Brand management capability collaborative evaluation method and system based on big data

Abstract

The invention discloses a brand management capability collaborative evaluation method and system based on big data, and relates to the technical field of big data, by constructing an insight information set Ins, market insight results dispersed in multi-source data are collected in a unified manner, so that brand management decisions have clear and traceable insight sources; on the basis, a decision information set Dec is formed, and the insight result is converted into clear decision description information containing brand management objects, management state directions and urgency degrees, so that the situation that analysis reports are sufficient but decision pointing is not clear in reality is avoided. Furthermore, an execution feedback set Exe is formed through collection and collection, so that each brand management decision description information can correspond to actually generated feedback data in an execution stage, and the problem that a decision is formed but the execution condition cannot be traced generally in brand management is solved; and a manager can intuitively judge the state of the current brand management system.

Description

Technical Field

[0001] This invention relates to the field of big data technology, specifically to a collaborative evaluation method and system for brand management capabilities based on big data. Background Technology

[0002] As businesses become increasingly digital, brand-related management activities generate a large amount of heterogeneous data across multiple levels, including marketing communications, user communication, and market feedback. Analyzing and evaluating this data has become a crucial tool for companies to enhance their brand management capabilities. This data is used to comprehensively measure the overall performance of the brand across different management stages, functional departments, and business phases. Compared to evaluation methods based on single indicators or perspectives, the integrated analysis of multi-dimensional data reflects the synergy and effectiveness of brand management capabilities at the overall level.

[0003] In practice, existing brand management capability evaluations typically focus on the statistical analysis of final outcome indicators, such as brand exposure, user favorability, and conversion rate changes, while paying less attention to the interrelationships between key decision-making stages in the brand management process. Brand management activities often involve multiple consecutive stages, including market insight formation, management decision-making, and execution feedback collection. However, in existing evaluation systems, these stages are often treated in isolation, or their inherent connections are completely ignored. When market analysis and insights are relatively thorough, but decisions fail to be effectively translated into action, or execution results fail to provide timely feedback and influence subsequent decisions, existing evaluation methods may still yield high brand management capability ratings, thus masking actual coordination deficiencies in the management process. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a collaborative evaluation method and system for brand management capabilities based on big data, which solves the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a collaborative evaluation method for brand management capabilities based on big data, comprising the following steps: S1. Collect multi-source data generated during brand management and market insights, and construct an insight information set Ins based on the multi-source data; S2. Process the insight information set Ins to extract insight elements related to brand management decisions and form a decision information set Dec. S3. Collect execution behavior data and feedback data generated during the brand management decision-making process, and aggregate and process them to form an execution feedback set Exe; S4. Based on the insight information set Ins, the decision information set Dec, and the execution feedback set Exe, perform correlation analysis on the correspondence between insight generation, decision formation, and execution feedback to generate the decision chain integrity evaluation result Chn. S5. Based on the decision chain integrity evaluation result Chn, output the collaborative evaluation result Syn, which reflects the collaborative status of brand management capabilities.

[0006] Preferably, S1 includes S11; S11. By connecting to brand management-related data sources through interfaces, reading logs, and subscribing to data, collect multi-source raw data related to the generation of market insights during the brand management process; The multi-source raw data includes brand communication data, user interaction data, and market feedback data; After the data collection is completed, the data source identification processing and data time identification processing are performed on the multi-source raw data respectively, so that each data is associated with the corresponding data source type and generation time information, forming the insight raw data set Raw.

[0007] Preferably, S1 further includes S12; S12. Perform insight element extraction processing on the acquired raw data set of insights; The insight element extraction process includes: extracting insight theme elements Top based on the theme features of data content, extracting insight attitude elements Att based on the attitude features of users or market feedback, and extracting insight trend elements Trd based on data change trends; The extracted insight theme elements (Top), insight attitude elements (Att), and insight trend elements (Trd) are combined to construct the insight information set (Ins).

[0008] Preferably, S2 includes S21; S21. Traverse the insight information set Ins, and determine each insight result consisting of the insight topic element Top, the insight attitude element Att corresponding to the insight topic element Top, and the insight trend element Trd as an insight unit Unt. Record the correspondence between the insight unit Unt and its source insight information, and then determine the trend anomaly. The trend anomaly determination includes topic relevance determination, attitude significance determination, and trend anomaly determination. To determine the relevance of a theme, for each insight unit Unt, the insight theme element Top is read, and the theme identifier information corresponding to the insight theme element Top is compared item by item with the pre-set brand management decision scope information. When the theme identification information is determined to fall within the scope of brand management decisions, the insight unit Unt is retained to enter the attitude salience determination; Insight units (Unt) that fail the topic relevance assessment will no longer be processed. Attitude salience determination: For the insight unit Unt that passes the topic relevance determination, the corresponding insight attitude element Att is read, and the attitude distribution results represented by the insight attitude element Att are analyzed; When the analysis results show that the attitude state under the insight topic is positively or negatively concentrated, the insight unit Unt is determined by attitude salience judgment. Insight units Unt that fail the attitude significance determination will no longer participate in subsequent processing; Trend anomaly determination: For the insight unit Unt that has passed both topic relevance determination and attitude significance determination, read the corresponding insight trend element Trd, and compare the time change results represented by the insight trend element Trd. When the comparison results show that the insight topic and attitude status maintain the same direction of change in a continuous time interval, or deviate from the historical change interval in a short time interval, the insight unit Unt is determined to pass the trend anomaly judgment. For the insight unit Unt that passes the trend anomaly determination, perform the following extraction of identifier results: Extract topic identifiers from the Top elements of the insight topic; Extract the attitude determination result from the insight attitude element Att; Extract trend determination results from the aforementioned trend insight element Trd; The topic identification results, attitude judgment results, and trend judgment results are then combined to form a decision candidate item; All the generated decision candidate items are collected to form the decision candidate set Pre.

[0009] Preferably, S2 further includes S22; S22. Process each decision candidate item in the decision candidate set Pre, and read the topic identification result, attitude judgment result and trend judgment result contained in the decision candidate item; Based on the topic identification results, determine the brand management object corresponding to the decision candidate item; Based on the attitude judgment results, determine the current management status direction of the brand management object. Negative attitudes correspond to management statuses that need to be corrected or suppressed, while positive attitudes correspond to management statuses that need to be maintained or strengthened. Based on the trend determination results, the urgency of the management status direction in the time dimension is determined, where continuous changes or abnormal fluctuations correspond to high urgency, and non-continuous changes correspond to normal urgency. The brand management object, management status and direction, and urgency are combined to form a complete description of brand management decision-making information; All the brand management decision descriptions are collected to form a decision information set, Dec.

[0010] Preferably, S3 includes S31; S31. After the brand management decision enters the execution stage, collect execution process data related to the brand management decision description information in the decision information set Dec; The execution process data is used to reflect the actual execution status of the brand management object under the corresponding management state direction and urgency level; Simultaneously, feedback data generated after the implementation of brand management decisions is collected, and the feedback data is used to reflect the observable changes in the state of the brand management object. During the data collection process, the execution process data and feedback data are respectively associated with the corresponding brand management objects, so that they can establish a correspondence with the management status direction and urgency of the brand management objects; The feedback data is then time-stamped to reflect its timing relative to the brand management decision-making process. After completing the association and time identification, the execution process data and feedback data collected for the same brand management object under the same management status direction and urgency are collected to form a set of execution feedback results corresponding to the brand management decision description information; All generated execution feedback results are aggregated to form the execution feedback set Exe.

[0011] Preferably, S4 includes S41; S41. Based on the insight information set Ins and the decision information set Dec, perform correlation analysis on the correspondence between the insight generation stage and the decision formation stage. In the aforementioned association analysis process, the insight theme element Top determined in the insight information set Ins is used as the association basis, and it is matched item by item with the brand management object determined in the decision information set Dec. It is determined whether each brand management decision description information has a corresponding insight source in the insight information set Ins, and the matching status between the brand management decision description information and the insight generation stage is recorded to form the insight decision correspondence result ChnId. The matching status includes the status of valid matching relationships and the status of broken matching relationships; When any brand management decision description information is matched with the corresponding insight theme element Top in the insight information set Ins, it is determined that a valid matching correspondence has been established between the brand management decision description information and the insight generation stage. When any brand management decision description information fails to match the corresponding insight theme element Top in the insight information set Ins, it is determined that there is a break in the matching of brand management decision description information during the insight generation stage.

[0012] Preferably, S4 further includes S42; S42. Based on the decision information set Dec and the execution feedback set Exe, perform correlation analysis on the correspondence between the decision formation stage and the execution feedback stage. In the aforementioned correlation analysis and processing, the brand management object, management status direction, and urgency determined in the decision information set Dec are used as the correlation basis. They are compared item by item with the feedback data collected in the execution feedback set Exe to determine whether each brand management decision description information has a corresponding feedback data combination in the execution feedback set Exe. The comparison status between the brand management decision description information and the execution feedback stage is recorded to form the decision execution feedback correspondence result ChnDe. Among them, the comparison states include the comparison of valid correspondence states and the comparison of broken states; When any brand management decision description information is matched with a corresponding combination of feedback data in the execution feedback set Exe, the status of establishing a valid correspondence between the brand management decision description information and the execution feedback stage is determined. When any brand management decision description information fails to match the corresponding feedback data combination in the execution feedback set Exe, it is determined that there is a break in the comparison of brand management decision description information in the execution feedback stage. The resulting insight-decision correspondence result ChnId and the decision execution feedback correspondence result ChnDe are integrated to generate the decision chain integrity evaluation result Chn.

[0013] Preferably, S5 includes S51; S51. Based on the decision chain integrity evaluation result Chn, the insight decision correspondence result ChnId and the decision execution feedback correspondence result ChnDe contained therein are summarized and processed. By reading the status of each brand management decision description information in the insight generation stage and the execution feedback stage, and based on the combination of the corresponding status, the collaborative status of the brand management decision description information in the decision chain is determined. The collaborative status corresponding to the description information of each brand's management decisions is uniformly identified and aggregated to form a collaborative evaluation result Syn, which is used to reflect the overall collaborative status of brand management capabilities.

[0014] The brand management capability collaborative evaluation system based on big data includes a brand management data collection module, a management data extraction module, a brand management feedback data collection module, a decision matching collaborative analysis module, and a collaborative evaluation module. The brand management data acquisition module collects multi-source data generated during the brand management process and market insights, and constructs an insight information set Ins based on the multi-source data; The management data extraction module processes the insight information set Ins to extract insight elements related to brand management decisions, forming a decision information set Dec. The brand management feedback data collection module collects execution behavior data and feedback data generated during the brand management decision-making process, and aggregates and processes them to form an execution feedback set Exe; The decision matching and collaborative analysis module, based on the insight information set Ins, the decision information set Dec, and the execution feedback set Exe, performs correlation analysis on the correspondence between insight generation, decision formation, and execution feedback, and generates a decision chain integrity evaluation result Chn. The collaborative evaluation module outputs a collaborative evaluation result Syn based on the decision chain integrity evaluation result Chn, which reflects the collaborative status of brand management capabilities.

[0015] This invention provides a collaborative evaluation method and system for brand management capabilities based on big data, which has the following beneficial effects: (1) By constructing an insight information set Ins, market insight results scattered across multiple data sources are uniformly collected, giving brand management decisions a clear and traceable source of insight. On this basis, by forming a decision information set Dec, the insight results are transformed into clear decision description information containing the brand management object, management status direction, and urgency, thus avoiding the situation where the analysis report is sufficient but the decision direction is unclear. Furthermore, by collecting and aggregating an execution feedback set Exe, each brand management decision description information can be matched with the actual feedback data generated during the execution stage, solving the common problem in brand management where decisions have been formed but the execution status is untraceable. By generating a decision chain integrity evaluation result Chn, the correspondence between insight generation, decision formation, and execution feedback is analyzed item by item, thus clearly identifying which brand management decision description information comes from effective insights and which decisions have gaps during the execution stage. Finally, by outputting a collaborative evaluation result Syn, the overall collaborative status of brand management capabilities is centrally represented, enabling managers to intuitively judge whether the current brand management system is in a state of consistent collaboration between insight, decision, and execution, or whether there are situations where insights are sufficient but execution is insufficient or execution behavior is disconnected from the original insights.

[0016] (2) By decomposing the insight results in the insight information set Ins into insight units Unt composed of insight theme element Top, insight attitude element Att and insight trend element Trd, and performing theme relevance judgment, attitude significance judgment and trend anomaly judgment in sequence, only insight units Unt with decision significance can enter the subsequent processing flow, thereby avoiding the problem of a large number of general and noisy insight results interfering with decision-making in brand management practice.

[0017] Building upon this foundation, thematic identification results, attitude judgment results, and trend judgment results are extracted from the insight unit Unt that has passed through the entire judgment process, and a decision candidate set Pre is constructed. This ensures that each candidate item entering the decision-making stage has a clear management direction. Furthermore, in S22, the candidate items in the decision candidate set Pre are uniformly transformed into brand management decision description information that includes the brand management object, management status direction, and urgency level. This information is then aggregated into a decision information set Dec, ensuring that brand management decisions are no longer presented as general suggestions or unstructured conclusions, but are fixed in a decision description form with clear objects, clear directions, and clear priorities.

[0018] (3) By forming the insight-decision correspondence result ChnId, each brand management decision description can be clearly identified as originating from the effective insight theme element Top in the insight information set Ins, thus avoiding situations where the decision content cannot be traced back to its insight basis in actual management. Additionally, by forming the decision execution feedback correspondence result ChnDe, the existence of corresponding feedback data combinations in the execution feedback set Exe for each brand management decision description is clearly distinguished, thus objectively reflecting whether the decision has been implemented in the execution stage. Based on the integration of the above two correspondence results, the decision chain integrity evaluation result Chn is formed, which presents the previously implicit and scattered stage connection issues in the brand management process in a structured manner. Furthermore, the summary processing of the decision chain integrity evaluation result Chn further outputs the collaborative evaluation result Syn, so that the collaborative state of brand management capabilities no longer relies on human experience or subjective judgment, but is uniformly identified based on the state combination of each brand management decision description in the insight generation stage and the execution feedback stage. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the steps of the collaborative evaluation method for brand management capabilities based on big data according to the present invention. Figure 2 This is a schematic diagram of the collaborative evaluation system for brand management capabilities based on big data, as described in this invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] Example 1 This invention provides a collaborative evaluation method for brand management capabilities based on big data. Please refer to [link / reference]. Figure 1 This includes the following steps: S1. Collect multi-source data generated during brand management and market insights, and construct an insight information set Ins based on the multi-source data; S2. Process the insight information set Ins to extract insight elements related to brand management decisions and form a decision information set Dec. S3. Collect execution behavior data and feedback data generated during the brand management decision-making process, and aggregate and process them to form an execution feedback set Exe; S4. Based on the insight information set Ins, the decision information set Dec, and the execution feedback set Exe, perform correlation analysis on the correspondence between insight generation, decision formation, and execution feedback to generate the decision chain integrity evaluation result Chn. S5. Based on the decision chain integrity evaluation result Chn, output the collaborative evaluation result Syn, which reflects the collaborative status of brand management capabilities.

[0022] In this embodiment, through the above steps S1 to S5, the big data-based collaborative evaluation method for brand management capabilities provided by the present invention realizes a structured characterization and collaborative status evaluation of the entire process of insight generation, decision formation, and execution feedback in brand management, effectively solving the problem that existing brand management evaluation methods only evaluate based on result indicators and cannot identify internal breaks in the management process.

[0023] Specifically, this method constructs an insight information set (Ins) to unify and aggregate market insights scattered across multiple data sources, ensuring that brand management decisions have a clear and traceable source of insights. Building upon this, it forms a decision information set (Dec) to transform the insights into clear decision descriptions containing the brand management object, management status, and urgency, thus avoiding the situation where analysis reports are comprehensive but decision-making directions are unclear. Furthermore, by collecting and aggregating an execution feedback set (Exe), each brand management decision description is linked to actual feedback data generated during the execution phase, solving the common problem in brand management where decisions are made but their execution is untraceable.

[0024] Building upon this foundation, this method generates a decision chain integrity evaluation result (Chn) to analyze the correspondence between insight generation, decision formation, and execution feedback item by item. This allows for the clear identification of which brand management decision descriptions originate from valid insights and which decisions suffer from gaps in the execution phase. Finally, by outputting a collaborative evaluation result (Syn), the overall collaborative status of brand management capabilities is centrally represented, enabling managers to intuitively determine whether the current brand management system is in a state of consistent collaboration between insight, decision-making, and execution, or whether there are situations where insights are sufficient but execution is insufficient, or where execution behavior is disconnected from the original insights.

[0025] For example, in a real-world brand management scenario, when a brand management target consistently exhibits negative attitude characteristics in the insight information set Ins, but the corresponding decision information set Dec clearly indicates a high-urgency management state direction, yet no corresponding feedback data is collected in the execution feedback set Exe, the generated decision chain integrity evaluation result Chn will clearly indicate a break in the decision-making process during the execution feedback stage, thus reflecting a state of insufficient collaboration in the collaboration evaluation result Syn. Through this approach, this method can help companies accurately identify collaboration shortcomings in their brand management capabilities without altering existing hardware conditions, providing a direct basis for subsequent management optimization.

[0026] Example 2 Specifically: S1 includes S11; S11. By connecting to brand management-related data sources through interfaces, reading logs, and subscribing to data, collect multi-source raw data related to the generation of market insights during the brand management process; The multi-source raw data includes brand communication data, user interaction data, and market feedback data; After the data collection is completed, the data source identification processing and data time identification processing are performed on the multi-source raw data respectively, so that each data is associated with the corresponding data source type and generation time information, forming the insight raw data set Raw; Among them, brand communication data refers to the exposure, reach, or communication records generated by the brand in advertising, content publishing, and communication channels; User interaction data: This refers to data on user comments, clicks, feedback, or interactive behaviors generated on brand-related platforms; Market feedback data: This refers to data reflecting market reactions to the brand, including evaluations, complaints, or survey results. Data source identification processing: This indicates that the source category of the collected data is labeled to distinguish the functional role of different data in the brand management process; Data time stamp processing: This means uniformly labeling the generation time of the collected data to establish the time sequence relationship required for subsequent insight analysis.

[0027] S1 further includes S12; S12. Perform insight element extraction processing on the acquired raw data set of insights; The insight element extraction process includes: extracting insight theme elements Top based on the theme features of data content, extracting insight attitude elements Att based on the attitude features of users or market feedback, and extracting insight trend elements Trd based on data change trends; The extracted insight theme elements Top, insight attitude elements Att, and insight trend elements Trd are combined to construct the insight information collection Ins; It should be noted that: Top Insight Elements: This refers to the data content categories extracted from the raw data set of Insights that reflect the focus of brand management. The extraction method for the Top insight theme elements is as follows: perform content clustering or keyword merging on the data content, and group data content with the same or similar semantic orientation into the same theme category, thereby determining the theme direction that is frequently focused on or concentrated in brand management activities; The Top Insights result is defined as: consisting of at least one topic identifier and a set of data content associated with that topic identifier, wherein the topic identifier is used to uniquely represent a brand management focus topic, and the set of data content represents the original or processed data records belonging to that topic; the effect is to clarify the core issue area around which the brand management insights revolve; Attitude Insights (Att): This refers to data elements that reflect the attitudes or biases of users or the market towards brand-related topics. The extraction method of the insight attitude element Att is as follows: perform attitude discrimination processing on the data content associated with the insight topic element Top, distinguish the positive attitude, neutral attitude or negative attitude shown by it, so as to form the attitude distribution corresponding to each insight topic. The result of the Attitude Insight Element Att is defined as follows: it consists of attitude category information and data distribution information corresponding to the attitude category information, wherein the attitude category information represents the attitude type under the corresponding insight theme, and the data distribution information represents the proportion or quantity of each attitude category in the insight theme; the effect is to reflect the emotional state of brand perception under different insight themes. Trend Insight Element (Trd): Represents a data element used to reflect the characteristics of how insight topics and corresponding attitudes change over time; The extraction method of the insight trend element Trd is as follows: based on the data time identifier, the changes of the insight topic element Top and the insight attitude element Att in different time periods are compared and analyzed to identify their upward, downward or fluctuating development trends. The result of the Trend Insight Element Trd is defined as follows: it consists of trend direction information and trend change magnitude information, wherein the trend direction information indicates the direction of change of the corresponding insight theme and attitude over time, and the trend change magnitude information indicates the degree of change; the effect is to reflect the dynamic change direction of the brand management insight results.

[0028] In this embodiment, through the processing steps S11 and S12 described above, the present invention achieves unified collection, structured organization, and multi-dimensional element extraction of multi-source brand-related data in the brand management insight generation stage. This effectively solves the problems of scattered insight data sources, single analysis dimensions, and difficulty in comprehensively utilizing insight results in existing brand management practices. Specifically, by performing unified data source identification and data time identification processing on brand communication data, user interaction data, and market feedback data, a raw insight data set (Raw) is formed. This ensures that data from different business systems and channels have consistent comparability in terms of time sequence and source attributes, laying a stable data foundation for subsequent insight analysis.

[0029] Building upon this foundation, by extracting the Topics (Top), Attitudes (Att), and Trends (Trd) elements from the raw data set Raw, and combining these three types of elements to construct the Ins insight information set, brand management insights move beyond fragmented data statistics or single-dimensional analysis. Instead, they form a complete insight structure that simultaneously encompasses the market focus, user attitude changes, and trends. For example, in practical applications, when content related to a specific product feature frequently appears in brand communication data, the extracted Topics (Top) element clearly identifies that this feature has become a focus of brand management. Further combining this with the Attitudes (Att) element derived from user interaction data clearly distinguishes whether users positively approve of or negatively question the feature. Simultaneously, by analyzing data changes over different time periods using the Trends (Trd) element, it's possible to determine whether this focus is a short-term fluctuation or a continuously evolving trend.

[0030] Through the above methods, this invention forms a set of insight information Ins with a clear structure, complete dimensions, and temporal continuity during the insight generation stage. This enables brand managers to obtain multi-faceted and traceable insights into brand-related issues before making decisions, avoiding the situation in reality where one-sided judgments are made based on a single data source or static indicators. This provides a more reliable and systematic insight foundation for the formation of subsequent brand management decisions.

[0031] Example 3 Specifically: S2 includes S21; S21. Traverse the insight information set Ins, and determine each insight result consisting of the insight topic element Top, the insight attitude element Att corresponding to the insight topic element Top, and the insight trend element Trd as an insight unit Unt. Record the correspondence between the insight unit Unt and its source insight information, and then determine the trend anomaly. The trend anomaly determination includes topic relevance determination, attitude significance determination, and trend anomaly determination. To determine the relevance of a theme, for each insight unit Unt, the insight theme element Top is read, and the theme identifier information corresponding to the insight theme element Top is compared item by item with the pre-set brand management decision scope information. When the theme identification information is determined to fall within the scope of brand management decisions, the insight unit Unt is retained to enter the attitude salience determination; Insight units (Unt) that fail the topic relevance assessment will no longer be processed. Attitude salience determination: For the insight unit Unt that passes the topic relevance determination, the corresponding insight attitude element Att is read, and the attitude distribution results represented by the insight attitude element Att are analyzed; When the analysis results show that the attitude state under the insight topic is positively or negatively concentrated, the insight unit Unt is determined by attitude salience judgment. Insight units Unt that fail the attitude significance determination will no longer participate in subsequent processing; Trend anomaly determination: For the insight unit Unt that has passed both topic relevance determination and attitude significance determination, read the corresponding insight trend element Trd, and compare the time change results represented by the insight trend element Trd. When the comparison results show that the insight topic and attitude status maintain the same direction of change in a continuous time interval, or deviate from the historical change interval in a short time interval, the insight unit Unt is determined to pass the trend anomaly judgment. For the insight unit Unt that passes the trend anomaly determination, perform the following extraction of identifier results: Extract topic identifiers from the Top elements of the insight topic; Extract the attitude determination result from the insight attitude element Att; Extract trend determination results from the aforementioned trend insight element Trd; The topic identification results, attitude judgment results, and trend judgment results are then combined to form a decision candidate item; All the generated decision candidate items are collected to form a decision candidate set Pre; It should be noted that: Topic relevance determination: This refers to the process of determining whether the insight unit Unt falls within the scope of brand management decision-making. The determination criteria are as follows: the theme identification information represented by the insight theme element Top is matched with the decision scope information pre-set in brand management; the determination result is a binary result, that is, it belongs to the brand management decision scope or does not belong to the brand management decision scope. Attitude salience determination: This refers to the process of determining whether the attitude state reflected by the insight attitude element Att has decision-making significance. The determination method is as follows: Analyze the distribution of the attitude under the corresponding topic. When positive or negative attitudes form a significant concentration under the topic, the attitude state is determined to have management signal significance. The determination result is either passing the attitude salience determination or failing the attitude salience determination. Trend Anomaly Determination: This refers to the process of determining whether management intervention is needed for the time-varying state reflected by the trend element Trd. The determination method is to compare the consistency of the change direction of the change result in multiple consecutive time intervals, or to determine whether it deviates significantly from the historical change interval. The determination result is either that there is a continuous change or abnormal fluctuation, or that there is no continuous change or abnormal fluctuation. Decision candidate elements: These represent the information results extracted from the insight unit Unt that has gone through the entire decision-making process and can be directly used to form brand management decisions. Specifically, they include: theme identification results, which are used to indicate the management focus; attitude judgment results, which are used to indicate the direction of management signals; and trend judgment results, which are used to indicate the urgency or characteristics of change in management. Decision candidate set Pre: represents a set consisting of multiple decision candidate items, each of which originates from an insight unit Unt that has passed all decision steps; its function is to transform the analysis results of the insight stage into a well-structured, quantity-controlled input set that can be directly entered into the decision-making stage.

[0032] S2 further includes S22; S22. Process each decision candidate item in the decision candidate set Pre, and read the topic identification result, attitude judgment result and trend judgment result contained in the decision candidate item; Based on the topic identification results, determine the brand management object corresponding to the decision candidate item; Based on the attitude judgment results, determine the current management status direction of the brand management object. Negative attitudes correspond to management statuses that need to be corrected or suppressed, while positive attitudes correspond to management statuses that need to be maintained or strengthened. Based on the trend determination results, the urgency of the management status direction in the time dimension is determined, where continuous changes or abnormal fluctuations correspond to high urgency, and non-continuous changes correspond to normal urgency. The brand management object, management status and direction, and urgency are combined to form a complete description of brand management decision-making information; All the brand management decision descriptions are collected to form a decision information set, Dec. It should be noted that: The process of determining brand management objects based on topic identifier results is as follows: read the topic identifier results contained in the decision candidate items, and use the topic identifier results as the only matching basis to match them with the pre-set set of brand management objects; When the topic identifier result matches an object identifier in the brand management object set, the object is determined to be the brand management object corresponding to the current decision candidate item; after the matching is completed, no other object identifiers are introduced for the decision candidate item, thereby completing the determination of the brand management object; After identifying the brand management object, the process of determining the management status direction based on the attitude judgment result is as follows: read the attitude judgment result corresponding to the brand management object and classify the attitude judgment result into status. When the attitude judgment result is identified as a negative attitude, the management status direction of the current brand management object will be determined as a correction or suppression state. When the attitude determination result is identified as a positive attitude, the management status direction of the current brand management object is determined to be either maintain or strengthen the status; after the above classification is completed, the management status direction is fixed as the unique status direction of the current decision candidate item. After determining the management status direction, the process of determining the urgency based on the trend judgment results is as follows: read the trend judgment results corresponding to the brand management object and its management status direction, and make a status judgment on the trend judgment results. When the trend determination result is identified as a state of continuous change or abnormal fluctuation, the urgency level corresponding to the management state direction is determined as high urgency. When the trend determination result is identified as a non-continuous change state, the urgency corresponding to the management state direction is determined as the normal urgency; after the determination is completed, the urgency is determined as the final time dimension attribute of the current decision candidate item.

[0033] In this embodiment, through the processing flow of S21 and S22 described above, the present invention achieves orderly screening of insight results, determination of decision value, and solidification of decision structure in the brand management decision-making stage, effectively solving the problem in real-world brand management where there is sufficient insight information but it is difficult to transform it into clear decisions. Specifically, by decomposing the insight results in the insight information set Ins into insight units Unt composed of insight theme element Top, insight attitude element Att, and insight trend element Trd, and sequentially performing theme relevance determination, attitude significance determination, and trend anomaly determination, only insight units Unt with decision significance can enter the subsequent processing flow, thereby avoiding the problem in brand management practice where a large number of general and noisy insight results indiscriminately interfere with decision-making.

[0034] Building upon this foundation, thematic identification results, attitude judgment results, and trend judgment results are extracted from the insight unit Unt that has passed through the entire judgment process, and a decision candidate set Pre is constructed. This ensures that each candidate item entering the decision-making stage has a clear management direction. Furthermore, in S22, the candidate items in the decision candidate set Pre are uniformly transformed into brand management decision description information that includes the brand management object, management status direction, and urgency level. This information is then aggregated into a decision information set Dec, ensuring that brand management decisions are no longer presented as general suggestions or unstructured conclusions, but are fixed in a decision description form with clear objects, clear directions, and clear priorities.

[0035] For example, in real-world brand management scenarios, when multiple insight topics exist simultaneously, traditional methods often rely on manual judgment to determine which issues require priority, which is easily influenced by subjective experience. However, in the method of this invention, only those insight units Unt that simultaneously demonstrate a high degree of relevance to the scope of brand management decisions, exhibit a clearly concentrated attitude, and show continuous changes or abnormal fluctuations in trends within the insight information set Ins are included in the decision candidate set Pre, and ultimately reflected as high-urgency brand management decision description information in the decision information set Dec. Through this approach, this invention achieves a systematic transformation from insight conclusions to actionable decision descriptions during the decision-making stage, significantly improving the controllability, consistency, and feasibility of the brand management decision-making process.

[0036] Example 4 Specifically: S3 includes S31; S31. After the brand management decision enters the execution stage, collect execution process data related to the brand management decision description information in the decision information set Dec; The execution process data is used to reflect the actual execution status of the brand management object under the corresponding management state direction and urgency level; Simultaneously, feedback data generated after the implementation of brand management decisions is collected, and the feedback data is used to reflect the observable changes in the state of the brand management object. During the data collection process, the execution process data and feedback data are respectively associated with the corresponding brand management objects, so that they can establish a correspondence with the management status direction and urgency of the brand management objects; The feedback data is then time-stamped to reflect its timing relative to the brand management decision-making process. After completing the association and time identification, the execution process data and feedback data collected for the same brand management object under the same management status direction and urgency are collected to form a set of execution feedback results corresponding to the brand management decision description information; All generated execution feedback results are collected to form an execution feedback set Exe; Among them, execution process data refers to the data records generated after the brand management decision enters the execution stage, which are used to objectively reflect the decision execution facts related to the brand management object identified in the decision information set Dec; the execution process data can at least indicate whether the corresponding brand management object has performed execution behavior under the given management state direction and urgency conditions, and the occurrence of execution behavior.

[0037] Feedback data: refers to data records related to the brand management object collected during and after the execution of brand management decisions; the feedback data comes from the data collection results related to the brand management object, and is used to record the feedback information generated after the execution of brand management decisions, and the feedback data is objectively collected data and does not contain subjective evaluation information.

[0038] Association Identifier: When collecting execution process data and feedback data, the data is associated with the corresponding brand management object, so that each piece of execution process data and feedback data can establish a clear correspondence with the corresponding brand management object, and further associate it with the management status direction and urgency of the brand management object.

[0039] Time stamping: This refers to the time stamping of the collected feedback data to record the generation sequence of the feedback data relative to the brand management decision execution process, thereby distinguishing the temporal relationship between the feedback data and the decision execution process.

[0040] Execution feedback results: These represent the data combinations formed by aggregating execution process data and feedback data collected under the same management state and urgency conditions for the same brand management object. Each execution feedback result serves as the smallest unit of the execution feedback set Exe and establishes a corresponding relationship with a brand management decision description in the decision information set Dec.

[0041] In this embodiment, through the processing flow of S31 described above, the present invention achieves objective recording of decision implementation and structured collection of execution feedback facts during the brand management execution phase, effectively solving the problem that it is difficult to systematically record whether execution has occurred and what feedback has been generated in the actual brand management process. Specifically, by synchronously collecting execution process data and feedback data related to the brand management decision description information in the decision information set Dec, and performing association identification and time identification processing respectively, the data generated during the execution phase no longer exists in a scattered and unaligned form, but can establish a clear correspondence with the specific brand management object, management status direction, and urgency.

[0042] Building upon this foundation, by aggregating execution process data and feedback data collected from the same brand management object under the same management state and urgency conditions, execution feedback results are formed that correspond one-to-one with the brand management decision description information. Furthermore, an execution feedback set (Exe) is constructed, enabling the brand management execution phase to possess a decision-centric feedback record structure for the first time. For example, in actual brand management, when the decision information set (Dec) explicitly requires high-urgency correction or suppression management of a certain brand management object, the execution feedback set (Exe) not only reflects whether the management action was actually executed but also, through time-stamped feedback data, reflects the timing of relevant feedback information after execution. This avoids situations where management decisions have been issued, but whether execution has been implemented and whether feedback has been generated after implementation cannot be confirmed.

[0043] Through the above methods, this invention achieves standardized recording and traceable collection of data in the brand management execution phase without introducing subjective evaluation or relying on manual review. This makes the execution feedback set Exe a reliable factual basis for subsequent decision chain integrity analysis, providing real, continuous and verifiable execution-level input for the systematic evaluation of brand management capabilities.

[0044] Example 5 Specifically: S4 includes S41; S41. Based on the insight information set Ins and the decision information set Dec, perform correlation analysis on the correspondence between the insight generation stage and the decision formation stage. In the aforementioned association analysis process, the insight theme element Top determined in the insight information set Ins is used as the association basis, and it is matched item by item with the brand management object determined in the decision information set Dec. It is determined whether each brand management decision description information has a corresponding insight source in the insight information set Ins, and the matching status between the brand management decision description information and the insight generation stage is recorded to form the insight decision correspondence result ChnId. The matching status includes the status of valid matching relationships and the status of broken matching relationships; When any brand management decision description information is matched with the corresponding insight theme element Top in the insight information set Ins, it is determined that a valid matching correspondence has been established between the brand management decision description information and the insight generation stage. When any brand management decision description information fails to match the corresponding insight theme element Top in the insight information set Ins, it is determined that there is a break in the matching of brand management decision description information during the insight generation stage.

[0045] S4 further includes S42; S42. Based on the decision information set Dec and the execution feedback set Exe, perform correlation analysis on the correspondence between the decision formation stage and the execution feedback stage. In the aforementioned correlation analysis and processing, the brand management object, management status direction, and urgency determined in the decision information set Dec are used as the correlation basis. They are compared item by item with the feedback data collected in the execution feedback set Exe to determine whether each brand management decision description information has a corresponding feedback data combination in the execution feedback set Exe. The comparison status between the brand management decision description information and the execution feedback stage is recorded to form the decision execution feedback correspondence result ChnDe. Among them, the comparison states include the comparison of valid correspondence states and the comparison of broken states; When any brand management decision description information is matched with a corresponding combination of feedback data in the execution feedback set Exe, the status of establishing a valid correspondence between the brand management decision description information and the execution feedback stage is determined. When any brand management decision description information fails to match the corresponding feedback data combination in the execution feedback set Exe, it is determined that there is a break in the comparison of brand management decision description information in the execution feedback stage. The resulting insight-decision correspondence result ChnId and the decision execution feedback correspondence result ChnDe are integrated to generate the decision chain integrity evaluation result Chn.

[0046] S5 includes S51; S51. Based on the decision chain integrity evaluation result Chn, the insight decision correspondence result ChnId and the decision execution feedback correspondence result ChnDe contained therein are summarized and processed. By reading the status of each brand management decision description information in the insight generation stage and the execution feedback stage, and based on the combination of the corresponding status, the collaborative status of the brand management decision description information in the decision chain is determined. The collaborative status corresponding to the description information of each brand's management decisions is uniformly identified and aggregated to form a collaborative evaluation result Syn, which is used to reflect the overall collaborative status of brand management capabilities.

[0047] In this embodiment, through the processing flow of S41, S42, and S51 described above, the present invention achieves an explicit characterization and collaborative status output of the connection relationship between the three stages of insight generation, decision formation, and execution feedback in the brand management evaluation stage. This effectively solves the problem in real-world brand management where each stage produces output, but the overall synergy cannot be determined. Specifically, by forming the insight-decision correspondence result ChnId in S41, each brand management decision description can be clearly identified as originating from the valid insight topic element Top in the insight information set Ins, thereby avoiding situations in actual management where the insight basis for decision content cannot be traced.

[0048] Furthermore, by forming the decision execution feedback correspondence result ChnDe in S42, it is clearly distinguished whether the description information of each brand management decision has a corresponding feedback data combination in the execution feedback set Exe, thereby objectively reflecting whether the decision has been implemented in the execution stage. Based on the integration of the above two correspondence results, the decision chain integrity evaluation result Chn is formed, which makes the originally implicit and scattered stage connection problems in the brand management process structurally presented.

[0049] Building upon this, the S51 algorithm aggregates and processes the decision chain integrity evaluation results (Chn), further outputting the collaborative evaluation results (Syn). This ensures that the collaborative state of brand management capabilities no longer relies on human experience or subjective judgment, but is uniformly identified based on the combination of states of each brand management decision description information in the insight generation and execution feedback stages. For example, in a real-world brand management scenario, when a brand management decision description is identified as having a valid matching relationship in the insight decision correspondence result (ChnId), but is identified as having a break in the comparison in the decision execution feedback correspondence result (ChnDe), the resulting collaborative evaluation result (Syn) will clearly reflect a lack of collaboration in the execution stage of the decision. This indicates to managers that the problem is not due to insufficient insight or a decision error, but rather a failure of the execution level to effectively connect with the preceding stages.

[0050] Through the above methods, this invention transforms the evaluation of brand management capabilities from simply focusing on results to systematically identifying the collaborative state within the management process. This enables managers to accurately pinpoint the stages in the brand management process that truly need improvement based on the collaborative evaluation results (Syn), providing a direct and actionable basis for subsequent management adjustments.

[0051] Example 6 For a collaborative evaluation system of brand management capabilities based on big data, please refer to... Figure 2 Specifically, it includes a brand management data collection module, a management data extraction module, a brand management feedback data collection module, a decision matching and collaborative analysis module, and a collaborative evaluation module; The brand management data acquisition module collects multi-source data generated during the brand management process and market insights, and constructs an insight information set Ins based on the multi-source data; The management data extraction module processes the insight information set Ins to extract insight elements related to brand management decisions, forming a decision information set Dec. The brand management feedback data collection module collects execution behavior data and feedback data generated during the brand management decision-making process, and aggregates and processes them to form an execution feedback set Exe; The decision matching and collaborative analysis module, based on the insight information set Ins, the decision information set Dec, and the execution feedback set Exe, performs correlation analysis on the correspondence between insight generation, decision formation, and execution feedback, and generates a decision chain integrity evaluation result Chn. The collaborative evaluation module outputs a collaborative evaluation result Syn based on the decision chain integrity evaluation result Chn, which reflects the collaborative status of brand management capabilities.

[0052] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A collaborative evaluation method for brand management capabilities based on big data, characterized by: Includes the following steps: S1. Collect multi-source data generated during brand management and market insights, and construct an insight information set Ins based on the multi-source data; S2. Process the insight information set Ins to extract insight elements related to brand management decisions and form a decision information set Dec. S3. Collect execution behavior data and feedback data generated during the brand management decision-making process, and aggregate and process them to form an execution feedback set Exe; S4. Based on the insight information set Ins, the decision information set Dec, and the execution feedback set Exe, perform correlation analysis on the correspondence between insight generation, decision formation, and execution feedback to generate the decision chain integrity evaluation result Chn. S5. Based on the decision chain integrity evaluation result Chn, output the collaborative evaluation result Syn, which reflects the collaborative status of brand management capabilities.

2. The collaborative evaluation method for brand management capabilities based on big data as described in claim 1, characterized in that: S1 includes S11; S11. By connecting to brand management-related data sources through interfaces, reading logs, and subscribing to data, collect multi-source raw data related to the generation of market insights during the brand management process; The multi-source raw data includes brand communication data, user interaction data, and market feedback data; After the data collection is completed, the data source identification processing and data time identification processing are performed on the multi-source raw data respectively, so that each data is associated with the corresponding data source type and generation time information, forming the insight raw data set Raw.

3. The collaborative evaluation method for brand management capabilities based on big data as described in claim 2, characterized in that: S1 further includes S12; S12. Perform insight element extraction processing on the acquired raw data set of insights; The insight element extraction process includes: extracting insight theme elements Top based on the theme features of data content, extracting insight attitude elements Att based on the attitude features of users or market feedback, and extracting insight trend elements Trd based on data change trends; The extracted insight theme elements Top, insight attitude elements Att, and insight trend elements Trd are combined to construct the insight information set Ins.

4. The collaborative evaluation method for brand management capabilities based on big data as described in claim 3, characterized in that: S2 includes S21; S21. Traverse the insight information set Ins, and determine each insight result consisting of the insight topic element Top, the insight attitude element Att corresponding to the insight topic element Top, and the insight trend element Trd as an insight unit Unt. Record the correspondence between the insight unit Unt and its source insight information, and then determine the trend anomaly. The trend anomaly determination includes topic relevance determination, attitude significance determination, and trend anomaly determination. To determine the relevance of a theme, for each insight unit Unt, the insight theme element Top is read, and the theme identifier information corresponding to the insight theme element Top is compared item by item with the pre-set brand management decision scope information. When the theme identification information is determined to fall within the scope of brand management decisions, the insight unit Unt is retained to enter the attitude salience determination; Insight units (Unt) that fail the topic relevance assessment will no longer be processed. Attitude salience determination: For the insight unit Unt that passes the topic relevance determination, the corresponding insight attitude element Att is read, and the attitude distribution results represented by the insight attitude element Att are analyzed; When the analysis results show that the attitude state under the insight topic is positively or negatively concentrated, the insight unit Unt is determined by attitude salience judgment. Insight units Unt that fail the attitude significance determination will no longer participate in subsequent processing; Trend anomaly determination: For the insight unit Unt that has passed both topic relevance determination and attitude significance determination, read the corresponding insight trend element Trd, and compare the time change results represented by the insight trend element Trd. When the comparison results show that the insight topic and attitude status maintain the same direction of change in a continuous time interval, or deviate from the historical change interval in a short time interval, the insight unit Unt is determined to pass the trend anomaly judgment. For the insight unit Unt that passes the trend anomaly determination, perform the following extraction of identifier results: Extract topic identifiers from the Top elements of the insight topic; Extract the attitude determination result from the insight attitude element Att; Extract trend determination results from the aforementioned trend insight element Trd; The topic identification results, attitude judgment results, and trend judgment results are then combined to form a decision candidate item; All the generated decision candidate items are collected to form the decision candidate set Pre.

5. The collaborative evaluation method for brand management capabilities based on big data as described in claim 4, characterized in that: S2 further includes S22; S22. Process each decision candidate item in the decision candidate set Pre, and read the topic identification result, attitude judgment result and trend judgment result contained in the decision candidate item; Based on the topic identification results, determine the brand management object corresponding to the decision candidate item; Based on the attitude judgment results, determine the current management status direction of the brand management object. Negative attitudes correspond to management statuses that need to be corrected or suppressed, while positive attitudes correspond to management statuses that need to be maintained or strengthened. Based on the trend determination results, the urgency of the management status direction in the time dimension is determined, where continuous changes or abnormal fluctuations correspond to high urgency, and non-continuous changes correspond to normal urgency. The brand management object, management status and direction, and urgency are combined to form a complete description of brand management decision-making information; All the brand management decision descriptions are collected to form a decision information set, Dec.

6. The collaborative evaluation method for brand management capabilities based on big data as described in claim 5, characterized in that: S3 includes S31; S31. After the brand management decision enters the execution stage, collect execution process data related to the brand management decision description information in the decision information set Dec; Simultaneously, feedback data generated after the implementation of brand management decisions is collected, and the feedback data is used to reflect the observable changes in the state of the brand management object. During the data collection process, the execution process data and feedback data are respectively associated with the corresponding brand management objects, so that they can establish a correspondence with the management status direction and urgency of the brand management objects; The feedback data is then time-stamped to reflect its timing relative to the brand management decision-making process. After completing the association and time identification, the execution process data and feedback data collected for the same brand management object under the same management status direction and urgency are collected to form a set of execution feedback results corresponding to the brand management decision description information; All generated execution feedback results are aggregated to form the execution feedback set Exe.

7. The collaborative evaluation method for brand management capabilities based on big data as described in claim 6, characterized in that: S4 includes S41; S41. Based on the insight information set Ins and the decision information set Dec, perform correlation analysis on the correspondence between the insight generation stage and the decision formation stage. In the aforementioned association analysis process, the insight theme element Top determined in the insight information set Ins is used as the association basis, and it is matched item by item with the brand management object determined in the decision information set Dec. It is determined whether each brand management decision description information has a corresponding insight source in the insight information set Ins, and the matching status between the brand management decision description information and the insight generation stage is recorded to form the insight decision correspondence result ChnId. The matching status includes the status of valid matching relationships and the status of broken matching relationships; When any brand management decision description information is matched with the corresponding insight theme element Top in the insight information set Ins, it is determined that a valid matching correspondence has been established between the brand management decision description information and the insight generation stage. When any brand management decision description information fails to match the corresponding insight theme element Top in the insight information set Ins, it is determined that there is a break in the matching of brand management decision description information during the insight generation stage.

8. The collaborative evaluation method for brand management capabilities based on big data as described in claim 7, characterized in that: S4 further includes S42; S42. Based on the decision information set Dec and the execution feedback set Exe, perform correlation analysis on the correspondence between the decision formation stage and the execution feedback stage. In the aforementioned correlation analysis and processing, the brand management object, management status direction, and urgency determined in the decision information set Dec are used as the correlation basis. They are compared item by item with the feedback data collected in the execution feedback set Exe to determine whether each brand management decision description information has a corresponding feedback data combination in the execution feedback set Exe. The comparison status between the brand management decision description information and the execution feedback stage is recorded to form the decision execution feedback correspondence result ChnDe. Among them, the comparison states include the comparison of valid correspondence states and the comparison of broken states; When any brand management decision description information is matched with a corresponding combination of feedback data in the execution feedback set Exe, the status of establishing a valid correspondence between the brand management decision description information and the execution feedback stage is determined. When any brand management decision description information fails to match the corresponding feedback data combination in the execution feedback set Exe, it is determined that there is a break in the comparison of brand management decision description information in the execution feedback stage. The resulting insight-decision correspondence result ChnId and the decision execution feedback correspondence result ChnDe are integrated to generate the decision chain integrity evaluation result Chn.

9. The collaborative evaluation method for brand management capabilities based on big data as described in claim 8, characterized in that: S5 includes S51; S51. Based on the decision chain integrity evaluation result Chn, the insight decision correspondence result ChnId and the decision execution feedback correspondence result ChnDe contained therein are summarized and processed. By reading the status of each brand management decision description information in the insight generation stage and the execution feedback stage, and based on the combination of the corresponding status, the collaborative status of the brand management decision description information in the decision chain is determined. The collaborative status corresponding to the description information of each brand's management decisions is uniformly identified and aggregated to form a collaborative evaluation result Syn, which is used to reflect the overall collaborative status of brand management capabilities.

10. A big data-based collaborative evaluation system for brand management capabilities, applied to the big data-based collaborative evaluation method for brand management capabilities as described in any one of claims 1 to 9, characterized in that: It includes a brand management data collection module, a management data extraction module, a brand management feedback data collection module, a decision matching and collaborative analysis module, and a collaborative evaluation module; The brand management data acquisition module collects multi-source data generated during the brand management process and market insights, and constructs an insight information set Ins based on the multi-source data; The management data extraction module processes the insight information set Ins to extract insight elements related to brand management decisions, forming a decision information set Dec. The brand management feedback data collection module collects execution behavior data and feedback data generated during the brand management decision-making process, and aggregates and processes them to form an execution feedback set Exe; The decision matching and collaborative analysis module, based on the insight information set Ins, the decision information set Dec, and the execution feedback set Exe, performs correlation analysis on the correspondence between insight generation, decision formation, and execution feedback, and generates a decision chain integrity evaluation result Chn. The collaborative evaluation module outputs a collaborative evaluation result Syn based on the decision chain integrity evaluation result Chn, which reflects the collaborative status of brand management capabilities.

Images