CN116051191A - New media advertisement putting recommendation system based on data analysis - Google Patents
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Abstract
The invention belongs to the field of advertisement delivery, relates to a data analysis technology, and is used for solving the problem that targeted delivery recommendation cannot be carried out by combining various customer attributes of commodities to be sold by a user, in particular to a new media advertisement delivery recommendation system based on data analysis, which comprises a delivery recommendation platform, wherein the delivery recommendation platform is in communication connection with a consumption analysis module, a delivery analysis module, a conversion monitoring module and a storage module, and the consumption analysis module is used for carrying out consumption attribute analysis on the commodities delivered by the advertisement: the method comprises the steps that purchasing data of the advertised commodity in L1 month is called through a storage module, the purchasing data comprise ages and sexes of purchasing users, and an age range is formed by age maximum values and age minimum values of the purchasing users; according to the invention, the consumption attribute analysis is carried out on the commodity put in the advertisement, then different recommendation modes are adopted for recommending the advertisement put in different commodities according to the classification, and the conversion rate of the advertisement put in is improved.
Description
Technical Field
The invention belongs to the field of advertisement delivery, relates to a data analysis technology, and particularly relates to a new media advertisement delivery recommendation system based on data analysis.
Background
Advertisement marketing refers to the process that enterprises develop and promote products through advertisements, direct purchase of consumers is facilitated, sales of the products are enlarged, knowledge, reputation and influence activities of the enterprises are improved, along with globalization of economy and rapid development of market economy, the advertisement marketing activities play an increasingly important role in the enterprise marketing strategy, and are an important component part in the enterprise marketing combination;
in the prior art, a new media advertisement putting recommendation system can only conduct advertisement putting recommendation analysis on users through a single recommendation mode, and can not conduct targeted putting recommendation by combining various client attributes of commodities to be sold by the users, so that advertisement putting conversion rate is low;
aiming at the technical problems, the application provides a solution.
Disclosure of Invention
The invention aims to provide a new media advertisement putting recommendation system based on data analysis, which is used for solving the problem that putting recommendation can not be performed in a targeted manner by combining various client attributes of commodities to be sold by a user.
The technical problems to be solved by the invention are as follows: how to provide a new media advertisement putting recommendation system which can be used for carrying out targeted putting recommendation by combining various client attributes of commodities to be sold by users.
The aim of the invention can be achieved by the following technical scheme:
the new media advertisement putting recommendation system based on data analysis comprises a putting recommendation platform, wherein the putting recommendation platform is in communication connection with a consumption analysis module, a putting analysis module, a conversion monitoring module and a storage module;
the consumption analysis module is used for carrying out consumption attribute analysis on the commodity put in the advertisement: the method comprises the steps of calling purchase data of an advertisement commodity in L1 month through a storage module, wherein the purchase data comprise the age and sex of a purchase user, forming an age range by the age maximum value and the age minimum value of the purchase user, dividing the age range into a plurality of age intervals, obtaining the number of the purchase users with the ages in the age intervals and marking the number as a purchase value of the age interval, marking the L1 age intervals with the maximum purchase value as a protruding interval, establishing a purchase set of the purchase value of the protruding interval, calculating variance of the purchase set to obtain a purchase coefficient, obtaining a purchase threshold through the storage module, comparing the purchase coefficient of the purchase set with the purchase threshold, and marking the purchase characteristic, the sex characteristic and the age recommended range of the commodity through comparison results;
the delivery analysis module is used for carrying out delivery platform recommendation analysis on commodities: if the purchasing characteristics of the commodities are concentrated, adopting a screening mode to carry out throwing analysis and obtaining a recommended object; if the purchasing characteristics of the commodity are scattered, carrying out release analysis by adopting a conversion mode and obtaining a recommended object;
the recommendation object is sent to a release recommendation platform, and the release recommendation platform sends the recommendation object to a mobile phone terminal of a user after receiving the recommendation object;
the conversion monitoring module is used for monitoring and analyzing the advertisement putting conversion state of the user.
As a preferred embodiment of the invention, the specific process of comparing the purchase coefficients of the purchase set with the purchase threshold comprises: if the purchase coefficient is smaller than the purchase threshold, judging that the user purchasing power of the salient region has similarity, carrying out purchase coefficient calculation on the L1+1 age regions with the largest purchase power value, comparing with the purchase threshold again, and so on until the purchase coefficient is not smaller than the purchase threshold, judging that the user purchasing power of the salient region has no similarity, forming an age recommended range of the commodity by the minimum boundary value and the maximum boundary value of the salient region with the last purchase coefficient calculation, and marking the purchasing characteristics of the commodity as concentrated; if the purchase coefficient is greater than or equal to the purchase threshold, judging that the user purchasing power of the salient region does not have similarity, forming an age recommendation range of the commodity by the age region with the largest purchase value, and marking the purchase characteristics of the commodity as scattered; the gender of the purchasing user within the age recommendation range is counted: if the number of purchasing users with the gender being male occupies half of the total number of purchasing users, marking the gender characteristic of the commodity as male; otherwise, marking the sex characteristic of the commodity as female; and sending the purchase characteristics, the gender characteristics and the age recommendation range of the commodity to a delivery analysis module through a delivery recommendation platform.
As a preferred embodiment of the invention, the specific process of carrying out the delivery analysis by adopting the screening mode comprises the following steps: marking software with an advertisement putting function as an putting object, screening registered users of the putting object according to the sex characteristics and age recommendation range of commodities to obtain screened users of the putting object, and obtaining using data SY, online data SX and active data HY of the screened users of the putting object; screening the use data SY of the user as the total duration of screening the use of the object in the last L2 months; screening the online data SX of the user as the total number of times of the user logging in the object in the last L2 months; screening the active data HY of the user as the total number of active operation performed by the user in the released object in the last L2 months, wherein the active operation comprises the following steps: adding shopping carts, adding collection, praying and forwarding; obtaining a recommendation coefficient TJ of the throwing object by carrying out numerical calculation on the usage data SY, the online data SX and the active data HY; and marking the put object with the largest recommendation coefficient TJ value as a recommendation object.
As a preferred embodiment of the invention, the specific process of carrying out the delivery analysis by adopting the conversion mode comprises the following steps: marking software with an advertisement putting function as a putting object, and acquiring click data DJ, price data JG and user data YH of the putting object, wherein the click data DJ of the putting object is the total number of times that the advertisement of the putting object is clicked in the last L1 months, the price data JG of the putting object is the advertisement putting unit price value of the putting object, and the user data YH of the putting object is the total number of registered users of the putting object; obtaining a value coefficient JZ of the object to be put through numerical calculation of click data DJ, price data JG and user data YH; and marking the object with the largest value coefficient JZ as a recommended object.
As a preferred implementation mode of the invention, the specific process of monitoring and analyzing the advertisement putting conversion state of the user by the conversion monitoring module comprises the following steps: the method comprises the steps that after advertisement putting is completed by a user, timing is conducted, when the time for completing advertisement putting reaches D1 days, the clicking rate DL of advertisement putting of commodities and the conversion rate ZL are obtained, wherein the clicking rate DL is the ratio of the clicked times of the commodity advertisements in the putting software in the last D1 days to the total display time, the conversion rate ZL is the ratio of the times of obtaining conversion marks in the putting software of the commodity advertisements in the last D1 days to the total advertisement putting cost, and the conversion marks comprise the received registration success page, the purchased success page and the downloaded success page; the conversion coefficient ZH of commercial advertisement delivery is obtained through numerical calculation of the click rate DL and the conversion rate ZL, a conversion threshold ZHmin is obtained through a storage module, the conversion coefficient ZH is compared with the conversion threshold ZHmin, and whether the advertisement delivery conversion state of a user meets the requirement or not is judged through a comparison result.
As a preferred embodiment of the present invention, the specific process of comparing the conversion factor ZH with the conversion threshold ZHmin comprises: if the conversion coefficient ZH is smaller than the conversion threshold ZHmin, judging that the advertisement putting conversion state of the user does not meet the requirement, and sending a re-recommendation signal to the putting recommendation platform by the conversion monitoring module, wherein the putting recommendation platform sends the re-recommendation signal to a mobile phone terminal of the user after receiving the re-recommendation signal; if the conversion coefficient ZH is greater than or equal to the conversion threshold ZHmin, judging that the advertisement putting conversion state of the user meets the requirement, and sending a conversion qualified signal to the putting recommendation platform by the conversion monitoring module, and sending the conversion qualified signal to a mobile phone terminal of the user after the putting recommendation platform receives the conversion qualified signal.
The working method of the new media advertisement putting recommendation system based on data analysis comprises the following steps:
step one: carrying out consumption attribute analysis on the commodity put in the advertisement and obtaining the purchase characteristics, sex characteristics and age recommendation range of the commodity;
step two: selecting a screening mode or a conversion mode through the purchase characteristics of the commodity to carry out release analysis and marking a recommended object;
step three: monitoring and analyzing advertisement putting conversion states of users: and (3) after completing the advertisement putting, the user counts time, calculates the conversion coefficient of the commodity in the putting software when the time for completing the advertisement putting reaches D1 day, and judges whether the conversion state of the advertisement putting meets the requirement or not according to the numerical value of the conversion coefficient.
The invention has the following beneficial effects:
1. the consumption analysis module can analyze the consumption attribute of the commodity put in the advertisement, and mark the purchasing characteristics of the commodity by the age layer and the dispersion degree of the target user of the commodity, so that the commodity is classified according to the purchasing characteristics, and then different recommendation modes are adopted for recommending the advertisement put in different commodities according to the classification, so that the conversion rate of the advertisement put in is improved;
2. the goods can be subjected to recommendation analysis of a delivery platform through a delivery analysis module, the goods with different purchasing characteristics are recommended in a delivery mode by adopting two different delivery analysis modes, the goods with concentrated purchasing user age levels are screened from the audience surface of delivery software by adopting a screening mode, and the goods with the purchasing user age levels and scattered goods are screened from the integral conversion condition of the delivery software by adopting a conversion mode, so that the goods of all types can be matched with proper delivery software;
3. the conversion monitoring module can monitor and analyze the advertisement putting conversion state of the user and obtain a conversion coefficient, and the conversion coefficient is used for feeding back the advertisement conversion rate of the commodity, so that the suitability of putting software and the commodity is monitored through a feedback result, and the putting scheme is replaced in time when the suitability does not meet the requirement.
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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 new media advertisement delivery recommendation system based on data analysis comprises a delivery recommendation platform, wherein the delivery recommendation platform is in communication connection with a consumption analysis module, a delivery analysis module, a conversion monitoring module and a storage module.
The consumption analysis module is used for carrying out consumption attribute analysis on the commodity put in the advertisement: the method comprises the steps of calling purchase data of an advertisement commodity in L1 month through a storage module, wherein the purchase data comprise ages and sexes of purchase users, forming an age range by age maximum values and age minimum values of the purchase users, dividing the age range into a plurality of age intervals, obtaining the number of the purchase users with ages in the age intervals and marking the number as a purchase value of the age interval, marking the L1 age intervals with the maximum purchase value as a protruding interval, establishing a purchase set by the purchase value of the protruding interval, calculating variance of the purchase set to obtain a purchase coefficient, obtaining a purchase threshold by the storage module, and comparing the purchase coefficient of the purchase set with the purchase threshold: if the purchase coefficient is smaller than the purchase threshold, judging that the user purchasing power of the salient region has similarity, carrying out purchase coefficient calculation on the L1+1 age regions with the largest purchase power value, comparing with the purchase threshold again, and so on until the purchase coefficient is not smaller than the purchase threshold, judging that the user purchasing power of the salient region has no similarity, forming an age recommended range of the commodity by the minimum boundary value and the maximum boundary value of the salient region with the last purchase coefficient calculation, and marking the purchasing characteristics of the commodity as concentrated; if the purchase coefficient is greater than or equal to the purchase threshold, judging that the user purchasing power of the salient region does not have similarity, forming an age recommendation range of the commodity by the age region with the largest purchase value, and marking the purchase characteristics of the commodity as scattered; the gender of the purchasing user within the age recommendation range is counted: if the number of purchasing users with the gender being male occupies half of the total number of purchasing users, marking the gender characteristic of the commodity as male; otherwise, marking the sex characteristic of the commodity as female; the purchasing characteristics, sex characteristics and age recommendation range of the commodity are sent to a delivery analysis module through a delivery recommendation platform; and (3) carrying out consumption attribute analysis on the commodity subjected to advertisement delivery, and marking purchasing characteristics of the commodity by means of age and dispersion degree of target users of the commodity, so that the commodity is classified according to the purchasing characteristics, and then different commodity is recommended to carry out advertisement delivery in different recommendation modes according to the classification, so that the conversion rate of advertisement delivery is improved.
The delivery analysis module is used for carrying out delivery platform recommendation analysis on commodities: if the purchasing characteristics of the commodities are concentrated, adopting a screening mode to carry out release analysis: marking software with an advertisement putting function as an putting object, screening registered users of the putting object according to the sex characteristics and age recommendation range of commodities to obtain screened users of the putting object, and obtaining using data SY, online data SX and active data HY of the screened users of the putting object; screening the use data SY of the user as the total duration of screening the use of the object in the last L2 months; screening the online data SX of the user as the total number of times of the user logging in the object in the last L2 months; screening the active data HY of the user as the total number of active operation performed by the user in the released object in the last L2 months, wherein the active operation comprises the following steps: adding shopping carts, adding collection, praying and forwarding; obtaining a recommended coefficient TJ of a delivery object through a formula TJ=α1×SY+α2×SX+α3×HY, wherein α1, α2 and α3 are proportionality coefficients, and α1 > α2 > α3 > 1; marking a put object with the maximum recommendation coefficient TJ value as a recommendation object; if the purchase characteristics of the commodity are scattered, adopting a conversion mode to carry out release analysis: marking software with an advertisement putting function as a putting object, and acquiring click data DJ, price data JG and user data YH of the putting object, wherein the click data DJ of the putting object is the total number of times that the advertisement of the putting object is clicked in the last L1 months, the price data JG of the putting object is the advertisement putting unit price value of the putting object, and the user data YH of the putting object is the total number of registered users of the putting object; obtaining a value coefficient JZ of a delivery object through a formula JZ= (beta 1 x DJ+beta 2 x YH)/(beta 3 x JG), wherein beta 1, beta 2 and beta 3 are all proportional coefficients, and beta 3 is more than beta 2 is more than beta 1 is more than 1; marking a put object with the largest value coefficient JZ value as a recommended object; the recommendation object is sent to a release recommendation platform, and the release recommendation platform sends the recommendation object to a mobile phone terminal of a user after receiving the recommendation object; the method comprises the steps of carrying out recommendation analysis on commodities on a delivery platform, carrying out delivery recommendation on commodities with different purchasing characteristics by adopting two different delivery analysis modes, screening from an audience surface of delivery software by adopting a screening mode aiming at the commodities with concentrated purchasing user age levels, and screening from the whole conversion condition of the delivery software by adopting a conversion mode aiming at the commodities with the purchasing user age levels and scattered, so that the commodities of all types can be matched with proper delivery software.
The conversion monitoring module is used for monitoring and analyzing the advertisement putting conversion state of the user: the method comprises the steps that after advertisement putting is completed by a user, timing is conducted, when the time for completing advertisement putting reaches D1 days, the clicking rate DL of advertisement putting of commodities and the conversion rate ZL are obtained, wherein the clicking rate DL is the ratio of the clicked times of the commodity advertisements in the putting software in the last D1 days to the total display time, the conversion rate ZL is the ratio of the times of obtaining conversion marks in the putting software of the commodity advertisements in the last D1 days to the total advertisement putting cost, and the conversion marks comprise the received registration success page, the purchased success page and the downloaded success page; obtaining a conversion coefficient ZH of commercial advertisement delivery through a formula ZH=γ1XDL+γ2ZL, wherein γ1 and γ2 are both proportional coefficients, γ1 > γ2 > 1, obtaining a conversion threshold value ZHmin through a storage module, and comparing the conversion coefficient ZH with the conversion threshold value ZHmin: if the conversion coefficient ZH is smaller than the conversion threshold ZHmin, judging that the advertisement putting conversion state of the user does not meet the requirement, and sending a re-recommendation signal to the putting recommendation platform by the conversion monitoring module, wherein the putting recommendation platform sends the re-recommendation signal to a mobile phone terminal of the user after receiving the re-recommendation signal; if the conversion coefficient ZH is greater than or equal to a conversion threshold ZHmin, judging that the advertisement putting conversion state of the user meets the requirement, and sending a conversion qualified signal to a putting recommendation platform by a conversion monitoring module, wherein the putting recommendation platform sends the conversion qualified signal to a mobile phone terminal of the user after receiving the conversion qualified signal; monitoring and analyzing the advertisement putting conversion state of the user, obtaining a conversion coefficient, and feeding back the advertisement conversion rate of the commodity through the conversion coefficient, so that the suitability of putting software and the commodity is monitored through a feedback result, and when the suitability does not meet the requirement, the putting scheme is replaced in time.
Example two
As shown in fig. 2, a new media advertisement delivery recommendation method based on data analysis includes the following steps:
step one: carrying out consumption attribute analysis on the advertised commodity to obtain purchasing characteristics, sex characteristics and age recommendation range of the commodity, carrying out advertisement delivery recommendation on different commodities by adopting different recommendation modes, and improving the conversion rate of advertisement delivery;
step two: selecting a screening mode or a conversion mode through the purchase characteristics of the commodities to carry out release analysis and marking a recommended object, so that each type of commodity can be matched with proper release software;
step three: monitoring and analyzing advertisement putting conversion states of users: and (3) after finishing advertisement putting, the user counts time, calculates the conversion coefficient of the commodity in the putting software when the time for finishing advertisement putting reaches D1 day, judges whether the conversion state of advertisement putting meets the requirement or not according to the numerical value of the conversion coefficient, monitors the suitability of the putting software and the commodity, and timely changes the putting scheme when the suitability does not meet the requirement.
The new media advertisement putting recommendation system based on data analysis is characterized in that in operation, consumption attribute analysis is carried out on the commodities put by advertisements, and purchasing characteristics, sex characteristics and age recommendation range of the commodities are obtained; selecting a screening mode or a conversion mode through the purchase characteristics of the commodity to carry out release analysis and marking a recommended object; monitoring and analyzing advertisement putting conversion states of users: and (3) after completing the advertisement putting, the user counts time, calculates the conversion coefficient of the commodity in the putting software when the time for completing the advertisement putting reaches D1 day, and judges whether the conversion state of the advertisement putting meets the requirement or not according to the numerical value of the conversion coefficient.
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: formula tj=α1×sy+α2×sx+α3×hy; collecting a plurality of groups of sample data by a person skilled in the art and setting a corresponding recommendation coefficient for each group of sample data; substituting the set recommended 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 4.48, 3.69 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 recommended 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 recommended coefficient is proportional to the value of the usage 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 (7)
1. The new media advertisement putting recommendation system based on data analysis is characterized by comprising a putting recommendation platform, wherein the putting recommendation platform is in communication connection with a consumption analysis module, a putting analysis module, a conversion monitoring module and a storage module;
the consumption analysis module is used for carrying out consumption attribute analysis on the commodity put in the advertisement: the method comprises the steps of calling purchase data of an advertisement commodity in L1 month through a storage module, wherein the purchase data comprise the age and sex of a purchase user, forming an age range by the age maximum value and the age minimum value of the purchase user, dividing the age range into a plurality of age intervals, obtaining the number of the purchase users with the ages in the age intervals and marking the number as a purchase value of the age interval, marking the L1 age intervals with the maximum purchase value as a protruding interval, establishing a purchase set of the purchase value of the protruding interval, calculating variance of the purchase set to obtain a purchase coefficient, obtaining a purchase threshold through the storage module, comparing the purchase coefficient of the purchase set with the purchase threshold, and marking the purchase characteristic, the sex characteristic and the age recommended range of the commodity through comparison results;
the delivery analysis module is used for carrying out delivery platform recommendation analysis on commodities: if the purchasing characteristics of the commodities are concentrated, adopting a screening mode to carry out throwing analysis and obtaining a recommended object; if the purchasing characteristics of the commodity are scattered, carrying out release analysis by adopting a conversion mode and obtaining a recommended object;
the recommendation object is sent to a release recommendation platform, and the release recommendation platform sends the recommendation object to a mobile phone terminal of a user after receiving the recommendation object;
the conversion monitoring module is used for monitoring and analyzing the advertisement putting conversion state of the user.
2. The new media advertising recommendation system based on data analysis of claim 1, wherein comparing purchase coefficients of a purchase set to purchase thresholds comprises: if the purchase coefficient is smaller than the purchase threshold, judging that the user purchasing power of the salient region has similarity, carrying out purchase coefficient calculation on the L1+1 age regions with the largest purchase power value, comparing with the purchase threshold again, and so on until the purchase coefficient is not smaller than the purchase threshold, judging that the user purchasing power of the salient region has no similarity, forming an age recommended range of the commodity by the minimum boundary value and the maximum boundary value of the salient region with the last purchase coefficient calculation, and marking the purchasing characteristics of the commodity as concentrated; if the purchase coefficient is greater than or equal to the purchase threshold, judging that the user purchasing power of the salient region does not have similarity, forming an age recommendation range of the commodity by the age region with the largest purchase value, and marking the purchase characteristics of the commodity as scattered; the gender of the purchasing user within the age recommendation range is counted: if the number of purchasing users with the gender being male occupies half of the total number of purchasing users, marking the gender characteristic of the commodity as male; otherwise, marking the sex characteristic of the commodity as female; and sending the purchase characteristics, the gender characteristics and the age recommendation range of the commodity to a delivery analysis module through a delivery recommendation platform.
3. The new media advertisement delivery recommendation system based on data analysis of claim 2, wherein the specific process of delivering analysis using a screening mode comprises: marking software with an advertisement putting function as an putting object, screening registered users of the putting object according to the sex characteristics and age recommendation range of commodities to obtain screened users of the putting object, and obtaining using data SY, online data SX and active data HY of the screened users of the putting object; screening the use data SY of the user as the total duration of screening the use of the object in the last L2 months; screening the online data SX of the user as the total number of times of the user logging in the object in the last L2 months; screening the active data HY of the user as the total number of active operation performed by the user in the released object in the last L2 months, wherein the active operation comprises the following steps: adding shopping carts, adding collection, praying and forwarding; obtaining a recommendation coefficient TJ of the throwing object by carrying out numerical calculation on the usage data SY, the online data SX and the active data HY; and marking the put object with the largest recommendation coefficient TJ value as a recommendation object.
4. The new media advertisement delivery recommendation system based on data analysis of claim 3, wherein the specific process of performing delivery analysis using conversion mode comprises: marking software with an advertisement putting function as a putting object, and acquiring click data DJ, price data JG and user data YH of the putting object, wherein the click data DJ of the putting object is the total number of times that the advertisement of the putting object is clicked in the last L1 months, the price data JG of the putting object is the advertisement putting unit price value of the putting object, and the user data YH of the putting object is the total number of registered users of the putting object; obtaining a value coefficient JZ of the object to be put through numerical calculation of click data DJ, price data JG and user data YH; and marking the object with the largest value coefficient JZ as a recommended object.
5. The new media advertisement delivery recommendation system based on data analysis of claim 4, wherein the specific process of monitoring and analyzing the advertisement delivery conversion status of the user by the conversion monitoring module comprises: the method comprises the steps that after advertisement putting is completed by a user, timing is conducted, when the time for completing advertisement putting reaches D1 days, the clicking rate DL of advertisement putting of commodities and the conversion rate ZL are obtained, wherein the clicking rate DL is the ratio of the clicked times of the commodity advertisements in the putting software in the last D1 days to the total display time, the conversion rate ZL is the ratio of the times of obtaining conversion marks in the putting software of the commodity advertisements in the last D1 days to the total advertisement putting cost, and the conversion marks comprise the received registration success page, the purchased success page and the downloaded success page; the conversion coefficient ZH of commercial advertisement delivery is obtained through numerical calculation of the click rate DL and the conversion rate ZL, a conversion threshold ZHmin is obtained through a storage module, the conversion coefficient ZH is compared with the conversion threshold ZHmin, and whether the advertisement delivery conversion state of a user meets the requirement or not is judged through a comparison result.
6. The new media advertising recommendation system based on data analysis of claim 5, wherein the specific process of comparing the conversion factor ZH with the conversion threshold ZHmin comprises: if the conversion coefficient ZH is smaller than the conversion threshold ZHmin, judging that the advertisement putting conversion state of the user does not meet the requirement, and sending a re-recommendation signal to the putting recommendation platform by the conversion monitoring module, wherein the putting recommendation platform sends the re-recommendation signal to a mobile phone terminal of the user after receiving the re-recommendation signal; if the conversion coefficient ZH is greater than or equal to the conversion threshold ZHmin, judging that the advertisement putting conversion state of the user meets the requirement, and sending a conversion qualified signal to the putting recommendation platform by the conversion monitoring module, and sending the conversion qualified signal to a mobile phone terminal of the user after the putting recommendation platform receives the conversion qualified signal.
7. A new media advertisement delivery recommendation system based on data analysis according to any of claims 1-6, wherein the working method of the new media advertisement delivery recommendation system based on data analysis comprises the steps of:
step one: carrying out consumption attribute analysis on the commodity put in the advertisement and obtaining the purchase characteristics, sex characteristics and age recommendation range of the commodity;
step two: selecting a screening mode or a conversion mode through the purchase characteristics of the commodity to carry out release analysis and marking a recommended object;
step three: monitoring and analyzing advertisement putting conversion states of users: and (3) after completing the advertisement putting, the user counts time, calculates the conversion coefficient of the commodity in the putting software when the time for completing the advertisement putting reaches D1 day, and judges whether the conversion state of the advertisement putting meets the requirement or not according to the numerical value of the conversion coefficient.
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