CN114331536A - Marketing control method and device - Google Patents
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Abstract
The disclosure relates to a marketing management and control method and device. The method comprises the following steps: acquiring a user network behavior score of each user when the user operates in the network; acquiring an intercommunication behavior score of each user for intercommunication with a merchant; acquiring the preference degree of each user to a merchant product; acquiring a marketing response score corresponding to each user according to a preset user network behavior weight value, a user network behavior score, a preset interworking behavior weight value, an interworking behavior score, a preset preference weight value and a preference degree; and determining the marketing strategy for each user according to the marketing response score corresponding to each user. The marketing response score is calculated from three dimensions of user network behaviors, intercommunication behaviors between the user and the merchant and preference of the user to the merchant products, so that the target user is mined, the enterprise is helped to accurately obtain customers, and the outbound call completing rate and the conversion rate are effectively improved.
Description
Technical Field
The disclosure relates to the technical field of marketing, in particular to a marketing control method and device.
Background
At present, there are two ways for accurately mining target users based on operator big data, one is a user intention response/component model based on machine learning training, and the other is rule screening based on expert experience. However, the former has certain requirements on the number of samples, the number of features, the granularity and the computing resources, and is not suitable for the cold start stage of the service. The later method relies on expert experience to output rules for screening target users, can quickly test and improve the return on investment in a short time, but cannot quantify and meet the requirement of refinement.
Disclosure of Invention
In order to overcome the problems in the related art, the embodiments of the present disclosure provide a marketing management and control method and apparatus. The technical scheme is as follows:
according to a first aspect of the embodiments of the present disclosure, there is provided a marketing management and control method, the method including:
acquiring a user network behavior score of each user when the user operates in the network;
acquiring an intercommunication behavior score of each user for intercommunication with a merchant;
acquiring the preference degree of each user to a merchant product;
acquiring a marketing response score corresponding to each user according to a preset user network behavior weight value, a user network behavior score, a preset interworking behavior weight value, an interworking behavior score, a preset preference weight value and a preference degree;
and determining the marketing strategy for each user according to the marketing response score corresponding to each user.
In one embodiment, the obtaining the user network behavior score when the user operates in the network includes:
acquiring a first time attenuation coefficient according to the current time and the generation time of the user network behavior when the user operates in the network;
acquiring a first time of accessing a target page in a target network by the user on the t day; t is an integer greater than or equal to 1;
acquiring the use duration of the target page accessed by the user on the t day;
obtaining the use flow of the user accessing the target page on the t day;
and acquiring the user network behavior score according to the first time attenuation coefficient, the first times, a preset weight coefficient corresponding to the first times, the use duration, a preset weight coefficient corresponding to the use duration, the use flow and a preset weight coefficient corresponding to the use flow.
In one embodiment, the obtaining the interworking behavior score of the user interworking with the merchant includes:
acquiring a second time attenuation coefficient according to the current time and the generation time of the intercommunication behavior of the user and the merchant;
acquiring a second time of intercommunication between the user and the merchant on the t day;
acquiring the number of merchants primarily contacted in a preset period;
and obtaining the intercommunicating behavior score according to the second time attenuation coefficient, the second times, the preset weight corresponding to the second times, the number of merchants and the preset weight of the number of merchants.
In one embodiment, the obtaining of the preference of the user for the merchant product includes:
acquiring the times of browsing a target product by a user;
acquiring the number of products which belong to the same type as the target product browsed by the user;
and acquiring the preference degree according to the times and the number.
In one embodiment, the preset user network behavior weight value is greater than the preset interworking behavior weight value; the preset interworking behavior weight value is greater than the preset preference weight value.
In one embodiment, the determining a marketing strategy for each of the users according to the marketing response score corresponding to each of the users comprises:
when the marketing response score corresponding to the user is smaller than a first preset threshold value, prompting that the user is called out manually;
when the marketing response score corresponding to the user is greater than or equal to the first preset threshold, prompting to develop user reach through a preset outbound mode, wherein the preset outbound mode comprises the following steps: short messages or robots.
In one embodiment, the determining a marketing strategy for each of the users according to the marketing response score corresponding to each of the users comprises:
sequencing the users according to the sequence of the marketing response scores corresponding to the users from large to small;
and outputting the sequencing result.
In one embodiment, the determining a marketing strategy for each of the users according to the marketing response score corresponding to each of the users comprises:
when the marketing response score corresponding to the user is smaller than a second preset threshold value, deleting the user;
and when the marketing response score corresponding to the user is greater than or equal to the second preset threshold value, prompting the user to reach.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects: the marketing management and control method provided by the embodiment of the disclosure comprises the following steps: acquiring a user network behavior score of each user when the user operates in the network; acquiring an intercommunication behavior score of each user for intercommunication with a merchant; acquiring the preference degree of each user to a merchant product; acquiring a marketing response score corresponding to each user according to a preset user network behavior weight value, a user network behavior score, a preset interworking behavior weight value, an interworking behavior score, a preset preference weight value and a preference degree; and determining the marketing strategy for each user according to the marketing response score corresponding to each user. The marketing response score is calculated from three dimensions of user network behaviors, intercommunication behaviors between the user and the merchant and preference of the user to the merchant products, so that the target user is mined, the enterprise is helped to accurately obtain customers, and the outbound call completing rate and the conversion rate are effectively improved.
According to a second aspect of the embodiments of the present disclosure, there is provided a marketing management and control device, including:
the first acquisition module is used for acquiring the network behavior score of each user when the user operates in the network;
the second acquisition module is used for acquiring the intercommunicating behavior score of each user intercommunicating with the merchant;
the third acquisition module is used for acquiring the preference degree of each user to the merchant products;
the fourth acquisition module is used for acquiring the marketing response score corresponding to each user according to the preset user network behavior weight value, the user network behavior score, the preset interworking behavior weight value, the interworking behavior score, the preset preference weight value and the preference degree;
and the determining module is used for determining the marketing strategy for each user according to the marketing response score corresponding to each user.
In one embodiment, the first obtaining module includes:
the first obtaining submodule is used for obtaining a first time attenuation coefficient according to the current time and the generation time of the user network behavior when the user operates in the network;
the second obtaining submodule is used for obtaining the first times of accessing a target page in a target network by the user on the t day; t is an integer greater than or equal to 1;
the third obtaining submodule is used for obtaining the use duration of the target page accessed by the user on the t day;
the fourth obtaining submodule is used for obtaining the use flow of the user accessing the target page at the t day;
and the network behavior score obtaining submodule is used for obtaining the user network behavior score according to the first time attenuation coefficient, the first times, the preset weight coefficient corresponding to the first times, the use duration, the preset weight coefficient corresponding to the use duration, the use flow and the preset weight coefficient corresponding to the use flow.
In one embodiment, the second obtaining module includes:
the fifth obtaining submodule is used for obtaining a second time attenuation coefficient according to the current time and the generation time of the intercommunication behavior of the user and the merchant;
a sixth obtaining submodule, configured to obtain a second number of times that the user communicates with the merchant on the ttth day;
the seventh obtaining submodule is used for obtaining the number of merchants which are primarily contacted in a preset period;
and the eighth obtaining submodule is used for obtaining the intercommunicating behavior score according to the second time attenuation coefficient, the second times, the preset weight corresponding to the second times, the number of merchants and the preset weight of the number of merchants.
In one embodiment, the third obtaining module includes:
the ninth acquisition submodule is used for acquiring the times of browsing the target product by the user;
a tenth obtaining submodule, configured to obtain the number of products that belong to the same type as the target product browsed by the user;
and the eleventh obtaining submodule is used for obtaining the preference degrees according to the times and the number.
In one embodiment, the preset user network behavior weight value is greater than the preset interworking behavior weight value; the preset interworking behavior weight value is greater than the preset preference weight value.
In one embodiment, the determining module includes:
the first prompting submodule is used for prompting that the user is called out manually when the marketing response score corresponding to the user is smaller than a first preset threshold value;
a first outbound sub-module, configured to prompt a user to reach a preset outbound mode when the marketing response score corresponding to the user is greater than or equal to the first preset threshold, where the preset outbound mode includes: short messages or robots.
In one embodiment, the determining module includes:
the sequencing submodule is used for sequencing the users according to the sequence of the marketing response scores corresponding to the users from large to small;
and the output submodule is used for outputting the sequencing result.
In one embodiment, the determining module includes:
the deleting submodule is used for deleting the user when the marketing response score corresponding to the user is smaller than a second preset threshold value;
and the second prompting submodule is used for prompting the user to reach when the marketing response score corresponding to the user is greater than or equal to the second preset threshold value.
According to a third aspect of the embodiments of the present disclosure, there is provided a marketing management and control device, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
acquiring a user network behavior score of each user when the user operates in the network;
acquiring an intercommunication behavior score of each user for intercommunication with a merchant;
acquiring the preference degree of each user to a merchant product;
acquiring a marketing response score corresponding to each user according to a preset user network behavior weight value, a user network behavior score, a preset interworking behavior weight value, an interworking behavior score, a preset preference weight value and a preference degree;
and determining the marketing strategy for each user according to the marketing response score corresponding to each user.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of any one of the first aspects.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flow chart illustrating a marketing management method according to an exemplary embodiment.
Fig. 2 is a flowchart illustrating a step S101 in a marketing management method according to an exemplary embodiment.
Fig. 3 is a flowchart illustrating a step S102 of the marketing management method according to an exemplary embodiment.
Fig. 4 is a flowchart of step S103 in the marketing management and control method according to the second exemplary embodiment.
Fig. 5 is a block diagram illustrating a marketing management apparatus according to an exemplary embodiment.
Fig. 6 is a block diagram illustrating the first acquisition module 11 in the marketing management apparatus according to an exemplary embodiment.
Fig. 7 is a block diagram illustrating the second acquisition module 12 in the marketing management control device according to an exemplary embodiment.
Fig. 8 is a block diagram illustrating the third acquisition module 13 in the marketing management control apparatus according to an exemplary embodiment.
Fig. 9 is a block diagram illustrating the determination module 15 in the marketing management apparatus according to an exemplary embodiment.
Fig. 10 is a block diagram illustrating the determination module 15 in the marketing management apparatus according to an exemplary embodiment.
Fig. 11 is a block diagram illustrating the determination module 15 in the marketing management apparatus according to an exemplary embodiment.
Fig. 12 is a block diagram illustrating a marketing management apparatus 80 according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
Fig. 1 is a flowchart illustrating a marketing management method according to an exemplary embodiment, as shown in fig. 1, the method including the following steps S101-S105:
in step S101, a user network behavior score when each user operates in the network is acquired.
In one embodiment, as shown in FIG. 2, this step S101 includes the following sub-steps S1011-S1015:
in step S1011, a first time attenuation coefficient is obtained according to the current time and the generation time of the user network behavior when the user operates in the network;
the user network behavior of the user when operating in the network may include: the user network behavior of the user when operating in the APP may also be the user network behavior of the user when operating in the Web, which is not limited by the present disclosure.
In step S1012, a first number of times that the user accesses a target page in the target network on the tth day is obtained; t is an integer greater than or equal to 1;
in step S1013, a usage duration of the user accessing the target page on the tth day is obtained;
in step S1014, the usage traffic of the user accessing the target page on the tth day is acquired;
in step S1015, a user network behavior score is obtained according to the first time attenuation coefficient, the first number of times, the preset weight corresponding to the first number of times, the usage duration, the preset weight corresponding to the usage duration, the usage traffic, and the preset weight corresponding to the usage traffic.
The user network behavior score is calculated by internet surfing data and comprises Application program (App) data and internet (English: web) data. Due to the advent of the mobile internet era, APP and web browsing are the most likely to represent the recent focus of attention of users. Accessing different APPs or Uniform Resource Locators (URLs) represents different user requirements. Accessing different pages of the same APP represents different demand phases.
Meanwhile, considering that the user demand has strong timeliness, a timestamp of a user generated behavior (occurrence time of the behavior) is used, that is, a method is defined for measuring timeliness, specifically, a first time attenuation coefficient can be obtained by the current time and the generation time of the user network behavior when the user operates in the network, and the first time attenuation coefficient can be obtained by the following formula:
p(t)＝1-α(T-t)β,T≥t
where p (T) is a first time attenuation coefficient, α and β are demand attenuation parameters, T is a current time, and T is a time stamp of the network behavior generated by the user (time of the network behavior generated by the user). When T is T, p (T) is 1, which means that the demand is strong in timeliness. The setting of the specific parameters can be determined according to the service scene and the requirements.
Wherein, the larger p (t) is, the closer the occurrence time of the user network behavior is to the current time, and the smaller p (t) is, the farther the occurrence time of the user network behavior is from the current time.
For example: a is 1/8, β is 1/2, and the time when the user generates the network behavior is 64 days from the current time, then p (t) is 1-1/8 × 641/20; the time when the user generates the network behavior is 4 days from the current time, then p (t) is 1-1/8 × 41 /2＝3/4。
The user network behavior score may then be obtained by the following formula:
where w is the user network behavior score, WebiIs a target APP or target Web; VT (t, Web)i) Is the first number of times a Web or APP target page is accessed on day t; VD (t, Web)i) The service time of accessing a Web or APP target page on the t day is set; VV (t, Web)i) The service flow of accessing a Web or APP target page on the t day; theta is an observation period; a is1Is a predetermined weight coefficient corresponding to the first order, a2Is a preset weight coefficient corresponding to the duration of use, a3Is to use flow-corresponding predictionsSetting a weight coefficient.
And theta is an observation period and is different according to different service scene periods. Taking the car purchasing scenario as an example, for example, it takes 1 month from car watching to car purchasing, and then θ at this time is 1 month from car watching to car purchasing.
The network behavior is an operation when browsing on a related APP or a web, different behaviors reflect different requirements of a user, the weight of the different behaviors can be defined according to business experience, and the weight of the different behaviors can be determined by a machine learning method.
Taking a certain product of a certain APP as an example, the weight of submitted information > the weight of viewed product details > the weight of viewed product poster.
Since the user's network behavior may also be related to other factors, in one embodiment, the user's network behavior score may also be derived by the following formula:
in this formula, VX is a reservation feature, a4The preset weight coefficient corresponding to the reserved feature, and the meanings of other parameters are the same as the above formula, which is not described herein again.
In step S102, an interworking behavior score of each user interworking with the merchant is obtained.
In one embodiment, as shown in FIG. 3, this step S102 includes the following sub-steps S1021-S1024:
in step S1021, a second time attenuation coefficient is obtained according to the current time and the generation time of the intercommunication action of the user and the merchant;
in step S1022, a second number of times that the user communicates with the merchant on the tth day is obtained;
in step S1023, the number of merchants initially contacted in a preset period is obtained;
in step S1024, the interworking behavior score is obtained according to the second time attenuation coefficient, the second number of times, the preset weight coefficient corresponding to the second number of times, the number of merchants, and the preset weight coefficient of the number of merchants.
Meanwhile, considering the timeliness of the intercommunication behavior, the second time attenuation coefficient can be obtained through the current time and the generation time of the intercommunication behavior of the user and the merchant, and the second time attenuation coefficient can be obtained through the following formula:
wherein p '(T) is a second time attenuation coefficient, α', β 'are demand attenuation parameters, T is the current time, and T' is the generation time of the interworking behavior of the user and the merchant. When T 'is T, p' (T) is 1, which means that the demand is strong in timeliness. The setting of the specific parameters can be determined according to the service scene and the requirements.
Wherein, the larger p '(t) is, the closer the interworking behavior for the user to intercommunicate with the merchant is to the current time, and the smaller p' (t) is, the farther the interworking behavior for the user to intercommunicate with the merchant is from the current time.
After long-term accumulation, the user requirements can be reflected by combining the merchant/enterprise data and the operator data, and specifically, the intercommunicating behavior score can be obtained through the following formula:
c denotes the intercommunicating behavioral score, BuiIs a merchant (merchants may include: businesses and merchants); CT (t, Bu)i) Is the second number of intercommunications with the merchant on day t; b1Is a preset weight coefficient corresponding to the second number; b3Is a preset weight coefficient of the number of merchants; p' (t) is a second time attenuation coefficient; CF (Bu)i) Is the number of merchants initially contacted during the period theta.
And further mining the user requirements from the intercommunication level by combining the enterprise/merchant yellow page data and the operator big data.
Since the user's interworking behavior may also be related to other factors, in one embodiment, the user interworking behavior score may also be derived by the following formula:
in this formula, CX is a reserved feature, b2Is a preset weight coefficient corresponding to the reserved characteristic; the other parameters have the same meanings as the above formula and are not described herein again.
In step S103, the preference of each user for the merchant product is obtained.
In one embodiment, as shown in FIG. 4, this step S103 includes the following sub-steps S1031-S1033:
in step S1031, the number of times that the user browses the target product is acquired;
in step S1032, the number of products that the user browses and the target product belong to the same type is obtained;
in step S1033, a preference degree is acquired according to the number and the number.
The preference of the user to the merchant products can further refine the requirements of the user.
Specifically, the product matching degree reflects the concentration degree of the product browsed by the user, and what is actually reflected behind the product matching degree is the preference degree of the product, such as price, brand, risk and the like. The higher the degree of matching, the more interesting the user is in a certain type of product. Let the preference of the user to the merchant product be p, and the frequency of browsing the product A by the user be pnumThe number of the same kind of products browsed by the user is ptotalThen p is defined as:
for example, the interest rate of financial loan products on the market is 18/24/36 different, and if the interest rate of products with higher product matching degree is 18%, it can indicate that the user is interested in low-interest rate products, and reflect the user risk preference.
In step S104, a marketing response score corresponding to each user is obtained according to the preset user network behavior weight value, the user network behavior score, the preset interworking behavior weight value, the interworking behavior score, the preset preference weight value, and the preference degree.
Let s denote the marketing response score corresponding to the user, which may also be referred to as the user intent score, and which reflects a "probability". c represents the intercommunication behavior score, w represents the user network behavior score, and p represents the preference of the user to the merchant product. And judging the user requirement through w and c, and delineating the approximate user range. And subdividing the user requirements by p to circle users suitable for a specific product. s is defined as:
s＝β1w+β2c+β3p
in the above formula, s represents the marketing response score corresponding to the user; beta is a1Representing a preset user network behavior weight value; beta is a2Representing a preset user network behavior weight value; beta is a3Representing a preset user network behavior weight value.
The selection of each factor is summarized based on the customer obtaining method in each industry, and the influence on the user is different. The network advertisement and the telephone are used as main contact modes for getting a guest, and target users are very directly positioned from network and intercommunication behaviors, so the weight is larger.
In one implementation, the preset user network behavior weight value is greater than the preset interworking behavior weight value; the preset interworking behavior weight value is greater than the preset preference weight value. That is, w > c > p.
Taking a consumer product mobile phone as an example, users with mobile phone requirements can be defined through w and c, but mobile phones have numerous brands and different prices, mobile phone brands and prices in which the users are interested can be subdivided through p, and brand preference and price preference of the users are reflected.
The p is introduced in a time-sharing mode when the user intention is determined, the idea of accurately digging thousands of faces of thousands of people is reflected, the target users are further positioned, the number of the target users is reduced, and meanwhile the investment return rate is improved.
In step S105, a marketing strategy for each user is determined according to the marketing response score corresponding to each user.
Wherein, the higher the marketing response score corresponding to the user is, the higher the intention of the user is.
In one embodiment, this step S105 includes the following sub-steps A1-A2:
at a1, when the marketing response score corresponding to the user is less than a first preset threshold, the user is prompted to manually call out.
In a2, when the marketing response score corresponding to the user is greater than or equal to a first preset threshold, prompting the user to reach by a preset outbound mode, wherein the preset outbound mode includes: short messages or robots.
For the users with larger marketing response scores, the users can be prompted to call in a manual outbound mode, and compared with the users with lower marketing response scores, the users can be reached in forms of short messages with lower cost, robot outbound and the like.
In one embodiment, this step S105 includes the following sub-steps B1-B2:
in B1, sorting the users in the order of the marketing response scores corresponding to the users from large to small;
in B2, the sorting result is output.
The marketing response scores are ordered from top to bottom to allow the customer to make an on-period manual outbound based on the order.
In one embodiment, this step S105 includes the following sub-steps C1-C2:
in C1, when the marketing response score corresponding to the user is smaller than a second preset threshold, deleting the user;
in C2, when the marketing response score corresponding to the user is greater than or equal to a second preset threshold, the user is prompted to reach.
Only the users with larger marketing response scores are shown to the customers, so that the data volume viewed by the customers can be effectively reduced.
When the weighting coefficients of w, c and p are determined, the appropriate weighting coefficients of w, c and p, a first preset threshold and a second preset threshold can be determined through the accumulated samples, so that the corresponding marketing response score and the success rate of the user are positively correlated. And verified by actually reaching the user. And continuously adjusting to finally reach a stable state.
Further, the online preliminary screening log is processed to obtain the network operation type of the user, the total operation times of each network operation type of the user, the type of the merchant having an intercommunication behavior with the user, the intercommunication times of the user and each merchant type, the times of browsing the target product by the user, and the number of the similar products of the user browsing and the target product.
Specifically, according to the service requirement, the following service fields are processed and accumulated:
type, the field of industry corresponding to the record, such as real estate, home decoration, loan;
the Behavior corresponding to the record of the Behavior, such as 'loan user digging', can be 'logging in loan APP', 'browsing loan product poster', 'submitting application', and the like. The key customer-obtaining nodes can be defined by industry according to expert experience;
time, which records the Time of occurrence of the action;
object, causing the user to record the product produced, such as a certain gold bar;
from, this record of the app or web name produced;
expansion, extended attributes, such as traffic.
The above information can be extracted from the above service fields, and the specific extraction method is similar to that in the related art, and is not described herein again.
The marketing management and control method provided by the embodiment of the disclosure comprises the following steps: acquiring a user network behavior score of each user when the user operates in the network; acquiring an intercommunication behavior score of each user for intercommunication with a merchant; acquiring the preference degree of each user to a merchant product; acquiring a marketing response score corresponding to each user according to a preset user network behavior weight value, a user network behavior score, a preset interworking behavior weight value, an interworking behavior score, a preset preference weight value and a preference degree; and determining the marketing strategy for each user according to the marketing response score corresponding to each user. The yellow page data is matched with the operator data, and the marketing response score is calculated from three dimensions of user network behaviors, intercommunication behaviors between the user and the merchant and the preference degree of the user to the merchant products, so that the target user is mined, the enterprise is helped to accurately obtain the customer, and the outbound call completing rate and the conversion rate are effectively improved.
Furthermore, the method in the disclosure does not need to have certain requirements on the number of samples, the number of features, the granularity and the computing resources based on the method of the user intention response/component model of machine learning training in the related art, and the method is suitable for the cold start stage of the business. In addition, the method disclosed by the invention does not need to rely on expert experience to output rules, so that quantification can be realized to meet the requirements for refinement.
Specifically, in the method, the target user is mined from two dimensions of a network and an intercommunication behavior by matching the disclosed yellow page data with operator data, and the requirement of the target user is subdivided by the product matching degree; and calculating a marketing response score of the user based on the data timeliness; thereby effectively improving the call completing rate and the conversion rate of the outbound call.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods.
Fig. 5 is a block diagram illustrating a marketing management apparatus, which may be implemented as part or all of an electronic device through software, hardware, or a combination of both, according to an example embodiment. As shown in fig. 5, the marketing management and control apparatus includes:
a first obtaining module 11, configured to obtain a user network behavior score when each user operates in a network;
a second obtaining module 12, configured to obtain an interworking behavior score for interworking between each user and a merchant;
a third obtaining module 13, configured to obtain a preference degree of each user for a merchant product;
a fourth obtaining module 14, configured to obtain a marketing response score corresponding to each user according to the preset user network behavior weight value, the user network behavior score, the preset interworking behavior weight value, the interworking behavior score, the preset preference weight value, and the preference degree;
and the determining module 15 is configured to determine a marketing strategy for each user according to the marketing response score corresponding to each user.
In one embodiment, as shown in fig. 6, the first obtaining module 11 includes:
the first obtaining sub-module 111 is configured to obtain a first time attenuation coefficient according to a current time and a generation time of a user network behavior when a user operates in a network;
a second obtaining sub-module 112, configured to obtain a first number of times that the user accesses a target page in a target network on a t-th day; t is an integer greater than or equal to 1;
a third obtaining sub-module 113, configured to obtain a use duration of the user accessing the target page on the tth day;
a fourth obtaining sub-module 114, configured to obtain a usage traffic of the user accessing the target page on the tth day;
a network behavior score obtaining sub-module 115, configured to obtain the user network behavior score according to the first time attenuation coefficient, the first number of times, a preset weight coefficient corresponding to the first number of times, the usage duration, a preset weight coefficient corresponding to the usage duration, the usage traffic, and a preset weight coefficient corresponding to the usage traffic.
In one embodiment, as shown in fig. 7, the second obtaining module 12 includes:
a fifth obtaining submodule 121, configured to obtain a second time attenuation coefficient according to the current time and the generation time of the interworking behavior of the user and the merchant;
a sixth obtaining sub-module 122, configured to obtain a second number of times that the user communicates with the merchant on the ttth day;
the seventh obtaining sub-module 123, configured to obtain the number of merchants that are primarily contacted in a preset period;
an eighth obtaining submodule 124, configured to obtain the interworking behavior score according to the second time attenuation coefficient, the second number of times, a preset weight corresponding to the second number of times, the number of merchants, and a preset weight of the number of merchants.
In one embodiment, as shown in fig. 8, the third obtaining module 13 includes:
a ninth obtaining sub-module 131, configured to obtain the number of times that the user browses the target product;
a tenth obtaining submodule 132, configured to obtain the number of products that belong to the same type as the target product browsed by the user;
an eleventh obtaining submodule 133, configured to obtain the preference degree according to the number and the number.
In one embodiment, the preset user network behavior weight value is greater than the preset interworking behavior weight value; the preset interworking behavior weight value is greater than the preset preference weight value.
In one embodiment, as shown in fig. 9, the determining module 15 includes:
the first prompting sub-module 151 is configured to prompt that the user is called out manually when the marketing response score corresponding to the user is smaller than a first preset threshold;
a first outbound sub-module 152, configured to prompt the user to reach the marketing response score corresponding to the user by a preset outbound mode when the marketing response score corresponding to the user is greater than or equal to the first preset threshold, where the preset outbound mode includes: short messages or robots.
In one embodiment, as shown in fig. 10, the determining module 15 includes:
the sorting submodule 153 is configured to sort the users in an order from a large marketing response score to a small marketing response score corresponding to each user;
an output sub-module 154 for outputting the sorting result.
In one embodiment, as shown in fig. 11, the determining module 15 includes:
the deleting submodule 155 is configured to delete the user when the marketing response score corresponding to the user is smaller than a second preset threshold;
the second prompting sub-module 156 is configured to prompt the user to reach when the marketing response score corresponding to the user is greater than or equal to the second preset threshold.
According to a third aspect of the embodiments of the present disclosure, there is provided a marketing management and control apparatus including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
acquiring a user network behavior score of each user when the user operates in the network;
acquiring an intercommunication behavior score of each user for intercommunication with a merchant;
acquiring the preference degree of each user to a merchant product;
acquiring a marketing response score corresponding to each user according to a preset user network behavior weight value, a user network behavior score, a preset interworking behavior weight value, an interworking behavior score, a preset preference weight value and a preference degree;
and determining the marketing strategy for each user according to the marketing response score corresponding to each user.
The processor may be further configured to:
in one embodiment, the obtaining the user network behavior score when the user operates in the network includes:
acquiring a first time attenuation coefficient according to the current time and the generation time of the user network behavior when the user operates in the network;
acquiring a first time of accessing a target page in a target network by the user on the t day; t is an integer greater than or equal to 1;
acquiring the use duration of the target page accessed by the user on the t day;
obtaining the use flow of the user accessing the target page on the t day;
and acquiring the user network behavior score according to the first time attenuation coefficient, the first times, a preset weight coefficient corresponding to the first times, the use duration, a preset weight coefficient corresponding to the use duration, the use flow and a preset weight coefficient corresponding to the use flow.
In one embodiment, the obtaining the interworking behavior score of the user interworking with the merchant includes:
acquiring a second time attenuation coefficient according to the current time and the generation time of the intercommunication behavior of the user and the merchant;
acquiring a second time of intercommunication between the user and the merchant on the t day;
acquiring the number of merchants primarily contacted in a preset period;
and obtaining the intercommunicating behavior score according to the second time attenuation coefficient, the second times, the preset weight corresponding to the second times, the number of merchants and the preset weight of the number of merchants.
In one embodiment, the obtaining of the preference of the user for the merchant product includes:
acquiring the times of browsing a target product by a user;
acquiring the number of products which belong to the same type as the target product browsed by the user;
and acquiring the preference degree according to the times and the number.
In one embodiment, the preset user network behavior weight value is greater than the preset interworking behavior weight value; the preset interworking behavior weight value is greater than the preset preference weight value.
In one embodiment, the determining a marketing strategy for each of the users according to the marketing response score corresponding to each of the users comprises:
when the marketing response score corresponding to the user is smaller than a first preset threshold value, prompting that the user is called out manually;
when the marketing response score corresponding to the user is greater than or equal to the first preset threshold, prompting to develop user reach through a preset outbound mode, wherein the preset outbound mode comprises the following steps: short messages or robots.
In one embodiment, the determining a marketing strategy for each of the users according to the marketing response score corresponding to each of the users comprises:
sequencing the users according to the sequence of the marketing response scores corresponding to the users from large to small;
and outputting the sequencing result.
In one embodiment, the determining a marketing strategy for each of the users according to the marketing response score corresponding to each of the users comprises:
when the marketing response score corresponding to the user is smaller than a second preset threshold value, deleting the user;
and when the marketing response score corresponding to the user is greater than or equal to the second preset threshold value, prompting the user to reach.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Fig. 12 is a block diagram illustrating a marketing management apparatus 80, which is suitable for a terminal device, according to an exemplary embodiment. For example, the apparatus 80 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
The apparatus 80 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.
The processing component 802 generally controls overall operation of the device 80, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 80. Examples of such data include instructions for any application or method operating on the device 80, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The power supply component 806 provides power to the various components of the device 80. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 80.
The multimedia component 808 includes a screen that provides an output interface between the device 80 and the user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 80 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 80 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the device 80. For example, the sensor assembly 814 may detect the open/closed status of the device 80, the relative positioning of the components, such as a display and keypad of the device 80, the change in position of the device 80 or a component of the device 80, the presence or absence of user contact with the device 80, the orientation or acceleration/deceleration of the device 80, and the change in temperature of the device 80. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate wired or wireless communication between the apparatus 80 and other devices. The device 80 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 80 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 804 comprising instructions, executable by the processor 820 of the apparatus 80 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
A non-transitory computer readable storage medium, wherein instructions, when executed by a processor of an apparatus 80, enable the apparatus 80 to perform the marketing management method described above, the method comprising:
acquiring a user network behavior score of each user when the user operates in the network;
acquiring an intercommunication behavior score of each user for intercommunication with a merchant;
acquiring the preference degree of each user to a merchant product;
acquiring a marketing response score corresponding to each user according to a preset user network behavior weight value, a user network behavior score, a preset interworking behavior weight value, an interworking behavior score, a preset preference weight value and a preference degree;
and determining the marketing strategy for each user according to the marketing response score corresponding to each user.
In one embodiment, the obtaining the user network behavior score when the user operates in the network includes:
acquiring a first time attenuation coefficient according to the current time and the generation time of the user network behavior when the user operates in the network;
acquiring a first time of accessing a target page in a target network by the user on the t day; t is an integer greater than or equal to 1;
acquiring the use duration of the target page accessed by the user on the t day;
obtaining the use flow of the user accessing the target page on the t day;
and acquiring the user network behavior score according to the first time attenuation coefficient, the first times, a preset weight coefficient corresponding to the first times, the use duration, a preset weight coefficient corresponding to the use duration, the use flow and a preset weight coefficient corresponding to the use flow.
In one embodiment, the obtaining the interworking behavior score of the user interworking with the merchant includes:
acquiring a second time attenuation coefficient according to the current time and the generation time of the intercommunication behavior of the user and the merchant;
acquiring a second time of intercommunication between the user and the merchant on the t day;
acquiring the number of merchants primarily contacted in a preset period;
and obtaining the intercommunicating behavior score according to the second time attenuation coefficient, the second times, the preset weight corresponding to the second times, the number of merchants and the preset weight of the number of merchants.
In one embodiment, the obtaining of the preference of the user for the merchant product includes:
acquiring the times of browsing a target product by a user;
acquiring the number of products which belong to the same type as the target product browsed by the user;
and acquiring the preference degree according to the times and the number.
In one embodiment, the preset user network behavior weight value is greater than the preset interworking behavior weight value; the preset interworking behavior weight value is greater than the preset preference weight value.
In one embodiment, the determining a marketing strategy for each of the users according to the marketing response score corresponding to each of the users comprises:
when the marketing response score corresponding to the user is smaller than a first preset threshold value, prompting that the user is called out manually;
when the marketing response score corresponding to the user is greater than or equal to the first preset threshold, prompting to develop user reach through a preset outbound mode, wherein the preset outbound mode comprises the following steps: short messages or robots.
In one embodiment, the determining a marketing strategy for each of the users according to the marketing response score corresponding to each of the users comprises:
sequencing the users according to the sequence of the marketing response scores corresponding to the users from large to small;
and outputting the sequencing result.
In one embodiment, the determining a marketing strategy for each of the users according to the marketing response score corresponding to each of the users comprises:
when the marketing response score corresponding to the user is smaller than a second preset threshold value, deleting the user;
and when the marketing response score corresponding to the user is greater than or equal to the second preset threshold value, prompting the user to reach.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A marketing management and control method is characterized by comprising the following steps:
acquiring a user network behavior score of each user when the user operates in the network;
acquiring an intercommunication behavior score of each user for intercommunication with a merchant;
acquiring the preference degree of each user to a merchant product;
acquiring a marketing response score corresponding to each user according to a preset user network behavior weight value, a user network behavior score, a preset interworking behavior weight value, an interworking behavior score, a preset preference weight value and a preference degree;
and determining the marketing strategy for each user according to the marketing response score corresponding to each user.
2. The method of claim 1, wherein the obtaining the user network behavior score of the user when operating in the network comprises:
acquiring a first time attenuation coefficient according to the current time and the generation time of the user network behavior when the user operates in the network;
acquiring a first time of accessing a target page in a target network by the user on the t day; t is an integer greater than or equal to 1;
acquiring the use duration of the target page accessed by the user on the t day;
obtaining the use flow of the user accessing the target page on the t day;
and acquiring the user network behavior score according to the first time attenuation coefficient, the first times, a preset weight coefficient corresponding to the first times, the use duration, a preset weight coefficient corresponding to the use duration, the use flow and a preset weight coefficient corresponding to the use flow.
3. The method of claim 1, wherein obtaining the interworking behavior score of the user interworking with the merchant comprises:
acquiring a second time attenuation coefficient according to the current time and the generation time of the intercommunication behavior of the user and the merchant;
acquiring a second time of intercommunication between the user and the merchant on the t day;
acquiring the number of merchants primarily contacted in a preset period;
and obtaining the intercommunicating behavior score according to the second time attenuation coefficient, the second times, the preset weight corresponding to the second times, the number of merchants and the preset weight of the number of merchants.
4. The method of claim 1, wherein the obtaining user preferences for merchant products comprises:
acquiring the times of browsing a target product by a user;
acquiring the number of products which belong to the same type as the target product browsed by the user;
and acquiring the preference degree according to the times and the number.
5. The method of claim 1, wherein the predetermined user network behavior weight value is greater than the predetermined interworking behavior weight value; the preset interworking behavior weight value is greater than the preset preference weight value.
6. The method of claim 1, wherein determining a marketing strategy for each of the users based on the marketing response score for each of the users comprises:
when the marketing response score corresponding to the user is smaller than a first preset threshold value, prompting that the user is called out manually;
when the marketing response score corresponding to the user is greater than or equal to the first preset threshold, prompting to develop user reach through a preset outbound mode, wherein the preset outbound mode comprises the following steps: short messages or robots.
7. The method of claim 1, wherein determining a marketing strategy for each of the users based on the marketing response score for each of the users comprises:
sequencing the users according to the sequence of the marketing response scores corresponding to the users from large to small;
and outputting the sequencing result.
8. The method of claim 1, wherein determining a marketing strategy for each of the users based on the marketing response score for each of the users comprises:
when the marketing response score corresponding to the user is smaller than a second preset threshold value, deleting the user;
and when the marketing response score corresponding to the user is greater than or equal to the second preset threshold value, prompting the user to reach.
9. A marketing management and control device, comprising:
the first acquisition module is used for acquiring the network behavior score of each user when the user operates in the network;
the second acquisition module is used for acquiring the intercommunicating behavior score of each user intercommunicating with the merchant;
the third acquisition module is used for acquiring the preference degree of each user to the merchant products;
the fourth acquisition module is used for acquiring the marketing response score corresponding to each user according to the preset user network behavior weight value, the user network behavior score, the preset interworking behavior weight value, the interworking behavior score, the preset preference weight value and the preference degree;
and the determining module is used for determining the marketing strategy for each user according to the marketing response score corresponding to each user.
10. A computer-readable storage medium having stored thereon computer instructions, which when executed by a processor, carry out the steps of the method according to any one of claims 1 to 8.
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