WO2019196502A1

WO2019196502A1 - Marketing activity quality assessment method, server, and computer readable storage medium

Info

Publication number: WO2019196502A1
Application number: PCT/CN2018/123580
Authority: WO
Inventors: 杨亮吉; 陈旷雨; 王思晶
Original assignee: 深圳壹账通智能科技有限公司
Priority date: 2018-04-13
Filing date: 2018-12-25
Publication date: 2019-10-17
Also published as: CN108805603A

Abstract

The present application discloses a marketing activity quality assessment method, said method comprising: acquiring all activity data concerning a current activity and other activities in a database; pre-processing the activity data; using a pre-established activity quality assessment model to process the activity data so as to obtain an activity quality score of the current activity and activity quality scores of other activities; and presenting an activity effect on a bubble diagram, and performing comparisons between the activities. The present application further provides a server and a computer readable storage medium. The marketing activity quality assessment method, the server, and the computer readable storage medium provided in the present application can clearly compare the effects of activities in various aspects, thereby facilitating the improvement of marketing activity efficiency, and providing directions and ideas for subsequent activity optimization and new activity planning.

Description

Marketing activity quality assessment method, server and computer readable storage medium

This application claims priority to Chinese Patent Application No. 201810331128.4, entitled "Marketing Activity Quality Assessment Method, Server and Computer Readable Storage Media", filed on April 13, 2018, the entire contents of which are The citations are incorporated herein by reference.

Technical field

The present application relates to the field of data analysis technologies, and in particular, to a marketing activity quality assessment method, a server, and a computer readable storage medium.

Background technique

With the rapid development of Internet technology, whether it is a new type of Internet enterprise or most traditional entities, the development of Internet marketing is a necessary means for all enterprises to survive and develop in the future. Internet marketing is a marketing method in which companies achieve marketing goals through online networks, computer communications, and digital interactive media. Internet marketing has the characteristics of low cost, high efficiency, wide spread, good effect, and strong real-time performance. However, compared with traditional marketing activities, the evaluation of Internet marketing activities faces many problems. For example, many qualitative indicators and hidden indicators require enterprise mining, while the input-output ratio, market size, and profit growth used by traditional marketing activities The indicators such as rate are obviously not enough, and how to automatically realize the historical vertical comparison of Internet marketing activities and the horizontal comparison in the same period is also an urgent need. Therefore, how to comprehensively evaluate the advantages and disadvantages of Internet marketing activities, and provide direction and ideas for future event optimization and new event planning has become a major problem that needs to be solved.

Summary of the invention

In view of this, the present application proposes a marketing activity quality assessment method, a server, and a computer readable storage medium to solve the problem of how to comprehensively evaluate the merits of Internet marketing activities.

First of all, in order to achieve the above object, the present application proposes a marketing activity quality assessment method, the method comprising the steps of:

Obtain activity data of the event and other activities in the database, the activity data including the activity cost indicator, the customer indicator, the sales indicator, and the customer activity indicator;

Performing dimensionless processing on the activity data based on a first formula to normalize the activity data, the first formula is:

Where Xi is the standardized activity data, and Yi is the value after Xi standardization.

Representing the summation of multiple values to be normalized;

The pre-established activity quality assessment model processes the standardized activity data to obtain the activity quality score of the event and the activity quality score of other activities;

Comparing the activity quality score of the activity with the activity quality score of the other activity, outputting the evaluation result of the activity according to the comparison result, and displaying the activity quality score of the activity and other activities on the bubble chart Activity quality score.

Calculate the average of the data for the month prior to the event; and

The growth data is obtained by subtracting the average of the period during which the event is held from the average of the month prior to the event.

In addition, in order to achieve the above object, the present application further provides a server, including a memory, a processor, and a marketing activity quality evaluation system stored on the memory and operable on the processor, the marketing activity quality evaluation system. The steps of the marketing activity quality assessment method as described above are implemented when executed by the processor.

Further, to achieve the above object, the present application further provides a computer readable storage medium storing a marketing activity quality evaluation system, the marketing activity quality evaluation system being executable by at least one processor, The step of causing the at least one processor to perform the marketing activity quality assessment method as described above.

Compared with the prior art, the marketing activity quality evaluation method, the server and the computer readable storage medium proposed by the present application first acquire all activity data of the event and the history activity, delineate the storage space, and all the activities described. Data is stored in the storage space; secondly, the activity data is pre-processed; then, the pre-established activity quality assessment model processes the pre-processed activity data to obtain an activity quality score; and finally, in the bubble chart Show the effect of the event and compare the activities. Using the marketing activity quality assessment method, server and computer readable storage medium proposed in this application, the advantages and disadvantages of Internet marketing activities can be comprehensively and comprehensively evaluated. The historical vertical and horizontal comparison of Internet marketing activities can clearly compare activities. The effects of various aspects are conducive to improving the efficiency of marketing activities, providing direction and ideas for future event optimization and new event planning, which is more convenient, faster and more accurate than the prior art.

DRAWINGS

1 is a schematic diagram of an optional hardware architecture of the server of the present application;

2 is a schematic diagram of a program module of a first embodiment of a marketing activity quality evaluation system of the present application;

3 is a schematic diagram of a program module of a second embodiment of the marketing activity quality evaluation system of the present application;

4 is a schematic flow chart of a first embodiment of a method for evaluating a quality of a marketing activity of the present application;

5 is a schematic flow chart of a second embodiment of a method for evaluating a quality of a marketing activity of the present application;

6 is a schematic flow chart of a third embodiment of a method for evaluating a quality of marketing activities of the present application;

The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings.

detailed description

In order to make the objects, technical solutions, and advantages of the present application more comprehensible, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the application and are not intended to be limiting. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without departing from the inventive scope are the scope of the present application.

It should be noted that the descriptions of "first", "second" and the like in the present application are for the purpose of description only, and are not to be understood as indicating or implying their relative importance or implicitly indicating the number of indicated technical features. . Thus, features defining "first" or "second" may include at least one of the features, either explicitly or implicitly. In addition, the technical solutions between the various embodiments may be combined with each other, but must be based on the realization of those skilled in the art, and when the combination of the technical solutions is contradictory or impossible to implement, it should be considered that the combination of the technical solutions does not exist. Nor is it within the scope of protection required by this application.

Referring to FIG. 1, it is a schematic diagram of an optional hardware architecture of the server 1 of the present application.

In this embodiment, the server 1 may include, but is not limited to, the memory 11, the processor 12, and the network interface 13 being communicably connected to each other through a system bus. It is pointed out that Figure 1 only shows the server 1 with the components 11-13, but it should be understood that not all illustrated components are required to be implemented, and more or fewer components may be implemented instead.

The server 1 may be a computing device such as a rack server, a blade server, a tower server, or a rack server. The server 1 may be an independent server or a server cluster composed of multiple servers.

The memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), a random access memory (RAM), a static Random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like. In some embodiments, the memory 11 may be an internal storage unit of the server 1, such as a hard disk or memory of the server 1. In other embodiments, the memory 11 may also be an external storage device of the server 1, such as a plug-in hard disk equipped on the server 1, a smart memory card (SMC), and a secure digital (Secure) Digital, SD) cards, flash cards, etc. Of course, the memory 11 can also include both the internal storage unit of the server 1 and its external storage device. In this embodiment, the memory 11 is generally used to store an operating system installed in the server 1 and various types of application software, such as program code of the marketing activity quality evaluation system 2. Further, the memory 11 can also be used to temporarily store various types of data that have been output or are to be output.

The processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 12 is typically used to control the overall operation of the server 1. In this embodiment, the processor 12 is configured to run program code or process data stored in the memory 11, such as running the marketing activity quality assessment system 2 and the like.

The network interface 13 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the server 1 and other electronic devices.

So far, the hardware structure and functions of the devices related to this application have been described in detail. Hereinafter, various embodiments of the present application will be made based on the above description.

First, the present application proposes a marketing activity quality assessment system 2.

Referring to FIG. 2, it is a program module diagram of the first embodiment of the marketing activity quality assessment system 2 of the present application.

In this embodiment, the marketing activity quality assessment system 2 includes a series of computer program instructions stored in the memory 11, and when the computer program instructions are executed by the processor 12, the quality of the marketing activities of the embodiments of the present application can be implemented. Evaluation operation. In some embodiments, the campaign quality assessment system 2 can be divided into one or more modules based on the particular operations implemented by the various portions of the computer program instructions. For example, in FIG. 3, the marketing activity quality assessment system 2 can be divided into a data acquisition module 21, a pre-processing module 22, a scoring module 23, and a display module 24. among them:

The data obtaining module 21 is configured to acquire all activity data of the current activity and other activities in the database.

Specifically, in order to compare the effects of this event and other activities, and to obtain activity data of this event and other activities, other activities include activities held in conjunction with the event and events held in history.

Specifically, the acquired activity data includes an activity cost indicator, a customer indicator, a sales indicator, and a customer activity indicator, wherein the cost indicator includes a marketing resource cost, an internal channel promotion cost, an external channel promotion cost, a business development cost, and a customer. The indicators include the number of registered persons, the number of accounts opened, the sales indicators include the number of purchases, the order quantity and sales volume, and the customer activity indicators include the number of customer logins and the number of logins.

The pre-processing module 22 is configured to perform pre-processing on the activity data to perform dimensionless processing on the activity data, so that the activity has dimensionless characteristics.

Specifically, the pre-processing includes the steps of: calculating an average value of the activity data during an activity;

Calculate the average of the data for the month prior to the event; and

Specifically, the collected daily indicators are subtracted from the daily average value of the previous month of the activity, and the growth data is obtained, and the data is sorted according to the basis. For example, activity A should be held once a month. Take Activity A as an example. Obtain activity A. The number of accounts opened this month and the number of accounts opened last month, R2, based on R1 and R2. The daily average R11 and R22 of the number of accounts opened this month and last month are based on the daily average value R11 of this month and the daily average value R22 of the previous month, and the growth data (R11-R22) of this month is compared with the average daily opening value of the previous month. Further, for different activities A and B, as long as they have the same purpose, for example, to increase turnover, order quantity, number of accounts, etc., then the method of calculating the growth data is as described above. The method is similar. I will not repeat them here.

The scoring module 23 is configured to process the activity data using a pre-established activity quality assessment model to obtain an activity quality score of the activity and an activity quality score of other activities.

Specifically, the activity quality assessment model is mainly used to process the pre-processed activity data to obtain an activity quality score, and the activity quality assessment model calculates the activity quality score includes: performing the activity growth data Sorting, calculating the activity quality score of the event and the activity quality score of other activities according to the sorted activity growth data.

Specifically, the number of activities may be sorted according to different ranks according to different ranks, for example, the number of account opening data is sorted in descending order, and the sales volume increase data is in accordance with the largest to smallest. Sort in order.

The activity quality score of the activity and the activity quality score of other activities can be obtained by calculating the sorted activity growth data by a specific method. Specifically, the specific method may include performing data segmentation and corresponding to the sorted growth data. The base score is formed into the basic score interval, for example, the aforementioned growth data is divided into one percentile points, and the corresponding score is 0.01 to 1 minute. For example, if the growth figures of the number of accounts for activity A, activity B, activity C, and activity D are 30, 20, 10, and 0, respectively, the percentiles corresponding to the numerical growth data of the account holder are 1, 0.75, respectively. 0.5, 0.25, corresponding to the score interval of 0.01 to 1, the corresponding base scores are 1, 0.75, 0.5, 0.25. It should be noted that using the percentile method to obtain the base score is to treat the same type of data in all activities as a percentile, for example, the number of accounts for multiple activities, the cost of business development for all activities, and all activities. After the percentile of the same type of data, it is necessary to match each percentile to the interval of [0.01, 1] to obtain the corresponding base score.

Specifically, the specific method further includes determining different weights for different activity data. For example, according to the actual service scenario, the scoring weights are determined for the indicators participating in the scoring, each weight value is 0 to 1, and the weights are added to 1.

Specifically, the steps of obtaining the weights of the indicators include:

Obtain the activity data after standardization of each indicator, for example, the activity indicators are X1, X2, X3, ..., Xi, and assume that the values normalized to each indicator data are Y1, Y2, ..., Yi;

Find the information entropy of each indicator: According to the definition of information entropy in information theory, the information entropy of a set of data:

E _i = -ln(n) ^-1 ∑p _i lnp _i , where P _i =Y _i /∑Y _i

Determine the weight of each indicator: According to the calculation formula of information entropy, calculate the information entropy of each index as E1, E2,..., Ek, and the weight is: Wi=(1-Ei)/(k-∑Ei), i=1 , 2,...,k.

Specifically, the step of obtaining the activity quality score further includes:

Get the score for each metric by the following formula:

Pi=Yi*Wi, i=1, 2,...,16

The activity quality score is obtained by the following formula:

Specifically, the specific method further includes calculating the indicator base score by substituting the actual index value of each activity into each score interval, and multiplying the weight by the sum to obtain the comprehensive score.

The display module 24 compares the activity quality score of the current activity with the activity quality score of other activities, outputs an evaluation result of the activity according to the comparison result, and displays the activity of the activity on the bubble chart. Quality scores and activity quality scores for other activities.

Specifically, after the activity comprehensive score is obtained, the actual cost and important indicator value of the activity are collected (for example, one activity whose main purpose is to promote transaction volume, and the important indicator value is the transaction amount during the activity). The activity scores, cost and important indicator values are marked as three output values on the bubble chart to visually compare the advantages and disadvantages between the activities at the same time; historical activities can also be introduced as a reference. It is also possible to fit an activity effect average line for each activity of the same period as a reference, and above and below the average line, it is higher than the average effect of the concurrent activities.

Further, the marketing activity quality assessment system 2 can continuously update the quality sub-evaluation model to ensure that the evaluation effect is objective.

Specifically, the new activity indicator values are included in all historical activities for sorting, and 100 percentiles are re-defined, and the interval values corresponding to each of the 0.01-1 individual scores. This standard can be presented to the event planner to quantitatively split the subsequent activity goals and properly control the activity process.

Referring to FIG. 3, it is a program module diagram of the second embodiment of the marketing activity quality assessment system 2 of the present application. In this embodiment, the marketing activity quality assessment system 2 includes a standardization module 25 in addition to the data acquisition module 21, the preprocessing module 22, the scoring module 23, and the display module 24 in the first embodiment.

Calculate the average of the data for the month prior to the event; and

Specifically, the collected daily indicators are subtracted from the daily average value of the previous month of the activity, and the growth data is obtained, and the data is sorted according to the basis. For example, activity A should be held once a month. Take Activity A as an example. Obtain activity A. The number of accounts opened this month and the number of accounts opened last month, R2, based on R1 and R2. The daily average R11 and R22 of the number of accounts opened this month and last month are based on the daily average value R22 of the previous month and the daily average value R22 of the previous month. The growth data (R11-R22) of this month is compared with the average daily value of the account opened last month. Further, for different activities A and B, as long as they have the same purpose, for example, to increase turnover, order quantity, number of accounts, etc., then the method of calculating the growth data is as described above. The method is similar. I will not repeat them here.

Specifically, the activity quality assessment model is mainly used to process the pre-processed activity data to obtain an activity quality score, and the functions of the activity quality assessment model include:

Performing data segmentation on the input data and corresponding to the basic score interval to form a base score, for example, dividing the growth data R11-R22 into one hundred points, and correspondingly the base score is 0.01 to 1 minute;

Determining different weights for different activity data. For example, according to the actual business scenario, the scoring weights are determined for the indicators participating in the scoring, each weight value is 0 to 1, and the weights are added to 1;

Substituting the actual indicator values of each activity into each score interval to calculate the indicator base score, and multiplying by the weights to obtain a comprehensive score.

The display module 24 is configured to compare the activity quality score of the current activity with the activity quality score of other activities, output an evaluation result of the activity according to the comparison result, and display the activity on the bubble chart. Activity quality scores and activity quality scores for other activities.

Specifically, after the activity comprehensive score is obtained, the actual cost and important indicator value of the activity are collected (for example, one main purpose is to promote the activity of the transaction volume, and the important indicator value is the transaction amount during the activity period). The activity scores, cost and important indicator values are marked as three output values on the bubble chart to visually compare the advantages and disadvantages between the activities at the same time; historical activities can also be introduced as a reference. It is also possible to fit an activity effect average line for each activity of the same period as a reference, and above and below the average line, it is higher than the average effect of the concurrent activities.

The difference between this embodiment and the first embodiment is that the embodiment further includes a standardization module 25 with respect to the first embodiment.

The normalization module 25 is configured to perform dimensionless processing on the activity data based on the first formula.

Specifically, since the nature of the activity data is different and the unit of measurement is different, the model cannot be directly input for calculation and analysis, so the extracted raw data needs to be standardized and dimensionlessly processed. The common standardized dimensionless processing method includes the threshold method and the proportion. Law and standardization law, etc.

The first formula for preprocessing the activity data by the specific gravity method and using the specific gravity method to perform dimensionless processing on the activity is:

Where Yi is the value after Xi standardization,

Represents the sum of multiple values to be normalized. The function of step b is to perform dimensionless processing on each data for different characteristics of different types of data units, for example, the cost unit is a unit, the unit of the customer index is a number, and the number of units of the sales indicator is a number, etc., To avoid distortion due to different units in the calculation, it is necessary to standardize the data without dimensioning.

In addition, the present application also proposes a method for evaluating the quality of marketing activities.

Referring to FIG. 4, it is a schematic flowchart of the first embodiment of the quality assessment method for marketing activities of the present application. In this embodiment, the order of execution of the steps in the flowchart shown in FIG. 4 may be changed according to different requirements, and some steps may be omitted.

In step S110, all activity data of the event and other activities in the database are acquired.

Step S120, preprocessing the activity data to standardize the activity data.

Specifically, the collected daily indicators are subtracted from the daily average value of the previous month of the activity, and the growth data is obtained, and the data is sorted according to the basis. For example, activity A should be held once a month. Take Activity A as an example. Obtain activity A. The number of accounts opened this month and the number of accounts opened last month, R2, based on R1 and R2. The daily average values R11 and R22 of the number of accounts opened this month and last month are based on the daily average value R22 of the previous month and the daily average value R22 of the previous month, and the growth data R11-R22 of this month is compared with the average daily opening value of the previous month. Further, for different activities A and B, as long as they have the same purpose, for example, to increase turnover, order quantity, number of accounts, etc., then the method of calculating the growth data is as described above. The method is similar. I will not repeat them here.

Step S130, the pre-established activity quality assessment model processes the activity data to obtain an activity quality score of the activity and an activity quality score of the other activities.

Specifically, the activity quality assessment model can be composed of three aspects: data segmentation, determination of indicator weights, and score output.

Data segmentation: By dividing each index value that has been sorted in the previous step into 100 quantile points, the interval of each quantile is corresponding to 0.01 to 1 minute.

Determining the weight: According to the actual business scenario, the scoring weights are determined for the indicators participating in the scoring, each weight value is 0 to 1, and the weights of ownership are added to 1.

Score output: Substitute the actual indicator value of each activity into each score interval to calculate the indicator base score, and multiply by the weight to get the comprehensive score.

Specifically, the steps of obtaining the weights of the indicators include:

Find the information entropy of each indicator: According to the definition of information entropy in information theory, the information entropy of a set of data: E _i =-ln(n) ^-1 ∑p _i lnp _i , where P _i =Y _i /∑Y _i

Get the score for each metric by the following formula:

Pi=Yi*Wi, i=1, 2,...,16

The activity quality score is obtained by the following formula:

Specifically, the activity quality assessment model may also be a three-layer structure model established based on the analytic hierarchy process, the activity quality assessment model includes three layers, the first layer is the activity quality effect, and the second layer is the cost indicator, and the customer is obtained. Indicators, sales indicators and customer activity indicators, the third layer is the specific indicators of the second layer of indicators, wherein the cost indicators include marketing resource costs, internal channel promotion costs, external channel promotion costs, business development costs, and customer acquisition indicators including registration The number of people, the number of accounts opened, the sales indicators include the number of purchases, order volume and sales volume. The customer activity indicators include the number of customer logins and the number of logins.

Step S140, comparing the activity quality score of the current activity with the activity quality score of other activities, outputting the evaluation result of the activity according to the comparison result, and displaying the activity quality score of the activity on the bubble chart and Activity quality scores for other activities.

As shown in FIG. 5, it is a schematic flowchart of a second embodiment of the method for evaluating quality of marketing activities of the present application. In this embodiment, the step S210 of the marketing activity quality evaluation method, S230-S250 is similar to the steps S110-S140 of the first embodiment, except that the method further includes step S220.

The method includes the following steps:

In step S210, all activity data of the event and other activities in the database are obtained.

Step S220, performing dimensionless processing on the activity data based on the first formula.

Where Yi is the value after Xi standardization,

Step S230, preprocessing the activity data.

Step S240, the pre-established activity quality assessment model processes the activity data to obtain an activity quality score of the activity and an activity quality score of other activities.

Step S250, comparing the activity quality score of the current activity with the activity quality score of other activities, outputting the evaluation result of the activity according to the comparison result, and displaying the activity quality score of the activity on the bubble chart and Activity quality scores for other activities.

As shown in FIG. 6, it is a schematic flowchart of a third embodiment of the method for evaluating quality of marketing activities of the present application. In this embodiment, the step S130 of the marketing activity quality assessment method and the activity quality assessment model in step S240 of the second embodiment to score the activity data includes the following steps:

In step S310, the data is segmented.

Specifically, by dividing each index value that has been sorted in the previous step into 100 quantile points, the interval of each quantile point is corresponding to 0.01 to 1 minute.

In step S320, the indicator weight is determined.

Specifically, according to the actual business scenario, the scoring weights are determined for the indicators participating in the scoring, each weight value is 0 to 1, and the weights of ownership are added to 1.

Specifically, the steps of obtaining the weights of the indicators include:

E _i = -ln(n) ^-1 ∑p _i lnp _i , where P _i =Y _i /∑Y _i

In step S330, the scores of the respective indicators are obtained by the following formula:

Pi=Yi*Wi, i=1, 2,...,16

The activity quality score is obtained by the following formula:

The score is calculated and the score is output.

The serial numbers of the embodiments of the present application are merely for the description, and do not represent the advantages and disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better. Implementation. Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk, The optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present application.

The above is only a preferred embodiment of the present application, and is not intended to limit the scope of the patent application, and the equivalent structure or equivalent process transformations made by the specification and the drawings of the present application, or directly or indirectly applied to other related technical fields. The same is included in the scope of patent protection of this application.

Claims

A marketing activity quality assessment method is applied to a server, characterized in that the method comprises the steps of:

Obtain activity data of the event and other activities in the database, the activity data including the activity cost indicator, the customer indicator, the sales indicator, and the customer activity indicator;

Performing dimensionless processing on the activity data based on a first formula to normalize the activity data, the first formula is:
i=1, 2, ..., 16, where Xi is the normalized activity data, and Yi is the value after Xi normalization,
Representing the summation of multiple values to be normalized;

The pre-established activity quality assessment model processes the standardized activity data to obtain the activity quality score of the event and the activity quality score of other activities;

Comparing the activity quality score of the activity with the activity quality score of the other activity, outputting the evaluation result of the activity according to the comparison result, and displaying the activity quality score of the activity and other activities on the bubble chart Activity quality score.
The method for evaluating a quality of a marketing activity according to claim 1, wherein the cost indicator comprises a marketing resource cost, an internal channel promotion cost, an external channel promotion cost, and a business development cost, and the customer index includes a number of registered persons and an account opening. The number of people, the sales indicators include the number of purchases, the order quantity and the sales volume. The customer activity indicators include the number of customer logins and the number of logins.
The marketing activity quality evaluation method according to claim 2, wherein the step "pre-established activity quality assessment model processes the standardized activity data to obtain an activity quality score of the event and activities of other activities. Quality Score" includes:

Calculate activity growth data after the event; and

Based on the activity growth data, the activity quality score of the event and the activity quality score of other activities are calculated.
The marketing activity quality assessment method according to claim 3, wherein the calculating the activity growth data comprises the steps of:

Calculating an average of the activity data during the activity;

Calculate the average of the data for the month prior to the event; and

The growth data is obtained by subtracting the average of the duration of the event from the average of the month prior to the event.
The marketing activity quality assessment method according to claim 4, wherein the same type of growth data in the growth data is sorted from large to small.
The marketing activity quality assessment method according to claim 5, wherein calculating the activity quality score of the activity and the activity quality score of the other activities comprises the following steps:

Data segmentation, the same-type growth data is divided into 100 quantile points to obtain the percentile of the same kind of growth data, and the respective percentiles of the same-type growth data are corresponding to [0.01, 1] The score of the score, using the corresponding score as the base score;

Determining the weight of the indicator, using the information entropy method to obtain the weight of the indicator; and

Multiplying the base score by the weight to obtain a single item score, and summing each of the individual item scores to obtain an activity quality score of the event and an activity quality score of other activities.
The method for evaluating a quality of a marketing activity according to claim 6, wherein the step of obtaining the indicator weight comprises:

Obtain the activity data after standardization of each indicator, wherein the activity indicators are X1, X2, X3...Xi, and the values normalized to each indicator data are Y1, Y2...Yi;

The information entropy of each indicator is obtained according to the second formula, and the second formula is:

E i = -ln(n) -1 ∑p i lnp i , where P i =Y i /∑Y i ;

The weight of each indicator is determined according to a third formula, the third formula is:

Wi = (1-Ei) / (k - ∑ Ei), i = 1, 2, ..., k.
A server, comprising: a memory, a processor, and a campaign quality assessment system stored on the processor and operable on the processor, the campaign quality assessment system being processed The following steps are implemented when the device is executed:

Obtain activity data of the event and other activities in the database, the activity data including the activity cost indicator, the customer indicator, the sales indicator, and the customer activity indicator;

Performing dimensionless processing on the activity data based on a first formula to normalize the activity data, the first formula is:
i=1, 2, ..., 16, where Xi is the normalized activity data, and Yi is the value after Xi normalization,
Representing the summation of multiple values to be normalized;

The pre-established activity quality assessment model processes the standardized activity data to obtain the activity quality score of the event and the activity quality score of other activities;

Comparing the activity quality score of the activity with the activity quality score of the other activity, outputting the evaluation result of the activity according to the comparison result, and displaying the activity quality score of the activity and other activities on the bubble chart Activity quality score.
The server according to claim 8, wherein the cost indicator comprises a marketing resource cost, an internal channel promotion cost, an external channel promotion cost, and a business development cost, and the customer indicator includes a number of registered persons, an account opening number, and a sales indicator. Including the number of purchases, order quantity and sales volume, the customer activity indicators include the number of customer logins and the number of logins.
The server according to claim 9, wherein the step "pre-established activity quality assessment model processes the standardized activity data to obtain an activity quality score of the event and an activity quality score of other activities" includes :

Calculate activity growth data after the event; and

Based on the activity growth data, the activity quality score of the event and the activity quality score of other activities are calculated.
The server according to claim 10, wherein the calculating the activity growth data comprises the steps of:

Calculating an average of the activity data during the activity;

Calculate the average of the data for the month prior to the event; and

The growth data is obtained by subtracting the average of the duration of the event from the average of the month prior to the event.
The server according to claim 11, wherein the same type of growth data in said growth data is sorted from large to small.
The server according to claim 12, wherein calculating the activity quality score of the event and the activity quality score of the other activities comprises the following steps:

Data segmentation, the same-type growth data is divided into 100 quantile points to obtain the percentile of the same kind of growth data, and the respective percentiles of the same-type growth data are corresponding to [0.01, 1] The score of the score, using the corresponding score as the base score;

Determining the weight of the indicator, using the information entropy method to obtain the weight of the indicator; and

Multiplying the base score by the weight to obtain a single item score, and summing each of the individual item scores to obtain an activity quality score of the event and an activity quality score of other activities.
The server according to claim 13, wherein the step of obtaining the index weight comprises:

Obtain the activity data after standardization of each indicator, wherein the activity indicators are X1, X2, X3...Xi, and the values normalized to each indicator data are Y1, Y2...Yi;

The information entropy of each indicator is obtained according to the second formula, and the second formula is:

E i = -ln(n) -1 ∑p i lnp i , where P i =Y i /∑Y i ;

The weight of each indicator is determined according to a third formula, the third formula is:

Wi = (1-Ei) / (k - ∑ Ei), i = 1, 2, ..., k.
A computer readable storage medium storing a campaign activity quality assessment system, the campaign activity quality assessment system being executable by at least one processor to cause the at least one processor to perform the following steps:

Obtain activity data of the event and other activities in the database, the activity data including the activity cost indicator, the customer indicator, the sales indicator, and the customer activity indicator;

Performing dimensionless processing on the activity data based on a first formula to normalize the activity data, the first formula is:
i=1, 2, ..., 16, where Xi is the normalized activity data, and Yi is the value after Xi normalization,
Representing the summation of multiple values to be normalized;

The pre-established activity quality assessment model processes the standardized activity data to obtain the activity quality score of the event and the activity quality score of other activities;

Comparing the activity quality score of the activity with the activity quality score of the other activity, outputting the evaluation result of the activity according to the comparison result, and displaying the activity quality score of the activity and other activities on the bubble chart Activity quality score.
The computer readable storage medium according to claim 15, wherein the cost indicator comprises a marketing resource cost, an internal channel promotion cost, an external channel promotion cost, and a business development cost, and the customer indicator includes a number of registered persons and an account opening. The number of people, the sales indicators include the number of purchases, the order quantity and the sales volume. The customer activity indicators include the number of customer logins and the number of logins.
The computer readable storage medium according to claim 16, wherein the step "pre-established activity quality assessment model processes the standardized activity data to obtain an activity quality score of the event and activities of other activities. Quality Score" includes:

Calculate activity growth data after the event; and

Based on the activity growth data, the activity quality score of the event and the activity quality score of other activities are calculated.
The computer readable storage medium of claim 17, wherein the calculating the activity growth data comprises the steps of:

Calculating an average of the activity data during the activity;

Calculate the average of the data for the month prior to the event; and

The growth data is obtained by subtracting the average of the duration of the event from the average of the month prior to the event.
The computer readable storage medium according to claim 18, wherein the same type of growth data in the growth data is sorted from large to small;

The calculation of the activity quality score of the event and the activity quality score of other activities include the following steps:

Data segmentation, the same-type growth data is divided into 100 quantile points to obtain the percentile of the same kind of growth data, and the respective percentiles of the same-type growth data are corresponding to [0.01, 1] The score of the score, using the corresponding score as the base score;

Determining the weight of the indicator, using the information entropy method to obtain the weight of the indicator; and

Multiplying the base score by the weight to obtain a single item score, and summing each of the individual item scores to obtain an activity quality score of the event and an activity quality score of other activities.
The computer readable storage medium of claim 19, wherein the step of obtaining the indicator weight comprises:

Obtain the activity data after standardization of each indicator, wherein the activity indicators are X1, X2, X3...Xi, and the values normalized to each indicator data are Y1, Y2...Yi;

The information entropy of each indicator is obtained according to the second formula, and the second formula is:

E i = -ln(n) -1 ∑p i lnp i , where P i =Y i /∑Y i ;

The weight of each indicator is determined according to a third formula, the third formula is:

Wi = (1-Ei) / (k - ∑ Ei), i = 1, 2, ..., k.