CN114020687A - User retention analysis method, device, equipment and storage medium - Google Patents
User retention analysis method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to artificial intelligence and provides a user retention analysis method, a device, equipment and a storage medium. The method can obtain the sample information of a plurality of sample users on a plurality of initial indexes, generate the representation information of the plurality of sample users on the plurality of initial indexes according to the sample information, carry out standardization processing on the representation information to obtain the feature information of the plurality of sample users on the plurality of initial indexes, analyze the index feature value of each initial index according to the feature information, select the target index from the plurality of initial indexes according to the index feature value, obtain the user information of the user to be detected on the target index according to the user retention analysis request when receiving the user retention analysis request, generate the retention result of the user to be detected according to the user information and the target index, and improve the retention analysis accuracy and efficiency. Furthermore, the invention also relates to a block chain technique, and the retention result can be stored in the block chain.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a user retention analysis method, device, equipment and storage medium.
Background
With the development of artificial intelligence, retention analysis algorithms also develop. In the current user retention analysis, the loss condition of the user can be determined by analyzing the behaviors of the user on a plurality of preset label dimensions. However, the number of dimensions of the preset label dimension is large, so that the analysis efficiency of the user loss condition is low.
In order to improve the analysis efficiency of user retention analysis, currently, a factor analysis algorithm is adopted to perform dimensionality reduction processing on the plurality of preset label dimensions, however, in this way, the number of dimensions is reduced, and meanwhile, important information in user information is lost, so that the accuracy of user retention analysis is low.
Therefore, on the premise of ensuring the accuracy of user retention analysis, how to improve the efficiency of user retention analysis becomes a problem which needs to be solved urgently.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a user retention analysis method, device, apparatus and storage medium, which can improve the efficiency of user retention analysis while ensuring the accuracy of user retention analysis.
In one aspect, the present invention provides a user retention analysis method, including:
acquiring sample information of a plurality of sample users on a plurality of initial indexes;
generating characterization information of the plurality of sample users on the plurality of initial indexes according to the sample information;
carrying out standardization processing on the characterization information to obtain characteristic information of the plurality of sample users on the plurality of initial indexes;
analyzing an index characteristic value on each initial index according to the characteristic information;
selecting a target index from the plurality of initial indexes according to the index characteristic value;
when a user retention analysis request is received, acquiring user information of a user to be detected on the target index according to the user retention analysis request;
and generating a retention result of the user to be detected according to the user information and the target index.
According to a preferred embodiment of the present invention, the generating the characterization information of the plurality of sample users on the plurality of initial indicators according to the sample information includes:
comparing the information format of the sample information with the configuration format in the type template, and determining the type corresponding to the configuration format which is the same as the information format as the index type of the plurality of initial indexes;
determining the initial index of which the index type is not a preset type as a characteristic index;
screening information corresponding to the initial index of which the index type is not a preset type from the sample information as first characteristic information, and determining the rest information except the first characteristic information in the sample information as second characteristic information;
mapping the first characteristic information to obtain numerical value information;
determining the numerical information and the second characteristic information as target characteristic information;
acquiring information of each sample user on the plurality of initial indexes from the target characteristic information as a row vector;
and splicing the row vector of each sample user to obtain the characterization information.
According to a preferred embodiment of the present invention, the normalizing the characterization information to obtain feature information of the plurality of sample users on the plurality of initial indicators includes:
calculating average index information of the plurality of sample users on each initial index according to the characterization information;
analyzing the characterization information and the average index information according to the following formula to obtain difference information of each initial index:
wherein S is the difference information, xijThe characterization information of the ith sample user on the jth initial index is referred to,
the average index information of the jth initial index is referred to, and n is the total amount of users of the plurality of sample users;
and standardizing the characterization information according to the average index information and the difference information to obtain the characteristic information.
According to a preferred embodiment of the present invention, the analyzing the index feature value on each initial index according to the feature information includes:
calculating the characteristic information according to the following formula to obtain the index correlation of any two initial indexes:
wherein r ishqIs the index correlation degree of the h initial index and the q initial index, n is the total amount of the users, XkhIs the characteristic information, X, of the kth sample user on the h initial indexkqThe characteristic information of the kth sample user on the qth initial index is referred to;
analyzing the dispersion degree of the characteristic information on each initial index to obtain the information dispersion degree of each initial index;
sequencing the index correlation degrees in sequence to obtain an initial diagonal matrix, and filling the initial diagonal matrix according to the information dispersion to obtain an index feature matrix;
and calculating the characteristic value of the index characteristic matrix as the index characteristic value.
According to a preferred embodiment of the present invention, the selecting a target index from the plurality of initial indexes according to the index feature value includes:
selecting an index characteristic value which is greater than or equal to a preset characteristic threshold value from the index characteristic values as a target characteristic value, and determining an initial index corresponding to the target characteristic value as an index to be selected;
sequencing the indexes to be selected according to the sequence of the target characteristic values from large to small to obtain a characteristic sequence;
calculating the cumulative contribution rate according to the characteristic sequence and the target characteristic value:
wherein, PtMeans the cumulative contribution rate, lambda, of the first t indexes to be selected in the characteristic sequencekThe target characteristic values corresponding to the first k indexes to be selected in the characteristic sequence are referred to, and t is 1,2,3 …, m;
and if the accumulated contribution rate is greater than or equal to a preset contribution threshold value, determining the t previous indexes to be selected as the target indexes.
According to a preferred embodiment of the present invention, the acquiring, according to the user retention analysis request, the user information of the user to be tested on the target index includes:
analyzing the message of the analysis request retained by the user to obtain the data information carried by the message;
extracting a user identification code from the data information, and determining a binding terminal associated with the user identification code;
generating an information acquisition request according to the user identification code and the target index, and sending the information acquisition request to the binding terminal;
after a preset time interval, receiving an acquisition response result sent by the binding terminal, and extracting a secret key from the acquisition response result;
acquiring ciphertext information corresponding to the user identification code;
and decrypting the ciphertext information based on the key, and determining the information which is decrypted successfully as the user information.
According to a preferred embodiment of the present invention, the user information includes information to be detected of the user to be detected on each target index, and the generating a retention result of the user to be detected according to the user information and the target index includes:
normalizing the index characteristic value corresponding to the target index to obtain an index weight of each target index;
calculating the product of each piece of information to be tested and each index weight to obtain the value of the user to be tested on each target index;
and calculating the sum of a plurality of scores to obtain the retention result.
In another aspect, the present invention further provides a user retention analysis apparatus, including:
the acquisition unit is used for acquiring sample information of a plurality of sample users on a plurality of initial indexes;
the generating unit is used for generating the characterization information of the plurality of sample users on the plurality of initial indexes according to the sample information;
the processing unit is used for carrying out standardization processing on the characterization information to obtain characteristic information of the plurality of sample users on the plurality of initial indexes;
the analysis unit is used for analyzing the index characteristic value of each initial index according to the characteristic information;
the selecting unit is used for selecting a target index from the plurality of initial indexes according to the index characteristic value;
the obtaining unit is further configured to obtain, when a user retention analysis request is received, user information of a user to be tested on the target index according to the user retention analysis request;
the generating unit is further configured to generate a retention result of the user to be tested according to the user information and the target index.
In another aspect, the present invention further provides an electronic device, including:
a memory storing computer readable instructions; and
a processor executing computer readable instructions stored in the memory to implement the user retention analysis method.
In another aspect, the present invention further provides a computer-readable storage medium, in which computer-readable instructions are stored, and the computer-readable instructions are executed by a processor in an electronic device to implement the user retention analysis method.
According to the technical scheme, the representation information is subjected to standardization processing, so that the influence of the scale difference between different initial indexes on the index characteristic value can be eliminated, the selection accuracy of the target index can be improved, the selection efficiency of the target index can be improved by analyzing the index characteristic value on each initial index, the quantity of the target index can be reduced by selecting the target index through the index characteristic value, the analysis efficiency of the retained result can be improved, important information in the initial indexes can be prevented from being lost, the selection accuracy of the target index can be improved, and the analysis accuracy of the retained result can be ensured.
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FIG. 1 is a flow chart of a preferred embodiment of a user retention analysis method of the present invention.
Fig. 2 is a functional block diagram of a preferred embodiment of the user retention analysis apparatus of the present invention.
FIG. 3 is a schematic structural diagram of an electronic device implementing a preferred embodiment of the user retention analysis method according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a flow chart of a preferred embodiment of the user retention analysis method of the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.
The user retention analysis method can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
The user retention analysis method is applied to one or more electronic devices, which are devices capable of automatically performing numerical calculation and/or information processing according to computer readable instructions set or stored in advance, and the hardware thereof includes but is not limited to a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The electronic device may be any electronic product capable of performing human-computer interaction with a user, for example, a Personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a game machine, an interactive Internet Protocol Television (IPTV), a smart wearable device, and the like.
The electronic device may include a network device and/or a user device. Wherein the network device includes, but is not limited to, a single network electronic device, an electronic device group consisting of a plurality of network electronic devices, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of hosts or network electronic devices.
The network in which the electronic device is located includes, but is not limited to: the internet, a wide area Network, a metropolitan area Network, a local area Network, a Virtual Private Network (VPN), and the like.
And S10, acquiring sample information of a plurality of sample users on a plurality of initial indexes.
In at least one embodiment of the present invention, the plurality of sample users refer to test users for performing dimension reduction processing on the plurality of initial indicators, and the plurality of sample users may be internal employees of an enterprise.
The initial indexes refer to preset labels for analyzing the user churn condition, for example, the initial indexes may include, but are not limited to: the number of attendance days, the number of valid contacts, etc. indicate the user's liveness, wherein the number of valid contacts refers to the total number of people establishing contact in the enterprise.
The sample information refers to relevant information corresponding to the plurality of initial indexes of the plurality of sample users, for example, the sample information of the sample user a on the attendance day label is 18 days/month.
In at least one embodiment of the present invention, the electronic device may obtain the sample information from a user library according to the plurality of sample users and the plurality of initial indicators.
And S11, generating the characterization information of the plurality of sample users on the plurality of initial indexes according to the sample information.
In at least one embodiment of the present invention, the characterization information refers to a quantization value used for indicating the sample information, for example, the characterization information may be a matrix, and each row vector in the matrix may be used for characterizing the sample information of each sample user on the plurality of initial indicators. And the total amount of elements in the characterization information is a product result of the total amount of the users of the plurality of sample users and the total amount of the indexes of the plurality of initial indexes.
In at least one embodiment of the present invention, the electronic device generating, according to the sample information, the characterization information of the plurality of sample users on the plurality of initial indicators includes:
comparing the information format of the sample information with the configuration format in the type template, and determining the type corresponding to the configuration format which is the same as the information format as the index type of the plurality of initial indexes;
determining the initial index of which the index type is not a preset type as a characteristic index;
screening information corresponding to the initial index of which the index type is not a preset type from the sample information as first characteristic information, and determining the rest information except the first characteristic information in the sample information as second characteristic information;
mapping the first characteristic information to obtain numerical value information;
determining the numerical information and the second characteristic information as target characteristic information;
acquiring information of each sample user on the plurality of initial indexes from the target characteristic information as a row vector;
and splicing the row vector of each sample user to obtain the characterization information.
The type template stores a plurality of mapping relations between configuration formats and types. The configuration formats are used for indicating the variable format corresponding to each type.
The indicator types include, but are not limited to: short integer, long integer, character type, etc.
The preset type refers to short integer type, long integer type and the like.
Through the analysis of the index types, the characteristic indexes can be accurately screened out, and then the sample information corresponding to the characteristic indexes is subjected to mapping processing, so that the generation of numerical value information is facilitated, and the representation intuitiveness of the representation information to the initial indexes can be improved because the representation information only contains integer information.
Specifically, the mapping processing of the first feature information by the electronic device to obtain numerical information includes:
traversing a preset mapping table according to each character in the first characteristic information, and acquiring a number corresponding to each character in the preset mapping table;
and splicing a plurality of numbers according to the character sequence of each character in the first characteristic information to obtain the numerical value information.
The preset mapping table stores mapping relations between a plurality of characters and a plurality of numbers.
The numbers can be accurately acquired through the preset mapping table, and then the numerical information can be accurately generated according to the character sequence.
And S12, carrying out standardization processing on the characterization information to obtain characteristic information of the plurality of sample users on the plurality of initial indexes.
In at least one embodiment of the present invention, the characteristic information is information obtained by normalizing the characterization information, and the variation ranges of different initial indexes can be in the same data interval by normalizing the characterization information. For example, the variation range of the initial index a is [1000, 2000], the variation range of the initial index B is [0.01, 1], and after the characterization information is normalized, the variation range of the initial index a in the feature information can be [0, 1], and the variation range of the initial index B can be [0, 1 ].
In at least one embodiment of the present invention, the normalizing, by the electronic device, the characterization information to obtain feature information of the plurality of sample users on the plurality of initial indicators includes:
calculating average index information of the plurality of sample users on each initial index according to the characterization information;
analyzing the characterization information and the average index information according to the following formula to obtain difference information of each initial index:
wherein S is the difference information, xijMeans that the ith sample user is at the jthThe characterization information on the initial index is obtained,
the average index information of the jth initial index is referred to, and n is the total amount of users of the plurality of sample users;
and standardizing the characterization information according to the average index information and the difference information to obtain the characteristic information.
The difference information of each initial index can be quickly generated through the average index information on each initial index, and then the characteristic information can be accurately generated according to the difference information of each initial index, so that the initial indexes with different scales have different effects on dimension reduction processing, and the generation accuracy of the target index is improved.
In this embodiment, by performing the standardization processing on the characterization information, it can be avoided that the initial index with a large scale plays a decisive role in performing the dimension reduction processing on the plurality of initial indexes, and the initial index with a small scale is ignored in the dimension reduction processing, so that the accuracy of the dimension reduction processing is improved.
Specifically, the normalizing the characterization information by the electronic device according to the average indicator information and the difference information to obtain the feature information includes:
calculating a difference value between the representation information and the average index information to obtain a deviation value;
and calculating the ratio of the deviation value in the difference value information to obtain the characteristic information.
And S13, analyzing the index characteristic value on each initial index according to the characteristic information.
In at least one embodiment of the invention, the metric characteristic value is used to indicate the degree of influence of each initial metric on the retention analysis of the user.
In at least one embodiment of the present invention, the electronic device analyzing the index feature value on each initial index according to the feature information includes:
calculating the characteristic information according to the following formula to obtain the index correlation of any two initial indexes:
wherein r ishqIs the index correlation degree of the h initial index and the q initial index, n is the total amount of the users, XkhIs the characteristic information, X, of the kth sample user on the h initial indexkqThe characteristic information of the kth sample user on the qth initial index is referred to;
analyzing the dispersion degree of the characteristic information on each initial index to obtain the information dispersion degree of each initial index;
sequencing the index correlation degrees in sequence to obtain an initial diagonal matrix, and filling the initial diagonal matrix according to the information dispersion to obtain an index feature matrix;
and calculating the characteristic value of the index characteristic matrix as the index characteristic value.
For example: the index correlation includes: the index correlation degree of the 1 st initial index and the 2 nd initial index is-4, the index correlation degree of the 1 st initial index and the 3 rd initial index is 3, the index correlation degree of the 2 nd initial index and the 3 rd initial index is 2, the index correlation degrees are sequentially sequenced, and the obtained initial diagonal matrix is:
the information dispersion of the 1 st initial index is 1, the information dispersion of the 2 nd initial index is 3, the information dispersion of the 3 rd initial index is 0, and then the index feature matrix is:
the index feature matrix can be accurately generated through the index correlation and the information dispersion, and then index feature values in the plurality of initial indexes are divided according to the index feature matrix, so that the plurality of initial indexes which are related to each other can be converted into unrelated target indexes.
Specifically, the analyzing, by the electronic device, the dispersion degree of the feature information on each initial indicator, and obtaining the information dispersion degree of each initial indicator includes:
and for each initial index, calculating the variance value of the characteristic information to obtain the information dispersion.
The calculation process of the variance value belongs to the prior art, and is not described in detail herein.
And S14, selecting a target index from the plurality of initial indexes according to the index characteristic value.
In at least one embodiment of the present invention, the target indicator refers to an initial indicator in which the indicator characteristic value is greater than a preset characteristic threshold, and the cumulative contribution rate is greater than or equal to a preset contribution threshold.
In at least one embodiment of the present invention, the electronic device selecting a target index from the plurality of initial indexes according to the index feature value includes:
selecting an index characteristic value which is greater than or equal to a preset characteristic threshold value from the index characteristic values as a target characteristic value, and determining an initial index corresponding to the target characteristic value as an index to be selected;
sequencing the indexes to be selected according to the sequence of the target characteristic values from large to small to obtain a characteristic sequence;
calculating the cumulative contribution rate according to the characteristic sequence and the target characteristic value:
wherein, PtMeans the cumulative contribution rate, lambda, of the first t indexes to be selected in the characteristic sequencekThe target characteristic values corresponding to the first k indexes to be selected in the characteristic sequence are referred to, and t is 1,2,3 …, m;
and if the accumulated contribution rate is greater than or equal to a preset contribution threshold value, determining the t previous indexes to be selected as the target indexes.
The preset feature threshold and the preset contribution threshold can be set according to requirements.
Through the preset characteristic threshold value, the indexes to be selected can be quickly screened out, then the indexes to be selected are sequenced, calculation of the cumulative contribution rate can be facilitated, through analysis of the cumulative contribution rate, loss of important information in the plurality of initial indexes can be avoided, and selection accuracy of the target index is improved.
And S15, when receiving the user retention analysis request, acquiring the user information of the user to be detected on the target index according to the user retention analysis request.
In at least one embodiment of the present invention, the information carried in the user retention analysis request includes, but is not limited to: and the user identification code of the user to be tested is the user needing to analyze the loss condition. The user information refers to information corresponding to the target index of the user to be tested.
In at least one embodiment of the present invention, the obtaining, by the electronic device, the user information of the user to be tested on the target indicator according to the user retention analysis request includes:
analyzing the message of the analysis request retained by the user to obtain the data information carried by the message;
extracting a user identification code from the data information, and determining a binding terminal associated with the user identification code;
generating an information acquisition request according to the user identification code and the target index, and sending the information acquisition request to the binding terminal;
after a preset time interval, receiving an acquisition response result sent by the binding terminal, and extracting a secret key from the acquisition response result;
acquiring ciphertext information corresponding to the user identification code;
and decrypting the ciphertext information based on the key, and determining the information which is decrypted successfully as the user information.
The user identification code is a code capable of uniquely indicating the user to be detected.
The binding terminal is a terminal associated with the user to be tested.
The preset time interval can be set according to requirements. For example, the preset time interval may be five minutes.
The ciphertext information and the binding terminal can be accurately determined through the user identification code, the acquisition accuracy of the user information is improved, leakage of the user information caused by the safety problem of the binding terminal can be avoided through setting of the preset time interval, the safety of the user information is improved, and the acquisition validity of the user information can be improved through generation and sending of the information acquisition request.
And S16, generating a retention result of the user to be detected according to the user information and the target index.
It is emphasized that the retention result may also be stored in a node of a block chain in order to further ensure privacy and security of the retention result.
In at least one embodiment of the present invention, the retention result is used to characterize the loss condition of the user to be tested. For example, if the retention result is 0.8, the user to be tested has an attrition probability of 20%.
In at least one embodiment of the present invention, the user information includes information to be detected of the user to be detected on each target index, and the generating, by the electronic device, a retention result of the user to be detected according to the user information and the target index includes:
normalizing the index characteristic value corresponding to the target index to obtain an index weight of each target index;
calculating the product of each piece of information to be tested and each index weight to obtain the value of the user to be tested on each target index;
and calculating the sum of a plurality of scores to obtain the retention result.
For example, if the index feature value corresponding to the target index 1 is 0.2, the index feature value corresponding to the target index 2 is 0.8, and the index feature value corresponding to the target index 3 is 0.6, after normalization, the weight of the index corresponding to the target index 1 is 0.125, the weight of the index corresponding to the target index 2 is 0.5, and the weight of the index corresponding to the target index 3 is 0.375.
By carrying out normalization processing on the index characteristic value, the analysis uniformity of the target index can be improved, and the accuracy of the retained result is improved.
According to the technical scheme, the representation information is subjected to standardization processing, so that the influence of the scale difference between different initial indexes on the index characteristic value can be eliminated, the selection accuracy of the target index can be improved, the selection efficiency of the target index can be improved by analyzing the index characteristic value on each initial index, the quantity of the target index can be reduced by selecting the target index through the index characteristic value, the analysis efficiency of the retained result can be improved, important information in the initial indexes can be prevented from being lost, the selection accuracy of the target index can be improved, and the analysis accuracy of the retained result can be ensured.
Fig. 2 is a functional block diagram of a preferred embodiment of the user retention analysis apparatus according to the present invention. The user retention analysis apparatus 11 includes an acquisition unit 110, a generation unit 111, a processing unit 112, an analysis unit 113, and a selection unit 114. The module/unit referred to herein is a series of computer readable instruction segments that can be accessed by the processor 13 and perform a fixed function and that are stored in the memory 12. In the present embodiment, the functions of the modules/units will be described in detail in the following embodiments.
The acquisition unit 110 acquires sample information of a plurality of sample users on a plurality of initial indexes.
In at least one embodiment of the present invention, the plurality of sample users refer to test users for performing dimension reduction processing on the plurality of initial indicators, and the plurality of sample users may be internal employees of an enterprise.
The initial indexes refer to preset labels for analyzing the user churn condition, for example, the initial indexes may include, but are not limited to: the number of attendance days, the number of valid contacts, etc. indicate the user's liveness, wherein the number of valid contacts refers to the total number of people establishing contact in the enterprise.
The sample information refers to relevant information corresponding to the plurality of initial indexes of the plurality of sample users, for example, the sample information of the sample user a on the attendance day label is 18 days/month.
In at least one embodiment of the present invention, the obtaining unit 110 may obtain the sample information from a user library according to the plurality of sample users and the plurality of initial indicators.
The generating unit 111 generates the characterization information of the plurality of sample users on the plurality of initial indexes according to the sample information.
In at least one embodiment of the present invention, the characterization information refers to a quantization value used for indicating the sample information, for example, the characterization information may be a matrix, and each row vector in the matrix may be used for characterizing the sample information of each sample user on the plurality of initial indicators. And the total amount of elements in the characterization information is a product result of the total amount of the users of the plurality of sample users and the total amount of the indexes of the plurality of initial indexes.
In at least one embodiment of the present invention, the generating unit 111 generates the characterization information of the plurality of sample users on the plurality of initial indicators according to the sample information, including:
comparing the information format of the sample information with the configuration format in the type template, and determining the type corresponding to the configuration format which is the same as the information format as the index type of the plurality of initial indexes;
determining the initial index of which the index type is not a preset type as a characteristic index;
screening information corresponding to the initial index of which the index type is not a preset type from the sample information as first characteristic information, and determining the rest information except the first characteristic information in the sample information as second characteristic information;
mapping the first characteristic information to obtain numerical value information;
determining the numerical information and the second characteristic information as target characteristic information;
acquiring information of each sample user on the plurality of initial indexes from the target characteristic information as a row vector;
and splicing the row vector of each sample user to obtain the characterization information.
The type template stores a plurality of mapping relations between configuration formats and types. The configuration formats are used for indicating the variable format corresponding to each type.
The indicator types include, but are not limited to: short integer, long integer, character type, etc.
The preset type refers to short integer type, long integer type and the like.
Through the analysis of the index types, the characteristic indexes can be accurately screened out, and then the sample information corresponding to the characteristic indexes is subjected to mapping processing, so that the generation of numerical value information is facilitated, and the representation intuitiveness of the representation information to the initial indexes can be improved because the representation information only contains integer information.
Specifically, the generating unit 111 performs mapping processing on the first feature information, and obtaining numerical value information includes:
traversing a preset mapping table according to each character in the first characteristic information, and acquiring a number corresponding to each character in the preset mapping table;
and splicing a plurality of numbers according to the character sequence of each character in the first characteristic information to obtain the numerical value information.
The preset mapping table stores mapping relations between a plurality of characters and a plurality of numbers.
The numbers can be accurately acquired through the preset mapping table, and then the numerical information can be accurately generated according to the character sequence.
The processing unit 112 normalizes the characterization information to obtain feature information of the plurality of sample users on the plurality of initial indicators.
In at least one embodiment of the present invention, the characteristic information is information obtained by normalizing the characterization information, and the variation ranges of different initial indexes can be in the same data interval by normalizing the characterization information. For example, the variation range of the initial index a is [1000, 2000], the variation range of the initial index B is [0.01, 1], and after the characterization information is normalized, the variation range of the initial index a in the feature information can be [0, 1], and the variation range of the initial index B can be [0, 1 ].
In at least one embodiment of the present invention, the processing unit 112 normalizes the characterization information to obtain feature information of the plurality of sample users on the plurality of initial indicators, including:
calculating average index information of the plurality of sample users on each initial index according to the characterization information;
analyzing the characterization information and the average index information according to the following formula to obtain difference information of each initial index:
wherein S is the difference information, xijThe characterization information of the ith sample user on the jth initial index is referred to,
is the average index information of the jth initial index, n is the multiple samplesThe total number of users;
and standardizing the characterization information according to the average index information and the difference information to obtain the characteristic information.
The difference information of each initial index can be quickly generated through the average index information on each initial index, and then the characteristic information can be accurately generated according to the difference information of each initial index, so that the initial indexes with different scales have different effects on dimension reduction processing, and the generation accuracy of the target index is improved.
In this embodiment, by performing the standardization processing on the characterization information, it can be avoided that the initial index with a large scale plays a decisive role in performing the dimension reduction processing on the plurality of initial indexes, and the initial index with a small scale is ignored in the dimension reduction processing, so that the accuracy of the dimension reduction processing is improved.
Specifically, the processing unit 112 normalizes the characterization information according to the average indicator information and the difference information, and obtaining the feature information includes:
calculating a difference value between the representation information and the average index information to obtain a deviation value;
and calculating the ratio of the deviation value in the difference value information to obtain the characteristic information.
The analysis unit 113 analyzes the index feature value on each initial index based on the feature information.
In at least one embodiment of the invention, the metric characteristic value is used to indicate the degree of influence of each initial metric on the retention analysis of the user.
In at least one embodiment of the present invention, the analyzing unit 113, which analyzes the index feature value on each initial index based on the feature information, includes:
calculating the characteristic information according to the following formula to obtain the index correlation of any two initial indexes:
wherein r ishqIs the index correlation degree of the h initial index and the q initial index, n is the total amount of the users, XkhIs the characteristic information, X, of the kth sample user on the h initial indexkqThe characteristic information of the kth sample user on the qth initial index is referred to;
analyzing the dispersion degree of the characteristic information on each initial index to obtain the information dispersion degree of each initial index;
sequencing the index correlation degrees in sequence to obtain an initial diagonal matrix, and filling the initial diagonal matrix according to the information dispersion to obtain an index feature matrix;
and calculating the characteristic value of the index characteristic matrix as the index characteristic value.
For example: the index correlation includes: the index correlation degree of the 1 st initial index and the 2 nd initial index is-4, the index correlation degree of the 1 st initial index and the 3 rd initial index is 3, the index correlation degree of the 2 nd initial index and the 3 rd initial index is 2, the index correlation degrees are sequentially sequenced, and the obtained initial diagonal matrix is:
the information dispersion of the 1 st initial index is 1, the information dispersion of the 2 nd initial index is 3, the information dispersion of the 3 rd initial index is 0, and then the index feature matrix is:
the index feature matrix can be accurately generated through the index correlation and the information dispersion, and then index feature values in the plurality of initial indexes are divided according to the index feature matrix, so that the plurality of initial indexes which are related to each other can be converted into unrelated target indexes.
Specifically, the analyzing unit 113 analyzes the degree of dispersion of the feature information on each initial index, and obtaining the information dispersion of each initial index includes:
and for each initial index, calculating the variance value of the characteristic information to obtain the information dispersion.
The calculation process of the variance value belongs to the prior art, and is not described in detail herein.
The selecting unit 114 selects a target index from the plurality of initial indexes according to the index feature value.
In at least one embodiment of the present invention, the target indicator refers to an initial indicator in which the indicator characteristic value is greater than a preset characteristic threshold, and the cumulative contribution rate is greater than or equal to a preset contribution threshold.
In at least one embodiment of the present invention, the selecting unit 114 selects a target index from the plurality of initial indexes according to the index feature value includes:
selecting an index characteristic value which is greater than or equal to a preset characteristic threshold value from the index characteristic values as a target characteristic value, and determining an initial index corresponding to the target characteristic value as an index to be selected;
sequencing the indexes to be selected according to the sequence of the target characteristic values from large to small to obtain a characteristic sequence;
calculating the cumulative contribution rate according to the characteristic sequence and the target characteristic value:
wherein, PtMeans the cumulative contribution rate, lambda, of the first t indexes to be selected in the characteristic sequencekThe target characteristic values corresponding to the first k indexes to be selected in the characteristic sequence are referred to, and t is 1,2,3 …, m;
and if the accumulated contribution rate is greater than or equal to a preset contribution threshold value, determining the t previous indexes to be selected as the target indexes.
The preset feature threshold and the preset contribution threshold can be set according to requirements.
Through the preset characteristic threshold value, the indexes to be selected can be quickly screened out, then the indexes to be selected are sequenced, calculation of the cumulative contribution rate can be facilitated, through analysis of the cumulative contribution rate, loss of important information in the plurality of initial indexes can be avoided, and selection accuracy of the target index is improved.
When receiving a user retention analysis request, the obtaining unit 110 obtains the user information of the user to be tested on the target index according to the user retention analysis request.
In at least one embodiment of the present invention, the information carried in the user retention analysis request includes, but is not limited to: and the user identification code of the user to be tested is the user needing to analyze the loss condition. The user information refers to information corresponding to the target index of the user to be tested.
In at least one embodiment of the present invention, the obtaining unit 110 obtains the user information of the user to be tested on the target indicator according to the user retention analysis request, where the obtaining unit includes:
analyzing the message of the analysis request retained by the user to obtain the data information carried by the message;
extracting a user identification code from the data information, and determining a binding terminal associated with the user identification code;
generating an information acquisition request according to the user identification code and the target index, and sending the information acquisition request to the binding terminal;
after a preset time interval, receiving an acquisition response result sent by the binding terminal, and extracting a secret key from the acquisition response result;
acquiring ciphertext information corresponding to the user identification code;
and decrypting the ciphertext information based on the key, and determining the information which is decrypted successfully as the user information.
The user identification code is a code capable of uniquely indicating the user to be detected.
The binding terminal is a terminal associated with the user to be tested.
The preset time interval can be set according to requirements. For example, the preset time interval may be five minutes.
The ciphertext information and the binding terminal can be accurately determined through the user identification code, the acquisition accuracy of the user information is improved, leakage of the user information caused by the safety problem of the binding terminal can be avoided through setting of the preset time interval, the safety of the user information is improved, and the acquisition validity of the user information can be improved through generation and sending of the information acquisition request.
The generating unit 111 generates a retention result of the user to be tested according to the user information and the target index.
It is emphasized that the retention result may also be stored in a node of a block chain in order to further ensure privacy and security of the retention result.
In at least one embodiment of the present invention, the retention result is used to characterize the loss condition of the user to be tested. For example, if the retention result is 0.8, the user to be tested has an attrition probability of 20%.
In at least one embodiment of the present invention, the user information includes information to be measured of the user to be measured on each target index, and the generating unit 111 generates the retention result of the user to be measured according to the user information and the target index, including:
normalizing the index characteristic value corresponding to the target index to obtain an index weight of each target index;
calculating the product of each piece of information to be tested and each index weight to obtain the value of the user to be tested on each target index;
and calculating the sum of a plurality of scores to obtain the retention result.
For example, if the index feature value corresponding to the target index 1 is 0.2, the index feature value corresponding to the target index 2 is 0.8, and the index feature value corresponding to the target index 3 is 0.6, after normalization, the weight of the index corresponding to the target index 1 is 0.125, the weight of the index corresponding to the target index 2 is 0.5, and the weight of the index corresponding to the target index 3 is 0.375.
By carrying out normalization processing on the index characteristic value, the analysis uniformity of the target index can be improved, and the accuracy of the retained result is improved.
According to the technical scheme, the representation information is subjected to standardization processing, so that the influence of the scale difference between different initial indexes on the index characteristic value can be eliminated, the selection accuracy of the target index can be improved, the selection efficiency of the target index can be improved by analyzing the index characteristic value on each initial index, the quantity of the target index can be reduced by selecting the target index through the index characteristic value, the analysis efficiency of the retained result can be improved, important information in the initial indexes can be prevented from being lost, the selection accuracy of the target index can be improved, and the analysis accuracy of the retained result can be ensured.
Fig. 3 is a schematic structural diagram of an electronic device according to a preferred embodiment of the present invention, which implements a user retention analysis method.
In one embodiment of the present invention, the electronic device 1 includes, but is not limited to, a memory 12, a processor 13, and computer readable instructions stored in the memory 12 and executable on the processor 13, such as a user retention analysis program.
It will be appreciated by a person skilled in the art that the schematic diagram is only an example of the electronic device 1 and does not constitute a limitation of the electronic device 1, and that it may comprise more or less components than shown, or some components may be combined, or different components, e.g. the electronic device 1 may further comprise an input output device, a network access device, a bus, etc.
The Processor 13 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The processor 13 is an operation core and a control center of the electronic device 1, and is connected to each part of the whole electronic device 1 by various interfaces and lines, and executes an operating system of the electronic device 1 and various installed application programs, program codes, and the like.
Illustratively, the computer readable instructions may be partitioned into one or more modules/units that are stored in the memory 12 and executed by the processor 13 to implement the present invention. The one or more modules/units may be a series of computer readable instruction segments capable of performing specific functions, which are used for describing the execution process of the computer readable instructions in the electronic device 1. For example, the computer readable instructions may be divided into an acquisition unit 110, a generation unit 111, a processing unit 112, an analysis unit 113, and a selection unit 114.
The memory 12 may be used for storing the computer readable instructions and/or modules, and the processor 13 implements various functions of the electronic device 1 by executing or executing the computer readable instructions and/or modules stored in the memory 12 and invoking data stored in the memory 12. The memory 12 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to use of the electronic device, and the like. The memory 12 may include non-volatile and volatile memories, such as: a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other storage device.
The memory 12 may be an external memory and/or an internal memory of the electronic device 1. Further, the memory 12 may be a memory having a physical form, such as a memory stick, a TF Card (Trans-flash Card), or the like.
The integrated modules/units of the electronic device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the above embodiments may be implemented by hardware that is configured to be instructed by computer readable instructions, which may be stored in a computer readable storage medium, and when the computer readable instructions are executed by a processor, the steps of the method embodiments may be implemented.
Wherein the computer readable instructions comprise computer readable instruction code which may be in source code form, object code form, an executable file or some intermediate form, and the like. The computer-readable medium may include: any entity or device capable of carrying said computer readable instruction code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM).
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
In conjunction with fig. 1, the memory 12 in the electronic device 1 stores computer-readable instructions that implement a method of user retention analysis, and the processor 13 executes the computer-readable instructions to implement:
acquiring sample information of a plurality of sample users on a plurality of initial indexes;
generating characterization information of the plurality of sample users on the plurality of initial indexes according to the sample information;
carrying out standardization processing on the characterization information to obtain characteristic information of the plurality of sample users on the plurality of initial indexes;
analyzing an index characteristic value on each initial index according to the characteristic information;
selecting a target index from the plurality of initial indexes according to the index characteristic value;
when a user retention analysis request is received, acquiring user information of a user to be detected on the target index according to the user retention analysis request;
and generating a retention result of the user to be detected according to the user information and the target index.
Specifically, the processor 13 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer readable instructions, which is not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The computer readable storage medium has computer readable instructions stored thereon, wherein the computer readable instructions when executed by the processor 13 are configured to implement the steps of:
acquiring sample information of a plurality of sample users on a plurality of initial indexes;
generating characterization information of the plurality of sample users on the plurality of initial indexes according to the sample information;
carrying out standardization processing on the characterization information to obtain characteristic information of the plurality of sample users on the plurality of initial indexes;
analyzing an index characteristic value on each initial index according to the characteristic information;
selecting a target index from the plurality of initial indexes according to the index characteristic value;
when a user retention analysis request is received, acquiring user information of a user to be detected on the target index according to the user retention analysis request;
and generating a retention result of the user to be detected according to the user information and the target index.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. The plurality of units or devices may also be implemented by one unit or device through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A user retention analysis method, comprising:
acquiring sample information of a plurality of sample users on a plurality of initial indexes;
generating characterization information of the plurality of sample users on the plurality of initial indexes according to the sample information;
carrying out standardization processing on the characterization information to obtain characteristic information of the plurality of sample users on the plurality of initial indexes;
analyzing an index characteristic value on each initial index according to the characteristic information;
selecting a target index from the plurality of initial indexes according to the index characteristic value;
when a user retention analysis request is received, acquiring user information of a user to be detected on the target index according to the user retention analysis request;
and generating a retention result of the user to be detected according to the user information and the target index.
2. The user retention analysis method of claim 1, wherein said generating characterization information of the plurality of sample users over the plurality of initial metrics from the sample information comprises:
comparing the information format of the sample information with the configuration format in the type template, and determining the type corresponding to the configuration format which is the same as the information format as the index type of the plurality of initial indexes;
determining the initial index of which the index type is not a preset type as a characteristic index;
screening information corresponding to the initial index of which the index type is not a preset type from the sample information as first characteristic information, and determining the rest information except the first characteristic information in the sample information as second characteristic information;
mapping the first characteristic information to obtain numerical value information;
determining the numerical information and the second characteristic information as target characteristic information;
acquiring information of each sample user on the plurality of initial indexes from the target characteristic information as a row vector;
and splicing the row vector of each sample user to obtain the characterization information.
3. The method of claim 1, wherein the normalizing the characterization information to obtain characterization information of the plurality of sample users on the plurality of initial metrics comprises:
calculating average index information of the plurality of sample users on each initial index according to the characterization information;
analyzing the characterization information and the average index information according to the following formula to obtain difference information of each initial index:
wherein S is the difference information, xijThe characterization information of the ith sample user on the jth initial index is referred to,
the average index information of the jth initial index is referred to, and n is the total amount of users of the plurality of sample users;
and standardizing the characterization information according to the average index information and the difference information to obtain the characteristic information.
4. The user retention analysis method according to claim 3, wherein said analyzing an indicator feature value on each initial indicator according to the feature information comprises:
calculating the characteristic information according to the following formula to obtain the index correlation of any two initial indexes:
wherein r ishqIs the index correlation degree of the h initial index and the q initial index, n is the total amount of the users, XkhIs the characteristic information, X, of the kth sample user on the h initial indexkqThe characteristic information of the kth sample user on the qth initial index is referred to;
analyzing the dispersion degree of the characteristic information on each initial index to obtain the information dispersion degree of each initial index;
sequencing the index correlation degrees in sequence to obtain an initial diagonal matrix, and filling the initial diagonal matrix according to the information dispersion to obtain an index feature matrix;
and calculating the characteristic value of the index characteristic matrix as the index characteristic value.
5. The user retention analysis method according to claim 1, wherein said selecting a target metric from the plurality of initial metrics according to the metric feature value comprises:
selecting an index characteristic value which is greater than or equal to a preset characteristic threshold value from the index characteristic values as a target characteristic value, and determining an initial index corresponding to the target characteristic value as an index to be selected;
sequencing the indexes to be selected according to the sequence of the target characteristic values from large to small to obtain a characteristic sequence;
calculating the cumulative contribution rate according to the characteristic sequence and the target characteristic value:
wherein, PtMeans the cumulative contribution rate, lambda, of the first t indexes to be selected in the characteristic sequencekThe target characteristic values corresponding to the first k indexes to be selected in the characteristic sequence are referred to, and t is 1,2,3 …, m;
and if the accumulated contribution rate is greater than or equal to a preset contribution threshold value, determining the t previous indexes to be selected as the target indexes.
6. The user retention analysis method according to claim 1, wherein the obtaining user information of the user to be tested on the target indicator according to the user retention analysis request comprises:
analyzing the message of the analysis request retained by the user to obtain the data information carried by the message;
extracting a user identification code from the data information, and determining a binding terminal associated with the user identification code;
generating an information acquisition request according to the user identification code and the target index, and sending the information acquisition request to the binding terminal;
after a preset time interval, receiving an acquisition response result sent by the binding terminal, and extracting a secret key from the acquisition response result;
acquiring ciphertext information corresponding to the user identification code;
and decrypting the ciphertext information based on the key, and determining the information which is decrypted successfully as the user information.
7. The user retention analysis method according to claim 1, wherein the user information includes information to be tested of the user to be tested on each target index, and the generating the retention result of the user to be tested according to the user information and the target index includes:
normalizing the index characteristic value corresponding to the target index to obtain an index weight of each target index;
calculating the product of each piece of information to be tested and each index weight to obtain the value of the user to be tested on each target index;
and calculating the sum of a plurality of scores to obtain the retention result.
8. A user retention analysis apparatus, characterized in that the user retention analysis apparatus comprises:
the acquisition unit is used for acquiring sample information of a plurality of sample users on a plurality of initial indexes;
the generating unit is used for generating the characterization information of the plurality of sample users on the plurality of initial indexes according to the sample information;
the processing unit is used for carrying out standardization processing on the characterization information to obtain characteristic information of the plurality of sample users on the plurality of initial indexes;
the analysis unit is used for analyzing the index characteristic value of each initial index according to the characteristic information;
the selecting unit is used for selecting a target index from the plurality of initial indexes according to the index characteristic value;
the obtaining unit is further configured to obtain, when a user retention analysis request is received, user information of a user to be tested on the target index according to the user retention analysis request;
the generating unit is further configured to generate a retention result of the user to be tested according to the user information and the target index.
9. An electronic device, characterized in that the electronic device comprises:
a memory storing computer readable instructions; and
a processor executing computer readable instructions stored in the memory to implement the user retention analysis method of any of claims 1 to 7.
10. A computer-readable storage medium characterized by: the computer readable storage medium having stored therein computer readable instructions for execution by a processor in an electronic device to implement the user retention analysis method of any of claims 1 to 7.
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| CN112036641A (en) * | 2020-08-31 | 2020-12-04 | 中国平安人寿保险股份有限公司 | Retention prediction method, device, computer equipment and medium based on artificial intelligence |
| CN112182069A (en) * | 2020-09-30 | 2021-01-05 | 中国平安人寿保险股份有限公司 | Agent retention prediction method and device, computer equipment and storage medium |
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