TWI752546B - Evaluation system and evaluation method - Google Patents

Abstract

An evaluation system and evaluation method, which first provides integrated information including a plurality of candidate items, uses one of the plurality of candidate items as a designated item, and then defines a rating range based on the designated item to generate a target that meets the rating range group; the target group includes a target object and at least one comparison object, and then the other of the plural candidate items is used as the target item to calculate the order of the target object and the comparison object in the target item, so it can effectively evaluate the market value of the target audience.

Description

Evaluation system and evaluation method

The present invention relates to an evaluation system and an evaluation method, in particular to an evaluation system and an evaluation method that can be dynamically analyzed.

With the maturity of Web2.0 technology, commenting systems or user-interactive online services are developing rapidly, and these services can accumulate a large amount of data, so in the field of online marketing, influencer marketing is booming.

In the current situation of online marketing, most products are sold through endorsements to increase sales. For example, a manufacturer invites internet celebrities (commonly known as internet celebrities) as distributors. Among them, each internet celebrity has a different number of fans, ranging from millions to thousands, but they all have certain influence.

However, for manufacturers (such as advertisers or brand owners), it is currently difficult to effectively aggregate the relevant values of Internet celebrities and then make judgments, so it is often only based on the volume of Internet celebrities (such as the number of subscriptions of Youtube Internet celebrities) As a basis for judging whether to act as an agent, it cannot effectively reflect the contribution of the Internet celebrity to the sales of the product, resulting in the selection of an inappropriate Internet celebrity as an agent, and the payment of an endorsement fee that does not match the price code to the Internet celebrity. For example, the number of subscriptions of the first YouTube influencer is one million, but the number of shares of each video and audio data is only a single digit, while the number of subscriptions of the second YouTube influencer is 100,000, but the number of shares of each audio and video data is only one digit. There are millions, but manufacturers usually choose to pay high endorsement fees to the first Youtube influencer as a distributor, but only produce the publicity effect of single-digit sharers.

Therefore, how to overcome the above-mentioned problems of the prior art has actually become an urgent problem to be overcome in the current industry.

In view of the above-mentioned deficiencies in the prior art, the present invention provides an evaluation system, which includes: a database, which provides integrated information, wherein the integrated information includes basic items and plural candidate items compiled through plural behavior data, and the The basic item includes a target object and a plurality of comparison objects, so that both the target object and the plurality of comparison objects correspond to the plurality of candidate items; the selection module is connected to the database by communication to make one of the plurality of candidate items As a designated item, and an evaluation range is defined based on the designated item to generate a target group that conforms to the evaluation range, wherein the target group includes the target object and part of the plurality of comparison objects; and an analysis module is communicatively connected to the A selection module, using at least one of the other of the plurality of candidate items as the target item, calculates the target score obtained by the target object in the target item, wherein the target score is the comparison of the target object with the target item The percentile into which the ordering of the comparison objects is converted.

The aforementioned evaluation system further includes a retrieval module that communicates with the database, searches and collects the plurality of behavior data, and transmits it to the database for aggregation to generate the integrated information.

In the aforementioned evaluation system, the analysis module scores the target object and the comparison object based on the target item, and performs the ranking according to the scoring result to calculate the target score.

In the aforementioned evaluation system, the evaluation range is floating.

In the aforementioned evaluation system, the analysis module obtains the potential score by calculating two target scores of the target object based on the target item before and after a period of time.

The present invention also provides an evaluation method, which includes: providing integrated information, wherein the integrated information includes basic items and a plurality of candidate items compiled from a plurality of behavioral data, and the The basic item includes a target object and a plurality of comparison objects, so that both the target object and the plurality of comparison objects correspond to the plurality of candidate items; one of the plurality of candidate items is used as a specified item, and an evaluation is defined based on the specified item range to generate a target group that meets the evaluation range, wherein the target group includes the target object and a part of the plurality of comparison objects; and at least one of the other of the plurality of candidate items is used as a target item to calculate the The target score obtained by the target object in the target item, wherein the target score is the percentile converted into the ranking of the target object compared with the comparison object.

In the aforementioned evaluation method, it includes searching and collecting the plural behavioral data for aggregation to generate the integrated information.

In the aforementioned evaluation method, the target object and the comparison object are scored based on the target item, and the ranking is performed according to the scoring result to calculate the target score.

In the aforementioned evaluation method, the evaluation range is floating.

In the aforementioned evaluation method, before and after a period of time, calculating the two target scores of the target object based on the target item, and then obtaining the potential score.

As can be seen from the above, in the evaluation system and the evaluation method of the present invention, the target score is obtained mainly by sorting the target object and the comparison object that meet the evaluation range for the target item. Therefore, compared with the prior art, for For manufacturers, it can judge the effective influence of the target object (such as Internet celebrity) according to the comparison results of the same level (within the evaluation range) (that is, the ranking or the target score), so as to estimate the sales contribution of the target object to the product Therefore, it is possible to select an appropriate internet celebrity as an agent, and pay an endorsement fee that matches the price to the internet celebrity.

1: Evaluation System

1a: host

10: Capture module

11: The first database

12: Second database

13: Processing modules

14: Select the module

15: Analysis module

8: Electronics

9: Personnel

F1, F2: Transmission direction

S20~S24: Steps

FIG. 1 is a schematic diagram of the structure of the evaluation system of the present invention.

Fig. 1' is a schematic diagram of the hardware configuration of the evaluation system of the present invention.

FIG. 2 is a schematic flowchart of the evaluation method of the present invention.

Figures 3-1 to 3-4 are the calculation result reports generated by the evaluation method of the evaluation system operation of the present invention.

Figures 4-1 to 4-4 are the calculation result reports generated by the evaluation method of the evaluation system operation of the present invention.

The following specific embodiments are used to illustrate the implementation of the present invention, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification.

It should be noted that the structures, proportions, sizes, etc. shown in the drawings in this specification are only used to cooperate with the contents disclosed in the specification for the understanding and reading of those who are familiar with the art, and are not intended to limit the implementation of the present invention. Therefore, it has no technical significance. Any modification of the structure, change of the proportional relationship or adjustment of the size should still fall within the scope of the present invention without affecting the effect and the purpose that the present invention can achieve. The technical content disclosed by the invention can be covered within the scope. Meanwhile, the terms such as "above" and "a" quoted in this specification are only for the convenience of description, and are not used to limit the scope of implementation of the present invention. Substantially changed technical content should also be regarded as the scope of the present invention.

FIG. 1 is a schematic diagram of the structure of an evaluation system 1 of the present invention. As shown in FIG. 1 , the evaluation system 1 includes: a capture module 10 , a first database 11 , a second database 12 , a processing module 13 , a selection module 14 and an analysis Module 15.

In this embodiment, as shown in FIG. 1 ′, the evaluation system 1 is installed in a host 1a, such as a server, with various processors or other suitable computer devices, so that in operation, it can communicate with at least one The electronic devices 8 transmit signals to each other, as shown in the transmission direction F1 in FIG. 1 ′. For example, the electronic device 8 is a computer product, such as a smart phone, a notebook computer, a tablet computer, or the like, which is connected to the host 1a for communication.

The capture module 10 is used for collecting initial information and storing the initial information in the first database 11 .

In this embodiment, the capture module 10 includes a web crawler model to automatically search and collect platform data published on the Internet as the initial information. For example, the initial information can come from an online platform, including Facebook fan page information, Instagram business account information, LINE official account information, Youtube channel information, Google Maps business information or other platform information, etc., Its content has a basic profile or business account known to the public, and the network platform is configured with a user interface for the public to communicate and interact with the network platform through the network, so as to use the network platform leave a record of the interaction.

Specifically, taking a business fan page on Facebook as an example, after the business publishes an article, the public can express their opinions on the article on the business fan page, such as like, comment or share interactive items. Or, taking Youtube as an example, after the publisher publishes the audio-visual material, the viewers watch the audio-visual material, the number of views of the audio-visual content will automatically increase by one time. Or, for each business that has registered information on a Google map (map), the public can rate the business on the Google map. Therefore, these platform data can be automatically searched and collected by the web crawler model.

The first database 11 is communicatively connected to the capture module 10 to store the initial information, such as receiving platform data provided by the web crawler model of the capture module 10 .

In this embodiment, the data type of the first database 11 is the online behavior data type, so the first database 11 can be input manually (the transmission direction shown in FIG. 1 ′). F2) Save the platform information. For example, the operator of an e-commerce platform (as shown in Figure 1') can also collect sales records of each commodity. The sales records can be regarded as the interaction between the public and the online platform for initial information, so manual labor can be used. The input method inputs the sales record into the first database 11 .

Furthermore, non-online behavior data can also be stored in the first database 11 by manual input (transmission direction F2 as shown in FIG. 1'). For example, school test scores. Therefore, as long as the initial information conforms to the behavior data type, there is no special limitation.

The second database 12 is communicatively connected to the first database 11 to store integrated information obtained after the initial information has been integrated.

In this embodiment, the processing module 13 performs data collection on the initial information in the first database 11 to obtain basic items and candidate items of each website platform, which are used as the integration information and then stored. into the second database 12 . For example, the basic item includes name, identification number (eg, network ID), address, age, and/or others, and the candidate item includes the estimated time period processed (eg, calculated) by the processing module 13 The degree to which people in the country interact and communicate on the online platform, so that each basic item corresponds to some candidate items. For example, since the data sources of the initial information are numerous, the processing module 13 classifies the data according to the data sources, so that the data from the same data source is aggregated into the integrated information.

Specifically, taking the data source of Facebook as the integrated information as an example, the candidate items of the public fan page are obtained through the Facebook Graph API (Application Programming Interface), such as the number of likes, and the candidate items It can also be the number of emotional interactions, the number of likes, the number of comments, the number of shares, or the quantitative value of other candidate items for each article in the past 30 days. Or, taking the data source of Youtube as the integrated information as an example, the candidate items of the public channel, such as the Subscriber Count, can be obtained through the Youtube Data API, and the candidate item can also be each video and audio data of the public channel in the past 30 days The number of views, likes, down likes, plays, comments or other quantitative values of candidate items, etc., among which, in actual commercial considerations, the same Youtube channel may have multiple median videos, and The number of views (views) of each audio and video data is relatively large, so taking the median of the number of views of all audio and video data of the channel (that is, regardless of the maximum number of views and the minimum number of views) can more reliably present the Youtube channel. Contribution of marketing activities, integrated information as shown in the following table:

。

.

The selection module 14 communicates with the second database 12 to group the integrated information to obtain grouping information including the target group.

In this embodiment, the selection module 14 is grouped by filtering similar items, so that one of the candidate items is used as a designated item, and an evaluation range is defined based on the designated item for evaluation. As the basis for the target group, make the target group meet the conditions of the evaluation range. For example, the basic item for selecting the integrated information is Youtube's Internet celebrity, and the number of Internet celebrities' subscriptions (that is, the number of fans) is used as the basis for subsequent potential evaluation. The exposure of the first influencer with 10,000 subscriptions and the second influencer with 50,000 subscriptions is obviously different, and cannot reflect the contribution of the second influencer compared to the first influencer in the same marketing campaign. Therefore, when evaluating the contribution of the second influencer (or target object), the influencer with a similar level of subscriptions should be used as the comparison object. Specifically, the number of subscriptions is used as a designated item, and the upper limit (such as 100,000) and the lower limit (such as 10,000) to form the required evaluation range (such as 10,000 to 100,000 subscriptions), so you can select the influencers that meet the evaluation range in the integrated information, so that the Internet celebrities who meet the evaluation range (such as fans The third internet celebrity with 80,000 followers, the fourth internet celebrity with 20,000 followers or other comparison objects with the number of followers) and the target object (such as the second internet celebrity) are listed as the target group, and the aggregated information does not match. Other net reds in the evaluation range are regarded as other groups, such as the first category group (fans The number of threads is less than 10,000), the second group (the number of fans is more than 100,000), or other groups, so the grouping information as shown in the following table can be obtained:

。

.

Furthermore, the evaluation range is a floating type, that is, the upper limit and lower limit of the specified item (the number of subscriptions) can be adjusted according to requirements. For example, at the same point in time, if the target object's subscription number (such as 50,000) is the main component and the upper limit of the subscription number is defined as 60,000 and the lower limit is 40,000, another evaluation range (such as 40,000) will be formed. ~60,000 subscriptions), and then form another target group, grouping information as shown in the following table:

。因此，該選取模組14係以該目標對象為主定義出各種評比範圍而形成多種目標組，以供後續評估作業用。應可理解地，該評比範圍之上限及下限可與該目標對 象之指定項目呈等值(如上述)，亦可為非等值(如基於5萬訂閱數，上限為10萬訂閱數，下限為4萬訂閱數，使評比範圍為4萬~10萬訂閱數)，並無特別限制。

. Therefore, the selection module 14 defines various evaluation ranges based on the target object to form various target groups for subsequent evaluation operations. It should be understood that the upper and lower limits of the evaluation range can be equivalent to the specified items of the target object (as above), or they can be non-equivalent (eg based on 50,000 subscriptions, the upper limit is 100,000 subscriptions, and the lower limit is The number of subscriptions is 40,000, making the evaluation range from 40,000 to 100,000 subscriptions), and there is no special limit.

Furthermore, the target group includes a target object and one or more comparison objects, and the target object is assigned to the comparison objects as a rating base (such as the number of Internet celebrities included in the target group).

In other embodiments, if the integrated information is a rating service, such as Google Map, the selection module 14 can use the price as the specified item to perform the screening of the same-level item.

The analysis module 15 is communicatively connected to the selection module 14 to calculate the target score obtained by the target object for the target item based on the target group.

In the present embodiment, the analysis module 15 selects at least one candidate item different from the specified item as the target item, performs scoring based on the target item, and then sorts according to the scoring result, and then calculates the target object in the target item. The target score in the target item, wherein the scoring method can be absolute or relative, the scoring result of the absolute system is an absolute score, and the scoring result of the relative system is a relative score. For example, the average number of likes of video content in the past 30 days is used as the target item. If the absolute score is used, the average number of likes will be used as the absolute score for scoring, so the subsequent sorting will be based on the absolute score, as shown in the following table:

。另一方面，若將近30天影音內容之平均按讚數及平均倒讚數均作為目標項目，且採用相對分數，則依據平均按讚數之加成(如乘以1)與該平均倒讚數之加成(如乘以-0.1)之總和作為相對分數進行評分，故後續係依據該相對分數進行排 序。由於該加成之方式繁多，故若採用不同之加成方式，則會產生不同之評分結果，因而產生不同之排序，如下兩表所示：

. On the other hand, if the average number of likes and the average number of downvotes of the video content in the past 30 days are used as the target item, and the relative score is adopted, then the addition of the average number of likes (for example, multiplied by 1) and the average downvote will be used as the target item. The sum of the addition of the numbers (for example, multiplied by -0.1) is used as the relative score for scoring, so the subsequent sorting is based on the relative score. Due to the many ways of adding, if different adding methods are used, different scoring results will be generated, and thus different rankings will be generated, as shown in the following two tables:

或

or

。由上述兩表可知，網紅「CCC」之排名由第一名變為第三名。應可理解地，使用者可依需求設定相對分數之定義，如將圖案量化(笑臉圖案得10分，哭臉圖案得-2分)，再進行加成總和之評分。因此，使用者(即使用該評估系統者)可依需求選取所需之目標項目及評分方式，以產生有價值之排序。

. It can be seen from the above two tables that the ranking of the Internet celebrity "CCC" has changed from the first place to the third place. It should be understood that the user can set the definition of the relative score according to the needs, such as quantifying the pattern (10 points for a smiley face pattern, -2 points for a crying face pattern), and then add a total score. Therefore, users (ie, those who use the evaluation system) can select desired target items and scoring methods according to their needs, so as to generate valuable rankings.

Furthermore, the target score is a percentile (Percentile) converted from the ranking of the target object compared with the comparison object, so as to reflect the value of the target object in the same level. For example, if the first type of target group has a total of 100 internet celebrities (including one target object and 99 other comparison objects) as the evaluation base, based on the target item (such as the average number of likes of video and audio content in the past 30 days or 30 days of likes) Inside video If the target object is ranked last (that is, the target object has won 0 comparison objects), its target score is 1% (ie (0 +1)/100), and if the ranking result of the target object is the first (that is, the target object has won 99 comparison objects), its target score is 100% (ie (99+1)/100), so If it has won 59 comparison objects, the ranking result of the target object in the target item is 41st, and its target score is 60% (ie (59+1)/100). It should be noted that when the target object and the comparison object have the same score, the target object can be regarded as winning (or not winning) the comparison object as required.

Or, select the second type of target group, which has 80 Internet celebrities (including target objects) as the evaluation base, to sort based on the target item, if the target object wins 74 comparison objects, then the target object is in the target item The ranking result is 6th, and its target score is 93.75% (ie (74+1)/80). Or, select the third target group, which has 200 Internet celebrities (including target objects) as the evaluation base, to sort based on the target item, if the target object wins 119 comparison objects, then the target object is in the target The ranking result in the project is No. 81, and its target score is 60% (ie (119+1)/200).

Therefore, the analysis module 15 can sort according to any one of various target groups to calculate the target score of the target object in the target item, and in different target groups, the target object is in the target item The resulting target scores may or may not be the same, as shown in the table below:

，但基於該目標組之比較對象之數量，仍可反映出該目標對象(第二網紅)相較於不同競爭對手(比較對象)對於同一行銷活動所展現之可能貢獻度，以供使用者(即使用該評估系統者)判斷該目標對象之市場價值。具體地，雖然第一與第 三目標組同分，但第三目標組之評比基數較多，故使用者可依據第三目標組之網紅價格估價；或者，雖然第二目標組較高分，但第二目標組之評比基數較少，致使其目標分數較為不客觀，故使用者可不考量第二目標組之目標分數，而以第一或第三目標組進行估價。

, but based on the number of comparison objects in the target group, it can still reflect the possible contribution of the target object (second influencer) to the same marketing activity compared with different competitors (comparison objects) for the user (that is, using the evaluation system) to determine the market value of the target object. Specifically, although the first and third target groups have the same score, the third target group has more evaluation bases, so users can make an evaluation based on the Internet celebrity price of the third target group; or, although the second target group has a higher score , but the evaluation base of the second target group is small, which makes its target score less objective. Therefore, the user can ignore the target score of the second target group and use the first or third target group for evaluation.

In addition, the analysis module 15 can further evaluate the future potential of the target object based on the target score. For example, before and after a period of time, sorting is performed based on the same target item to obtain a potential score for the user to evaluate the future potential of the target object. Specifically, the first type of target group is sorted based on the target item, the target object's target score in the target item on the first day is 60 points, and the target score in the target item on the hundredth day is 80 points, Then subtract the two target scores (ie 80-60=+20) to obtain a potential score of "positive 20 points", so the future potential of the target object is shown as positive growth. It should be understood that if the target score in the target item on the 100th day is 40 points, the potential score obtained is "minus 20 points" (ie 40-60=-20), which makes the future potential of the target object showing negative growth.

In addition, although the specified item (number of subscriptions) of the target object (Internet celebrity) may change over time (such as the upgrade status of the number of subscriptions from 10,000 to 100,000, or the number of subscriptions from 10,000 to 1,000), degraded status), so that the evaluation range may change accordingly (for example, from the range of 9,000 to 11,000 people to the range of 90,000 to 110,000 people), and the target object is compared to the comparison objects and their evaluation bases. Obviously different, but since the ranking result is converted into percentiles as the target score, it can still reflect the future potential of the target object. Specifically, the upgrade status is shown in the following table:

，故使用者若將兩目標分數相減而獲取潛力分數(-36分)，仍可反映出該目標對象對於外界因素之未來潛力，以供使用者判斷該目標對象之競爭力，如上表所示之負成長，其可反映出該網紅即使處於追蹤數暴增至10萬人次之升級狀態，但於未來市場並不具競爭力。

, so if the user subtracts the two target scores to obtain the potential score (-36 points), it can still reflect the future potential of the target object to external factors, so that the user can judge the competitiveness of the target object, as shown in the above table The negative growth shown can reflect that the Internet celebrity is not competitive in the future market even though the number of followers has surged to 100,000.

Or, the degraded status as shown in the following table:

，其反映出正成長(即潛力分數為+1)，故該網紅雖處於追蹤數只剩1千人次之降級狀態，但於未來市場仍具競爭力。因此，該分析模組15藉由一目標時段之前、後時間點所得之目標分數能評估該目標對象之未來潛力，以供使用者更客觀判斷該目標對象之競爭力，故不論該目標對象之指定項目是否升級或降級，該分析模組15所獲取之潛力分數，仍可反映出該目標對象對於外界因素之競爭力。

, which reflects positive growth (that is, the potential score is +1), so although the influencer is in a downgrade state with only 1,000 followers remaining, it will still be competitive in the future market. Therefore, the analysis module 15 can evaluate the future potential of the target object based on the target scores obtained at the time points before and after a target period, so that the user can more objectively judge the competitiveness of the target object. Whether the designated item is upgraded or downgraded, the potential score obtained by the analysis module 15 can still reflect the competitiveness of the target object against external factors.

It can be seen from the above that the evaluation accuracy of the analysis module 15 may be affected by the following factors:

The first point is that if the level of the specified item of the target object is high (for example, the number of subscriptions exceeds one million), the number of comparison objects in the target group is small (for example, the evaluation base is less than 10), and the target group that can be generated from the grouping information If there are few types (for example, less than 10 types of target groups), when the ranking of the target objects changes, the target score changes greatly. For example, if the number of subscriptions (designated items) of a well-known Internet celebrity (target object) is one million, there are only three types of target groups that can be generated (for example, the evaluation base of the target group is five Internet celebrities, four Internet celebrities or three internet celebrities), so when the rank of the target object in the target group (taking a total of four internet celebrities as an example) changes, the target score will change greatly, such as 1/4 percentage of 25% The quantile (ie, the bottom of the target group) becomes the 75th percentile (ie, the second-ranked target) of the 3/4 percentile, which changes a total of 50 percentiles.

The second point is that if the data capture volume of the platform is more (that is, the data volume of the integrated information is more), the types of target groups that can be generated by the grouping information may be more, so the comparison objects in the target group will be more and more. more, so that the target score has more reference value. For example, compared with the fact that the first database 11 only has hundreds of Internet celebrities, if the first database 11 has tens of thousands of Internet celebrities, the grouping information may generate more types of target groups. The more accurate the ranking (target score) in the same evaluation range (such as 1,000 to 10,000 subscriptions), as shown in the following table:

。因此，相較於該目標分數為90%(或90分)之目標對象，該目標分數為80%(或80分)之目標對象之評估更具有參考價值。

. Therefore, compared with the target object whose target score is 90% (or 90 points), the evaluation of the target object whose target score is 80% (or 80 points) has more reference value.

The third point is that if the evaluation range is larger (or the gap between the upper limit and the lower limit is too large), although there may be more comparison objects in the target group, it is also possible to include two comparison objects with too large gaps in one of the targets. In the group, for example, the aforementioned third and fourth influencers are assigned to the same target group.

FIG. 2 is a schematic flowchart of the evaluation method of the present invention. In this embodiment, the evaluation method described above is operated by the evaluation system 1 .

In step S20, first, initial information is collected. In this embodiment, the initial information is collected into the first database 11 by the capture module 10 or manual input.

In step S21, based on the initial information, an integration operation is performed to obtain integrated information. In this embodiment, the processing module 13 collects the initial information of the first database 11 to obtain the integrated information, and stores the integrated information in the second database 12 .

In step S22, based on the integrated information, a screening operation is performed to obtain grouping information. In this embodiment, the specified item is selected by the selection module 14 to form an evaluation range after inputting parameters into the selection module 14 (eg, step S220 ), and the screening operation is completed.

For example, the selection module 14 can operate the same-level item screening method by a mathematical equation, such as the following equation (A):

，其中，以網紅為例，P表示所有粉絲互動網頁，f(p _i )表示其中一物件之屬性(即該指定項目)或該目標對象的粉絲數，σ係作為步驟S220之用以調整該屬性之篩選條件之參數(其值小於1)，f(p _j )表示該屬性之限制(或該評比範圍之上限與下限)，p _j 表示該比較對象，U _i 係為目標組。

Wherein, in an example network red, P denotes all the fans interactive pages, f (p _i) which represents the object of a fan attributes (i.e., the designated items) or the number of the target object, [sigma] line as in step S220 to adjust the The parameter of the filter condition of the attribute (its value is less than 1), f ( p _j ) represents the limit of the attribute (or the upper and lower limits of the evaluation range), p _j represents the comparison object, and U _i is the target group.

In step S23, a calculation operation is performed based on one of the target groups in the grouping information. In this embodiment, the analysis module 15 performs a calculation operation to calculate the target score obtained by the target object in the target item at a point in time.

For example, the analysis module 15 can perform calculation operations on mathematical equations, such as the following calculation equation (B):

其中，h(p _i )表示該物件之另一屬性(或目標項目)，h(p _j )表示該比較對象之另一屬性(或目標項目)，n表示其中一種目標組，n(U _i )表示其中一種目標組之評比基數，PR(P _i )表示目標分數(百分位數)，score(P _i )表示該目標對象於該評比基數中之百分比，即score(P _i )之最大值為1(即第一名，即贏過所有比較對象)， 而最小值為

(即最後一名，即未贏過任一比較對象)。因此，當該分析模組 15接收各種目標組後，可演算出該目標對象於各種目標組中之目標分數，使該目標分數具有參考價值。

Among them, h( p _i ) represents another property (or target item) of the object, h( p _j ) represents another property (or target item) of the comparison object, n represents one of the target groups, n( U _i ) represents the evaluation base of one of the target groups, PR( P _i ) represents the target score (percentile), and score( P _i ) represents the percentage of the target object in the evaluation base, that is, the maximum score( P _{i )} The value is 1 (i.e. first place, i.e. wins all comparisons), and the minimum value is

(i.e. last place, i.e. not winning any of the comparison objects). Therefore, after the analysis module 15 receives various target groups, it can calculate the target score of the target object in the various target groups, so that the target score has reference value.

Therefore, in the calculation operation of step S23, various target groups can be selected according to requirements, and then the possible contribution of the target object can be judged therein, or the potential of the target object can be judged in the evaluation operation of step S24. It should be understood that since the target group is used for the calculation operation, the more the comparison objects, the better, so as to increase the reference value of the ranking of the calculation operation. If the number of influencers in the target group is too small, then The reference value of the sorting of arithmetic operations is not high.

In step S24, an evaluation operation is performed based on the target score. In this embodiment, the analysis module 15 calculates the difference between the target scores between two time points as a potential score to evaluate the future potential of the target object.

For example, the analysis module 15 may obtain the first target score score( P _i ) _{t of} the target object at the first time point t, and after a period of time (ie, the second time point t+1), perform the Another calculation to obtain the second target score score( P _i ) _t+1 , then subtract the first and second target scores score( P _i ) _t , score( P _i ) _t+1 , as shown in the formula below :

g ( P _i )= score ( P _i ) _{t +1} - score ( P _i ) _t , to obtain a potential score g(P _i ) for evaluating the future potential of the target object.

Therefore, the evaluation method described is based on the comparison of objects with similar conditions to generate a very meaningful ranking, so it can be applied to the evaluation of career matching endorsements (or Internet celebrities), evaluation of high-quality restaurants, evaluation of student potential, and evaluation of opinion leaders ( Key Opinion Leader) market potential evaluation, student learning ability evaluation or other target object evaluation, etc., to judge whether an opinion leader has value in the target project in the market relative to the competitors of the same level. For example, the evaluation method filters out the target group by selecting a specified item suitable for comparison (such as the number of YouTube channel subscriptions), and then calculates the ranking and percentile values according to the target item (the cumulative number of YouTube videos viewed within 30 days). , and then compare the percentile of the same opinion leader over time to evaluate whether the opinion leader is on the market for the target project (the cumulative number of views of Youtube videos within 30 days) when the number of subscriptions continues to increase. ) remains in the lead (i.e. potential score).

The above-mentioned influencer evaluation can target the following number (such as the number of fans or subscriptions) of a certain influencer (target object) as the specified item f ( p _i ), and the difference ±10% can be used as the parameter σ, so that the Other net reds within this range of fans (evaluation range) are used as the comparison object p _i to generate the target group U _i , and then the number of interactions of public posts within 30 days is used as the target item h( p _i ), based on the The median rating of the target item is the ranking or target score (hundredthile) of the influencer (target object) in the target group, as shown in the following table:

。進一步，基於同一指定項目，觀測二月底與三月底的排序，可評估該網紅(目標對象)之未來成長潛力，如+10(即80-70=+10)之正成長。

. Further, based on the same designated project, observing the ranking at the end of February and the end of March can evaluate the future growth potential of the Internet celebrity (target object), such as positive growth of +10 (ie 80-70=+10).

The above-mentioned high-quality hotel screening is based on browsing the online reservation data (the initial information of the first database 11 ), and can target the double room of a certain hotel (target object) (the integrated information in the second database 12 ) The tax-included price (one of the candidate items of the integrated information) is used as the specified item, and the price difference ±10% is extracted as the parameter to obtain other hotels within this price range (evaluation range) as the comparison object, and the Target group (that is, the comparison list), and then use the guest evaluation scores within 30 days as the target item to evaluate the hotel (target object) double room in the same level comparison list (the target group) according to the median of the target item. The ranking or target score in the following table:

。進一步，基於同一指定項目，觀測2019年底與2020年底的排序，可評估該飯店(目標對象)之未來成長潛力，如-5(即90-95=-5)之負成長。

. Further, based on the same designated project, observing the ranking at the end of 2019 and the end of 2020 can evaluate the future growth potential of the hotel (target object), such as negative growth of -5 (ie 90-95=-5).

The aforementioned potential student evaluation is to collect initial information by manual input, and then take the intelligence test score of a certain high school student (target object) as the specified item, and use the score difference ±10% as the parameter to obtain this score range ( Other high school students within the evaluation range) are used as the comparison objects to generate the target group (ie, the comparison list), and then take the test scores of a certain subject (mathematics) in a certain semester (the first semester of senior high school) as the target item, in accordance with the target item The average (such as the average of three tests) is used to evaluate the ranking or target score of the student (target object) in the subject compared to other students in the target group, as shown in the following table:

。進一步，基於同一指定項目，觀測高二上與高二下的目標分數，可評估該高中學生之未來成長潛力，如+5(即95-90=+5)之正成長。

. Further, based on the same specified item, the target scores of the upper and lower secondary school students can be observed to evaluate the future growth potential of the high school students, such as positive growth of +5 (ie, 95-90=+5).

Fig. 3-1 to Fig. 3-4 and Fig. 4-1 to Fig. 4-4 are detailed descriptions of the actual operation of the evaluation method of the evaluation system 1 of the present invention, which are authorized by Facebook data.

First, a large number of fan page lists are collected by the web crawler model of the capture module 10 (or manually input or read lists compiled by third-party websites) as initial information, and the processing module 13 uses Graph The API publicly obtains the current number of fans of each fan page in the initial information and the information of the post on the fan page (including the number of likes of the post, etc.), which can be used as integrated information to complete the construction of the second database 12, so that It can store the number of followers of each fan page and the information of each post.

私人廚房」(https：//www.facebook.com/ABGKitchen/)作為目標對象，並以其粉絲數作為指定項目(2020年03月20日至04月20日，其粉絲數為725,369)，再取差異參數±10%內的粉絲數作為評比範圍，即選取粉絲數652,832至797,906之粉絲頁作為比較對象(共66個粉絲頁)，以形成目標組(如圖3-1至圖3-4所示之演算結果報表之67個粉絲頁)。 Next, the selection module 14 selects the fan page "Amy

Private Kitchen” (https://www.facebook.com/ABGKitchen/) as the target object and the number of fans as the designated project (from March 20 to April 20, 2020, the number of fans was 725,369), and then Take the number of fans within ±10% of the difference parameter as the evaluation range, that is, select the fan pages with the number of fans from 652,832 to 797,906 as the comparison object (a total of 66 fan pages) to form the target group (as shown in Figure 3-1 to Figure 3-4). 67 fan pages of the calculation results report shown).

私人廚房」的貼文互動分數之中位數係為1875.2，且該目標分數係為95.5(即(63+1)/67，贏過63名粉絲頁)。 After that, the analysis module 15 performs a calculation operation, which takes the social interaction as the target item, and the calculation method of each post interaction score adopts a relative system, such as adding the emotional interaction number (eg, multiplying by 0.1) , the sum of the addition of the number of comments (eg multiplied by 0.1) and the number of shares (eg multiplied by 1), so all the fan pages that have posted within 30 days can calculate the median (ie relative score) of the target item, The calculation result report shown in Figure 3-1 to Figure 3-4 is used for sorting, so the "Amy

The median post interaction score of "Private Kitchen" is 1875.2, and the target score is 95.5 (ie (63+1)/67, winning 63 fan pages).

私人廚房」之粉絲數從725,369成長至741,016，該選取模組14取差異參數±10%內的粉絲數作為評比範圍，即選取粉絲數666,914至815,118之粉絲頁作為比較對象(共65個粉絲頁)，以形成目標組(如圖4-1至圖4-4所示之演算結果報表之66個粉絲頁)，故該分析模組15進行另一次演算作業，其採用與上述相同之計算方式，以獲取該目標對象之目標項目 之中位數為2037.5，如圖4-1至圖4-4所示之演算結果報表，且該目標分數係為98.4(即(64+1)/66，贏過64名粉絲頁)。進一步，該分析模組15可藉由該時段(即30天)之前後(即04月20日~05月20日)，基於該目標項目所計算出該粉絲頁「Amy

私人廚房」之兩個目標分數(即98.4與95.5)，演算出該目標對象之潛力分數為+2.9(即98.4-95.5)，其呈現正成長，故於進行評估作業時，使用者可判斷該粉絲頁「Amy

私人廚房」之未來應具有競爭力。 Further, the analysis module 15 performs an evaluation operation. After 30 days (April 21 to May 20, 2020), the fan page "Amy

The number of fans of "Private Kitchen" has grown from 725,369 to 741,016. The selection module 14 takes the number of fans within ±10% of the difference parameter as the evaluation range, that is, the fan pages with the number of fans from 666,914 to 815,118 are selected as the comparison objects (65 fan pages in total). ) to form a target group (66 fan pages of the calculation result report as shown in Figure 4-1 to Figure 4-4), so the analysis module 15 performs another calculation operation, which adopts the same calculation method as above , to obtain the median of the target item of the target object is 2037.5, the calculation result report shown in Figure 4-1 to Figure 4-4, and the target score is 98.4 (ie (64+1)/66, won 64 fan pages). Further, the analysis module 15 can calculate the fan page “Amy

The two target scores (i.e. 98.4 and 95.5) of “Private Kitchen”, the potential score of the target object is calculated to be +2.9 (i.e. 98.4-95.5), which is showing positive growth. Fan page "Amy

The future of private kitchens should be competitive.

To sum up, the evaluation system and the evaluation method of the present invention, by dynamically grouping the target object with respect to the specified item (ie, a floating evaluation range), score the target item in the target group, and By calculating the target score, it can effectively tap potential target objects in the market (such as Internet celebrities, stock market, real estate market, tourism or others).

The above embodiments are used to illustrate the principles and effects of the present invention, but not to limit the present invention. Any person skilled in the art can make modifications to the above embodiments without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the right of the present invention should be listed in the scope of the patent application described later.

1: Evaluation System

10: Capture module

11: The first database

12: Second database

13: Processing modules

14: Select the module

15: Analysis module

Claims (8)

An evaluation system includes: a database, which provides integrated information, wherein the integrated information includes basic items and plural candidate items compiled through plural behavioral data, and the basic item includes a target object and plural comparison objects , so that both the target object and the plurality of comparison objects correspond to the plurality of candidate items; the selection module is communicatively connected to the database, so that one of the plurality of candidate items is used as a designated item, and the target object corresponds to The specified item is mainly defined to define an evaluation range, so as to generate a target group that meets the evaluation range, wherein the target group includes the target object and some of the plurality of comparison objects, and the evaluation range is based on the target object corresponding to The specified item is a floating type, so that the upper limit and the lower limit of the specified item are adjustable based on the target object; and an analysis module is communicatively connected to the selection module for the other of the plurality of candidate items At least one of them is used as the target item, and the target score obtained by the target object in the target item is calculated, wherein the target score is the percentile converted into the ranking of the target object compared with the comparison object . The evaluation system as claimed in claim 1 further comprises a retrieval module communicating with the database, which searches and collects the plurality of behavior data, and transmits the data to the database for aggregation to generate the integrated information. The evaluation system of claim 1, wherein the analysis module scores the target object and the comparison object based on the target item, and performs the ranking according to the scoring result to calculate the target score. The evaluation system of claim 1, wherein the analysis module obtains the potential score by calculating two target scores of the target object based on the target item before and after a period of time. An evaluation method includes: providing integrated information through a database, wherein the integrated information includes a basic item and a plurality of candidate items compiled from plural behavioral data, and the basic item includes a target object and plural comparison objects , so that both the target object and the plurality of comparison objects correspond to the plurality of candidate items; the selection module defines one of the plurality of candidate items as the specified item, and defines the specified item corresponding to the target object as the main an evaluation range to generate a target group conforming to the evaluation range, wherein the target group includes the target object and some of the plural comparison objects, and the evaluation range is floating based on the specified item corresponding to the target object, The upper limit and the lower limit of the specified item are adjustable based on the target object; and the analysis module takes at least one of the other of the plurality of candidate items as the target item to calculate the target object in the target item The obtained target score, wherein the target score is the percentile converted into the ranking of the target object compared to the comparison object. The evaluation method as described in claim 5, further comprising searching and collecting the plurality of behavior data through a retrieval module communicatively connected to the database, so that the processing module communicatively connected to the database performs aggregation to generate the integrated information . The evaluation method according to claim 5, further comprising scoring the target object and the comparison object based on the target item by the analysis module, and performing the ranking according to the scoring result to calculate the target score . The evaluation method according to claim 5 further includes before and after a period of time that the analysis module calculates the two target scores of the target object based on the target item, and then obtains the potential score.

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