CN115408586A - Intelligent channel operation data analysis method, system, equipment and storage medium - Google Patents
Intelligent channel operation data analysis method, system, equipment and storage medium Download PDFInfo
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Abstract
The utility model relates to an intelligence channel operation data analysis method, system, equipment and storage medium, its method is including the channel operation data of real-time acquisition full life cycle, right channel operation data carries out data cleaning and handles, obtains effective user data, right effective user data carries out user portrait and handles, obtains user portrait data, according to user portrait data, the curve of reserving about the channel user reserves is found to user portrait data, obtains user's curve graph of reserving, discerns channel abnormal data on the user's curve of reserving, and right channel abnormal data carries out data tagging, obtains channel mark data, according to user reserves curve and calculates channel user conversion rate to according to the calculation result generation is used for throwing in the tactics optimization instruction that the strategy was optimized to the channel mark data, so that pass through tactics optimization instruction optimizes channel mark data. The method and the device have the effect of improving the accuracy of the channel operation data analysis result.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to an intelligent channel operation data analysis method, system, equipment and storage medium.
Background
At present, with the rapid development of the big data era, the delivery channels of enterprises are also rapidly iterated, and common delivery channels such as information flow advertisements, media promotion, search engines and the like, no matter the delivery channels are paid channels or free channels, the enterprises need to pay certain operation cost, so that the complex and various promotion channels also put higher requirements on channel operation, and particularly in the field of e-commerce, the selection of the promotion channels of e-commerce products often determines the sales volume of the e-commerce products.
The existing channel operation data analysis mode is generally to pull all channel operation data of channels through a third-party statistical tool to perform data analysis, so as to judge whether the channels are high-quality channels according to analysis results and perform corresponding advertisement putting.
For the related technologies, the inventor thinks that there is a defect that false channel traffic affects the accuracy of the analysis result of the channel operation data.
Disclosure of Invention
In order to improve the accuracy of the channel operation data analysis result, the application provides an intelligent channel operation data analysis method, system, equipment and storage medium.
The above object of the present invention is achieved by the following technical solutions:
an intelligent channel operation data analysis method is provided, and comprises the following steps:
acquiring channel operation data of a full life cycle in real time, and performing data cleaning processing on the channel operation data to obtain effective user data;
carrying out user portrait processing on the effective user data to obtain user portrait data;
constructing a retention curve retained by a channel user according to the user portrait data to obtain a user retention curve graph;
identifying channel abnormal data on the user retention curve, and performing data marking on the channel abnormal data to obtain channel marking data;
and calculating the channel user conversion rate according to the user retention curve, and generating a strategy optimization instruction for optimizing a channel delivery strategy according to a calculation result so as to optimize the channel mark data through the strategy optimization instruction.
By adopting the technical scheme, channel flow cheating is generally uniformly carried out through a timing simulation starting behavior and a fixed browsing behavior, and random browsing behavior and click conversion behavior of real users are difficult to generate, so that invalid operation data with fixed user behaviors and no real conversion rate are favorably cleared by carrying out data cleaning processing on channel operation data in a whole life cycle, effective user data are obtained, user portrayal is favorably realized through the effective user data, the error influence of false channel flow on the user portrayal is favorably reduced, channel abnormal data such as data inflection points and breakpoints are favorably marked through a user retention curve graph, the channel abnormal data are favorably and pertinently optimized, multi-dimensional optimization on a channel delivery strategy is favorably carried out according to the user retention condition and the channel user conversion rate through calculation of the channel user conversion rate, the optimized channel delivery strategy is more suitable for the real channel flow, and the accuracy of an analysis result of the channel operation data is improved.
The present application may be further configured in a preferred example to: the processing of the user portrait on the effective user data to obtain the user portrait data specifically includes:
acquiring an original channel delivery strategy according with an enterprise channel delivery target;
performing data analysis processing on the original channel delivery strategy to obtain a user characteristic index for drawing a user portrait;
identifying a user unique identification code and user behavior data in the channel operation data;
clustering the user characteristic indexes and the user behavior data according to the unique user identification code to obtain a user clustering model;
and inputting the effective user data into the user clustering model to obtain user portrait data.
By adopting the technical scheme, the channel delivery purposes of each enterprise are different, if the e-commerce industry is used for promoting commodity sales, the news website is used for increasing news browsing volume, the delivery purposes are different, and the targeted user groups are different, so that the engagement degree of the user portrait and the enterprise delivery purposes is improved, the original channel delivery strategy is analyzed, the user characteristic indexes meeting the enterprise delivery purposes are obtained, the user data with the optimal engagement degree can be screened out according to the user characteristic indexes, the user unique identification code has uniqueness, the false flow is difficult to simulate the user unique identification code, the portrait is clustered and associated with the user behavior data according to the user unique identification code, effective user data meeting the user characteristic indexes can be rapidly associated and aggregated according to a user clustering model, the user portrait data is obtained, and the user rate of channel operation data is improved.
The present application may be further configured in a preferred example to: after the inputting the valid user data into the user clustering model to obtain user portrait data, the method includes:
performing path tracking processing on the user behavior data to obtain a user behavior path;
according to the user behavior path, path planning is carried out when a channel user reaches a preset conversion target, and a user conversion path is obtained;
acquiring an actual behavior path of a channel user in real time in the channel operation process according to the user conversion path;
performing path fitting processing on the actual behavior path and the user conversion path to obtain path deviation data;
and generating a path optimization instruction which corresponds to the path deviation data and is used for carrying out delivery path adjustment on the original channel delivery strategy according to the path deviation data.
By adopting the technical scheme, because the user behavior paths of each channel user are different, when the user behavior data are prejudged, the user behavior is usually tracked through the user behavior paths, therefore, the user behavior data distributed in scattered points are drawn into visual user behavior paths, and path planning is carried out by combining with an expected conversion target, so that the user conversion path is obtained, wherein the expected conversion target is obtained by analyzing in a channel releasing strategy, the channel releasing strategy is adjusted through the user conversion path, so that the channel releasing content is more suitable for the user requirements, the user experience is improved, path deviation data is obtained through fitting of the actual behavior paths and the user conversion paths, so that channel releasing materials are optimized in time according to the path deviation data, the channel releasing path is more suitable for the user requirements, the releasing time of the user actual behavior paths among the expected conversion targets is shortened through dynamic adjustment of releasing channels, the releasing cost is reduced, and the conversion efficiency of the channels is also improved.
The present application may be further configured in a preferred example to: according to the user portrait data, establish the curve of persisting about channel user persists, obtain the user and persist the curve graph, specifically include:
identifying user newly-added data and user login data in the channel operation data, wherein the user login data is the next-day login data of a newly-added user;
calculating retention rate according to the user newly added data and the user login data to obtain user retention rate;
and drawing a user retention curve corresponding to the user portrait data according to the user retention rate to obtain a user retention curve graph.
By adopting the technical scheme, the user retention curve graph of the real user usually shows exponential attenuation change, the false flow is usually set fixedly by a machine, and fixed login or browsing behaviors are carried out at fixed time and fixed point, so that the generated user retention curve is usually smooth and unchangeable, and the effect of random browsing change of the real flow is difficult to simulate.
The application may be further configured in a preferred example to: the channel abnormal data on the user retention curve are identified and are subjected to data marking, and channel marking data are obtained, and the method specifically comprises the following steps:
acquiring natural flow data in the channel operation data, wherein the natural flow data comprises natural user login data and natural user newly added data;
constructing a standard retention curve about the natural flow data according to the natural user login data and the natural user newly added data;
comparing the characteristics of the standard retention curve and the user retention curve to obtain channel abnormal data;
and carrying out data marking on the channel abnormal data according to the user characteristic indexes in the user portrait data to obtain channel marking data.
By adopting the technical scheme, the phenomenon that a user changes an IP address suddenly or the browsing time is excessively increased causes a data inflection point or a breakpoint of a user retention curve to form channel abnormal data, the user retention change condition of the channel abnormal data is too random and uncontrollable, and the reference significance of error compensation of user portrayal is not large, so that in order to reduce the influence of invalid user retention data on the data analysis accuracy, the user retention curve is drawn into a standard retention curve through the user login condition and the user addition condition of natural flow data, a comparison reference basis is provided for the user retention curve conforming to a channel delivery strategy, abnormal data which is different from the standard retention curve in the user retention curve is obtained according to a comparison result, the channel abnormal data is subjected to data marking through the user characteristic indexes, the channel abnormal data which do not conform to the user characteristic indexes is subjected to accurate compensation according to the user characteristic indexes, and the error compensation accuracy of operation data analysis is improved.
The application may be further configured in a preferred example to: the method includes the steps of calculating channel user conversion rate according to the user retention curve, and generating a strategy optimization instruction for optimizing a channel delivery strategy according to a calculation result, so that channel marking data are optimized through the strategy optimization instruction, and specifically includes the following steps:
acquiring conversion times information of channel users corresponding to the effective user data in real time;
calculating the conversion times information and user login data which accord with the user retention curve to obtain channel user conversion rate;
constructing a user funnel model according with the user retention curve according to the channel user conversion rate;
and inputting the channel operation data into the user funnel model, and generating a strategy optimization instruction which corresponds to the channel user conversion rate and is used for optimizing a channel delivery strategy.
By adopting the technical scheme, the false flow generated by channel cheating usually is set by a machine to carry out simulation behaviors of macro indexes such as starting simulation, clicking simulation or browsing simulation, but certain technical difficulty exists in the simulation of user conversion rate, usually the false flow only stays at a macro index expression level, when the macro index of channel operation data is excellent and the real conversion rate of a user is very low, the possibility of higher flow false exists is indicated, therefore, the calculation of the user conversion rate is carried out by combining user login data in a user retention curve through obtaining user conversion time information corresponding to effective user data, the effectiveness of the corresponding channel operation data is judged according to the user conversion rate, a user funnel model is built through the channel user conversion rate, the deep analysis of user transfer conditions according to the user funnel model is facilitated, multiple links in the channel operation process are supervised and managed, the channel operation data are subjected to multi-dimensional analysis according to the user funnel model, the channel putting strategy is further optimized according to the analysis result, the channel putting strategy is further optimized, and the operation data is more true.
The present application may be further configured in a preferred example to: calculating the conversion rate of the channel user according to the user retention curve, and generating a strategy optimization instruction for optimizing the channel delivery strategy according to the calculation result so as to optimize the channel marking data through the strategy optimization instruction, and further comprising:
comparing the channel user conversion rate with an expected conversion index preset in the channel putting strategy to obtain a conversion rate comparison result;
performing risk assessment on the channel user according to the conversion rate comparison result to obtain a user risk assessment result;
judging whether the channel user is a risk user or not according to the user risk evaluation result;
if so, carrying out data clearing processing on the risk user so as to enable the channel operation data to be more fit with the user conversion rate under the natural flow.
By adopting the technical scheme, because the false flow of the channel usually exists in a channel macroscopic level, such as the residence time of a page, the access depth and the like, the risk channel with the false flow usually shows that the user macroscopic data shows good performance, and the conversion rate and the retention rate are low, the judgment of whether the channel user conversion rate meets the expected conversion index is facilitated by comparing the channel user conversion rate with the preset expected conversion index, and whether the delivery effect of the channel meets the channel delivery requirement of an enterprise is further judged, and whether the channel user is a risk user is judged according to the user risk assessment result, such as the false user with good user macroscopic data and low conversion rate, so that the data removal processing is carried out on the risk user, the error influence of invalid users on the channel operation data analysis result is reduced, and the data analysis result of the channel operation data more fits the user conversion rate under the natural flow.
The second objective of the present invention is achieved by the following technical solutions:
there is provided an intelligent channel operation data analysis system, including:
the data acquisition module is used for acquiring channel operation data of a full life cycle in real time and carrying out data cleaning processing on the channel operation data to obtain effective user data;
the user portrait module is used for carrying out user portrait processing on the effective user data to obtain user portrait data;
the retention curve construction module is used for constructing a retention curve retained by the channel user according to the user portrait data to obtain a user retention curve graph;
the data marking module is used for identifying channel abnormal data on the user retention curve and carrying out data marking on the channel abnormal data to obtain channel marking data;
and the strategy optimization module is used for calculating the channel user conversion rate according to the user retention curve and generating a strategy optimization instruction for optimizing the channel delivery strategy according to the calculation result so as to optimize the channel mark data through the strategy optimization instruction.
By adopting the technical scheme, channel flow cheating is generally uniformly carried out through a timing simulation starting behavior and a fixed browsing behavior, and a random browsing behavior and a click conversion behavior of a real user are difficult to generate, so that invalid operation data with fixed user behaviors and no real conversion rate are favorably cleared by carrying out data cleaning processing on channel operation data in a whole life cycle, effective user data are obtained, user portrayal is favorably carried out through the effective user data, the error influence of false channel flow on the user portrayal is favorably reduced, channel abnormal data such as data inflection points and breakpoints are favorably marked by a user retention curve graph, the channel abnormal data are favorably optimized in a targeted mode, multi-dimensional optimization of a channel delivery strategy is favorably carried out according to the user retention condition and the channel user conversion rate through calculation of the channel user conversion rate, the optimized channel delivery strategy is more suitable for the real channel flow, and the accuracy of an analysis result of the channel operation data is improved.
The third purpose of the present application is achieved by the following technical solutions:
a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the intelligent channel operation data analysis method when executing the computer program.
The fourth purpose of the present application is achieved by the following technical solutions:
a computer-readable storage medium, which stores a computer program, which when executed by a processor, implements the steps of the above-mentioned intelligent channel operation data analysis method.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the channel operation data of the whole life cycle is subjected to data cleaning, invalid operation data with fixed user behaviors and no real conversion rate are cleared, so that effective user data are obtained, user portrayal is carried out through the effective user data, the error influence of false channel flow on the user portrayal is reduced, channel abnormal data such as inflection points and breakpoints of the data are subjected to data marking through a user retention curve graph, so that the channel abnormal data are optimized in a targeted mode, multi-dimensional optimization of a channel delivery strategy according to the user retention condition and the channel user conversion rate is facilitated through calculation of the channel user conversion rate, the optimized channel delivery strategy is more fit with the real channel flow, and the accuracy of an analysis result of the channel operation data is improved;
2. the original channel delivery strategy is analyzed, so that a user characteristic index which accords with an enterprise delivery target is obtained, the user data with the optimal fit degree can be screened according to the user characteristic index, and the user unique identification code has uniqueness, so that false flow is difficult to simulate the user unique identification code, the user characteristic index and the user behavior data are clustered and associated according to the user unique identification code, effective user data which accords with the user characteristic index can be associated and aggregated quickly according to a user clustering model, user portrait data is obtained, and the user portrait rate of channel operation data is improved;
3. the method comprises the steps of drawing user behavior data distributed in a scattered point mode into a user behavior path, and planning a path by combining an expected conversion target, so that a user conversion path is obtained, wherein the expected conversion target is obtained by analyzing a channel putting strategy, the channel putting strategy is adjusted through the user conversion path, so that the channel putting content is more suitable for the user requirement, the user experience is improved, path deviation data is obtained through fitting of an actual behavior path and the user conversion path, so that channel putting materials are timely optimized according to the path deviation data, the channel putting path is more suitable for the user requirement, the putting time of the actual behavior path of a user reaching the expected conversion target is shortened through dynamic adjustment of a putting channel, the channel putting cost is reduced, and the conversion efficiency of channel putting is improved.
Drawings
Fig. 1 is a flowchart illustrating an implementation of an intelligent channel operation data analysis method in an embodiment of the present application.
Fig. 2 is a flowchart illustrating implementation of step S20 of the intelligent channel operation data analysis method in an embodiment of the present application.
Fig. 3 is a flowchart of another implementation of step S20 of the intelligent channel operation data analysis method in an embodiment of the present application.
Fig. 4 is a flowchart illustrating implementation of step S30 of the intelligent channel operation data analysis method according to an embodiment of the present application.
Fig. 5 is a flowchart illustrating implementation of step S40 of the intelligent channel operation data analysis method in an embodiment of the present application.
Fig. 6 is a flowchart of an implementation of step S50 of the intelligent channel operation data analysis method in an embodiment of the present application.
Fig. 7 is a flowchart of another implementation of step S50 of the intelligent channel operation data analysis method in an embodiment of the present application.
Fig. 8 is a schematic structural diagram of an intelligent channel operation data analysis system in an embodiment of the present application.
Fig. 9 is a schematic diagram of an internal structure of a computer device for implementing an intelligent channel operation data analysis method according to an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the accompanying drawings.
In an embodiment, as shown in fig. 1, the present application discloses an intelligent channel operation data analysis method, which specifically includes the following steps:
s10: and acquiring channel operation data of the whole life cycle in real time, and performing data cleaning processing on the channel operation data to obtain effective user data.
Specifically, a developer pulls channel operation data according to a preset channel data packet, if a user clicks corresponding product connection, a short link between the user and a product is established, so that the channel operation data is pulled in real time according to the short link between each channel and the user, the channel operation data comprises a user IP address, user personal information, user retention condition, user conversion condition and the like, the channel operation data in the whole life cycle comprises all user behavior data during the period from the user registration to the user logout, and partial suspicious or unnecessary flows are filtered by a preset anti-cheating rule, such as suspicious flows with frequently changed IP addresses, invalid flows with the activity lower than a preset standard, incomplete invalid flows of the data packet and the like, so that effective user data after invalid flows are filtered by data cleaning.
S20: and performing user portrait processing on the effective user data to obtain user portrait data.
Specifically, in order to make the user portrait data more fit the delivery target of the enterprise, the original channel delivery strategy is analyzed, and the user characteristic index and the user behavior data are associated by combining the unique user identification code, so as to obtain the user portrait data, as shown in fig. 2, step S20 specifically includes:
s101: and acquiring an original channel delivery strategy which accords with the delivery target of the enterprise channel.
Specifically, a channel delivery strategy is formulated according to a product label and user group positioning, and an original channel delivery strategy is obtained by analyzing delivery behaviors of a product, for example, comprehensive analysis is performed according to product popularization mode selection, target user group selection, popularization effect selection, payment mode selection and the like of a user, so that the original channel delivery strategy is obtained, and selection of multiple delivery modes such as search engine delivery, social media delivery, information flow advertisement delivery and the like is obtained.
S102: and carrying out data analysis processing on the original channel delivery strategy to obtain a user characteristic index for drawing the user portrait.
Specifically, data analysis is performed on the original channel delivery strategy, including channel user characteristics, channel delivery scenes and channel information, so that user characteristic indexes such as target population age, hobbies, channel activity duration, purchasing power and the like are obtained.
S103: and identifying the user unique identification code and the user behavior data in the channel operation data.
Specifically, according to the channel operation data, a user mobile phone number or real-name authentication information when a user registers is obtained and used as a mobile phone unique identification code, a pre-packaged monitoring data packet detects specific user behaviors in the channel operation data, the specific user behaviors include user behaviors such as user clicking, browsing and purchasing, and the user behavior data are automatically obtained through the monitoring data packet when the user behaviors reach a preset trigger condition.
S104: and clustering the user characteristic indexes and the user behavior data according to the unique user identification code to obtain a user clustering model.
Specifically, clustering is performed on users according to each user characteristic index, for example, female users in the age range of 18-28 are divided into a class, users with channel active duration exceeding a preset value, for example, 5 hours are divided into a class, clustering centroids with corresponding number are set according to the number of the user characteristic indexes, all users in the same channel are repeatedly classified, and the user characteristic indexes and user behavior data are associated according to the classification result, so that a user clustering model is obtained.
S105: and inputting the effective user data into the user clustering model to obtain the user portrait data.
Specifically, when effective user data is input into the user clustering model, the effective user data is classified according to the user characteristic indexes, and user behavior data in the classified effective user data is associated with the user characteristic indexes, so that user portrait data about the user unique identification code is obtained, for example, user portrait data corresponding to the associated user unique identification code is a female user with an age range of 18-28, channel activity is higher than an average level, and behavior data such as browsing time, purchasing power and the like are higher than that of a male user.
In this embodiment, in order to describe the user behavior more accurately, after inputting the valid user data into the user clustering model to obtain the user portrait data, as shown in fig. 3, step S20 further includes:
s201: and carrying out path tracking processing on the user behavior data to obtain a user behavior path.
Specifically, user behavior data of each effective user behavior, such as user behavior data of behaviors of generating real click, browsing or purchasing and the like, is obtained through a pre-packaged data monitoring packet, the user behavior data distributed in a scattered manner is connected according to behavior generation time, accordingly the trend of the user behavior data is obtained, and a user behavior path is obtained according to the trend of the user behavior data.
S202: and planning a path when the channel user reaches a preset conversion target according to the user behavior path to obtain a user conversion path.
Specifically, user behavior paths are screened through a decision tree algorithm, an optimal path that the current user position reaches a preset conversion target is obtained, for example, the next behavior of the user is predicted according to the user behavior paths, random sampling is carried out in expected behaviors of a plurality of users according to the decision tree algorithm, and data training is carried out on sampled samples, so that a conversion path which reaches the preset conversion target most quickly is planned.
S203: and acquiring the actual behavior path of the channel user in real time in the channel operation process according to the user conversion path.
Specifically, accurate pushing is performed on the user according to the user conversion path, if the preset conversion target of the user conversion path is to promote commodity transaction, when the user enters a pushing channel, the attention of the user is attracted in a pop-up window or material optimizing and pushing mode, order transaction is promoted, actual behavior data of the user is obtained in real time in the process that the product is pushed on the user conversion path, and the actual behavior path is drawn.
S204: and performing path fitting processing on the actual behavior path and the user conversion path to obtain path deviation data.
Specifically, path fitting is carried out on the actual behavior path and the user transformation path, the actual behavior path of the user is judged to accord with the user transformation path within an error range according to a fitting result, and if yes, the user transformation path is indicated to be the optimal user transformation path; if not, further optimizing the user conversion path according to the path deviation data between the user actual behavior path and the user conversion path, such as adjusting the content of the pushed material, adjusting the pushing mode, such as changing the popup window pushing into video page pushing, and the like.
S205: and generating a path optimization instruction which corresponds to the path deviation data and is used for carrying out delivery path adjustment on the original channel delivery strategy according to the path deviation data.
Specifically, according to the path deviation data, such as an order closing stage on a user conversion path, a user only stays at a product browsing stage to cause the path deviation data between the actual behavior path and the user conversion path, and the path deviation data is combined with user portrait data to judge the reason of the path deviation data, such as low payment capacity, so as to generate a path optimization instruction corresponding to the path deviation data and used by the user to adjust the release path, such as adjusting a high-price product to a time period with high user purchasing power for release, and adjusting a low-price product to a time period with low user purchasing power for release.
S30: and constructing a retention curve retained by the channel user according to the user portrait data to obtain a user retention curve graph.
Specifically, judge whether the user is the target crowd who accords with the channel popularization object according to the user portrait data to draw the portrait data according to the user and construct the user retention curve like user behavior, as shown in fig. 4, step S30 specifically includes:
s301: and identifying user new data and user login data in the channel operation data, wherein the user login data is the next day login data of the new user.
Specifically, a pre-packaged data monitoring packet is triggered according to a registration button when a user registers, newly-added user data is obtained according to the activation condition of the data monitoring packet, a short link between a user side and an operation background is built according to a unique user identification code, the data monitoring packet is triggered through the login operation of the user to record the login data of the user, and therefore the login data of the user is obtained.
S302: and calculating the retention rate according to the user new data and the user login data to obtain the user retention rate.
Specifically, according to a preset retention rate formula, if a quotient between the user login data and the newly added user data is used as the user retention rate, if the user login number is 200 people, and the corresponding user newly added number is 1000 people, the user retention rate is 20%.
S303: according to the user retention rate, drawing a user retention curve corresponding to the user portrait data to obtain a user retention curve graph.
Specifically, the adjacent user behavior data are correlated according to the user retention rate in the adjacent operation time period, so that a user retention curve is drawn, and a user retention curve graph is obtained.
S40: and identifying channel abnormal data on the user retention curve, and performing data marking on the channel abnormal data to obtain channel marking data.
Specifically, in order to reduce the error influence of the channel abnormal data on the channel operation data analysis, the channel marking data is obtained by identifying and marking abnormal user data such as data inflection points, breakpoints and the like on the user retention curve, as shown in fig. 5, step S40 specifically includes:
s401: and acquiring natural flow data in the channel operation data, wherein the natural flow data comprises natural user login data and natural user newly added data.
Specifically, the natural flow data refers to product browsing or payment data which is generated by a user through keyword search, user fission and the like before a product is not subjected to payment promotion, and the natural flow data is obtained by recording user access conditions when the product is not subjected to a channel promotion state, wherein the user login number in the non-promotion state is natural user login data, the user registration number in the non-promotion state is newly added data for natural users,
s402: and constructing a standard retention curve about the natural flow data according to the natural user login data and the natural user newly added data.
Specifically, the retention rate of the natural flow is calculated according to the natural user login data and the natural user newly-increased data of the adjacent operation periods, if the number of the newly-increased natural users obtained on the same day is 1000, and the number of the naturally-increased natural user login data obtained on the next day is 500, the retention rate of the natural flow is 50%, and according to the retention rate of the natural flow data, the user data under the adjacent operation periods are connected, so that a standard retention curve is drawn, and an effective data reference is provided for the user retention condition under the channel popularization state.
S403: and comparing the standard retention curve with the user retention curve to obtain channel abnormal data.
Specifically, the operation period is taken as a reference, the user retention curve and the standard retention curve in the same operation period are compared in a characteristic mode, the operation period is divided into an activation stage, a retention stage and a loss stage, if in the retention stage, the average user browsing time length of the standard retention curve is 3 hours, and the average user browsing time length in the user retention curve is 1 hour, the difference between the characteristics of the two curves in the average browsing time length is large, the average browsing time length is 2 hours, and the reason that the user retention characteristic is abnormal can be judged according to channel abnormal data.
S404: and performing data marking on the channel abnormal data according to the user characteristic indexes in the user portrait data to obtain channel marking data.
Specifically, data marking is carried out on channel abnormal data according to user characteristic indexes in the user portrait data, such as user purchasing power, user liveness, user preference and the like, if the channel abnormal data with the average user browsing duration being lower than a standard retention curve is marked to be low in user liveness, the channel abnormal data on the user retention curve is marked through each user characteristic index, and channel marking data which are in line with the user portrait data are obtained.
S50: and calculating the channel user conversion rate according to the user retention curve, and generating a strategy optimization instruction for optimizing the channel delivery strategy according to the calculation result so as to optimize the channel marking data through the strategy optimization instruction.
Specifically, the channel delivery strategy is optimized in a multi-dimensional manner by combining the user retention curve with the user conversion rate, so that the channel delivery strategy is more suitable for the user demand of the real channel flow, as shown in fig. 6, step S50 specifically includes:
s501: and acquiring the conversion times information of the channel user corresponding to the effective user data in real time.
Specifically, the effective user data is monitored in real time, the conversion frequency information of the channel user corresponding to the effective user data is counted, and if the conversion target is analyzed according to an original channel putting strategy, if the channel putting target of an e-commerce enterprise generates a purchasing behavior for the user, and if the channel putting target of a news media enterprise generates a page browsing behavior for the user, the real purchasing frequency of the effective channel user or the page browsing frequency of the effective channel user is counted, if the browsing time is more than 30 seconds, the effective browsing is performed once, so that the conversion frequency information of the channel user is obtained, and if the purchasing behavior frequency of 500 effective channel users is 300, the conversion frequency information is 300.
S502: and calculating the conversion frequency information and the user login data which accords with the user retention curve to obtain the channel user conversion rate.
Specifically, according to a preset channel conversion rate calculation formula, if the quotient of the conversion number information and the user login data is used as the channel user conversion rate, if the user login data is that the number of login persons is 500, the conversion number meeting the channel delivery target is 200, and the channel user conversion rate is 40%.
S503: and constructing a user funnel model according with the user retention curve according to the channel user conversion rate.
Specifically, according to the conversion rate of a channel user, a channel operation link is planned when the user arrives at a channel delivery target from registration, if the channel operation link is planned by using commodity purchase as the delivery target, if the user clicking to enter a commodity homepage is set as a target user, the user clicking to enter the commodity homepage is set as a first link, page browsing data of the target user is obtained as a second link, the commodity is added into a shopping cart to be set as a third link, the user entering a payment page is set as a fourth link, successful payment is set as a fifth link, user behavior data of each link is obtained respectively, and the user behavior data of each link is associated according to a unique identification code of the user, so that a user funnel model is obtained.
S504: and inputting the channel operation data into the user funnel model, and generating a strategy optimization instruction which corresponds to the channel user conversion rate and is used for optimizing the channel delivery strategy.
Specifically, channel operation data is input into a user funnel model, and user conversion rate in each link is calculated according to a unique identification code of a user, so that the problem links of user loss or low conversion rate are optimized in a targeted manner according to the conversion condition of each link, for example, for users with short page browsing time, a channel delivery strategy can be optimized by optimizing a product detail page or changing a product introduction mode and the like; and for a user with low payment conversion rate, analyzing the purchasing power of the user, pushing commodities with corresponding price to the user, and the like, so as to optimize the channel delivery strategy.
In this embodiment, in order to better perform comprehensive evaluation on the channel delivery policy so as to screen out an effective channel for accurate marketing, as shown in fig. 7, after step S50, the method further includes:
s601: and comparing the conversion rate of the channel user with an expected conversion index preset in the channel putting strategy to obtain a conversion rate comparison result.
Specifically, if the expected conversion target is that the user payment conversion rate is 50%, and the actual channel user conversion rate is 20%, the conversion rate comparison result is that the conversion rate difference between the channel user conversion rate and the expected conversion target is 30%.
S602: and performing risk assessment on the channel user according to the conversion rate comparison result to obtain a user risk assessment result.
Specifically, a preset conversion rate difference threshold value is 10% in the channel operation strategy, that is, within an error range, a conversion rate difference value between an actual channel user conversion rate and an expected conversion target is less than 10%, if a conversion rate comparison result is greater than or equal to 10%, it is determined that a false flow exists in a corresponding channel, so that a macro base number of the user conversion rate is too large, so that the actual user conversion rate is far less than an expected conversion index, and a user risk assessment result is high risk; if the conversion rate comparison result is less than 10%, the possibility that the corresponding channel user is real flow is judged to be high, and the user risk assessment result is low risk.
S603: and judging whether the channel user is a risk user or not according to the user risk evaluation result.
Specifically, if the user risk assessment result is high risk, it indicates that the channel corresponding to the channel has a channel false phenomenon, and the corresponding channel user is a risk user; if the user risk evaluation result is low risk, the corresponding channel flow is indicated to be real flow, and the corresponding channel user is a non-risk user.
S604: if so, carrying out data clearing processing on the risk user so as to enable the channel operation data to be more fit with the user conversion rate under the natural flow.
Specifically, if the channel user is a risk user, it is indicated that the corresponding channel operation data does not meet actual operation requirements, and the effect on achieving the enterprise delivery target is not large, the user data determined as the risk user is subjected to data removal, so that the error influence of false flow data on the data analysis accuracy is reduced.
S605: and if not, continuing to monitor the data of the non-risk user.
Specifically, if the channel user is a non-risk user, it is indicated that the corresponding channel operation data is user data under the real flow, and the non-risk user is continuously monitored through a short link established during user registration.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
In an embodiment, an intelligent channel operation data analysis system is provided, and the intelligent channel operation data analysis system corresponds to the intelligent channel operation data analysis method in the above embodiment one to one. As shown in fig. 8, the intelligent channel operation data analysis system includes a data acquisition module, a user profile module, a retention curve construction module, a data marking module, and a policy optimization module. The detailed description of each functional module is as follows:
and the data acquisition module is used for acquiring the channel operation data of the whole life cycle in real time, and performing data cleaning processing on the channel operation data to obtain effective user data.
And the user portrait module is used for carrying out user portrait processing on the effective user data to obtain user portrait data.
And the retention curve building module is used for building a retention curve retained by the channel user according to the user portrait data to obtain a user retention curve graph.
And the data marking module is used for identifying the channel abnormal data on the user retention curve and carrying out data marking on the channel abnormal data to obtain channel marking data.
And the strategy optimization module is used for calculating the channel user conversion rate according to the user retention curve and generating a strategy optimization instruction for optimizing the channel delivery strategy according to the calculation result so as to optimize the channel marking data through the strategy optimization instruction.
Optionally, the user representation module comprises:
and the strategy acquisition submodule is used for acquiring an original channel delivery strategy which accords with an enterprise channel delivery target.
And the index acquisition submodule is used for carrying out data analysis processing on the original channel delivery strategy to obtain a user characteristic index for drawing the user portrait.
And the data identification submodule is used for identifying the user unique identification code and the user behavior data in the channel operation data.
And the clustering model building submodule is used for clustering the user characteristic indexes and the user behavior data according to the unique user identification code to obtain a user clustering model.
And the user portrait data acquisition submodule is used for inputting the effective user data into the user clustering model to obtain the user portrait data.
In this embodiment, in order to improve the accuracy of user portrayal and shorten the operation cycle when the user reaches the preset transformation target, after inputting valid user data into the user clustering model and obtaining the user portrayal data, the user portrayal module further includes:
and the path tracking submodule is used for carrying out path tracking processing on the user behavior data to obtain a user behavior path.
And the path planning submodule is used for planning a path when the channel user reaches a preset conversion target according to the user behavior path to obtain a user conversion path.
And the actual behavior path acquisition submodule is used for acquiring the actual behavior path of the channel user in real time in the channel operation process according to the user conversion path.
And the path fitting sub-module is used for performing path fitting processing on the actual behavior path and the user conversion path to obtain path deviation data.
And the path optimization instruction generation submodule is used for generating a path optimization instruction which corresponds to the path deviation data and is used for carrying out delivery path adjustment on the original channel delivery strategy according to the path deviation data.
Optionally, the retention curve constructing module includes:
and the data identification submodule is used for identifying user new-added data and user login data in the channel operation data, wherein the user login data is the next-day login data of the new user.
And the retention rate calculation submodule is used for calculating the retention rate according to the user newly-added data and the user login data to obtain the user retention rate.
And the retention curve drawing submodule is used for drawing a user retention curve corresponding to the user portrait data according to the user retention rate to obtain a user retention curve graph.
Optionally, the data marking module includes:
and the natural flow obtaining submodule is used for obtaining natural flow data in the channel operation data, wherein the natural flow data comprises natural user login data and natural user newly added data.
And the standard retention curve drawing submodule is used for constructing a standard retention curve related to the natural flow data according to the natural user login data and the natural user newly added data.
And the characteristic comparison submodule is used for comparing the characteristics of the standard retention curve and the user retention curve to obtain channel abnormal data.
And the data marking submodule is used for carrying out data marking on the channel abnormal data according to the user characteristic indexes in the user portrait data to obtain channel marking data.
Optionally, the policy optimization module includes:
and the conversion time obtaining submodule is used for obtaining the conversion time information of the channel user corresponding to the effective user data in real time.
And the conversion rate calculation submodule is used for calculating the conversion time information and the user login data which accord with the user retention curve to obtain the channel user conversion rate.
And the funnel model construction submodule is used for constructing a user funnel model which accords with the user retention curve according to the channel user conversion rate.
And the strategy optimization instruction generation submodule is used for inputting the channel operation data into the user funnel model, generating a strategy optimization instruction which corresponds to the channel user conversion rate and is used for optimizing the channel delivery strategy.
In this embodiment, in order to carry out integrated management to channel operation data, in order to select effective channel simultaneously and carry out accurate input, the strategy optimization module still includes:
and the conversion index comparison submodule is used for comparing the conversion rate of the channel user with an expected conversion index preset in the channel putting strategy to obtain a conversion rate comparison result.
And the risk evaluation processing submodule is used for carrying out risk evaluation on the channel user according to the conversion rate comparison result to obtain a user risk evaluation result.
And the risk user judgment submodule is used for judging whether the channel user is a risk user according to the user risk evaluation result.
And the data clearing processing submodule is used for clearing data of the risk users if the data are true so as to enable the channel operation data to be more fit with the user conversion rate under the natural flow.
For specific limitations of the intelligent channel operation data analysis system, reference may be made to the above limitations of the intelligent channel operation data analysis method, which is not described herein again. The modules in the intelligent channel operation data analysis system can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 9. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing the channel operation data and analysis data generated in the process of carrying out data analysis on the channel operation data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an intelligent channel operation data analysis method.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the steps of the above-mentioned intelligent channel operation data analysis method.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. An intelligent channel operation data analysis method, characterized in that the intelligent channel operation data analysis method comprises:
acquiring channel operation data of a full life cycle in real time, and performing data cleaning processing on the channel operation data to obtain effective user data;
carrying out user portrait processing on the effective user data to obtain user portrait data;
constructing a retention curve retained by a channel user according to the user portrait data to obtain a user retention curve graph;
identifying channel abnormal data on the user retention curve, and performing data marking on the channel abnormal data to obtain channel marking data;
and calculating the channel user conversion rate according to the user retention curve, and generating a strategy optimization instruction for optimizing a channel delivery strategy according to a calculation result so as to optimize the channel mark data through the strategy optimization instruction.
2. The intelligent channel operation data analysis method of claim 1, wherein the user portrait processing of the valid user data to obtain user portrait data specifically comprises:
acquiring an original channel delivery strategy according with an enterprise channel delivery target;
performing data analysis processing on the original channel delivery strategy to obtain a user characteristic index for drawing a user portrait;
identifying a user unique identification code and user behavior data in the channel operation data;
clustering the user characteristic indexes and the user behavior data according to the unique user identification code to obtain a user clustering model;
and inputting the effective user data into the user clustering model to obtain user portrait data.
3. The intelligent channel operation data analysis method of claim 2, wherein after the inputting the valid user data into the user clustering model to obtain user profile data, comprising:
performing path tracking processing on the user behavior data to obtain a user behavior path;
according to the user behavior path, path planning is carried out when a channel user reaches a preset conversion target, and a user conversion path is obtained;
acquiring an actual behavior path of a channel user in real time in the channel operation process according to the user conversion path;
performing path fitting processing on the actual behavior path and the user conversion path to obtain path deviation data;
and generating a path optimization instruction which corresponds to the path deviation data and is used for adjusting the delivery path of the original channel delivery strategy according to the path deviation data.
4. The intelligent channel operation data analysis method according to claim 1, wherein the constructing a retention curve about channel user retention according to the user profile data to obtain a user retention curve graph specifically comprises:
identifying user newly-added data and user login data in the channel operation data, wherein the user login data is the next-day login data of a newly-added user;
calculating retention rate according to the user newly added data and the user login data to obtain user retention rate;
and drawing a user retention curve corresponding to the user portrait data according to the user retention rate to obtain a user retention curve graph.
5. The intelligent channel operation data analysis method according to claim 1, wherein the identifying of the channel abnormal data on the user retention curve and the data marking of the channel abnormal data are performed to obtain channel marking data specifically comprises:
acquiring natural flow data in the channel operation data, wherein the natural flow data comprises natural user login data and natural user newly added data;
constructing a standard retention curve about the natural flow data according to the natural user login data and the natural user newly added data;
comparing the standard retention curve with the user retention curve to obtain channel abnormal data;
and carrying out data marking on the channel abnormal data according to the user characteristic indexes in the user portrait data to obtain channel marking data.
6. The intelligent channel operation data analysis method according to claim 1, wherein the calculating a channel user conversion rate according to the user retention curve, and generating a policy optimization instruction for optimizing a channel delivery policy according to a calculation result, so as to optimize the channel mark data through the policy optimization instruction specifically comprises:
acquiring conversion times information of channel users corresponding to the effective user data in real time;
calculating the conversion frequency information and the user login data which accord with the user retention curve to obtain the channel user conversion rate;
constructing a user funnel model according with the user retention curve according to the channel user conversion rate;
and inputting the channel operation data into the user funnel model, and generating a strategy optimization instruction which corresponds to the channel user conversion rate and is used for optimizing a channel delivery strategy.
7. The method for analyzing operation data of an intelligent channel according to claim 6, wherein the calculating a user conversion rate of the channel according to the user retention curve and generating a policy optimization instruction for optimizing a channel delivery policy according to a calculation result, so that after the channel mark data is optimized by the policy optimization instruction, the method further comprises:
comparing the channel user conversion rate with an expected conversion index preset in the channel putting strategy to obtain a conversion rate comparison result;
performing risk assessment on the channel user according to the conversion rate comparison result to obtain a user risk assessment result;
judging whether the channel user is a risk user or not according to the user risk evaluation result;
if yes, data clearing processing is conducted on the risk users, and therefore the channel operation data can be more fit with the user conversion rate under the natural flow.
8. An intelligent channel operation data analysis system, comprising:
the data acquisition module is used for acquiring channel operation data of a full life cycle in real time and carrying out data cleaning processing on the channel operation data to obtain effective user data;
the user portrait module is used for carrying out user portrait processing on the effective user data to obtain user portrait data;
the retention curve construction module is used for constructing a retention curve retained by the channel user according to the user portrait data to obtain a user retention curve graph;
the data marking module is used for identifying channel abnormal data on the user retention curve and carrying out data marking on the channel abnormal data to obtain channel marking data;
and the strategy optimization module is used for calculating the conversion rate of the channel user according to the user retention curve and generating a strategy optimization instruction for optimizing the channel delivery strategy according to the calculation result so as to optimize the channel marking data through the strategy optimization instruction.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the intelligent channel operation data analysis method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the intelligent channel operation data analysis method according to any one of claims 1 to 7.
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