Game marketing system and method based on big data
Through big data analysis, identifying the degree of users' preference for games and dynamically adjusting the push frequency, it solves the problem that existing game marketing methods are difficult to accurately identify users' preferences, and improves the popularity of the game and user stickiness.
Patent Information
- Application Number
- CN202411648722.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-19
- Publication Date
- 2025-02-28
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing game marketing methods are difficult to accurately identify the degree of love for the game by different groups, resulting in unreasonable information push, which may arouse user dislike and affect the popularity and reputation of the game.
Through big data analysis, multiple audience types are divided, the degree of love is calculated based on the user's game duration, consumption records, login times and interactive data, potential users are selected, and push frequency is dynamically adjusted based on the game downloads and views.
It realizes a more accurate judgment of user preferences, which facilitates game information push; dynamically adjusts the push frequency, avoids user disgust caused by excessive information push, and improves the popularity of the game and user stickiness.
Smart Images
Figure CN119537697A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of game marketing technology, and specifically relates to a game marketing system and method based on big data. Background Art
[0002] Game marketing refers to the behavior of game developers or publishers to increase game popularity, attract players, increase sales and profits through a series of strategies and means. In the fiercely competitive game market, effective marketing strategies are crucial to the success of games. Through precise market positioning, attractive promotional content and diversified promotion channels, game marketing can quickly increase the popularity and exposure of games, attract the attention of potential players, and thus increase the number of downloads and activity of games.
[0003] Most of the existing game marketing methods push game features, gameplay, characters and other information to players through social media platforms, so as to increase game popularity and attract players. However, different groups of people have different preferences for games. For example, some older people do not pay attention to the game itself. If they are pushed game information blindly, it will cause disgust, thus affecting the reputation of the game. Summary of the invention
[0004] The purpose of the present invention is to provide a game marketing system and method based on big data to solve the problems faced by the above-mentioned background technology.
[0005] The purpose of the present invention can be achieved through the following technical solutions: A game marketing system based on big data, the system comprising: A large database storing a large amount of user data; An information collection module, which is used to collect data information related to game marketing from a large database to form a data information set A; A user selection module, which is used to analyze and process the data information in the data information set A to screen potential users; An information push module, which is used to push game information to potential users at different push intervals according to a set push frequency; An adjustment module is used to adjust the push frequency of each push interval according to the game download data and game browsing data in each push interval.
[0006] Furthermore, the method for the user selection module to screen potential users is: Divide users into multiple audience types according to their industries, and obtain game marketing-related data information of all users in each audience type, including game time, consumption records, login times, and game-related interaction data; By formula Get the preference value of each user ; The love value you will get With the preset love threshold Make a comparison; when , then the user is judged to be a potential user; in, as well as is the preset proportional coefficient, X is the type matching value, is the age matching value, The game consumption value of the user. The number of times the user logs into the game. is the number of interactions, For game duration, The duration of interaction.
[0007] Furthermore, the method for obtaining the type matching value X is: Get the user's game type data. If the game type played by the user is exactly the same as the marketing game type, set X=1. If the game type played by the user is partially the same as the marketing game type, set X=0.8. If the game type played by the user is completely different from the marketing game type, set X=0.2.
[0008] Furthermore, the age matching value The acquisition method is: Get the user's age data. If the user is in the youth stage, then , if the user is in middle age, then , if the user is in the elderly stage, then .
[0009] Furthermore, the method in which the information push module pushes information is as follows: The set push time period is divided into n push intervals, and sufficient game download data and game browsing data are obtained from the data information set A. According to the historical average game download volume and the historical average game browsing volume in each push interval, a push frequency is set for each push interval, and the game information is pushed according to the set push frequency.
[0010] Furthermore, the working method of the adjustment module is: According to the game download volume and game browsing volume in each push interval, a download volume curve over time for each push interval is drawn up. And the curve of game browsing volume changing over time ; By formula Get the judgment value ; The judgment value obtained The preset judgment threshold interval To compare: when When the push frequency is adjusted in the next push time period; in, is the average number of game downloads during the current push period. is the average number of game views during the current push period. is the average number of game downloads during the last push period. is the average number of game views during the last push period. This is the preset standard curve of download volume changing over time. It is the preset standard curve of page views changing with time. is the start time of the push interval, is the end time of the push interval, as well as is the preset weight coefficient.
[0011] Furthermore, the adjustment module working method also includes: Adjusting the push frequency includes increasing the push frequency and decreasing the push frequency; when , the push frequency within the push interval will be increased in the next push time period, specifically through the formula Adjust the push frequency to ; when , the push frequency in the next push time interval will be reduced, specifically through the formula Adjust the push frequency to ; in, To set the initial push frequency, is the preset conversion factor, The intensity of activity rewards for the next push period.
[0012] A game marketing method based on big data, wherein the marketing method is controlled and executed by the game marketing system based on big data.
[0013] Beneficial effects of the present invention: The present invention obtains relevant data information of users from a large database, and then analyzes it, divides the users into multiple audience types according to their industries, and analyzes the degree of love for games of each user in each audience type, so as to screen potential users according to the love degree, so as to more accurately judge the user's love for games and facilitate the push of subsequent game information; The present invention also divides the push time period into multiple push intervals according to the different behavior patterns and living habits of users, and adjusts the set push frequency according to the game download volume and browsing situation in each push interval. In this way, the push frequency can be dynamically adjusted more reasonably to keep the push frequency within a reasonable range, avoiding user disgust caused by too frequent information push. In this way, it can ensure that marketing activities can reach target users more effectively, improve the game's popularity and user stickiness, and more effectively attract user participation.
[0014] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.
[0016] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION
[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.
[0018] In one embodiment, a game marketing system based on big data is disclosed. Figure 1 As shown, the marketing system includes: Large databases contain massive amounts of user data; An information collection module is used to collect data information related to game marketing from a large database to form a data information set A; User selection module, which is used to analyze and process the data information in data information set A to screen potential users; The information push module is used to push game information to potential users at different push intervals according to the set push frequency; The adjustment module adjusts the push frequency of each push interval according to the game download data and game browsing data in each push interval.
[0019] Through the above technical scheme, the present application obtains relevant data information of users from a large database for analysis, divides the users into multiple audience types according to the different industries they are in, and analyzes the degree of each user's love for the game within each audience type, so as to screen potential users according to the degree of love, so as to more accurately judge the user's love for the game and facilitate the subsequent push of game information; at the same time, the push time period is divided into multiple push intervals according to the different behavior patterns and living habits of users, and the set push frequency is adjusted according to the game download volume and browsing situation within each push interval, so that the push frequency can be more reasonably adjusted dynamically, so that the push frequency is always within a reasonable range, avoiding too frequent information pushes that cause user disgust.
[0020] As an implementation mode of the present invention, the method for the user selection module to screen potential users is: Divide users into multiple audience types according to their industries, and obtain game marketing-related data information of all users in each audience type, including game time, consumption records, login times, and game-related interaction data; By formula Get the preference value of each user ; The love value you will get With the preset love threshold Make a comparison; when , then the user is judged to be a potential user; in, as well as is the preset proportional coefficient, X is the type matching value, is the age matching value, The game consumption value of the user. The number of times the user logs into the game. is the number of interactions, For game duration, The interaction time, the type matching value X is obtained by obtaining the game type data of the user. If the game type played by the user is exactly the same as the marketing game type, then X=1; if the game type played by the user is partially the same as the marketing game type, then X=0.8; if the game type played by the user is completely different from the marketing game type, then X=0.2; and the age matching value The acquisition method is: Get the user's age data. If the user is in the youth stage, then , if the user is in middle age, then , if the user is in the elderly stage, then .
[0021] Through the above technical solution, this embodiment provides a specific method for the user selection module to screen potential users. First, the users are divided into multiple audience types according to their industries. Then, the data information related to game marketing of all users in each audience type is analyzed, including but not limited to game time, consumption records, login times, and game-related interaction data. In this way, different game audience groups can be selected according to different industries, and then the formula is used to select different game audience groups. Get the preference value of each user , in the formula, The game consumption value of the user. The number of times the user logs into the game. is the number of interactions, For game duration, is the interaction time. It can be seen that when a user has game behavior for a period of time and the game consumption value The higher the number of game logins, and the number of times they interacted with the game on social media The higher the game time, the longer the game time Or interaction time The higher it is, the more fond the user is of the game, and the greater the possibility that the user is a potential user. Potential users are also related to the user's age and the type of game they play. For example, young people love games more than the elderly. For example, some users like to play music games but not competitive games. Therefore, obtain the user's game type data. If the type of game the user plays is exactly the same as the marketing game type, let X=1; if the type of game the user plays is partially the same as the marketing game type, let X=0.8; if the type of game the user plays is completely different from the marketing game type, let X=0.2; at the same time, obtain the user's age data. If the user is in the youth stage, let , if the user is in middle age, then , if the user is in the elderly stage, then In this way, the type matching value and age matching value of each user can be determined according to the user's age group and the type of game they like to play. Finally, the formula Conduct comprehensive analysis to determine the preference level of each user , it can be seen that the greater the degree of preference, the greater the possibility that it is a potential user, so the degree of preference obtained will be With the preset love threshold To compare; when In this way, potential users can be found according to the gaming behavior habits, age group and game types of each user, so that game information can be pushed more accurately.
[0022] It should be noted that the preset proportionality factor as well as , preset love threshold All of them can be determined based on empirical data, while the user's game consumption value, game time, and interaction time can be converted into unified numbers for calculation. The specific numbers can be determined artificially based on actual conditions, so I will not elaborate on them here.
[0023] As an implementation mode of the present invention, the method for information pushing by the information push module is: The set push time period is divided into n push intervals, and sufficient game download data and game browsing data are obtained from the data information set A. According to the historical average game download volume and the historical average game browsing volume in each push interval, a push frequency is set for each push interval, and the game information is pushed according to the set push frequency.
[0024] Through the above technical solution, this embodiment provides a specific method for information push by the information push module. First, a push time period is set, such as 24 hours as a push time period, and the set push time period is divided into n push intervals, such as divided by hours, and the push time period is divided into 24 push intervals, and sufficient game download data and game browsing data are obtained from the data information set A, and the game download data and game browsing data of each push interval are obtained, so as to obtain the historical average game download volume and the historical average game browsing volume in each push interval. According to the historical average game download volume and the historical average game browsing volume, the push frequency is set for each push interval, so as to push the game information according to the set push frequency, such as increasing the push frequency during the user's leisure time period and reducing the push frequency during the user's sleeping time period. In this way, different push frequencies can be set in different time periods, so that a time period that is more suitable for pushing information can be selected according to the user's behavioral habits to carry out game marketing, thereby attracting users.
[0025] As an embodiment of the present invention, the working method of the adjustment module is: according to the game download volume and game browsing volume in each push interval, a download volume change curve over time is drawn up for each push interval. And the curve of game browsing volume changing over time ; By formula Get the judgment value ; The judgment value obtained The preset judgment threshold interval To compare: when When the push frequency is adjusted in the next push time period; in, is the average number of game downloads during the current push period. is the average number of game views during the current push period. is the average number of game downloads during the last push period. is the average number of game views during the last push period. This is the preset standard curve of download volume changing over time. It is the preset standard curve of page views changing with time. is the start time of the push interval, is the end time of the push interval, as well as is the preset weight coefficient; Among them, adjusting the push frequency includes increasing the push frequency and reducing the push frequency; when , the push frequency within the push interval will be increased in the next push time period, specifically through the formula Adjust the push frequency to ; when , the push frequency in the next push time interval will be reduced, specifically through the formula Adjust the push frequency to ; in, To set the initial push frequency, is the preset conversion factor, The intensity of activity rewards for the next push period.
[0026] Through the above technical solution, this implementation provides a method for the adjustment module to dynamically adjust the set push frequency. First, according to the game download volume and game browsing volume in each push interval, a download volume change curve over time for each push interval is drawn up. And the curve of game browsing volume changing over time , and then by the formula Get the judgment value ; formula It indicates the difference between the game download volume within the push interval and the standard game download volume. The larger the value, the more users can be obtained within the push interval, and the more push frequency is required. The same formula It indicates the difference between the game views within the push interval and the standard game views. The larger the value, the greater the push frequency required. The formula It indicates the ratio of the average downloads and views in the current push time period to the average downloads and views in the previous push time period. The larger the value, the more push frequency is needed. Therefore, the formula Get the judgment value After that, the obtained judgment value is compared with the preset judgment threshold interval. For comparison, when If the push frequency is not set correctly, the push frequency in the next push time interval will be adjusted. The push frequency adjustment includes increasing the push frequency and decreasing the push frequency. , the push frequency within the push interval will be increased in the next push time period, specifically through the formula Adjust the push frequency to , it can be seen from the formula that when the judgment value is The larger the value, the greater the judgment threshold. The more, the better the game marketing situation is, and the more push frequency needs to be increased in the next push time period. The greater the activity reward set in the next push time period, the more push frequency is needed. , then in the next push time period, the formula Adjust the push frequency to ; Similarly when , the push frequency in the next push time interval will be reduced, specifically through the formula Adjust the push frequency to , it can be seen from the formula that when the judgment value is The smaller it is, the closer it is to the judgment threshold The larger the difference between the two, the worse the game marketing situation is, and the more the push frequency needs to be appropriately reduced in the next push time period. The smaller the activity reward strength set in the next push time period, the more the push frequency needs to be reduced. Therefore, when , in the next push time period, the formula Adjust the push frequency to In this way, we can conduct a comprehensive analysis based on the changes in game downloads, page views, and game activity rewards in different time periods to dynamically adjust the game push frequency. This ensures that marketing activities can more effectively reach target users, increase game awareness and user stickiness, and more effectively attract user participation.
[0027] It should be noted that as well as The preset weight coefficient as well as , preset judgment threshold interval , preset standard curve of download volume changing over time , preset standard curve of page views changing over time , preset conversion factor All can be determined based on historical data and experience data, and the intensity of the activity rewards for the next push time period This can be determined independently based on the intensity of game activities at different times, so I won’t go into details here.
[0028] In addition, the calculation formulas and parameters involved in the calculations in the present invention are all dimensionally processed in advance, and the process of dimensionless processing is well known in the industry and will not be described here.
[0029] A game marketing method based on big data, wherein the marketing method is controlled and executed by one of the above-mentioned game marketing systems based on big data.
[0030] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.
Claims
1. A game marketing system based on big data, characterized in that: The system comprises: A large database storing a large amount of user data; An information collection module, which is used to collect data information related to game marketing from a large database to form a data information set A; A user selection module, which is used to analyze and process the data information in the data information set A to screen potential users; An information push module, which is used to push game information to potential users at different push intervals according to a set push frequency; An adjustment module is used to adjust the push frequency of each push interval according to the game download data and game browsing data in each push interval.
2. A game marketing system based on big data according to claim 1, characterized in that: The method for the user selection module to screen potential users is: Divide users into multiple audience types according to their industries, and obtain game marketing-related data information of all users in each audience type, including game time, consumption records, login times, and game-related interaction data; By formula Get the preference value of each user ; The love value you will get With the preset love threshold Make a comparison; when , then the user is judged to be a potential user; in, as well as is the preset proportional coefficient, X is the type matching value, is the age matching value, The game consumption value of the user. The number of times the user logs into the game. is the number of interactions, For game duration, The duration of interaction.
3. A game marketing system based on big data according to claim 2, characterized in that: The method for obtaining the type matching value X is: Get the user's game type data. If the game type played by the user is exactly the same as the marketing game type, set X=1. If the game type played by the user is partially the same as the marketing game type, set X=0.
8. If the game type played by the user is completely different from the marketing game type, set X=0.
2.
4. A game marketing system based on big data according to claim 2, characterized in that: The age matching value The acquisition method is: Get the user's age data. If the user is in the youth stage, then , if the user is in middle age, then , if the user is in the elderly stage, then .
5. The game marketing system based on big data according to claim 1, characterized in that: The method for the information push module to push information is: The set push time period is divided into n push intervals, and sufficient game download data and game browsing data are obtained from the data information set A. According to the historical average game download volume and the historical average game browsing volume in each push interval, a push frequency is set for each push interval, and the game information is pushed according to the set push frequency.
6. A game marketing system based on big data according to claim 5, characterized in that: The working method of the adjustment module is: According to the game download volume and game browsing volume in each push interval, a download volume curve over time for each push interval is drawn up. And the curve of game browsing volume changing over time ; By formula Get the judgment value ; The judgment value obtained The preset judgment threshold interval To compare: when When the push frequency is adjusted in the next push time period; in, is the average number of game downloads during the current push period. is the average number of game views during the current push period. is the average number of game downloads during the last push period. is the average number of game views during the last push period. This is the preset standard curve of download volume changing over time. It is the preset standard curve of page views changing with time. is the start time of the push interval, is the end time of the push interval, as well as is the preset weight coefficient.
7. A game marketing system based on big data according to claim 6, characterized in that: The adjustment module working method also includes: Adjusting the push frequency includes increasing the push frequency and decreasing the push frequency; when , the push frequency within the push interval will be increased in the next push time period, specifically through the formula Push frequency is adjusted to ; when , the push frequency in the next push time interval will be reduced, specifically through the formula Adjust the push frequency to ; in, The initial push frequency set, is the preset conversion factor, The intensity of activity rewards for the next push period.
8. A game marketing method based on big data, characterized in that: The marketing method is controlled and executed by the big data-based game marketing system described in any one of claims 1-7.