Game activity flow analysis method and device, equipment and storage medium

Abstract

The invention discloses a game activity flow analysis method and device, equipment and a storage medium, and the method comprises the steps: obtaining activity operation information of a game activity, the activity operation information comprises target group information, group grouping information, an activity triggering task and an activity behavior type; determining a first number of target users in the target group according to the target group information, dividing the target group into a plurality of sub-groups according to the group grouping information, and determining a second number of target users in each sub-group; when the target user completes the activity triggering task, an activity rule is sent to the target user, and the activity rule is used for indicating the user to execute a target behavior corresponding to the activity behavior type; based on a preset statistical period, counting a third number of target users completing the activity triggering task in the sub-group and counting a fourth number of target users executing the target behavior in the sub-group; and generating a flow funnel model of the game activity according to the first quantity, the second quantity, the third quantity and the fourth quantity.

Description

Technical Field

[0001] The present application relates to the field of game technology, and in particular to a method, device, equipment and storage medium for analyzing game activity traffic. Background Art

[0002] With the rapid development of mobile Internet and increasingly fierce competition in the game market, everything from company development strategies to activity operation strategies needs to respond quickly to changes in the market, users, and competitors. And behind the rapid response is the support of accurate and efficient data analysis. In the game operation scenario, the formulation of activity strategies and the response and adjustment during the activity operation stage are equally important. When the activity is running, the activity operation status, user feedback, and key behavior conversion can be observed, and the activity strategy can be adjusted in time, thereby improving the overall effect of the activity.

[0003] In the prior art, after a game activity has been online for a period of time, the game operator will propose activity analysis requirements for the activity, query the activity data in the corresponding data source according to the data analysis requirements, and generate data reports based on the activity data and feedback them to the game operator. It usually takes two to three days for the game operator to submit the analysis requirements and get the data reports, which leads to a relatively delayed activity analysis and strategy adjustment time, making it impossible to observe the activity operation status in real time and adjust the activity strategy in time, thus affecting the execution effect of the activity. Summary of the invention

[0004] The present application provides a method, device, equipment and storage medium for analyzing traffic of game activities, so as to count in real time the number of users who complete the activity triggering tasks and execute the target behaviors in the subgroups of the target group during the activity operation stage, and generate a real-time traffic funnel model of the activity in combination with the number of users of the target group and the subgroups. The traffic funnel model can be used to observe the operation of the activity in real time so as to adjust the activity strategy and detect activity anomalies in a timely manner, thus solving the problem in the prior art that it is impossible to observe the activity in real time and adjust the activity strategy in a timely manner, and effectively improving the execution effect of the activity.

[0005] In a first aspect, the present application provides a method for analyzing game activity traffic, comprising:

[0006] Acquire activity operation information of the game activity, wherein the activity operation information includes target group information, group group information, activity triggering tasks, and activity behavior types;

[0007] Determine a first number of target users in a target group according to the target group information, divide the target group into a plurality of subgroups according to the group grouping information, and determine a second number of target users in each of the subgroups;

[0008] When the target user completes the activity triggering task, an activity rule is sent to the target user, where the activity rule is used to instruct the user to perform a target behavior corresponding to the activity behavior type;

[0009] Based on a preset statistical period, counting a third number of target users in the subgroup who complete the activity triggering task and counting a fourth number of target users in the subgroup who perform the target behavior;

[0010] A traffic funnel model of the game activity is generated according to the first number, the second number, the third number, and the fourth number.

[0011] In a second aspect, the present application provides a game activity flow analysis device, comprising:

[0012] An activity information acquisition module is configured to acquire activity operation information of a game activity, wherein the activity operation information includes target group information, group group information, activity triggering tasks, and activity behavior types;

[0013] a group quantity determination module, configured to determine a first quantity of target users in a target group according to the target group information, divide the target group into a plurality of subgroups according to the group grouping information, and determine a second quantity of target users in each of the subgroups;

[0014] An activity rule sending module is configured to send an activity rule to the target user when the target user completes the activity triggering task, wherein the activity rule is used to instruct the user to perform a target behavior corresponding to the activity behavior type;

[0015] A first statistical module is configured to count, based on a preset statistical period, a third number of target users in the subgroup who complete the activity triggering task and a fourth number of target users in the subgroup who perform the target behavior;

[0016] The activity traffic analysis module is configured to generate a traffic funnel model of the game activity according to the first number, the second number, the third number and the fourth number.

[0017] In a third aspect, the present application provides a game activity traffic analysis device, comprising:

[0018] one or more processors;

[0019] A memory stores one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the game activity traffic analysis method as described in the first aspect.

[0020] In a fourth aspect, the present application provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to perform the game activity traffic analysis method as described in the first aspect.

[0021] In the present application, activity operation information of a game activity is obtained, and the activity operation information includes target group information, group grouping information, activity triggering tasks, and activity behavior types; a first number of target users in the target group is determined according to the target group information, and the target group is divided into multiple subgroups according to the group grouping information, and a second number of target users in each subgroup is determined; when the target user completes the activity triggering task, an activity rule is sent to the target user, and the activity rule is used to instruct the user to perform the target behavior corresponding to the activity behavior type; based on a preset statistical period, a third number of target users who complete the activity triggering task in the subgroup is counted, and a fourth number of target users who perform the target behavior in the subgroup is counted; a traffic funnel model for the game activity is generated according to the first number, the second number, the third number, and the fourth number. Through the above-mentioned technical means, the game activities can be broken down into multiple operation nodes such as determining the target group, grouping the target group, executing the activity triggering task and executing the target behavior, so as to count the traffic of multiple operation nodes, and generate a real-time traffic funnel model for the game activities according to the top-down order of multiple operation nodes and the statistical traffic of the operation nodes. The traffic funnel model can be used to observe the traffic conversion rate of the upper and lower operation nodes in real time, and then understand the activity operation status in real time, so as to adjust the activity strategy and detect activity anomalies in time, thus solving the problem of being unable to observe the activity situation in real time and adjust the activity strategy in time in the existing technology, and effectively improving the execution effect of the activity. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of a method for analyzing game activity traffic provided by an embodiment of the present application;

[0023] Figure 2 This is one of the schematic diagrams of the operation path of the game activity provided in this embodiment;

[0024] Figure 3 is a flowchart of sending activity rules to target users provided by an embodiment of the present application;

[0025] Figure 4 This is the second schematic diagram of the operation path of the game activity provided in the embodiment of the present application;

[0026] Figure 5 is a flowchart of counting the third quantity and the fourth quantity provided in an embodiment of the present application;

[0027] Figure 6 This is the third schematic diagram of the operation path of the game activity provided in the embodiment of the present application;

[0028] Figure 7 is a flow chart of generating a traffic funnel model provided by an embodiment of the present application;

[0029] Figure 8 is a schematic diagram of a flow funnel model provided in an embodiment of the present application;

[0030] Fig. 9 is a schematic diagram of a flow funnel model provided in an embodiment of the present application;

[0031] Fig.10 is a schematic diagram of a flow funnel model provided in an embodiment of the present application;

[0032] Fig.11 It is a structural schematic diagram of a game activity flow analysis device provided in an embodiment of the present application;

[0033] Fig.12 It is a structural schematic diagram of a game activity traffic analysis device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical scheme and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. It is understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for the convenience of description, only the part related to the present application but not all the contents are shown in the accompanying drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow chart describes each operation (or step) as a sequential process, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of each operation can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0035] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0036] In the more common existing implementation method, after a game activity has been online for a period of time, the game operation proposes activity analysis requirements for the activity, queries the activity data in the corresponding data source according to the data analysis requirements, and generates data reports based on the activity data and feeds them back to the game operation. It usually takes two to three days for the game operation to submit the analysis requirements and get the data report, which leads to a relatively delayed time for activity analysis and strategy adjustment, and it is impossible to observe the activity operation status in real time and adjust the activity strategy in time, thus affecting the execution effect of the activity.

[0037] To solve the above problems, this embodiment provides a game activity traffic analysis method to count in real time the number of users who complete activity triggering tasks and perform target behaviors in subgroups of the target group during the activity operation stage, and generate a real-time traffic funnel model of the activity based on the number of users in the target group and subgroups. The traffic funnel model can be used to observe the activity operation status in real time, so as to adjust the activity strategy and detect activity anomalies in a timely manner, thereby effectively improving the execution effect of the activity.

[0038] The game activity traffic analysis method provided in this embodiment can be executed by a game activity traffic analysis device, which can be implemented by software and / or hardware, and can be composed of two or more physical entities or one physical entity. For example, the game activity traffic analysis device can be a server that operates game activities.

[0039] The game activity traffic analysis device is installed with at least one type of operating system, wherein the operating system includes but is not limited to Android system, Linux system and Windows system. The game activity traffic analysis device can install at least one application based on the operating system, and the application can be an application that comes with the operating system or an application downloaded from a third-party device or server. In this embodiment, the game activity traffic analysis device has at least an application that can execute the game activity traffic analysis method.

[0040] For ease of understanding, this embodiment is described by taking a server as an example of the main body executing the game activity traffic analysis method.

[0041] Figure 1 A flowchart of a method for analyzing game activity traffic provided by an embodiment of the present application is given. Figure 1 , the game activity traffic analysis method specifically includes:

[0042] S110, obtaining activity operation information of the game activity, where the activity operation information includes target group information, group group information, activity triggering tasks, and activity behavior types.

[0043] For example, a game activity can be viewed as an activity for certain user groups to obtain game rewards by performing certain game behaviors. The game activity can be broken down into multiple operation nodes ordered from top to bottom. During operation, the game activity can be gradually promoted in the order of the corresponding operation nodes. Figure 2 This is one of the schematic diagrams of the operation path of the game activity provided in this embodiment. Figure 2 As shown, the game activity can be broken down into the first operation node, the second operation node, the third operation node and the fourth operation node. These four operation nodes are arranged from top to bottom to form the operation path of the game activity. The first operation node is to determine the target group, the second operation node is to group the target group, the third operation node is to send the activity rules to the players who complete the activity triggering task, and the fourth operation node is to issue activity rewards to the players who perform the target behavior. Among them, the target group is the player group that can participate in the game activity, the activity triggering task is the task that the player needs to complete to trigger the game activity, and the target behavior is the game behavior that the player needs to perform when obtaining the activity reward of the game activity. The above four operation nodes can be understood as the basic operation nodes of the game activity. On the basis of these four operation nodes, the operator can add other operation nodes according to the operation needs.

[0044] For example, suppose the game activity is to recall players who have been offline for more than 30 days, and the players who have been offline for more than 30 days are divided into female players and male players. After the players who have been offline for more than 30 days log in to the game, the activity rules of the recall activity are sent to the players, and the recall rewards are sent to the players after the players complete the recall task based on the activity rules. Based on this game activity, the target group can be determined as players who have been offline for more than 30 days at the first operation node, and the target group can be grouped into female players and male players who have been offline for more than 30 days at the second operation node. At the third operation node, it can be determined that the player has completed the activity triggering task when the player logs in to the game, and at the fourth operation node, it can be determined that the player has performed the target behavior when the player completes the recall task.

[0045] After the game activity is decomposed into multiple operation nodes, the activity operation information of the game activity can be obtained through the multiple operation nodes, and the activity operation information includes information such as the groups, tasks, behaviors, and conditions involved in each operation node. Specifically, after the game activity is decomposed into the first operation node, the second operation node, the third operation node, and the fourth operation node, the target group information can be obtained through the first operation node, the group grouping information can be obtained through the second operation node, the activity triggering task can be obtained through the third operation node, and the activity behavior type corresponding to the target behavior can be obtained through the fourth operation node. Among them, the target group information is used to indicate the corresponding target group, and the group grouping information is used to indicate the grouping rules of the target group.

[0046] It should be noted that any game activity can be broken down into multiple operation nodes sorted from top to bottom. This embodiment aims to count the traffic at each operation node in real time, so as to generate a traffic funnel model that reflects the conversion rate between the upper and lower operation nodes based on the real-time traffic statistics, so that the operation personnel can observe the execution of the game activities in real time through the traffic funnel model, adjust the operation strategy of the game activities in time, and maximize the execution effect of the game activities.

[0047] S120: Determine a first number of target users in the target group according to the target group information, divide the target group into multiple subgroups according to the group grouping information, and determine a second number of target users in each subgroup.

[0048] refer to Figure 2 The first operation node is the first step in promoting the game activity, that is, the target group of the game activity must be determined first before determining which players to send the activity rules to. In this embodiment, the statistical traffic of the first operation node is used to count the number of players who can participate in the game activity, so the first number of target users in the target group can be determined as the statistical traffic of the first operation node. After obtaining the target group information through the first operation node, the target users who meet the target group information can be screened out from the game users, and the first number of target users can be counted. For example, if the target group is players who have been offline for more than 30 days, the number of players who have been offline for more than 30 days can be counted to obtain the first number.

[0049] In this embodiment, the target group information may include not only information such as the level or identity of the target users in the target group, but also user portraits. The user portraits classify the user's preferences, habits, etc. The target users in the target group may be screened out through the user portraits, and then the first number of target users may be counted.

[0050] refer to Figure 2 , the second operation node is the second step in the advancement of the game activity, that is, to group the target group after determining the target group, and the purpose of grouping the target group is to conduct AB testing on different groups to determine which group's activity execution effect is better, so as to adjust the activity operation strategy or plan subsequent game activities. In this embodiment, the statistical traffic of the second operation node is used to count the number of players who can participate in the game activity in each group, and the second number of target users in the multiple subgroups into which the target group is divided can be determined as the statistical traffic of the second operation node. It should be noted that since the target group is divided into multiple subgroups, the second operation node includes the statistical traffic corresponding to the multiple subgroups. In the traffic funnel model generated subsequently, the multiple statistical traffic of the second operation node are displayed in parallel in the traffic funnel model.

[0051] After the group grouping information is obtained through the second operation node, the target group can be divided into multiple subgroups according to the group grouping information, and the second number of target users in the subgroups can be counted. For example, if the group grouping information divides the target group into female players and male players, the female players in the target group are divided into the same subgroup and the number of female players in the subgroup is counted to obtain the second number of target users in the subgroup, and the male players in the target group are divided into the same subgroup and the number of male players in the subgroup is counted to obtain the second number of target users in the subgroup.

[0052] It should be noted that the group grouping information includes grouping rules based on the player's level, gender, behavior, preference, age, etc., and may also include random grouping rules. For example, the group grouping information may be used to identify the target group based on the group grouping information.

[0053] S130. When the target user completes the activity triggering task, an activity rule is sent to the target user, where the activity rule is used to instruct the user to perform a target behavior corresponding to the activity behavior type.

[0054] Exemplarily, the third operation node is to confirm that the target user in the target group completes the activity triggering task. When the target user in the target group completes the activity triggering task, it is confirmed that the target user triggers the game activity, and then the activity rules of the game activity are sent to the target user, so that the target user performs the target behavior of the game activity under the guidance of the activity rules. When the target user performs the target behavior, the activity reward can be issued to the target user. For example, when the activity triggering task is the player logging into the game, and the target behavior is to complete the recall task, the activity rules are sent to the target user when the target user logs into the game. The activity rules record the instruction information on how the player completes the recall task. After the target user completes the recall task under the instruction of the activity rules, the activity reward is issued to the target user.

[0055] In one embodiment, when the first behavior event of the target user completing the activity triggering task is received, the activity rules can be sent to the target user according to a preset reach strategy. The reach strategy can be an SDK pop-up window, SMS and / or mall guidance, etc.

[0056] In another embodiment, in order to test which reach strategy has a better reach effect, different reach strategies can be used to send activity rules to target users in different subgroups. Figure 3 1 is a flow chart of sending activity rules to target users provided by an embodiment of the present application. Figure 3 As shown, the step of sending the activity rules to the target user specifically includes S1301-S1303:

[0057] S1301. Obtain the first behavior event of the user completing the activity triggering task through a synchronous interface.

[0058] This embodiment is described by taking the activity triggering task of logging into the game as an example. When the user logs into the game, a login event is triggered, and the game client uploads the login event to the server, and the server receives the login event through the synchronization interface. It should be noted that for the behavior events received by the synchronization interface, the server can respond in a timely manner and then provide feedback to the user. This embodiment obtains the first behavior event through the synchronization interface so that the server can quickly send the activity rules to the user based on the first behavior event, so that the player can quickly learn about the game activities that he can participate in, thereby increasing the probability and enthusiasm of the player to participate in the activities.

[0059] S1302: When the user corresponding to the first behavior event is the target user, obtain the corresponding target activity reach strategy in the activity reach strategy according to the subgroup matched by the target user.

[0060] Exemplarily, after acquiring the first behavior event, the server may acquire the corresponding user information according to the user identifier carried by the first behavior event, and determine whether the user is the target user in the target group according to the user information. For example, when the target group is players who have been offline for more than 30 days, the most recent online time in the user information may be acquired. If the most recent online time is more than 30 days away from the current time, the user corresponding to the first behavior event is determined to be the target user. Otherwise, the user corresponding to the first behavior event is determined not to be the target user. If the user corresponding to the first behavior event is not the target user, it indicates that the user is not eligible to participate in the game activity, and the activity rules are not sent to the user. If the user corresponding to the first behavior event is the target user, it indicates that the user is eligible to participate in the game activity, and the activity rules are sent to the user.

[0061] Furthermore, the activity operation information also includes multiple activity reach strategies, and the activity reach strategy is a strategy for sending activity rules to users, which includes SDK pop-up windows, text messages, and mall guidance. In the second operation node of the game activity, not only the grouping rules of the target group are defined, but also the activity reach strategy of each subgroup after grouping is defined. Therefore, the activity reach strategy corresponding to each subgroup can be obtained through the second operation node. Correspondingly, when it is determined that the user corresponding to the first behavior event is the target user, the subgroup to which the target user belongs is determined, and the activity reach strategy corresponding to the subgroup is the target activity reach strategy used when sending activity rules to the target user. For example, the second operation node randomly divides the target group into subgroup A, subgroup B, and subgroup C. Subgroup A uses SDK pop-up windows to send activity rules, subgroup B uses text messages to send activity rules, and subgroup C uses mall guidance to send activity rules. If the target user belongs to subgroup A, the SDK pop-up window is determined to be the target activity reach strategy for the target user.

[0062] S1303: Send activity rules to target users according to the target activity triggering strategy.

[0063] For example, if the target activity triggering strategy of the target user is an SDK pop-up window, the activity rules are delivered to the target user through the SDK pop-up window; if the target activity triggering strategy of the target user is a text message, the activity rules are delivered to the target user through a text message; if the target activity triggering strategy of the target user is a mall guide, the activity rules are delivered to the target user through a mall guide. This embodiment adopts different activity triggering strategies for different sub-groups, which can not only test the reach effects of various activity reach strategies, but also adapt to the habits of different sub-groups in understanding activity rules, thereby improving the participation of players in game activities.

[0064] In one embodiment, if the activity operator believes that some players in the target group are not qualified to participate in the game activity, for example, players with a level below 30 or a recharge amount below 100 cannot participate in the game's recall activity, a fifth operation node can be added between the third operation node and the fourth operation node. The fifth operation node sends activity rules to players who complete the activity triggering task and meet the activity contact conditions, and the third operation node is adjusted to determine the players who complete the activity triggering task. Figure 4 This is the second schematic diagram of the operation path of the game activity provided in the embodiment of the present application. Figure 4 As shown, the game activity can be disassembled into the first operation node, the second operation node, the third operation node, the fifth operation node and the fourth operation node, and these five operation nodes are arranged from top to bottom to form the operation path of the game activity. In this embodiment, the activity operation information includes the activity touch condition, and the activity touch condition is the condition satisfied by the target user who sends the activity rule, and the activity touch condition can be obtained through the fifth operation node. After determining that the target user completes the activity triggering task, it is determined whether the target user meets the activity touch condition according to the user information of the target user. If the target user meets the activity touch condition, the activity rule is sent to the target user, and if the target user does not meet the activity touch condition, the activity rule is not sent to the target user. For example, if the activity touch condition is that the player level is above level 30, then when the target user completes the activity triggering task, the game level of the target user is obtained, and when the game level is greater than or equal to 30, the activity rule is sent to the target user, otherwise the activity rule is not sent to the target user. This embodiment can filter some players in the target group through the activity touch condition to improve the quality of players participating in game activities.

[0065] S140. Based on a preset statistical period, count a third number of target users in the subgroup who complete the activity triggering task and count a fourth number of target users in the subgroup who perform the target behavior.

[0066] The preset statistical period is the time interval between two consecutive activity data analysis time nodes, that is, the preset statistical period specifies the time node for analyzing the activity data of the game activity. At each time node, the traffic of each operation node is automatically counted, and the traffic funnel model of the game activity is generated based on the statistical traffic. The preset statistical period can be set to one hour or one day, etc. If the preset statistical period is set to one hour, a new traffic funnel model will be generated every hour. The operator can observe the execution of the game activity in real time based on the traffic funnel model generated every hour, so as to adjust the game activity operation strategy in time.

[0067] Exemplarily, when the time node for data analysis is reached, the third number of target users who complete the activity triggering task in each subgroup and the fourth number of target users who perform the target behavior in each subgroup are counted. It can be understood that the statistical traffic of the third operation node is used to count the number of players who complete the activity triggering task, so the third number of target users who complete the activity triggering task in the subgroup can be determined as the statistical traffic of the third operation node. The statistical traffic of the fourth operation node is used to count the number of players who perform the target behavior, so the number of players who perform the target behavior in the subgroup can be determined as the statistical traffic of the fourth operation node. It should be noted that since the target group is divided into multiple subgroups, the third operation node and the fourth operation node also include statistical traffic corresponding to multiple subgroups. In the traffic funnel model generated subsequently, multiple statistical traffic of the third operation node and the fourth operation node are displayed in parallel in the traffic funnel model.

[0068] During the advancement of the third operation node and the fourth operation node, the first behavior event of the user completing the activity triggering task can be written into the first message queue, and the second behavior event of the user performing the target behavior can be written into the second message queue. Among them, the first message queue and the second message queue can be kafka. The first message queue and the second message queue can temporarily cache the first behavior event and the second behavior event, so that when the time node of data analysis arrives, the first behavior event and the second behavior event can be read from the first message queue and the second message queue for consumption and processing, thereby realizing asynchronous response to the first behavior event and the second behavior event, alleviating the pressure of real-time response of the server. Accordingly, Figure 5 : is a flowchart of counting the third quantity and the fourth quantity provided by the embodiment of the present application. Figure 5 As shown, the step of counting the third quantity and the fourth quantity specifically includes S1401-S1402:

[0069] S1401. Count a third number of target users who complete an activity triggering task in a subgroup according to a first behavior event cached in a first message queue.

[0070] Exemplarily, the cached first behavior event is read from the first message queue, the corresponding user information is obtained according to the user account corresponding to the first behavior event, and it is determined whether the user corresponding to the first behavior event is the target user according to the user information, thereby determining the number of target users in the first message queue. If the data analysis requirement is to analyze the activity data of the game activity up to the current time node, the number of target users in the first message queue is added to the third number counted at the time node of the previous data analysis to obtain the third number counted at the current time node. If the data analysis requirement is to analyze the activity data from the time node of the previous data analysis to the current time node, the number of target users in the first message queue is used as the third number counted at the current node.

[0071] S1402. Count a fourth number of target users who perform the target behavior in the subgroup according to the second behavior event cached in the second message queue.

[0072] Exemplarily, the cached second behavior event is read from the second message queue, the corresponding user information is obtained according to the user account corresponding to the second behavior event, and it is determined whether the user corresponding to the second behavior event is the target user according to the user information, thereby determining the number of target users in the second message queue. If the data analysis requirement is to analyze the activity data of the game activity up to the current time node, the number of target users in the second message queue is added to the fourth number counted at the time node of the previous data analysis to obtain the fourth number counted at the current time node. If the data analysis requirement is to analyze the activity data from the time node of the previous data analysis to the current time node, the number of target users in the second message queue is used as the fourth number counted at the current node.

[0073] It should be noted that if the number of target users in the first message queue and the second message queue is not deduplicated, the number of times the target users complete the activity triggering task and execute the target task is counted instead of the number of users who complete the activity triggering task and execute the target task. If the data analysis requirement is to analyze the number of people in each operation node, the number of times the target users complete the activity triggering task and execute the target task can be used to generate a traffic funnel model; if the data analysis requirement is to analyze the number of people in each operation node, the first behavior event and the second behavior event of the same user in the first message queue and the second message queue can be deduplicated, and the number of users who complete the activity triggering task and execute the target task can be obtained based on the first behavior event and the second behavior event after deduplication, and the number of users who complete the activity triggering task and execute the target task can be used to generate a traffic funnel model.

[0074] Furthermore, when the activity operation information includes the activity reach conditions, the fifth number of target users reached by the activity rules in the subgroup may be counted; and the sixth number of target users filtered by the activity reach conditions in the subgroup may be counted. Figure 4 , the fifth operation node sends activity rules to players who have completed the activity triggering task and meet the activity reaching conditions. The statistical traffic of the fifth operation node is used to count the number of players that can be reached by the activity rules. Therefore, the fifth number of target users reached by the activity rules in the subgroup can be determined as the statistical traffic of the fifth operation node. In addition, the statistical traffic of the fifth operation node can also be used to count the number of players who have completed the activity triggering task but do not meet the activity reaching conditions. Therefore, the sixth number of target users filtered by the activity reaching conditions in the subgroup can also be determined as the statistical traffic of the fifth operation node. For example, the number of target users who have completed the activity triggering task is 1,000, of which 800 meet the activity reaching conditions, then the activity rules are sent to these 800 people, and 200 people do not meet the activity reaching conditions, so these 200 people are filtered and the activity rules are not sent to them. Afterwards, it can be counted that the fifth number is 800 and the sixth number is 200.

[0075] In one embodiment, in order to determine whether the activity rewards meet the expectations of the players, the number of players who use the activity rewards may be counted. When the number of players is small, it indicates that the activity rewards are not attractive to most players, and the activity rewards of the game activities may be adjusted. Alternatively, in order to test which activity rewards are more in line with the expectations of the players, multiple activity rewards may be issued to the players, and then the number of players who use various activity rewards may be counted, and the activity rewards of the game activities may be adjusted according to the statistical results. Exemplarily, after receiving the activity rules, the target user may perform the target behavior under the instructions of the activity rules, and after the target user performs the target behavior, the activity reward is sent to the target user. Afterwards, when the time node for data analysis arrives, the seventh number of target users who use the activity rewards in the subgroup is counted. For example, if you want to count the number of users who use the activity rewards, you may add a sixth operation node in the game activity. The sixth operation node is located after the fourth operation node, and the sixth operation node is for determining the target players who use the activity rewards. Exemplarily, Figure 6 This is the third schematic diagram of the operation path of the game activity provided in the embodiment of the present application. Figure 6 As shown, the game activity can be broken down into the first operation node, the second operation node, the third operation node, the fourth operation node and the sixth operation node, and these five operation nodes are arranged from top to bottom to form the operation path of the game activity. It can be understood that the statistical flow of the sixth operation node is used to count the number of players who use the activity rewards, so the seventh number of target users who use the activity rewards in the subgroup can be determined as the statistical flow of the sixth operation node.

[0076] In this embodiment, in order to count the seventh number of target users who use activity rewards in the subgroup, the third behavior event of the user using the activity reward can be stored in the third message queue. When the time node of data analysis arrives, the third behavior event is read from the third message queue, and user information is obtained according to the user account corresponding to the third behavior event. It is determined whether the user corresponding to the third behavior event is the target user based on the user information, and then the number of third behavior events of the target user in the third message queue is counted as the seventh number.

[0077] S150. Generate a traffic funnel model for the game activity according to the first quantity, the second quantity, the third quantity, and the fourth quantity.

[0078] Exemplarily, when a game activity is broken down into a first operation node, a second operation node, a third operation node, and a fourth operation node, a first quantity, a second quantity, a third quantity, and a fourth quantity can be obtained correspondingly, and the first quantity, the second quantity, the third quantity, and the fourth quantity are sorted from top to bottom to obtain a traffic funnel model.

[0079] In one embodiment, the traffic conversion rate between the front and rear operation nodes may be calculated to generate a traffic funnel model according to the traffic conversion rate. Figure 7 This is a flow chart of generating a traffic funnel model provided by an embodiment of the present application. Figure 7 As shown, the steps of generating the traffic funnel model include S1501-S1503:

[0080] S1501. Determine the first number, the second number, the third number, and the fourth number as statistical traffic of corresponding operation nodes in the operation path of the game activity respectively; wherein the operation nodes corresponding to the first number, the second number, the third number, and the fourth number are arranged in order from front to back in the operation path.

[0081] S1502: Determine the ratio of the statistical traffic corresponding to the previous operation node to the statistical traffic corresponding to the next operation node as the conversion rate of the next operation node.

[0082] S1503. Based on the order of each operation node in the operation path, the conversion rate of each operation node is sorted to obtain a traffic funnel model of the game activity.

[0083] refer to Figure 2, the first quantity, the second quantity, the third quantity and the fourth quantity are respectively determined as the statistical traffic of the first operation node, the second operation node, the third operation node and the fourth operation node. The second operation node is the node after the first operation node, and the ratio of the second quantity to the first quantity can be determined as the conversion rate of the second operation node. For example, the second quantity includes the second quantity of target users in each subgroup, then the conversion rate of the second operation node is the proportion of each subgroup in the target group. The third operation node is the node after the second operation node, and the ratio of the third quantity to the second quantity of a subgroup can be determined as the conversion rate of the subgroup in the third operation node. The fourth operation node is the node after the third operation node, and the ratio of the fourth quantity to the third quantity of a subgroup can be determined as the conversion rate of the subgroup in the fourth operation node. Sort the conversion rates of the first operation node, the second operation node, the third operation node and the fourth operation node from top to bottom to obtain a traffic funnel model of the game activity. Exemplarily, Figure 8 Schematic diagram of the flow funnel model provided in the embodiment of the present application. Figure 8 As shown, the first layer of the traffic funnel model shows the conversion rate and statistical traffic of the first operating node. The statistical traffic of the first operating node is the first number of target users in the target group. The second layer of the traffic funnel model shows the conversion rate and statistical traffic of the three subgroups of the second operating node. The conversion rate of subgroup A is the proportion of subgroup A in the target group, and the statistical traffic of subgroup A is the second number of target users in the subgroup. The same is true for subgroups B and C. The third layer of the traffic funnel model shows the conversion rate and statistical traffic of the three subgroups of the third operating node. The conversion rate of subgroup A is the proportion of target users in subgroup A who complete the activity triggering task in subgroup A, and the statistical traffic of subgroup A is the third number of target users in subgroup A who complete the activity triggering task. The same is true for subgroups B and C. The fourth layer of the traffic funnel model shows the conversion rate and statistical traffic of the three subgroups of the fourth operation node. The conversion rate of subgroup A is the proportion of target users in subgroup A who perform target behaviors among target users who complete the activity triggering tasks. The statistical traffic of subgroup A is the fourth number of target users who perform target behaviors in subgroup A. The same applies to subgroups B and C.

[0084] Furthermore, when the game activity is decomposed into the first operation node, the second operation node, the third operation node, the fifth operation node and the fourth operation node, the first quantity, the second quantity, the third quantity, the fourth quantity, the fifth quantity and the sixth quantity can be obtained accordingly. A traffic funnel model of the game activity can be generated according to the first quantity, the second quantity, the third quantity, the fourth quantity, the fifth quantity and the sixth quantity; wherein the operation nodes corresponding to the fifth quantity and the sixth quantity are located in parallel between the operation nodes corresponding to the third quantity and the fourth quantity. Figure 4, determine the first quantity, the second quantity, the third quantity and the fourth quantity as the statistical traffic of the first operation node, the second operation node, the third operation node and the fourth operation node respectively, and determine the fifth quantity and the sixth quantity as the statistical traffic of the fifth operation node. Determine the ratio of the second quantity to the first quantity as the conversion rate of the second operation node. Determine the ratio of the third quantity to the second quantity of a certain subgroup as the conversion rate of the subgroup in the third operation node. Determine the ratio of the fifth quantity to the second quantity of a certain subgroup as the conversion rate of the subgroup in the fifth operation node, and determine the ratio of the sixth quantity to the second quantity of a certain subgroup as the filtering rate of the subgroup in the fifth operation node. Determine the ratio of the fourth quantity to the fifth quantity of a certain subgroup as the conversion rate of the subgroup in the fourth operation node. Sort the conversion rates of the first operation node, the second operation node, the third operation node, the fifth operation node and the fourth operation node from top to bottom, and set the filtering rate and conversion rate of the fifth operation node side by side to obtain a traffic funnel model of the game activity. Exemplary, Fig. 9 Schematic diagram of the flow funnel model provided in the embodiment of the present application. Fig. 9 As shown, the first, second, third and fifth layers of the traffic funnel model respectively show the conversion rate and statistical traffic of the first operation node, the conversion rate and statistical traffic of each subgroup of the second operation node, the conversion rate and statistical traffic of each subgroup of the third operation node, and the conversion rate and statistical traffic of each subgroup of the fourth operation node. The description of the first, second and third layers can be referred to Figure 8 The descriptions of the first, second, and third layers shown are not repeated here. The conversion rate of subgroup A in the fourth layer is the proportion of target users in subgroup A who perform target behaviors among target users who complete the activity triggering task and meet the activity reaching conditions. The statistical traffic of subgroup A in the fourth layer is the fourth number of target users who perform target behaviors in subgroup A. The same is true for subgroups B and C. The fourth layer shows the conversion rate, filtering rate, and statistical traffic of the three subgroups of the fifth operation node. The conversion rate of subgroup A is the proportion of target users in subgroup A who complete the activity triggering task and meet the activity reaching conditions among target users who complete the activity triggering task. The filtering rate of subgroup A is the proportion of target users who complete the activity triggering task but do not meet the activity reaching conditions among target users who complete the activity triggering task. The statistical traffic corresponding to the conversion rate is the fifth number of target users in subgroup A who complete the activity triggering task and meet the activity reaching conditions. The statistical traffic corresponding to the filtering rate is the sixth number of target users in subgroup A who complete the activity triggering task but do not meet the activity reaching conditions. The same is true for subgroups B and C.

[0085] When the game activity is decomposed into the first operation node, the second operation node, the third operation node, the fourth operation node and the sixth operation node, the first quantity, the second quantity, the third quantity, the fourth quantity and the seventh quantity can be obtained accordingly. The traffic funnel model of the game activity can be generated according to the first quantity, the second quantity, the third quantity, the fourth quantity and the seventh quantity; wherein the operation node corresponding to the seventh quantity is located after the operation node corresponding to the fourth quantity. Figure 6 , determine the first quantity, the second quantity, the third quantity and the fourth quantity as the statistical traffic of the first operation node, the second operation node, the third operation node, the fourth operation node and the sixth operation node respectively. Determine the ratio of the second quantity to the first quantity as the conversion rate of the second operation node. Determine the ratio of the third quantity to the second quantity of a subgroup as the conversion rate of the subgroup in the third operation node. Determine the ratio of the fourth quantity to the third quantity of a subgroup as the conversion rate of the subgroup in the fourth operation node. Determine the ratio of the seventh quantity to the fourth quantity of a subgroup as the conversion rate of the subgroup in the sixth operation node. Sort the conversion rates of the first operation node, the second operation node, the third operation node, the fourth operation node and the sixth operation node from top to bottom to obtain a traffic funnel model of the game activity. Exemplarily, Fig.10 Schematic diagram of the flow funnel model provided in the embodiment of the present application. Fig.10 As shown, the first, second, third, fourth and fifth layers of the traffic funnel model respectively show the conversion rate and statistical traffic of the first operation node, the conversion rate and statistical traffic of each subgroup of the second operation node, the conversion rate and statistical traffic of each subgroup of the third operation node, the conversion rate and statistical traffic of each subgroup of the fourth operation node, and the conversion rate and statistical traffic of each subgroup of the sixth operation node. The description of the first, second, third and fourth layers can be referred to Figure 8 The descriptions of the first, second, third and fourth layers are not repeated here. The conversion rate of subgroup A in the fifth layer is the proportion of target users who use activity rewards in subgroup A among target users who perform target behaviors, and the statistical flow of subgroup A is the seventh number of target users who use activity rewards in subgroup A. The same is true for subgroups B and C.

[0086] When the game activity is decomposed into the first operation node, the second operation node, the third operation node, the fifth operation node, the fourth operation node and the sixth operation node, the first quantity, the second quantity, the third quantity, the fourth quantity, the fifth quantity, the sixth quantity and the seventh quantity can be obtained accordingly. The traffic funnel model of the game activity can be generated based on the first quantity, the second quantity, the third quantity, the fourth quantity, the fifth quantity, the sixth quantity and the seventh quantity. The specific process can be combined with reference Fig. 9 and Fig.10The generation process of the traffic funnel model shown is not repeated here.

[0087] Through the above traffic funnel model, operators can observe the conversion rate between the two previous and next operation nodes, determine the abnormal operation node based on the conversion rate, and then adjust the operation strategy or perform abnormal diagnosis on the game operation based on the abnormal operation node.

[0088] In summary, the game activity traffic analysis method provided in the embodiment of the present application obtains activity operation information of the game activity, and the activity operation information includes target group information, group grouping information, activity triggering tasks and activity behavior types; determines a first number of target users in the target group according to the target group information, divides the target group into multiple subgroups according to the group grouping information, and determines a second number of target users in each subgroup; sends activity rules to the target user when the target user completes the activity triggering task, and the activity rules are used to instruct the user to perform the target behavior corresponding to the activity behavior type; based on a preset statistical period, counts the third number of target users who complete the activity triggering task in the subgroup and counts the fourth number of target users who perform the target behavior in the subgroup; generates a traffic funnel model for the game activity according to the first number, the second number, the third number and the fourth number. Through the above-mentioned technical means, the game activities can be broken down into multiple operation nodes such as determining the target group, grouping the target group, executing the activity triggering task and executing the target behavior, so as to count the traffic of multiple operation nodes, and generate a real-time traffic funnel model for the game activities according to the top-down order of multiple operation nodes and the statistical traffic of the operation nodes. The traffic funnel model can be used to observe the traffic conversion rate of the upper and lower operation nodes in real time, and then understand the activity operation status in real time, so as to adjust the activity strategy and detect activity anomalies in time, thus solving the problem of being unable to observe the activity situation in real time and adjust the activity strategy in time in the existing technology, and effectively improving the execution effect of the activity.

[0089] Based on the above embodiments, Fig.11 This is a schematic diagram of the structure of a game activity flow analysis device provided in an embodiment of the present application. Fig.11 The game activity flow analysis device provided in this embodiment specifically includes: an activity information acquisition module 21, a group quantity determination module 22, an activity rule sending module 23, a first statistics module 24 and an activity flow analysis module 25.

[0090] The activity information acquisition module 21 is configured to acquire activity operation information of the game activity, and the activity operation information includes target group information, group group information, activity triggering tasks and activity behavior types;

[0091] A group quantity determination module 22 is configured to determine a first quantity of target users in a target group according to the target group information, divide the target group into a plurality of subgroups according to the group grouping information, and determine a second quantity of target users in each subgroup;

[0092] The activity rule sending module 23 is configured to send the activity rule to the target user when the target user completes the activity triggering task, and the activity rule is used to instruct the user to perform the target behavior corresponding to the activity behavior type;

[0093] The first statistical module 24 is configured to count a third number of target users who complete the activity triggering task in the subgroup and a fourth number of target users who perform the target behavior in the subgroup based on a preset statistical period;

[0094] The activity traffic analysis module 25 is configured to generate a traffic funnel model of the game activity according to the first quantity, the second quantity, the third quantity and the fourth quantity.

[0095] On the basis of the above embodiment, the activity traffic analysis module 25 includes: a statistical traffic determination unit, configured to respectively determine the first number, the second number, the third number and the fourth number as the statistical traffic of the corresponding operation nodes in the operation path of the game activity; wherein the operation nodes corresponding to the first number, the second number, the third number and the fourth number are sorted in sequence from front to back in the operation path; a conversion rate determination unit, configured to determine the ratio of the statistical traffic corresponding to the previous operation node to the statistical traffic corresponding to the next operation node as the conversion rate of the next operation node; a first model determination unit, configured to sort the conversion rates of each operation node based on the order of each operation node in the operation path, and obtain a traffic funnel model of the game activity.

[0096] On the basis of the above embodiment, the activity operation information also includes multiple activity reach strategies; accordingly, the activity rule sending module 23 includes: a synchronous receiving unit, configured to obtain the first behavior event of the user completing the activity triggering task through the synchronous interface; a strategy determination unit, configured to obtain the corresponding target activity reach strategy from multiple activity reach strategies according to the subgroup matched by the target user when the user corresponding to the first behavior event is the target user; a first rule sending unit, configured to send the activity rule to the target user according to the target activity triggering strategy.

[0097] On the basis of the above embodiment, the activity operation information also includes an activity reaching condition; accordingly, the activity rule sending module 23 includes: a second rule sending unit, configured to send the activity rule to the target user when the target user meets the activity reaching condition.

[0098] On the basis of the above embodiments, the game activity flow analysis device also includes a second statistical module, which includes: a first statistical unit, configured to count the fifth number of target users reached by the activity rules in the subgroup after sending the activity rules to the target users; a second statistical unit, configured to count the sixth number of target users filtered by the activity reach conditions in the subgroup; accordingly, the activity flow analysis module also includes: a second model determination unit, configured to generate a flow funnel model of the game activity based on the first number, the second number, the third number, the fourth number, the fifth number and the sixth number; wherein the operation nodes corresponding to the fifth number and the sixth number are located side by side between the operation nodes corresponding to the third number and the fourth number.

[0099] Based on the above embodiment, the game activity flow analysis device also includes a third statistical module, which includes: a reward sending unit, configured to send an activity reward to the target user after the target user performs the target behavior corresponding to the activity behavior type; a third statistical unit, configured to count the seventh number of target users who use the activity reward in the subgroup; accordingly, the activity flow analysis module also includes: a third model determination unit, configured to generate a flow funnel model of the game activity based on the first number, the second number, the third number, the fourth number and the seventh number; wherein the operation node corresponding to the seventh number is located after the operation node corresponding to the fourth number.

[0100] Based on the above embodiment, the game activity traffic analysis device also includes a behavior event acquisition module, which is configured to write a first behavior event of a user completing an activity triggering task into the first message queue and a second behavior event of a user performing the target behavior into the second message queue before counting the third number of target users who complete the activity triggering task in the subgroup and counting the fourth number of target users who perform the target behavior in the subgroup; accordingly, the first statistical module includes: a third statistical unit, configured to count the third number of target users who complete the activity triggering task in the subgroup based on the first behavior event cached in the first message queue; and a fourth statistical unit, configured to count the fourth number of target users who perform the target behavior in the subgroup based on the second behavior event cached in the second message queue.

[0101] As described above, the game activity traffic analysis device provided by the embodiment of the present application obtains activity operation information of the game activity, and the activity operation information includes target group information, group grouping information, activity triggering tasks and activity behavior types; determines a first number of target users in the target group according to the target group information, divides the target group into multiple subgroups according to the group grouping information, and determines a second number of target users in each subgroup; sends activity rules to the target user when the target user completes the activity triggering task, and the activity rules are used to instruct the user to perform the target behavior corresponding to the activity behavior type; based on a preset statistical period, counts the third number of target users who complete the activity triggering task in the subgroup and counts the fourth number of target users who perform the target behavior in the subgroup; generates a traffic funnel model for the game activity according to the first number, the second number, the third number and the fourth number. Through the above-mentioned technical means, the game activities can be broken down into multiple operation nodes such as determining the target group, grouping the target group, executing the activity triggering task and executing the target behavior, so as to count the traffic of multiple operation nodes, and generate a real-time traffic funnel model for the game activities according to the top-down order of multiple operation nodes and the statistical traffic of the operation nodes. The traffic funnel model can be used to observe the traffic conversion rate of the upper and lower operation nodes in real time, and then understand the activity operation status in real time, so as to adjust the activity strategy and detect activity anomalies in time, thus solving the problem of being unable to observe the activity situation in real time and adjust the activity strategy in time in the existing technology, and effectively improving the execution effect of the activity.

[0102] The game activity traffic analysis device provided in the embodiment of the present application can be used to execute the game activity traffic analysis method provided in the above embodiment, and has corresponding functions and beneficial effects.

[0103] Fig.12 This is a schematic diagram of the structure of a game activity flow analysis device provided in an embodiment of the present application, with reference to Fig.12 The game activity traffic analysis device includes: a processor 31, a memory 32, a communication device 33, an input device 34, and an output device 35. The number of processors 31 in the game activity traffic analysis device can be one or more, and the number of memories 32 in the game activity traffic analysis device can be one or more. The processor 31, memory 32, communication device 33, input device 34, and output device 35 of the game activity traffic analysis device can be connected through a bus or other means.

[0104] The memory 32, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the game activity traffic analysis method of any embodiment of the present application (for example, the activity information acquisition module 21, the group quantity determination module 22, the activity rule sending module 23, the first statistics module 24 and the activity traffic analysis module 25 in the game activity traffic analysis device). The memory 32 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function; the storage data area may store data created according to the use of the device, etc. In addition, the memory 32 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network and a combination thereof.

[0105] The communication device 33 is used for data transmission.

[0106] The processor 31 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 32, that is, implements the above-mentioned game activity traffic analysis method.

[0107] The input device 34 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 35 may include a display device such as a display screen.

[0108] The game activity traffic analysis device provided above can be used to execute the game activity traffic analysis method provided in the above embodiment, and has corresponding functions and beneficial effects.

[0109] An embodiment of the present application also provides a storage medium containing computer executable instructions, which, when executed by a computer processor, are used to execute a game activity traffic analysis method, the game activity traffic analysis method comprising: obtaining activity operation information of the game activity, the activity operation information comprising target group information, group grouping information, activity triggering tasks and activity behavior types; determining a first number of target users in the target group according to the target group information, dividing the target group into multiple subgroups according to the group grouping information, and determining a second number of target users in each subgroup; when the target user completes the activity triggering task, sending an activity rule to the target user, the activity rule being used to instruct the user to perform a target behavior corresponding to the activity behavior type; based on a preset statistical period, counting a third number of target users who complete the activity triggering task in the subgroup and counting a fourth number of target users who perform the target behavior in the subgroup; generating a traffic funnel model for the game activity according to the first number, the second number, the third number and the fourth number.

[0110] Storage medium - any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media, such as CD-ROM, floppy disk or tape device; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (such as hard disk or optical storage); registers or other similar types of memory elements, etc. Storage media may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the first computer system in which the program is executed, or may be located in a different second computer system, which is connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media residing in different locations (for example, in different computer systems connected by a network). The storage medium may store program instructions (for example, embodied as a computer program) that can be executed by one or more processors.

[0111] Of course, the storage medium containing computer executable instructions provided in the embodiments of the present application is not limited to the above-mentioned game activity traffic analysis method, and the computer executable instructions can also execute the relevant operations in the game activity traffic analysis method provided in any embodiment of the present application.

[0112] The game activity traffic analysis device, storage medium, and game activity traffic analysis equipment provided in the above embodiments can execute the game activity traffic analysis method provided in any embodiment of the present application. For technical details not described in detail in the above embodiments, please refer to the game activity traffic analysis method provided in any embodiment of the present application.

[0113] The above are only preferred embodiments of the present application and the technical principles used. The present application is not limited to the specific embodiments herein, and various obvious changes, readjustments and substitutions that can be made by those skilled in the art will not deviate from the protection scope of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. A method for analyzing game activity traffic, characterized in that: include: Acquire activity operation information of the game activity, wherein the activity operation information includes target group information, group group information, activity triggering tasks, and activity behavior types; Determine a first number of target users in a target group according to the target group information, divide the target group into a plurality of subgroups according to the group grouping information, and determine a second number of target users in each of the subgroups; When the target user completes the activity triggering task, an activity rule is sent to the target user, where the activity rule is used to instruct the user to perform a target behavior corresponding to the activity behavior type; Based on a preset statistical period, counting a third number of target users in the subgroup who complete the activity triggering task and counting a fourth number of target users in the subgroup who perform the target behavior; A traffic funnel model of the game activity is generated according to the first number, the second number, the third number, and the fourth number.

2. The method for analyzing game activity flow according to claim 1, characterized in that: Generating the traffic funnel model of the game activity according to the first quantity, the second quantity, the third quantity, and the fourth quantity includes: Determine the first number, the second number, the third number, and the fourth number as statistical flows of corresponding operation nodes in the operation path of the game activity, respectively; wherein the operation nodes corresponding to the first number, the second number, the third number, and the fourth number are arranged in order from front to back in the operation path; The ratio of the statistical flow corresponding to the previous operation node to the statistical flow corresponding to the next operation node is determined as the conversion rate of the next operation node; Based on the order of each operation node in the operation path, the conversion rate of each operation node is sorted to obtain a traffic funnel model of the game activity.

3. The method for analyzing game activity flow according to claim 1, characterized in that: The activity operation information also includes a plurality of activity contact strategies; accordingly, when the target user completes the activity triggering task, the activity rules are sent to the target user, including: Acquire the first behavior event of the user completing the activity triggering task through the synchronization interface; In a case where the user corresponding to the first behavior event is a target user, obtaining a corresponding target activity reach strategy from the multiple activity reach strategies according to a subgroup matched by the target user; Sending activity rules to the target user according to the target activity triggering strategy.

4. The method for analyzing game activity flow according to claim 1, characterized in that: The activity operation information also includes activity contact conditions; accordingly, sending activity rules to the target user includes: When the target user meets the activity reaching condition, the activity rules are sent to the target user.

5. The method for analyzing game activity flow according to claim 4, characterized in that: After sending the activity rules to the target user, the method further includes: Counting a fifth number of target users reached by the activity rule in the subgroup; Counting a sixth number of target users filtered by the activity reach condition in the subgroup; Accordingly, generating the traffic funnel model of the game activity according to the first quantity, the second quantity, the third quantity, and the fourth quantity includes: A traffic funnel model of the game activity is generated based on the first quantity, the second quantity, the third quantity, the fourth quantity, the fifth quantity and the sixth quantity; wherein the operation nodes corresponding to the fifth quantity and the sixth quantity are located side by side between the operation nodes corresponding to the third quantity and the fourth quantity.

6. The method for analyzing game activity flow according to claim 1, characterized in that: After sending the activity rules to the target user, the method further includes: After the target user performs the target behavior corresponding to the activity behavior type, sending an activity reward to the target user; Counting a seventh number of target users in the subgroup who use the activity reward; Accordingly, generating the traffic funnel model of the game activity according to the first quantity, the second quantity, the third quantity, and the fourth quantity includes: A traffic funnel model of the game activity is generated according to the first quantity, the second quantity, the third quantity, the fourth quantity and the seventh quantity; wherein the operation node corresponding to the seventh quantity is located after the operation node corresponding to the fourth quantity.

7. The method for analyzing game activity flow according to claim 1, characterized in that: Before counting the third number of target users in the subgroup who have completed the activity triggering task and counting the fourth number of target users in the subgroup who have performed the target behavior, the method further includes: Writing a first behavior event of the user completing the activity triggering task into a first message queue, and writing a second behavior event of the user performing the target behavior into a second message queue; Correspondingly, counting a third number of target users in the subgroup who complete the activity triggering task and counting a fourth number of target users in the subgroup who perform the target behavior include: counting a third number of target users in the subgroup who have completed the activity triggering task according to the first behavior event cached in the first message queue; According to the second behavior event cached in the second message queue, a fourth number of target users in the subgroup who perform the target behavior is counted.

8. A game activity flow analysis device, characterized in that: include: An activity information acquisition module is configured to acquire activity operation information of a game activity, wherein the activity operation information includes target group information, group group information, activity triggering tasks, and activity behavior types; a group quantity determination module, configured to determine a first quantity of target users in a target group according to the target group information, divide the target group into a plurality of subgroups according to the group grouping information, and determine a second quantity of target users in each of the subgroups; An activity rule sending module is configured to send an activity rule to the target user when the target user completes the activity triggering task, wherein the activity rule is used to instruct the user to perform a target behavior corresponding to the activity behavior type; A first statistical module is configured to count, based on a preset statistical period, a third number of target users in the subgroup who complete the activity triggering task and a fourth number of target users in the subgroup who perform the target behavior; The activity traffic analysis module is configured to generate a traffic funnel model of the game activity according to the first number, the second number, the third number and the fourth number.

9. A game activity flow analysis device, characterized in that: include: one or more processors; A memory stores one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the game activity traffic analysis method as described in any one of claims 1-7.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to execute the game activity traffic analysis method as described in any one of claims 1-7 when executed by a computer processor.

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