Data analysis method, system and equipment of multi-service system and medium

Abstract

The invention provides a data analysis method for a multi-service system. The method comprises the following steps: acquiring a marketing event from a marketing system and a sales event from a sales system in real time; performing format conversion processing on the marketing event and the sales event to obtain initial marketing deconstruction data and initial sales deconstruction data in a unified format, and caching the initial marketing deconstruction data and the initial sales deconstruction data; performing aggregation processing on all cached initial marketing deconstruction data and initial sales deconstruction data according to a time sequence to generate a user link; extracting initial sales deconstruction data of the user links in a preset time range, and grouping the initial sales deconstruction data to generate a target group; and extracting initial marketing deconstruction data of the user link in a preset time range to perform aggregation analysis processing on the target group to generate a target analysis result. According to the data analysis method, system and device for the multi-service system and the medium, the data analysis efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a data analysis method, system, device and medium for a multi-business system. Background Art

[0002] Currently, the data platform contains multiple different business systems, which are responsible for carrying out the use of different marketing tools. Business system users are relatively proficient in independent marketing tools and recycling. However, for a certain type of users under the data platform, when they need to analyze the costs and values ​​generated by different marketing strategies and tool combinations, they often calculate and recycle data based on the indicators in the indicator library. For multi-granular business indicators under the same business that are not in the public indicator library, as well as overlapping marketing costs between different businesses, it is impossible to integrate the data of multiple business systems for recycling and analysis, resulting in low data analysis efficiency. Therefore, there is room for improvement. Summary of the invention

[0003] In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a data analysis method, system, device and medium for a multi-business system, so as to solve the problem of low efficiency of data analysis in multiple business systems in the prior art.

[0004] To achieve the above objectives and other related objectives, the present invention provides a data analysis method for a multi-business system, comprising:

[0005] Acquire marketing events from a marketing system and sales events from a sales system in real time; wherein the formats of the marketing events and the sales events are structured data or unstructured data;

[0006] Performing format conversion processing on the marketing event and the sales event to obtain initial marketing deconstructed data and initial sales deconstructed data in a unified format, and caching the initial marketing deconstructed data and the initial sales deconstructed data;

[0007] Aggregate all cached initial marketing deconstructed data and initial sales deconstructed data in chronological order to generate user links;

[0008] Extracting the initial sales deconstruction data of the user link within a preset time range and grouping them to generate a target group;

[0009] The initial marketing deconstruction data of the user link within a preset time range is extracted to perform aggregate analysis processing on the target group to generate a target analysis result.

[0010] In one embodiment of the present invention, the marketing event includes a marketing event type, a marketing event occurrence time, and marketing behavior information, and the sales event includes a user identifier, a sales event type, a sales event occurrence time, and sales behavior information; wherein the marketing event type and the sales event type are associated with each other;

[0011] The steps of performing format conversion processing on the marketing event and the sales event to obtain initial marketing deconstructed data and initial sales deconstructed data in a unified format, and caching the initial marketing deconstructed data and the initial sales deconstructed data include:

[0012] Filter marketing events and sales events to obtain filtered marketing events and corresponding sales events;

[0013] The filtered marketing events and sales events are deconstructed to obtain initial marketing deconstructed data and initial sales deconstructed data in a unified format, and the initial marketing deconstructed data and the initial sales deconstructed data are cached.

[0014] In one embodiment of the present invention, the step of screening the marketing events and sales events to obtain the screened marketing events and sales events includes:

[0015] Filtering marketing events according to preset marketing behavior conditions to obtain marketing events whose marketing behavior information meets the preset marketing behavior conditions;

[0016] The sales events are filtered according to the marketing event types of the filtered marketing events to obtain sales events whose sales event types are correlated with the marketing event types.

[0017] In one embodiment of the present invention, the steps of deconstructing the filtered marketing events and sales events to obtain initial marketing deconstructed data and initial sales deconstructed data in a unified format, and caching the initial marketing deconstructed data and the initial sales deconstructed data include:

[0018] Determine the format of marketing events and corresponding sales events;

[0019] If the format of the marketing event and / or the corresponding sales event is structured data, the marketing event and / or the corresponding sales event are deconstructed according to a preset parsing format to generate initial marketing deconstructed data or initial sales deconstructed data;

[0020] If the format of the marketing event and / or the corresponding sales event is unstructured data, the marketing event and / or the corresponding sales event are deconstructed according to a preset Groovy script to generate initial marketing deconstructed data or initial sales deconstructed data.

[0021] In one embodiment of the present invention, the step of extracting the initial sales deconstructed data of the user link within a preset time range and grouping them to generate a target group includes:

[0022] Extracting the initial sales deconstructed data of the user link within a preset time range, and performing data cleaning processing on the data to generate a target cleaning result;

[0023] The target cleaning results are grouped according to preset grouping conditions to generate target groups.

[0024] In one embodiment of the present invention, the step of extracting the initial sales deconstructed data of the user link within a preset time range, performing data cleaning processing on the data, and generating a target cleaning result includes:

[0025] Extracting initial sales deconstruction data of the user link within a preset time range;

[0026] The initial sales deconstructed data is screened according to the sales event type and the sales behavior information to obtain the screened initial sales deconstructed data, and the screened initial sales deconstructed data is expressed as intermediate sales deconstructed data, wherein the number of the intermediate sales deconstructed data of the same sales event type and satisfying the preset sales behavior condition is greater than a preset threshold;

[0027] The user identification, sales event type, sales behavior information and sales event occurrence time of the intermediate sales deconstruction data are extracted as the target cleaning result.

[0028] In one embodiment of the present invention, the step of extracting the initial marketing deconstructed data of the user link within a preset time range and performing aggregate analysis processing on the target group to generate a target analysis result includes:

[0029] Extracting the initial marketing deconstruction data of the user link within a preset time range;

[0030] According to the sales event type in the target group, initial marketing deconstruction data corresponding to the marketing event type associated with the sales event type is filtered out;

[0031] The target group is aggregated and analyzed based on the marketing event occurrence time and / or marketing behavior information of the filtered initial marketing deconstruction data to generate a target analysis result.

[0032] The present invention also provides a data analysis system for a multi-business system, comprising:

[0033] An event acquisition module, used to acquire marketing events from a marketing system and sales events from a sales system in real time; wherein the formats of the marketing events and the sales events are structured data or unstructured data;

[0034] a format conversion module, used to perform format conversion processing on the marketing event and the sales event, obtain initial marketing deconstructed data and initial sales deconstructed data in a unified format, and cache the initial marketing deconstructed data and the initial sales deconstructed data;

[0035] A link aggregation module, used to aggregate all the cached initial marketing deconstructed data and the initial sales deconstructed data in chronological order to generate a user link;

[0036] A grouping processing module, used for extracting the initial sales deconstruction data of the user link within a preset time range, grouping them, and generating a target group;

[0037] The data analysis module is used to extract the initial marketing deconstruction data of the user link within a preset time range, perform aggregate analysis on the target group, and generate a target analysis result.

[0038] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the data analysis method for a multi-business system as described in any one of the above items are implemented.

[0039] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the data analysis method for a multi-business system as described in any one of the above items are implemented.

[0040] As described above, the data analysis method, system, device and medium of a multi-business system of the present invention have the following beneficial effects: it can support the access of different business systems, and can realize the integrated analysis of data of multiple different business systems, which can improve the data analysis efficiency of multiple business systems and reduce marketing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of an application environment of a data analysis method for a multi-business system provided by an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of a flow chart of a data analysis method for a multi-business system provided by an embodiment of the present invention;

[0043] Figure 3 for Figure 2 A partial flow chart of step S40;

[0044] Figure 4 Shown is a structural block diagram of a data analysis system for a multi-business system provided by an embodiment of the present invention;

[0045] Figure 5 Shown is a structural schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0047] The illustrations provided in the following embodiments are only used to schematically illustrate the basic concept of the present invention. Therefore, the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout type may also be more complicated.

[0048] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0049] It should be noted that the data analysis method of multiple business systems can realize the integration of data from multiple business systems through pre-set configuration strategies, thereby facilitating the analysis of the integrated data and improving the efficiency and accuracy of analysis. For example, multiple business systems can be a marketing system on the business side and a sales system on the user side. When the marketing event in the marketing system is to set the amount and quantity of red envelopes in a marketing activity, configuration strategies such as the order model, deposit, and handling fee standards can be set in the marketing system. Configuration strategies usually exist in the form of files, databases, or environment variables, and are managed through specific configuration tools or interfaces. Configuration data usually stores system configuration information in formats such as XML, JSON, and YAML.

[0050] See also Figures 1 to 5 The present invention provides a data analysis method, system, device and medium for a multi-business system, which relates to the field of data processing technology and can be specifically applied to the analysis of data in multiple business systems in a data platform to obtain the marketing cost of users from a global perspective and improve the analysis efficiency of data. The following is a detailed description through specific embodiments.

[0051] The present invention proposes a data analysis method for a multi-business system, which can be applied in Figure 1 In the application environment. The client 10 may include multiple business systems, the server 20 may include but not limited to a data analysis system, and the client 10 may communicate with the server 20 through a network. The server 20 may store a data configuration strategy for obtaining data provided by the client 10, and complete the integrated analysis of the data through the configuration strategy, and finally output the target analysis result.

[0052] See also Figure 2 The present invention provides a data analysis method for a business system, which may include the following steps:

[0053] Step S10: Acquire marketing events from the marketing system and sales events from the sales system in real time; wherein the formats of the marketing events and sales events are structured data or unstructured data.

[0054] Step S20: Perform format conversion processing on the marketing events and sales events to obtain initial marketing deconstructed data and initial sales deconstructed data in a unified format, and cache the initial marketing deconstructed data and initial sales deconstructed data.

[0055] Step S30: Aggregate all cached initial marketing deconstructed data and initial sales deconstructed data in chronological order to generate user links.

[0056] Step S40: extract the initial sales deconstruction data of the user links within a preset time range, group them, and generate a target group.

[0057] Step S50: extract the initial marketing deconstruction data of the user links within a preset time range, perform aggregate analysis on the target group, and generate a target analysis result.

[0058] In one embodiment of the present invention, when step S10 is executed, marketing events from the marketing system and sales events from the sales system are obtained in real time. Specifically, multiple business systems may include but are not limited to marketing systems on the business side and sales systems on the user side. A business developer can communicate with the client 10 through a configuration interface to obtain marketing events from the marketing system and sales events from the sales system in real time. Among them, the format of marketing events and sales events is structured data or unstructured data. In this embodiment, the marketing events of the marketing system may be, for example, coupon issuance events, coupon verification events, lottery participation events, and cash withdrawal events. The sales events of the sales system may be user order events, user order completion events, user order acceptance events, valuation events, and the like.

[0059] Furthermore, structured data refers to data with a fixed format or pattern, usually represented in a table or key-value pair, where each column represents a specific attribute and each row represents a specific record. Structured data is easy to process and query, and is usually stored in a database. In addition, the type of structured data can be in comma-separated values ​​(CSV), JSON or XML format. For example, in a marketing system, a typical example of structured data may be a table containing the following fields: marketing event type, marketing event occurrence time, marketing behavior information. Marketing behavior information may include product IDs and other related attributes.

[0060] Unstructured data refers to data that has no clear format or structure and cannot usually be put directly into a database table. Unstructured data can include but is not limited to text documents, emails, audio files, video files, images, and social media posts, and unstructured data usually needs to be preprocessed before it can be analyzed.

[0061] In one embodiment of the present invention, when executing step S20, the marketing events and sales events are formatted, initial marketing deconstructed data and initial sales deconstructed data in a unified format are obtained, and the initial marketing deconstructed data and initial sales deconstructed data are cached. Specifically, the following steps may be included.

[0062] Step S21: Filter marketing events and sales events to obtain filtered marketing events and corresponding sales events.

[0063] Step S22: Deconstruct the filtered marketing events and sales events to obtain initial marketing deconstructed data and initial sales deconstructed data in a unified format, and cache the initial marketing deconstructed data and initial sales deconstructed data.

[0064] In one embodiment of the present invention, when step S21 is executed, the marketing events and sales events are screened to obtain the screened marketing events and corresponding sales events. It should be noted that the marketing events may include but are not limited to the marketing event type, the time when the marketing event occurs, and the marketing behavior information. The sales event may include but is not limited to the user identifier, the sales event type, the time when the sales event occurs, and the sales behavior information. Among them, the marketing behavior information can be determined according to the marketing event type, and the sales behavior information can be determined according to the corresponding sales event type. For example, when the marketing event type is the issuance of coupons, the corresponding marketing behavior information can be the coupon ID, coupon type, coupon amount, and coupon quantity. When the sales event type is a user order, the corresponding sales behavior information can be the user order number, the user order amount, the number of coupons used by the user, the type of coupons used by the user, and the like.

[0065] Furthermore, step S21 may include steps S211 to S212, which are described in detail as follows.

[0066] Step S211: Filter marketing events according to preset marketing behavior conditions to obtain marketing events whose marketing behavior information meets the preset marketing behavior conditions.

[0067] Step S212: Filter the sales events according to the marketing event types of the filtered marketing events to obtain sales events whose sales event types are correlated with the marketing event types.

[0068] In one embodiment of the present invention, when step S211 is executed, the marketing events are filtered according to the preset marketing behavior conditions to obtain the marketing events whose marketing behavior information meets the preset marketing behavior conditions. Specifically, a large amount of marketing event data may be generated in the marketing events. In order to better understand and optimize the effect of the marketing activities, some preset marketing behavior conditions can be set to filter these event data to achieve the screening of the core data source.

[0069] In addition, the preset marketing behavior conditions can be determined according to the type of marketing event. For example, when the marketing event type is coupon redemption, the corresponding marketing behavior information may include the coupon type, the coupon redemption quantity, and the coupon ID. The preset marketing behavior conditions can be that the corresponding coupon redemption quantity is greater than or equal to 10, or it can be that the coupon redemption quantity is greater than or equal to 20, or it can be that the coupon redemption quantity is greater than or equal to other values. However, it is not limited to this, and the preset marketing behavior conditions can also be set according to the coupon redemption type. For example, the preset marketing behavior conditions can be that the coupon redemption type is a 50% discount coupon, or it can be a 10% discount coupon, or it can be a 10% discount coupon. Therefore, it is only necessary to filter the marketing events according to the preset marketing behavior conditions to obtain marketing events whose marketing behavior information meets the preset behavior conditions.

[0070] In one embodiment of the present invention, when step S212 is executed, the sales events are filtered according to the marketing event type of the filtered marketing events to obtain sales events whose sales event types are correlated with the marketing event types. Specifically, the sales type field in the sales event and the marketing type field in the filtered marketing event are correlated to obtain sales events corresponding to the sales type field that can be mapped with the marketing type field in the marketing event, thereby filtering the sales events.

[0071] In addition, the marketing type field and the sales type field that are related to each other can be represented as related business features. For example, the filtered marketing event can be a coupon write-off event, then the marketing type field can be "coupon write-off", and the corresponding marketing behavior information can be the coupon ID. The sales event can be a coupon order event, then the corresponding sales type field can be "coupon order", and the corresponding sales behavior information can be the order ID. That is, when the order ID is the same as the coupon ID, the sales type field "coupon order" can be defined as being related to the marketing type field "coupon write-off", then the sales event can be retained, and the related business features can be "coupon write-off" and "coupon order".

[0072] In one embodiment of the present invention, when step S22 is executed, the filtered marketing events and sales events are deconstructed to obtain initial marketing deconstructed data and initial sales deconstructed data in a unified format, and the initial marketing deconstructed data and initial sales deconstructed data are cached. Specifically, step S22 may include step S221, which is described in detail as follows.

[0073] Step S221: Determine the format of the marketing event and the corresponding sales event:

[0074] If the format of the marketing event and / or the corresponding sales event is structured data, the marketing event and / or the corresponding sales event is deconstructed according to a preset parsing format to generate initial marketing deconstructed data or initial sales deconstructed data.

[0075] If the format of the marketing event and / or the corresponding sales event is unstructured data, the marketing event and / or the corresponding sales event are deconstructed according to a preset Groovy script to generate initial marketing deconstructed data or initial sales deconstructed data.

[0076] In one embodiment of the present invention, when executing step S221, specifically, if the marketing event and / or the corresponding sales event exists in the form of structured data, then these data can be deconstructed according to a preset parsing format to generate initial marketing deconstructed data or initial sales deconstructed data. The preset parsing format can be a CSV file format, an Excel table format, a table format in a relational database, or JSON. In one embodiment of the present invention, the preset parsing format can be a JSON format.

[0077] If the marketing event and / or the corresponding sales event exists in the form of unstructured data, these data can be deconstructed according to a preset Groovy script to generate corresponding initial marketing deconstructed data or initial sales deconstructed data.

[0078] For example, in one embodiment of the present invention, when the formats of marketing events and corresponding sales events are both structured data and are stored in CSV files, a JSON object can be created for each marketing behavior in the marketing event according to a preset parsing format, including but not limited to marketing behavior information, marketing event type, and marketing event occurrence time. However, it is not limited to this. For sales events, a JSON object can be created for each sales behavior in the sales event according to a preset parsing format, including but not limited to user identification (ID), sales event type, sales event occurrence event, and sales behavior information. Among them, sales behavior information may include order ID, order total, etc.

[0079] Furthermore, the JSON object corresponding to the marketing event and the JSON object corresponding to the sales event may be stored in arrays corresponding to the values, respectively, to obtain the corresponding initial marketing deconstructed data and the corresponding initial sales deconstructed data.

[0080] Furthermore, caching the initial marketing deconstruction data and the initial sales deconstruction data refers to temporarily storing the obtained initial marketing deconstruction data and the initial sales deconstruction data respectively, so as to facilitate subsequent data extraction. The storage time for temporary storage of data can be 1 week, 2 weeks, or other storage periods.

[0081] In one embodiment of the present invention, when executing step S30, all cached initial marketing deconstruction data and initial sales deconstruction data are aggregated in chronological order to generate user links. Specifically, all cached initial marketing deconstruction data and initial sales deconstruction data can be subjected to link aggregation in chronological order to obtain corresponding user links. Link aggregation refers to sorting all data records in the initial marketing deconstruction data and the initial sales deconstruction data in chronological order.

[0082] Furthermore, a user link refers to a series of events that a user experiences while using a product or service. These events can be marketing events and sales events, where marketing events can be reward distribution and coupon redemption on the business side. Sales events can include but are not limited to user clicks, browsing, purchases, and other behaviors. By tracking these events and connecting them, a complete user behavior trajectory can be constructed, thereby better understanding the user's decision-making process, preference changes, and responses to different marketing activities.

[0083] See also Figure 2In one embodiment of the present invention, when executing step S40, the initial sales deconstruction data of the user links within the preset time range is extracted and grouped to generate a target group. Specifically, step S40 may include steps S41 to S42, which are described in detail as follows.

[0084] Step S41: extract the initial sales deconstruction data of the user link within a preset time range, perform data cleaning processing on it, and generate a target cleaning result.

[0085] Step S42: group the target cleaning results according to preset grouping conditions to generate target groups.

[0086] See also Figure 3 In one embodiment of the present invention, when executing step S41, the initial sales deconstruction data of the user link within the preset time range is extracted, and data cleaning processing is performed on it to generate a target cleaning result. Specifically, step S41 may include steps S411 to S412, which are described in detail as follows.

[0087] Step S411: extracting initial sales deconstruction data of user links within a preset time range.

[0088] Step S412: filter the initial sales deconstruction data according to the sales event type and sales behavior information to obtain the filtered initial sales deconstruction data, and represent it as intermediate sales deconstruction data, wherein the number of intermediate sales deconstruction data of the same sales event type and satisfying the preset sales behavior conditions is greater than a preset threshold.

[0089] Step S413: extract the user ID, sales event type, sales behavior information and sales event occurrence time of the intermediate sales deconstruction data as the target cleaning result.

[0090] In one embodiment of the present invention, when executing step S411, specifically, extracting the initial sales deconstruction data of the user link within the preset time range may be extracting all the initial sales deconstruction data in the user link within 24 hours after 0:00 on the same day. However, it is not limited thereto, and may also be within other preset time ranges.

[0091] In one embodiment of the present invention, when executing step S412, specifically, filtering the initial sales deconstruction data according to the sales event type and sales behavior information means filtering the sales event type and sales behavior information of the initial sales deconstruction data based on the initial sales deconstruction data extracted in step S411.

[0092] Furthermore, the preset sales behavior conditions may actually include but are not limited to preset sales event types and preset sales behavior information. First, the initial sales deconstruction data extracted in step S411 may be screened according to the preset sales event type to obtain at least one initial sales deconstruction data that satisfies the preset sales event type. Secondly, the at least one initial sales deconstruction data that satisfies the preset sales event type may be screened according to the preset sales behavior information to obtain initial sales structure data that satisfies the preset sales behavior information. In addition, whenever there is an initial sales deconstruction data that satisfies both the preset sales event type and the preset sales behavior information, the number of the initial sales deconstruction data is counted and added by one to obtain multiple initial sales deconstruction data of the same sales event type and that meet the preset sales behavior conditions, and they are represented as intermediate sales deconstruction data.

[0093] For example, in one embodiment of the present invention, when the sales event is a user order event, the initial sales deconstruction data in the user link obtained in step S411 may be initial sales deconstruction data of multiple sales event types. For example, it can be an order type using a coupon, an order type not using a coupon, and the preset sales event type can be an order type using a coupon. Then, the initial sales deconstruction data of all order types using coupons can be filtered out according to the preset sales event type. Subsequently, the preset sales behavior information can also be set to a single user order amount greater than or equal to 100. Therefore, after obtaining the initial sales deconstruction data of all order types using coupons, these initial sales deconstruction data can be filtered again according to the preset sales behavior information to obtain the initial sales deconstruction data of the order type using coupons and the single user order amount greater than or equal to 100, and these initial sales deconstruction data are counted.

[0094] In one embodiment of the present invention, when step S413 is executed, the user identification, sales event type, sales behavior information and sales event occurrence time of the intermediate sales deconstruction data are extracted as the target cleaning result. Specifically, in one embodiment of the present invention, when the sales event corresponding to the intermediate sales deconstruction data is an order event, the corresponding sales event type can be an order with a coupon and an order without a coupon, the user identification is the user order ID, and the corresponding sales behavior information is the coupon type, coupon ID, order amount, and number of orders.

[0095] In one embodiment of the present invention, when step S42 is executed, the target cleaning results are grouped according to the preset grouping conditions to generate a target group. Specifically, the preset grouping conditions can be determined according to the specific sales event, or according to the target cleaning results. For example, the preset grouping conditions can be determined according to the sales event type, or according to the sales behavior information, or according to the time when the sales event occurs.

[0096] For example, in one embodiment of the present invention, when the sales event is a user order event, the preset grouping condition may be the type of order placed using different coupons, the amount of the user's order, or the number of times the user has placed an order. However, the preset grouping condition may also be the user's order time, other marketing behavior information, or user attributes. The user attributes may include but are not limited to the user's age, the user's gender, or the user's region.

[0097] In one embodiment of the present invention, when executing step S50, the initial marketing deconstruction data of the user link within the preset time range is extracted to perform aggregate analysis on the target group to generate a target analysis result. Specifically, step S50 may include steps S51 to S53, which are described in detail as follows.

[0098] Step S51: extracting initial marketing deconstruction data of user links within a preset time range.

[0099] Step S52: According to the sales event types in the target group, filter out the initial marketing deconstructed data corresponding to the marketing event types associated with the sales event types.

[0100] Step S53: Perform aggregate analysis on the target group based on the marketing event occurrence time and / or marketing behavior information of the screened initial marketing deconstruction data to generate a target analysis result.

[0101] In one embodiment of the present invention, when step S51 is executed, the initial marketing deconstruction data of the user link within the preset time range is extracted. Specifically, the initial marketing deconstruction data of the user link within the preset time range can be extracted by extracting all the initial marketing deconstruction data in the user link within 24 hours after 0:00 on the same day. However, it is not limited to this, and it can also be within other preset time ranges.

[0102] In one embodiment of the present invention, when step S52 is executed, the initial marketing deconstructed data corresponding to the marketing event type associated with the sales event type is screened out according to the sales event type in the target group. Specifically, the initial marketing deconstructed data extracted in step S51 can be screened according to the corresponding sales event type in the target group to obtain the initial marketing deconstructed data corresponding to the marketing event type associated with the sales event type.

[0103] In one embodiment of the present invention, when step S53 is executed, the target group is aggregated and analyzed based on the marketing event occurrence time and / or marketing behavior information of the filtered initial marketing deconstruction data to generate a target analysis result. Specifically, because the marketing event type of the filtered initial marketing deconstruction data is associated with the sales event type of the target group, the target group can be aggregated and analyzed based on the marketing event occurrence time and / or marketing behavior information of the initial marketing deconstruction data.

[0104] For example, when the sales event type of the target group is when users use different coupons to place orders, then the marketing event type corresponding to the corresponding initial marketing deconstruction data can be the redemption of different types of coupons, wherein the marketing behavior information in the initial marketing deconstruction data can include the redemption number of 50% discount coupons, the redemption number of 30% discount coupons, and the redemption number of 10% discount coupons. Therefore, based on the above marketing behavior information, the ratio of the number of users who place orders using 50% discount coupons to the redemption number of 50% discount coupons, the ratio of the number of users who place orders using 30% discount coupons to the redemption number of 30% discount coupons, and the ratio of the number of users who place orders using 10% discount coupons to the redemption number of 10% discount coupons in each target group can be analyzed to obtain multiple ratio results, which are the target analysis results. However, it is not limited to this, and different aggregation analyses can also be performed based on other types of marketing events and sales events to obtain different target analysis results.

[0105] Furthermore, after obtaining the target analysis results, the initial analysis results can also be formatted according to the preset target data format, and the format of the data after the format conversion is structured data. Specifically, since the target analysis results are usually in the form of original analysis, which may not be the most suitable form for further use or display, it is necessary to organize and format-convert them according to the target data format required by the business side, so that the generated analysis data is easier to understand or adapted to specific application scenarios. In addition, the analysis data after the format conversion has a structured data format, such as a table format, JSON format, etc.

[0106] See also Figure 4 The present invention also provides a data analysis system for a multi-service system, which corresponds to the data analysis method in the above embodiment. The data analysis system may include but is not limited to an event acquisition module 100, a format conversion module 200, a link aggregation module 300, a packet processing module 400, and a data analysis module 500. The functional modules are described in detail as follows:

[0107] The event acquisition module 100 may be used to acquire marketing events from a marketing system and sales events from a sales system in real time; wherein the formats of the marketing events and sales events are structured data or unstructured data.

[0108] The format conversion module 200 can be used to perform format conversion processing on marketing events and sales events, obtain initial marketing deconstructed data and initial sales deconstructed data in a unified format, and cache the initial marketing deconstructed data and initial sales deconstructed data.

[0109] The link aggregation module 300 may be used to aggregate all cached initial marketing deconstructed data and initial sales deconstructed data in chronological order to generate user links.

[0110] The grouping processing module 400 may be used to extract the initial sales deconstruction data of the user links within a preset time range, group them, and generate a target group.

[0111] The data analysis module 500 may be used to extract the initial marketing deconstruction data of the user links within a preset time range, perform aggregate analysis processing on the target group, and generate a target analysis result.

[0112] For the specific definition of the data analysis system of the multi-business system, please refer to the definition of the data analysis method above, which will not be repeated here. Each module in the above-mentioned data analysis system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0113] An embodiment of the present invention also provides an electronic device, comprising: one or more processors; a storage system for storing one or more programs, when the one or more programs are executed by one or more processors, the electronic device implements the data analysis method of the multi-business system provided in the above-mentioned embodiments.

[0114] See also Figure 5 , Figure 5 The structure diagram of the computer system of the electronic device suitable for implementing the embodiment of the present invention is shown. It should be noted that: Figure 5 The computer system 300 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0115] like Figure 4As shown, the computer system 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 303 or the program loaded from the storage part 303 to the random access memory (RAM) 302, such as executing the method in the above embodiment. In the RAM 302, various programs and data required for system operation are also stored. The CPU 301, the ROM 302 and the RAM 303 are connected to each other through the bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0116] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a cathode ray tube (CRT), an organic light emitting diode (OLED), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 303 as needed.

[0117] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the system of the present invention are executed.

[0118] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, system or device. A computer program contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0119] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0120] The units involved in the embodiments of the present invention may be implemented by software or hardware, and the units described may also be arranged in a processor. The names of these units do not, in some cases, limit the units themselves.

[0121] Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor of a computer, causes the computer to execute the data analysis method for a multi-business system as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0122] Another aspect of the present invention further provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the data analysis method of the multi-business system provided in each of the above embodiments.

[0123] In summary, the present invention provides a data analysis method, system, device and medium for a multi-business system, which obtains marketing events from a marketing system and sales events from a sales system in real time, performs format conversion processing on marketing events and sales events, obtains initial marketing deconstruction data and initial sales deconstruction data in a unified format, and caches them, and then aggregates all cached initial marketing deconstruction data and initial sales deconstruction data in chronological order to generate user links, and then extracts the initial sales deconstruction data of user links within a preset time range to group them to generate a target group, extracts the initial marketing deconstruction data of user links within a preset time range to aggregate and analyze the target group, and generates a target analysis result. The present invention can support access to different business systems, and can realize integrated analysis of data from multiple different business systems, which can improve the data analysis efficiency of multiple business systems and reduce marketing costs.

[0124] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A data analysis method for a multi-business system, characterized in that: The data analysis method comprises: Acquire marketing events from a marketing system and sales events from a sales system in real time; wherein the formats of the marketing events and the sales events are structured data or unstructured data; Performing format conversion processing on the marketing event and the sales event to obtain initial marketing deconstructed data and initial sales deconstructed data in a unified format, and caching the initial marketing deconstructed data and the initial sales deconstructed data; Aggregate all the cached initial marketing deconstructed data and the initial sales deconstructed data in chronological order to generate a user link; Extracting the initial sales deconstruction data of the user link within a preset time range and grouping them to generate a target group; The initial marketing deconstruction data of the user link within a preset time range is extracted to perform aggregate analysis processing on the target group to generate a target analysis result.

2. The data analysis method for a multi-business system according to claim 1, characterized in that: The marketing event includes a marketing event type, a marketing event occurrence time, and marketing behavior information; the sales event includes a user identifier, a sales event type, a sales event occurrence time, and sales behavior information; wherein the marketing event type and the sales event type are related to each other; The steps of converting the format of the marketing event and the sales event to obtain initial marketing deconstructed data and initial sales deconstructed data in a unified format, and caching the initial marketing deconstructed data and the initial sales deconstructed data include: Filter marketing events and sales events to obtain filtered marketing events and corresponding sales events; The filtered marketing events and sales events are deconstructed to obtain initial marketing deconstructed data and initial sales deconstructed data in a unified format, and the initial marketing deconstructed data and the initial sales deconstructed data are cached.

3. The data analysis method for a multi-business system according to claim 2, characterized in that: The step of screening the marketing events and sales events to obtain the screened marketing events and sales events comprises: Filtering marketing events according to preset marketing behavior conditions to obtain marketing events whose marketing behavior information meets the preset marketing behavior conditions; The sales events are filtered according to the marketing event types of the filtered marketing events to obtain sales events whose sales event types are correlated with the marketing event types.

4. The data analysis method for a multi-business system according to claim 2, characterized in that: The steps of deconstructing the filtered marketing events and sales events to obtain initial marketing deconstructed data and initial sales deconstructed data in a unified format, and caching the initial marketing deconstructed data and the initial sales deconstructed data include: Determine the format of marketing events and corresponding sales events; If the format of the marketing event and / or the corresponding sales event is structured data, the marketing event and / or the corresponding sales event are deconstructed according to a preset parsing format to generate initial marketing deconstructed data or initial sales deconstructed data; If the format of the marketing event and / or the corresponding sales event is unstructured data, the marketing event and / or the corresponding sales event are deconstructed according to a preset Groovy script to generate initial marketing deconstructed data or initial sales deconstructed data.

5. The data analysis method for a multi-business system according to claim 1, characterized in that: The step of extracting the initial sales deconstruction data of the user link within a preset time range and grouping them to generate a target group comprises: Extracting the initial sales deconstructed data of the user link within a preset time range, and performing data cleaning processing on the data to generate a target cleaning result; The target cleaning results are grouped according to preset grouping conditions to generate target groups.

6. The data analysis method for a multi-business system according to claim 5, characterized in that: The step of extracting the initial sales deconstructed data of the user link within a preset time range, performing data cleaning processing on the data, and generating a target cleaning result includes: Extracting initial sales deconstruction data of the user link within a preset time range; The initial sales deconstructed data is screened according to the sales event type and the sales behavior information to obtain the screened initial sales deconstructed data, and the screened initial sales deconstructed data is expressed as intermediate sales deconstructed data, wherein the number of the intermediate sales deconstructed data of the same sales event type and satisfying the preset sales behavior condition is greater than a preset threshold; The user identification, sales event type, sales behavior information and sales event occurrence time of the intermediate sales deconstruction data are extracted as the target cleaning result.

7. The data analysis method for a multi-business system according to claim 1, characterized in that: The step of extracting the initial marketing deconstruction data of the user link within a preset time range, performing aggregate analysis processing on the target group, and generating a target analysis result target group comprises: Extracting the initial marketing deconstruction data of the user link within a preset time range; According to the sales event type in the target group, initial marketing deconstruction data corresponding to the marketing event type associated with the sales event type is filtered out; The target group is aggregated and analyzed based on the marketing event occurrence time and / or marketing behavior information of the filtered initial marketing deconstruction data to generate a target analysis result.

8. A data analysis system for a multi-business system, characterized in that: The data analysis system comprises: An event acquisition module, used to acquire marketing events from a marketing system and sales events from a sales system in real time; wherein the formats of the marketing events and the sales events are structured data or unstructured data; a format conversion module, used to perform format conversion processing on the marketing event and the sales event, obtain initial marketing deconstructed data and initial sales deconstructed data in a unified format, and cache the initial marketing deconstructed data and the initial sales deconstructed data; A link aggregation module, used to aggregate all the cached initial marketing deconstructed data and the initial sales deconstructed data in chronological order to generate a user link; A grouping processing module, used for extracting the initial sales deconstruction data of the user link within a preset time range, grouping them, and generating a target group; The data analysis module is used to extract the initial marketing deconstruction data of the user link within a preset time range, perform aggregate analysis on the target group, and generate a target analysis result.

9. A computer device, characterized in that: It comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the data analysis method for a multi-business system as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the data analysis method for a multi-business system according to any one of claims 1 to 7 are implemented.

Images