle threshold;
representing the first sales cycle
And the second sales cycle
A difference of (d);
indicating that the sold financial product B is in the second sale period
Internal heat coefficient;
the first sales cycle
And a second sales cycle
The calculating step of the difference value of (a) is:
wherein the content of the first and second substances,
representing the first sales cycle
And the second sales cycle
A difference of (d);
representing a first sales period required by the financial product A to be sold to complete a preset sales target;
indicating a second sales cycle required for a marketing strategy when the same kind of sold financial product B as the financial product A to be sold completes the preset sales target of the financial product A to be sold.
It should be noted that, in the above technical solution of the embodiment of the present invention, in the process of calculating and determining the preset period threshold, not only the second sales period is considered
The number of times that all financial products of the same type as financial product B are purchased also takes into account the financial product B in the second sale period
The specific calculation step of the preset period threshold is integrated and constructed by various influence factors, the accuracy of the result of calculating the preset period threshold is ensured, and the method is a first sales period
And a second sales cycle
The difference value of the time interval and the preset period threshold value are compared to obtain the accuracy of the comparison result, so that whether the financial product A to be sold directly adopts the marketing strategy of the sold financial product B or needs to be optimized and adjusted on the basis of the marketing strategy of the sold financial product B can be accurately judged, and the financial product A to be sold is the financial product to be soldThe product A provides a market promotion strategy reference with pertinence, shortens the market promotion strategy formulation period of the financial product A to be sold, improves the market promotion efficiency of the financial product A to be sold, improves the promotion flow of the financial product A to be sold, and reduces the time cost, the labor cost and the financial cost of the financial product A to be sold on market promotion.
It should be noted that many of the functional units described in this specification have been referred to as modules, in order to more particularly emphasize their implementation independence. When the modules can be implemented by software, all the modules that can be implemented by software can build corresponding hardware circuits to implement corresponding functions without considering the cost, considering the level of the existing hardware technology.
Example two:
as shown in fig. 2 and fig. 3, a method for optimizing a financial product marketing strategy based on big data according to a second embodiment of the present invention, which utilizes the above-mentioned system for optimizing a financial product marketing strategy based on big data, includes the following steps:
step 100: according to the return on investment rate, the investment amount, the return on investment period and the purchasing power index of the current market of the financial product A to be sold, calculating a first sales period required by the financial product A to be sold to complete a preset sales target
；
Wherein a represents the investment amount of the financial product A to be sold; b represents the return on investment for the financial product A to be sold; c represents a return on investment period; d represents the purchasing power index of the current market for the fund a to be sold;
calculating the first sale period by the return on investment rate, the investment amount, the return on investment period and the purchasing power index of the current market
Is only a conventional technical scheme in the prior art; but others calculate the first sales cycle by the above-mentioned return on investment, investment amount, return on investment cycle and purchasing power index of the current market
The method of the embodiment is not described in detail; the embodiment of the present application only considers that the above scheme belongs to a preferred technical scheme in the embodiment, and the first sales cycle is calculated for other modes
The embodiment is also protected.
Step 200: obtaining a marketing strategy when the same type of sold financial product B of the financial product A to be sold completes the preset sales target of the financial product A to be sold and has the same return on investment rate, investment amount, return on investment period and market purchasing power index according to a big data processing and analyzing module, and adopting a second sales period required by the marketing strategy
；
Step 300: calculating the first sales period
And the second sales cycle
The difference value of (a), wherein,
if the first sales period
And the second sales cycle
When the difference value is smaller than the preset period threshold value, the financial product A to be sold directly adopts the marketing strategy of the sold financial product B to sell;
if the first sales period
And the second sales cycle
When the difference value is greater than the preset period threshold value, performing optimization adjustment on the basis of the marketing strategy of the sold financial product B.
Specifically, the big data processing and analyzing module 13 includes: a command receiving module 131, a collection scheme making module 132, a collection scheme executing module 133, an information data storing module 134, a calculation screening module 135 and an information data feedback module 136; the big data processing and analyzing module 13 utilizes the command receiving module 131, the collection scheme making module 132, the collection scheme executing module 133, the information data storing module 134, the calculation screening module 135 and the information data feedback module 136 to execute the specific scheme of step 200; the main implementation of the step 200 executed in the first embodiment is as follows:
step 210 is executed: the command receiving module receives data of the return on investment rate, the investment amount, the return on investment period and the purchasing power index of the current market of the financial product A to be sold, which are sent by an upper computer;
step 220 is executed: the collection scheme making module makes an information data collection scheme of the same type of sold financial product B of the financial product A to be sold according to the data of the return on investment rate, the investment amount, the return on investment period and the purchasing power index of the current market, which are sent by the upper computer and received by the command receiving module, wherein the information data of the sold financial product B comprises: completing the preset sale of the financial product A to be sold with a same-type sold financial product B as the financial product A to be soldMarketing strategy data at the time of the target and a second sales cycle required to adopt the marketing strategy
Data;
step 230 is executed: the collection plan execution module executes the information data collection plan of the sold financial product B formulated by the collection plan formulation module, and collects the information data of the financial product B; specifically, the embodiment of the present invention designs an information data collection scheme for a sold financial product B of the same type as the financial product a to be sold, the information data collection scheme being for collecting information data of the sold financial product B.
Step 240 is executed: the information data storage module stores the information data of the sold financial product B collected by the collection scenario execution module;
step 250 is executed: the calculation screening module screens and filters the information data of the sold financial product B stored in the information data storage module, so that the accuracy of the collected information data of the sold financial product B is improved;
step 260 is executed: and the information data feedback module feeds back the information data of the sold financial product B filtered by the calculation and screening module to the upper computer.
By adopting the embodiment of the invention, the technical function of the big data processing and analyzing module is specifically adopted when the step 200 is executed, and the function is realized on the basis of hardware, and the advantage of the big data technology on data processing is based, so that the big data processing and analyzing module can be directly used on the existing network, the performance of the big data processing and analyzing module can be improved on the premise of not increasing hardware in a large amount, and better support is provided for an upper computer.
In a still further aspect; the researchers think that the information data of the sold financial product B collected through the aforementioned operations is massive data, and still need to further filter the collected information data of the sold financial product B;
in order to ensure the accuracy of the information data of the sold financial product B collected by the collection scenario execution module stored in the information data storage module, the calculation filtering module needs to filter the information data of the sold financial product B collected by the collection scenario execution module stored in the information data storage module.
In practice, when the operation of step 250 is performed, the information data of the sold financial product B stored in the information data storage module is information data about the sold financial product B in various aspects, such as: the data of actually required return on investment, investment amount, return on investment period, market purchasing power index and the like can be collected, and meanwhile, the information data of the sold financial product B which is not needed too much (for example, the sales return condition of the sold financial product B and the like) can be collected;
the information data of the sold financial product B actually collected is preset and is pre-stored in the screening policy. Similarly, the "sold financial product B" in the information data of the sold financial product B that actually needs to be collected is only a code or a general term, and is not specific to a certain "sold financial product B", and the initial filtering words are stored in the filtering policy.
When the operation of step 250 is executed, the calculation screening module performs screening and filtering on the information data of the sold financial product B stored in the information data storage module, so as to improve the accuracy of the collected information data of the sold financial product B (which is equivalent to adjusting and optimizing a preset screening policy), specifically including the following steps:
step 2501 is executed: calling a preset screening strategy to obtain an initial screening and filtering word in the preset screening strategy; judging whether the initial screening and filtering words of the preset screening strategy in the calculation screening module are related to the information data (data in the aspect) of the sold financial product B stored in the information data storage module (mainly judging whether text similarity is related); if the current initial screening filtering words of the preset screening strategy in the calculation screening module are not related to the current certain information data (data in the aspect) of the sold financial product B stored in the information data storage module (namely the similarity of the text is lower than a standard threshold value and is considered as not related), deleting the current initial screening filtering words from the preset screening strategy;
step 2502 is executed: if the current initial screening filtering words of the preset screening strategy in the calculation screening module are related to certain current information data (aspect data) of the sold financial product B stored in the information data storage module (namely the text similarity is higher than or equal to a standard threshold value and is considered to be related), calculating the screening efficiency value of the current initial screening filtering words, further verifying and modifying the current initial screening filtering words according to the screening efficiency value, and finally selecting the optimal target preset screening strategy. In the process of executing step 2502, researchers in the embodiments of the present invention believe that the initial screening filter term is not perfectly preset, and further modify and verify the initial screening filter term, so as to finally find the optimal screening filter term.
According to the invention, a screening strategy is preset in a calculation screening module, the preset screening strategy is called to carry out correlation calculation, initial screening and filtering words are adjusted, effective screening and filtering words are generated, and the screening strategy is perfected; finally, processing is carried out according to the adjusted preset screening strategy and the information data of the sold financial product B to obtain actually required information data; the embodiment of the invention enables the preset screening strategy in the calculation screening module to be more concise and effective, and improves the data processing capacity, the data processing efficiency, the screening and filtering efficiency and the accuracy of the screening and filtering results of the calculation screening module on the information data of the sold financial product B stored in the information data storage module.
It should be noted that the screening efficiency value of the present invention represents the screening effect of the preset screening policy on the information data of the sold financial product B stored in the information data storage module, and the higher the screening efficiency value is, the more effective the corresponding preset screening policy is.
Specifically, in a further technical solution, the execution scheme for executing step 2502 is as follows:
execution of step 25021: calculating the total screening efficiency value of the current screening and filtering words;
execution step 25022: simplifying the total screening efficiency value of the screening and filtering words according to the total number of the information data of the sold financial product B stored in the information data storage module;
step 25023 is executed: comparing the simplification processing result with a preset simplification processing threshold value;
execution of step 250231: if the simplification processing result is larger than or equal to the preset simplification processing threshold value, retaining the corresponding current screening and filtering words, and identifying the current screening and filtering words as the optimal screening and filtering words; finally, summarizing all the optimal screening and filtering words to form an optimal target preset screening strategy;
step 250232: and if the simplification processing result is less than the preset simplification processing threshold value, deleting the corresponding current screening and filtering words.
Preferably, the simplified processing of the invention can adopt normalization processing, after data normalization, the optimization process of the optimal target screening and filtering words becomes gentle obviously, the optimal target screening and filtering words can be converged more easily and correctly, the accuracy of the simplified processing result is ensured, the simplified processing result is accurate, thereby the accuracy of the comparison result of the simplified processing result and the preset simplified processing threshold value can be ensured, the retention of the optimal screening and filtering words and the deletion of the non-optimal screening and filtering words are finally realized, by the implementation scheme of the implementation step 2502, an accurate judgment basis is provided for the retention of the optimal screening and filtering words and the deletion of the optimal screening and filtering words, the accuracy of the judgment result is ensured, thereby ensuring the accuracy of the information data of the sold financial product B stored in the information data storage module.
Specifically, in order to reduce the data processing load of the calculation screening module and further improve the accuracy of the result of the filtering performed by the calculation screening module, between the step 240 and the step 250, the following steps (where the step of performing specifically means that before the calculation screening module is used to filter the information data of the sold financial product B stored in the information data storage module, the information data of the sold financial product B stored in the information data storage module needs to be deduplicated) are further required to be performed, and the steps of performing specifically are:
step 245 is executed: establishing a preset deduplication strategy according to the information data of the plurality of sold financial products B stored in the information data storage module, and associating the information data of the plurality of sold financial products B stored in the information data storage module with the preset deduplication strategy one by one;
step 246 is executed: the preset duplication elimination strategy is provided with name parameters and content parameters corresponding to the information data of the sold financial product B stored in the information data storage module;
step 247 is executed: the information data of the sold financial product B stored in the information data storage module is compared with the duplication elimination strategy according to the name parameter and the content parameter, and the information data of the sold financial product B stored in the information data storage module is duplicated after comparison with the information data of the plurality of sold financial products B stored in the information data storage module corresponding to the same duplication elimination strategy, so that each duplication elimination strategy retains one information data of the sold financial product B.
In the execution step 247, the information data of the sold financial product B stored in the information data storage module is compared with the duplication elimination policy, and the specific execution steps are as follows:
go to step 2471: filtering out information data of the sold financial product B which is stored in the information data storage module and does not correspond to the name in the information data of the sold financial product B according to the name parameter in the duplication removing strategy;
step 2472 is performed: and filtering out the information data of the sold financial product B, which does not correspond to the content in the information data of the sold financial product B, stored in the information data storage module according to the content parameters in the duplication elimination strategy.
Through the duplication elimination method, the information data of the plurality of sold financial products B stored in the information data storage module are associated with the preset duplication elimination strategy one by one, and the preset duplication elimination strategy is provided with name parameters and content parameters corresponding to the information data of the sold financial products B stored in the information data storage module; filtering out information data of the sold financial product B which is stored in the information data storage module and does not correspond to the name in the information data of the sold financial product B according to the name parameter in the duplication removing strategy; according to the content parameters in the duplication elimination strategy, filtering out the information data of the sold financial product B which does not correspond to the content in the information data of the sold financial product B stored in the information data storage module, and setting in this way, the information data of a larger number of the sold financial products B stored in the information data storage module can be deduplicated, the efficiency of deduplication processing of the information data of the sold financial products B stored in the information data storage module is improved, and the burden of data processing amount of the calculation screening module in the subsequent screening processing process of the information data of the sold financial product B stored in the information data storage module is further reduced, and a foundation is laid for further improving the screening efficiency of the calculation screening module in screening the filtering results and the accuracy of the screening results.
Specifically, in step 300, the specific implementation steps of calculating the preset period threshold value are as follows:
step 3001 is performed: based on the second sales cycle
And the number of times that all financial products of the same type as the sold financial product B are purchased is counted for all financial productsRanking to obtain the second sale period of the sold financial product B
The popularity ranking of all financial products in the bank;
step 3002 is executed: based on the sold financial product B in the second sale period
Calculating the popularity ranking of all financial products in the second sale period of the sold financial product B
Inner heat coefficient:
;
wherein the content of the first and second substances,
in the second sale period for the sold financial product B
Internal heat coefficient;
in the second sale period for the sold financial product B
The popularity ranking of the financial products in the bank;
in the second sale period for the sold financial product B
The number of purchases made;
step 3003 is executed: based on the sold fundMelting product B in the second sale period
And calculating the preset period threshold value according to the internal heat coefficient:
;
wherein the content of the first and second substances,
representing the preset cycle threshold;
representing the first sales cycle
And the second sales cycle
A difference of (d);
indicating that the sold financial product B is in the second sale period
Internal heat coefficient;
the first sales cycle
And a second sales cycle
The calculating step of the difference value of (a) is:
wherein the content of the first and second substances,
representing the first sales cycle
And the second sales cycle
A difference of (d);
representing a first sales period required by the financial product A to be sold to complete a preset sales target;
indicating a second sales cycle required for a marketing strategy when the same kind of sold financial product B as the financial product A to be sold completes the preset sales target of the financial product A to be sold.
The specific implementation steps for calculating the preset period threshold value not only consider the second sales period based on the preset period threshold value in the process of calculating and determining the preset period threshold value
The number of times that all financial products of the same type as financial product B are purchased also takes into account the financial product B in the second sale period
The specific calculation step of the preset period threshold is integrated and constructed by various influence factors, the accuracy of the result of calculating the preset period threshold is ensured, and the method is a first sales period
And a second sales cycle
Difference of (2) to a preset valueThe accuracy of the comparison result of the period threshold value can accurately judge whether the financial product A to be sold directly adopts the marketing strategy of the sold financial product B or needs to be optimized and adjusted on the basis of the marketing strategy of the sold financial product B, a targeted marketing strategy reference is provided for the financial product A to be sold, the market promotion strategy formulation period of the financial product A to be sold is shortened, the market promotion efficiency of the financial product A to be sold is improved, the promotion flow of the financial product A to be sold is improved, and the time cost, the labor cost and the financial cost spent on marketing of the financial product A to be sold are reduced.
While the preferred embodiments of the present invention have been described, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A financial product marketing strategy optimization system based on big data is characterized by comprising the following steps:
a first sale period calculation module, configured to calculate a first sale period required by the financial product a to be sold to complete a preset sale target according to the return on investment, the investment amount, the return on investment period, and the purchasing power index of the current market of the financial product a to be sold
；
A second sales cycle calculation module, configured to obtain, according to the big data processing and analyzing module, a marketing strategy when the same type of sold financial product B as the financial product a to be sold completes the preset sales target of the financial product a to be sold and a second sales cycle required by the marketing strategy, where the same type of sold financial product B has the same return on investment rate, investment amount, return on investment cycle, and purchasing power index of the current market
；
A sales cycle comparison module for calculating the first sales cycle
And the second sales cycle
The difference value of (a), wherein,
if the first sales period
And the second sales cycle
When the difference value is smaller than the preset period threshold value, the financial product A to be sold directly adopts the marketing strategy of the sold financial product B to sell;
if the first sales period
And the second sales cycle
Is greater than a preset period threshold, then the sold financial is processedAnd optimizing and adjusting the product B on the basis of the market promotion strategy.
2. The optimization system of the big data based financial product marketing strategy according to claim 1, wherein the specific calculation steps of the preset period threshold are as follows:
based on the second sales cycle
Ranking all financial products according to the number of times that all financial products of the same type as the sold financial product B are purchased to obtain the sold financial product B in the second sale period
The popularity ranking of all financial products in the bank;
based on the sold financial product B in the second sale period
Calculating the popularity ranking of all financial products in the second sale period of the sold financial product B
Inner heat coefficient:
;
wherein the content of the first and second substances,
in the second sale period for the sold financial product B
Internal heat coefficient;
in the second sale period for the sold financial product B
The popularity ranking of the financial products in the bank;
in the second sale period for the sold financial product B
The number of purchases made;
based on the sold financial product B in the second sale period
And calculating the preset period threshold value according to the internal heat coefficient:
;
wherein the content of the first and second substances,
representing the preset cycle threshold;
representing the first sales cycle
And the second sales cycle
A difference of (d);
indicating that the sold financial product B is in theSecond sales cycle
Internal heat coefficient;
the first sales cycle
And a second sales cycle
The calculating step of the difference value of (a) is:
wherein the content of the first and second substances,
representing the first sales cycle
And the second sales cycle
A difference of (d);
representing a first sales period required by the financial product A to be sold to complete a preset sales target;
indicating a second sales cycle required for a marketing strategy when the same kind of sold financial product B as the financial product A to be sold completes the preset sales target of the financial product A to be sold.
3. A method for optimizing a market promotion strategy for financial products based on big data, which utilizes the system for optimizing a market promotion strategy for financial products based on big data according to any one of claims 1-2, comprising the steps of:
according to the return on investment rate, the investment amount, the return on investment period and the purchasing power index of the current market of the financial product A to be sold, calculating a first sales period required by the financial product A to be sold to complete a preset sales target
；
Obtaining a marketing strategy when the same type of sold financial product B of the financial product A to be sold completes the preset sales target of the financial product A to be sold and has the same return on investment rate, investment amount, return on investment period and market purchasing power index according to a big data processing and analyzing module, and adopting a second sales period required by the marketing strategy
；
Calculating the first sales period
And the second sales cycle
The difference value of (a), wherein,
if the first sales period
And the second sales cycle
When the difference value is less than the preset period threshold value, the financial product A to be sold directly adopts the marketing strategy of the sold financial product B to sell；
If the first sales period
And the second sales cycle
When the difference value is greater than the preset period threshold value, performing optimization adjustment on the basis of the marketing strategy of the sold financial product B.
4. The method for optimizing financial product marketing strategy based on big data as claimed in claim 3, wherein the big data processing analysis module comprises: a command receiving module, a collection scheme making module, a collection scheme executing module, an information data storage module, a calculation screening module and an information data feedback module, wherein,
the command receiving module is used for receiving data of the return on investment rate, the investment amount, the return on investment period and the purchasing power index of the current market of the financial product A to be sold, which are sent by the upper computer;
the collection scheme making module is configured to make an information data collection scheme for the same kind of sold financial product B as the financial product a to be sold according to the data of the return on investment rate, the investment amount, the return on investment period, and the purchasing power index of the current market, which are sent by the upper computer and received by the command receiving module, where the information data of the sold financial product B includes: market promotion strategy data when the same type of sold financial product B as the financial product A to be sold completes the preset sales target of the financial product A to be sold and a second sales cycle required for adopting the market promotion strategy
Data;
the collection plan execution module is used for executing the plan formulated by the collection plan formulation module
The information data collection scheme of the sold financial product B is used for collecting the information data of the sold financial product B;
the information data storage module: for storing the information data of the sold financial product B collected by the collection scenario execution module;
the calculation screening module is used for screening and filtering the information data of the sold financial product B stored in the information data storage module, so that the accuracy of the collected information data of the sold financial product B is improved;
and the information data feedback module is used for feeding back the information data of the sold financial product B filtered by the calculation and screening module to the upper computer.
5. The method for optimizing financial product marketing strategy based on big data of claim 4, wherein the computing and filtering module is configured to filter the information data of the sold financial product B collected by the collection scheme execution module and stored in the information data storage module, and the steps of the filtering module are as follows:
calling a preset screening strategy to obtain initial screening and filtering words in the preset screening strategy; judging whether the initial screening filtering words of the preset screening strategy are related to the information data of the sold financial product B stored in the information data storage module; deleting the current initial screening filtering words from the preset screening strategy if the current initial screening filtering words of the preset screening strategy in the calculation screening module are not related to the current certain information data of the sold financial product B stored in the information data storage module;
if the current initial screening filtering words of the preset screening strategy in the calculation screening module are related to the current certain information data of the sold financial product B stored in the information data storage module, calculating the screening efficiency value of the current initial screening filtering words, further verifying and modifying the current initial screening filtering words according to the screening efficiency value, and finally selecting the optimal target preset screening strategy.
6. The method for optimizing the financial product marketing strategy based on big data according to claim 5, wherein the current initial screening filtering words are further verified and modified according to the screening efficiency value, and an optimal target preset screening strategy is finally selected, specifically comprising the following steps:
calculating the total screening efficiency value of the current screening and filtering words;
simplifying the total screening efficiency value of the screening and filtering words according to the total number of the information data of the sold financial product B stored in the information data storage module;
comparing the simplification processing result with a preset simplification processing threshold value;
if the simplification processing result is larger than or equal to the preset simplification processing threshold value, retaining the corresponding current screening and filtering words, and identifying the current screening and filtering words as the optimal screening and filtering words; finally, summarizing all the optimal screening and filtering words to form an optimal target preset screening strategy;
and if the simplification processing result is less than the preset simplification processing threshold value, deleting the corresponding current screening and filtering words.
7. The method for optimizing financial product marketing strategy based on big data according to claim 6, wherein the calculation filtering module is configured to perform deduplication on the information data of the sold financial product B stored in the information data storage module before performing filtering on the information data of the sold financial product B stored in the information data storage module, and the detailed steps are as follows:
establishing a preset deduplication strategy according to the information data of the plurality of sold financial products B stored in the information data storage module, and associating the information data of the plurality of sold financial products B stored in the information data storage module with the preset deduplication strategy one by one;
the preset duplication elimination strategy is provided with name parameters and content parameters corresponding to the information data of the sold financial product B stored in the information data storage module;
the information data of the sold financial product B stored in the information data storage module is compared with the duplication elimination strategy according to the name parameter and the content parameter, and the information data of the sold financial product B stored in the information data storage module is duplicated after comparison with the information data of the plurality of sold financial products B stored in the information data storage module corresponding to the same duplication elimination strategy, so that each duplication elimination strategy retains one information data of the sold financial product B.
8. The method for optimizing financial product marketing strategy based on big data according to claim 7, wherein the information data of the sold financial product B stored in the information data storage module is compared with the deduplication strategy according to the name parameter and the content parameter, and the specific steps are as follows:
filtering out information data of the sold financial product B which is stored in the information data storage module and does not correspond to the name in the information data of the sold financial product B according to the name parameter in the duplication removing strategy;
and filtering out the information data of the sold financial product B, which does not correspond to the content in the information data of the sold financial product B, stored in the information data storage module according to the content parameters in the duplication elimination strategy.
9. The method for optimizing financial product marketing strategies based on big data as claimed in claim 5, wherein the method for calculating the screening efficiency value of the current initial screening filter term comprises the following specific steps:
and if one piece of information data of the sold financial product B corresponds to one preset screening strategy, considering that the screening efficiency value of the preset screening strategy relative to the corresponding information data of the sold financial product B is a first numerical value.
10. The method for optimizing the financial product marketing strategy based on big data according to claim 3, wherein the specific calculation steps of the preset period threshold are as follows:
based on the second sales cycle
Ranking all financial products according to the number of times that all financial products of the same type as the sold financial product B are purchased to obtain the sold financial product B in the second sale period
The popularity ranking of all financial products in the bank;
based on the sold financial product B in the second sale period
Calculating the popularity ranking of all financial products in the second sale period of the sold financial product B
Inner heat coefficient:
;
wherein the content of the first and second substances,
in the second sale period for the sold financial product B
Internal heat coefficient;
in the second sale period for the sold financial product B
The popularity ranking of the financial products in the bank;
in the second sale period for the sold financial product B
The number of purchases made;
based on the sold financial product B in the second sale period
And calculating the preset period threshold value according to the internal heat coefficient:
;
wherein the content of the first and second substances,
representing the preset cycle threshold;
representing the first sales cycle
And the second sales cycle
A difference of (d);
indicating that the sold financial product B is in the second sale period
Internal heat coefficient;
the first sales cycle
And a second sales cycle
The calculating step of the difference value of (a) is:
wherein the content of the first and second substances,
representing the first sales cycle
And the second sales cycle
A difference of (d);
representing a first sales period required by the financial product A to be sold to complete a preset sales target;
indicating that the same type of sold financial product B as the financial product A to be sold completes the preset sales target of the financial product A to be soldA second sales period required by the temporal marketing strategy.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110463585.0A CN112991071A (en) | 2021-04-28 | 2021-04-28 | Financial product market promotion strategy optimization system and method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110463585.0A CN112991071A (en) | 2021-04-28 | 2021-04-28 | Financial product market promotion strategy optimization system and method based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112991071A true CN112991071A (en) | 2021-06-18 |
Family
ID=76340462
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110463585.0A Pending CN112991071A (en) | 2021-04-28 | 2021-04-28 | Financial product market promotion strategy optimization system and method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112991071A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111062756A (en) * | 2019-12-17 | 2020-04-24 | 深圳市承和润文化传播股份有限公司 | Sales data analysis method and system |
| CN111160971A (en) * | 2019-12-30 | 2020-05-15 | 深圳市云积分科技有限公司 | Method and device for optimizing marketing strategy based on marketing effect |
| CN112732786A (en) * | 2020-12-31 | 2021-04-30 | 平安科技（深圳）有限公司 | Financial data processing method, device, equipment and storage medium |
-
2021
- 2021-04-28 CN CN202110463585.0A patent/CN112991071A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111062756A (en) * | 2019-12-17 | 2020-04-24 | 深圳市承和润文化传播股份有限公司 | Sales data analysis method and system |
| CN111160971A (en) * | 2019-12-30 | 2020-05-15 | 深圳市云积分科技有限公司 | Method and device for optimizing marketing strategy based on marketing effect |
| CN112732786A (en) * | 2020-12-31 | 2021-04-30 | 平安科技（深圳）有限公司 | Financial data processing method, device, equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111861174B (en) | Credit assessment method for user portrait | |
| US8620725B2 (en) | System and method for enhancing credit and debt collection | |
| JP2008129984A (en) | Transaction support system, transaction support program and recording medium thereof | |
| CN109102386A (en) | Risk control method and device based on big data | |
| US20210334812A1 (en) | System and method for managing chargeback risk | |
| CN113177839A (en) | Credit risk assessment method, device, storage medium and equipment | |
| CN116823469A (en) | Financial transaction platform based on big data and transaction method thereof | |
| CN113327164A (en) | Risk control method and device for futures trading and computer equipment | |
| CN113052684A (en) | Risk prediction method of financial product, related device and computer storage medium | |
| TWM613536U (en) | Investment risk scoring system for fund commodities | |
| CN112991071A (en) | Financial product market promotion strategy optimization system and method based on big data | |
| CN111951050A (en) | Financial product recommendation method and device | |
| KR20240029504A (en) | Method for configuration of data economy for financial service and apparatus for performing the method | |
| CN112037006A (en) | Credit risk identification method and device for small and micro enterprises | |
| CN113656702B (en) | User behavior prediction method and device | |
| KR102095022B1 (en) | Method, device and program for trading stocks using articles analysis | |
| CN113781056A (en) | Method and device for predicting user fraud behavior | |
| CN114330962A (en) | Financial data analysis platform based on artificial intelligence | |
| TWI776370B (en) | Investment risk scoring method and system for fund commodities | |
| CN116681526B (en) | Securities quantitative trade monitoring method | |
| CN113240191A (en) | System and method for optimizing fund investment market promotion strategy | |
| KR102587477B1 (en) | Method for providing financial service based on data economy and apparatus for performing the method | |
| KR102587476B1 (en) | Method for adjustment of financial service matrix and apparatus for performing the method | |
| CN110782342B (en) | Method and device for verifying correctness of new channel feature engineering based on binary classification model | |
| CN113988890A (en) | Cross-border electric commerce and enterprise assessment method, device and equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210618 |
|
| RJ01 | Rejection of invention patent application after publication |