CN112734233A - Method and device for confirming quality of newly added customer of APP (application) promotion channel - Google Patents

Method and device for confirming quality of newly added customer of APP (application) promotion channel Download PDF

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CN112734233A
CN112734233A CN202110027808.9A CN202110027808A CN112734233A CN 112734233 A CN112734233 A CN 112734233A CN 202110027808 A CN202110027808 A CN 202110027808A CN 112734233 A CN112734233 A CN 112734233A
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郑晓峰
杨云清
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Shanghai Yizhuo Network Technology Co ltd
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Abstract

The invention provides a method and a device for confirming the quality of newly added customers of an APP (application) popularization channel, wherein the method comprises the steps of carrying out subjective weight determination on each newly added customer quality evaluation index through an AHP (advanced high-performance analysis) analytic hierarchy process to obtain the subjective weight of each newly added customer quality evaluation index; determining objective weights of the newly added client quality evaluation indexes through an entropy weight method to obtain the objective weights of the newly added client quality evaluation indexes; combining the subjective weight and the objective weight of each newly added client quality evaluation index through a power average synthesis method to obtain the final weight of each newly added client quality evaluation index; and calculating the quality scores of the newly added customers and confirming the head channels according to the final weight of each newly added customer quality evaluation index. The invention has the beneficial effects that: the defect that advertisement operators cannot estimate the abnormity in time is overcome, and therefore accuracy and authority of the weight evaluation result are improved.

Description

Method and device for confirming quality of newly added customer of APP (application) promotion channel
Technical Field
The invention relates to a method and a device for confirming the quality of a newly added client, in particular to a method and a device for confirming the quality of the newly added client of an APP popularization channel.
Background
The method comprises the steps that new users obtained after the APP carries out advertisement promotion, the most active period of all new users on the same day is the new period on the same day, the quality of the new users obtained from a channel is preliminarily evaluated by combining the customer obtaining cost, the number of activated users, the per-person advertisement clicking times, the single advertisement clicking income and the new increase cost rate on the new day, and the quality condition of the new users of the channel can be effectively judged.
The prior method comprises the following steps: the quality evaluation of the newly added users in the current channel is mainly combined with the data related to the customer obtaining cost and the newly added cost rate in the same day to carry out experience evaluation.
The method has the following defects: due to the fact that experience assessment is carried out, the subjective performance is high, the relationship between the learning level of an evaluator and the inspection requirement is high, and the objective performance is not high.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: a method and a device for confirming the quality of newly added customers of an APP promotion channel are provided, so that the accuracy of a weight evaluation result is improved.
In order to solve the technical problems, the invention adopts the technical scheme that: a method for confirming the quality of newly added customers of an APP promotion channel comprises the following steps,
s10, carrying out subjective weight determination on each newly added customer quality evaluation index through an AHP analytic hierarchy process to obtain the subjective weight of each newly added customer quality evaluation index;
s20, determining the objective weight of each newly added client quality evaluation index through an entropy weight method to obtain the objective weight of each newly added client quality evaluation index;
s30, combining the subjective weight and the objective weight of each newly added customer quality evaluation index through a power average synthesis method to obtain the final weight of each newly added customer quality evaluation index;
and S40, calculating the quality scores of the newly added customers according to the final weight of each newly added customer quality evaluation index, and confirming the head channels.
Further, the newly added customer quality evaluation indexes comprise the number of activations, activation cost, per-person click times, first-day return rate and single click income.
Further, step S10 specifically includes,
s11, decomposing the target layer according to the requirement, and determining a plurality of newly added customer quality evaluation indexes;
s12, constructing a judgment matrix according to the quality evaluation indexes of the newly added customers, and scoring the relative importance between every two indexes by experts by using a nine-level scale method;
s13, carrying out normalization processing on the column vectors of the judgment matrix through an arithmetic mean method to obtain the subjective weight of each newly added customer quality evaluation index;
and S14, performing consistency check on the judgment matrix.
Further, in step S12, specifically,
the structure determination matrix a ═ aij)n×nThe relative importance between every two indexes is scored by experts by using a nine-level scale method, n is the number of newly added customer quality evaluation indexes, aijRepresenting the importance of the element i and the element j to the target, wherein the importance is the same when the value is 1, the importance of the element i is slightly more important than the element j when the value is 3, the importance of the element i is more important than the element j when the value is 5, the importance of the element i is more important than the element j when the value is 7, the importance of the element i is more important than the element j when the value is 9, the importance of the element i is more important than the element j when the value is 2, 4, 6 and 8;
Figure BDA0002889958160000021
if and only if aji＝m；
Wherein, aijRepresenting the importance of the element i and the element j to the target; m takes on values of 1, 2.
Further, in step S13, specifically,
taking an arithmetic mean method of column vectors, and carrying out column vector normalization processing:
Figure BDA0002889958160000022
i, j takes 1, a. Summing by rows;
Figure BDA0002889958160000023
then normalization processing is carried out to obtain
Figure BDA0002889958160000024
Then there are: wC＝{w1，w2，...，wn}；
Wherein, aijRepresenting the importance of element i and element j to the target, bi、
Figure BDA0002889958160000025
wiFor calculating a feature vector WCIntermediate variable of (1), WCAnd the feature vector is the subjective weight of the newly added client quality evaluation index.
Further, in step S14, specifically,
calculating a maximum feature root:
Figure BDA0002889958160000031
calculating a consistency index
Figure BDA0002889958160000032
Proportion of consistency
Figure BDA0002889958160000033
When CR is less than 0.1, the matrix is considered to have satisfactory consistency, otherwise, the judgment matrix needs to be adjusted;
wherein λ ismaxThe root is the maximum characteristic root, CI is the consistency index, and CR is the consistency ratio; and RI is a random consistency index corresponding to n newly-added customer quality evaluation indexes.
Further, step S20 specifically includes,
s21, taking activation cost xijReciprocal of (2)
Figure BDA0002889958160000034
Maximize its display;
s22, calculating the proportion of the ith scheme in the jth index
Figure BDA0002889958160000035
(j takes 1, 2.. ang., n)1；n1The number of sample data collected is the number of cases;
s23, calculating the entropy of the j index
Figure BDA0002889958160000036
Wherein
Figure BDA0002889958160000037
S24, calculating the difference coefficient g of the j indexj＝1-ej；
S25, calculating weight
Figure BDA0002889958160000038
(j is 1, 2,. n), WQ＝{w1，w2，...，wn}，WQAnd the objective weight of each newly added client quality evaluation index is obtained.
Further, in step S30, the subjective weight and the objective weight of each new customer quality evaluation index are combined by a power average synthesis method to obtain a final weight of each new customer quality evaluation index by using the following formula:
Figure BDA0002889958160000039
(k, i, j takes 1, 2.. n), W ═ W { (W)1，w2，…，wn}; w is the final weight of the newly added customer quality evaluation index.
Further, step S40 specifically includes,
s41, according to the final weight of each newly added customer quality evaluation index, through a formula:
Figure BDA00028899581600000310
Figure BDA00028899581600000311
i, taking 1, 2,. n1, and calculating a quality score of the newly added customer;
and S42, ranking the channels by adding the quality scores of the clients, and combining the two-eight rules to obtain the head popularization channel.
The invention also provides a device for confirming the quality of the newly added customer of the APP promotion channel, which comprises,
the subjective weight calculation module is used for carrying out subjective weight determination on each newly added customer quality evaluation index through an AHP (analytic hierarchy process) to obtain the subjective weight of each newly added customer quality evaluation index;
the objective weight calculation module is used for determining objective weights of the newly added client quality evaluation indexes through an entropy weight method to obtain the objective weights of the newly added client quality evaluation indexes;
the final weight calculation module is used for combining the subjective weight and the objective weight of each newly added customer quality evaluation index through a power average synthesis method to obtain the final weight of each newly added customer quality evaluation index;
and the head channel confirming module is used for calculating the quality scores of the newly added customers and confirming the head channels according to the final weight of each newly added customer quality evaluation index.
The invention has the beneficial effects that: the weight coefficient of each newly added client quality evaluation index is determined by combining an AHP (analytic hierarchy process) and an entropy weight method, and the final weight of each newly added client quality evaluation index is determined by combining a power average combination method, so that the subjectivity of quality judgment of newly added users according to experience is eliminated to a certain extent, the actual service condition and expert experience are integrated, the defect that advertising operators cannot estimate abnormality in time is overcome, and the accuracy and authority of weight evaluation results are improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the mechanisms shown in the drawings without creative efforts.
Fig. 1 is a flowchart of a method for confirming the quality of a newly added customer of an APP promotion channel according to an embodiment of the present invention;
fig. 2 is a block diagram of an apparatus for confirming the quality of a newly added client in an APP promotion channel according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the description of the invention relating to "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying any relative importance or implicit indication of the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
Referring to fig. 1, a first embodiment of the present invention is: a method for confirming the quality of newly added customers of an APP promotion channel comprises the following steps,
step S10, carrying out subjective weight determination on each newly added customer quality evaluation index through an AHP analytic hierarchy process to obtain the subjective weight of each newly added customer quality evaluation index; the newly added customer quality evaluation indexes comprise the number of activations, activation cost, per-person click times, first-day rate of reimbursement and single click income.
In a specific application, a new customer-related data set of 30 channels is collected, as shown in table 1 below;
table 1: adding new client related data table
Figure BDA0002889958160000051
Figure BDA0002889958160000061
Wherein, the step S10 specifically includes,
s11, decomposing the target layer according to the requirement, and determining a plurality of newly added customer quality evaluation indexes;
s12, constructing a judgment matrix according to the quality evaluation indexes of the newly added customers, and scoring the relative importance between every two indexes by experts by using a nine-level scale method, as shown in the following table 2;
table 2: newly-added client quality evaluation index scoring table
Figure BDA0002889958160000071
In step S12, specifically, the step,
the structure determination matrix a ═ aij)n×nThe relative importance between every two indexes is scored by experts by using a nine-level scale method, n is the number of newly added customer quality evaluation indexes, aijRepresenting the importance of the element i and the element j to the target, wherein the importance is the same when the value is 1, the importance of the element i is slightly more important than the element j when the value is 3, the importance of the element i is more important than the element j when the value is 5, the importance of the element i is more important than the element j when the value is 7, the importance of the element i is more important than the element j when the value is 9, the importance of the element i is more important than the element j when the value is 2, 4, 6 and 8;
Figure BDA0002889958160000072
if and only if aji＝m；
Wherein, aijRepresenting the importance of the element i and the element j to the target; m takes on values of 1, 2.
The matrix constructed according to Table 2 above is
Figure BDA0002889958160000073
S13, carrying out normalization processing on the column vectors of the judgment matrix through an arithmetic mean method to obtain the subjective weight of each newly added customer quality evaluation index;
further, in step S13, specifically,
taking an arithmetic mean method of column vectors, and carrying out column vector normalization processing:
Figure BDA0002889958160000074
i, j takes 1, a. Summing by rows;
Figure BDA0002889958160000081
then normalization processing is carried out to obtain
Figure BDA0002889958160000082
Then there are: wc＝{w1，w2，...，wn}；
Wherein, aijRepresenting the importance of element i and element j to the target, bi、
Figure BDA0002889958160000083
wiFor calculating a feature vector WCIntermediate variable of (1), WCAnd the feature vector is the subjective weight of the newly added client quality evaluation index.
By performing normalization processing, the feature vector (each index weight of AHC analytic hierarchy process) W of the judgment matrix A is obtainedc＝(0.2613，0.1560，0.4831，0.0401，0.0595)。
And S14, performing consistency check on the judgment matrix.
In step S14, specifically, the step,
calculating a maximum feature root:
Figure BDA0002889958160000084
calculating a consistency index
Figure BDA0002889958160000085
Proportion of consistency
Figure BDA0002889958160000086
When CR is less than 0.1, the matrix is considered to have satisfactory consistency, otherwise, the judgment matrix needs to be adjusted;
wherein λ ismaxThe root is the maximum characteristic root, CI is the consistency index, and CR is the consistency ratio; RI is a random consistency index corresponding to n newly added customer quality evaluation indexes, and specific numerical values are shown in table 3 below.
Through the above calculation mode: calculating to obtain maximum characteristic root value of lambdamax5.2408, the consistency index CI is 0.0602, the consistency ratio CR is 0.0538 < 0.1, and the consistency test is satisfied.
Table 3: consistency index value table
n 1 2 3 4 5 6 7 8 9 10 11
RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 1.51
Step S20, determining objective weight of each newly added client quality evaluation index through an entropy weight method to obtain the objective weight of each newly added client quality evaluation index;
further, step S20 specifically includes,
s21, taking activation cost xijReciprocal of (2)
Figure BDA0002889958160000087
Maximize its display; the current activation cost can be known, the smaller the activation cost is, the better the activation cost is, the reciprocal of the collected observation data is taken, the display of the observation data is maximized, and the trend of the observation data is consistent with that of other indexes.
S22, calculating the proportion of the ith scheme in the jth index
Figure BDA0002889958160000091
(j takes 1, 2.. ang., n)1；n1Is the number of sample data collected.
S23, calculating the entropy of the j index
Figure BDA0002889958160000092
Wherein
Figure BDA0002889958160000093
Calculated, e ═ e (0.9319, 0.9978, 0.9940, 0.9845, 0.9755);
s24, calculating the difference coefficient g of the j indexj＝1-ej；
Calculating g ═ (0.0681, 0.0022, 0.0060, 0.0155, 0.0245);
s25, calculating weight
Figure BDA0002889958160000094
(j is 1, 2,. n), WQ＝{w1，w2，...，wn}，WQAnd the objective weight of each newly added client quality evaluation index is obtained.
And calculating to obtain: wQ＝(0.5856，0.0191，0.0516，0.1336，0.2102)；
S30, combining the subjective weight and the objective weight of each newly added customer quality evaluation index through a power average synthesis method to obtain the final weight of each newly added customer quality evaluation index;
further, in step S30, the subjective weight and the objective weight of each new customer quality evaluation index are combined by a power average synthesis method to obtain a final weight of each new customer quality evaluation index by using the following formula:
Figure BDA0002889958160000095
(k, i, j takes 1, 2.. n), W ═ W { (W)1，w2，...，wn}; w is the final weight of the newly added customer quality evaluation index.
And calculating to obtain: w ═ (0.3911, 0.0545, 0.1578, 0.0732, 0.1118);
and S40, calculating the quality scores of the newly added customers according to the final weight of each newly added customer quality evaluation index, and confirming the head channels.
Further, step S40 specifically includes,
s41, adding new guests according to the new guestsAnd the final weight of the user quality evaluation index is calculated by the formula:
Figure BDA0002889958160000096
Figure BDA0002889958160000097
i, taking 1, 2,. n1, and calculating a quality score of the newly added customer;
and S42, ranking the channels by adding the quality scores of the clients, combining the second-eight rules to obtain the head promotion channel, and taking the first six channels as main delivery channels and the last six channels as abandoning channels according to the second-eight rules as shown in the following table 4.
Table 4: newly added client quality scoring table
Figure BDA0002889958160000101
Figure BDA0002889958160000111
According to the method, the weight coefficient of each newly added client quality evaluation index is determined by combining an AHP (analytic hierarchy process) and an entropy weight method, and the final weight of each newly added client quality evaluation index is determined by combining a power average combination method, so that the subjectivity of quality judgment of newly added users according to experience is eliminated to a certain extent, the actual service condition and expert experience are integrated, the defect that advertisement operators cannot estimate abnormality in time is overcome, and the accuracy and authority of a weight evaluation result are improved.
As shown in fig. 2, the second embodiment of the present invention is: a device for confirming the quality of newly added customers of APP promotion channels comprises,
the subjective weight calculation module 10 is configured to perform subjective weight determination on each newly added client quality evaluation index through an AHP analytic hierarchy process to obtain a subjective weight of each newly added client quality evaluation index;
the objective weight calculation module 20 is configured to perform objective weight determination on each newly added client quality evaluation index through an entropy weight method to obtain an objective weight of each newly added client quality evaluation index;
the final weight calculation module 30 is used for combining the subjective weight and the objective weight of each newly added customer quality evaluation index through a power average synthesis method to obtain the final weight of each newly added customer quality evaluation index;
and the head channel confirming module 40 is used for calculating the quality scores of the newly added customers and confirming the head channels according to the final weight of each newly added customer quality evaluation index.
It should be noted that, as can be clearly understood by those skilled in the art, for a specific implementation process of the apparatus for confirming the quality of the newly added customer of the APP promotion channel, reference may be made to the corresponding description in the foregoing method embodiment, and for convenience and brevity of description, details are not described here again.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. A method for confirming the quality of newly added customers of an APP promotion channel is characterized in that: comprises the following steps of (a) carrying out,
s10, carrying out subjective weight determination on each newly added customer quality evaluation index through an AHP analytic hierarchy process to obtain the subjective weight of each newly added customer quality evaluation index;
s20, determining the objective weight of each newly added client quality evaluation index through an entropy weight method to obtain the objective weight of each newly added client quality evaluation index;
s30, combining the subjective weight and the objective weight of each newly added customer quality evaluation index through a power average synthesis method to obtain the final weight of each newly added customer quality evaluation index;
and S40, calculating the quality scores of the newly added customers according to the final weight of each newly added customer quality evaluation index, and confirming the head channels.
2. The method for identifying the quality of the newly added customers of the APP promotion channel as claimed in claim 1, wherein: the newly added customer quality evaluation indexes comprise the number of activations, activation cost, per-person click times, first-day rate of reimbursement and single click income.
3. The method for identifying the quality of the newly added customers of the APP promotion channel as claimed in claim 2, wherein: the step S10 specifically includes the steps of,
s11, decomposing the target layer according to the requirement, and determining a plurality of newly added customer quality evaluation indexes;
s12, constructing a judgment matrix according to the quality evaluation indexes of the newly added customers, and scoring the relative importance between every two indexes by experts by using a nine-level scale method;
s13, carrying out normalization processing on the column vectors of the judgment matrix through an arithmetic mean method to obtain the subjective weight of each newly added customer quality evaluation index;
and S14, performing consistency check on the judgment matrix.
4. The method for identifying the quality of the newly added customers of the APP promotion channel as claimed in claim 3, wherein: in step S12, specifically, the step,
the structure determination matrix a ═ aij)n×nThe relative importance between every two indexes is scored by experts by using a nine-level scale method, n is the number of newly added customer quality evaluation indexes, aijRepresenting the importance of the element i and the element j to the target, wherein the importance is the same when the value is 1, the importance of the element i is slightly more important than the element j when the value is 3, the importance of the element i is more important than the element j when the value is 5, the importance of the element i is more important than the element j when the value is 7, the importance of the element i is more important than the element j when the value is 9, the importance of the element i is more important than the element j when the value is 2, 4, 6 and 8;
Figure FDA0002889958150000021
if and only if aji＝m；
Wherein, aijRepresenting the importance of the element i and the element j to the target; m takes on values of 1, 2.
5. The method for identifying the quality of the newly added customers of the APP promotion channel as claimed in claim 4, wherein: in step S13, specifically, the step,
taking an arithmetic mean method of column vectors, and carrying out column vector normalization processing:
Figure FDA0002889958150000022
i, j takes 1, a. Summing by rows;
Figure FDA0002889958150000023
then normalization processing is carried out to obtain
Figure FDA0002889958150000024
Then there are: wC＝{w1，w2，...，wn}；
Wherein, aijRepresenting the importance of element i and element j to the target, bi、
Figure FDA00028899581500000212
wiFor calculating a feature vector WCIntermediate variable of (1), WCAnd the feature vector is the subjective weight of the newly added client quality evaluation index.
6. The method for identifying the quality of the newly added customers of the APP promotion channel as claimed in claim 5, wherein: in step S14, specifically, the step,
calculating a maximum feature root:
Figure FDA0002889958150000025
calculating a consistency index
Figure FDA0002889958150000026
Proportion of consistency
Figure FDA0002889958150000027
When CR is less than 0.1, the matrix is considered to have satisfactory consistency, otherwise, the judgment matrix needs to be adjusted;
wherein λ ismaxThe root is the maximum characteristic root, CI is the consistency index, and CR is the consistency ratio; and RI is a random consistency index corresponding to n newly-added customer quality evaluation indexes.
7. The method for identifying the quality of the newly added customers of the APP promotion channel as claimed in claim 6, wherein: the step S20 specifically includes the steps of,
s21, taking activation cost xijReciprocal of (2)
Figure FDA0002889958150000028
Maximize its display;
s22, calculating the proportion of the ith scheme in the jth index
Figure FDA0002889958150000029
(j takes 1, 2.. ang., n)1；n1The number of sample data collected is the number of cases;
s23, calculating the entropy of the j index
Figure FDA00028899581500000210
Wherein
Figure FDA00028899581500000211
S24, calculating the difference coefficient g of the j indexj＝1-ej；
S25, calculating weight
Figure FDA0002889958150000031
(j is 1, 2,. n), WQ＝{w1，w2，...，wn}，WQFor each new additionObjective weight of customer quality assessment indicators.
8. The method for identifying the quality of the newly added customers of the APP promotion channel as claimed in claim 7, wherein: in step S30, the subjective weight and the objective weight of each newly added customer quality evaluation index are combined by a power-averaging synthesis method to obtain a final weight of each newly added customer quality evaluation index, using the following formula:
Figure FDA0002889958150000032
(k, i, j takes 1, 2.. n), W ═ W { (W)1，w2，...，wn}; w is the final weight of the newly added customer quality evaluation index.
9. The method for identifying the quality of the newly added customers of the APP promotion channel as claimed in claim 8, wherein: the step S40 specifically includes the steps of,
s41, according to the final weight of each newly added customer quality evaluation index, through a formula:
Figure FDA0002889958150000033
Figure FDA0002889958150000034
i, taking 1, 2,. n1, and calculating a quality score of the newly added customer;
and S42, ranking the channels by adding the quality scores of the clients, and combining the two-eight rules to obtain the head popularization channel.
10. The utility model provides a confirm device of newly-increased customer quality of APP popularization channel which characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the subjective weight calculation module is used for carrying out subjective weight determination on each newly added customer quality evaluation index through an AHP (analytic hierarchy process) to obtain the subjective weight of each newly added customer quality evaluation index;
the objective weight calculation module is used for determining objective weights of the newly added client quality evaluation indexes through an entropy weight method to obtain the objective weights of the newly added client quality evaluation indexes;
the final weight calculation module is used for combining the subjective weight and the objective weight of each newly added customer quality evaluation index through a power average synthesis method to obtain the final weight of each newly added customer quality evaluation index;
and the head channel confirming module is used for calculating the quality scores of the newly added customers and confirming the head channels according to the final weight of each newly added customer quality evaluation index.
CN202110027808.9A 2021-01-08 2021-01-08 Method and device for confirming quality of newly added customer of APP (application) promotion channel Pending CN112734233A (en)

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