A customer acquisition method, a digital intelligence marketing system, a terminal device, and a storage medium

Abstract

The present invention discloses a customer acquisition method, a digital intelligence marketing system, a terminal device, and a storage medium. The method includes: obtaining at least one piece of historical placement information that meets a preset matching condition with respect to the placement information to be placed for a product; determining non-target users of the placement information to be placed according to the negative evaluations of each piece of the historical placement information; determining non-target user groups according to the number of non-target users in each user group; determining, from each of the user groups, target user groups that do not have common clustering conditions with the clustering condition set of the non-target user groups, or at least one clustering condition of which is mutually exclusive with a certain clustering condition in the clustering condition set of the non-target user groups according to the clustering condition set of the non-target user groups; and determining target users of the placement information to be placed from each of the target user groups. By implementing the present invention, the memory consumption during target user acquisition can be reduced, and the efficiency of target user acquisition can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a customer acquisition method, a digital intelligence marketing system, a terminal device, and a storage medium. Background Art

[0002] In the marketing field, delivering product information to users is an important means of product dissemination. To improve the marketing success rate of products, a digital intelligence marketing system is usually used to select target users of the product for information delivery.

[0003] In related technologies, the method of obtaining target users through a digital intelligence marketing system is to screen and match user data that conforms to the product characteristics from various user data according to the product characteristics of the product, so as to determine the target users who need to receive product information. However, this method requires traversing and converting all user data, resulting in a large amount of memory consumption when obtaining target users, increasing the hardware cost, and affecting the efficiency of obtaining target users. Summary of the Invention

[0004] Embodiments of the present invention provide a customer acquisition method, a digital intelligence marketing system, a terminal device, and a storage medium, which can reduce the memory consumption when obtaining target users and improve the efficiency of obtaining target users.

[0005] An embodiment of the present invention provides a customer acquisition method, including: obtaining at least one piece of historical delivery information that meets a preset matching condition with the information to be delivered of the product;

[0006] Determining non-target users of the information to be delivered according to the negative evaluations of the historical delivery information;

[0007] Determining non-target user groups according to the number of non-target users in each user group;

[0008] Determining, from each user group, target user groups that do not have common clustering conditions with the clustering condition set of the non-target user group, or at least one clustering condition is mutually exclusive with a certain clustering condition in the clustering condition set of the non-target user group, according to the clustering condition set of the non-target user group;

[0009] Determining target users of the information to be delivered from each target user group;

[0010] Wherein, each user group is provided with a corresponding clustering condition set, and users who meet the clustering condition set are classified into the corresponding user group; the clustering condition set includes at least one clustering condition.

[0011] Further, it further includes: determining at least one common clustering condition according to the clustering condition sets of each user group;

[0012] Determine a set of users that meet the public clustering conditions according to the user data of each user;

[0013] When the public clustering condition exists in the set of clustering conditions of the user group, determine each user belonging to the user group according to the set of users that meet the public clustering conditions and the sets of users that meet each independent clustering condition of the user group;

[0014] Wherein, the independent clustering condition is a clustering condition in the set of clustering conditions of the user group except the public clustering condition.

[0015] Furthermore, it further includes: determining that the independent clustering conditions of any two user groups are mutually exclusive conditions, and obtaining the set of users that meet the independent clustering conditions of the other user group according to the set of users that meet the independent clustering conditions of one of the user groups.

[0016] Furthermore, the user data of each user belonging to any user group records a unique identifier corresponding to the user group, and the user group to which the non-target user belongs is determined according to the user data of the non-target user.

[0017] Furthermore, determining the non-target users of the to-be-delivered information according to the negative evaluations of each historical delivery information includes:

[0018] Determine alternative users who post the negative evaluation according to the negative evaluation of any historical delivery information;

[0019] When the number of positive evaluations of the alternative users for each historical delivery information is less than a preset number, determine the alternative users as the non-target users of the to-be-delivered information.

[0020] Furthermore, determining the non-target user group according to the number of non-target users in each user group includes:

[0021] Obtain alternative user groups in which the proportion of the number of non-target users in each user group reaches a preset proportion;

[0022] Determine that the number of users who post positive evaluations for at least one of the historical delivery information in the alternative user groups is less than a preset number, and determine the alternative user groups as non-target user groups.

[0023] Furthermore, it further includes: determining that the historical delivery information does not exist, and obtaining a set of target clustering conditions whose matching degree with the information characteristics reaches a preset matching degree according to the information characteristics of the to-be-delivered information;

[0024] According to the target clustering condition set, the users in each user group where the clustering condition set has an intersection with the target clustering condition set are determined as the target users of the information to be delivered.

[0025] Based on the above method item embodiments, the present invention correspondingly provides device item embodiments;

[0026] An embodiment of the present invention provides a digital intelligence marketing system, including: an information matching module, a first processing module, a second processing module, a user screening module, and a user acquisition module;

[0027] The information matching module is used to obtain at least one historical delivery information that meets the preset matching conditions for the information to be delivered of the product;

[0028] The first processing module is used to determine the non-target users of the information to be delivered according to the negative evaluations of each historical delivery information;

[0029] The second processing module is used to determine the non-target user groups according to the number of non-target users in each user group;

[0030] The user screening module is used to determine, according to the clustering condition set of the non-target user groups, each target user group in each user group that has no common clustering conditions with the clustering condition set of the non-target user groups, or at least one clustering condition is mutually exclusive with a certain clustering condition in the clustering condition set of the non-target user groups;

[0031] The user acquisition module is used to determine the target users of the information to be delivered from each target user group;

[0032] Wherein, each user group is provided with a corresponding clustering condition set, and the users who meet the clustering condition set are divided into the corresponding user groups; the clustering condition set includes at least one clustering condition.

[0033] Based on the above method item embodiments, the present invention provides another embodiment;

[0034] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the customer acquisition method provided in any of the above method item embodiments of the present application.

[0035] Based on the method item embodiments of the present invention, another embodiment is provided:

[0036] Another embodiment of the present invention provides a storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the storage medium is located to execute the customer acquisition method provided in any one of the above method embodiments of the present invention.

[0037] By implementing the embodiments of the present invention, the following beneficial effects can be achieved:

[0038] The embodiments of the present invention provide a customer acquisition method, device, terminal device, and storage medium. The method obtains at least one piece of historical placement information that meets a preset matching condition with the placement information to be placed of a product, determines non-target users of the placement information to be placed according to the negative evaluations of each piece of historical placement information, determines non-target user groups according to the number of non-target users in each user group, and then determines, according to the clustering condition set of the non-target user groups, each target user group in each user group that has no common clustering condition with the clustering condition set of the non-target user groups, or at least one clustering condition is mutually exclusive with a certain clustering condition in the clustering condition set of the non-target user groups, so as to determine each target user of the placement information to be placed from each target user group. Thus, only by using the non-target users of the placement information to be placed, each user group can be screened to screen out the target user groups from each user group to determine each target user of the placement information to be placed, without having to traverse and transform the user data of all users, thereby reducing the memory consumption when obtaining target users and improving the efficiency of obtaining target users. Description of the Drawings

[0039] Figure 1 is a schematic flowchart of the customer acquisition method provided by an embodiment of the present invention.

[0040] Figure 2 is a schematic flowchart of the customer acquisition method provided by another embodiment of the present invention.

[0041] Figure 3 is a schematic flowchart of the customer acquisition method provided by still another embodiment of the present invention.

[0042] Figure 4 is a schematic structural diagram of a digital intelligence marketing system provided by an embodiment of the present invention.

[0043] Figure 5 is a schematic structural diagram of a digital intelligence marketing system provided by another embodiment of the present invention. Detailed Embodiments

[0044] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] See Figure 1 , which is a schematic flowchart of a customer acquisition method provided by an embodiment of the present invention, including:

[0046] Step S101: Obtain at least one piece of historical placement information that meets a preset matching condition for the product's to-be-placed information.

[0047] Among them, the product's to-be-placed information can be advertising information or introduction information to be placed for the product, etc., and it can include feature information for indicating product features, such as the name, type, target population, function, features, and price of the product. The presentation style of the to-be-placed information can be in styles such as text, video, or voice.

[0048] For the to-be-placed information, the information content of the to-be-placed information can be matched with each piece of placed information. Among them, the placed information can be advertising information or introduction information that has been placed for a certain product, etc., and it can also include feature information for indicating product features, such as the name, type, target population, function, features, and price of the product. By matching the information content of the to-be-placed information with each piece of placed information, placed information that meets the preset matching condition, such as the content matching degree reaching the preset matching degree, can be obtained from each piece of placed information as the historical placement information that meets the preset matching condition for the product's to-be-placed information.

[0049] Step S102: Determine the non-target users of the to-be-placed information according to the negative evaluations of each piece of the historical placement information.

[0050] Specifically, for any historical delivery information, all evaluation information for the historical delivery information can be obtained through an API interface from a database storing the evaluation information of any historical delivery information. For any evaluation information, whether the evaluation information is a negative evaluation or a non-negative evaluation can be determined based on the grade evaluation information corresponding to the evaluation information. Exemplarily, when commenting on historical delivery information, grade evaluation information for rating the historical delivery information is usually attached, such as grade evaluation information from 1 to 5 stars. And the grade evaluation information can be used to reflect whether the user has a negative evaluation or a non-negative evaluation of the historical delivery information. For example, if the grade evaluation information is from 3 to 5 stars, it is a non-negative evaluation; if the grade evaluation information is from 1 to 2 stars, it is a negative evaluation. Alternatively, whether the evaluation information is a negative evaluation or a non-negative evaluation can be determined by matching the keywords extracted from the evaluation information with each preset negative evaluation entry. Alternatively, semantic analysis can also be performed on the evaluation information to determine whether the evaluation information is a negative evaluation or a non-negative evaluation. For any negative evaluation in the evaluation information of each historical delivery information, the user who posted the negative evaluation can be determined to identify the user as a non-target user for the information to be delivered.

[0051] Step S103: Determine the non-target user group according to the number of non-target users in each user group.

[0052] Among them, each user group is provided with a corresponding clustering condition set, and users meeting the clustering condition set are classified into the corresponding user group. The clustering condition set includes at least one clustering condition.

[0053] Specifically, before determining the target users, each user can be clustered according to the user data of each user, and each user can be assigned to the corresponding user group. For any user group, it is set with a corresponding clustering condition set, and the clustering condition set includes at least one clustering condition, such as a combination of multiple clustering conditions or a single clustering condition. The clustering condition can be a composite condition or a single condition, and the composite condition can be a logical combination of multiple single conditions. Exemplarily, the clustering condition set can be (A AND B) OR C, where A AND B is a composite condition composed of single condition A and single condition B, and C is a single condition. The single condition can be set according to the actual situation, such as annual income being greater than a preset value, annual income being less than a preset value, age being less than a preset age, age being greater than a preset age, or job requirements, etc.

[0054] Through the pre-stored user data of each user, the user data of each user can be matched with the clustering condition set of the user group to classify each user into the corresponding user group. Among them, the user data can be uploaded by the user himself / herself or obtained through publicly available data acquisition channels. The user data can include the user's annual income, occupation, age, and fixed assets, etc.

[0055] For any user group, non-target users can be found among all the users in the user group to determine the number of non-target users in the user group. To facilitate the subsequent determination of the user group to which a user belongs, each user group is provided with a unique identifier corresponding to the user group. After dividing each user into the corresponding user group, the unique identifier of the user group is written into the user data of the user, so that the user data belonging to the user group records the unique identifier corresponding to the user group. At this time, by identifying the unique identifier in the user data of the non-target users, the user group to which the non-target users belong can be determined, without traversing all the users in the user group, improving the search efficiency of the number of non-target users in the user group.

[0056] After determining the number of non-target users in any user group, it can be judged whether the number of non-target users in the user group reaches a preset number, or whether the proportion of the number of non-target users in the user group reaches a preset ratio. If so, it can be determined that the user group is a non-target user group.

[0057] Step S104: According to the clustering condition set of the non-target user group, determine from each of the user groups the target user groups that do not have common clustering conditions with the clustering condition set of the non-target user group, or at least one clustering condition of which is mutually exclusive with a certain clustering condition in the clustering condition set of the non-target user group.

[0058] Among them, the common clustering condition refers to the clustering condition that exists in at least two clustering condition sets at the same time. Specifically, the rules of each user group are compared pairwise to obtain the common clustering condition. For example, the clustering condition set of user group 1 is (A AND B) OR C, and the clustering condition set of user group 2 is (A AND B) OR D. By comparing user group 1 and user group 2, the common clustering condition can be determined as (A AND B). Mutually exclusive clustering conditions mean that there is no intersection between the clustering conditions. For example, the clustering condition set of user group 1 is (A AND B) OR C, and the clustering condition set of user group 2 is (A AND B) OR D. The clustering condition C is the occupational requirement, and the clustering condition D is that the annual income is greater than the preset value. Then, by comparing the clustering conditions of user group 1 and user group 2, it can be determined that the clustering condition C and the clustering condition D are mutually exclusive.

[0059] Compare the clustering condition set of the non-target user group with the clustering condition set of any user group to determine whether there are common clustering conditions or mutually exclusive clustering conditions between the two clustering condition sets. If there are no common clustering conditions between the two clustering condition sets, the user group can be determined as a target user group; or, if there are mutually exclusive clustering conditions between the two clustering condition sets, the user group can be determined as a target user group.

[0060] Step S105: Determine the target users of the information to be delivered from each of the target user groups.

[0061] Specifically, all users in each user group can be determined as the target users of the information to be delivered; or, all users in each user group except the non-target users can be determined as the target users of the information to be delivered.

[0062] By obtaining at least one historical delivery information that meets the preset matching conditions for the information to be delivered of the product, determining the non-target users of the information to be delivered according to the negative evaluations of each historical delivery information, determining the non-target user groups according to the number of non-target users in each user group, and then determining, according to the clustering condition set of the non-target user groups, each target user group in each user group that does not have a common clustering condition with the clustering condition set of the non-target user groups, or at least one clustering condition is mutually exclusive with a certain clustering condition in the clustering condition set of the non-target user groups, so as to determine the target users of the information to be delivered from each target user group. Thus, only the non-target users of the information to be delivered are used to screen each user group to screen out the target user groups from each user group to determine the target users of the information to be delivered, without having to traverse and convert the user data of all users, thereby being able to reduce the memory consumption when obtaining target users and improve the efficiency of obtaining target users.

[0063] In addition, since users who are interested in the product may not evaluate the delivery information of the product, and the non-target users who only post negative evaluations are usually users who clearly have no interest in the product, using non-target users for reverse screening of target user groups can more comprehensively screen out users who may be interested in the product.

[0064] As Figure 2 shown, in a preferred embodiment, for the division of the user groups to which each user belongs, it may include step S1011: Determine at least one common clustering condition according to the clustering condition sets of each of the user groups.

[0065] Specifically, compare the rules of each user group pairwise to obtain the common clustering condition. For example, if the clustering condition set of user group 1 is (A AND B) OR C, and the clustering condition set of user group 2 is (A AND B) OR D, comparing user group 1 and user group 2, the common clustering condition can be determined as (A AND B).

[0066] Step S1012: Determine the user set that meets the common clustering condition according to the user data of each user.

[0067] Specifically, for each common clustering condition, all user data that meet the common clustering condition can be determined based on the user data of each user stored in the database, so as to form a user set that meets the common clustering condition with all the users corresponding to these user data.

[0068] Step S1013: When the common clustering condition exists in the clustering condition set of the user group, determine each user belonging to the user group according to the user set of the common clustering condition and the user sets that meet each independent clustering condition of the user group.

[0069] Among them, the independent clustering condition is the clustering condition in the clustering condition set of the user group except the common clustering condition.

[0070] Specifically, when determining each user belonging to a certain user group, if the clustering condition set of the user group includes a certain common clustering condition, such as the clustering condition set of the user group is (A AND B) OR C, and it includes the common clustering condition A AND B, then the user set of the common clustering condition A AND B can be obtained. At the same time, according to the user data of each user stored in the database, the user set that meets the independent clustering condition C can be found from each user, so as to perform a logical combination of the user set of the common clustering condition and the user set that meets the independent clustering condition to obtain each user belonging to the user group. When determining each user belonging to a certain user group, since the user set that meets the common clustering condition has been determined first, if there is a common clustering condition in the user group, the corresponding user set can be directly obtained through this common clustering condition, without having to traverse and transform the user data in the database, improving the processing efficiency when determining each user in a certain user group.

[0071] In a preferred embodiment, the method for determining each user belonging to a certain user group further includes:

[0072] Determine that the independent clustering conditions of any two of the user groups are mutually opposite conditions, and obtain the user set of the independent clustering conditions of the other user group according to the user set of the independent clustering conditions of one of the user groups.

[0073] Specifically, when determining each user belonging to a certain user group, if the independent clustering condition of a certain user group and the independent clustering condition of another user group are mutually opposite conditions, after obtaining the user set of the independent clustering event of a certain user group, the user set composed of the remaining users can be directly determined as the user set of the independent clustering conditions of the other user group, without having to traverse and transform the user data in the database, improving the processing efficiency.

[0074] Among them, if two independent clustering conditions have no intersection and their union covers all users, it can be determined that the two independent clustering conditions are mutually exclusive events. For example, if the independent clustering condition D is that the age is less than N, and the independent clustering condition E is that the age is greater than or equal to N, it can be determined that the two independent clustering conditions are mutually exclusive events.

[0075] In a preferred embodiment, determining the non-target users of the to-be-delivered information according to the negative evaluations of each of the historical delivery information includes:

[0076] Determining alternative users who post the negative evaluations according to the negative evaluation of any one of the historical delivery information;

[0077] In the case where the number of positive evaluations of each of the historical delivery information by the alternative users is less than a preset number, determining the alternative users as the non-target users of the to-be-delivered information.

[0078] Specifically, for any one of the negative evaluations in the evaluation information of the historical delivery information, the user who posts the negative evaluation can be determined to be used as the alternative user.

[0079] For any alternative user, all evaluation information of the alternative user for each historical delivery information can be obtained, and it is detected whether the number of positive evaluations in each evaluation information is less than a preset number. If the number of positive evaluations in each evaluation information is greater than or equal to the preset number, such as there are positive evaluations in each evaluation information, it means that the user is only not interested in a certain specific historical delivery information, so this alternative user can be ignored; if the number of positive evaluations in each evaluation information is less than the preset number, such as it is detected that there are no positive evaluations in each evaluation information, it means that the user is not interested in the delivery information similar to the historical delivery information. At this time, the alternative user can be determined as the non-target user of the to-be-delivered information.

[0080] In a preferred embodiment, determining the non-target user group according to the number of non-target users in each user group includes:

[0081] Obtaining alternative user groups in each user group where the proportion of the number of non-target users reaches a preset proportion;

[0082] Determining that the number of users who post positive evaluations for at least one of the historical delivery information in the alternative user group is less than a preset number, and determining the alternative user group as the non-target user group.

[0083] Specifically, for any user group, non-target users can be found from all users in the user group to determine the number of non-target users in the user group. After determining the number of non-target users in any user group, it can be judged whether the proportion of the number of non-target users in the user group reaches a preset proportion. If so, the user group can be determined as the alternative user group.

[0084] For any alternative user group, it is possible to detect the evaluation information released by each user in the alternative user group for each historical delivery information, so as to determine the number of users in the alternative user group who have released positive evaluations for at least one historical delivery information. If the number of such users is less than a preset number, it can be determined that all users in the alternative user group are likely not interested in delivery information similar to the historical delivery information. In this case, the alternative user group can be determined as a non-target user group. Among them, the preset number can be set according to the actual situation.

[0085] In a preferred embodiment, the customer acquisition method further includes:

[0086] Step S106: Determine that the historical delivery information does not exist, and obtain a target clustering condition set whose matching degree with the information characteristics of the to-be-delivered information reaches a preset matching degree according to the information characteristics of the to-be-delivered information.

[0087] Specifically, when obtaining historical delivery information that meets the preset matching conditions with the to-be-delivered information, if no historical delivery information that meets the preset matching conditions with the to-be-delivered information is found, such as when the to-be-delivered information is an advertisement information for the first time, the information characteristics of the to-be-delivered information can be obtained to match the information characteristics of the to-be-delivered information with the clustering condition sets of each user group. Among them, the information characteristics can be the description characteristics of the to-be-delivered information, such as keywords reflecting the name, type, target population, function, features, and price of the product, etc. Matching the information characteristics of the to-be-delivered information with the clustering condition set can be to determine whether a certain information characteristic of the to-be-delivered information meets a certain clustering condition in the clustering condition set. If so, it can be determined that the matching degree between the information characteristics of the to-be-delivered information and the clustering condition set reaches the preset matching degree.

[0088] If the matching degree between the information characteristics of the to-be-delivered information and a certain clustering condition set reaches the preset matching degree, then it can be determined that the clustering condition set is the target clustering condition set.

[0089] Step S107: According to the target clustering condition set, determine the users in each user group whose clustering condition set has an intersection with the target clustering condition set as the target users of the to-be-delivered information.

[0090] Specifically, for any set of clustering conditions, it is possible to detect whether there is an intersection between at least one clustering condition in the set of clustering conditions and at least one clustering condition in the target set of clustering conditions. For example, if a clustering condition in the set of clustering conditions is that the age is less than 30 years old, and a clustering condition in the target set of clustering conditions is that the age is less than 40 years old, then it can be determined that there is an intersection between the set of clustering conditions and the target set of clustering conditions. If there is an intersection between at least one clustering condition in the set of clustering conditions and at least one clustering condition in the target set of clustering conditions, then it can be determined that there is an intersection between the set of clustering conditions and the target set of clustering conditions, so as to determine the users in the user group corresponding to the set of clustering conditions as the target users of the information to be delivered. Thus, even if no historical delivery information is found, it is possible to determine the target users from the user group through the set of clustering conditions that match the information characteristics of the information to be delivered, without having to traverse and transform the user data of all users, thereby reducing the memory consumption when obtaining the target users and improving the efficiency of obtaining the target users.

[0091] Based on the above method embodiment, a corresponding apparatus embodiment is provided;

[0092] As Figure 4 shown, another embodiment of the present invention provides a digital intelligence marketing system, including: an information matching module, a first processing module, a second processing module, a user screening module, and a user obtaining module;

[0093] The information matching module is used to obtain at least one piece of historical delivery information that meets the preset matching conditions with the information to be delivered of the product;

[0094] The first processing module is used to determine the non-target users of the information to be delivered according to the negative evaluations of the historical delivery information;

[0095] The second processing module is used to determine the non-target user groups according to the number of non-target users in each user group;

[0096] The user screening module is used to determine, from each user group, the target user groups that do not have common clustering conditions with the clustering condition set of the non-target user group, or at least one clustering condition of which is mutually exclusive with a certain clustering condition in the clustering condition set of the non-target user group;

[0097] The user obtaining module is used to determine the target users of the information to be delivered from each target user group;

[0098] Wherein, each user group is provided with a corresponding clustering condition set, and the users who meet the clustering condition set are divided into the corresponding user groups; the clustering condition set includes at least one clustering condition.

[0099] AsFigure 5 As shown in Figure 5 , another embodiment of the present invention provides a digital intelligence marketing system, further including a user clustering module. The user clustering module is used to determine a user set that meets the common clustering conditions according to the user data of each user; when the common clustering conditions exist in the clustering condition set of the user group, determine each user belonging to the user group according to the user set that meets the common clustering conditions and the user sets that meet the independent clustering conditions of each user group; where the independent clustering conditions are the clustering conditions in the clustering condition set of the user group except the common clustering conditions.

[0100] In a preferred embodiment, the user clustering module is further used to: determine that the independent clustering conditions of any two user groups are mutually opposite conditions, and obtain the user set of the independent clustering conditions of another user group according to the user set of the independent clustering conditions of one of the user groups.

[0101] In a preferred embodiment, the user data of each user belonging to any user group records a unique identifier corresponding to the user group, and the user group to which the non-target user belongs is determined according to the user data of the non-target user.

[0102] In a preferred embodiment, the first processing module is specifically used to: determine an alternative user who posts a negative evaluation according to the negative evaluation of any historical delivery information; when the number of positive evaluations of the alternative user for each historical delivery information is less than a preset number, determine the alternative user as the non-target user of the information to be delivered.

[0103] In a preferred embodiment, the second processing module is specifically used to: obtain alternative user groups in each user group where the proportion of the number of non-target users reaches a preset proportion; determine that the number of users who post positive evaluations for at least one of the historical delivery information in the alternative user groups is less than a preset number, and determine the alternative user groups as non-target user groups.

[0104] In a preferred embodiment, the user acquisition module is further used to: determine that the historical delivery information does not exist, obtain a target clustering condition set whose matching degree with the information characteristics of the information to be delivered reaches a preset matching degree according to the information characteristics of the information to be delivered; according to the target clustering condition set, determine the users in each user group whose clustering condition set has an intersection with the target clustering condition set as the target users of the information to be delivered.

[0105] It can be understood that the above device item embodiments correspond to the method item embodiments of the present invention, and can implement the customer acquisition method provided by any one of the above method item embodiments of the present invention.

[0106] It should be noted that the device embodiments described above are merely illustrative. The units / modules described as separate components may or may not be physically separated, and the components shown as units / modules may or may not be physical units / modules. That is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative work. The schematic diagram is only an example of the digital intelligence marketing system, and does not constitute a limitation on the digital intelligence marketing system. It may include more or fewer components than shown in the figure, or combine some components, or different components.

[0107] Based on the above method item embodiments, another embodiment is provided;

[0108] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the customer acquisition method provided in any one of the above method item embodiments of the present invention is implemented.

[0109] Exemplarily, the computer program can be divided into one or more modules / units. The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the terminal device.

[0110] The terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that, for example, the terminal device may further include input / output devices, network access devices, a bus, etc.

[0111] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, 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, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device, and connects various parts of the entire terminal device through various interfaces and lines.

[0112] The memory can be used to store the computer program and / or modules. The processor realizes various functions of the terminal device by running or executing the computer program and / or modules stored in the memory, and by calling the data stored in the memory. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function (such as the sound display function, the image display function, etc.); the data storage area can store the data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, memory, plug-in hard disks, Smart Media Cards (SMCs), Secure Digital (SD) cards, Flash Cards, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0113] Based on the above invention embodiments, a corresponding storage medium embodiment is provided;

[0114] Another embodiment of the present invention provides a storage medium. The storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the storage medium is located to execute the customer acquisition method provided in any one of the above method embodiments of the present invention.

[0115] Among them, the storage medium is a computer storage medium. If the modules / units integrated in the device / terminal device are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0116] The above is the preferred implementation manner of the present invention. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A customer acquisition method, characterized in that, Including: Obtain at least one piece of historical placement information that meets the preset matching conditions for the placement information of the product; Determine the non-target users of the placement information according to the negative evaluations of each piece of the historical placement information; Determine the non-target user groups according to the number of the non-target users in each user group; According to the clustering condition set of the non-target user group, determine, from each of the user groups, the target user groups that have no common clustering conditions with the clustering condition set of the non-target user group, or at least one clustering condition is mutually exclusive with a certain clustering condition in the clustering condition set of the non-target user group; Determine the target users of the placement information from each of the target user groups; Wherein, each of the user groups is provided with a corresponding clustering condition set, and the users who meet the clustering condition set are classified into the corresponding user groups; The clustering condition set includes at least one clustering condition.

2. The customer acquisition method according to claim 1, wherein It further includes: Determine at least one common clustering condition according to the clustering condition sets of each of the user groups; Determine the user set that meets the common clustering condition according to the user data of each user; When the common clustering condition exists in the clustering condition set of the user group, determine each user belonging to the user group according to the user set of the common clustering condition and the user sets that meet the respective independent clustering conditions of the user group; Wherein, the independent clustering condition is the clustering condition in the clustering condition set of the user group except the common clustering condition.

3. The customer acquisition method according to claim 2, wherein It further includes: Determine that the independent clustering conditions of any two of the user groups are mutually opposite conditions, and obtain the user set of the independent clustering conditions of the other user group according to the user set of the independent clustering conditions of one of the user groups.

4. The customer acquisition method according to any one of claims 1 to 3, characterized in that, The user data of the users belonging to any one of the user groups records the unique identifier corresponding to the user group, and the user group to which the non-target user belongs is determined according to the user data of the non-target user.

5. The customer acquisition method according to claim 1, wherein Determine the non-target users of the placement information according to the negative evaluations of each piece of the historical placement information, including: Determine the alternative users who post the negative evaluations according to the negative evaluations of any one of the historical placement information; When the number of positive evaluations of the alternative users for each piece of the historical placement information is less than the preset number, determine the alternative users as the non-target users of the placement information.

6. The customer acquisition method according to claim 1, wherein Determine the non-target user groups according to the number of the non-target users in each user group, including: Obtain the alternative user groups in each of the user groups where the proportion of the number of the non-target users reaches the preset proportion; Determine that the number of users who post positive evaluations for at least one piece of the historical placement information in the alternative user groups is less than the preset number, and determine the alternative user groups as the non-target user groups.

7. The customer acquisition method according to any one of claims 1-3 or 5-6, characterized in that, It further includes: Determine that the historical placement information does not exist, and obtain the target clustering condition set with a matching degree reaching the preset matching degree according to the information characteristics of the placement information; According to the target clustering condition set, determine the users in each user group whose clustering condition set has an intersection with the target clustering condition set as the target users of the placement information.

8. A digital marketing system, characterized in that, Including: An information matching module, a first processing module, a second processing module, a user screening module, and a user acquisition module; The information matching module is configured to obtain at least one piece of historical placement information that meets a preset matching condition with the to-be-placed information of the product; The first processing module is configured to determine non-target users of the to-be-placed information according to the negative evaluations of the historical placement information; The second processing module is configured to determine a non-target user group according to the number of non-target users in each user group; The user screening module is configured to determine, from each user group, target user groups that do not have a common clustering condition with the clustering condition set of the non-target user group, or at least one clustering condition of which is mutually exclusive with a certain clustering condition in the clustering condition set of the non-target user group, according to the clustering condition set of the non-target user group; The user acquisition module is configured to determine target users of the to-be-placed information from each of the target user groups; Wherein, each user group is provided with a corresponding clustering condition set, and users who meet the clustering condition set are divided into the corresponding user groups; The clustering condition set includes at least one clustering condition.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the customer acquisition method according to any one of claims 1-7 is implemented.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the storage medium is located to execute the customer acquisition method according to any one of claims 1-7.

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