CN118014661B - Advertisement putting management system based on big data - Google Patents
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Abstract
The invention belongs to the field of advertisement delivery, relates to a data analysis technology, and aims to solve the problem that an advertisement delivery management system in the prior art cannot conduct targeted advertisement delivery operation on users according to user tags, and particularly relates to an advertisement delivery management system based on big data, which comprises a delivery management platform, wherein the delivery management platform is in communication connection with a user management module, a delivery analysis module, a benefit analysis module and a storage module; the user management module is used for carrying out management analysis on the platform user; the delivery analysis module is used for carrying out advertisement delivery analysis on the platform user; the profit analysis module is used for analyzing the profit of advertisement delivery; according to the invention, the platform user can be managed and analyzed, the user activity parameter and the consumption parameter in the management period are analyzed and calculated to obtain the value coefficient, the put value of the user is fed back through the value coefficient, and the value label of the user is updated at the end time of each management period.
Description
Technical Field
The invention belongs to the field of advertisement delivery, relates to a data analysis technology, and particularly relates to an advertisement delivery management system based on big data.
Background
The advertisement delivery platforms can be classified according to different standards, and can be classified into search engines, social media, video platforms, portal websites and the like according to platform types; cost-effectiveness analysis and optimization of advertisement delivery requires comprehensive consideration of a plurality of factors and application of a plurality of methods; through data-driven decision making and practice, the effect and benefit of advertisement delivery can be continuously improved.
In the prior art, the advertisement delivery management system cannot customize user tags for consumption habits and operation habits of users, and further cannot perform targeted advertisement delivery operation on the users according to the user tags, so that advertisement delivery benefits cannot be improved.
The application provides a solution to the technical problem.
Disclosure of Invention
The invention aims to provide an advertisement putting management system based on big data, which is used for solving the problem that the advertisement putting management system in the prior art cannot carry out targeted advertisement putting operation on users according to user tags;
the technical problems to be solved by the invention are as follows: how to provide an advertisement delivery management system based on big data, which can perform targeted advertisement delivery operation on users according to user labels.
The aim of the invention can be achieved by the following technical scheme:
The advertisement delivery management system based on big data comprises a delivery management platform, wherein the delivery management platform is in communication connection with a user management module, a delivery analysis module, a benefit analysis module and a storage module;
The user management module is used for carrying out management analysis on the platform user: generating a management period, dividing the management period into a plurality of management periods, acquiring active data HY, consumption data XF and ordering data XD of a user in the management period at the end time of the management period, and obtaining a value coefficient JZ of the user in the management period by carrying out numerical calculation on the active data HY, the consumption data XF and the ordering data XD; marking a user as a put object or a pending object through a value coefficient JZ;
The delivery analysis module is used for carrying out advertisement delivery analysis on the platform user: at the beginning time of the management period, acquiring category data ZL, amount data JE and purchase data GM of the object to be put in the last management period; obtaining a random coefficient SJ of the object to be put through numerical calculation of the category data ZL, the amount data JE and the purchase data GM; marking the throwing label of the throwing object as concentrated throwing or random throwing through a random coefficient SJ; performing analysis on the delivery objects with the delivery tags being intensively delivered;
The profit analysis module is used for analyzing the profit of advertisement delivery.
As a preferred implementation mode of the invention, the active data HY is the total number of times the user logs in the platform in the management period, the consumption data XF is the total amount consumed by the user in the platform in the management period, and the ordering data XF is the total number of times the user orders in the platform in the management period.
As a preferred embodiment of the present invention, the specific process of marking a user as a put object or a pending object includes: the value threshold JZmin is obtained through the storage module, and the value coefficient JZ of the user in the management period is compared with the value threshold JZmin: if the value coefficient JZ is greater than or equal to a value threshold JZmin, judging that the user has a release value, marking the value label of the user in the next management period as release, and marking the corresponding user as a release object; if the value coefficient JZ is smaller than the value threshold JZmin, determining that the user does not have the release value, marking the value label of the user in the next management period as pending, and marking the corresponding user as a pending object.
As a preferred embodiment of the present invention, the category data ZL is a category number value of the commodity purchased by the delivery object in the last management period, and the acquiring process of the amount data JE includes: acquiring the amount spent by the object to be put in purchasing each commodity type in the last management period and marking the amount as the amount value of the commodity type, and performing variance calculation on the amount values of all commodity types of the object to be put in the last management period to obtain amount data JE; the acquisition process of the purchase data GM includes: and acquiring the number of times of the purchase of each commodity type by the object in the last management period, marking the number of times as the purchase value of the commodity type, and calculating variance of the purchase values of all commodity types of the object in the last management period to obtain the purchase data GM.
As a preferred embodiment of the present invention, the specific process of marking the delivery label of the delivery object as concentrated delivery or random delivery includes: the random threshold SJmax is obtained through the storage module, and the random coefficient SJ of the object to be put is compared with the random threshold SJmax: if the random coefficient SJ is larger than or equal to a random threshold SJmax, marking the release label of the release object as random release; and if the random coefficient SJ is smaller than the random threshold SJmax, marking the delivery label of the delivery object as concentrated delivery.
As a preferred embodiment of the present invention, the specific process of performing analysis on a delivery object whose delivery label is a concentrated delivery includes: purchase values of various commodity types purchased by the object in the last management period form a purchase set, the purchase value with the largest value in the purchase set is removed, variance calculation is carried out on the purchase set to obtain new purchase data GM, a purchase threshold GMmax is obtained through a storage module, and the purchase data GM is compared with the purchase threshold GMmax: if the purchase data GM is greater than or equal to the purchase threshold GMmax, rejecting the purchase value with the largest value in the purchase set again, and recalculating the new purchase data GM until the purchase data GM is smaller than the purchase threshold GMmax; if the purchase data GM is smaller than the purchase threshold GMmax, marking the commodity type corresponding to the removed purchase value as the execution type of the delivery object, and matching the advertisement corresponding to the execution type with the delivery object.
As a preferred embodiment of the invention, the specific process of analyzing the benefits of advertisement delivery by the benefits analysis module comprises the following steps: marking a put object at the beginning time of a management period as a basic object, marking a put object at the end time of the management period as an analysis object, marking coincident users in the analysis object and the basic object as conversion objects, marking the number of conversion objects as conversion values of the management period, marking the ratio of the conversion values to the number of the basic objects as conversion coefficients of the management period, acquiring a conversion threshold value through a storage module, and comparing the conversion coefficients with the conversion threshold value: if the conversion coefficient is smaller than the conversion threshold, judging that the advertising income in the management time period does not meet the requirement, and carrying out conversion optimization analysis on the management time period; if the conversion coefficient is greater than or equal to the conversion threshold value, judging that the advertising income in the management period meets the requirement; the specific process of carrying out conversion optimization analysis on the management time period comprises the following steps: marking the ratio of the number of the conversion objects which are put in the tag as concentrated to the total number of the conversion objects as concentrated expression values, acquiring a concentrated expression threshold value through a storage module, and comparing the concentrated expression values with the concentrated expression threshold value: if the centralized representation value is smaller than the centralized representation threshold value, generating a centralized delivery optimizing signal and sending the centralized delivery optimizing signal to a mobile phone terminal of a manager through a delivery management platform; and if the centralized representation value is greater than or equal to the centralized representation threshold value, generating a random delivery optimization signal and sending the random delivery optimization signal to a mobile phone terminal of a manager through a delivery management platform.
As a preferred embodiment of the invention, the working method of the advertisement delivery management system based on big data comprises the following steps:
Step one: performing management analysis on platform users: generating a management period, dividing the management period into a plurality of management periods, acquiring active data HY, consumption data XF and next data XD of a user in the management period at the end time of the management period, and calculating a numerical value to obtain a value coefficient JZ; marking a user as a put object or a pending object through a value coefficient JZ;
step two: carrying out advertisement delivery analysis on a platform user: at the beginning time of the management period, obtaining the type data ZL, the amount data JE and the purchase data GM of the object to be put in the last management period, performing numerical calculation to obtain a random coefficient SJ of the object to be put in, and marking a put-in label of the object to be put in through the random coefficient SJ;
Step three: and analyzing the benefits of the advertisement delivery, and performing conversion optimization analysis on the management time when the benefits of the advertisement delivery of the management time do not meet the requirements.
The invention has the following beneficial effects:
1. The platform user can be managed and analyzed through the user management module, the user activity parameters and the consumption parameters in the management time period are analyzed and calculated to obtain a value coefficient, the throwing value of the user is fed back through the value coefficient, the value label of the user is updated at the ending time of each management time period, and the advertising income is improved;
2. The advertisement delivery analysis module is used for analyzing the advertisement delivery of the platform user and analyzing the consumption data of the delivery object in the last management period to obtain a random coefficient, so that the consumption habit of the delivery object is fed back according to the random coefficient, targeted advertisement delivery is carried out according to the delivery label of the delivery object, and the conversion rate of the advertisement is improved;
3. the profit of advertisement delivery can be analyzed through the profit analysis module, the marking condition of the delivery object at the beginning and ending time of the management time interval is analyzed to obtain a conversion coefficient, the conversion efficiency of advertisement delivery in the management time interval is fed back through the conversion coefficient, the conversion optimization analysis is carried out when the conversion efficiency is abnormal, and the conversion rate of advertisement delivery in the subsequent management time interval is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a first embodiment of the present invention;
Fig. 2 is a flowchart of a method according to a second embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious 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.
Example 1
As shown in FIG. 1, the advertisement delivery management system based on big data comprises a delivery management platform, wherein the delivery management platform is in communication connection with a user management module, a delivery analysis module, a benefit analysis module and a storage module.
The user management module is used for carrying out management analysis on the platform user: generating a management period and dividing the management period into a plurality of management periods, and acquiring active data HY, consumption data XF and next data XD of a user in the management period at the end time of the management period, wherein the active data HY is the total number of times the user logs in a platform in the management period, the consumption data XF is the total amount of consumption of the user in the platform in the management period, the next data XF is the total number of times the user places in the platform in the management period, and the value coefficient JZ of the user in the management period is obtained through a formula JZ=α1XHY+α2XF+α3XF, wherein α1, α2 and α3 are proportionality coefficients, and α1 > α2 > α3 > 1; the value threshold JZmin is obtained through the storage module, and the value coefficient JZ of the user in the management period is compared with the value threshold JZmin: if the value coefficient JZ is greater than or equal to a value threshold JZmin, judging that the user has a release value, marking the value label of the user in the next management period as release, and marking the corresponding user as a release object; if the value coefficient JZ is smaller than the value threshold JZmin, judging that the user does not have the release value, marking the value label of the user in the next management period as pending, and marking the corresponding user as a pending object; and carrying out management analysis on the platform user, analyzing and calculating the user activity parameters and the consumption parameters in the management time period to obtain a value coefficient, feeding back the throwing value of the user through the value coefficient, updating the value label of the user at the ending time of each management time period, and improving the advertising income.
The delivery analysis module is used for carrying out advertisement delivery analysis on the platform user: at the beginning time of the management period, acquiring category data ZL, amount data JE and purchase data GM of the object in the last management period, wherein the category data ZL is the category number value of the commodity purchased by the object in the last management period, and the acquiring process of the amount data JE comprises the following steps: acquiring the amount spent by the object to be put in purchasing each commodity type in the last management period and marking the amount as the amount value of the commodity type, and performing variance calculation on the amount values of all commodity types of the object to be put in the last management period to obtain amount data JE; the acquisition process of the purchase data GM includes: acquiring the number of times of purchasing each commodity type by the delivery object in the last management period, marking the number of times as the purchase value of the commodity type, and performing variance calculation on the purchase values of all commodity types of the delivery object in the last management period to obtain purchase data GM; obtaining a random coefficient SJ of a delivery object through a formula SJ=β1 xZL- β2 xJE- β3 xGM, wherein β1, β2 and β3 are proportionality coefficients, and β1 > β2 > β3 > 1; the random threshold SJmax is obtained through the storage module, and the random coefficient SJ of the object to be put is compared with the random threshold SJmax: if the random coefficient SJ is larger than or equal to a random threshold SJmax, marking the release label of the release object as random release; if the random coefficient SJ is smaller than the random threshold SJmax, marking the delivery label of the delivery object as concentrated delivery; performing analysis on the delivery objects with the delivery tags being intensively delivered: purchase values of various commodity types purchased by the object in the last management period form a purchase set, the purchase value with the largest value in the purchase set is removed, variance calculation is carried out on the purchase set to obtain new purchase data GM, a purchase threshold GMmax is obtained through a storage module, and the purchase data GM is compared with the purchase threshold GMmax: if the purchase data GM is greater than or equal to the purchase threshold GMmax, rejecting the purchase value with the largest value in the purchase set again, and recalculating the new purchase data GM until the purchase data GM is smaller than the purchase threshold GMmax; if the purchase data GM is smaller than the purchase threshold GMmax, marking the commodity type corresponding to the removed purchase value as the execution type of the object to be put, and matching the advertisement corresponding to the execution type with the object to be put; and carrying out advertisement delivery analysis on the platform user, analyzing consumption data of the delivery object in the last management period to obtain a random coefficient, feeding back the consumption habit of the delivery object according to the random coefficient, carrying out targeted advertisement delivery according to the delivery label of the delivery object, and improving the conversion rate of advertisements.
The profit analysis module is used for analyzing the profit of advertisement delivery: marking a put object at the beginning time of a management period as a basic object, marking a put object at the end time of the management period as an analysis object, marking coincident users in the analysis object and the basic object as conversion objects, marking the number of conversion objects as conversion values of the management period, marking the ratio of the conversion values to the number of the basic objects as conversion coefficients of the management period, acquiring a conversion threshold value through a storage module, and comparing the conversion coefficients with the conversion threshold value: if the conversion coefficient is smaller than the conversion threshold, judging that the advertising income in the management time period does not meet the requirement, and carrying out conversion optimization analysis on the management time period; if the conversion coefficient is greater than or equal to the conversion threshold value, judging that the advertising income in the management period meets the requirement; the specific process of carrying out conversion optimization analysis on the management time period comprises the following steps: marking the ratio of the number of the conversion objects which are put in the tag as concentrated to the total number of the conversion objects as concentrated expression values, acquiring a concentrated expression threshold value through a storage module, and comparing the concentrated expression values with the concentrated expression threshold value: if the centralized representation value is smaller than the centralized representation threshold value, generating a centralized delivery optimizing signal and sending the centralized delivery optimizing signal to a mobile phone terminal of a manager through a delivery management platform; if the centralized representation value is greater than or equal to the centralized representation threshold value, generating a random delivery optimizing signal and sending the random delivery optimizing signal to a mobile phone terminal of a manager through a delivery management platform; and analyzing the profit of advertisement delivery, analyzing the marking condition of the delivery object at the beginning and ending time of the management time interval to obtain a conversion coefficient, feeding back the conversion efficiency of advertisement delivery in the management time interval through the conversion coefficient, and carrying out conversion optimization analysis when the conversion efficiency is abnormal to improve the conversion rate of advertisement delivery in the subsequent management time interval.
Example two
As shown in fig. 2, an advertisement delivery management method based on big data includes the following steps:
Step one: performing management analysis on platform users: generating a management period, dividing the management period into a plurality of management periods, acquiring active data HY, consumption data XF and next data XD of a user in the management period at the end time of the management period, and calculating a numerical value to obtain a value coefficient JZ; marking a user as a put object or a pending object through a value coefficient JZ;
step two: carrying out advertisement delivery analysis on a platform user: at the beginning time of the management period, obtaining the type data ZL, the amount data JE and the purchase data GM of the object to be put in the last management period, performing numerical calculation to obtain a random coefficient SJ of the object to be put in, and marking a put-in label of the object to be put in through the random coefficient SJ;
Step three: and analyzing the benefits of the advertisement delivery, and performing conversion optimization analysis on the management time when the benefits of the advertisement delivery of the management time do not meet the requirements.
The advertisement putting management system based on big data generates a management period and divides the management period into a plurality of management periods, acquires active data HY, consumption data XF and next data XD of a user in the management period at the end time of the management period, and calculates a value coefficient JZ by a numerical value; marking a user as a put object or a pending object through a value coefficient JZ; at the beginning time of the management period, obtaining the type data ZL, the amount data JE and the purchase data GM of the object to be put in the last management period, performing numerical calculation to obtain a random coefficient SJ of the object to be put in, and marking a put-in label of the object to be put in through the random coefficient SJ; and analyzing the benefits of the advertisement delivery, and performing conversion optimization analysis on the management time when the benefits of the advertisement delivery of the management time do not meet the requirements.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: the formula jz=α1×hy+α2×xf+α3×xd; collecting a plurality of groups of sample data by a person skilled in the art and setting a corresponding value coefficient for each group of sample data; substituting the set value coefficient and the acquired sample data into a formula, forming a ternary one-time equation set by any three formulas, screening the calculated coefficient and taking an average value to obtain values of alpha 1, alpha 2 and alpha 3 of 4.63, 2.52 and 2.17 respectively;
The size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding value coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected, for example, the value coefficient is proportional to the value of the active data.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (4)
1. The advertisement delivery management system based on big data is characterized by comprising a delivery management platform, wherein the delivery management platform is in communication connection with a user management module, a delivery analysis module, a benefit analysis module and a storage module;
The user management module is used for carrying out management analysis on the platform user: generating a management period, dividing the management period into a plurality of management periods, acquiring active data HY, consumption data XF and ordering data XD of a user in the management period at the end time of the management period, and obtaining a value coefficient JZ of the user in the management period by carrying out numerical calculation on the active data HY, the consumption data XF and the ordering data XD; marking a user as a put object or a pending object through a value coefficient JZ;
The delivery analysis module is used for carrying out advertisement delivery analysis on the platform user: at the beginning time of the management period, acquiring category data ZL, amount data JE and purchase data GM of the object to be put in the last management period; obtaining a random coefficient SJ of the object to be put through numerical calculation of the category data ZL, the amount data JE and the purchase data GM; marking the throwing label of the throwing object as concentrated throwing or random throwing through a random coefficient SJ; performing analysis on the delivery objects with the delivery tags being intensively delivered;
The profit analysis module is used for analyzing the profit of advertisement delivery;
The specific process of marking the user as a put object or a pending object comprises the following steps: the value threshold JZmin is obtained through the storage module, and the value coefficient JZ of the user in the management period is compared with the value threshold JZmin: if the value coefficient JZ is greater than or equal to a value threshold JZmin, judging that the user has a release value, marking the value label of the user in the next management period as release, and marking the corresponding user as a release object; if the value coefficient JZ is smaller than the value threshold JZmin, judging that the user does not have the release value, marking the value label of the user in the next management period as pending, and marking the corresponding user as a pending object;
The category data ZL is the category number value of the commodity purchased by the delivery object in the last management period, and the acquiring process of the amount data JE comprises the following steps: acquiring the amount spent by the object to be put in purchasing each commodity type in the last management period and marking the amount as the amount value of the commodity type, and performing variance calculation on the amount values of all commodity types of the object to be put in the last management period to obtain amount data JE; the acquisition process of the purchase data GM includes: acquiring the number of times of purchasing each commodity type by the delivery object in the last management period, marking the number of times as the purchase value of the commodity type, and performing variance calculation on the purchase values of all commodity types of the delivery object in the last management period to obtain purchase data GM;
The specific process for marking the delivery label of the delivery object as concentrated delivery or random delivery comprises the following steps: the random threshold SJmax is obtained through the storage module, and the random coefficient SJ of the object to be put is compared with the random threshold SJmax: if the random coefficient SJ is larger than or equal to a random threshold SJmax, marking the release label of the release object as random release; if the random coefficient SJ is smaller than the random threshold SJmax, marking the delivery label of the delivery object as concentrated delivery;
The specific process for performing analysis on the delivery objects with the delivery tags being intensively delivered comprises the following steps: purchase values of various commodity types purchased by the object in the last management period form a purchase set, the purchase value with the largest value in the purchase set is removed, variance calculation is carried out on the purchase set to obtain new purchase data GM, a purchase threshold GMmax is obtained through a storage module, and the purchase data GM is compared with the purchase threshold GMmax: if the purchase data GM is greater than or equal to the purchase threshold GMmax, rejecting the purchase value with the largest value in the purchase set again, and recalculating the new purchase data GM until the purchase data GM is smaller than the purchase threshold GMmax; if the purchase data GM is smaller than the purchase threshold GMmax, marking the commodity type corresponding to the removed purchase value as the execution type of the delivery object, and matching the advertisement corresponding to the execution type with the delivery object.
2. The big data based advertisement putting management system according to claim 1, wherein the active data HY is total number of times the user logs in the platform in the management period, the consumption data XF is total amount consumed by the user in the platform in the management period, and the ordering data XF is total number of times the user orders in the platform in the management period.
3. The big data based advertising management system of claim 2, wherein the specific process of analyzing the revenue of the advertising by the revenue analysis module comprises: marking a put object at the beginning time of a management period as a basic object, marking a put object at the end time of the management period as an analysis object, marking coincident users in the analysis object and the basic object as conversion objects, marking the number of conversion objects as conversion values of the management period, marking the ratio of the conversion values to the number of the basic objects as conversion coefficients of the management period, acquiring a conversion threshold value through a storage module, and comparing the conversion coefficients with the conversion threshold value: if the conversion coefficient is smaller than the conversion threshold, judging that the advertising income in the management time period does not meet the requirement, and carrying out conversion optimization analysis on the management time period; if the conversion coefficient is greater than or equal to the conversion threshold value, judging that the advertising income in the management period meets the requirement; the specific process of carrying out conversion optimization analysis on the management time period comprises the following steps: marking the ratio of the number of the conversion objects which are put in the tag as concentrated to the total number of the conversion objects as concentrated expression values, acquiring a concentrated expression threshold value through a storage module, and comparing the concentrated expression values with the concentrated expression threshold value: if the centralized representation value is smaller than the centralized representation threshold value, generating a centralized delivery optimizing signal and sending the centralized delivery optimizing signal to a mobile phone terminal of a manager through a delivery management platform; and if the centralized representation value is greater than or equal to the centralized representation threshold value, generating a random delivery optimization signal and sending the random delivery optimization signal to a mobile phone terminal of a manager through a delivery management platform.
4. A big data based advertising management system according to any of claims 1-3, characterized in that the working method of the big data based advertising management system comprises the following steps:
Step one: performing management analysis on platform users: generating a management period, dividing the management period into a plurality of management periods, acquiring active data HY, consumption data XF and next data XD of a user in the management period at the end time of the management period, and calculating a numerical value to obtain a value coefficient JZ; marking a user as a put object or a pending object through a value coefficient JZ;
step two: carrying out advertisement delivery analysis on a platform user: at the beginning time of the management period, obtaining the type data ZL, the amount data JE and the purchase data GM of the object to be put in the last management period, performing numerical calculation to obtain a random coefficient SJ of the object to be put in, and marking a put-in label of the object to be put in through the random coefficient SJ;
Step three: and analyzing the benefits of the advertisement delivery, and performing conversion optimization analysis on the management time when the benefits of the advertisement delivery of the management time do not meet the requirements.
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