CN112330098B - Intelligent calculation method and device for KOL (KOL) capacity attribute - Google Patents

Intelligent calculation method and device for KOL (KOL) capacity attribute Download PDF

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CN112330098B
CN112330098B CN202011108820.4A CN202011108820A CN112330098B CN 112330098 B CN112330098 B CN 112330098B CN 202011108820 A CN202011108820 A CN 202011108820A CN 112330098 B CN112330098 B CN 112330098B
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CN112330098A (en
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孔晓晴
李百川
劳晓敏
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Youmi Technology Co ltd
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Abstract

The invention discloses an intelligent calculation method and device for KOL (KOL) capacity attribute, wherein the method comprises the following steps: acquiring contribution data (such as sales contribution data and/or browsing contribution data) of a certain video with a certain KOL to target commodities sold with the video within a certain target time period; calculating the commodity carrying capacity attribute of the KOL according to the total commodity data of the target commodity in the target time period and the contribution data of the commodity carrying video to the target commodity in the target time period; the KOL is any one of a plurality of KOL, the target commodity is any one of a plurality of commodities sold by the KOL through a commodity video, and the commodity video is any one of all commodity videos issued by the KOL for selling the target commodity in a commodity manner. Therefore, the method and the device can calculate the carrying capacity attribute of the KOL according to the contribution data of the carrying video of the KOL to the commodity sold by the carrying video of the KOL, and improve the accuracy of the calculated carrying capacity attribute of the KOL.

Description

Intelligent calculation method and device for KOL (KOL) capacity attribute
Technical Field
The invention relates to the technical field of intellectualization, in particular to an intelligent calculation method and device for KOL (KOL) capacity attribute.
Background
With the rapid development of the internet, internet users are increasing. In order to expand the audience range and influence of commodities, the marketing mode of commodities introduces video marketing based on internet besides traditional advertising marketing, for example: an advertiser may choose multiple video bloggers (also referred to as "KOLs", key opinion leaders) to publish videos for a commodity or commodities, which may also be referred to as on-demand videos.
In practical application, the influence (also called as "capacity attribute") of the video blogger is generally evaluated according to the amount of vermicelli of the video blogger and the amount of interactive data (such as forwarding amount, comment amount, browsing amount, etc.) of the released video, so as to select a corresponding video blogger to popularize the commodity. However, practice discovers that in order to improve the influence of the video bloggers, some video bloggers purchase vermicelli amounts, interactive data amounts and the like in a money-consuming manner, which results in the problem of low influence accuracy of the video bloggers determined according to the vermicelli amounts, the interactive data amounts of released videos and the like. It can be seen that how to improve the accuracy of the determined capacity attribute of the video blogger is particularly important.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an intelligent calculation method and device for the KOL capacity attribute, which can improve the accuracy of the determined capacity attribute of the video blogger.
In order to solve the technical problem, the first aspect of the present invention discloses an intelligent calculation method for KOL capacity attribute, which comprises:
acquiring contribution data of a certain cargo video of a certain KOL to target commodities sold by the cargo video in a certain target time period;
calculating the commodity carrying capacity attribute of the KOL according to the total commodity data of the target commodity in the target time period and the contribution data of the carried video to the target commodity in the target time period;
the KOL is any one of a plurality of KOL, the target commodity is any one of a plurality of commodities sold by the KOL through a commodity carrying video, and the commodity carrying video is any one of all commodity carrying videos issued by the KOL for selling the target commodity in a commodity carrying manner.
As an optional implementation manner, in the first aspect of the present invention, after calculating the capability attribute of the KOL according to total commodity data of the target commodity in the target time period and the contribution data of the video-with-goods to the target commodity in the target time period, the method further includes:
Acquiring cargo capacity attribute correction data corresponding to the cargo video;
according to the acquired cargo carrying capacity attribute correction data, performing correction operation on the calculated cargo carrying capacity attribute of the KOL to obtain a corrected cargo carrying capacity attribute of the KOL;
wherein the capacity attribute correction data includes one or more of a total exposure time of the video, a total release time of the video, and a combination of the KOL's man attribute data.
As an optional implementation manner, in the first aspect of the present invention, the calculating the capability attribute of the KOL according to total commodity data of the target commodity in the target time period and the contribution data of the video-with-commodity to the target commodity in the target time period includes:
calculating the ratio of the contribution data of the video with goods to the target goods in the target time period to the total goods data of the target goods in the target time period, and obtaining the contribution duty ratio of the video with goods to the target goods in the target time period;
determining the calculated contribution duty cycle as a capacity attribute of the KOL; or,
And analyzing the contribution level corresponding to the contribution duty ratio, and determining the analyzed contribution level as the capacity attribute of the KOL.
As an optional implementation manner, in the first aspect of the present invention, the contribution data includes a combination of one or more of sales contribution data, browsing contribution data, and conversion contribution data of the video in the target period of time for the target commodity;
wherein before calculating the capacity attribute of the KOL according to the total commodity data of the target commodity in the target time period and the contribution data of the video-in-band to the target commodity in the target time period, the method further comprises:
and determining the data category of the contribution data, and collecting total commodity data of the target commodity, which is matched with the data category in the target time period, according to the data category, and taking the total commodity data of the target commodity in the target time period.
As an optional implementation manner, in the first aspect of the present invention, the calculating a ratio of the contribution data of the video with goods to the target goods in the target time period to the total goods data of the target goods in the target time period, to obtain a contribution duty ratio of the video with goods to the target goods in the target time period includes:
When the data category comprises at least two categories, for each data category, calculating the ratio of target contribution data of the target commodity to target total commodity data of the target commodity in the target time period by the video with goods in the target time period to obtain the contribution duty ratio of the data category, determining the weight value corresponding to the data category, and calculating the product of the contribution duty ratio of the data category and the weight value corresponding to the data category to obtain the product corresponding to the data category, wherein the target contribution data and the target total commodity data are matched with the data category;
and executing summation operation on products corresponding to all the data categories to obtain the contribution duty ratio of the video with goods to the target commodity in the target time period.
As an alternative embodiment, in the first aspect of the present invention, the method further includes:
acquiring all commodities sold by the carried video in the carried video set of the KOL, and performing classification operation on all the acquired commodities according to commodity categories to obtain commodity sets of a plurality of different commodity categories;
And calculating the contribution duty ratio of the video with goods corresponding to all the goods in the goods set for the goods set of any goods category, and determining the contribution duty ratio of the KOL for the goods category according to the contribution duty ratio of the video with goods corresponding to all the goods in the goods set.
As an optional implementation manner, in the first aspect of the present invention, for a commodity set of any commodity category, a contribution ratio of the KOL to the commodity category is equal to an average value or a median of contribution ratios of all the video-with-commodity corresponding to all the commodities in the commodity set;
and, the method further comprises:
for any commodity category, calculating the contribution rank of each KOL in the KOL set according to the contribution duty ratio of each KOL in the KOL set for the commodity category, and calculating the capability attribute of each KOL under the commodity category according to the contribution rank of each KOL in the KOL set, wherein the KOL set comprises a plurality of KOL with commodity categories of commodities sold through the commodity video bands as the commodity categories.
In a second aspect, the present invention discloses an intelligent computing device for KOL capability attribute, the device comprising:
the acquisition module is used for acquiring contribution data of a certain cargo video of a certain KOL to target commodities sold by the cargo video in a certain target time period;
A first calculation module, configured to calculate a capability attribute of the KOL according to total commodity data of the target commodity in the target time period and the contribution data of the video-with-commodity to the target commodity in the target time period;
the KOL is any one of a plurality of KOL, the target commodity is any one of a plurality of commodities sold by the KOL through a commodity carrying video, and the commodity carrying video is any one of all commodity carrying videos issued by the KOL for selling the target commodity in a commodity carrying manner.
As an optional implementation manner, in the second aspect of the present invention, the obtaining module is further configured to obtain, after the first calculating module calculates the capacity attribute of the KOL according to total commodity data of the target commodity in the target period and the contribution data of the video-in-band to the target commodity in the target period, capacity attribute correction data corresponding to the video-in-band;
wherein the apparatus further comprises:
the correction module is used for executing correction operation on the calculated capacity attribute of the KOL according to the acquired capacity attribute correction data to obtain the corrected capacity attribute of the KOL;
Wherein the capacity attribute correction data includes one or more of a total exposure time of the video, a total release time of the video, and a combination of the KOL's man attribute data.
As an alternative embodiment, in a second aspect of the present invention, the first computing module includes:
the calculation sub-module is used for calculating the ratio of the contribution data of the video with goods to the target goods in the target time period to the total goods data of the target goods in the target time period to obtain the contribution duty ratio of the video with goods to the target goods in the target time period;
a determination submodule for determining the calculated contribution duty cycle as a capacity attribute of the KOL; or analyzing the contribution level corresponding to the contribution duty ratio, and determining the analyzed contribution level as the capacity attribute of the KOL.
As an optional implementation manner, in the second aspect of the present invention, the contribution data includes a combination of one or more of sales contribution data, browsing contribution data, and conversion contribution data of the video in the target period of time to the target commodity;
Wherein the apparatus further comprises:
a determining module, configured to determine a data category of the contribution data before the first calculating module calculates the capability attribute of the KOL according to total commodity data of the target commodity in the target time period and the contribution data of the video-in-band to the target commodity in the target time period;
and the acquisition module is used for acquiring total commodity data of the target commodity, which is matched with the data category in the target time period, according to the data category, and taking the total commodity data of the target commodity in the target time period as the total commodity data of the target commodity.
In a second aspect of the present invention, as an optional implementation manner, the calculating submodule calculates a ratio of the contribution data of the video with goods to the target goods in the target time period to the total goods data of the target goods in the target time period, so as to obtain a specific mode that the contribution ratio of the video with goods to the target goods in the target time period is:
when the data category comprises at least two categories, for each data category, calculating the ratio of target contribution data of the target commodity to target total commodity data of the target commodity in the target time period by the video with goods in the target time period to obtain the contribution duty ratio of the data category, determining the weight value corresponding to the data category, and calculating the product of the contribution duty ratio of the data category and the weight value corresponding to the data category to obtain the product corresponding to the data category, wherein the target contribution data and the target total commodity data are matched with the data category;
And executing summation operation on products corresponding to all the data categories to obtain the contribution duty ratio of the video with goods to the target commodity in the target time period.
As an optional implementation manner, in the second aspect of the present invention, the obtaining module is further configured to obtain all goods sold by the carried video in the carried video collection of the KOL;
wherein the apparatus further comprises:
the classification module is used for performing classification operation on all the acquired commodities according to commodity categories to obtain commodity sets of a plurality of different commodity categories;
the second calculation module is used for calculating the contribution duty ratio of the video with goods corresponding to all the goods in the goods set for the goods set of any goods category, and determining the contribution duty ratio of the KOL for the goods category according to the contribution duty ratio of the video with goods corresponding to all the goods in the goods set.
As an optional implementation manner, in the second aspect of the present invention, for a commodity set of any commodity category, the contribution ratio of the KOL to the commodity category is equal to the average value or the median of the contribution ratios of all the video-with-commodity corresponding to all the commodities in the commodity set;
The second calculation module is further configured to calculate, for any commodity category, a contribution rank of each KOL in the KOL set according to a contribution duty ratio of each KOL in the KOL set for the commodity category, and calculate, for each KOL in the KOL set, a capacity attribute of each KOL under the commodity category according to the contribution rank of each KOL in the KOL set, where the KOL set includes a plurality of KOLs with commodity categories of commodities sold through a video band as the commodity category.
In a third aspect, the present invention discloses another intelligent computing device for KOL capability attribute, the device comprising:
a memory storing executable program code;
a processor coupled to the memory;
the processor calls the executable program code stored in the memory to execute part or all of the steps in the intelligent calculation method for the KOL capacity attribute disclosed in the first aspect of the embodiment of the invention.
In a fourth aspect, an embodiment of the present invention discloses a computer storage medium, where computer instructions are stored, where the computer instructions are used to execute part or all of the steps in the intelligent method for calculating the KOL capability attribute disclosed in the first aspect of the embodiment of the present invention when the computer instructions are called.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, contribution data (such as sales contribution data and/or browsing contribution data) of a certain carried video of a certain KOL to a target commodity sold by the carried video in a certain target time period is obtained; calculating the commodity carrying capacity attribute of the KOL according to the total commodity data of the target commodity in the target time period and the contribution data of the commodity carrying video to the target commodity in the target time period; the KOL is any one of a plurality of KOL, the target commodity is any one of a plurality of commodities sold by the KOL through a commodity video, and the commodity video is any one of all commodity videos issued by the KOL for selling the target commodity in a commodity manner. Therefore, the method and the device can calculate the goods carrying capacity attribute of the KOL according to the contribution data of the goods carried and sold by the goods carrying video of the KOL, and compared with the goods carrying capacity attribute of the KOL determined according to the vermicelli quantity, the interaction data quantity of the released video and the like, the method and the device improve the accuracy of the calculated goods carrying capacity attribute of the KOL, further can provide accurate reference basis for selecting proper KOL and can recommend KOL meeting the requirements of different advertisers.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an intelligent calculation method for the KOL capacity attribute according to an embodiment of the present invention;
FIG. 2 is a flow chart of another method for intelligent computation of KOL capacity attributes according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a KOL capacity attribute intelligent computing device according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an intelligent computing device for KOL throughput attributes in accordance with another embodiment of the present invention;
FIG. 5 is a schematic diagram of an intelligent computing device for a KOL capacity attribute according to an embodiment of the invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or article that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or article.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention discloses an intelligent calculation method and device for the carrying capacity attribute of a KOL, which can calculate the carrying capacity attribute of the KOL according to the contribution data of the carrying video of the KOL to the commodity sold by the carrying video of the KOL. The following will describe in detail.
Example one (method side example)
Referring to fig. 1, fig. 1 is a flow chart of an intelligent calculation method for KOL capacity attribute according to an embodiment of the invention. The method described in fig. 1 is applied to a computing device of the KOL capability attribute, where the computing device may be a corresponding computing terminal, computing device or server, and the server may be a local server or a cloud server, which is not limited by the embodiment of the present invention. As shown in FIG. 1, the intelligent calculation method of the KOL capacity attribute may include the following operations:
101. the computing device obtains contribution data of a certain video with a certain KOL to target commodities sold with the video with the goods in a certain target time period.
In the embodiment of the present invention, the KOL is any one of a plurality of KOLs, the target commodity is any one of a plurality of commodities sold by the KOL through a commodity carrying video, and the commodity carrying video is any one of all commodity carrying videos issued by the KOL for selling the target commodity with the commodity carrying.
In the embodiment of the invention, the target time period can be a time period selected or input manually, or can be a time period intelligently determined according to the release starting time of the video with goods, because the video with goods has relatively strong influence on goods when being just released, and the video with goods has relatively weak influence on goods after being released for a period of time, the mode of intelligently determining the target time period (especially the starting date or starting time of the target time period) according to the release starting time of the video with goods is beneficial to improving the accuracy of the goods carrying capacity attribute of the KOL which is determined according to the contribution data of the target time period, and can also reduce the data quantity and save the computing resources of the computing device. For example, if the current date is 9 months 29, the release start date of the video with goods is 9 months 20, the start date of the target time period may be 9 months 23.
Optionally, before performing step 101, the method may further include the following operations:
the computing device determines a target time period;
when the target time period is manually input or selected, the computing device determines the starting time of the target time period, judges whether the starting time of the target time period is not earlier than the time determined according to the release starting time of the video with goods, and triggers the execution of step 101 when the judgment result is yes; when the judgment result is negative, the computing device outputs a prompt message, wherein the prompt message is used for prompting the artificial input or the selected target time period to be too early.
Further optionally, the hint message may include an error offset of the KOL capacity attribute calculated from contribution data corresponding to a current target time period compared to the KOL capacity attribute calculated from contribution data corresponding to a time period having a start time not earlier than the determined time period. Still further, the alert message may also include a time determined based on the start time of release of the video in stock, to facilitate quick selection or entry of the appropriate target time period by the relevant personnel.
102. The calculating device calculates the commodity carrying capacity attribute of the KOL according to the total commodity data of the target commodity in the target time period and the contribution data of the carried video to the target commodity in the target time period.
In an embodiment of the present invention, the capability attribute of the KOL calculated in step 102 may be specifically understood as the capability attribute of the video-in-band of the KOL for the target commodity.
In an alternative embodiment, after performing the finishing step 102, the method may further comprise the following operations:
the computing device acquires the cargo capacity attribute correction data corresponding to the cargo video;
the computing device executes a correction operation on the calculated capacity attribute of the KOL according to the acquired capacity attribute correction data to obtain the corrected capacity attribute of the KOL.
Wherein the capacity attribute correction data includes one or more of a total exposure time of the video, a total release time of the video, and a combination of the KOL's man attribute data.
It can be seen that, in this alternative embodiment, after the marketability attribute of the KOL is initially calculated, the marketability attribute of the KOL is further improved by correcting the marketability attribute of the KOL according to one or more of the total exposure time of the marketable video, the total release time of the marketable video, and the data of the marketer attribute of the KOL.
In an alternative embodiment, the calculating means calculates the marketability attribute of the KOL according to the total commodity data of the target commodity in the target time period and the contribution data of the video with the commodity to the target commodity in the target time period, and may include:
The calculating device calculates the ratio of contribution data of the video with goods to the target goods in the target time period to total goods data of the target goods in the target time period, and the contribution duty ratio of the video with goods to the target goods in the target time period is obtained;
the computing device determining the calculated contribution duty as a capacity attribute of the KOL; or,
the computing device analyzes the contribution level corresponding to the contribution ratio and determines the analyzed contribution level as the marketability attribute of the KOL.
In this alternative embodiment, the total merchandise data of the target merchandise in the target period is equal to the merchandise data of the target merchandise at the end time of the target period minus the merchandise data of the target merchandise at the start time of the target period.
It can be seen that this alternative embodiment enables quantification of the capacity attribute of the KOL by determining the contribution duty or the contribution level as the capacity attribute of the KOL when determining the capacity attribute of the KOL from the contribution data and the total commodity data, making the capacity attribute of the KOL more intuitive.
In this alternative embodiment, further optionally, the contribution data may include a combination of one or more of sales contribution data, browsing contribution data, and conversion contribution data of the video in stock to the target commodity over the target time period. Wherein the data category of sales contribution data is sales category, the data category of browsing contribution data is browsing category, and the data category of conversion contribution data is conversion contribution category, and it may include contribution data associated with the marketability attribute of KOL in addition to sales contribution data, browsing contribution data, for example: download amount data, forwarding amount data, and sharing amount data.
In this optional embodiment, further optionally, before the calculating means calculates the marketability attribute of the KOL from the total commodity data of the target commodity in the target period and the contribution data of the video-with-commodity to the target commodity in the target period, the method may further include the operations of:
the computing device determines the data category of the contribution data, and collects the total commodity data of the target commodity, which is matched with the data category in the target time period, according to the data category, and the total commodity data of the target commodity in the target time period is used as the total commodity data of the target commodity.
Therefore, the optional embodiment can determine the data category of the contribution data according to the data content of the contribution data before calculating the capacity attribute of the KOL, so as to collect the total commodity data matched with the data category, which is beneficial to improving the rationality and accuracy of the capacity attribute of the KOL calculated later.
Still further optionally, the calculating means calculates a ratio of contribution data of the video with goods to the target goods in the target time period to total goods data of the target goods in the target time period, to obtain a contribution duty ratio of the video with goods to the target goods in the target time period, and may include:
When the data categories of the contribution data comprise at least two, for each data category, calculating the ratio of target contribution data of the target commodity to target total commodity data of the target commodity in the target time period by the video with goods in the target time period by the calculating device to obtain the contribution duty ratio of the data category, determining the weight value corresponding to the data category, and calculating the product of the contribution duty ratio of the data category and the weight value corresponding to the data category to obtain the product corresponding to the data category, wherein the target contribution data and the target total commodity data are matched with the data category;
and the computing device performs summation operation on products corresponding to all data categories to obtain the contribution duty ratio of the video with goods to the target commodity in the target time period.
The higher the weight value corresponding to the data category is, the greater the influence of the contribution data of the corresponding data category on the capacity attribute of the KOL is.
It can be seen that this alternative embodiment may consider contribution data of multiple data categories when calculating the capacity attribute of the KOL from the contribution data, and calculate the capacity attribute of the KOL from the contribution data of multiple data categories, which is beneficial to further improving the accuracy of the calculated capacity attribute of the KOL.
Still further, when the data category includes a sales category and a browsing category, the calculating means calculates a ratio of contribution data of the video with goods to the target goods in the target time period to total goods data of the target goods in the target time period, to obtain a contribution duty ratio of the video with goods to the target goods in the target time period, and may include:
the calculating device calculates the ratio of sales contribution data of the video with goods to the target goods in the target time period to total goods sales data of the target goods in the target time period to obtain a first contribution duty ratio, and calculates the ratio of browsing contribution data of the video with goods to the target goods in the target time period to total goods browsing data of the target goods in the target time period to obtain a second contribution duty ratio;
the computing device determines a first weight value corresponding to the sales category and a second weight value corresponding to the browsing category;
the calculating device calculates a first product of the first contribution duty ratio and the first weight value and a second product of the second contribution duty ratio and the second weight value, and calculates a sum of the first product and the second product to obtain the contribution duty ratio of the video with goods to the target goods in the target time period.
It can be seen that this alternative embodiment can comprehensively calculate the marketability attribute of the KOL based on the sales contribution data, the browse contribution data, and the corresponding weight values, which is beneficial to improving the accuracy of the calculated marketability attribute of the KOL.
In yet another alternative embodiment, the method may further comprise the operations of:
the method comprises the steps that a computing device obtains all commodities sold by a commodity video in a commodity video set of the KOL, and performs classification operation on all the obtained commodities according to commodity categories to obtain commodity sets of a plurality of different commodity categories;
and calculating the contribution duty ratio of the video with goods corresponding to all the goods in the goods set for the goods set of any goods category, and determining the contribution duty ratio of the KOL for the goods category according to the contribution duty ratio of the video with goods corresponding to all the goods in the goods set.
For a commodity set of any commodity category, the commodity video corresponding to all the commodities in the commodity set is specifically all the commodity videos sold in the commodity video set of KOL, and the contribution ratio of any commodity video corresponding to the commodity belongs to all the commodity videos of the commodity set, that is, the contribution ratio of the commodity video to the commodity in a corresponding time period, and the specific calculation mode is referred to the calculation mode described above, which is not described in detail in this alternative embodiment.
It can be seen that the optional embodiment can further determine the contribution ratio of the KOL to the commodity category after calculating the contribution ratio of the video with the KOL, and provides a calculation mode of the contribution ratio of the KOL to different commodity categories, so that the KOL meeting the commodity to be promoted and sold by different advertisers and having proper contribution ratio can be recommended.
In this optional embodiment, further optional, for a commodity set of any commodity category, the contribution duty ratio of the KOL for that commodity category is equal to the average or median of the contribution duty ratios of all the video-with-commodity corresponding to all the commodities in the commodity set.
Still further optionally, the method may further comprise the operations of:
for any commodity category, the computing device calculates a contribution rank of each KOL in the KOL set according to the contribution ratio of each KOL in the KOL set to the commodity category, and calculates the capacity attribute of each KOL under the commodity category according to the contribution rank of each KOL in the KOL set, wherein the KOL set comprises a plurality of KOL with commodity categories of commodities sold through the commodity video bands as the commodity categories.
Specifically, the KOL set is a KOL set corresponding to a commodity category, and the commodity category of the commodity sold by the released video with commodity is the commodity category. And the computing device computes the capacity attribute of each KOL under the commodity category according to the contribution rank of each KOL in the KOL set, may include:
The computing device computes the ratio of the contribution rank of each KOL in the KOL set to the total number of KOL included in the KOL set as the capability attribute of each KOL under the commodity category; or,
the computing device determines a ranking range in which each KOL in the set of KOLs is ranked for its contribution, and determines a ranking level for each KOL as a capacity attribute for each KOL under the commodity category based on the ranking range in which each KOL is ranked for its contribution.
Still further optionally, for any commodity category, before the computing device computes a contribution rank for each KOL in the set of KOLs from the contribution duty cycle for the commodity category for each KOL in the set of KOLs, the method may further include the operations of:
for any commodity category, the computing device acquires all KOL of the video with the commodity which is issued for selling any commodity of the commodity category with the commodity, and determines all the acquired KOL as a KOL set corresponding to the commodity category.
Still further alternatively, after the computing device obtains all KOLs of the video-in-band that have been issued for selling any one of the commodity categories in-band, the computing device may further perform the following operations:
For any one of all the obtained KOL, randomly extracting the released video with the KOL by a computing device, collecting video interaction data of the video with the video, judging whether the KOL is an abnormal KOL according to the relation between the video interaction data of the video with the video and the data of the KOL (such as the amount of vermicelli), filtering the KOL when the KOL is the abnormal KOL (such as the KOL with the video interaction data 10 times higher than the amount of vermicelli), and reserving the KOL when the KOL is not the abnormal KOL;
the computing device determines the residual KOL after filtering all abnormal KOL from all acquired KOL as a KOL set corresponding to the commodity category.
It should be noted that, when no abnormal KOL exists in all the obtained KOLs, the computing device executes the operation of determining all the obtained KOLs as the KOL set corresponding to the commodity category.
As can be seen, in this alternative embodiment, filtering operation is performed on abnormal KOL in the determined KOL set before calculating the capacity attribute of each KOL under the commodity category according to the contribution rank of each KOL in the KOL set, so as to reduce the scope of the KOL set, improve the calculation efficiency of calculating the contribution rank of each KOL in the KOL set, and further facilitate improving the calculation efficiency of calculating the capacity attribute of each KOL under the commodity category according to the contribution rank of each KOL in the KOL set. And the range of the KOL set is reduced, so that the efficiency of screening the KOL meeting the requirements of related personnel (such as advertisers) is improved.
As can be seen, in the alternative embodiment, the contribution rank of each KOL in the KOL set can be calculated according to the contribution ratio of each KOL to the commodity category, so as to calculate the capacity attribute of each KOL under the corresponding commodity category, improve the accuracy of the calculated capacity attribute of each KOL under the corresponding commodity category, and recommend KOLs meeting the requirements for related personnel (such as sellers selling commodities of different commodity categories, etc.), thereby being beneficial to purposefully recommending KOLs.
In yet another alternative embodiment, the method may further comprise:
for any KOL, the computing device calculates historical capacity attribute of the KOL under different commodity categories according to a plurality of non-overlapping historical time periods, draws a capacity attribute curve of the KOL under different commodity categories according to the plurality of calculated historical capacity attribute of the KOL under different commodity categories, and predicts a capacity attribute change trend of the KOL under different commodity categories according to the drawn capacity attribute curve of the KOL under different commodity categories.
Therefore, the optional embodiment can also estimate the change trend of the capacity attribute of the KOL under different commodity categories, can provide corresponding reference basis for relevant personnel when selecting the KOL, further can improve the matching degree of the KOL selected by the relevant personnel and the self demand, and can improve the efficiency of selecting the proper KOL by the relevant personnel to a certain extent.
In yet another alternative embodiment, the method may further comprise the operations of:
for any KOL, the computing device evaluates the value attribute (e.g., marketing price, etc.) of the KOL under different commodity categories and/or the composite value attribute of the KOL based on the capacity attribute of the KOL under different commodity categories.
Therefore, the optional embodiment can also intelligently evaluate the value attribute of the KOL under different commodity categories and/or the comprehensive value attribute of the KOL according to the capability attribute of the KOL under different commodity categories, can provide corresponding reference basis for relevant personnel when the KOL is selected, further can improve the matching degree of the KOL selected by the relevant personnel and the self requirement, and can improve the efficiency of the relevant personnel for selecting the proper KOL to a certain extent.
Therefore, compared with the KOL with capability attribute determined according to the vermicelli quantity, the interaction data quantity of the released video and the like, the method improves the accuracy of the calculated KOL with capability attribute, and further can provide accurate reference basis for selecting a proper KOL and recommending KOL meeting the requirements of different advertisers. In addition, the contribution ratio of the KOL to the commodity category can be further determined after the contribution ratio of the KOL to the commodity category is calculated, a calculation mode of the contribution ratio of the KOL to the different commodity categories is provided, so that the KOL meeting the requirements of different advertisers for promoting the commodity to be sold and having the proper contribution ratio is promoted, further, the contribution rank of each KOL in the KOL set can be calculated according to the contribution ratio of each KOL to the commodity category, the commodity carrying capacity attribute of each KOL in the corresponding commodity category is calculated, the accuracy of the calculated KOL carrying capacity attribute in the corresponding commodity category is improved, and the KOL meeting the requirements of related personnel (such as advertisers or sellers selling the commodity of different commodity categories) is recommended, so that the KOL is promoted in a targeted manner.
Example two (method side example)
Referring to fig. 2, fig. 2 is a flowchart illustrating another intelligent calculation method of KOL capacity attribute according to an embodiment of the invention. The method described in fig. 2 is applied to a computing device of the KOL capability attribute, where the computing device may be a corresponding computing terminal, computing device or server, and the server may be a local server or a cloud server, which is not limited by the embodiment of the present invention. As shown in FIG. 2, the intelligent calculation method of the KOL capacity attribute may include the following operations:
201. the computing device obtains all items sold with the carried video in the carried video collection of the KOL.
202. The computing device performs classification operation on all acquired commodities according to commodity categories to obtain commodity sets of a plurality of different commodity categories.
203. For any commodity category, the computing device computes a contribution rank of each KOL in the KOL set according to a contribution ratio of each KOL in the KOL set for the commodity category, and computes a capacity attribute of each KOL under the commodity category according to the contribution rank of each KOL in the KOL set.
In an alternative embodiment, the method may further comprise the following operations, prior to performing step 203:
For any commodity category, the computing device calculates a contribution duty cycle for each KOL in the set of KOLs for that commodity category.
Wherein the calculating means calculates a contribution duty ratio of the KOL for a certain commodity category may include:
the computing device determining a plurality of pick videos of all pick videos of the KOL for pick selling the commodity of the commodity category;
for any determined video with goods, the calculating device calculates the ratio of the contribution data of the video with goods to the goods sold with goods and the total goods data of the goods, and obtains the contribution duty ratio of the video with goods to the goods.
It should be noted that, the calculating device calculates the ratio of the contribution data of the commodity sold by the commodity video to the total commodity data of the commodity, and the specific implementation manner of obtaining the contribution ratio of the commodity video to the commodity is referred to the related detailed description in the first embodiment, which is not repeated in the embodiments of the present invention.
In another alternative embodiment, the method may further comprise the operations of:
for any commodity category, the computing device calculates a contribution rank of each KOL in the KOL set according to the contribution ratio of each KOL in the KOL set to the commodity category, and calculates the capacity attribute of each KOL under the commodity category according to the contribution rank of each KOL in the KOL set, wherein the KOL set comprises a plurality of KOL with commodity categories of commodities sold through the commodity video bands as the commodity categories.
It should be noted that, for the detailed description of the optional embodiment, please refer to the related detailed description in the first embodiment, and the detailed description of the embodiment of the present invention is omitted.
In yet another alternative embodiment, the method may further include steps 101-102 in the first embodiment, which is not described in detail.
Therefore, by implementing the method described by the embodiment of the invention, the contribution rank of each KOL in the KOL set can be calculated according to the contribution duty ratio of each KOL for the commodity category, so that the capacity attribute of each KOL under the corresponding commodity category is calculated, the accuracy of the calculated capacity attribute of each KOL under the corresponding commodity category is improved, and the KOL meeting the requirements of related personnel (such as advertisers or sellers selling commodities of different commodity categories) can be recommended, so that the targeted recommendation of the KOL is facilitated.
Example III (device side example)
Referring to fig. 3, fig. 3 is a schematic diagram illustrating a configuration of an intelligent computing device for KOL capability attribute according to an embodiment of the invention. The apparatus described in fig. 3 may be applied to a corresponding computing terminal, computing device, or server, and the server may be a local server or a cloud server, which is not limited by the embodiment of the present invention. As shown in fig. 3, the intelligent computing device of the KOL capacity attribute may include:
The acquiring module 301 is configured to acquire contribution data of a certain video with a certain KOL to a target commodity sold by the video with the certain video within a certain target time period.
The first calculating module 302 is configured to calculate the capacity attribute of the KOL according to the total commodity data of the target commodity in the target time period and the contribution data of the video with the commodity to the target commodity in the target time period.
The KOL is any one of a plurality of KOL, the target commodity is any one of a plurality of commodities sold by the KOL through the commodity video tape, and the commodity video is any one of all commodity videos issued by the KOL and used for selling the target commodity with the commodity.
It can be seen that implementing the apparatus described in fig. 3 can calculate the capacity attribute of the KOL according to the contribution data of the video of the KOL to the commodity sold by the KOL, thereby improving the accuracy of the calculated capacity attribute of the KOL.
In an alternative embodiment, the obtaining module 301 is further configured to obtain the capacity attribute correction data corresponding to the video with load after the first calculating module 302 calculates the capacity attribute of the KOL according to the total commodity data of the target commodity in the target time period and the contribution data of the video with load to the target commodity in the target time period.
In this alternative embodiment, as shown in fig. 4, the apparatus may further include:
the correction module 303 is configured to perform a correction operation on the calculated capacity attribute of the KOL according to the acquired capacity attribute correction data, so as to obtain a corrected capacity attribute of the KOL.
Wherein the capacity attribute correction data includes one or more of a total exposure time of the video, a total release time of the video, and a combination of the KOL's man attribute data.
It can be seen that the apparatus described in fig. 4 can further correct the calculated KOL capacity attribute according to one or more of the total exposure time of the loaded video, the total release time of the loaded video, and the data of the KOL's man attribute after the initial calculation of the KOL capacity attribute, thereby further improving the accuracy of the calculated KOL capacity attribute.
In yet another alternative embodiment, as shown in fig. 4, the first computing module 302 may include:
and the calculating submodule 3021 is used for calculating the ratio of the contribution data of the video with goods to the target goods in the target time period to the total goods data of the target goods in the target time period to obtain the contribution duty ratio of the video with goods to the target goods in the target time period.
A determination submodule 3022 for determining the calculated contribution duty cycle as a capacity attribute of KOL; or analyzing the contribution level corresponding to the contribution ratio, and determining the analyzed contribution level as the capacity attribute of the KOL.
It can be seen that implementing the apparatus described in fig. 4 also enables quantification of the KOL's capacity attribute by determining the contribution duty or contribution level as the capacity attribute of the KOL when determining the capacity attribute of the KOL based on the contribution data and the total commodity data, making the capacity attribute of the KOL more intuitive.
Optionally, the contribution data includes a combination of one or more of sales contribution data, browsing contribution data, and conversion contribution data of the video in stock to the target commodity over the target time period.
Further alternatively, as shown in fig. 4, the apparatus may further include:
a determining module 304, configured to determine a data category of the contribution data before the first calculating module 302 calculates the capability attribute of KOL according to the total commodity data of the target commodity in the target time period and the contribution data of the video with commodity to the target commodity in the target time period.
The collection module 305 is configured to collect, according to the data type, total commodity data of the target commodity, which is matched with the data type in the target time period, as total commodity data of the target commodity in the target time period.
It can be seen that, before the implementation of the apparatus described in fig. 4 calculates the capacity attribute of the KOL, the data type of the contribution data is determined according to the data content of the contribution data, so as to collect the total commodity data matched with the data type, which is beneficial to improving the rationality and accuracy of the capacity attribute of the KOL calculated later.
Still further alternatively, the calculating submodule 3021 calculates a ratio of contribution data of the video with goods to the target goods in the target time period to total goods data of the target goods in the target time period, and the specific mode of obtaining the contribution ratio of the video with goods to the target goods in the target time period is as follows:
when the data category comprises at least two categories, for each data category, calculating the ratio of target contribution data of the target commodity by the video with goods in the target time period to target total commodity data of the target commodity in the target time period to obtain the contribution duty ratio of the data category, determining the weight value corresponding to the data category, and calculating the product of the contribution duty ratio of the data category and the weight value corresponding to the data category to obtain the product corresponding to the data category, wherein the target contribution data and the target total commodity data are matched with the data category;
And executing summation operation on products corresponding to all data categories to obtain the contribution duty ratio of the video with goods to the target commodity in the target time period.
Taking the example that the data category includes a sales category and a browse category, the calculating submodule 3021 calculates a ratio of contribution data of the video with goods to the target goods in the target time period to total goods data of the target goods in the target time period, and a specific mode of obtaining the contribution ratio of the video with goods to the target goods in the target time period is as follows:
calculating the ratio of sales contribution data of the video with goods to the target goods in the target time period to total goods sales data of the target goods in the target time period to obtain a first contribution duty ratio, and calculating the ratio of browsing contribution data of the video with goods to the target goods in the target time period to total goods browsing data of the target goods in the target time period to obtain a second contribution duty ratio;
determining a first weight value corresponding to the sales category and a second weight value corresponding to the browsing category;
and calculating a first product of the first contribution duty ratio and the first weight value and a second product of the second contribution duty ratio and the second weight value, and calculating a sum of the first product and the second product to obtain the contribution duty ratio of the video with goods to the target goods in the target time period.
It can be seen that implementing the apparatus described in fig. 4 is also capable of comprehensively calculating the marketability attribute of the KOL based on the sales contribution data, the browse contribution data, and the corresponding weight values, which is beneficial to improving the accuracy of the calculated marketability attribute of the KOL.
In another alternative embodiment, the acquiring module 301 may be further configured to acquire all the commodities that are sold in the video in the KOL in the video collection. As shown in fig. 4, the apparatus may further include:
the classification module 305 is configured to perform a classification operation on all the obtained commodities according to the commodity categories, so as to obtain a commodity set of a plurality of different commodity categories.
The second calculating module 306 is configured to calculate, for a commodity set of any commodity category, contribution ratios of the video with commodities corresponding to all commodities in the commodity set, and determine a contribution ratio of the KOL for the commodity category according to the contribution ratios of the video with commodities corresponding to all commodities in the commodity set.
It can be seen that implementing the apparatus described in fig. 4 can further determine the contribution ratio of the KOL to the commodity category after calculating the contribution ratio of the video with the KOL, and provides a calculation manner of the contribution ratio of the KOL to different commodity categories, so as to be beneficial to recommending KOLs meeting the commodity to be promoted and sold to different advertisers and having proper contribution ratios.
In this optional embodiment, further optional, for a commodity set of any commodity category, the contribution duty ratio of the KOL for that commodity category is equal to the average or median of the contribution duty ratios of all the video-with-commodity corresponding to all the commodities in the commodity set.
In this optional embodiment, the second calculation module 306 is further optionally configured to calculate, for any commodity category, a contribution rank of each KOL in the KOL set according to a contribution ratio of each KOL in the KOL set for the commodity category, and calculate, for each KOL in the KOL set, a capacity attribute of each KOL under the commodity category according to the contribution rank of each KOL in the KOL set, where the KOL set includes a number of KOLs whose commodity category of commodities sold through a commodity video band is the commodity category.
It should be noted that this alternative embodiment may also be a separate embodiment.
Therefore, the device described in fig. 4 can also calculate the contribution rank of each KOL in the KOL set according to the contribution ratio of each KOL to the commodity category, so as to calculate the capacity attribute of each KOL under the corresponding commodity category, improve the accuracy of the calculated capacity attribute of each KOL under the corresponding commodity category, and recommend KOLs meeting the requirements for related personnel (such as sellers selling commodities of different commodity categories, etc.), thereby being beneficial to purposefully recommending KOLs.
Example IV
Referring to fig. 5, fig. 5 is a schematic diagram illustrating a configuration of an intelligent computing device for KOL capability attribute according to another embodiment of the present invention. As shown in fig. 5, the apparatus may include:
a memory 401 storing executable program codes;
a processor 402 coupled with the memory 401;
processor 402 invokes executable program code stored in memory 401 to perform some or all of the steps in the intelligent computing method of the KOL capacity attribute disclosed in either embodiment one or embodiment two of the present invention.
Example five
The embodiment of the invention discloses a computer storage medium which stores computer instructions for executing part or all of the steps in the intelligent calculation method of the KOL capacity attribute disclosed in the first or second embodiment of the invention when the computer instructions are called.
The apparatus embodiments described above are merely illustrative, wherein the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above detailed description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product that may be stored in a computer-readable storage medium including Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic disc Memory, tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Finally, it should be noted that: the disclosure of the intelligent calculation method and the intelligent calculation device for the KOL capacity attribute disclosed by the embodiment of the invention is only a preferred embodiment of the invention, and is only used for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims (9)

1. An intelligent calculation method for KOL capacity attribute, which is characterized by comprising the following steps:
acquiring contribution data of a certain cargo video of a certain KOL to target commodities sold by the cargo video in a certain target time period, wherein the contribution data comprises one or more combinations of sales contribution data, browsing contribution data and conversion contribution data of the cargo video to the target commodities in the target time period, and the target time period comprises a time period selected or input by a user and/or a time period determined according to the release starting moment of the cargo video;
Calculating the commodity carrying capacity attribute of the KOL according to the total commodity data of the target commodity in the target time period and the contribution data of the carried video to the target commodity in the target time period;
the KOL is any one of a plurality of KOL, the target commodity is any one of a plurality of commodities sold by the KOL through a commodity carrying video, and the commodity carrying video is any one of all commodity carrying videos issued by the KOL for selling the target commodity in a commodity carrying manner;
the calculating the capability attribute of the KOL according to the total commodity data of the target commodity in the target time period and the contribution data of the video-in-charge to the target commodity in the target time period comprises:
calculating the ratio of the contribution data of the video with goods to the target goods in the target time period to the total goods data of the target goods in the target time period, and obtaining the contribution duty ratio of the video with goods to the target goods in the target time period;
determining the calculated contribution duty cycle as a capacity attribute of the KOL; or,
And analyzing the contribution level corresponding to the contribution duty ratio, and determining the analyzed contribution level as the capacity attribute of the KOL.
2. The intelligent computing method of KOL capacity attribute according to claim 1, wherein after computing the KOL capacity attribute from total commodity data of the target commodity in the target period and the contribution data of the video-in-band to the target commodity in the target period, the method further comprises:
acquiring cargo capacity attribute correction data corresponding to the cargo video;
according to the acquired cargo carrying capacity attribute correction data, performing correction operation on the calculated cargo carrying capacity attribute of the KOL to obtain a corrected cargo carrying capacity attribute of the KOL;
wherein the capacity attribute correction data includes one or more of a total exposure time of the video, a total release time of the video, and a combination of the KOL's man attribute data.
3. The intelligent calculation method of KOL capacity attribute according to claim 1 or 2, characterized in that the method further comprises, before calculating the KOL capacity attribute from total commodity data of the target commodity in the target period and the contribution data of the video-in-charge to the target commodity in the target period:
And determining the data category of the contribution data, and collecting total commodity data of the target commodity, which is matched with the data category in the target time period, according to the data category, and taking the total commodity data of the target commodity in the target time period.
4. The intelligent calculation method for KOL capability attribute according to claim 3, wherein the calculating the ratio of the contribution data of the video-in-band to the target commodity in the target period to the total commodity data of the target commodity in the target period to obtain the contribution duty ratio of the video-in-band to the target commodity in the target period includes:
when the data category comprises at least two categories, for each data category, calculating the ratio of target contribution data of the target commodity to target total commodity data of the target commodity in the target time period by the video with goods in the target time period to obtain the contribution duty ratio of the data category, determining the weight value corresponding to the data category, and calculating the product of the contribution duty ratio of the data category and the weight value corresponding to the data category to obtain the product corresponding to the data category, wherein the target contribution data and the target total commodity data are matched with the data category;
And executing summation operation on products corresponding to all the data categories to obtain the contribution duty ratio of the video with goods to the target commodity in the target time period.
5. The intelligent computing method of KOL capacity attribute according to claim 1, 2 or 4, further comprising:
acquiring all commodities sold by the carried video in the carried video set of the KOL, and performing classification operation on all the acquired commodities according to commodity categories to obtain commodity sets of a plurality of different commodity categories;
and calculating the contribution duty ratio of the video with goods corresponding to all the goods in the goods set for the goods set of any goods category, and determining the contribution duty ratio of the KOL for the goods category according to the contribution duty ratio of the video with goods corresponding to all the goods in the goods set.
6. The intelligent computing method of KOL capacity attribute according to claim 5, wherein, for a commodity set of any commodity category, the contribution ratio of KOL to the commodity category is equal to the average or median of the contribution ratios of all the videos with commodities corresponding to all the commodities in the commodity set;
And, the method further comprises:
for any commodity category, calculating the contribution rank of each KOL in the KOL set according to the contribution duty ratio of each KOL in the KOL set for the commodity category, and calculating the capability attribute of each KOL under the commodity category according to the contribution rank of each KOL in the KOL set, wherein the KOL set comprises a plurality of KOL with commodity categories of commodities sold through the commodity video bands as the commodity categories.
7. An intelligent computing device for KOL capacity attributes, the device comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring contribution data of a certain video with a certain KOL to a target commodity sold by the video with the certain KOL in a certain target time period, the contribution data comprises one or more of sales contribution data, browsing contribution data and conversion contribution data of the video with the target commodity in the target time period, the target time period comprises a time period selected or input by a user, and/or a time period determined according to the release starting moment of the video with the certain KOL;
a first calculation module, configured to calculate a capability attribute of the KOL according to total commodity data of the target commodity in the target time period and the contribution data of the video-with-commodity to the target commodity in the target time period;
The KOL is any one of a plurality of KOL, the target commodity is any one of a plurality of commodities sold by the KOL through a commodity carrying video, and the commodity carrying video is any one of all commodity carrying videos issued by the KOL for selling the target commodity in a commodity carrying manner;
the first computing module includes:
the calculation sub-module is used for calculating the ratio of the contribution data of the video with goods to the target goods in the target time period to the total goods data of the target goods in the target time period to obtain the contribution duty ratio of the video with goods to the target goods in the target time period;
a determination submodule for determining the calculated contribution duty cycle as a capacity attribute of the KOL; or analyzing the contribution level corresponding to the contribution duty ratio, and determining the analyzed contribution level as the capacity attribute of the KOL.
8. An intelligent computing device for KOL capacity attributes, the device comprising:
a memory storing executable program code;
a processor coupled to the memory;
the processor invokes the executable program code stored in the memory to perform the intelligent computing method of the KOL capacity attribute of any one of claims 1-6.
9. A computer storage medium storing computer instructions for performing the intelligent computing method of the KOL capacity attribute according to any one of claims 1-6 when called.
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