Product marketing condition evaluation and optimization system based on big data

Abstract

The invention relates to the technical field of product marketing evaluation and optimization, in particular to a big data-based product marketing condition evaluation and optimization system, which comprises a data acquisition module, a server, a sales evaluation analysis module, a market evaluation analysis module, a user evaluation analysis module, a marketing comprehensive analysis module, a marketing optimization module, a display terminal and a cloud database. According to the invention, the marketing state, the market state and the user state data of the online financial products are comprehensively collected and analyzed, deep mining and analysis are carried out by using an advanced algorithm mode, the marketing state is accurately evaluated in real time, the marketing state is intelligently judged, and a targeted optimization signal is generated. Therefore, merchants can be assisted to quickly respond to market changes, precise marketing is implemented, marketing strategies are expanded, the marketing efficiency and the conversion rate of online financial products are improved, and the market competitiveness of the merchants is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of product marketing evaluation and optimization, and in particular to a product marketing status evaluation and optimization system based on big data. Background Art

[0002] In today's new era of deep integration of the Internet and finance, the Internet financial industry has ushered in an unprecedented period of vigorous development. Various financial products such as gold, stocks, and insurance have emerged at an unprecedented speed and scale through online platforms, which has not only greatly broadened consumers' investment channels, but also profoundly changed the structure of the financial market. This change has brought about an explosive growth in the types of financial products and a sharp expansion in the number of financial products, providing users with rich, diverse, convenient and efficient financial service options, but also put forward higher requirements for the marketing of financial products;

[0003] However, in the face of an increasingly complex market environment and diversified user needs, the marketing and promotion methods of traditional financial products have gradually revealed their limitations. In a market where information is changing rapidly and consumer preferences are changing rapidly, traditional models are often difficult to adapt quickly due to lagging information updates and an imperfect market feedback mechanism. This lag not only limits merchants' ability to capture and respond to market dynamics in real time, but also leads to a lack of real-time and accuracy in the evaluation and monitoring of marketing effects, which in turn affects the effectiveness of marketing strategies and the grasp of adjustment opportunities.

[0004] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0005] The purpose of the present invention is to provide a product marketing status evaluation and optimization system based on big data.

[0006] The purpose of the present invention can be achieved through the following technical solutions: a product marketing status evaluation and optimization system based on big data, comprising:

[0007] The data collection module is used to collect the sales status information, market status information and user status information of various financial products of merchants online, and transmit them to the sales evaluation and analysis module, the market evaluation and analysis module and the user evaluation and analysis module respectively through the server;

[0008] The sales evaluation and analysis module is used to receive the sales status information of various financial products online from merchants, and evaluate and analyze the sales status of various financial products online from merchants to obtain a sales status evaluation index;

[0009] The market evaluation and analysis module is used to receive the market status information of various online financial products of merchants, analyze the market status of various online financial products of merchants based on the information, and obtain the market status evaluation index;

[0010] The user evaluation analysis module is used to receive the user status information of various online financial products of the merchant, analyze the user status of various online financial products of the merchant based on the information, and obtain the user status evaluation index;

[0011] The marketing comprehensive analysis module is used to extract the sales status evaluation index, market status evaluation index and user status evaluation index of various online financial products of the merchant, perform normalization processing, and obtain the marketing status evaluation coefficient of various online financial products of the merchant. Based on this, the marketing status of various online financial products of the merchant is judged and analyzed to obtain A marketing optimization signal and B marketing optimization signal, and send them to the marketing optimization module;

[0012] The marketing optimization module includes an A optimization execution unit and a B optimization execution unit. The A optimization execution unit is used to receive the A marketing optimization signal, thereby performing targeted marketing optimization processing to obtain the target marketing personnel, thereby performing targeted push display to the target marketing personnel; the B optimization execution unit is used to receive the B marketing optimization signal, thereby performing extended marketing optimization processing to obtain the extended marketing level.

[0013] Furthermore, the sales status of various financial products of merchants online is evaluated and analyzed, and the specific analysis process is as follows:

[0014] By obtaining the sales value of various online financial products of merchants in a period of time in real time, a period of time is equally divided into i time points, and time is used as the horizontal coordinate and sales value is used as the vertical coordinate, thereby establishing a two-dimensional dynamic sales coordinate system, and using the method of constructing a point-plotting curve, the sales value of i time points is plotted on the two-dimensional dynamic sales coordinate system, thereby obtaining a sales waveform curve;

[0015] Get all the inflection points of the sales waveform curve, calculate the slope values ​​of all the inflection points, and calculate the standard deviation of the slope values ​​of all the inflection points to obtain the sales fluctuation values ​​of various financial products of the merchant online, and mark them as & f , where f represents the serial number of each type of online financial product of the merchant, and f=1, 2, 3…m, and m represents the total number of serial numbers of each type of online financial product of the merchant;

[0016] Extract the reference sales fluctuation values ​​of various online financial products of the merchant from the cloud database, obtain the reference sales fluctuation values ​​of various online financial products of the merchant, and mark them as & f * ;

[0017] According to the formula: Get the sales status evaluation index XSP f , where u represents a natural constant.

[0018] Furthermore, the market status of various online financial products of merchants is analyzed, and the specific analysis process is as follows:

[0019] By obtaining the price fluctuation value, transaction value and depth value of the market status information of various financial products of merchants online in real time over a period of time, and marking them as jbz f 、cjz f and qxz f , extract the three values ​​for normalization, according to the formula: Get the market status assessment index SCP f , among which, jbz f * 、cjz f * and qxz f * Respectively represent the reference price wave value, reference transaction value and reference depth value, |jbz f -jbz f * |、|cjz f -cjz f * | and |qxz f -qxz f * |represents price fluctuation difference, transaction difference and depth difference respectively, η1, η2 and η3 represent the weight coefficients of price fluctuation value, transaction value and depth value respectively, and η1>η2>η3.

[0020] Furthermore, the user status of various online financial products of the merchant is analyzed, and the specific analysis process is as follows:

[0021] By obtaining the click value, return visit value, stay time value and purchasing power value of the user status information of various financial products of the merchant online in real time over a period of time, and marking them as djz f h 、hfz f h 、tlz f h and nlz f h , extract the values ​​of the four and normalize them according to the formula: Get the user status evaluation index YHP f , where h represents the number of each user, and h=1, 2, 3…v, v represents the total number of user numbers, γ1, γ2, γ3 and γ4 represent the weight coefficients of click value, return visit value, stay time value and purchasing power value respectively, and γ1>γ2>γ3>γ4.

[0022] Furthermore, the marketing status of various online financial products of merchants is determined and analyzed, and the specific analysis process is as follows:

[0023] Compare and analyze the marketing status evaluation coefficients of various online financial products of the merchant with the corresponding preset reference comparison intervals. When the marketing status evaluation coefficients of various online financial products of the merchant are outside the corresponding preset reference comparison intervals, a marketing abnormality signal is generated;

[0024] Based on the captured marketing abnormality signal, the marketing status evaluation coefficient of the merchant's online financial product of this type is retrieved, and the difference between the merchant's online marketing status evaluation coefficient of this type of financial product and the preset marketing status evaluation threshold is calculated to obtain the marketing difference;

[0025] Set a marketing difference threshold, compare and analyze the marketing difference value with the marketing difference threshold, and generate marketing optimization signal A when the marketing difference value is greater than or equal to the marketing difference threshold; and generate marketing optimization signal B when the marketing difference value is less than the marketing difference threshold.

[0026] Furthermore, the specific solution analysis for the target marketing personnel is as follows:

[0027] Based on the captured A marketing optimization signal, the click value, return visit value, stay time value and purchasing power value of each user corresponding to this type of financial product of the merchant online are retrieved, and the four values ​​are extracted, multiplied by the corresponding weight coefficients and then added to obtain the visit evaluation value of each user corresponding to this type of financial product of the merchant online. The visit evaluation value of each user corresponding to this type of financial product of the merchant online is compared and analyzed with the preset visit evaluation threshold. When the visit evaluation value of this type of financial product of the merchant online corresponding to a certain user is greater than or equal to the preset visit evaluation threshold, the user corresponding to this type of financial product of the merchant online is determined to be a target marketer.

[0028] Furthermore, the specific solution analysis for the extended marketing level is as follows:

[0029] Based on the captured B marketing optimization signal, the user volume per unit time of the preset publicity port is monitored to obtain the user volume per unit time of the preset publicity port, and the user volume is compared with the preset user volume threshold for analysis. If the user volume is greater than the preset user volume threshold, the preset publicity port is determined to be an optional publicity port, and the conversion rate, benefit value and dissemination range value of the optional publicity port are obtained, and the values ​​of the conversion rate, benefit value and dissemination range value of the optional publicity port are extracted, multiplied by the corresponding weight coefficients and added together to obtain the publicity value of the optional publicity port. At the same time, the marketing difference of this type of financial products of the merchant online is retrieved, and the values ​​of the publicity value of the optional publicity port and the marketing difference of this type of financial products of the merchant online are extracted and multiplied by the corresponding weight coefficients and added together to obtain the extended marketing value. The extended marketing value is compared and matched with the extended marketing determination table stored in the cloud database to obtain the extended marketing level.

[0030] Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects:

[0031] 1. The present invention monitors and analyzes the sales status information of various financial products of merchants online by establishing a coordinate model and dynamically analyzing data, and can evaluate the sales status of various financial products of merchants online, and obtain a sales status evaluation index. At the same time, the present invention also monitors and analyzes the market status information and user status information of various financial products of merchants online by means of data analysis and formula calculation, and can evaluate the market status and user status of various financial products of merchants online, and obtain a market status evaluation index and a user status evaluation index, thereby ensuring the comprehensiveness and comprehensiveness of the evaluation, and providing more comprehensive and accurate data support for the subsequent evaluation and determination of the marketing status of various financial products of merchants online;

[0032] 2. The present invention obtains a marketing status evaluation coefficient by comprehensively analyzing the sales status evaluation index, market status evaluation index and user status evaluation index of various online financial products of merchants, and compares and analyzes the marketing status evaluation coefficient with a preset reference comparison interval, so as to timely discover financial products with abnormal marketing, and further determine the urgency of marketing optimization through difference calculation, thereby obtaining A marketing optimization signal and B marketing optimization signal. If A marketing optimization signal is captured, personalized push display is performed to the target marketing personnel, and high-potential users are accurately located by calculating the visit evaluation value (combined with the user's click value, return visit value, stay time value and purchasing power value), thereby improving the efficiency and conversion rate of marketing promotion; if B marketing optimization signal is captured, a wider range of potential customer groups are attracted by expanding marketing methods. This timely response mechanism enables merchants to quickly identify financial products with abnormal marketing, immediately take corresponding optimization measures, and timely adjust marketing promotion strategies to cope with market changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 It is the overall module block diagram of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] like Figure 1 As shown, the product marketing status evaluation and optimization system based on big data includes: a data acquisition module, a server, a sales evaluation and analysis module, a market evaluation and analysis module, a user evaluation and analysis module, a marketing comprehensive analysis module, a marketing optimization module, a display terminal and a cloud database; wherein the marketing optimization module includes an A optimization execution unit and a B optimization execution unit;

[0037] The data collection module is used to collect the sales status information, market status information and user status information of various financial products of merchants online, and transmit them to the sales evaluation and analysis module, the market evaluation and analysis module and the user evaluation and analysis module respectively through the server;

[0038] The cloud database is used to store the reference sales fluctuation values ​​of various online financial products of merchants, the historical purchase parameters of users, and the extended marketing decision table;

[0039] When the sales evaluation and analysis module receives the sales status information of various financial products online from the merchant, it evaluates and analyzes the sales status of various financial products online from the merchant. The specific analysis is as follows:

[0040] By obtaining the sales value of various online financial products of merchants in a period of time in real time, the period of time is equally divided into i time points, where i=1, 2, 3...n, and the time is used as the horizontal coordinate and the sales value is used as the vertical coordinate, thereby establishing a two-dimensional dynamic sales coordinate system, and using the point plotting curve construction method, the sales value of the i time point is plotted on the two-dimensional dynamic sales coordinate system, thereby obtaining a sales waveform curve;

[0041] The sales value is calculated by obtaining the sales volume, sales volume and sales price of various online financial products of the merchant at the current monitoring time point, multiplying the three values ​​by the corresponding weight coefficients and then adding them to obtain the sales value;

[0042] It should be noted that the weight coefficient is set according to the target demand. For example, the merchant's online financial products pay more attention to the growth of sales at the current stage, while not neglecting the increase in sales volume and the stability of sales prices. Therefore, the following weight coefficients can be set:

[0043] Sales weight coefficient: 0.5 (indicates that sales have the greatest impact on sales value);

[0044] Sales volume weight coefficient: 0.3 (indicates that sales volume also has a significant impact on sales value);

[0045] Sales price weight coefficient: 0.2 (indicates that the impact of sales price on sales value is relatively small, but still needs to be considered);

[0046] The sales value is calculated as follows: sales value = (sales amount × 0.5) + (sales volume × 0.3) + (sales price × 0.2), and the specific setting of the weight coefficient is determined by professionals in this field according to specific circumstances;

[0047] Get all the inflection points of the sales waveform curve and record them as g, where g = 1, 2, 3...n1, n1 is the total number of inflection points, and n1 ≤ n, and calculate the slope values ​​of g inflection points, and calculate the standard deviation of the slope values ​​of g inflection points, according to the formula Get the sales fluctuation value of various financial products of merchants online& f , where f represents the serial number of each type of financial product of the merchant online, and f = 1, 2, 3…m, m represents the total number of serial numbers of each type of financial product of the merchant online, x lz g represents the slope value of the g-th time data point, xl z * Represents the average value of all inflection point slope values, according to the formula:

[0048] Extract the reference sales fluctuation values ​​of various online financial products of the merchant from the cloud database, obtain the reference sales fluctuation values ​​of various online financial products of the merchant, and mark them as & f * ;

[0049] According to the formula: Get the sales status evaluation index XSP f , where u represents a natural constant;

[0050] Send the obtained sales status evaluation index to the marketing comprehensive analysis module;

[0051] When the market evaluation and analysis module receives the market status information of various online financial products of the merchant, it analyzes the market status of various online financial products of the merchant based on the information. The specific analysis is as follows:

[0052] By obtaining the price fluctuation value, transaction value and depth value of the market status information of various financial products of merchants online in real time over a period of time, and marking them as jbz f 、cjz f and qxz f , extract the three values ​​for normalization, according to the formula: Get the market status assessment index SCP f , among which, jbz f * 、cjz f * and qxz f * Respectively represent the reference price wave value, reference transaction value and reference depth value, |jbz f -jbz f * |、|cjz f -cjz f * | and |qxz f -qxz f * |represents price fluctuation difference, transaction difference and depth difference respectively, η1, η2 and η3 represent the weight coefficients of price fluctuation value, transaction value and depth value respectively, and η1>η2>η3;

[0053] It should be noted that the price volatility refers to the difference between the highest and lowest prices sold in the market where various online financial products of merchants are located within a period of time, reflecting the degree of volatility of financial product prices within the period of time, and is an important indicator for evaluating market activity and potential risks; the transaction value refers to the total transaction volume in the market where various online financial products of merchants are located within a period of time, reflecting the trading activity of the market and the overall demand and acceptance of the market for such financial products; the depth value refers to the order obtained in the market where various online financial products of merchants are located within a period of time, sorted by price level, and the number of buy orders and sell orders at each price level are counted respectively, and the number of buy orders and the number of sell orders are added together to obtain the depth value, which reflects the liquidity of the market, that is, the ability of the market to absorb a large number of transactions in a short period of time without causing drastic price fluctuations. The larger the depth value, the more stable the market is, and the easier it is for traders to trade at the expected price;

[0054] Send the obtained market status evaluation index to the marketing comprehensive analysis module;

[0055] When the user evaluation analysis module receives the user status information of various online financial products of the merchant, it analyzes the user status of various online financial products of the merchant based on the information. The specific analysis is as follows:

[0056] By obtaining the click value, return visit value, stay time value and purchasing power value of the user status information of various financial products of the merchant online in real time over a period of time, and marking them as djz f h 、hfz f h 、tlz f h and nlz f h , extract the values ​​of the four and normalize them according to the formula: Get the user status evaluation index YHP f , where h represents the number of each user, and h = 1, 2, 3…v, v represents the total number of user numbers, γ1, γ2, γ3 and γ4 represent the weight coefficients of click value, return visit value, stay time value and purchasing power value, respectively, and γ1>γ2>γ3>γ4;

[0057] It should be noted that the click value indicates the number of times the user clicks on the financial product-related link or button on the page. The more clicks, the more interest the user has. The return visit value indicates the number of times the user revisits the financial product page. The stay time value indicates the length of time the user stays on the financial product page. The purchasing power value indicates the purchasing power of the user who visits the financial product. The purchasing power value is specifically calculated as follows: extract the user's historical purchase parameters during the current monitoring period from the cloud database. The historical purchase parameters include the total purchase amount, purchase quantity, and purchase frequency. At the same time, extract the total purchase amount, purchase quantity, and purchase frequency values, multiply them by the corresponding weight coefficients, and then add them up to obtain the purchasing power value.

[0058] Send the obtained user status evaluation index to the marketing comprehensive analysis module;

[0059] When the marketing comprehensive analysis module receives the sales status evaluation index, market status evaluation index and user status evaluation index, it determines and analyzes the marketing status of various online financial products of the merchant based on the index. The specific analysis is as follows:

[0060] By extracting the sales status evaluation index XSP of various financial products of merchants online f , Market Status Assessment Index SCP f and user status evaluation index YHP fThe value of is normalized according to the formula: ZHP f =XSP f ×θ1+SCP f ×θ2+YHP f ×θ3, we can get the marketing status evaluation coefficient ZHP of various online financial products of merchants f , where θ1, θ2 and θ3 represent the weight coefficients of the sales status evaluation index, the market status evaluation index and the user status evaluation index respectively, and θ1>θ2>θ3;

[0061] Compare and analyze the marketing status evaluation coefficients of various online financial products of the merchant with the corresponding preset reference comparison intervals. When the marketing status evaluation coefficients of various online financial products of the merchant are within the corresponding preset reference comparison intervals, a normal marketing signal is generated; when the marketing status evaluation coefficients of various online financial products of the merchant are outside the corresponding preset reference comparison intervals, a marketing abnormality signal is generated;

[0062] Based on the captured marketing abnormality signal, the marketing status evaluation coefficient of the merchant's online financial product of this type is retrieved, and the difference between the merchant's online marketing status evaluation coefficient of this type of financial product and the preset marketing status evaluation threshold is calculated to obtain the marketing difference;

[0063] Set a marketing difference threshold, compare and analyze the marketing difference value with the marketing difference threshold, and generate marketing optimization signal A when the marketing difference value is greater than or equal to the marketing difference threshold; and generate marketing optimization signal B when the marketing difference value is less than the marketing difference threshold;

[0064] Sending the generated A marketing optimization signal and B marketing optimization signal to the marketing optimization module;

[0065] The marketing optimization module includes A optimization execution unit and B optimization execution unit;

[0066] The A optimization execution unit is used to receive the A marketing optimization signal, thereby performing targeted marketing optimization processing. The specific process is as follows:

[0067] According to the captured A marketing optimization signal, the click value, return visit value, stay time value and purchasing power value of each user corresponding to the merchant's online financial product are retrieved, and the four values ​​are extracted, multiplied by the corresponding weight coefficients and then added to obtain the visit evaluation value of each user corresponding to the merchant's online financial product. The visit evaluation value of each user corresponding to the merchant's online financial product is compared and analyzed with the preset visit evaluation threshold. When the visit evaluation value of a certain user corresponding to the merchant's online financial product is greater than or equal to the preset visit evaluation threshold, the user corresponding to the merchant's online financial product is determined to be a target marketing personnel, so as to push and display targeted marketing personnel, and display notifications on the display terminal;

[0068] The B optimization execution unit is used to receive the B marketing optimization signal, and then perform extended marketing optimization processing. The specific process is as follows:

[0069] According to the captured B marketing optimization signal, the user volume per unit time of the preset publicity port is monitored to obtain the user volume per unit time of the preset publicity port, and the user volume is compared and analyzed with the preset user volume threshold. If the user volume is greater than the preset user volume threshold, the preset publicity port is determined as an optional publicity port, and the conversion rate, benefit value and propagation range value of the optional publicity port are obtained. The conversion rate, benefit value and propagation range value of the optional publicity port are extracted, and the conversion rate, benefit value and propagation range value of the optional publicity port are multiplied by the corresponding weight coefficient and then added to obtain the publicity value of the optional publicity port. At the same time, the marketing difference of the merchant's online financial products of this type is retrieved to extract the publicity value of the optional publicity port and the merchant's online The marketing difference values ​​of this type of financial products are multiplied by the corresponding weight coefficients and then added to obtain the extended marketing value, and the extended marketing value is compared and matched with the extended marketing determination table stored in the cloud database to obtain the extended marketing level, and each extended marketing value obtained corresponds to an extended marketing level, and a display notification is made on the display terminal, wherein the extended marketing level includes first-level extended marketing, second-level extended marketing and third-level extended marketing, and each level of extended marketing corresponds to different extension frequencies and interval durations; for example, if this type of financial product needs first-level extended marketing, then this type of financial product is extended at the optional promotion port every hour and at a playback frequency of F times per hour;

[0070] It should be noted that the conversion rate refers to the proportion of users who are successfully converted into actual purchases among the total number of users of the promotional port; the benefit value refers to the ratio between the income obtained by the promotional port and the input cost; and the communication range value refers to the number of users that the promotional port can cover.

[0071] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Product marketing status evaluation and optimization system based on big data, characterized by: include: The data collection module is used to collect the sales status information, market status information and user status information of various financial products of merchants online, and transmit them to the sales evaluation and analysis module, the market evaluation and analysis module and the user evaluation and analysis module respectively through the server; The sales evaluation and analysis module is used to receive the sales status information of various financial products online from merchants, and evaluate and analyze the sales status of various financial products online from merchants to obtain a sales status evaluation index; The market evaluation and analysis module is used to receive the market status information of various online financial products of merchants, analyze the market status of various online financial products of merchants based on the information, and obtain the market status evaluation index; The user evaluation analysis module is used to receive the user status information of various online financial products of the merchant, analyze the user status of various online financial products of the merchant based on the information, and obtain the user status evaluation index; The marketing comprehensive analysis module is used to extract the sales status evaluation index, market status evaluation index and user status evaluation index of various online financial products of the merchant, perform normalization processing, and obtain the marketing status evaluation coefficient of various online financial products of the merchant. Based on this, the marketing status of various online financial products of the merchant is judged and analyzed to obtain A marketing optimization signal and B marketing optimization signal, and send them to the marketing optimization module; The marketing optimization module includes an A optimization execution unit and a B optimization execution unit. The A optimization execution unit is used to receive the A marketing optimization signal, thereby performing targeted marketing optimization processing to obtain the target marketing personnel, thereby performing targeted push display to the target marketing personnel; the B optimization execution unit is used to receive the B marketing optimization signal, thereby performing extended marketing optimization processing to obtain the extended marketing level.

2. The product marketing status evaluation and optimization system based on big data according to claim 1 is characterized in that: Evaluate and analyze the sales status of various financial products of merchants online. The specific analysis process is as follows: By obtaining the sales value of various online financial products of merchants in a period of time in real time, a period of time is equally divided into i time points, and time is used as the horizontal coordinate and sales value is used as the vertical coordinate, thereby establishing a two-dimensional dynamic sales coordinate system, and using the method of constructing a point-plotting curve, the sales value of i time points is plotted on the two-dimensional dynamic sales coordinate system, thereby obtaining a sales waveform curve; Get all the inflection points of the sales waveform curve, calculate the slope values ​​of all the inflection points, and calculate the standard deviation of the slope values ​​of all the inflection points to obtain the sales fluctuation values ​​of various financial products of the merchant online, and mark them as & f , where f represents the serial number of each type of online financial product of the merchant, and f=1, 2, 3…m, and m represents the total number of serial numbers of each type of online financial product of the merchant; Extract the reference sales fluctuation values ​​of various online financial products of the merchant from the cloud database, obtain the reference sales fluctuation values ​​of various online financial products of the merchant, and mark them as & f * ; According to the formula: Get the sales status evaluation index XSP f , where u represents a natural constant.

3. The product marketing status evaluation and optimization system based on big data according to claim 1 is characterized in that: Analyze the market status of various online financial products of merchants. The specific analysis process is as follows: By obtaining the price fluctuation value, transaction value and depth value of the market status information of various financial products of merchants online in real time over a period of time, and marking them as jbz f 、cjz f and qxz f , extract the three values ​​for normalization, according to the formula: Get the market status assessment index SCP f , Among them, jbz f * 、cjz f * and qxz f * Respectively represent the reference price wave value, reference transaction value and reference depth value, |jbz f -jbz f * |、|cjz f -cjz f * | and |qxz f -qxz f * |represents price fluctuation difference, transaction difference and depth difference respectively, η1, η2 and η3 represent the weight coefficients of price fluctuation value, transaction value and depth value respectively, and η1>η2>η3.

4. The product marketing status evaluation and optimization system based on big data according to claim 1 is characterized in that: Analyze the user status of various online financial products of merchants. The specific analysis process is as follows: By obtaining the click value, return visit value, stay time value and purchasing power value of the user status information of various financial products of the merchant online in real time over a period of time, and marking them as djz f h 、hfz f h 、tlz f h and nlz f h , extract the values ​​of the four and normalize them according to the formula: Get the user status evaluation index YHP f , where h represents the number of each user, and h=1, 2, 3…v, v represents the total number of user numbers, γ1, γ2, γ3 and γ4 represent the weight coefficients of click value, return visit value, stay time value and purchasing power value respectively, and γ1>γ2>γ3>γ4.

5. The product marketing status evaluation and optimization system based on big data according to claim 1 is characterized in that: The marketing status of various online financial products of merchants is determined and analyzed. The specific analysis process is as follows: Compare and analyze the marketing status evaluation coefficients of various online financial products of the merchant with the corresponding preset reference comparison intervals. When the marketing status evaluation coefficients of various online financial products of the merchant are outside the corresponding preset reference comparison intervals, a marketing abnormality signal is generated; Based on the captured marketing abnormality signal, the marketing status evaluation coefficient of the merchant's online financial product of this type is retrieved, and the difference between the merchant's online marketing status evaluation coefficient of this type of financial product and the preset marketing status evaluation threshold is calculated to obtain the marketing difference; Set a marketing difference threshold, compare and analyze the marketing difference value with the marketing difference threshold, and generate marketing optimization signal A when the marketing difference value is greater than or equal to the marketing difference threshold; and generate marketing optimization signal B when the marketing difference value is less than the marketing difference threshold.

6. The product marketing status evaluation and optimization system based on big data according to claim 1 is characterized in that: The specific solution analysis for target marketers is as follows: Based on the captured A marketing optimization signal, the click value, return visit value, stay time value and purchasing power value of each user corresponding to this type of financial product of the merchant online are retrieved, and the four values ​​are extracted, multiplied by the corresponding weight coefficients and then added to obtain the visit evaluation value of each user corresponding to this type of financial product of the merchant online. The visit evaluation value of each user corresponding to this type of financial product of the merchant online is compared and analyzed with the preset visit evaluation threshold. When the visit evaluation value of this type of financial product of the merchant online corresponding to a certain user is greater than or equal to the preset visit evaluation threshold, the user corresponding to this type of financial product of the merchant online is determined to be a target marketer.

7. The product marketing status evaluation and optimization system based on big data according to claim 1 is characterized in that: The specific solution analysis for the extended marketing level is as follows: Based on the captured B marketing optimization signal, the user volume per unit time of the preset publicity port is monitored to obtain the user volume per unit time of the preset publicity port, and the user volume is compared with the preset user volume threshold for analysis. If the user volume is greater than the preset user volume threshold, the preset publicity port is determined to be an optional publicity port, and the conversion rate, benefit value and dissemination range value of the optional publicity port are obtained, and the values ​​of the conversion rate, benefit value and dissemination range value of the optional publicity port are extracted, multiplied by the corresponding weight coefficients and added together to obtain the publicity value of the optional publicity port. At the same time, the marketing difference of this type of financial products of the merchant online is retrieved, and the values ​​of the publicity value of the optional publicity port and the marketing difference of this type of financial products of the merchant online are extracted and multiplied by the corresponding weight coefficients and added together to obtain the extended marketing value. The extended marketing value is compared and matched with the extended marketing determination table stored in the cloud database to obtain the extended marketing level.

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