Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to the field of customer engagement analysis and brand promotion strategies, more specifically, relates to customer response analysis through narrative techniques impact measurement system and method thereof.
BACKGROUND OF THE DISCLOSURE
[0002] The framework is helping organizations leverage various narrative techniques effectively, driving deeper emotional connections with customers. By analysing how different storytelling methods influence customer behaviour, businesses are continuously improving their marketing strategies and enhancing overall customer engagement. This approach ensures that promotional activities resonate with the target audience, leading to more meaningful and lasting interactions. By measuring how customers respond to different narratives, brands are optimizing their efforts to create content that emotionally resonates, increasing the likelihood of customer retention and advocacy. The ongoing analysis empowers businesses to refine their customer engagement tactics and tailor content to the preferences of their audience.
[0003] By incorporating a structured model to measure the impact of narrative techniques, the system is delivering valuable, data-driven insights that guide decision-making processes. Organizations are continuously obtaining real-time feedback on the effectiveness of brand messaging, enabling them to refine their marketing tactics and adapt to changing customer preferences. This leads to more informed and accurate decision-making, helping brands stay ahead of market trends. The system also allows businesses to identify which narrative strategies perform best, providing actionable insights for future campaigns. As a result, companies can make strategic adjustments based on solid data, improving the efficiency and effectiveness of their marketing efforts.
[0004] The system is optimizing the way brands communicate with their customers by offering a clear understanding of how different narrative elements affect customer perception. This allows businesses to continuously refine their messaging strategies to achieve greater clarity, consistency, and emotional appeal. As a result, companies are improving their brand image, fostering stronger customer loyalty, and driving higher conversion rates through well-targeted and impactful communications. The ongoing analysis of customer responses also ensures that brand messaging aligns with customer expectations and market trends, enabling businesses to strengthen their overall brand identity. By using this approach, organizations can communicate more effectively, ensuring that their marketing efforts resonate with their audience and lead to higher levels of brand recognition and trust.
[0005] Existing solutions in the market primarily focus on quantifiable metrics such as click-through rates, sales, and user engagement, without offering a deep understanding of how customers emotionally connect with the brand. These methods often fail to analyse the emotional and informational impact of narrative techniques on customers, leading to a limited understanding of the true effect of brand messaging. As a result, businesses are unable to fully comprehend the emotional responses behind customer actions, leading to less effective communication and marketing strategies. This gap prevents organizations from crafting truly resonant narratives that engage customers on a deeper level and fosters only superficial customer relationships.
[0006] Many existing narrative-driven marketing strategies depend heavily on generic data and predefined storytelling templates, which may not account for the unique needs and preferences of individual customer segments. These solutions often fail to consider the diverse emotional triggers and personalized factors that influence customer behaviour. This standardization limits the ability to tailor marketing efforts to specific audiences, thereby reducing the overall effectiveness of brand promotion campaigns. Organizations relying on these generic approaches may miss opportunities to create personalized and targeted content that genuinely appeals to their customers, leading to lower engagement and a lack of differentiation from competitors.
[0007] Existing systems for analysing customer responses to brand messaging often fail to keep up with rapidly changing consumer behaviour and preferences. Many of these solutions lack real-time adaptability and are not continuously updated with new insights from evolving market trends or customer sentiment. As a result, organizations relying on these systems are often working with outdated or irrelevant data, leading to missed opportunities for improving marketing efforts and customer engagement. Businesses struggle to stay aligned with shifting consumer demands, which ultimately hinders their ability to maintain customer loyalty and stay competitive in a dynamic market. This lack of adaptability in current solutions leaves organizations vulnerable to changing market dynamics and customer expectations.
[0008] Thus, in light of the above-stated discussion, there exists a need for a customer response analysis through narrative techniques impact measurement system and method thereof.
SUMMARY OF THE DISCLOSURE
[0009] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0010] According to illustrative embodiments, the present disclosure focuses on a customer response analysis through narrative techniques impact measurement system and method thereof which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0011] An objective of the present disclosure is to provide a framework for measuring the impact of different narrative techniques on customer responses in brand promotion activities.
[0012] Another objective of the present disclosure is to facilitate the analysis of customer engagement by leveraging storytelling and data-driven insights for more targeted brand promotion strategies.
[0013] Another objective of the present disclosure is to enable businesses to assess the effectiveness of various narrative techniques in influencing customer decision-making and brand loyalty.
[0014] Another objective of the present disclosure is to offer a structured method for organizations to evaluate both emotional and informational aspects of narrative techniques in marketing campaigns.
[0015] Another objective of the present disclosure is to enhance customer communication by providing businesses with actionable insights into how different narrative techniques affect customer perceptions and responses.
[0016] Another objective of the present disclosure is to improve marketing efforts by helping organizations understand the specific emotional triggers that influence customer behaviour.
[0017] Another objective of the present disclosure is to assist businesses in crafting personalized marketing strategies by analysing the impact of narrative techniques on different customer segments.
[0018] Another objective of the present disclosure is to provide a comprehensive system for measuring the effectiveness of brand messages through the lens of customer emotional and cognitive responses.
[0019] Yet another objective of the present disclosure is to improve customer retention by identifying the most engaging and impactful narrative techniques that resonate with target audiences.
[0020] Yet another objective of the present disclosure is to enable businesses to adapt their brand promotion strategies in real-time based on customer feedback and evolving market trends.
[0021] In light of the above, in one aspect of the present disclosure, a customer response analysis through narrative techniques impact measurement system is disclosed herein. The system comprises a data collection unit configured to gather customer response data, customer feedback and customer interaction data from various marketing platforms. The system includes an input interface connected to the data collection unit, configured to receive and process the customer response data, the customer feedback and the customer interaction data. The system also includes a processing unit connected to the input interface, configured to analyse the customer response data, the customer feedback and the customer interaction data. The processing unit comprises an input module, configured to receive the customer response data, the customer feedback and the customer interaction data from the input interface. The processing unit includes an emotional analysis module, configured to analyse the emotional response of customers from the customer response data, the customer feedback and the customer interaction data to different narrative techniques. The processing unit also includes an informational analysis module, configured to evaluate the informational content of brand promotional activities. The processing unit also includes a narrative technique classification module, configured to categorize various narrative techniques based on customer engagement levels from the customer response data, the customer feedback and the customer interaction data. The processing unit also includes a correlation analysis module, configured to identify correlations between different narrative techniques and the customer response data, the customer feedback and the customer interaction data. The processing unit also includes a response modelling module, configured to predict customer behaviour based on the various narrative technique analysis. The processing unit also includes an output decision module, configured to generate insights and recommendations based on the analysed data from the customer response data, the customer feedback and the customer interaction data. The processing unit also includes a feedback module, configured to provide real-time feedback on customer sentiment towards specific narrative techniques. The system also includes a user interface connected to the processing unit, configured to display the analysis results and recommendations in a user-friendly format. The system also includes a storage unit connected to the processing unit, configured to store the customer response data, the customer feedback and the customer interaction data, the insights, and historical brand promotion performance. The system also includes a communication network connected to the data collection unit, processing unit, user interface, and storage unit, configured to facilitate data transfer and communication between all components of the system.
[0022] In one embodiment, the data collection unit is further configured to collect data from social media platforms, email campaigns, mobile applications, and website interactions.
[0023] In one embodiment, the input interface is further configured to preprocess the customer response data, the customer feedback and the customer interaction data by removing noise and irrelevant information before transmission to the processing unit.
[0024] In one embodiment, the input module of the processing unit is further configured to categorize the customer response data, the customer feedback and the customer interaction data based on demographic parameters and engagement metrics.
[0025] In one embodiment, the emotional analysis module is further configured to perform sentiment analysis and emotional tone classification using a trained artificial intelligence model.
[0026] In one embodiment, the informational analysis module is further configured to assess the clarity, relevance, and perceived credibility of the informational content of the brand promotional activities.
[0027] In one embodiment, the narrative technique classification module is further configured to dynamically update the categories of narrative techniques based on continuous learning from customer engagement patterns.
[0028] In one embodiment, the correlation analysis module is further configured to generate statistical models that map the strength and direction of relationships between narrative techniques and customer engagement indicators.
[0029] In one embodiment, the feedback module is further configured to generate real-time alerts and reports for marketing teams based on immediate customer sentiment shifts towards specific narrative techniques.
[0030] In light of the above, in one aspect of the present disclosure, a method for customer response analysis through narrative techniques impact measurement is disclosed herein. The method comprising receiving customer response data, customer feedback, and customer interaction data from various marketing platforms by a data collection unit. The method includes receiving and processing the customer response data, the customer feedback, and the customer interaction data, and transmitting the processed data to a processing unit by an input interface. The method also includes analysing the customer response data, the customer feedback, and the customer interaction data by the processing unit connected to the input interface. The method also includes receiving the customer response data, the customer feedback, and the customer interaction data from the input interface by an input module of the processing unit. The method also includes analysing emotional responses of customers towards different narrative techniques from the customer response data, the customer feedback, and the customer interaction data by an emotional analysis module of the processing unit. The method also includes evaluating informational content associated with brand promotional activities by an informational analysis module of the processing unit. The method also includes categorizing various narrative techniques based on customer engagement levels by a narrative technique classification module of the processing unit. The method also includes identifying correlations between different narrative techniques and the customer response data, the customer feedback, and the customer interaction data by a correlation analysis module of the processing unit. The method also includes predicting customer behaviour based on the narrative technique analysis by a response modelling module of the processing unit. The method also includes generating insights and recommendations based on the analysed customer response data, the customer feedback, and the customer interaction data by an output decision module of the processing unit. The method also includes providing real-time feedback indicating customer sentiment towards specific narrative techniques by a feedback module of the processing unit. The method also includes displaying the generated insights and recommendations in a user-friendly format by a user interface connected to the processing unit. The method also includes storing the customer response data, the customer feedback, the customer interaction data, the insights, and historical brand promotion performance data by a storage unit connected to the processing unit. The method also includes facilitating continuous transfer of the customer response data, the customer feedback, the customer interaction data, and analysis results among the data collection unit, the processing unit, the user interface, and the storage unit by a communication network.
[0031] These and other advantages will be apparent from the present application of the embodiments described herein.
[0032] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0033] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0035] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0036] FIG. 1 illustrates a block diagram of a customer response analysis through narrative techniques impact measurement system and method thereof, in accordance with an exemplary embodiment of the present disclosure;
[0037] FIG. 2 illustrates a flowchart of the customer response analysis through narrative techniques impact measurement system, in accordance with an exemplary embodiment of the present disclosure;
[0038] FIG. 3 illustrates a flowchart of the method for customer response analysis through narrative techniques impact measurement, in accordance with an exemplary embodiment of the present disclosure;
[0039] FIG. 4 illustrates a perspective view of the narrative techniques impact on brand promotion, in accordance with an exemplary embodiment of the present disclosure;
[0040] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0041] The customer response analysis through narrative techniques impact measurement system and method thereof is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0042] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0043] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0044] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0045] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0046] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0047] Referring now to FIG. 1 to FIG. 4 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a perspective view of a customer response analysis through narrative techniques impact measurement system and method thereof 100, in accordance with an exemplary embodiment of the present disclosure.
[0048] The system 100 may include a data collection unit 102 configured to gather customer response data, customer feedback and customer interaction data from various marketing platforms, an input interface 104 connected to the data collection unit 102, configured to receive and process the customer response data, the customer feedback and the customer interaction data, a processing unit 106 connected to the input interface 104, configured to analyse the customer response data, the customer feedback and the customer interaction data, wherein the processing unit 106 comprises, an input module 108, configured to receive the customer response data, the customer feedback and the customer interaction data from the input interface 104, an emotional analysis module 110, configured to analyse the emotional response of customers from the customer response data, the customer feedback and the customer interaction data to different narrative techniques, an informational analysis module 112, configured to evaluate the informational content of brand promotional activities, a narrative technique classification module 114, configured to categorize various narrative techniques based on customer engagement levels from the customer response data, the customer feedback and the customer interaction data, a correlation analysis module 116, configured to identify correlations between different narrative techniques and the customer response data, the customer feedback and the customer interaction data, a response modelling module 118, configured to predict customer behaviour based on the various narrative technique analysis, an output decision module, configured to generate insights and recommendations based on the analysed data from the customer response data, the customer feedback and the customer interaction data, a feedback module 122, configured to provide real-time feedback on customer sentiment towards specific narrative techniques, a user interface 124 connected to the processing unit 106, configured to display the analysis results and recommendations in a user-friendly format, a storage unit 126 connected to the processing unit 106, configured to store the customer response data, the customer feedback and the customer interaction data, the insights, and historical brand promotion performance, a communication network 128 connected to the data collection unit 102, processing unit 106, user interface 124, and storage unit 126, configured to facilitate data transfer and communication between all components of the system 100.
[0049] The data collection unit 102 is further configured to collect data from social media platforms, email campaigns, mobile applications, and website interactions.
[0050] The input interface 104 is further configured to preprocess the customer response data, the customer feedback and the customer interaction data by removing noise and irrelevant information before transmission to the processing unit 106.
[0051] The input module 108 of the processing unit 106 is further configured to categorize the customer response data, the customer feedback and the customer interaction data based on demographic parameters and engagement metrics.
[0052] The emotional analysis module 110 is further configured to perform sentiment analysis and emotional tone classification using a trained artificial intelligence model.
[0053] The informational analysis module 112 is further configured to assess the clarity, relevance, and perceived credibility of the informational content of the brand promotional activities.
[0054] The narrative technique classification module 114 is further configured to dynamically update the categories of narrative techniques based on continuous learning from customer engagement patterns.
[0055] The correlation analysis module 116 is further configured to generate statistical models that map the strength and direction of relationships between narrative techniques and customer engagement indicators.
[0056] The feedback module 122 is further configured to generate real-time alerts and reports for marketing teams based on immediate customer sentiment shifts towards specific narrative techniques.
[0057] The method 100 may include receiving customer response data, customer feedback, and customer interaction data from various marketing platforms by a data collection unit 102, receiving and processing the customer response data, the customer feedback, and the customer interaction data, and transmitting the processed data to a processing unit 106 by an input interface 104, analysing the customer response data, the customer feedback, and the customer interaction data by the processing unit 106 connected to the input interface 104, receiving the customer response data, the customer feedback, and the customer interaction data from the input interface 104 by an input module 108 of the processing unit 106, analysing emotional responses of customers towards different narrative techniques from the customer response data, the customer feedback, and the customer interaction data by an emotional analysis module 110 of the processing unit 106, evaluating informational content associated with brand promotional activities by an informational analysis module 112 of the processing unit 106, categorizing various narrative techniques based on customer engagement levels by a narrative technique classification module 114 of the processing unit 106, identifying correlations between different narrative techniques and the customer response data, the customer feedback, and the customer interaction data by a correlation analysis module 116 of the processing unit 106, predicting customer behaviour based on the narrative technique analysis by a response modelling module 118 of the processing unit 106, generating insights and recommendations based on the analysed customer response data, the customer feedback, and the customer interaction data by an output decision module of the processing unit 106, providing real-time feedback indicating customer sentiment towards specific narrative techniques by a feedback module 122 of the processing unit 106, displaying the generated insights and recommendations in a user-friendly format by a user interface 124 connected to the processing unit 106, storing the customer response data, the customer feedback, the customer interaction data, the insights, and historical brand promotion performance data by a storage unit 126 connected to the processing unit 106, facilitating continuous transfer of the customer response data, the customer feedback, the customer interaction data, and analysis results among the data collection unit 102, the processing unit 106, the user interface 124, and the storage unit 126 by a communication network 128.
[0058] The data collection unit 102 is configured to gather customer response data, customer feedback, and customer interaction data from various marketing platforms. The data collection unit 102 collects information from social media platforms, email campaigns, mobile applications, and website interactions to provide a comprehensive view of customer engagement across diverse channels. The data collection unit 102 plays a critical role in ensuring the acquisition of high-quality data that reflects real-world customer reactions to brand promotional activities. By capturing real-time and historical customer data, the data collection unit 102 ensures that subsequent analyses are based on robust and representative datasets.
[0059] The input interface 104 is connected to the data collection unit 102 and is configured to receive and process the customer response data, the customer feedback, and the customer interaction data. The input interface 104 preprocesses the received data by removing noise and irrelevant information to ensure only meaningful and actionable data is transmitted to the processing unit 106. The input interface 104 guarantees the integrity and quality of the input data, preparing it for advanced analytical operations. The input interface 104 serves as a critical filtration and standardization point within the system 100, enabling seamless downstream analysis.
[0060] The processing unit 106 is connected to the input interface 104 and is configured to analyse the customer response data, the customer feedback, and the customer interaction data. The processing unit 106 organizes and structures the incoming data, activating a series of analytical modules for comprehensive evaluation. The processing unit 106 functions as the central computational engine, executing emotional analysis, informational evaluation, narrative technique classification, correlation mapping, customer behaviour prediction, insight generation, and feedback reporting, all in an integrated manner. The processing unit 106 ensures that data-driven insights are accurately and efficiently produced for decision-making.
[0061] The input module 108 within the processing unit 106 is configured to receive the customer response data, the customer feedback, and the customer interaction data from the input interface 104. The input module 108 categorizes the received data based on demographic parameters and engagement metrics, setting the foundation for more targeted and detailed analyses. The input module 108 ensures that the analytical modules downstream operate on well-organized datasets tailored to different customer segments and behavioural patterns. The input module 108 enhances the precision of the entire analytical workflow by providing properly classified and segmented data.
[0062] The emotional analysis module 110 within the processing unit 106 is configured to analyse the emotional response of customers from the customer response data, the customer feedback, and the customer interaction data to different narrative techniques. The emotional analysis module 110 performs sentiment analysis and emotional tone classification using a trained artificial intelligence model. The emotional analysis module 110 accurately identifies positive, negative, and neutral sentiments expressed by customers, offering deep insights into the emotional impact of various brand narratives. The emotional analysis module 110 ensures that emotional aspects of customer engagement are quantitatively and qualitatively captured.
[0063] The informational analysis module 112 within the processing unit 106 is configured to evaluate the informational content of brand promotional activities. The informational analysis module 112 assesses the clarity, relevance, and perceived credibility of the content shared with customers. The informational analysis module 112 measures how well customers understand and trust the information provided through brand narratives, thus highlighting the effectiveness of the brand’s communication strategies. The informational analysis module 112 guarantees that the intellectual substance of brand promotions is rigorously evaluated for strategic improvement.
[0064] The narrative technique classification module 114 within the processing unit 106 is configured to categorize various narrative techniques based on customer engagement levels from the customer response data, the customer feedback, and the customer interaction data. The narrative technique classification module 114 dynamically updates categories of narrative techniques based on continuous learning from observed customer engagement patterns. The narrative technique classification module 114 identifies and groups storytelling methods that resonate strongly with customers, enabling brands to optimize future campaigns. The narrative technique classification module 114 ensures systematic and adaptive management of narrative strategies.
[0065] The correlation analysis module 116 within the processing unit 106 is configured to identify correlations between different narrative techniques and the customer response data, the customer feedback, and the customer interaction data. The correlation analysis module 116 generates statistical models that map the strength and direction of relationships between narrative techniques and customer engagement indicators. The correlation analysis module 116 uncovers hidden patterns and linkages that reveal how specific storytelling methods influence customer behaviour and sentiment. The correlation analysis module 116 ensures actionable insights into optimizing brand communication approaches.
[0066] The response modelling module 118 within the processing unit 106 is configured to predict customer behaviour based on the various narrative technique analyses. The response modelling module 118 uses the classified narrative techniques and correlation mappings to simulate future customer responses to different storytelling strategies. The response modelling module 118 helps brands anticipate market trends, customer reactions, and potential outcomes of deploying particular narrative styles. The response modelling module 118 guarantees predictive analytics capabilities that enhance proactive decision-making in brand promotion planning.
[0067] The output decision module within the processing unit 106 is configured to generate insights and recommendations based on the analysed customer response data, the customer feedback, and the customer interaction data. The output decision module consolidates findings from emotional analysis, informational evaluation, narrative classification, correlation identification, and response modelling into comprehensive decision support outputs. The output decision module formulates actionable strategies and campaign recommendations that are presented to marketing teams to enhance customer engagement. The output decision module ensures that sophisticated analytical results are translated into practical, user-friendly guidance.
[0068] The feedback module 122 within the processing unit 106 is configured to provide real-time feedback on customer sentiment towards specific narrative techniques. The feedback module 122 generates immediate alerts and detailed reports for marketing teams whenever notable sentiment shifts are detected in relation to ongoing brand narratives. The feedback module 122 enables marketing professionals to adapt campaign strategies swiftly and address customer concerns or capitalize on positive engagement trends. The feedback module 122 ensures a dynamic, responsive feedback loop between customer sentiment and marketing action.
[0069] The user interface 124 connected to the processing unit 106 is configured to display the analysis results and recommendations in a user-friendly format. The user interface 124 presents complex analytical outputs, visualizations, and predictive models in a simplified and accessible manner that allows marketing teams to quickly grasp insights and take informed actions. The user interface 124 bridges the gap between advanced backend analytics and practical business applications by offering clear, intuitive displays. The user interface 124 ensures that stakeholders across different organizational levels can easily interpret and utilize the generated insights.
[0070] The storage unit 126 connected to the processing unit 106 is configured to store the customer response data, the customer feedback, the customer interaction data, the insights, and historical brand promotion performance. The storage unit 126 securely archives all datasets and analysis outputs, enabling long-term trend analysis, comparative studies, and knowledge accumulation. The storage unit 126 supports regulatory compliance by maintaining detailed records of customer data and promotional performance history. The storage unit 126 ensures that the system 100 has reliable, organized, and easily retrievable data resources for ongoing learning and strategic refinement.
[0071] The communication network 128 connected to the data collection unit 102, the processing unit 106, the user interface 124, and the storage unit 126 is configured to facilitate data transfer and communication between all components of the system 100. The communication network 128 ensures seamless, real-time data flow, synchronizing the activities of data collection, analysis, reporting, and storage modules. The communication network 128 maintains high-speed, secure, and error-free transmission of information, ensuring the integrity and efficiency of the entire system 100. The communication network 128 guarantees cohesive operation and continuous interaction among all parts of the system 100.
[0072] In one embodiment, the data collection unit 102 is configured to implement intelligent scraping algorithms that automatically prioritize customer response data, customer feedback, and customer interaction data based on recency and relevance to active brand promotional campaigns, ensuring that the most current and impactful data feeds into the input interface 104. The input interface 104 is further configured to use a normalization engine that standardizes the formats of the customer response data, the customer feedback, and the customer interaction data before transmitting the data to the processing unit 106. This ensures seamless compatibility with the emotional analysis module 110, the informational analysis module 112, and the narrative technique classification module 114 during subsequent processing stages. The emotional analysis module 110 within the processing unit 106 is also configured to map emotional fluctuations over time by tracking changes in customer sentiments in response to sequential narrative techniques, and this dynamic emotional trend information is transmitted directly to the correlation analysis module 116 for enhanced behavioural insights.
[0073] In one embodiment, the informational analysis module 112 is configured to detect and flag inconsistencies between the customer’s perceived brand messaging and the originally intended marketing information. Such discrepancies are highlighted and transmitted to the response modelling module 118, allowing predictions of customer behaviour to be refined for improved accuracy. The narrative technique classification module 114 is configured with an adaptive clustering mechanism that continuously redefines categories of narrative techniques by learning from evolving patterns in customer engagement levels. These redefined classifications are processed by the correlation analysis module 116, which generates weighted influence maps that quantify the relative impact of different narrative techniques on segmented customer groups. These influence maps are utilized by the output decision module 120 to deliver more tailored insights and recommendations, enhancing the strategic marketing decisions presented via the user interface 124.
[0074] In one embodiment, the feedback module 122 is configured to highlight immediate customer emotional deviations following brand promotional activities, transmitting real-time predictive adjustments to the response modelling module 118. This real-time feedback allows the predictions to evolve dynamically with customer sentiment shifts and enhances the accuracy of behavioural forecasting. Additionally, the feedback module 122 collaborates with the data collection unit 102 by feeding live emotional response metrics back into the data gathering process, enabling the data collection unit 102 to refine its prioritization algorithms based on emerging emotional trends.
[0075] In one embodiment, the data collection unit 102 is configured with an anomaly detection submodule that identifies and filters out bot-generated or inauthentic customer interaction data, thereby ensuring that the input interface 104 only processes genuine and meaningful customer feedback. The processing unit 106 is also configured with a narrative performance benchmarking module that systematically compares the current performance of narrative techniques against historical campaign baselines retrieved from the storage unit 126. The benchmarking results are visualized through the user interface 124, providing marketers with actionable trend insights. The user interface 124 is further enhanced by incorporating an interactive storytelling simulator that uses behavioural prediction data generated by the response modelling module 118 to allow strategists to simulate and visualize various customer emotional journeys under different narrative scenarios. This simulation capability improves strategic planning by offering foresight into potential customer engagement outcomes.
[0076] In one embodiment, the communication net work 128 is configured to support not only the seamless real-time data transfer between the data collection unit 102, the processing unit 106, the user interface 124, and the storage unit 126, but also an adaptive bandwidth optimization mechanism that prioritizes transmission of critical emotional fluctuation data and prediction updates during peak campaign periods. This ensures that all insights and responses remain current, even under heavy system loads, thereby maintaining the operational efficiency of the entire customer response analysis through narrative techniques impact measurement system 100.
[0077] In one embodiment, the data collection unit 102 is configured with an anomaly detection submodule that automatically identifies and filters out bot-generated customer interaction data, ensuring that only genuine customer feedback is transmitted to the input interface 104 and subsequently analysed by the processing unit 106.
[0078] In one embodiment, the processing unit 106 is configured to incorporate a narrative performance benchmarking module that compares the impact of current narrative techniques against historical campaign baselines stored in the storage unit 126, thereby providing the user interface 124 with performance trend visualizations.
[0079] In one embodiment, the feedback module 122 is configured to feed real-time customer sentiment metrics not only back into the processing unit 106 but also into the data collection unit 102, enabling adaptive tuning of data gathering strategies based on live emotional responses.
[0080] In one embodiment, the user interface 124 is configured with an interactive storytelling simulator that allows marketing strategists to visualize predicted customer emotional journeys based on different narrative scenarios modelled by the response modelling module 118.
[0081] FIG. 2 illustrates a flowchart of the customer response analysis through narrative techniques impact measurement system, in accordance with an exemplary embodiment of the present disclosure.
[0082] At 202, the data collection unit is gathering customer response data, customer feedback, and customer interaction data from various marketing platforms such as social media, e-commerce websites, and brand campaigns. The data collection unit is continuously accumulating a wide range of inputs that reflect customer sentiments, behaviours, and engagements with brand promotional activities.
[0083] At 204, the input interface is receiving the gathered customer response data, the customer feedback, and the customer interaction data from the data collection unit. The input interface is processing the incoming data by standardizing, formatting, and validating it to ensure accuracy before transmitting the processed information to the processing unit for further analysis.
[0084] At 206, the processing unit, which is connected to the input interface, is analysing the customer response data, the customer feedback, and the customer interaction data. The processing unit is initially organizing the data into a structured form suitable for detailed evaluation across various modules specialized for different analytical tasks.
[0085] At 208, the input module within the processing unit is specifically receiving the customer response data, the customer feedback, and the customer interaction data from the input interface. The input module is preparing and segmenting the received data so that it can be effectively utilized by the other analytical modules for deeper assessments.
[0086] At 210, the emotional analysis module is analysing the emotional responses of customers by extracting sentiment indicators, emotional tones, and psychological reactions embedded within the customer response data, the customer feedback, and the customer interaction data, particularly in relation to different narrative techniques used in brand promotions.
[0087] At 212, the informational analysis module is evaluating the informational content conveyed through the brand promotional activities. The module is focusing on the clarity, relevance, depth, and effectiveness of the informational elements as perceived by the customers based on their interaction data and feedback.
[0088] At 214, the narrative technique classification module is categorizing various narrative techniques based on customer engagement levels. The module is using the analysed emotional and informational data to classify storytelling styles, thematic approaches, and promotional strategies according to how strongly they influence customer actions and perceptions.
[0089] At 216, the correlation analysis module is identifying correlations between the different narrative techniques and the customer response data, the customer feedback, and the customer interaction data. The module is uncovering patterns and trends showing which narrative methods are most effective in driving specific types of customer behaviour.
[0090] At 218, the response modelling module is predicting customer behaviour based on the various narrative technique analyses. The module is utilizing the classified narrative techniques and identified correlations to simulate future customer responses and to anticipate the outcomes of deploying certain storytelling strategies.
[0091] At 220, the output decision module is generating insights and recommendations based on the analysed data and the predicted customer behaviours. These results are transmitted to the user interface, where they are displayed in a user-friendly format, and simultaneously stored in the storage unit for historical tracking, with the communication network facilitating seamless data transfer and communication among all system components.
[0092] FIG. 3 illustrates a flowchart of the method for customer response analysis through narrative techniques impact measurement, in accordance with an exemplary embodiment of the present disclosure.
[0093] At 302, receive customer response data, customer feedback, and customer interaction data from various marketing platforms by a data collection unit.
[0094] At 304, receive and processing the customer response data, the customer feedback, and the customer interaction data, and transmitting the processed data to a processing unit by an input interface.
[0095] At 306, analyse the customer response data, the customer feedback, and the customer interaction data by the processing unit connected to the input interface.
[0096] At 308, receive the customer response data, the customer feedback, and the customer interaction data from the input interface by an input module of the processing unit.
[0097] At 310, analyse emotional responses of customers towards different narrative techniques from the customer response data, the customer feedback, and the customer interaction data by an emotional analysis module of the processing unit.
[0098] At 312, evaluate informational content associated with brand promotional activities by an informational analysis module of the processing unit.
[0099] At 314, categorize various narrative techniques based on customer engagement levels by a narrative technique classification module of the processing unit.
[0100] At 316, identify correlations between different narrative techniques and the customer response data, the customer feedback, and the customer interaction data by a correlation analysis module of the processing unit.
[0101] At 318, predict customer behaviour based on the narrative technique analysis by a response modelling module of the processing unit.
[0102] At 320, generate insights and recommendations based on the analysed customer response data, the customer feedback, and the customer interaction data by an output decision module of the processing unit.
[0103] At 322, provide real-time feedback indicating customer sentiment towards specific narrative techniques by a feedback module of the processing unit.
[0104] At 324, display the generated insights and recommendations in a user-friendly format by a user interface connected to the processing unit.
[0105] At 326, store the customer response data, the customer feedback, the customer interaction data, the insights, and historical brand promotion performance data by a storage unit connected to the processing unit.
[0106] At 328, facilitate continuous transfer of the customer response data, the customer feedback, the customer interaction data, and analysis results among the data collection unit, the processing unit, the user interface, and the storage unit by a communication network.
[0107] FIG. 4 illustrates a perspective view of the narrative techniques impact on brand promotion, in accordance with an exemplary embodiment of the present disclosure. The narrative techniques impact on brand promotion is being initiated through customer emotional responses, where customers are inspired by an association of happiness, excitement, pride, and trust. The emotional stimuli are driving initial engagement that is progressively shaping the customer's perception and attachment towards the brand. Customers are being motivated by the same associations of happiness, excitement, pride, and trust, strengthening their desire to align with brand messaging and promotional campaigns.
[0108] Following this, customers are being connected by the association of happiness, excitement, pride, and trust, leading to deeper emotional ties that are critical for long-term brand loyalty formation. Customers are continuously feeling joy by associations of happiness, excitement, pride, and trust, amplifying positive emotional responses that reinforce customer-brand relationships.
[0109] Simultaneously, external factors such as the influence of seller's knowledge and the influence of seller's features are interacting with customer perceptions. User-generated content narrative techniques and the effect of user-generated content already experienced are further enriching customer experiences, providing authentic validation to brand messaging. The emotional engagement narrative technique is playing a pivotal role in driving inspiration, motivation, connection, and joy towards the brand, with a specific emphasis observed towards promotional activities related to brands like Apple.
[0110] Further engagement is being driven by the influence of slogan campaigns and participation with slogans such as "JUST DO IT," where metaphor narrative techniques are shaping customer imagination and association. Augmented reality narrative techniques are enhancing experiential engagement, where customers are already using augmented reality technology and gaining experiences that fortify their emotional bonds with the brand.
[0111] Ultimately, all emotional, informational, and experiential inputs are converging towards successful brand promotion and are leading into the establishment of sustainable brand loyalty as depicted through the connected narrative flow in FIG. 4
[0112] The best mode of operation for the customer response analysis through narrative techniques impact measurement system 100 begins with the data collection unit 102 gathering customer response data, customer feedback, and customer interaction data from a variety of marketing platforms, including but not limited to social media platforms, email campaigns, mobile applications, and website interactions. The data collection unit 102 ensures that the gathered data is comprehensive and represents diverse customer engagement activities related to brand promotional efforts. The collected data is then transmitted to the input interface 104, which is configured to receive, preprocess, and process the customer response data, the customer feedback, and the customer interaction data. During preprocessing, the input interface 104 removes noise and irrelevant information to ensure that only meaningful and high-quality data is forwarded to the processing unit 106 for analysis.
[0113] Upon receipt by the processing unit 106, the data is systematically analysed through several integrated modules. The input module 108 first categorizes the customer response data, the customer feedback, and the customer interaction data based on demographic parameters and engagement metrics, ensuring that subsequent analyses can be conducted with precision and relevance. The emotional analysis module 110 is then employed to conduct sentiment analysis and classify the emotional tone of customer responses using a trained artificial intelligence model. This enables the system to discern the emotional impact of various narrative techniques embedded within the brand promotions. Concurrently, the informational analysis module 112 evaluates the clarity, relevance, and perceived credibility of the informational content presented in the brand promotional activities, ensuring a holistic understanding of how the informational aspects influence customer responses.
[0114] The narrative technique classification module 114 plays a critical role by categorizing various narrative techniques based on customer engagement levels observed in the customer response data, the customer feedback, and the customer interaction data. Furthermore, the narrative technique classification module 114 is dynamically updated through continuous learning algorithms to adapt to evolving customer engagement patterns and emerging storytelling trends. The correlation analysis module 116 follows by identifying and quantifying statistical relationships between different narrative techniques and customer engagement indicators, generating robust statistical models that map the strength and direction of these relationships. These insights are pivotal for understanding which narrative techniques are most effective in influencing customer behaviour.
[0115] The response modelling module 118 utilizes the outputs from the previous analyses to predict future customer behaviour based on the identified narrative technique influences. This predictive capability allows businesses to proactively tailor their marketing strategies for optimal customer engagement. Following the predictive analysis, the output decision module generates actionable insights and recommendations based on the comprehensive analysis of the customer response data, the customer feedback, and the customer interaction data. These insights are crucial for marketing teams to refine their narrative approaches and maximize campaign effectiveness.
[0116] Real-time customer sentiment towards specific narrative techniques is monitored and reported by the feedback module 122, which generates immediate alerts and detailed reports for marketing teams, enabling rapid response to shifts in customer perception. The analysis results and generated recommendations are presented through a user interface 124 designed to display the information in a user-friendly and intuitive format, making it accessible for marketing and business decision-makers.
[0117] All customer response data, customer feedback, customer interaction data, generated insights, and historical brand promotion performance data are securely stored within the storage unit 126, ensuring that the data is available for longitudinal analysis and continuous improvement of narrative techniques. Throughout the entire process, the communication network 128 facilitates seamless and continuous data transfer and communication among the data collection unit 102, the processing unit 106, the user interface 124, and the storage unit 126, maintaining the efficiency and integrity of the system 100 operations. This integrated and dynamic workflow ensures that organizations can systematically measure, analyse, and optimize the impact of narrative techniques on brand promotion activities, leading to improved customer engagement, enhanced brand loyalty, and superior marketing effectiveness.
[0118] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0119] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0120] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0121] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0122] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. A customer response analysis through narrative techniques impact measurement system (100), the system (100) comprises:
a data collection unit (102) configured to gather customer response data, customer feedback and customer interaction data from various marketing platforms;
an input interface (104) connected to the data collection unit (102), configured to receive and process the customer response data, the customer feedback and the customer interaction data;
a processing unit (106) connected to the input interface (104), configured to analyse the customer response data, the customer feedback and the customer interaction data, wherein the processing unit (106) comprises:
an input module (108), configured to receive the customer response data, the customer feedback and the customer interaction data from the input interface (104);
an emotional analysis module (110), configured to analyse the emotional response of customers from the customer response data, the customer feedback and the customer interaction data to different narrative techniques;
an informational analysis module (112), configured to evaluate the informational content of brand promotional activities;
a narrative technique classification module (114), configured to categorize various narrative techniques based on customer engagement levels from the customer response data, the customer feedback and the customer interaction data;
a correlation analysis module (116), configured to identify correlations between different narrative techniques and the customer response data, the customer feedback and the customer interaction data;
a response modelling module (118), configured to predict customer behaviour based on the various narrative technique analysis;
an output decision module, configured to generate insights and recommendations based on the analysed data from the customer response data, the customer feedback and the customer interaction data;
a feedback module (122), configured to provide real-time feedback on customer sentiment towards specific narrative techniques;
a user interface (124) connected to the processing unit (106), configured to display the analysis results and recommendations in a user-friendly format;
a storage unit (126) connected to the processing unit (106), configured to store the customer response data, the customer feedback and the customer interaction data, the insights, and historical brand promotion performance;
a communication network (128) connected to the data collection unit (102), processing unit (106), user interface (124), and storage unit (126), configured to facilitate data transfer and communication between all components of the system (100).
2. The system (100) as claimed in claim 1, wherein the data collection unit (102) is further configured to collect data from social media platforms, email campaigns, mobile applications, and website interactions.
3. The system (100) as claimed in claim 1, wherein the input interface (104) is further configured to preprocess the customer response data, the customer feedback and the customer interaction data by removing noise and irrelevant information before transmission to the processing unit (106).
4. The system (100) as claimed in claim 1, wherein the input module (108) of the processing unit (106) is further configured to categorize the customer response data, the customer feedback and the customer interaction data based on demographic parameters and engagement metrics.
5. The system (100) as claimed in claim 1, wherein the emotional analysis module (110) is further configured to perform sentiment analysis and emotional tone classification using a trained artificial intelligence model.
6. The system (100) as claimed in claim 1, wherein the informational analysis module (112) is further configured to assess the clarity, relevance, and perceived credibility of the informational content of the brand promotional activities.
7. The system (100) as claimed in claim 1, wherein the narrative technique classification module (114) is further configured to dynamically update the categories of narrative techniques based on continuous learning from customer engagement patterns.
8. The system (100) claimed in claim 1, wherein the correlation analysis module (116) is further configured to generate statistical models that map the strength and direction of relationships between narrative techniques and customer engagement indicators.
9. The system (100) as claimed in claim 1, wherein the feedback module (122) is further configured to generate real-time alerts and reports for marketing teams based on immediate customer sentiment shifts towards specific narrative techniques.
10. A method for customer response analysis through narrative techniques impact measurement (100), the method (100) comprising:
receiving customer response data, customer feedback, and customer interaction data from various marketing platforms by a data collection unit (102);
receiving and processing the customer response data, the customer feedback, and the customer interaction data, and transmitting the processed data to a processing unit (106) by an input interface (104);
analysing the customer response data, the customer feedback, and the customer interaction data by the processing unit (106) connected to the input interface (104);
receiving the customer response data, the customer feedback, and the customer interaction data from the input interface (104) by an input module (108) of the processing unit (106);
analysing emotional responses of customers towards different narrative techniques from the customer response data, the customer feedback, and the customer interaction data by an emotional analysis module (110) of the processing unit (106);
evaluating informational content associated with brand promotional activities by an informational analysis module (112) of the processing unit (106);
categorizing various narrative techniques based on customer engagement levels by a narrative technique classification module (114) of the processing unit (106);
identifying correlations between different narrative techniques and the customer response data, the customer feedback, and the customer interaction data by a correlation analysis module (116) of the processing unit (106);
predicting customer behaviour based on the narrative technique analysis by a response modelling module (118) of the processing unit (106);
generating insights and recommendations based on the analysed customer response data, the customer feedback, and the customer interaction data by an output decision module of the processing unit (106);
providing real-time feedback indicating customer sentiment towards specific narrative techniques by a feedback module (122) of the processing unit (106);
displaying the generated insights and recommendations in a user-friendly format by a user interface (124) connected to the processing unit (106);
storing the customer response data, the customer feedback, the customer interaction data, the insights, and historical brand promotion performance data by a storage unit (126) connected to the processing unit (106);
facilitating continuous transfer of the customer response data, the customer feedback, the customer interaction data, and analysis results among the data collection unit (102), the processing unit (106), the user interface (124), and the storage unit (126) by a communication network (128).