CN109801097A - Analysis method, device, storage medium and the analytical equipment of operation data - Google Patents
Analysis method, device, storage medium and the analytical equipment of operation data Download PDFInfo
- Publication number
- CN109801097A CN109801097A CN201811536313.3A CN201811536313A CN109801097A CN 109801097 A CN109801097 A CN 109801097A CN 201811536313 A CN201811536313 A CN 201811536313A CN 109801097 A CN109801097 A CN 109801097A
- Authority
- CN
- China
- Prior art keywords
- data
- operation data
- background operation
- background
- feature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 27
- 238000003860 storage Methods 0.000 title claims abstract description 18
- 239000013598 vector Substances 0.000 claims abstract description 114
- 238000007405 data analysis Methods 0.000 claims abstract description 71
- 230000000694 effects Effects 0.000 claims abstract description 43
- 238000000034 method Methods 0.000 claims abstract description 33
- 230000006870 function Effects 0.000 claims description 80
- 238000012163 sequencing technique Methods 0.000 claims description 5
- 238000004590 computer program Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 6
- 238000004519 manufacturing process Methods 0.000 description 6
- 239000004065 semiconductor Substances 0.000 description 6
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 230000008901 benefit Effects 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000005336 cracking Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The analysis method of operation data, the analytical equipment of device, storage medium and operation data are disclosed, technical field of computer programs is belonged to.This method includes obtaining the foreground running income data of marketing activity and financial net profit data in different time intervals；Selected characteristic vector；For feature vector and preset function model, it is weighted analysis, obtains the function between backstage operation data and feature vector；Data analysis is carried out, the numerical value of characteristic coefficient corresponding with each impact factor is obtained；According to the order of quality of the numerical value of characteristic coefficient each impact factor corresponding with backstage operation data.The device, storage medium and equipment its marketing activity backstage operation data can be quantified, to obtain accurate operation data analysis result, it not only contributes to the operation situation for grasping this marketing activity, valuable reference can also be provided for subsequent marketing activity, so that the cash earnings that subsequent marketing activity obtains is maximum.
Description
Technical Field
The present invention relates to the field of computing and program technologies, and in particular, to a method and an apparatus for analyzing operation data, a storage medium, and an apparatus for analyzing operation data.
Background
In the prior art, the analysis and analysis method for the operation data generally focuses on the foreground operation data, while the analysis and analysis method for the background operation data is generally less, and the degree of the emphasis on the background operation data is relatively lower. Generally, background data is high in operation, product, development and communication costs in a business process, and businesses are prone to cracking, deformation and the like at different points, so that an analysis and analysis method for background operation data in the prior art is high in cost and low in accuracy.
Disclosure of Invention
In view of this, the invention provides an analysis method and device for operation data, a storage medium and an analysis device for operation data, by which analysis and analysis of background operation data are performed, which not only has lower cost but also has higher accuracy, thereby being more practical.
In order to achieve the first object, the technical solution of the method for analyzing operation data provided by the present invention is as follows:
the method for analyzing the operation data comprises the following steps:
acquiring foreground operation income data and financial net profit data of marketing activities in different time intervals;
obtaining corresponding background operation data of the marketing campaign in different time intervals according to the foreground operation income data and the financial net profit data of the marketing campaign in different time intervals;
obtaining a function between the background operation data of the marketing campaign and the feature vectors of the influence factors in each different time interval according to the background operation data of the marketing campaign in the different time intervals and the feature vectors of the influence factors of the background operation data and a preset function model;
performing data analysis on the function between the background operation data and the feature vector to obtain the numerical value of the feature coefficient corresponding to each influence factor;
and obtaining the quality sequence of each influence factor corresponding to the background operation data in the marketing campaign according to the numerical value of the characteristic coefficient corresponding to each influence factor.
The analysis method of the operation data provided by the invention can be further realized by adopting the following technical measures.
Preferably, when obtaining the background operation data corresponding to the time interval of the operation data from the operation data, an operation formula of the background operation data is r ═ m-f (x),
wherein,
f (x): background operation data obtained by weighting operation of characteristic vectors of each influence factor corresponding to the background operation data,
m: foreground operational revenue data corresponding to a time interval of the background operational data,
r: financial net profit data corresponding to a time interval of the background operational data.
Preferably, the data analysis of the function between the background operation data and the feature vector to obtain the numerical value of the feature coefficient corresponding to each influence factor specifically includes the following steps:
selecting background operation data in the same time interval, foreground operation income data corresponding to the time interval of the foreground operation data and financial net profit data corresponding to the time interval of the background operation data as a data group pair;
substituting a plurality of data sets into the preset function to obtain a multi-element linear equation set, wherein the preset function is the sum of products of a feature vector of each influence factor and a feature coefficient value corresponding to the feature vector, specific numerical values of the feature vector of each influence factor are known data obtained from a background in a corresponding time interval, and unknowns in the multi-element linear equation set are feature coefficients of the feature vector in the background operation data;
obtaining the numerical value of the characteristic coefficient of the characteristic vector in the background operation data by solving the multivariate linear equation set;
substituting the numerical value of the characteristic coefficient of the characteristic vector in the background operation data into the preset function to be used as the background operation data to be substituted into the operation formula of the background operation data, and obtaining a background operation data function reflecting the relation between the background operation data and the characteristic vector;
and specifying specific numerical values of the feature vectors in the background operation data function, and obtaining numerical values of the feature coefficients corresponding to the influence factors through the operation of the background operation data function.
Preferably, when the number of data group pairs substituted into the function between the background operation data and the feature vector is the same as the number of the feature vector, the multivariate linear equation set is directly solved to obtain the numerical value of the feature coefficient of each feature vector.
Preferably, when the number of data group pairs used for substituting the function between the background operation data and the feature vector is greater than the number of the feature vectors, the multivariate linear equation set is solved through a least square method, and an optimized value of the feature coefficient of each feature vector is obtained.
Preferably, the method for obtaining the good and bad sequence of the marketing campaign background operation data according to the operation data analysis result specifically includes the following steps:
arranging the eigenvectors corresponding to the characteristic coefficients according to the descending order of the characteristic coefficients;
and marking the sequence numbers of the feature vectors corresponding to the feature coefficients after arrangement to obtain the quality sequence of each influence factor corresponding to the background operation data.
In order to achieve the second object, the present invention provides an analysis device for operational data, comprising:
the invention provides an analysis device of operation data, comprising:
the data acquisition unit is used for acquiring foreground operation income data and financial net profit data of the marketing activities in different time intervals;
the computing unit is used for obtaining corresponding background operation data of the marketing activities in different time intervals according to the foreground operation income data and the financial net profit data of the marketing activities in different time intervals;
the function creating unit is used for obtaining a function between the background operation data of the marketing campaign and the feature vectors of the influence factors in each different time interval according to the background operation data of the marketing campaign in the different time intervals and the feature vectors of the influence factors of the background operation data and a preset function model;
the data analysis unit is used for carrying out data analysis on the function between the background operation data and the characteristic vector to obtain the numerical value of the characteristic coefficient corresponding to each influence factor;
and the sequencing unit is used for obtaining the quality sequence of each influence factor corresponding to the background operation data in the marketing activity according to the numerical value of the characteristic coefficient corresponding to each influence factor.
The operation data analysis device provided by the invention can be further realized by adopting the following technical measures.
As a preference, the first and second liquid crystal compositions are,
the data acquisition unit includes:
the first data acquisition module is used for acquiring background operation data of marketing activities in different time intervals;
the second data acquisition module is used for acquiring foreground operation income data corresponding to the time interval of the background operation data;
the third data acquisition module is used for acquiring financial net profit data corresponding to the time interval of the background operation data;
and/or the presence of a gas in the gas,
the unit includes:
a sorting module: the characteristic vectors corresponding to the characteristic coefficients are arranged according to the sequence of the characteristic coefficients from large to small;
a marking module: and the sequence number is used for marking the sequence numbers of the feature vectors corresponding to the sequence of the feature coefficients sorted from big to small to obtain the good and bad sequence of each influence factor corresponding to the background operation data.
In order to achieve the third object, the present invention provides a storage medium, comprising:
the storage medium provided by the invention stores an analysis program of the operation data, and the analysis program of the operation data is executed by the processor to realize the analysis method of the operation data provided by the invention.
In order to achieve the fourth object, the technical solution of the device for analyzing operation data provided by the present invention is as follows:
the operation data analysis device provided by the invention comprises a memory and an analysis program of operation data, wherein the processor registers the analysis program of the operation data which is positioned on the memory and can be operated on the processor, and when the analysis program of the operation data is executed by the processor, the operation data analysis method provided by the invention is realized.
The method and the device for analyzing the operation data, the storage medium and the analysis equipment of the operation data sequentially acquire background operation data of marketing activities in different time intervals, foreground operation income data corresponding to the time intervals of the background operation data and financial net profit data corresponding to the time intervals of the background operation data; according to foreground operation income data, financial net profit data and feature vectors of all influence factors corresponding to the background operation data and the background operation data, obtaining a function between the background operation data and the feature vectors according to a preset function model; performing data analysis on a function between the background operation data and the feature vector to obtain an operation data analysis result; and obtaining the quality sequence of each influence factor corresponding to the background operation data in the marketing activity according to the operation data analysis result. By the method, the device, the storage medium and the operation data analysis equipment, the marketing activity background operation data can be quantized, so that a relatively accurate operation data analysis result is obtained, the operation condition of the marketing activity can be mastered, valuable references can be provided for subsequent marketing activities, and cash benefits obtained by the subsequent marketing activities are maximized.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic structural diagram of an analysis device for operation data of a hardware operating environment according to an embodiment of the present invention.
Fig. 2 is a flowchart illustrating steps of a method for analyzing operation data according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating specific steps of a method for obtaining an operation data analysis result through data analysis in an operation data analysis method according to an embodiment of the present invention;
fig. 4(a) is a schematic diagram illustrating a time interval adjacent to another time interval in the operation data analysis method according to an embodiment of the present invention;
fig. 4(b) is a schematic diagram illustrating a time interval between one time interval and another time interval in the operation data analysis method according to the first embodiment of the present invention;
fig. 4(c) is a schematic diagram illustrating an operation data analysis method according to an embodiment of the present invention, wherein an overlapping time exists between one time interval and another time interval;
fig. 4(d) is a schematic diagram illustrating an operation data analysis method according to an embodiment of the present invention, in which a time interval is encompassed by another time interval;
fig. 5 is a schematic diagram illustrating a signal flow direction relationship between modules in an operation data analysis apparatus according to a second embodiment of the present invention;
fig. 6 is a schematic diagram illustrating a specific signal flow relationship between modules in an operation data analysis apparatus according to a second embodiment of the present invention.
Detailed Description
The invention aims to solve the problems in the prior art, and provides an operation data analysis method, an operation data analysis device, a storage medium and operation data analysis equipment.
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the method, apparatus, storage medium and device for analyzing operation data according to the present invention with reference to the accompanying drawings and preferred embodiments will be provided to explain the detailed implementation, structure, features and effects thereof. In the following description, different "one embodiment" or "an embodiment" refers to not necessarily the same embodiment. Furthermore, the features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, with the specific understanding that: both a and B may be included, a may be present alone, or B may be present alone, and any of the three cases can be provided.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an analysis device for operation data of a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the operation data analysis device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the analysis device of the operational data, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a data storage module, a network communication module, a user interface module, and an analysis program of operation data.
In the operation data analysis device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the operation data analysis device of the present invention may be provided in an operation data analysis device that calls an operation data analysis program stored in the memory 1005 by the processor 1001 and executes the operation data analysis method provided by the embodiment of the present invention.
Example one
Referring to fig. 2, a method for analyzing operation data according to an embodiment of the present invention includes the following steps:
step S101: and acquiring foreground operation income data and financial net profit data of the marketing activities in different time intervals.
The present embodiment is described below by taking 5 time intervals as an example:
specifically, in this embodiment, the obtaining of the background operation data in the marketing campaign in different time intervals may specifically be obtaining of the foreground operation income data in different time intervals, and the obtaining of the foreground operation income data in 5 time intervals may specifically be obtaining of the foreground operation income data in 5 time intervals, including m1、m2、m3、m4、m5(ii) a Obtaining financial net profit data corresponding to the 5 time intervals corresponding to the obtaining of the background operational data comprises r1、r2、r3、r4、 r5Wherein the financial net profit data is derived from the book net profit.
Step S102: and obtaining corresponding background operation data of the marketing campaign in different time intervals according to the foreground operation income data and the financial net profit data of the marketing campaign in different time intervals.
Specifically, in this embodiment, it is necessary to obtain the front platform operation income data m in 5 time intervals1、 m2、m3、m4、m5Financial net profit data r corresponding to said 5 time intervals1、r2、r3、r4、 r5Obtaining the background operation data f (x) of the marketing activities in 5 time intervals1)、f(x2)、f(x3)、f(x4)、 f(x5)。
Step S103: and obtaining a function between the background operation data of the marketing campaign and the feature vectors of the influence factors in each different time interval according to the background operation data of the marketing campaign in the different time intervals and the feature vectors of the influence factors of the background operation data and a preset function model.
In this embodiment, the selected feature vectors of each influence factor corresponding to the background operation data include: t is t1Duration of activity, d number of page clicks, t2-time of page click, J-cash value of prize dispensed to user, L-limit for user, function model between background operational data and said feature vector is f (x) x1t1+x2d+x3t2+x4J+x5L, wherein x1、x2、x3、x4、x5The feature coefficients for each feature vector in the background operation data function f (x) are respectively.
Step S104: and performing data analysis on the function between the background operation data and the feature vector to obtain the numerical value of the feature coefficient corresponding to each influence factor.
Specifically, the present embodimentIn an example, the function f (x) x according to the background operation data is defined as1t1+x2d+x3t2+x4J+x5In L, the feature coefficient x for each feature vector1、x2、x3、x4、x5And each feature vector t1Duration of activity, d number of page clicks, t2-time of page click, J-cash value of reward issued to user, L-data analysis against user's constraints, resulting in characteristic coefficients x corresponding to said impact factors1、x2、x3、x4、x5The value of (d);
step S105: and obtaining the quality sequence of each influence factor corresponding to the background operation data in the marketing campaign according to the numerical value of the characteristic coefficient corresponding to each influence factor.
Specifically, it is assumed that the numerical value of the feature coefficient corresponding to each influence factor is x in order1＝5、x2＝4、x3＝3、 x4＝2、x51, then x1、x2、x3、x4、x5The sequencing sequence from big to small is as follows: x is the number of1、x2、x3、 x4、x5. Due to the characteristic coefficient x1And the feature vector t1Correspondingly, the sequence number of the marked feature vector t1 is 1; characteristic coefficient x2If the sequence number of the marked feature vector d is 2, the marked feature vector d corresponds to the feature vector d; characteristic coefficient x3And the feature vector t2Correspondingly, marking the characteristic vector t2Has a sequence number of 3; characteristic coefficient x4If the sequence number of the marked feature vector J corresponds to the feature vector J, the sequence number of the marked feature vector J is 2; characteristic coefficient x5Corresponding to the feature vector L, the sequence number of the marked feature vector L is 5, and at the moment, the result of sequencing the feature vectors in sequence according to the sequence number is t1、d、t2J, L, in obtaining each influence factor corresponding to background operation data, the influence on marketing activity is larger when the ranking is more advanced, and the ranking is more advancedThe less impact on the marketing campaign, in which case guidance can be provided for the subsequent marketing campaign. For example, if a certain influence factor has a small influence on the marketing campaign, the investment on the influence factor is reduced in the subsequent marketing campaign; if the influence of a certain influence factor on the marketing activities is large and the financial net profit is improved, the investment on the influence factor is increased in the subsequent marketing activities; if the influence of a certain influence factor on the marketing campaign is large and the financial net profit is reduced, the investment on the influence factor is eliminated in the subsequent marketing campaign, and the final purpose is to obtain the financial net profit as much as possible under the condition that the investment is as small as possible.
Therefore, through the analysis method of the operation data provided by the embodiment of the invention, the influence factors of the background operation data in the marketing campaign can be quantized, so that a relatively accurate operation data analysis result is obtained, the operation condition of the marketing campaign can be mastered, valuable references can be provided for subsequent marketing campaigns, and the cash income obtained by the subsequent marketing campaigns is maximized.
In this embodiment, the predetermined function is r ═ m-f (x),
wherein,
f (x): background operation data obtained by weighting operation of characteristic vectors of each influence factor corresponding to the background operation data,
m: foreground operational revenue data corresponding to a time interval of the background operational data,
r: financial net profit data corresponding to a time interval of the background operational data.
It should be noted that the front stage operation income data m corresponding to the time interval of the back stage operation data can be obtained by conversion according to the difference between the running water accumulation at the last time and the running water accumulation at the starting time in the time interval, and the financial net profit data corresponding to the time interval of the back stage operation income data can be obtained by conversion according to the difference between the net profit sum at the last time and the net profit sum at the starting time in the time interval.
Referring to fig. 3, performing data analysis on the background operation data, the foreground operation income data corresponding to the time interval of the background operation data, and the financial net profit data corresponding to the time interval of the background operation data to obtain the numerical values of the feature coefficients corresponding to the influence factors specifically includes the following steps:
step S201: background operation data in the same time interval, foreground operation income data corresponding to the time interval of the foreground operation data and financial net profit data corresponding to the time interval of the background operation data are selected as a data set pair.
In particular, the method of manufacturing a semiconductor device,
in a first specific implementation of this embodiment, the logarithm of the selected data set pair is 5, i.e. { f (x) }1)、m1、r1}，{f(x2)、m2、r2}，{f(x3)、m3、r3}，{f(x4)、m4、r4、}， {f(x5)、m5、r5}。
In a second specific implementation manner of the embodiment, the logarithm of the selected data set is greater than 5, i.e., { f (x) }1)、m1、r1}，{f(x2)、m2、r2}，{f(x3)、m3、r3}，{f(x4)、m4、r4、}， {f(x5)、m5、r5}，…，{f(xn)、mn、rnWherein n is a positive integer greater than 5.
Step S202: and substituting a plurality of data sets into a function between the background operation data and the characteristic vector to obtain a multi-element linear equation set, wherein the preset function is the sum of products of the characteristic vector of each influence factor and the characteristic coefficient value corresponding to the characteristic vector, the specific numerical value of the characteristic vector of each influence factor is known data acquired from the background in a corresponding time interval, and the unknown number in the multi-element linear equation set is the characteristic coefficient of the characteristic vector in the background operation data.
In particular, the method of manufacturing a semiconductor device,
in this embodiment, the preset function is f (x) ═ x1t1+x2d+x3t2+x4J+x5L, wherein, t1Duration of activity, d number of page clicks, t2-the time spent on page click, J-the cash value of the reward issued to the user, L-the constraints for the user, are all known data obtained from the background within the corresponding time interval;
in a first specific implementation manner of this embodiment, the 5-tuple data sets are respectively substituted into functions between the background operation data and the feature vector, and the obtained 5-tuple linear equation set is as follows:
in a second specific implementation manner of this embodiment, the groups of data greater than 5 are respectively substituted into the function between the background operation data and the feature vector, and an n-ary linear equation set is obtained as follows:
wherein n is a positive integer greater than 5.
Step S203: and obtaining the numerical value of the characteristic coefficient of the characteristic vector in the background operation data by solving the multivariate linear equation set.
In particular, the method of manufacturing a semiconductor device,
in a first implementation manner of this embodiment, since there are 5 unknowns in the 5-membered first-order equation set, the background operation data function f (x) ═ x is obtained by directly solving the 5-membered first-order equation set (1)1t1+x2d+x3t2+x4J+x5Each characteristic coefficient x in L1、x2、x3、x4、x5The value of (c).
In a second specific implementation manner of this embodiment, since the number of equations in the n-ary equation system is greater than the number of unknowns, it is necessary to solve the n-ary equation system (2) by a least square method to obtain an optimized background operation data function f (x)' ═ x1＇t1+x2＇d+x3＇t2+x4＇J+x5Optimized characteristic coefficients x in' L1＇、x2＇、x3＇、x4＇、x5The value of'.
Step S204: and substituting the numerical value of the characteristic coefficient of the characteristic vector in the background operation data into the preset function to obtain a background operation data function reflecting the relation between the background operation data and the characteristic vector.
In particular, the method of manufacturing a semiconductor device,
in a first specific implementation manner of this embodiment, the obtained feature coefficients are substituted into the background operation data function f (x), and it is obtained that the independent variables are t1、d、t2J, L background operation data function f (x) x1t1+x2d+x3t2+x4J+x5L。
In a second specific implementation manner of this embodiment, the obtained optimized feature coefficients are substituted into the optimized background operation data function f (x)', that is, the obtained arguments are t respectively1、d、t2Optimized background run data function f (x)' x of J, L1＇t1+x2＇d+x3＇t2+x4＇J+x5＇L；
Step S205: and specifying the specific numerical value of each feature vector in the function of the background operation data, namely obtaining the numerical value of the feature coefficient corresponding to each influence factor through the operation of the function of the background operation data.
In particular, the method of manufacturing a semiconductor device,
in a first specific implementation manner of this embodiment, a specific numerical value of each feature vector in the function f (x) of the background operation data is specified, that is, an operation data analysis result corresponding to the specific numerical value of each feature vector is obtained through the operation of the function f (x) of the background operation data.
In a second specific implementation manner of this embodiment, the specific values of the feature vectors in the function f (x) 'of the optimized background operation data are specified, that is, the optimized operation data analysis result corresponding to the specific values of the feature vectors can be obtained through the operation of the function f (x)' of the background operation data.
In this embodiment, the method for obtaining the quality sequence of the marketing campaign background operation data according to the operation data analysis result specifically includes the following steps:
and arranging the eigenvectors corresponding to the characteristic coefficients according to the sequence of the characteristic coefficients from large to small.
In particular, the method of manufacturing a semiconductor device,
in a first specific implementation manner of the embodiment, the characteristic coefficients x are determined according to each characteristic coefficient1、x2、x3、x4、 x5The values of (a) are sorted from big to small, and the feature vectors t corresponding to the sequence of sorting the feature coefficients from big to small are arranged from front to back1、d、t2、J、L。
In a second specific implementation manner of the embodiment, the characteristic coefficients x are determined according to the characteristic coefficients1＇、x2＇、x3＇、 x4＇、x5The value of' is sorted from big to small, and the feature vectors t corresponding to the sorted order of the feature coefficients from big to small are arranged from front to back1、d、t2、J、L。
And marking the sequence numbers of the feature vectors corresponding to the feature coefficients after arrangement to obtain the quality sequence of each influence factor corresponding to the background operation data.
Specifically, in the present embodiment, the post-alignment feature vector t1、d、t2J, L, the sequence number is the order of the impact factors corresponding to the background operating data.
In this embodiment, the number of the time intervals of the background operation data is multiple, and at least one of a start point and an end point of each time interval is different from start points or end points of other time intervals. In this case, a plurality of different pairs of data sets can be obtained, see fig. 4(a), 4(b), 4(c) and 4(d), the relationship between the plurality of time intervals being selected from the group consisting of fig. 4(a) where two adjacent time intervals are not consecutive, and where the time interval t is t1Cut-off point T of2The page is also the time interval t2A starting point of (a); or two adjacent time intervals shown in FIG. 4(b), in which the time interval t is1Cut-off point T of2And the time interval t2Starting point T of3With a time interval T therebetween3-T2Is greater than zero; or a part of the overlapping time between two adjacent time intervals shown in FIG. 4(c), wherein the cut-off point T of the time interval T12And the time interval t2Starting point T of3With a time interval T therebetween3-T2Less than zero; or some of the time intervals shown in FIG. 4(d) are encompassed by other time intervals, in which case the time interval t is2Starting point T of2Later than the time interval t1Starting point T of1Time interval t2Cut-off point T of4Earlier than the time interval t1Cut-off point T of2In practice, the selection can be made according to actual requirements.
Example two
Referring to fig. 5 and fig. 6, an apparatus for analyzing operation data according to a second embodiment of the present invention includes:
the data obtaining unit 401 is configured to obtain foreground operation income data and financial net profit data of the marketing campaign in different time intervals.
Specifically, in this embodiment, the data acquiring unit 401 may further include:
a first data obtaining module 4011, configured to obtain background operation data of marketing activities in different time intervals, where the background operation data includes f (x)1)、f(x2)、f(x3)、f(x4)、f(x5)。
A second data obtaining module 4012, configured to obtain foreground operation income data corresponding to the time interval of the background operation data, where the foreground operation income data includes m1、m2、m3、m4、m5。
A third data obtaining module 4013 for obtaining financial net profit data corresponding to time intervals of the background operation data, including r1、r2、r3、r4、r5。
And the computing unit 402 is used for obtaining corresponding background operation data of the marketing campaign in different time intervals according to the foreground operation income data and the financial net profit data of the marketing campaign in different time intervals.
Specifically, in this embodiment, it is necessary to obtain the front platform operation income data m in 5 time intervals1、 m2、m3、m4、m5Financial net profit data r corresponding to said 5 time intervals1、r2、r3、r4、 r5Obtaining the background operation data f (x) of the marketing activities in 5 time intervals1)、f(x2)、f(x3)、f(x4)、 f(x5)。
A function creating unit 403, configured to obtain, according to a preset function model, a function between the background operation data and the feature vector, according to the background operation data, the foreground operation income data corresponding to the background operation data, the financial net profit data, and the feature vector of each influence factor.
Specifically, in this embodiment, the function created by the function creating unit 403 according to the preset function model is f (x) ═ x1t1+x2d+x3t2+x4J+x5L, wherein x1、x2、x3、x4、x5Respectively, the characteristic coefficient, t, for each characteristic vector in the background operation data function f (x)1Duration of activity, d number of page clicks, t2Time spent page clicks, J-cash value of the reward issued to the user, L-restrictions for the user.
A data analysis unit 404, configured to perform data analysis on the function between the background operation data and the feature vector to obtain a numerical value of a feature coefficient corresponding to each impact factor.
Specifically, in the present embodiment, the function f (x) is x according to the background operation data1t1+x2d+x3t2+x4J+x5In L, the feature coefficient x for each feature vector1、x2、x3、x4、x5And each feature vector t1Duration of activity, d number of page clicks, t2-time spent page click, J-cash value of reward issued to user, L-data analysis against user's restrictions, resulting in said operational data analysis result.
And the sorting unit 405 is configured to obtain, according to the operation data analysis result, a quality sequence of each influence factor corresponding to the background operation data in the marketing campaign.
Specifically, in this embodiment, the sorting unit may further include:
the sorting module 4051: and the characteristic vectors corresponding to the characteristic coefficients are arranged according to the sequence of the characteristic coefficients from large to small.
The marking module 4052 is configured to mark sequence numbers of the feature vectors corresponding to the sequence in which the feature coefficients are sorted from large to small, so as to obtain a good-bad sequence of each impact factor corresponding to the background operation data.
The operation data analysis device provided by the invention obtains background operation data of marketing activities in different time intervals, foreground operation income data corresponding to the time intervals of the background operation data and financial net profit data corresponding to the time intervals of the background operation data through a data obtaining unit 401; obtaining a function between the background operation data and the characteristic vector according to a preset function model and the characteristic vector of each influence factor according to the background operation data, foreground operation income data corresponding to the background operation data and the characteristic vector of each influence factor through a function creating unit; performing data analysis on a function between the background operation data and the feature vector through a data analysis unit to obtain an operation data analysis result; and obtaining the quality sequence of each influence factor corresponding to the background operation data in the marketing activity according to the operation data analysis result through a sequencing unit.
EXAMPLE III
The storage medium provided by the third embodiment of the present invention stores an analysis program of the operation data, and when the analysis program of the operation data is executed by the processor, the analysis method of the operation data provided by the present invention is implemented.
When the storage medium provided by the invention runs, background operation data of marketing activities in different time intervals, foreground operation income data corresponding to the time intervals of the background operation data and financial net profit data corresponding to the time intervals of the background operation data are sequentially acquired; according to foreground operation income data, financial net profit data and feature vectors of all influence factors corresponding to the background operation data and the background operation data, obtaining a function between the background operation data and the feature vectors according to a preset function model; performing data analysis on a function between the background operation data and the feature vector to obtain an operation data analysis result; and obtaining the quality sequence of each influence factor corresponding to the background operation data in the marketing activity according to the operation data analysis result. Through the storage medium and the analysis equipment of the operation data, the background operation data of the marketing campaign can be quantized, so that a relatively accurate operation data analysis result is obtained, the operation condition of the marketing campaign can be mastered, valuable reference can be provided for subsequent marketing campaigns, and cash income obtained by the subsequent marketing campaigns is maximized.
Example four
The operation data analysis device provided by the fourth embodiment of the invention comprises a memory and an analysis program of operation data which is registered on the memory and can be operated on the processor by the processor, and when the analysis program of the operation data is executed by the processor, the operation data analysis method provided by the invention is realized.
On the analysis equipment of the operation data, a data analysis program which is stored on a memory and can run on a processor sequentially acquires background operation data of marketing activities in different time intervals, foreground operation income data corresponding to the time intervals of the background operation data and financial net profit data corresponding to the time intervals of the background operation data; according to foreground operation income data, financial net profit data and feature vectors of all influence factors corresponding to the background operation data and the background operation data, obtaining a function between the background operation data and the feature vectors according to a preset function model; performing data analysis on a function between the background operation data and the feature vector to obtain an operation data analysis result; and obtaining the quality sequence of each influence factor corresponding to the background operation data in the marketing activity according to the operation data analysis result. Through this analytical equipment, can quantify marketing campaign backstage operation data to obtain comparatively accurate operation data analysis result, it not only is favorable to mastering this marketing campaign's operation situation, can also provide valuable reference for follow-up marketing campaign, makes the cash income that follow-up marketing campaign obtained the biggest.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. An operational data analysis method, comprising the steps of:
acquiring foreground operation income data and financial net profit data of marketing activities in different time intervals;
obtaining corresponding background operation data of the marketing campaign in different time intervals according to the foreground operation income data and the financial net profit data of the marketing campaign in different time intervals;
obtaining a function between the background operation data of the marketing campaign and the feature vectors of the influence factors in each different time interval according to the background operation data of the marketing campaign in the different time intervals and the feature vectors of the influence factors of the background operation data and a preset function model;
performing data analysis on the function between the background operation data and the feature vector to obtain the numerical value of the feature coefficient corresponding to each influence factor;
and obtaining the quality sequence of each influence factor corresponding to the background operation data in the marketing campaign according to the numerical value of the characteristic coefficient corresponding to each influence factor.
2. The method for analyzing operation data according to claim 1, wherein when obtaining the background operation data corresponding to the time interval of the operation data according to the operation data, an operational formula of the background operation data is r-m-f (x),
wherein,
f (x): background operation data obtained by weighting operation of characteristic vectors of each influence factor corresponding to the background operation data,
m: foreground operational revenue data corresponding to a time interval of the background operational data,
r: financial net profit data corresponding to a time interval of the background operational data.
3. The method for analyzing operation data according to claim 2, wherein the step of performing data analysis on the function between the background operation data and the feature vector to obtain the numerical value of the feature coefficient corresponding to each influence factor specifically includes the following steps:
selecting background operation data in the same time interval, foreground operation income data corresponding to the time interval of the foreground operation data and financial net profit data corresponding to the time interval of the background operation data as a data group pair;
substituting a plurality of data sets into the preset function to obtain a multi-element linear equation set, wherein the preset function is the sum of products of a feature vector of each influence factor and a feature coefficient value corresponding to the feature vector, specific numerical values of the feature vector of each influence factor are known data obtained from a background in a corresponding time interval, and unknowns in the multi-element linear equation set are feature coefficients of the feature vector in the background operation data;
obtaining the numerical value of the characteristic coefficient of the characteristic vector in the background operation data by solving the multivariate linear equation set;
substituting the numerical value of the characteristic coefficient of the characteristic vector in the background operation data into the preset function to obtain a background operation data function reflecting the relation between the background operation data and the characteristic vector;
and specifying specific numerical values of the feature vectors in the background operation data function, and obtaining numerical values of the feature coefficients corresponding to the influence factors through the operation of the background operation data function.
4. The method according to claim 3, wherein when the number of pairs of data used to substitute the function between the background operating data and the feature vector is the same as the number of the feature vectors, the system of linear equations is directly solved to obtain the value of the feature coefficient of each feature vector.
5. The method for analyzing operational data according to claim 3, wherein when the number of pairs of data used to substitute the function between the background operational data and the feature vector is greater than the number of the feature vectors, the system of equations.
6. The operation data analysis method according to claim 3, wherein the method for obtaining the order of goodness of the marketing campaign background operation data according to the operation data analysis result specifically comprises the following steps:
arranging the eigenvectors corresponding to the characteristic coefficients according to the descending order of the characteristic coefficients;
and marking the sequence numbers of the feature vectors corresponding to the feature coefficients after arrangement to obtain the quality sequence of each influence factor corresponding to the background operation data.
7. An apparatus for analyzing operational data, comprising:
the data acquisition unit is used for acquiring foreground operation income data and financial net profit data of the marketing activities in different time intervals;
the computing unit is used for obtaining corresponding background operation data of the marketing activities in different time intervals according to the foreground operation income data and the financial net profit data of the marketing activities in different time intervals;
the function creating unit is used for obtaining a function between the background operation data of the marketing campaign and the feature vectors of the influence factors in each different time interval according to the background operation data of the marketing campaign in the different time intervals and the feature vectors of the influence factors of the background operation data and a preset function model;
the data analysis unit is used for carrying out data analysis on the function between the background operation data and the characteristic vector to obtain the numerical value of the characteristic coefficient corresponding to each influence factor;
and the sequencing unit is used for obtaining the quality sequence of each influence factor corresponding to the background operation data in the marketing activity according to the numerical value of the characteristic coefficient corresponding to each influence factor.
8. The operational data analysis device according to claim 7,
the data acquisition unit includes:
the first data acquisition module is used for acquiring background operation data of marketing activities in different time intervals;
the second data acquisition module is used for acquiring foreground operation income data corresponding to the time interval of the background operation data;
the third data acquisition module is used for acquiring financial net profit data corresponding to the time interval of the background operation data;
and/or the presence of a gas in the gas,
the unit includes:
a sorting module: the characteristic vectors corresponding to the characteristic coefficients are arranged according to the sequence of the characteristic coefficients from large to small;
a marking module: and the sequence number is used for marking the sequence numbers of the feature vectors corresponding to the sequence of the feature coefficients sorted from big to small to obtain the good and bad sequence of each influence factor corresponding to the background operation data.
9. A storage medium having stored thereon an analysis program of operation data, the analysis program of operation data realizing the analysis method of operation data according to any one of claims 1 to 6 when executed by a processor.
10. An operation data analysis device, comprising a memory, and a processor for registering an operation data analysis program that is stored in the memory and operable on the processor, wherein when the operation data analysis program is executed by the processor, the operation data analysis device implements the operation data analysis method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811536313.3A CN109801097A (en) | 2018-12-14 | 2018-12-14 | Analysis method, device, storage medium and the analytical equipment of operation data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811536313.3A CN109801097A (en) | 2018-12-14 | 2018-12-14 | Analysis method, device, storage medium and the analytical equipment of operation data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109801097A true CN109801097A (en) | 2019-05-24 |
Family
ID=66556793
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811536313.3A Pending CN109801097A (en) | 2018-12-14 | 2018-12-14 | Analysis method, device, storage medium and the analytical equipment of operation data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109801097A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110413969A (en) * | 2019-07-10 | 2019-11-05 | 江苏省高速公路经营管理中心 | One kind runing financial data analysis system based on the pretreated highway of data fusion |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100114794A1 (en) * | 2008-10-31 | 2010-05-06 | Choudur Lakshminarayan | Prediction of financial performance for a given portfolio of marketing investments |
| US20100114624A1 (en) * | 2008-10-31 | 2010-05-06 | Choudur Lakshminarayan | System and method for optimizing financial performance generated by marketing investments under budget constraints |
| JP2012063928A (en) * | 2010-09-15 | 2012-03-29 | Sharp Corp | Factor analysis method, factor analysis device, and recording medium |
| CN106204091A (en) * | 2015-05-28 | 2016-12-07 | 阿里巴巴集团控股有限公司 | Data processing method and device |
| CN106599200A (en) * | 2016-12-14 | 2017-04-26 | 陕西学前师范学院 | Intelligent remote financial data anti-cheating system and anti-cheating method thereof |
| CN108764528A (en) * | 2018-04-23 | 2018-11-06 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | A kind of Daily treatment cost interval prediction method of allowed for influencing factors analysis |
-
2018
- 2018-12-14 CN CN201811536313.3A patent/CN109801097A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100114794A1 (en) * | 2008-10-31 | 2010-05-06 | Choudur Lakshminarayan | Prediction of financial performance for a given portfolio of marketing investments |
| US20100114624A1 (en) * | 2008-10-31 | 2010-05-06 | Choudur Lakshminarayan | System and method for optimizing financial performance generated by marketing investments under budget constraints |
| JP2012063928A (en) * | 2010-09-15 | 2012-03-29 | Sharp Corp | Factor analysis method, factor analysis device, and recording medium |
| CN106204091A (en) * | 2015-05-28 | 2016-12-07 | 阿里巴巴集团控股有限公司 | Data processing method and device |
| CN106599200A (en) * | 2016-12-14 | 2017-04-26 | 陕西学前师范学院 | Intelligent remote financial data anti-cheating system and anti-cheating method thereof |
| CN108764528A (en) * | 2018-04-23 | 2018-11-06 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | A kind of Daily treatment cost interval prediction method of allowed for influencing factors analysis |
Non-Patent Citations (1)
| Title |
|---|
| 王便芳: "提升营销效率的一种新思路—基于数理模型的系统组合", 河北经贸大学学报, vol. 34, no. 03 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110413969A (en) * | 2019-07-10 | 2019-11-05 | 江苏省高速公路经营管理中心 | One kind runing financial data analysis system based on the pretreated highway of data fusion |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111325353B (en) | Training data set supplier selection method, device, equipment and storage medium | |
| CN110622196B (en) | Evaluate models that rely on aggregated historical data | |
| US11507580B2 (en) | Methods and apparatus to partition data | |
| US20210103858A1 (en) | Method and system for model auto-selection using an ensemble of machine learning models | |
| Wernerfelt et al. | Estimating the value of offsite data to advertisers on meta | |
| CN110008973B (en) | A model training method, a method and device for determining a target user based on a model | |
| US20120036085A1 (en) | Social media variable analytical system | |
| US11182804B2 (en) | Segment valuation in a digital medium environment | |
| CN110598183A (en) | Flow distribution method, device, equipment and storage medium | |
| US7660786B2 (en) | Data independent relevance evaluation utilizing cognitive concept relationship | |
| Drake et al. | Classifying forecasts | |
| JP2025520029A (en) | Method and system for performing data capture | |
| CN109801097A (en) | Analysis method, device, storage medium and the analytical equipment of operation data | |
| CN110310162A (en) | The method and device that sample generates | |
| CN107679896B (en) | Evaluation method and evaluation system based on time sequence-section model | |
| US20230214451A1 (en) | System and method for finding data enrichments for datasets | |
| US11467943B2 (en) | System and method for struggle identification | |
| CN117151794A (en) | Advertisement task recommendation method and related device | |
| CN111242449A (en) | Enterprise information loss prediction method | |
| CN112348594A (en) | Method, device, computing equipment and medium for processing article demands | |
| CN115705581A (en) | Advertisement Recommendation Success Rate Prediction, Advertisement Recommendation Method and Device | |
| KR20210030134A (en) | Method and system for analyzing advertising synergy | |
| Balzer et al. | Mitigating Consequences of Prestige in Citations of Publications | |
| JP2023073438A (en) | Information processing device, estimation device, analysis device, information processing method, and computer program | |
| WO2025158610A1 (en) | Learning device, analysis device, learning method, and program |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20190524 |
|
| WD01 | Invention patent application deemed withdrawn after publication |