Personalized expansion marketing strategy method and system

Abstract

The invention relates to the technical field of commercial operation, and particularly provides a personalized extended marketing strategy method and system.The marketing activity business process is subdivided into a plurality of independent and reusable modules, and public data and logic among the modules are extracted to form a public skeleton; arranging a flow of a public module, and ensuring that all used activities follow a unified and necessary operation flow; enumerating constant mapping activity types and specific strategy implementation methods, creating strategy classes in combination with a singleton mode, and implementing independent development of different activity forms; a specific sub-process is packaged into an independent developable strategy, a pseudo-random number is generated by using a PRD algorithm, a winning probability P (N) is dynamically adjusted according to the lottery drawing times N and the increment C of each activity, and uniform distribution of the winning times in the total lottery drawing times is ensured. Compared with the prior art, the method can enhance the fairness and credibility of the activity.

Description

Technical Field

[0001] The present invention relates to the field of commercial operation technology, and specifically provides a personalized extended marketing strategy method and system. Background Art

[0002] E-commerce marketing business is extremely complex and changeable, especially with the booming trend of digital marketing. Different marketing strategy decisions will be made in different periods of the market, different stages of company development, different user groups, and continuous fluctuations in effects. Marketing strategies are often numerous, short-term, and complex and changeable in functions. When faced with business scenarios that change at any time, the scalability of the system becomes very important.

[0003] The traditional online lottery algorithm may use a computer-generated random number within a certain upper limit, and then determine whether you have won the prize by comparing it with the generated random number based on the winning rate set by the marketing campaign and the product of the upper limit.

[0004] Although it is said that "there are no real random numbers in computers", this random number is relatively close to a true random number. As a result, users who participated continuously in the early stage of the activity may win prizes, while users who participated later may not win prizes because the winning rate exceeded the limit or the prizes were limited. In other words, the number of winning times is unevenly distributed. These users who participated later but did not win prizes will think that they participated in an unfair marketing activity and have a poor experience. Summary of the invention

[0005] The present invention aims at solving the above-mentioned deficiencies of the prior art and provides a highly practical and personalized marketing strategy method.

[0006] A further technical task of the present invention is to provide a marketing strategy system that is reasonably designed, safe and applicable, and has personalized expansion.

[0007] The technical solution adopted by the present invention to solve its technical problem is:

[0008] A personalized and extended marketing strategy method that subdivides the marketing activity business process into multiple independent and reusable modules, and extracts the common data and logic between the modules to form a common skeleton;

[0009] Arrange the processes of common modules to ensure that all activities follow unified and necessary operation procedures;

[0010] Enumerate constants to map activity types and specific strategy implementation methods, create strategy classes in combination with the singleton pattern, and implement independent development of different activity forms;

[0011] The specific sub-processes are encapsulated as independent developable strategies, and the PRD algorithm is used to generate pseudo-random numbers. The winning probability P(N) is dynamically adjusted according to the number of draws N and the increment C of each activity to ensure that the number of wins is evenly distributed in the total number of draws.

[0012] Furthermore, the template method pattern is used to arrange the processes of the common modules mentioned above, and the basic information of the activity, activity products, activity stores, activity cities, activity pictures, activity tags, activity groups, activity prizes, activity rules and activity operation logs are saved in turn.

[0013] Furthermore, the factory method pattern is used, and enumeration constants are used to save the mapping relationship between activity types and specific strategy implementation methods, and then combined with the singleton pattern in the framework to create strategy classes.

[0014] Furthermore, the PRD algorithm is used to generate pseudo-random numbers, where N for each activity is stored in redis, and N is the number of draws, that is, the number of draws since the last draw in the current activity;

[0015] The increment C pre-prepares the corresponding values ​​of all integer probabilities between 1 and 100 in the memory when the application is started, and provides an algorithm for calculating the C value corresponding to when the probability value set for the activity is a decimal. The actual winning rates corresponding to all probabilities are stored in the application's memory. The mapping relationship is fixed in the median of multiple applications in a distributed system. Whether a prize is won is determined based on the number of draws for each activity, ensuring that the number of wins is evenly distributed in the total number of draws, where C is the actual winning rate increased each time.

[0016] A personalized and extended marketing strategy system that subdivides the marketing activity business process into multiple independent and reusable modules, and extracts the common data and logic between modules to form a common framework;

[0017] Arrange the processes of common modules to ensure that all activities follow unified and necessary operation procedures;

[0018] Enumerate constants to map activity types and specific strategy implementation methods, create strategy classes in combination with the singleton pattern, and implement independent development of different activity forms;

[0019] The specific sub-processes are encapsulated as independent developable strategies, and the PRD algorithm is used to generate pseudo-random numbers. The winning probability P(N) is dynamically adjusted according to the number of draws N and the increment C of each activity to ensure that the number of wins is evenly distributed in the total number of draws.

[0020] Furthermore, the template method pattern is used to arrange the processes of the common modules mentioned above, and the basic information of the activity, activity products, activity stores, activity cities, activity pictures, activity tags, activity groups, activity prizes, activity rules and activity operation logs are saved in turn.

[0021] Furthermore, the factory method pattern is used, and enumeration constants are used to save the mapping relationship between activity types and specific strategy implementation methods, and then combined with the singleton pattern in the framework to create strategy classes.

[0022] Furthermore, the PRD algorithm is used to generate pseudo-random numbers, where N for each activity is stored in redis, and N is the number of draws, that is, the number of draws since the last draw in the current activity;

[0023] The increment C pre-prepares the corresponding values ​​of all integer probabilities between 1 and 100 in the memory when the application is started, and provides an algorithm for calculating the C value corresponding to when the probability value set for the activity is a decimal. The actual winning rates corresponding to all probabilities are stored in the application's memory. The mapping relationship is fixed in the median of multiple applications in a distributed system. Whether a prize is won is determined based on the number of draws for each activity, ensuring that the number of wins is evenly distributed in the total number of draws, where C is the actual winning rate increased each time.

[0024] Compared with the prior art, the personalized extended marketing strategy method and system of the present invention has the following outstanding beneficial effects:

[0025] The componentized and modularized design proposed in the present invention greatly shortens the R&D cycle of marketing strategies and improves development efficiency. At the same time, the combination of multiple design patterns and independent development strategies enable the system to flexibly respond to complex and changing marketing needs and quickly connect to the development of third-party content associations or personalized functions.

[0026] The present invention ensures uniform distribution of the number of winning times by using the PRD pseudo-random distribution algorithm, thereby improving the user's sense of participation and satisfaction. In addition, the algorithm design avoids the problem of uneven distribution of the number of winning times, thereby enhancing the fairness and credibility of the activity. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Attached Figure 1It is a framework diagram of a personalized and extended marketing strategy method (I);

[0029] Attached Figure 2 It is a framework diagram of a personalized and extended marketing strategy method (II). DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the solution of the present invention, the present invention is further described in detail below in conjunction with specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] A best embodiment is given below:

[0032] like Figure 1-2 As shown, a personalized extended marketing strategy method in this embodiment divides the business processes involved in the marketing activities into modules and extracts the common skeleton, such as the activity data including activity products, activity stores, activity cities, activity pictures, activity rule settings, activity prize settings, etc. The most basic public method is provided according to the data structure involved in different businesses. When the activity is extended and developed, if no new modules are added, a set of public logic is used to save the public part of the data.

[0033] Use the template method mode to arrange the processes of the common modules mentioned above. Since all activities have some necessary and fixed operation processes, the basic information of the activities, activity products, activity stores, activity cities, activity pictures, activity tags, activity groups, activity prizes, activity rules, activity operation logs, etc. are saved in sequence.

[0034] Use the factory method pattern and enumeration constants to save the mapping relationship between activity types and specific strategy implementation methods, and then combine the singleton pattern in the framework to create strategy classes, so that different activity forms can be developed independently without interfering with each other.

[0035] When using the strategy pattern, when introducing a new form of activity, you only need to add the activity creation logic for the corresponding channel. For example, for personalized settings that involve other data structures or internal or external calls to third-party systems, you can encapsulate the specific sub-processes into independent developable strategies to achieve rapid expansion.

[0036] So far, three design patterns have been used: the template method pattern is used to extract the common skeleton of the business process, the strategy pattern is used to implement and encapsulate the calls of specific sub-process strategies, and the factory pattern can be used to create sub-process strategies.

[0037] The PRD algorithm is used to generate pseudo-random numbers. The N of each activity is stored in redis, where N is the number of draws, that is, the number of draws since the last draw in the current activity.

[0038] The increment C pre-prepares the corresponding values ​​of all integer probabilities between 1 and 100 in the memory when the application is started, and provides an algorithm for calculating the C value corresponding to when the probability value set for the activity is a decimal. The actual winning rates corresponding to all probabilities are stored in the application's memory. The mapping relationship is fixed in the median of multiple applications in a distributed system. Whether a prize is won is determined based on the number of draws for each activity, ensuring that the number of wins is evenly distributed in the total number of draws, where C is the actual winning rate increased each time.

[0039] For example:

[0040] In the early real-time strategy games War III and Dota, the algorithm for players to hit critical hits was as follows:

[0041] If there is no critical hit this time, the probability of the next critical hit will increase. The probability of successfully triggering a critical hit for the Nth time is:

[0042] P(N)=C*N

[0043] P(N) represents the probability of the action occurring at the Nth attack, N represents the number of attacks after the Nth modified probability (the minimum value is 1), and C represents the initial probability of a critical hit and the increment of the probability after each attack. It is a simple linear relationship. When N is large enough, P(N) will always tend to 1, that is, 100% critical hit.

[0044] Among them, C is calculated using the dichotomy method. This article only lists the actual values ​​corresponding to some probability values.

[0045] 1% -> 0.015604169167720937%;

[0046] 10% -> 1.474584478107266%;

[0047] 20% -> 5.570404294978182%;

[0048] 30% -> 11.894919272540397%;

[0049] 40% -> 20.154741360775407%;

[0050] 50% -> 30.210302534874202%;

[0051] 60% -> 42.264973081037425%;

[0052] 70% -> 57.14285714285715%;

[0053] 80% -> 75.00000000000003%;

[0054] 90% -> 88.8888888888889%;

[0055] 100% -> 100%.

[0056] If the expectation is a 25% critical hit rate, the first critical hit rate is approximately equal to 8.5%, the second critical hit rate is approximately equal to 17%, the third critical hit rate is approximately equal to 25.5%, and so on, increasing by 8.5% each time until it reaches 100%, then the next critical hit will definitely be a hit.

[0057] Once a critical hit occurs, the probability resets to 8.5%.

[0058] Based on the above method, a personalized and extended marketing strategy system in this embodiment subdivides the marketing activity business process into multiple independent and reusable modules, and extracts the common data and logic between the modules to form a common skeleton;

[0059] Arrange the processes of common modules to ensure that all activities follow unified and necessary operation procedures;

[0060] Enumerate constants to map activity types and specific strategy implementation methods, create strategy classes in combination with the singleton pattern, and implement independent development of different activity forms;

[0061] The specific sub-processes are encapsulated as independent developable strategies, and the PRD algorithm is used to generate pseudo-random numbers. The winning probability P(N) is dynamically adjusted according to the number of draws N and the increment C of each activity to ensure that the number of wins is evenly distributed in the total number of draws.

[0062] Use the template method mode to arrange the processes of the common modules mentioned above, and save the basic information of the activity, activity products, activity stores, activity cities, activity pictures, activity tags, activity groups, activity prizes, activity rules and activity operation logs in turn.

[0063] Use the factory method pattern and enumeration constants to save the mapping relationship between activity types and specific strategy implementation methods, and then combine the singleton pattern in the framework to create strategy classes.

[0064] Furthermore, the PRD algorithm is used to generate pseudo-random numbers, where N for each activity is stored in redis, and N is the number of draws, that is, the number of draws since the last draw in the current activity;

[0065] The increment C pre-prepares the corresponding values ​​of all integer probabilities between 1 and 100 in the memory when the application is started, and provides an algorithm for calculating the C value corresponding to when the probability value set for the activity is a decimal. The actual winning rates corresponding to all probabilities are stored in the application's memory. The mapping relationship is fixed in the median of multiple applications in a distributed system. Whether a prize is won is determined based on the number of draws for each activity, ensuring that the number of wins is evenly distributed in the total number of draws, where C is the actual winning rate increased each time.

[0066] The above-mentioned specific implementations are only specific cases of the present invention. The patent protection scope of the present invention includes but is not limited to the above-mentioned specific implementations. Any technical solutions that conform to the above-mentioned specific implementations of the present invention and any appropriate changes or substitutions made by ordinary technicians in the relevant technical field shall fall within the patent protection scope of the present invention.

[0067] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A personalized and extended marketing strategy method, characterized in that: Divide the marketing activity business process into multiple independent and reusable modules, and extract the common data and logic between modules to form a common framework; Arrange the processes of common modules to ensure that all activities follow unified and necessary operation procedures; Enumerate constants to map activity types and specific strategy implementation methods, create strategy classes in combination with the singleton pattern, and implement independent development of different activity forms; The specific sub-processes are encapsulated as independent developable strategies, and the PRD algorithm is used to generate pseudo-random numbers. The winning probability P(N) is dynamically adjusted according to the number of draws N and the increment C of each activity to ensure that the number of winning times is evenly distributed in the total number of draws.

2. A personalized extended marketing strategy method according to claim 1, characterized in that: Use the template method mode to arrange the processes of the common modules mentioned above, and save the basic information of the activity, activity products, activity stores, activity cities, activity pictures, activity tags, activity groups, activity prizes, activity rules and activity operation logs in turn.

3. A personalized extended marketing strategy method according to claim 2, characterized in that: Use the factory method pattern and enumeration constants to save the mapping relationship between activity types and specific strategy implementation methods, and then combine the singleton pattern in the framework to create strategy classes.

4. A personalized extended marketing strategy method according to claim 3, characterized in that: The PRD algorithm is used to generate pseudo-random numbers. The N of each activity is stored in redis, where N is the number of draws, that is, the number of draws since the last draw in the current activity. The increment C pre-prepares the corresponding values ​​of all integer probabilities between 1 and 100 in the memory when the application is started, and provides an algorithm for calculating the C value corresponding to when the probability value set for the activity is a decimal. The actual winning rates corresponding to all probabilities are stored in the application's memory. The mapping relationship is fixed in the median of multiple applications in a distributed system. Whether a prize is won is determined based on the number of draws for each activity, ensuring that the number of wins is evenly distributed in the total number of draws, where C is the actual winning rate increased each time.

5. A personalized and extended marketing strategy system, characterized in that: Divide the marketing activity business process into multiple independent and reusable modules, and extract the common data and logic between modules to form a common framework; Arrange the processes of common modules to ensure that all activities follow unified and necessary operation procedures; Enumerate constants to map activity types and specific strategy implementation methods, create strategy classes in combination with the singleton pattern, and implement independent development of different activity forms; The specific sub-processes are encapsulated as independent developable strategies, and the PRD algorithm is used to generate pseudo-random numbers. The winning probability P(N) is dynamically adjusted according to the number of draws N and the increment C of each activity to ensure that the number of winning times is evenly distributed in the total number of draws.

6. A personalized and extended marketing strategy system according to claim 5, characterized in that: Use the template method mode to arrange the processes of the common modules mentioned above, and save the basic information of the activity, activity products, activity stores, activity cities, activity pictures, activity tags, activity groups, activity prizes, activity rules and activity operation logs in turn.

7. A personalized and extended marketing strategy system according to claim 6, characterized in that: Use the factory method pattern and enumeration constants to save the mapping relationship between activity types and specific strategy implementation methods, and then combine the singleton pattern in the framework to create strategy classes.

8. A personalized and extended marketing strategy system according to claim 7, characterized in that: The PRD algorithm is used to generate pseudo-random numbers. The N of each activity is stored in redis, where N is the number of draws, that is, the number of draws since the last draw in the current activity. The increment C pre-prepares the corresponding values ​​of all integer probabilities between 1 and 100 in the memory when the application is started, and provides an algorithm for calculating the C value corresponding to when the probability value set for the activity is a decimal. The actual winning rates corresponding to all probabilities are stored in the application's memory. The mapping relationship is fixed in the median of multiple applications in a distributed system. Whether a prize is won is determined based on the number of draws for each activity, ensuring that the number of wins is evenly distributed in the total number of draws, where C is the actual winning rate increased each time.

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