CN110675183A

CN110675183A - Marketing object determining method, marketing promotion method and related device

Info

Publication number: CN110675183A
Application number: CN201910814725.7A
Authority: CN
Inventors: 马书超; 董泽伟; 朱松岭; 冯健
Original assignee: Alibaba Group Holding Ltd
Current assignee: Advanced New Technologies Co Ltd; Advantageous New Technologies Co Ltd
Priority date: 2019-08-30
Filing date: 2019-08-30
Publication date: 2020-01-10
Anticipated expiration: 2039-08-30
Also published as: CN110675183B

Abstract

The embodiment of the application provides a marketing object determining method, a marketing popularization method and a related device. The determination method comprises the following steps: acquiring a candidate marketing object group; and taking the behavior characteristics of the users in the candidate marketing object group corresponding to the first historical marketing activity as the input of the first prediction model, and predicting to obtain the marketing success rate of the users in the candidate user group. And selecting users with marketing success rate meeting preset requirements from the candidate marketing object group as a target marketing object group. The marketing promotion method comprises the following steps: respectively inputting the at least one incentive interest and the behavior characteristics of the third history marketing activities corresponding to the users in the target marketing object group into a second prediction model, and predicting the marketing success rate of the users in the target marketing object group corresponding to the at least one incentive interest; and determining and delivering a target incentive interest matched with the users in the target marketing object group from the at least one incentive interest based on the prediction result of the second prediction model.

Description

Marketing object determining method, marketing promotion method and related device

Technical Field

The embodiment of the application relates to the technical field of data processing, in particular to a marketing object determining method, a marketing popularization method and a related device.

Background

Existing marketing campaigns deliver certain incentive interests to users to mobilize the mood of the users. The factors determining the marketing effect are many, and mainly include: economic factors, population factors, social culture factors, incentive rights and interests factors, market factors and the like. Therefore, by a manual mode, it is difficult to make a careful incentive right delivery strategy, so that in many cases, a good marketing effect is not achieved after the marketing budget is spent.

In view of the above, how to reasonably use the marketing budget to achieve a better marketing effect is a technical problem that needs to be solved at present.

Disclosure of Invention

The embodiment of the application aims to provide a marketing object determining method, a marketing promotion method and a related device, which can reasonably use marketing budget to achieve better marketing effect.

In order to achieve the above purpose, the embodiments of the present application are implemented as follows:

in a first aspect, a method for determining a marketing object is provided, including:

acquiring a candidate marketing object group;

taking the behavior characteristics of the users in the candidate marketing object group corresponding to the first historical marketing activities as the input of a first prediction model, and predicting to obtain the marketing success rate of the users in the candidate user group; the first prediction model is obtained by training based on a first training data set, wherein the training data in the first training data set comprise behavior characteristics of a second historical marketing campaign corresponding to a sample user and a label indicating whether the second historical marketing campaign has marketing effect on the sample user;

and selecting users with the predicted marketing success rate meeting the preset requirement from the candidate marketing object group as a target marketing object group to be promoted.

In a second aspect, a marketing promotion method is provided, which includes:

acquiring a target marketing object group to be promoted;

respectively inputting at least one incentive right and the behavior characteristics of a third history marketing activity corresponding to the users in the target marketing object group into a second prediction model, and predicting the marketing success rate of the users in the target marketing object group corresponding to the at least one incentive right; the second prediction model is obtained by training based on a second training data set, wherein the training data of the second training set comprises behavior characteristics of a sample user corresponding to a fourth historical marketing activity, incentive rights and interests released by the fourth historical marketing activity to the sample user, and a label indicating whether the incentive rights and interests released by the fourth historical marketing activity to the sample user play a marketing effect on the sample user;

determining a target incentive interest matched with the users in the target marketing object group from the at least one incentive interest based on the marketing success rate of the users in the target marketing object group corresponding to the at least one incentive interest;

delivering the matched targeted incentive interests to users in the targeted marketing object population.

In a third aspect, an apparatus for determining a marketing object is provided, including:

the first acquisition module is used for acquiring a candidate marketing object group;

the first prediction module is used for predicting the marketing success rate of the users in the candidate user group by taking the behavior characteristics of the users in the candidate marketing object group corresponding to the first historical marketing activity as the input of a first prediction model; the first prediction model is obtained by training based on a first training data set, wherein the training data in the first training data set comprise behavior characteristics of a second historical marketing campaign corresponding to a sample user and a label indicating whether the second historical marketing campaign has marketing effect on the sample user;

and the first determining module is used for selecting a user with a predicted marketing success rate meeting the preset requirement from the candidate marketing object group as a target marketing object group to be promoted.

In a fourth aspect, an electronic device is provided comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being executed by the processor to:

acquiring a candidate marketing object group;

In a fifth aspect, a computer-readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, performs the steps of:

acquiring a candidate marketing object group;

In a sixth aspect, a marketing promotion device is provided, comprising:

the second acquisition module is used for acquiring a target marketing object group to be promoted;

the second prediction module is used for inputting at least one incentive right and the behavior characteristics of a third history marketing activity corresponding to the users in the target marketing object group into a second prediction model respectively, and predicting the marketing success rate of the users in the target marketing object group corresponding to the at least one incentive right; the second prediction model is obtained by training based on a second training data set, wherein the training data of the second training set comprises behavior characteristics of a sample user corresponding to a fourth historical marketing activity, incentive rights and interests released by the fourth historical marketing activity to the sample user, and a label indicating whether the incentive rights and interests released by the fourth historical marketing activity to the sample user play a marketing effect on the sample user;

a second determination module, configured to determine a target incentive interest matching the user in the target marketing object group from the at least one incentive interest based on a marketing success rate of the user in the target marketing object group corresponding to the at least one incentive interest;

and the right and interest releasing module is used for releasing the matched target incentive right and interest to the users in the target marketing object group.

In a seventh aspect, an electronic device is provided that includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being executed by the processor to:

acquiring a target marketing object group to be promoted;

In an eighth aspect, a computer readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:

acquiring a target marketing object group to be promoted;

Based on the scheme of the embodiment of the application, on one hand, the marketing success rate of the users in the candidate marketing object group is obtained through the first prediction model in a prediction mode, and the target marketing object group which can be effectively popularized is determined from the candidate marketing object group according to the marketing success rate of the users, so that the marketing budget is prevented from being wasted on the users which cannot play a marketing effect. On the other hand, after the target marketing object group is determined, the marketing success rates corresponding to different incentive rights and interests of the users in the target marketing object group are predicted and obtained through the second prediction model, and the target incentive rights and interests matched with the users in the target marketing object group are selected from the incentive rights and interests for delivery, so that balance is found between ensuring the marketing success rate and reducing marketing budget expenditure, and the technical effect of applying the marketing budget to the blades is achieved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative efforts.

Fig. 1 is a schematic step diagram of a method for determining a marketing object according to an embodiment of the present disclosure.

Fig. 2 is a schematic step diagram of a marketing promotion method according to an embodiment of the present application.

Fig. 3 is a schematic step diagram of a marketing object determination method and a marketing promotion method combined in an actual application scenario.

Fig. 4 is a schematic structural diagram of a device for determining a marketing object according to an embodiment of the present application.

Fig. 5 is a schematic structural diagram of a marketing promotion device provided in the embodiment of the present application.

Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application.

As described above, the factors determining the marketing effect are very complicated and vary from person to person. Based on a manual mode, a careful incentive interest releasing strategy is difficult to make, so that in many cases, a good marketing effect is not achieved after the marketing budget is spent. Therefore, the present application aims to provide a technical solution to the above-mentioned problems.

Fig. 1 is a flowchart of a method for determining a marketing object according to an embodiment of the present application. The method shown in fig. 1 may be performed by a corresponding apparatus, comprising:

and step S102, acquiring a candidate marketing object group.

The determination method of the marketing candidate group is not limited to the only one, and the embodiment of the present application is not limited in particular.

Step S104, taking the behavior characteristics of the users in the candidate marketing object group corresponding to the first historical marketing activity as the input of a first prediction model, and predicting the marketing success rate of the users in the candidate user group; the first prediction model is obtained by training based on a first training data set, and the training data in the first training data set comprises behavior characteristics of the sample user corresponding to the second historical marketing campaign and a label indicating whether the second historical marketing campaign has marketing effect on the sample user.

Wherein the first prediction model may be, but is not limited to, a classification model. For example: iterative decision tree models, logistic regression models, random forest models, naive bayes models, support vector machine models, and the like.

It should be understood that different classification models correspond to prediction results in different presentation manners, and the embodiments of the present application are not particularly limited. For example, the first predictive model may represent in a binary manner whether the user can be marketed successfully or whether the user cannot be marketed successfully. As another example, the first predictive model may represent the amount of probability that the user will be successful in marketing in a scored manner.

And S106, selecting the users with the predicted marketing success rate meeting the preset requirement from the candidate marketing object group as the target marketing object group to be promoted.

Based on the determination method of the marketing object shown in fig. 1, it can be known that: according to the scheme of the embodiment of the application, the marketing success rate of the user can be predicted based on the first model, and the target marketing object group to be promoted is reasonably selected according to the marketing success rate, so that the situation that incentive rights and interests are put on the user with lower marketing success rate is avoided, and a better marketing effect can be realized on the premise that the marketing budget is limited.

The method of the embodiments of the present application is described in detail below.

Specifically, the method for determining the marketing object in the embodiment of the application mainly comprises the following stages:

and step one, determining a candidate marketing object group.

At this stage, inactive users may be determined as a group of candidate marketing objects.

And stage two, training a first prediction model based on the first training data set.

Specifically, the training data in the first training data set contains behavior characteristics of the sample user corresponding to the second historical marketing campaign and a label indicating whether the second historical marketing campaign has marketing effect on the sample user.

And the behavior characteristics of the sample user corresponding to the second historical marketing activity are used as the input of the first prediction model, and a label indicating whether the second historical marketing activity has marketing effect on the sample user is used as the output of the first prediction model.

During the training process, the first prediction model outputs a training result. In this stage, a loss function of the first prediction model can be derived based on maximum likelihood estimation, and the loss between the training result and the label is calculated based on the loss function. Finally, with the purpose of reducing loss, the weight values of the user features in the first prediction model are optimized, and therefore the training effect is achieved.

In practical application, the sample user can be determined from the users of the historical marketing promotion, and whether the second historical marketing activity has marketing effect on the sample user or not is further analyzed according to the behavior data of the second historical marketing activity responded by the sample user.

And stage three, predicting the marketing success rate of the users in the candidate marketing object group.

At this stage, the behavior characteristics of the users in the marketing object candidate group in the first historical marketing campaign can be input into the first prediction model, and the marketing success rate of the users in the marketing object candidate group is predicted by the first prediction model.

Similarly, the behavior characteristics of the users in the first historical marketing campaign in the marketing object candidate group can be determined according to the behavior data of the users responding to the first historical marketing campaign.

It should be noted that, as a reasonable prediction scheme, the future result should be predicted based on the past data. Therefore, preferably, the time corresponding to the second historical marketing campaign is earlier than the time corresponding to the first historical marketing campaign, so as to ensure that the occurrence time of the behavior feature used in the training phase is earlier than the occurrence time of the behavior feature used in the prediction phase.

And step four, screening the candidate marketing object groups to obtain a target marketing object group to be promoted.

In this stage, the users with the marketing success rate greater than the preset threshold value in the candidate marketing object group may be determined as the target marketing object group, or a preset number of users may be selected from the candidate marketing object group as the target marketing object group according to the descending order of the marketing success rate.

It should be appreciated that users in the targeted marketing object population may be considered users who respond to the marketing campaign with a high probability, and thus, putting incentive rights to users in the targeted marketing object population may avoid wasting the marketing budget to some extent.

In addition, incentive rights are also important factors in deciding whether to play a marketing role for the user. For example, the incentive interests are of too little value, and even if delivered to users in the targeted marketing object group, marketing failures may result. If the value of the incentive interest is too large, the investment coverage is small on the premise of limited marketing budget, and the expected marketing benefit cannot be achieved.

Therefore, the embodiment of the application also provides a marketing promotion method which is used for delivering proper incentive rights and interests to users.

Fig. 2 is a flowchart of a marketing promotion method according to an embodiment of the present application. The method shown in fig. 2 may be performed by a corresponding apparatus, comprising:

and step S202, acquiring a target marketing object group to be promoted.

The target marketing object group may be determined based on the method shown in fig. 1, and details are not repeated herein for example.

Step S204, respectively inputting the behavior characteristics of the third history marketing activities corresponding to the at least one incentive interest and the users in the target marketing object group into a second prediction model, and predicting the marketing success rate of the users in the target marketing object group corresponding to the at least one incentive interest; the second prediction model is obtained by training based on a second training data set, and the training data of the second training set comprises behavior characteristics of the sample user corresponding to a fourth historical marketing campaign, incentive rights and interests released by the fourth historical marketing campaign to the sample user, and a label indicating whether the incentive rights and interests released by the fourth historical marketing campaign to the sample user have marketing effects on the sample user.

Wherein the second prediction model may be, but is not limited to, a classification model. For example: iterative decision tree models, logistic regression models, random forest models, naive bayes models, support vector machine models, and the like.

It should be understood that different classification models correspond to prediction results in different presentation manners, and the embodiments of the present application are not particularly limited. For example, the second prediction model may use a binary method to represent that the incentive right can have a marketing effect on the user, or that the incentive right cannot have a marketing effect on the user. For another example, the second predictive model may represent the probability of successful incentive interests to the user in a scored manner.

And S206, determining the target incentive rights matched with the users in the target marketing object group from the at least one incentive rights based on the marketing success rate of the users in the target marketing object group corresponding to the at least one incentive rights.

Specifically, the target incentive rights and interests matched with the users in the target marketing object group can be further determined by combining the marketing cost upper limit of the group and the marketing success rate of at least one incentive rights and interests corresponding to each user in the target marketing object group.

If the upper limit of the marketing cost is not considered, the incentive right with a smaller amount can be selected as the target incentive right of the user on the basis of ensuring the marketing success rate.

And step S208, delivering the matched target incentive interest to the users in the target marketing object group.

In this step, channels such as short message, push, corner mark, waist seal, benefit payment and the like can be adopted to deliver the target incentive rights and interests to the users in the target user group.

The implementation manner of the target incentive right is not unique, and may be, by way of exemplary introduction, a red packet, a coupon, a bonus, and the like, which is not specifically limited in the embodiment of the present application.

Based on the marketing promotion method shown in fig. 2, it can be known that: according to the scheme of the embodiment of the application, the marketing success rate of different incentive rights to the user can be predicted based on the second model, and the reasonable target incentive rights are selected to be released to the user according to the prediction result, so that balance is found between the guarantee of the marketing success rate and the reduction of marketing budget expenditure, and the technical effect of applying the marketing budget to the knife edge is achieved.

Specifically, the marketing promotion method in the embodiment of the application mainly comprises the following stages:

and in the first stage, acquiring a target marketing object group to be promoted.

Specifically, in this stage, the target marketing object group to be promoted may be determined based on the above method for determining marketing objects, and the principle is the same, and therefore, the description is omitted here for illustration.

And stage two, training a second prediction model based on two training data sets.

Specifically, the training data in the second training data set includes behavior characteristics of the sample user corresponding to the fourth historical marketing campaign, incentive rights and interests issued by the fourth historical marketing campaign to the sample user, and a label indicating whether the incentive rights and interests issued by the fourth historical marketing campaign to the sample user have marketing effects on the sample user. And the behavior characteristics of the sample user corresponding to the fourth historical marketing activity and the incentive right of the fourth historical marketing activity to the sample user are used as the input of the second prediction model, and a label indicating whether the incentive right of the fourth historical marketing activity to the sample user has a marketing effect on the sample user is used as the output of the second prediction model.

During the training process, the second prediction model outputs the training result. In this stage, a loss function of the second prediction model can be derived based on the maximum likelihood estimation, and the loss between the training result and the label is calculated based on the loss function. And finally, optimizing the user characteristic weight and the weighted value of the incentive right in the second prediction model with the aim of reducing the loss, thereby achieving the training effect.

In practical application, the sample user can be determined from the users of the historical marketing promotion in the stage, and the behavior data of the fourth historical marketing activity is responded according to the sample user, so that whether the fourth historical marketing activity has marketing effect on the sample user is further analyzed.

And step three, predicting the marketing success rate of each incentive interest corresponding to the users in the target marketing object group.

In particular, at least one incentive benefit may be provided to users in the targeted marketing object population. Wherein different incentive interests correspond to different interest amounts. The larger the equity amount, the higher the corresponding marketing success rate and marketing budget expenditure. The smaller the equity, the lower the corresponding marketing success rate and marketing budget expenditure.

In the stage, the different incentive interests and the behavior characteristics of the users in the target marketing object group in the historical third history marketing activities are respectively input into the first prediction model, and the marketing success rate of the users in the target marketing object group to the different incentive interests is predicted by the first prediction model.

Similarly, the behavior characteristics of the marketing campaign in the third history of the users in the targeted marketing object group can be determined according to the behavior data of the users responding to the marketing campaign in the third history.

It should be noted that, as a reasonable prediction scheme, the future result should be predicted based on the past data. Therefore, as a preferable scheme, the time corresponding to the fourth historical marketing campaign is earlier than the time corresponding to the third historical marketing campaign, so as to ensure that the occurrence time of the behavior feature used in the training phase is earlier than the occurrence time of the behavior feature used in the prediction phase.

And fourthly, determining the target incentive rights and interests finally released to the users in the target marketing object group from the at least one incentive rights and interests based on the marketing success rate of the users in the target marketing object group corresponding to the at least one incentive rights and interests.

Specifically, on the premise that the marketing cost upper limit of the target marketing object group is not exceeded, in the step, one of the incentive rights and interests with the lowest marketing success rate meeting certain requirements can be selected as the target incentive right and interest.

For example, incentive interests with marketing success rates greater than 70% may be identified as producing effective marketing results after delivery. Assuming that the equity amount of the incentive equity is divided into three grades of 50, 75 and 100 (the higher the equity amount value is, the higher the corresponding cost is), the marketing success rate of the incentive equity with the equity amount of 50 to the user A is predicted to be 20%, the marketing success rate of the incentive equity with the equity amount of 75 to the user A is 70%, and the marketing success rate of the incentive equity with the equity amount of 100 to the user A is 85% by the marketing success rate prediction model, then the incentive equity with the equity amount of 75 can be used as the target incentive equity of the user A in the step.

In addition, when the target incentive rights and interests of the users in the target marketing object group are determined, the adaptive adjustment can be carried out according to the marketing cost upper limit of the target user group. For example, after the target incentive interest is determined, if the marketing cost upper limit of the target user group is exceeded, a part of users can be deleted from the target user group according to a certain preset strategy.

And step five, delivering the matched target incentive rights and interests to the users in the target marketing object group.

In this stage, the matched target incentive rights can be released to the users in the target marketing object group by using the modes of short message, pushing, card, corner mark, waist seal, benefit payment and the like, and the releasing mode is not unique, so the description is not repeated herein by way of example.

For convenience of understanding, the method for determining the marketing object shown in fig. 1 and the marketing promotion method shown in fig. 2 are exemplarily described below with reference to an actual application scenario.

In the application scenario, the targeted payment application stimulates the inactive users to use the targeted payment application more by putting a marketing mode of the red envelope equity. Specifically, the process shown in fig. 3 includes:

step S301, determining an inactive user group in the target payment application as a candidate marketing object group.

In this step, users whose balance in the target payment application has not reached the first preset threshold and/or users whose deactivation time in the target payment application has reached the second preset threshold may be determined as the inactive user group, but not limited to.

And step S302, predicting the marketing success rate of the users in the candidate marketing object group based on the first prediction model.

In this step, the behavior characteristics of the users in the marketing candidate object group corresponding to the first historical marketing campaign are determined based on the usage data of the users in the marketing candidate object group for the target payment application in the first historical marketing campaign.

And then, inputting the behavior characteristics of the users in the marketing object candidate group corresponding to the first historical marketing activity into the first prediction model, and predicting the marketing success rate of the users in the marketing object candidate group by the first prediction model.

As described above, the first prediction model is obtained by training the behavior characteristics of the sample user corresponding to the second historical marketing campaign as input, and the label indicating whether the second historical marketing campaign has a marketing effect on the sample user is output.

Wherein the behavioral characteristics of the sample user corresponding to the second historical marketing campaign may be determined based on the usage data of the sample user for the targeted payment application over the period of the second historical marketing campaign. The label indicating whether the second historical marketing campaign has marketing effect on the sample user may be determined according to whether the sample user receives incentive interests placed by the second historical marketing campaign within the second historical marketing campaign period, and/or the sample is determined using the amount of change in usage frequency of the targeted payment application after receiving the incentive interests placed by the second historical marketing campaign.

In this step, if the sample user receives the right of the red envelope released by the second historical marketing campaign, and/or the sample user receives the right of the red envelope released by the second historical marketing campaign and then the usage frequency variation of the target payment application reaches a certain mark (if the increase reaches 40%), the fourth historical marketing campaign is determined to have a marketing effect on the sample user.

Step S303, determining users who can be effectively motivated to use the target payment application from the candidate marketing object group as a target marketing object group.

In this step, users in the marketing object candidate group with a predicted marketing success rate greater than a certain criterion (e.g., 60%) may be determined as users who may be effectively motivated to use the targeted payment application.

Step S304, configuring the red packet rights and interests of different red packet amounts for the users in the target attrition user group.

And S305, predicting the marketing success rate of the users in the target lost user group aiming at different red packet amounts.

In this step, the behavior characteristics of the marketing campaign corresponding to the third history of users in the targeted marketing target group are determined based on the usage data of the users in the targeted marketing target group for the targeted payment application in the marketing campaign.

And then, respectively inputting the behavior characteristics of marketing activities of the different red envelope sums and the third history corresponding to the users in the target marketing object group into a second prediction model, and predicting the marketing success rate of each red envelope sum corresponding to the users in the target marketing object group by the second prediction model.

As described above, the third prediction model is obtained by training the behavior characteristics of the fourth historical marketing campaign corresponding to the sample user and the different red packet amounts as input, and the label indicating whether the different red packet amounts have marketing effects on the sample user is output.

Wherein the behavioral characteristics of the sample user corresponding to the fourth historical marketing campaign may be determined based on the usage data of the sample user for the targeted payment application over the period of the fourth historical marketing campaign. The label indicating whether the amount of the red package released by the fourth historical marketing campaign to the sample user has a marketing effect on the sample user is determined based on whether the sample user receives the right to the red package released by the fourth historical marketing campaign, and/or the usage frequency variation of the target payment application by the sample user after receiving the right to the red package released by the fourth historical marketing campaign.

In this step, if the sample user receives the right of the red envelope released by the fourth historical marketing campaign, and/or the sample user receives the right of the red envelope released by the fourth historical marketing campaign and then the usage frequency variation of the target payment application reaches a certain mark (if the increase reaches 40%), the right of the red envelope released by the fourth historical marketing campaign is determined to have a marketing effect on the sample user.

And step S306, determining the matched target red envelope amount for the users in the target marketing object group.

In this step, it can be determined that the red envelope equity with the marketing success rate of more than 75% can produce effective marketing effect after being put in.

Assume that the targeted marketing object population includes user a, user B, and user C. The red packet rights and interests are divided into 8-element red packet, 15-element red packet and 25-element red packet.

Predicting by a second prediction model to obtain:

the marketing success rate of the user a corresponding to the 8-yuan red packet is 20%, the marketing success rate corresponding to the 15-yuan red packet is 75%, and the marketing success rate corresponding to the 25-yuan red packet is 90%.

The marketing success rate of the user B corresponding to the 8-yuan red packet is 80%, the marketing success rate corresponding to the 15-yuan red packet is 90%, and the marketing success rate corresponding to the 25-yuan red packet is 95%.

The marketing success rate of the user C corresponding to the 8-yuan red packet is 0%, the marketing success rate corresponding to the 15-yuan red packet is 10%, and the marketing success rate corresponding to the 25-yuan red packet is 75%.

On the premise of not considering marketing budget, the marketing campaign should release 15-element red packages to the user A, release 8-element red packages to the user B and release 25-element red packages to the user C.

If the marketing cost upper limit of the targeted marketing object group is 40 yuan, a part of users need to be removed from the targeted marketing object group in order not to exceed the marketing budget. Considering that the user C needs to invest 25 dollars to obtain a certain marketing effect, this step may remove the user C from the targeted marketing object group in order to reduce the marketing budget.

And step S307, delivering the matched red envelope equity of the target red envelope amount to the users in the target marketing object group.

In correspondence to the method for determining a marketing target, as shown in fig. 4, an embodiment of the present application further provides a device 400 for determining a marketing target, including:

a first obtaining module 410, obtaining a candidate marketing object group;

the first prediction module 420 is used for predicting the marketing success rate of the users in the candidate user group by taking the behavior characteristics of the users in the candidate marketing object group corresponding to the first historical marketing activity as the input of a first prediction model; the first prediction model is obtained by training based on a first training data set, wherein the training data in the first training data set comprise behavior characteristics of a second historical marketing campaign corresponding to a sample user and a label indicating whether the second historical marketing campaign has marketing effect on the sample user;

and the first determining module 430 selects a user with a predicted marketing success rate meeting a preset requirement from the candidate marketing object group as a target marketing object group to be promoted.

The determination means based on the marketing object shown in fig. 4 may know that: according to the scheme of the embodiment of the application, the marketing success rate of the user can be predicted based on the first model, and the target marketing object group to be promoted is reasonably selected according to the marketing success rate, so that the situation that incentive rights and interests are put on the user with low marketing success rate is avoided, and a better marketing effect can be realized on the premise that the marketing budget is limited.

Optionally, the second historical marketing campaign corresponds to a time that is earlier than the time corresponding to the first historical marketing campaign.

Optionally, the first obtaining module 410, when executed, specifically determines at least some inactive users in the targeted payment application as a group of candidate marketing objects; wherein the first historical marketing campaign and the second historical marketing campaign are both marketing campaigns initiated by the targeted payment application.

Optionally, the inactive users include: and the balance in the target payment application does not reach the user with the first preset threshold value, and/or the user with the deactivation time reaching the second preset threshold value in the target payment application.

Optionally, the behavioral characteristics of the user in the marketing object candidate group corresponding to the first historical marketing campaign are determined based on the usage data of the user for the targeted payment application in the first historical marketing campaign period;

the behavioral characteristics of the sample user corresponding to the second historical marketing campaign are determined based on usage data of the sample user for the targeted payment application over a period of the second historical marketing campaign.

Obviously, the determining apparatus of the embodiment of the present application may be the subject of the determining method shown in fig. 1, and thus the determining apparatus can implement the functions of the determining method in fig. 1 and fig. 3. Since the principle is the same, the detailed description is omitted here.

Corresponding to the marketing promotion method, as shown in fig. 5, an embodiment of the present application further provides a marketing promotion device 500, including:

a second obtaining module 510, for obtaining a target marketing object group to be promoted;

the second prediction module 520 is used for inputting the at least one incentive right and the behavior characteristics of the third history marketing activities corresponding to the users in the target marketing object group into a second prediction model respectively, and predicting the marketing success rate of the users in the target marketing object group corresponding to the at least one incentive right; the second prediction model is obtained by training based on a second training data set, wherein the training data of the second training set comprises behavior characteristics of a sample user corresponding to a fourth historical marketing activity, incentive rights and interests released by the fourth historical marketing activity to the sample user, and a label indicating whether the incentive rights and interests released by the fourth historical marketing activity to the sample user play a marketing effect on the sample user;

a second determining module 530, configured to determine a target incentive interest matching the user in the target marketing object group from the at least one incentive interest based on the marketing success rate of the user in the target marketing object group corresponding to the at least one incentive interest;

and the equity releasing module 540 is used for releasing the matched target incentive equity to the users in the target marketing object group.

Based on the marketing promotion device shown in fig. 5, it can be known that: according to the scheme of the embodiment of the application, the marketing success rate of different incentive rights to the user can be predicted based on the second model, and the reasonable target incentive rights are selected to be released to the user according to the prediction result, so that balance is found between the guarantee of the marketing success rate and the reduction of marketing budget expenditure, and the technical effect of applying the marketing budget to the knife edge is achieved.

Optionally, the fourth historical marketing campaign corresponds to a time earlier than the third historical marketing campaign.

Optionally, the third historical marketing campaign and the fourth historical marketing campaign are both marketing campaigns initiated by a targeted payment application; wherein the behavioral characteristics of the user in the targeted marketing object population corresponding to the third historical marketing campaign are determined based on the usage data of the user for the targeted payment application over the period of the third historical marketing campaign; the behavioral characteristics of the sample user corresponding to the fourth historical marketing campaign are determined based on the usage data of the sample user for the targeted payment application over the period of the fourth historical marketing campaign.

Optionally, the at least one incentive right corresponds to rights that are different from one another.

Optionally, the interest releasing module 540 determines, when executed, a target incentive interest matching the user in the target user group from the at least one incentive interest based on the marketing cost upper limit of the target user group and the marketing success rate of the at least one incentive interest corresponding to the user in the target user group.

Obviously, the marketing promotion device according to the embodiment of the present application can be used as the execution theme of the marketing promotion method shown in fig. 1, so that the marketing promotion device can implement the functions of the marketing promotion method in fig. 2 and fig. 3. Since the principle is the same, the detailed description is omitted here.

Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 6, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.

The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (peripheral component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 6, but that does not indicate only one bus or one type of bus.

And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.

The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program, and can form a determining device of the marketing object on a logic level, and is specifically used for executing the following operations:

acquiring a candidate marketing object group;

Or, the processor reads the corresponding computer program from the nonvolatile memory to the memory and then runs the computer program, so that the marketing promotion device can be formed on a logical level, and is specifically configured to perform the following operations:

acquiring a target marketing object group to be promoted;

The method for determining the marketing object according to the embodiment shown in fig. 1 of the present application or the marketing promotion method according to the embodiment shown in fig. 2 of the present application may be implemented in or by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.

It should be understood that the electronic device of the embodiment of the present application may implement the functions of the above-mentioned marketing object determination device in the embodiments shown in fig. 1 and 3, or the functions of the above-mentioned marketing promotion device in the embodiments shown in fig. 2 and 3. Since the principle is the same, the description is omitted here for illustration.

Of course, besides the software implementation, the electronic device of the present application does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices.

Furthermore, an embodiment of the present application also provides a computer-readable storage medium storing one or more programs, where the one or more programs include instructions.

Wherein. The instructions, when executed by a portable electronic device comprising a plurality of applications, enable the portable electronic device to perform the method of determining a marketing object of the embodiment shown in fig. 1, and in particular to perform the following method:

acquiring a candidate marketing object group;

Alternatively, the instructions, when executed by a portable electronic device comprising a plurality of application programs, can cause the portable electronic device to perform the marketing promotion method of the embodiment shown in fig. 2, and is specifically configured to perform the following method:

acquiring a target marketing object group to be promoted;

As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.

Claims

1. A method of determining a marketing object, comprising:

acquiring a candidate marketing object group;

2. The method of claim 1, wherein the first and second light sources are selected from the group consisting of,

the second historical marketing campaign corresponds to a time that is earlier than a time corresponding to the first historical marketing campaign.

3. The method according to claim 1 or 2,

obtaining a population of candidate marketing objects, comprising:

determining at least some inactive users in the targeted payment application as a population of candidate marketing objects; wherein the first historical marketing campaign and the second historical marketing campaign are both marketing campaigns initiated by the targeted payment application.

4. The method of claim 3, wherein the first and second light sources are selected from the group consisting of,

the inactive users include: and the balance in the target payment application does not reach the user with the first preset threshold value, and/or the user with the deactivation time reaching the second preset threshold value in the target payment application.

5. The method of claim 4, wherein the first and second light sources are selected from the group consisting of,

the behavior characteristics of the users in the marketing object candidate group corresponding to the first historical marketing campaign are determined based on the usage data of the users for the target payment application in the first historical marketing campaign period;

6. A marketing promotion method, comprising:

acquiring a target marketing object group to be promoted;

7. The method of claim 6, wherein the first and second light sources are selected from the group consisting of,

the time corresponding to the fourth historical marketing campaign is earlier than the time corresponding to the third historical marketing campaign.

8. The method of claim 6, wherein the first and second light sources are selected from the group consisting of,

the third historical marketing campaign and the fourth historical marketing campaign are both marketing campaigns initiated by a targeted payment application;

wherein the behavioral characteristics of the user in the targeted marketing object population corresponding to the third historical marketing campaign are determined based on the usage data of the user for the targeted payment application over the period of the third historical marketing campaign;

the behavioral characteristics of the sample user corresponding to the fourth historical marketing campaign are determined based on the usage data of the sample user for the targeted payment application over the period of the fourth historical marketing campaign.

9. The method according to any one of claims 6-8,

the at least one incentive right corresponds to rights that are different from one another.

10. The method according to any one of claims 6-8,

determining a target incentive interest matching the users in the targeted marketing object group from the at least one incentive interest based on the marketing success rate of the users in the targeted marketing object group corresponding to the at least one incentive interest, comprising:

and determining a target incentive interest matched with the users in the target user group from the at least one incentive interest based on the marketing cost upper limit of the target user group and the marketing success rate of the users in the target user group corresponding to the at least one incentive interest.

11. A marketing object determination apparatus comprising:

12. An electronic device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being executed by the processor to:

acquiring a candidate marketing object group;

13. A computer-readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of:

acquiring a candidate marketing object group;

14. A marketing promotion device comprising:

15. An electronic device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being executed by the processor to:

acquiring a target marketing object group to be promoted;

16. A computer-readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of:

acquiring a target marketing object group to be promoted;