TWM576700U - Intelligent mining system for enterprise GOLD customers - Google Patents

Abstract

This creation proposes an enterprise gold customer intelligence exploration system, which includes: memory storage of customer data including multiple financial features, multiple initial screening conditions, multiple initial weightings, comprehensive screening conditions, comprehensive weighting, at least one rating, etc. a comprehensive score and a preset adjustment range, the operator is electrically connected to the memory to obtain each of the above-mentioned materials, and is used for generating an initial score according to the financial feature, the initial screening condition and the initial weighting amount, and according to the initial score, the comprehensive screening condition and The integrated weighted quantity generates a comprehensive score and transmits it to the memory, and compares the comprehensive score and the rating range to produce the rating result, and the display interface is electrically connected to the operator, and receives and presents the rating result transmitted by the operator.

Description

Enterprise Gold Customer Wisdom Exploration System

This creation is about a smart data mining system based on big data, especially a smart exploration system for financial clients.

In today's big data era, in order to efficiently develop potential customers, many companies use data analysis to initially find a suitable customer list and classify them according to different analysis results to help companies serve different types of customers.

However, in the financial industry, because the information changes rapidly and the needs of the enterprise change at any time, the detailed process of data analysis often needs to be updated and maintained in order to reflect the needs of the enterprise in a timely manner. However, with the artificially updated data analysis process, it often needs to be tried and corrected several times to be enabled. The update speed often cannot keep up with the environmental changes of the financial industry, and the time and cost it consumes. It is also an extra burden.

Therefore, there is still a need for an enterprise gold customer intelligence exploration system to improve the above problems.

The creation is to provide a smart exploration system for enterprise customers, which can modify the detailed process of the analysis process according to the difference between the analysis results and the actual conditions, so as to be applied in the financial industry.

This creation is related to an enterprise gold customer intelligent exploration system, comprising: a memory for storing a customer data including multiple financial features, multiple initial screening conditions, multiple initial weighting factors, a comprehensive screening condition, and a comprehensive a weighting amount, at least one rating range, a comprehensive score, a reference score, and a preset adjustment range; an operator electrically connected to the memory to obtain the customer data, the initial screening conditions, and the initial weighting The quantity, the comprehensive screening condition, the comprehensive weighting amount, the at least one rating range, the comprehensive score, the reference score and the preset adjustment range are used according to the financial features, the initial screening conditions and the initial Weighting the amount, generating an initial score, and generating the comprehensive score according to the initial score, the comprehensive screening condition and the comprehensive weighting amount, and transmitting the comprehensive score to the memory, and comparing the comprehensive score and the at least one rating range to generate a rating a result; and a display interface electrically connected to the operator for receiving the rating result transmitted by the operator, and presenting the rating node .

The creation lies in providing a smart exploration system for enterprise customers, which can analyze multiple potential customers according to different financial characteristics, compare some analysis results with actual data, and automatically correct the analysis process according to the comparison results, so as to be applied in the financial industry.

The above description of the disclosure and the following description of the embodiments are intended to illustrate and explain the spirit and principles of the present invention, and to provide further explanation of the scope of the patent application of the present invention.

The detailed features and advantages of the present invention are described in detail below in the embodiments, which are sufficient to enable any skilled artisan to understand the technical contents of the present invention and implement it according to the contents, the scope of the patent application and the drawings. Anyone familiar with the relevant art can easily understand the purpose and advantages of this creation. The following examples are intended to further illustrate the scope of this creation, but do not limit the scope of the creation in any way.

Please refer to FIG. 1 , which is a structural diagram of the enterprise gold customer intelligence exploration system 1 . As shown in FIG. 1, the enterprise customer smart exploration system 1 includes a memory 11, an arithmetic unit 13, and a display interface 15. The memory 11 may be any hard disk or other storage function device for storing customer data including multiple financial features, multiple initial screening conditions, multiple initial weighting amounts, comprehensive screening conditions, comprehensive weighting amount, At least one rating range, comprehensive score, reference score, and preset adjustment range. The computing unit 13 can be a central processor unit (CPU) or other computing device, and is electrically connected to the memory 11 for calculating and analyzing customer data stored in the memory 11. In detail, the operator 13 may generate an initial score according to the initial screening conditions and the initial weighting amount, and then generate a comprehensive score according to the initial score, the comprehensive screening condition, and the comprehensive weighting amount, and may compare the comprehensive score and at least one. The scope of the evaluation, the results of the evaluation of this customer data, as a reference for evaluating potential customers. In addition, when the difference value between the comprehensive score and the reference score falls within the preset adjustment range, the operator 13 may selectively adjust the above-mentioned weighting amount and the screening condition according to the difference value, so as to be between the comprehensive score and the reference score. Values can approximate each other. The display interface 15 can be any display device that can be interconnected with the computing unit 13, such as a screen electrically coupled to the computing unit 13, or a flat panel display communicably coupled to the computing unit 13 via wireless transmission. In the present creation, the display interface 15 is for receiving the rating results from the operator 13 and presenting the rating results. Briefly, the enterprise gold customer intelligence exploration system 1 disclosed in the present invention is based on the operation of the computing unit 13 and calculates and analyzes the customer data stored in the memory 11 to generate a plurality of analysis results and presents them in the display interface 15. So that users can find potential customers more efficiently through the results of these analyses.

In addition, it should be additionally noted that each financial feature has a feature value or a preset value, wherein the feature value is associated with the initial screening conditions of the financial feature, and only one of the initial screening conditions corresponds to For this feature value. For example, the revenue growth rate, the operating profit rate and the pre-tax net profit rate mentioned in the preceding paragraph, the relevant information of the financial characteristics is a percentage of the data presented, and this data is the financial conditions. The feature value, and by the initial screening condition corresponding to the feature value, the initial weighting amount of the financial condition can be further calculated; this type of financial feature can be referred to as a "numerical financial feature." On the other hand, due to the large range of financial condition data (for example, debt ratio), or the fact that the data is presented in the form of ratings (for example, TCRI rating), the initial screening of financial conditions with the above characteristics Conditions can be presented in the form of data ranges or rating ranges, and different data ranges or rating ranges can correspond to different initial weightings to increase the integrity of the calculation process; in addition, this type of financial feature can be called "Series financial features."

To specifically describe the calculation process of the enterprise gold customer smart exploration system 1, please refer to FIG. 2, which is a schematic diagram of the operation of the enterprise gold customer smart exploration system 1 according to an embodiment. A to E shown in Figure 2 are different financial features, a1 to e3 are initial screening conditions, aw1 to ew3 are the initial weightings corresponding to each initial screening condition, and both IS1 and IS2 are initial scores, sw1 and sw2. For the combined weighting, SS1 and SS2 are comprehensive scores. In the present embodiment, the foregoing financial features include revenue growth rate A, business benefit rate B, pre-tax net profit ratio C, debt ratio D, and TCRI rating E, and the corresponding screening conditions and weighting amounts are as follows: Table 1  <TABLE border="1" borderColor="#000000" width="85%"><TBODY><tr><td> Financial Features</td><td> Initial Filter Conditions (Graphic Code) </td> <td> Initial Weighted Value</td><td> Initial Weighted Quantity (Graphic Code) </td></tr><tr><td> Revenue Growth Rate A </td><td> Growth Rate> 0 (a1) </td><td> 15 </td><td> Growth rate*15 points (aw1) </td></tr><tr><td> Growth rate ≦0 (a2) </ Td><td> 0 </td><td> 0 points (aw2) </td></tr><tr><td> Business benefit rate B </td><td> Growth rate>0 (b1) </td><td> 15 </td><td> Growth rate*15 points (bw1) </td></tr><tr><td> Growth rate ≦0 (b2) </td><td > 0 </td><td> 0 points (bw2) </td></tr><tr><td> pre-tax net profit rate C </td><td> growth rate >0 (c1) </td ><td> 15 </td><td> Growth rate*15 points (cw1) </td></tr><tr><td> Growth rate ≦0 (c2) </td><td> 0 < /td><td> 0 points (cw2) </td></tr><tr><td> debt ratio D </td><td> over 200% (d1) </td><td> 0 < /td><td> 0 points (dw1) </td></tr><tr><td> More than 100% and less than 200% (d2) </td><td> 5 </td><td> 5 points (dw2) </td></tr><tr><td> 100% or less (d3) </td><td> 10 </td><td> 10 points (dw3) </td>< /tr><tr><td> TCR I rating E </td><td> 1 or more and 4 or less (e1) </td><td> 10 </td><td> 10 points (ew1) </td></tr>< Tr><td> 5 or more and 6 or less (e2) </td><td> 5 </td><td> 5 points (ew2) </td></tr><tr><td> 7 ~8, etc., no rating, etc. (e3) </td><td> 0 </td><td> 0 points (ew3) </td></tr></TBODY></TABLE> Note that the initial weighting amount of the revenue growth rate A, the operating profit rate B, and the pre-tax net profit rate C is 15 points, and in the present embodiment, the initial scoring algorithm is the initial weighting corresponding to all financial features. The sum of the quantities. Among the financial characteristics shown in Table 1, the revenue growth rate A, the operating profit ratio B, and the pre-tax net profit ratio C are the aforementioned “numerical financial characteristics”, and the debt ratio D and the TCRI rating E are “grade type”. Financial characteristics." Specifically, the "numerical financial feature" obtains the operation result of the numerical financial feature based on the feature value; for example, if the revenue growth rate A is 10% and the characteristic value is 10%, the operator 13 Multiply the eigenvalue (10%) by the initial weighting value (15) to obtain its initial weighting amount (1.5 points). On the other hand, the “grade financial feature” obtains the operation result of the financial feature of the series according to the preset value; for example, the debt ratio D and the TCRI rating E are both set to be 1 when the liability When the ratio D is 150%, the arithmetic unit 13 multiplies the initial weighting value (5) by the preset value (1) to obtain an initial weighting amount (5 minutes).  

To further illustrate the comprehensive screening criteria and the combined weighting, please continue to refer to Figure 2. In order to more effectively develop customers with financial potential, users can design different comprehensive screening conditions and comprehensive weighting according to different initial screening conditions. For example, suppose the primary target group is to lock the company's overall revenue to be positive growth and profit, the self-owned capital sufficient debt ratio is low, and the TCRI credit rating is superior, then the first group of comprehensive screening conditions And the comprehensive weighting can be set as shown in Table 2 Table 2  <TABLE border="1" borderColor="#000000" width="85%"><TBODY><tr><td> Groups</td><td> Financial Features</td><td> Initial Filter Conditions (Graphic code) </td><td> Integrated weighted value (illustrated code) </td><td> Integrated weighted amount (illustrated code) </td></tr><tr><td> Group 1 </td><td> Revenue Growth Rate A </td><td> Growth Rate>0 (a1) </td><td> 150% (sw1) </td><td> Initial Rating*150 % (SS1) </td></tr><tr><td> Business Benefit Rate B </td><td> Growth Rate>0 (b1) </td></tr><tr><td> Pre-tax net profit ratio C </td><td> Growth rate>0 (c1) </td></tr><tr><td> Debt ratio D </td><td> 100% or less (d3) < /td></tr><tr><td> TCRI rating E </td><td> 1 or more and 4 or less (e1) </td></tr></TBODY></TABLE> On the other hand, assuming that the secondary target customer group is the company's revenue is positive growth and both are profitable, the self-owned fund sufficient debt ratio is low, and the TCRI credit rating is medium, then the second group of comprehensive screening conditions and comprehensive weighting The amount can be set as shown in Table 3. table 3  <TABLE border="1" borderColor="#000000" width="85%"><TBODY><tr><td> Groups</td><td> Financial Features</td><td> Initial Filter Conditions (Graphic code) </td><td> Integrated weighted value (illustrated code) </td><td> Integrated weighted amount (illustrated code) </td></tr><tr><td> Group 2 </td><td> Revenue Growth Rate A </td><td> Growth Rate>0 (a1) </td><td> 140% (sw2) </td><td> Initial Rating*140 % (SS2) </td></tr><tr><td> Business Benefit Rate B </td><td> Growth Rate>0 (b1) </td></tr><tr><td> Pre-tax net profit ratio C </td><td> Growth rate>0 (c1) </td></tr><tr><td> Debt ratio D </td><td> 100% or less (d3) < /td></tr><tr><td> TCRI Rating E </td><td> 5 or more and 6 or less (e2) </td></tr></TBODY></TABLE> It should be noted that since the data shown in Table 3 is designed for the secondary target group (Group 2), the comprehensive weighted value can be lower than the combined weight of the main target group (Group 1) (see Table 2). Shown).  

To illustrate the above list, this paragraph will illustrate the two customer profiles shown in Tables 4 and 5. Table 4  <TABLE border="1" borderColor="#000000" width="85%"><TBODY><tr><td> Customer</td><td> Financial Features</td><td> Corresponding numeric size Or rating </td><td> initial weighting amount</td></tr><tr><td> A</td><td> revenue growth rate A </td><td> 20% < /td><td> 20%*15=3 points</td></tr><tr><td> Business benefit rate B </td><td> 25% </td><td> 25%* 15=3.75 points</td></tr><tr><td> Pre-tax net profit ratio C </td><td> 10% </td><td> 10%*15=1.5 points</td> </tr><tr><td> debt ratio D </td><td> 68% </td><td> 5 points</td></tr><tr><td> TCRI rating E < /td><td> 2, etc.</td><td> 10 points</td></tr></TBODY></TABLE> It can be seen from Table 2 that the initial weighting amount corresponding to each financial feature A customer's initial score IS1=3+3.75+1.5+5+10=23.25 points, and A customer meets the comprehensive screening criteria of the main target group (Group 1), so the comprehensive score of customer A is SS1=23.25*150% = 34.875 points. table 5  <TABLE border="1" borderColor="#000000" width="85%"><TBODY><tr><td> Customer</td><td> Financial Features</td><td> Corresponding numeric size Or rating </td><td> initial weighting amount</td></tr><tr><td> B</td><td> revenue growth rate A </td><td> 20% < /td><td> 20%*15=3 points</td></tr><tr><td> Business benefit rate B </td><td> 15% </td><td> 15%* 15=2.25 points</td></tr><tr><td> Pre-tax net profit rate C </td><td> 10% </td><td> 10%*15=1.5 points</td> </tr><tr><td> debt ratio D </td><td> 93% </td><td> 5 points</td></tr><tr><td> TCRI rating E < /td><td> 5 etc.</td><td> 5 points</td></tr></TBODY></TABLE> It can be seen from Table 3 that the initial score of customer B is IS2=3+2.25 +1.5+5+5=16.75 points, and the B customer meets the comprehensive screening criteria of the secondary target group (Group 2), so the comprehensive score of the customer B is SS2=16.75*140%=23.45 points.  

In order to specifically describe the subsequent calculation process, in the present embodiment, the aforementioned difference value is defined as: difference value = reference score - comprehensive score. Suppose that the reference score of A customer is 40 points, the reference score of customer B is 30 points, and the preset adjustment range is that the difference value is greater than 5 points (excluding 5 points), so it can be known that the difference value of customer A is 5.125, and B The difference value of the customer is 6.55; since the difference values of the two both fall within the preset adjustment range, the operator 13 selectively adjusts the above-mentioned initial weighting amount and the comprehensive weighting amount, calculates again and generates an adjusted comprehensive score, Replace the original comprehensive score. For example, in this embodiment, the comprehensive scores of the two customer data are lower than the reference score, so the operator 13 can increase the initial weighting value of the revenue growth rate A (ie, aw1 of FIG. 2), or simultaneously improve The combined weighted values of Group 1 and Group 2 (i.e., sw1 and sw2 of Figure 2) are used to generate a new composite score and compare it again with the reference score to gradually bring the composite score closer to the reference score. On the other hand, assuming that a customer is a stable customer and has a good cooperation record, the reference score may be more indicative than the B customer, so the operator 13 can gradually adjust the initial weight and synthesis based on the difference value of the customer A. The weighting amount, after the comprehensive score of the customer A can approach the reference score, and then calculate the data of the customer B with the adjusted initial weighting amount and the comprehensive weighting amount, so that the whole computing process can be more efficient.

It is worth mentioning that the above financial characteristics may also include receivables turnover rate, quick ratio, cash flow ratio, net worth ratio, company establishment period, industrial trend (weather status) or other special conditions (such as Index data such as exporting excellent manufacturers, government-backed industries, credit insurance fund guarantees, quality public works vendors or award-winning start-ups, etc., and setting more screening conditions and weightings, with more diversification Calculate the conditions to find a list of potential customers of various natures.

In summary, the present invention aims to provide a smart exploration system for enterprise customers, which can analyze multiple potential customers according to different financial characteristics, compare some analysis results with actual data, and automatically correct the analysis process according to the comparison results, so that Applied in the financial industry with changing environment.

Although the present invention has been disclosed above in the foregoing embodiments, it is not intended to limit the present invention. The changes and refinements that are made without departing from the spirit and scope of this creation are within the scope of patent protection of this creation. Please refer to the attached patent application scope for the scope of protection defined by this creation.

1‧‧‧Enterprise Customer Smart Exploration System

11‧‧‧ memory

13‧‧‧Operator

15‧‧‧Display interface

A‧‧‧revenue growth rate

B‧‧‧Business profit rate

C‧‧‧ pre-tax net profit rate

D‧‧‧ debt ratio

E‧‧‧TCRI Rating

A1, a2, b1, b2, c1, c2, d1, d2, d3, e1, e2, e3‧‧‧ initial screening conditions

Aw1, aw2, bw1, bw2, cw1, cw2, dw1, dw2, dw3, ew1, ew2, ew3‧‧‧ initial weighting

IS1, IS2‧‧‧ initial score

Group 1‧‧‧ main target group

Group 2‧‧‧ secondary target group

SS1, SS2‧‧‧ comprehensive score

Sw1, sw2‧‧‧ comprehensive weighting

FIG. 1 is a structural diagram of a smart exploration system of an enterprise gold client according to an embodiment of the present invention. FIG. 2 is a schematic diagram of the operation of the intelligent exploration system of the enterprise gold client according to an embodiment of the present invention.

Claims (5)

An enterprise gold customer intelligent exploration system comprises: a memory for storing a customer data comprising a plurality of financial features, a plurality of initial screening conditions, a plurality of initial weighting amounts, a comprehensive screening condition, a comprehensive weighting amount, at least a rating range, a comprehensive score and a preset adjustment range; an operator electrically connected to the memory to obtain the customer data, the initial screening conditions, the initial weighting amounts, the comprehensive screening condition, the The comprehensive weighting amount, the at least one rating range, the comprehensive rating and the preset adjustment range are used to generate an initial score according to the financial features, the initial screening conditions and the initial weighting amounts, and according to the initial The score, the comprehensive screening condition and the comprehensive weighting amount generate the comprehensive score and transmit to the memory, and compare the comprehensive score and the at least one rating range to generate a rating result; and a display interface electrically connected to the The operator is configured to receive the rating result transmitted by the operator and present the rating result. The search system of claim 1, wherein the memory further comprises a reference score, the operator compares the reference score with the comprehensive score to generate a difference value, and when the difference value falls into the preset In the adjustment range, the operator selectively adjusts the initial weighting amount and the comprehensive weighting amount according to the difference value, and according to the financial features, the initial screening conditions, the comprehensive screening condition, and the selective adjustment The initial weighting amount and the combined weighting amount generate an adjusted comprehensive score. The search system of claim 1, wherein each of the financial features further comprises a feature value associated with the initial screening conditions of the financial feature, and only one of the initial screening conditions corresponds to For the eigenvalue, only one of the initial weighting values belongs to the initial screening condition corresponding to the eigenvalue, and the operator obtains a plurality of operation results according to the eigenvalue or a preset value, and generates The initial score. The search system of claim 3, wherein the financial features further comprise a numerical financial feature, and the operator obtains the operational result of the numerical financial feature based on the characteristic value of the numerical financial feature. The search system of claim 3, wherein the financial features further comprise a first-order digital financial feature, and the operator obtains the operational result of the numerical financial feature according to the preset value.