A game promotion system and method based on data analysis

Abstract

The present invention relates to the field of game promotion and discloses a game promotion system and method based on data analysis. The system comprises: a data acquisition module that collects player login frequency, game duration, level completion rate, payment frequency and amount; regularly captures the game market size, growth rate, and popularity ranking of the current game type in each region from a game data platform; analyzes the download volume curve of competing games of the same type; a data analysis module obtains the interest index of each player based on player behavior data, and then performs group analysis on the players according to the interest index of each player; a promotion strategy generation module is used to customize promotion strategies for different player groups according to the data analysis results; an effect monitoring module calculates the promotion value index of the current game based on market data and competing product data, and then comprehensively evaluates the promotion effect of the current game based on the promotion value index and the player interest index, and builds a visual dashboard.

Description

Technical Field

[0001] The present invention relates to the field of game promotion, and in particular to a game promotion system and method based on data analysis. Background Art

[0002] With the rapid development of the gaming industry, market competition is becoming increasingly fierce. The number of games worldwide is growing exponentially, with thousands of new games released each year. This makes game promotion more difficult and poses enormous challenges to developers.

[0003] The rise of data analysis technology has brought hope to the game promotion dilemma. In the era of big data, the collection, storage, and processing of massive amounts of data have become possible. With the help of professional data mining and machine learning algorithms, it is possible to deeply analyze player behavior and accurately track market trends, thereby providing a scientific and quantitative basis for the formulation and optimization of game promotion strategies.

[0004] However, how to continuously attract existing game players and convert them into active players is a shortcoming of current game promotion. After most players are attracted by traditional game promotion strategies, the lack of follow-up promotion strategies in the subsequent process leads to a large number of player loss, resulting in serious waste of promotion resources invested in the early stage and causing losses. Therefore, it is urgent to build a game promotion system and method based on data analysis. Summary of the Invention

[0005] The purpose of the present invention is to provide a game promotion system and method based on data analysis to solve the above technical problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A game promotion system based on data analysis, comprising:

[0008] The data acquisition module is used to collect multi-dimensional data, and the multi-dimensional data includes:

[0009] Player behavior data, including player login frequency, game duration, level completion rate, payment frequency and amount;

[0010] Market data: Regularly capture regional game market size, growth rate, and popularity rankings of current game types from game data platforms;

[0011] Competitive product data: Analyze the download curves of competing games of the same type;

[0012] The data analysis module calculates each player's interest index based on player behavior data, and then performs group analysis on each player based on their interest index, dividing the players into high-frequency, medium-frequency, and low-frequency groups;

[0013] Promotion strategy generation module, used to customize promotion strategies for different player groups based on data analysis results;

[0014] The effect monitoring module calculates the promotion value index of the current game based on market data and competitor data, and then comprehensively evaluates the promotion effect of the current game based on the promotion value index and player interest index, and builds a visual dashboard.

[0015] As a further technical solution, the process of calculating each player's interest index based on player behavior data is as follows:

[0016] The obtained player behavior data is passed through the formula:

[0017]

[0018] Calculate the interest index F of the rth player r , i is the i-th player behavior data, 6 is the number of player behavior data items, q i is the parameter value of the i-th player behavior data, q imin ,q imax are the minimum and maximum values ​​of the i-th player behavior data, is the weight coefficient of the i-th player behavior data.

[0019] As a further technical solution, the process of performing group analysis on players based on each player's interest index is as follows:

[0020] The interest index F of the rth player r With the pre-set interest index range [F r0 , F r1 ] for comparison:

[0021] If F r ∈[F r0 , F r1 ], then the current player is judged to be a medium-frequency player;

[0022] If F r >F r1 , then the current player is judged to be a high-frequency player;

[0023] If F r >F r1 , then the current player is judged to be a low-frequency player.

[0024] As a further technical solution, based on market data and competitor data, the process of calculating the promotion value index of the current game is as follows:

[0025] Combine market data and competitor data through the formula:

[0026]

[0027] Calculate the promotion value index D of the current game;

[0028] Among them, t0 and t1 are the two time endpoints of a preset monitoring period, K(t) is the curve of the popularity ranking of the type of the current game over time during the monitoring period, f(x) is the judgment function about competing games, β is the game market size, and γ is the game market size growth rate.

[0029] As a further technical solution, the process of obtaining the judgment function f(x) about the competing game is:

[0030] like but

[0031] like but

[0032] Among them, g(t) is the download volume change curve of the competing game over time, g0(t) is the download volume reference change curve, Δθ is the reference value, is a constant and

[0033] As a further technical solution, the process of comprehensively evaluating the current game promotion effect based on the promotion value index and player interest index is as follows:

[0034] By formula:

[0035]

[0036] The promotion evaluation coefficient H is calculated;

[0037] Among them, N B N is the actual number of players in the current game at the end of a statistical period. A N is the actual number of players in the current game at the start time of a statistical period. C It is the ratio of high-frequency players to the actual number of players of the current game at the end of a statistical period. D0 is the reference value of the promotion value index.

[0038] Compare the calculated promotion evaluation coefficient H with the preset promotion evaluation threshold H0:

[0039] If H≥H0, the promotion value of the current game is evaluated to be good;

[0040] If H<H0, the promotion value of the current game is evaluated to be poor.

[0041] As a further technical solution, the visual dashboard supports multi-terminal display, including computers, tablets and mobile phones.

[0042] A game promotion method based on data analysis is implemented based on the game promotion system based on data analysis.

[0043] Beneficial effects of the present invention:

[0044] The present invention uses a data acquisition module to obtain multi-dimensional data, and focuses on analyzing player behavior data through a data analysis module, based on which players are divided into three groups: high-frequency, medium-frequency, and low-frequency. The promotion focus is on converting low-frequency players into medium-frequency and high-frequency players to increase the popularity of the game, while focusing on attracting new players and expanding the player base for high-frequency players. Subsequently, exclusive promotion strategies are formulated according to different groups, and existing players are stabilized on the basis of traditional methods to seize market share. The promotion value index is then calculated based on market and competitor data, and the promotion effect is evaluated in combination with the player interest index. A visual dashboard is built to provide support for real-time optimization of promotion strategies and achieve overall promotion optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention will be further described below with reference to the accompanying drawings.

[0046] Figure 1 It is the system logic block diagram of the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0048] See also Figure 1 As shown, the present invention is a game promotion system based on data analysis, comprising:

[0049] The data acquisition module is used to collect multi-dimensional data, and the multi-dimensional data includes:

[0050] Player behavior data, including player login frequency, game duration, level completion rate, payment frequency and amount;

[0051] Market data: Regularly capture regional game market size, growth rate, and popularity rankings of current game types from game data platforms;

[0052] Competitive product data: Analyze the download curves of competing games of the same type;

[0053] The data analysis module calculates each player's interest index based on player behavior data, and then performs group analysis on each player based on their interest index, dividing the players into high-frequency, medium-frequency, and low-frequency groups;

[0054] Promotion strategy generation module, used to customize promotion strategies for different player groups based on data analysis results;

[0055] The effect monitoring module calculates the promotion value index of the current game based on market data and competitor data, then comprehensively evaluates the promotion effect of the current game based on the promotion value index and player interest index, and builds a visual dashboard that supports multi-terminal display, including computers, tablets and mobile phones.

[0056] In this embodiment, after collecting multi-dimensional data through the data collection module, the data analysis module then conducts a focused analysis of player behavior data, thereby dividing players into three groups: high-frequency, medium-frequency, and low-frequency. Obviously, low-frequency and medium-frequency players are the top priorities of this promotion. The key is how to convert low-frequency players into medium-frequency or even high-frequency players, which can significantly improve the game's popularity ranking. For high-frequency players, the promotion focus is to attract new players, that is, to promote the current game to surrounding areas and increase the number of game players. Different promotion strategies are formulated for the above three groups. On the basis of traditional promotion strategies, the existing players of the current game can be maintained, ultimately achieving the goal of seizing market share. Finally, the promotion value index of the current game is calculated based on market data and competitive product data. The promotion effect of the current game is then comprehensively evaluated based on the promotion value index and player interest index. A visual dashboard is built to provide a judgment basis for real-time optimization of the promotion strategy, thereby achieving overall promotion optimization. It should be noted that existing traditional game promotion strategies are existing technologies and will not be elaborated here. For example, they include highlighting the differences from competing games, channel-based promotion, celebrity and internet celebrity endorsements, interesting science popularization, hot topic association, and free trials.

[0057] The process of calculating each player's interest index based on player behavior data is as follows:

[0058] The obtained player behavior data is passed through the formula:

[0059]

[0060] Calculate the interest index F of the rth player r , i is the i-th player behavior data, 6 is the number of player behavior data items, q i is the parameter value of the i-th player behavior data, q imin ,q imax are the minimum and maximum values ​​of the i-th player behavior data, is the weight coefficient of the i-th player behavior data, determined based on historical data analysis;

[0061] The process of performing segmentation analysis on players based on each player's interest index is as follows:

[0062] The interest index F of the rth player r With the pre-set interest index range [F r0 , F r1 ] for comparison:

[0063] If F r ∈[F r0 , F r1 ], then the current player is judged to be a medium-frequency player;

[0064] If F r >F r1 , then the current player is judged to be a high-frequency player;

[0065] If F r >F r1 , then the current player is judged to be a low-frequency player.

[0066] In this embodiment, a method for obtaining different interest indexes of each player is provided. Specifically, the collected behavior parameters of each player are first substituted into the formula Each player's behavior parameter is processed by cumulative calculation, and finally the interest index of each player is obtained. Obviously, the higher the player's login frequency, game time, level completion rate, payment frequency and amount, the higher the player's enthusiasm for the current game. In other words, the more willing the player is to promote the current game, the greater the player's stickiness. In addition, the slight changes in each player's behavior parameter can be discovered by cumulative multiplication, and the player group can be divided in real time and accurately, laying a solid foundation for the subsequent formulation of different promotion strategies. At the same time, a method for dividing player groups is provided. First, the interest index F of the rth player is calculated. r With the pre-set interest index range [F r0 , F r1 ] for comparison, when F r ∈[F r0 , F r1 ], the current player is a medium frequency player, when F r >F r1 When the current player is a high-frequency player, when F r >F r1 When , the current player is a low-frequency player;

[0067] It should be noted that the promotion strategies designed for different groups of players are shown in the following examples:

[0068] For high-frequency players:

[0069] 1. Exclusive gift pack strategy: This type of player is highly engaged and frequently participates in the game. Customize limited-edition premium game gift packs for them, containing rare items, limited-time skins, and exclusive characters. Gift packs not only enhance their gaming experience but also, through their scarcity, stimulate their desire to show off, prompting them to share in the gaming community and fostering word-of-mouth communication. For example, launch a themed gift pack each month exclusively for frequent players, promoting it through in-game announcements.

[0070] 2. Early Access Privileges: Give frequent players the opportunity to try out new gameplay and levels early, unlocking them days or even weeks before regular players. This gives them a special treat, stimulates their continued passion for the game, and allows them to collect feedback to optimize subsequent official releases. For example, before a major expansion pack update, frequent players may be invited to participate in the internal beta test.

[0071] 3. Community Leader Incentives: Identify opinion leaders among high-frequency players and grant them special in-game titles, such as "Glory Pioneer" and "Chief Experience Officer," to enhance their prestige within the gaming community. Encourage them to organize online and offline activities to boost the gaming social atmosphere. For every successful large-scale community event, award them with corresponding game points or physical peripheral rewards.

[0072] For mid-range players:

[0073] 1. Growth Support Program: For mid-frequency players who have a certain level of game engagement but may encounter bottlenecks during their advancement, we design personalized growth path guidance for them, along with periodic rewards. For example, when players reach specific levels or their character levels increase, we provide them with corresponding upgrade resource packs containing experience point bonus items and skill upgrade materials, helping them overcome bottlenecks and improve retention rates.

[0074] 2. Limited-time discount incentives: Regularly select a group of popular props and costumes and offer limited-time discount promotions to medium-frequency players to create a sense of urgency and encourage them to pay more frequently. Exclusive discount information can be pushed at fixed times of the week, such as weekends, through in-game pop-ups and email notifications, with eye-catching discount labels and original price comparisons to attract purchases.

[0075] 3. Social team incentives: Given that mid-frequency players are quite active, encourage them to participate more in social team play. Establish team tasks and reward mechanisms. When a team completes a specified number of dungeons and battles, all team members can receive additional rewards, such as gold coin bonuses and random treasure chests, to strengthen the social stickiness of the game and bring them closer to high-frequency players.

[0076] For low-frequency players:

[0077] 1. Newbie Gift Pack Reboot: Many low-frequency players may reduce their participation due to initial difficulties in getting started. We will re-send them an optimized Newbie Gift Pack, simplify the gift pack redemption process, increase early resource support, and help them easily restart the game experience. The gift pack also includes a link to a short video guide for new players to quickly familiarize them with the basic game operations and core gameplay.

[0078] 2. Lightweight event invitations: Plan some lightweight events that are short and easy to participate, such as limited-time sign-in raffles and simple casual game challenges, to attract low-frequency players to participate in their fragmented time. Prizes can be highly versatile game currencies or basic props. Each event cycle is limited to 1-3 days, lowering the participation threshold and rekindling their interest.

[0079] 3. Nostalgic content: Explore classic elements from past games and create themed promotional posters and short videos. These are then pushed to low-frequency players to evoke their past gaming memories. Combined with return rewards, players can receive a return gift pack containing replicas of popular items from previous versions as long as they log in to the game again, sparking emotional resonance and attracting them back to the game.

[0080] Based on market data and competitor data, the process of calculating the promotion value index of the current game is as follows:

[0081] Combine market data and competitor data through the formula:

[0082]

[0083] Calculate the promotion value index D of the current game;

[0084] Among them, t0 and t1 are the two time endpoints of a preset monitoring period, K(t) is the curve of the popularity ranking of the type of the current game over time during the monitoring period, f(x) is the judgment function about competing games, β is the game market size, and γ is the game market size growth rate.

[0085] The process of obtaining the judgment function f(x) about the competing game is as follows:

[0086] like but

[0087] like but

[0088] Among them, g(t) is the download volume change curve of the competing game over time, g0(t) is the download volume reference change curve, Δθ is the reference value, is a constant and

[0089] The process of comprehensively evaluating the current game promotion effect based on the promotion value index and player interest index is as follows:

[0090] By formula:

[0091]

[0092] The promotion evaluation coefficient H is calculated;

[0093] Among them, N B N is the actual number of players in the current game at the end of a statistical period. A N is the actual number of players in the current game at the start time of a statistical period. C The ratio of high-frequency players to actual players of the current game at the end of a statistical period. D0 is the reference value of the promotion value index, which is determined based on historical data and experimental data analysis.

[0094] Compare the calculated promotion evaluation coefficient H with the preset promotion evaluation threshold H0:

[0095] If H≥H0, the promotion value of the current game is evaluated to be good;

[0096] If H<H0, the promotion value of the current game is evaluated to be poor.

[0097] In this embodiment, in order to quickly determine the promotion value of the current game, specifically, firstly, the market data and the competitor data are combined through the formula: The promotion value index D of the current game is calculated; wherein, t0 and t1 are the two time endpoints of a preset monitoring period, K(t) is the change curve of the popularity ranking of the type of the current game over time during the monitoring period, f(x) is the judgment function of the competing games, β is the market size of the game, and γ is the growth rate of the market size of the game. It can be seen from the above formula that if the product of the market size of the game and the growth rate is larger, it means that the market of the game is good and is on an upward trend. Therefore, it is more valuable to promote the current game. Therefore, the promotion value index of the current game is affected by the method of 1+β*γ, thereby improving the accuracy of the promotion value index of the current game. At the same time, Calculate the cumulative change of the current game popularity ranking. Obviously, the larger the cumulative change, the faster the current game popularity increases, so the promotion value of the current game is higher. Add the influence of competing games and compare the cumulative change of competing games in a monitoring period with the reference value. Obviously, if the cumulative change of the competing game is greater than 1, it means that the download volume of the competing game is on a downward trend, otherwise it means that the download volume of the competing game is on an upward trend. Therefore, if but like but This indicates that the download volume of competing games is on a downward trend, and the number of new players is relatively low. At this time, maintaining existing players is an important means to maintain the popularity of the current game, so the promotion value of the current game is higher;

[0098] In order to evaluate the current game promotion strategy, we first use the formula:

[0099] The generalized evaluation coefficient H is calculated; obviously, if The larger the value, the more new players are added, and N C The larger the value, the higher the proportion of high-frequency players. Therefore, the better the overall promotion effect of the current game, the greater the promotion evaluation coefficient. If the ratio of the current game's promotion value index to the reference value is The larger it is, the higher the promotion value of the current game. By combining the above two parameters to establish an evaluation model for the current game promotion strategy, the promotion situation of the current game in each statistical period can be accurately and quickly evaluated, so as to adjust the promotion strategy in a timely and effective manner. The calculated promotion evaluation coefficient H is then compared with the preset promotion evaluation threshold H0: if H≥H0, the promotion value of the current game is evaluated to be good; if H<H0, the promotion value of the current game is evaluated to be poor.

[0100] A game promotion method based on data analysis is implemented based on the game promotion system based on data analysis.

[0101] It should be noted that the calculation formulas and various parameters involved in the calculations in the present invention have been dimensionally processed in advance, and the process of dimensionless processing is well known in the industry and will not be described here.

[0102] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A game promotion system based on data analysis, characterized in that: include: The data acquisition module is used to collect multi-dimensional data, and the multi-dimensional data includes: Player behavior data, including player login frequency, game duration, level completion rate, payment frequency and amount; Market data: Regularly capture regional game market size, growth rate, and popularity rankings of current game types from game data platforms; Competitive product data: Analyze the download curves of competing games of the same type; The data analysis module calculates each player's interest index based on player behavior data, and then performs group analysis on each player based on their interest index, dividing the players into high-frequency, medium-frequency, and low-frequency groups; Promotion strategy generation module, used to customize promotion strategies for different player groups based on data analysis results; The effect monitoring module calculates the promotion value index of the current game based on market data and competitor data, then comprehensively evaluates the promotion effect of the current game based on the promotion value index and player interest index, and builds a visual dashboard; Based on market data and competitor data, the process of calculating the promotion value index of the current game is as follows: Combine market data and competitor data through the formula: Calculate the promotion value index of the current game ; in, 、 are two time endpoints of a preset monitoring cycle, This is the curve of the popularity ranking of the current game type changing over time during the monitoring period. is the judgment function about the competing game, is the size of the game market, is the growth rate of the game market size; The process of comprehensively evaluating the current game promotion effect based on the promotion value index and player interest index is as follows: By formula: Calculate the promotion evaluation coefficient ; in, The actual number of players in the current game at the end of a statistical period. The actual number of players in the current game at the start of a statistical period. It is the ratio of high-frequency players to the actual number of players in the current game at the end of a statistical period. It is the reference value of the promotion value index; The calculated promotion evaluation coefficient Compared with the preset promotion evaluation threshold For comparison: like , then the promotion value of the current game is evaluated to be good; like , then the promotion value of the current game is evaluated to be poor.

2. The game promotion system based on data analysis according to claim 1, characterized in that: The process of calculating each player's interest index based on player behavior data is as follows: The obtained player behavior data is passed through the formula: Calculate the first Player's interest index , For the Player behavior data, is the number of items of player behavior data, For the The parameter value of the player behavior data, 、 Respectively The minimum and maximum values ​​of the player behavior data. For the The weight coefficient of the player behavior data.

3. The game promotion system based on data analysis according to claim 2, characterized in that: The process of performing segmentation analysis on players based on each player's interest index is as follows: The first Player's interest index and pre-set interest index range For comparison: like , then the current player is judged to be a medium-frequency player; like , then the current player is judged to be a high-frequency player; like , then the current player is judged to be a low-frequency player.

4. The game promotion system based on data analysis according to claim 1, characterized in that: Get the judgment function about the competing game The process is: like >1, then ; like ,but ; in, The download volume of competing games changes over time. The download volume reference change curve is shown below. is the reference value, is a constant and .

5. The game promotion system based on data analysis according to claim 1, characterized in that: The visual dashboard supports multi-terminal display, including computers, tablets and mobile phones.

6. A game promotion method based on data analysis, characterized in that: The method is implemented based on the game promotion system based on data analysis described in any one of claims 1-5.

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