Description:FIELD OF DISCLOSURE
[0001] The present disclosure relates generally relates to the field of consumer behavior analysis and marketing analytics. More specifically, it pertains to an integrated system for linking customer persona and experience to brand-based purchase decisions.
BACKGROUND OF THE DISCLOSURE
[0002] Understanding consumer behavior has been a central focus of marketing research for decades, driven by the need for businesses to enhance customer engagement and optimize brand strategies. The complex interplay between consumer personas, their experiences, and the purchase decisions they make has been widely explored in the fields of marketing, psychology, and behavioral economics. A consumer persona is a semi-fictional representation of a customer segment, often constructed using demographic, psychographic, and behavioral data. Personas help businesses segment their markets, understand consumer needs, and predict responses to marketing strategies. These personas are developed by analyzing data collected through surveys, social media interactions, purchasing history, and observational studies, providing a framework for companies to design more personalized experiences.
[0003] The concept of customer experience has emerged as a critical factor influencing purchase decisions. Customer experience encompasses every interaction a consumer has with a brand, ranging from pre-purchase research and advertising exposure to the in-store experience and post-purchase support. Studies have demonstrated that positive customer experiences are strongly correlated with brand loyalty, repeat purchases, and advocacy. Conversely, negative experiences can lead to decreased trust, customer attrition, and unfavorable word-of-mouth. Businesses have recognized that understanding not only who their customers are but also how they experience a brand is essential for sustaining a competitive advantage in increasingly crowded markets.
[0004] Brand-based purchase decisions are influenced by multiple dimensions of consumer perception. These dimensions include perceived quality, brand image, social influence, and emotional connection. Research in behavioral marketing has indicated that consumers often make purchase choices not solely based on functional product attributes but also on the psychological and emotional value attached to a brand. For instance, luxury brands rely heavily on creating a perception of exclusivity and prestige, which can outweigh considerations of price or utility. Similarly, mass-market brands may focus on creating trust, reliability, and accessibility to appeal to a broad consumer base. Understanding the mechanisms through which brand perception interacts with individual customer traits has been a longstanding challenge in marketing research.
[0005] The proliferation of digital platforms has transformed the way consumer data is collected, analyzed, and applied. Social media, e-commerce websites, and mobile applications generate vast amounts of data on consumer interactions, preferences, and sentiment. Big data analytics has enabled marketers to move beyond aggregate measures and segment consumers into highly detailed clusters, often referred to as micro-segments. Advanced techniques such as machine learning, sentiment analysis, and predictive modeling are increasingly used to infer behavioral patterns and anticipate purchase intent. These technologies facilitate the identification of correlations between consumer personas, brand engagement, and purchasing behavior, allowing companies to tailor offerings and marketing communications more precisely.
[0006] Psychological theories provide additional insight into the link between consumer persona and purchase decisions. The Theory of Planned Behavior (TPB) and the Theory of Reasoned Action (TRA) suggest that attitudes, subjective norms, and perceived behavioral control collectively influence intentions and actions. In the context of brand-based purchases, this implies that a consumer’s internalized preferences, social environment, and self-efficacy in making choices all contribute to their likelihood of buying a particular brand. Similarly, research in consumer psychology emphasizes the role of motivation, identity, and emotional resonance in guiding brand selection. A consumer’s persona defined by their values, lifestyle, and personality traits intersects with these psychological mechanisms to produce observable buying behaviors.
[0007] Customer journey mapping has emerged as a crucial tool for linking experiences to purchase outcomes. This approach visualizes the sequential touchpoints a consumer encounters from initial awareness to post-purchase engagement. By analyzing these touchpoints, businesses can identify critical moments that influence decision-making, such as first impressions created through advertisements, ease of navigation on a website, or interactions with customer support. Research indicates that experiences at these touchpoints do not operate in isolation; rather, they accumulate to form an overall perception of the brand. Consequently, mapping the journey enables marketers to diagnose gaps, optimize interactions, and enhance alignment between consumer expectations and brand delivery.
[0008] Historically, traditional marketing models relied heavily on surveys, focus groups, and interviews to infer consumer preferences. While these methods provided valuable insights, they were often limited by small sample sizes, respondent biases, and delayed feedback. The rise of digital channels has addressed many of these limitations, allowing real-time tracking of consumer behavior across multiple platforms. Techniques such as clickstream analysis, heatmaps, and A/B testing provide actionable insights into how users navigate digital environments and interact with brand content. This shift towards empirical, data-driven understanding has emphasized the need for integrated systems capable of combining multiple data sources, providing a holistic view of the consumer-brand relationship.
[0009] The importance of segmentation in marketing strategy cannot be overstated. Effective segmentation enables businesses to allocate resources efficiently, target communications more accurately, and develop products that resonate with specific customer groups. Traditional segmentation methods often relied on broad categories such as age, gender, income, or geographic location. However, modern approaches increasingly incorporate behavioral and psychographic dimensions, reflecting the realization that consumers within the same demographic can exhibit vastly different purchasing behaviors. Advanced analytical frameworks allow marketers to construct multidimensional profiles that capture the subtleties of consumer motivation, perception, and loyalty.
[0010] Brand equity research has also highlighted the interconnectedness of consumer perception and purchase decisions. Brand equity refers to the value a brand adds to a product, often measured through consumer awareness, perceived quality, and emotional attachment. High brand equity can justify premium pricing, encourage repeat purchases, and provide resilience against competitive pressures. Studies indicate that a consumer’s persona significantly influences how brand equity is perceived and acted upon. For example, a persona characterized by risk-aversion and emphasis on reliability may respond more favorably to brands with established reputations, while personas seeking novelty may be drawn to emerging or innovative brands. Understanding this dynamic is critical for developing marketing strategies that translate positive brand perception into tangible sales outcomes.
[0011] Thus, in light of the above-stated discussion, there exists a need for an integrated system for linking customer persona and experience to brand-based purchase decisions.
SUMMARY OF THE DISCLOSURE
[0012] 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.
[0013] According to illustrative embodiments, the present disclosure focuses on an integrated system for linking customer persona and experience to brand-based purchase decisions which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0014] An objective of the present disclosure is to identify and categorize key customer persona segments that influence brand-based purchase decisions.
[0015] Another objective of the present disclosure is to develop a comprehensive framework that integrates customer persona data with experiential factors to provide a holistic understanding of consumer behavior.
[0016] Another objective of the present disclosure is to analyze the impact of experiential factors, such as brand interactions, user engagement, and emotional responses, on customer purchase behavior.
[0017] Another objective of the present disclosure is to establish a link between customer personas and their corresponding brand experiences to predict purchase preferences accurately.
[0018] Another objective of the present disclosure is to design and implement a data-driven system that consolidates demographic, psychographic, and behavioral attributes of customers for better decision-making.
[0019] Another objective of the present disclosure is to enable predictive analytics that forecasts brand choice tendencies based on integrated persona and experience insights.
[0020] Another objective of the present disclosure is to enhance personalized marketing strategies by tailoring brand interactions according to individual customer personas and their experiential responses.
[0021] Another objective of the present disclosure is to visualize and interpret complex relationships between customer characteristics, experiences, and brand preferences for actionable business insights.
[0022] Another objective of the present disclosure is to evaluate the effectiveness of brand strategies by measuring their influence on different customer persona groups and experiential outcomes.
[0023] Yet another objective of the present disclosure is to provide actionable recommendations to organizations for optimizing brand positioning, engagement, and marketing campaigns based on integrated persona-experience insights.
[0024] In light of the above, an integrated system for linking customer persona and experience to brand-based purchase decisions comprises a data collection module configured to gather customer information from multiple sources. The system also includes a dynamic persona modeling module configured to generate adaptive customer personas based on the collected data and continuously update the personas in real-time as new customer interactions and behavioral inputs are received. The system also includes a customer experience analysis module configured to capture and process customer experience data. The system also includes a predictive decision intelligence module configured to apply machine learning algorithms to analyze patterns, emotional cues, and behavior from the integrated customer persona and experience data to predict and influence brand-based purchase decisions. The system also includes a feedback loop module configured to receive post-purchase evaluations and updated customer interactions to refine and adapt the dynamic personas and predictive decision models continuously. The system also includes an output interface module configured to generate personalized brand engagement, product recommendations, and marketing insights based on the predictive decision intelligence for enhancing purchase decisions.
[0025] In one embodiment, the data collection module gathers customer information from sources including web and mobile applications, customer relationship management (CRM) systems, third-party databases, social media platforms, and in-store sensors.
[0026] In one embodiment, the dynamic persona modeling module generates adaptive customer personas by integrating demographic, psychographic, behavioral, and geo-demographic attributes, and updates the personas in real-time based on live customer interactions and behavioral inputs.
[0027] In one embodiment, the customer experience analysis module captures data related to brand awareness, brand image and identity, brand loyalty, perceived brand value, and social proof, and correlates the experience data with the dynamic customer personas to form a comprehensive customer profile.
[0028] In one embodiment, the predictive decision intelligence module applies machine learning algorithms including supervised, unsupervised, and reinforcement learning techniques to analyze patterns, emotional cues, and customer behaviors for predicting purchase intent, perceived brand fit, and brand selection.
[0029] In one embodiment, the feedback loop module continuously receives post-purchase evaluations, customer feedback, and subsequent interaction data to refine and adapt the dynamic persona models and predictive decision algorithms.
[0030] In one embodiment, the output interface module generates personalized brand messaging, product recommendations, promotional offers, and marketing insights, and delivers the outputs via digital platforms including websites, mobile applications, email, and social media channels.
[0031] In one embodiment, the predictive decision intelligence module leverages natural language processing techniques to analyze textual customer feedback and social media interactions for sentiment and behavioral insights.
[0032] In one embodiment, the system further comprising integration with cloud-based computing infrastructure to provide scalable processing, storage, and analytics capabilities for handling large volumes of real-time customer data.
[0033] In one embodiment, the data collection module additionally receives inputs from in-store edge devices, IoT sensors, beacons, and NFC-enabled devices to capture offline customer behavior for inclusion in the dynamic persona models.
[0034] These and other advantages will be apparent from the present application of the embodiments described herein.
[0035] 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.
[0036] 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
[0037] 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.
[0038] 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:
[0039] FIG. 1 illustrates a flowchart outlining sequential step involved in an integrated system for linking customer persona and experience to brand-based purchase decisions, in accordance with an exemplary embodiment of the present disclosure;
[0040] FIG. 2 illustrates a customer persona to purchase decision framework, in accordance with an exemplary embodiment of the present disclosure.
[0041] Like reference, numerals refer to like parts throughout the description of several views of the drawing;
[0042] The integrated system linking customer persona and experience to brand-based purchase decisions, 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
[0043] 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.
[0044] 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.
[0045] 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.
[0046] 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.
[0047] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0048] Referring now to FIG. 1 to FIG. 2 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a flowchart outlining sequential step involved in an integrated system for linking customer persona and experience to brand-based purchase decisions, in accordance with an exemplary embodiment of the present disclosure.
[0049] An integrated system for linking customer persona and experience to brand-based purchase decisions 100 comprises a data collection module 102 configured to gather customer information from multiple sources. The data collection module 102 additionally receives inputs from in-store edge devices, IoT sensors, beacons, and NFC-enabled devices to capture offline customer behavior for inclusion in the dynamic persona models. The data collection module 102 gathers customer information from sources including web and mobile applications, customer relationship management (CRM) systems, third-party databases, social media platforms, and in-store sensors.
[0050] The system also includes a dynamic persona modeling module 104 configured to generate adaptive customer personas based on the collected data and continuously update the personas in real-time as new customer interactions and behavioral inputs are received. The dynamic persona modeling module 104 generates adaptive customer personas by integrating demographic, psychographic, behavioral, and geo-demographic attributes, and updates the personas in real-time based on live customer interactions and behavioral inputs.
[0051] The system also includes a customer experience analysis module 106 configured to capture and process customer experience data. The customer experience analysis module 106 captures data related to brand awareness, brand image and identity, brand loyalty, perceived brand value, and social proof, and correlates the experience data with the dynamic customer personas to form a comprehensive customer profile.
[0052] The system also includes a predictive decision intelligence module 108 configured to apply machine learning algorithms to analyze patterns, emotional cues, and behavior from the integrated customer persona and experience data to predict and influence brand-based purchase decisions. The predictive decision intelligence module 108 applies machine learning algorithms including supervised, unsupervised, and reinforcement learning techniques to analyze patterns, emotional cues, and customer behaviors for predicting purchase intent, perceived brand fit, and brand selection. The predictive decision intelligence module 108 leverages natural language processing techniques to analyze textual customer feedback and social media interactions for sentiment and behavioral insights.
[0053] The system also includes a feedback loop module 110 configured to receive post-purchase evaluations and updated customer interactions to refine and adapt the dynamic personas and predictive decision models continuously. The feedback loop module 110 continuously receives post-purchase evaluations, customer feedback, and subsequent interaction data to refine and adapt the dynamic persona models and predictive decision algorithms.
[0054] The system also includes an output interface module 112 configured to generate personalized brand engagement, product recommendations, and marketing insights based on the predictive decision intelligence for enhancing purchase decisions. The output interface module 112 generates personalized brand messaging, product recommendations, promotional offers, and marketing insights, and delivers the outputs via digital platforms including websites, mobile applications, email, and social media channels.
[0055] The system further comprising integration with cloud-based computing infrastructure to provide scalable processing, storage, and analytics capabilities for handling large volumes of real-time customer data.
[0056] FIG. 1 illustrates a flowchart outlining sequential step involved in an integrated system for linking customer persona and experience to brand-based purchase decisions.
[0057] At 102, the system first employs a data collection module that gathers detailed information from multiple sources, including online and offline interactions, mobile and web application usage, purchase history, customer feedback, and third-party data. This module ensures that a broad and diverse range of customer attributes such as demographic, psychographic, behavioral, and geo-demographic data is captured, providing a robust foundation for understanding each customer’s profile. By integrating data from varied touchpoints, the system ensures that the information reflects both static characteristics and dynamic behavioral patterns.
[0058] At 104, once the data is collected, it is processed by the dynamic persona modeling module, which generates adaptive customer personas. This module continuously updates these personas in real-time as new customer interactions and behavioral inputs are received, allowing the system to maintain an accurate and evolving representation of each customer. The adaptive personas incorporate information such as personal preferences, expectations, and previous purchase behavior, thereby enabling the system to capture subtle changes in customer behavior and attitudes. The real-time updating capability ensures that the personas remain relevant and reflective of the latest customer tendencies, bridging the gap between traditional static profiling and dynamic customer behavior.
[0059] At 106, the customer experience analysis module captures and processes data related to the customer’s evaluation of brands. This module analyzes factors such as brand awareness, brand image and identity, brand loyalty, value perception, and social proof, integrating them with the dynamic customer personas. By combining these two streams of information persona and experience the system creates a comprehensive view of the customer that encompasses both who the customer is and how the customer perceives and interacts with various brands. This integration is critical for understanding the decision-making context and for tailoring interventions that are most likely to influence purchase behavior.
[0060] At 108, the predictive decision intelligence module then applies machine learning algorithms to the integrated data to analyze patterns, behavioral cues, and emotional responses. By leveraging AI and predictive analytics, the system identifies trends, preferences, and decision drivers that influence brand-based purchase behavior. This module predicts which brands or products are most likely to resonate with each customer and determines personalized strategies to enhance engagement. The analysis not only informs product recommendations but also guides marketing messages and interactions, making the brand-customer engagement more targeted and effective.
[0061] At 110, a feedback loop module ensures the system’s continuous learning and adaptation. It receives post-purchase evaluations and updated customer interactions, feeding this information back into the dynamic persona models and predictive decision algorithms. This loop allows the system to refine its understanding of customer preferences and improve the accuracy of future predictions. By constantly incorporating new insights, the feedback loop ensures that the system evolves alongside changing customer behaviors, maintaining high relevance and precision in brand engagement strategies.
[0062] At 112, the output interface module translates the insights generated by the predictive decision intelligence module into actionable outcomes. This module generates personalized brand engagement strategies, product recommendations, and marketing insights tailored to each customer. By delivering these outputs through multiple channels such as mobile apps, websites, email campaigns, or in-store interactions the system effectively influences purchase decisions while enhancing the overall customer experience.
[0063] FIG. 2 illustrates a customer persona to purchase decision framework.
[0064] It begins with the concept of the customer persona, which represents a detailed profile of who the customer is. This persona is built using demographic factors such as age, gender, and income, along with psychographic aspects such as values and lifestyle choices. Behavioral traits, including shopping habits and interaction styles, are also considered, as well as geo-demographic information that situates the customer within a specific location and cultural environment. Together, these dimensions create a dynamic picture of the individual customer, forming the foundation for understanding their expectations and decision-making processes.
[0065] Once the customer persona is established, the next layer in the flow focuses on customer experience, which captures how customers perceive and evaluate brands. This includes their awareness of a brand, the image and identity that the brand projects, the loyalty the brand inspires, the value proposition it offers, and the influence of social proof, such as recommendations or community acceptance. This stage reflects the evaluative lens through which customers view different brand options, combining both emotional and rational responses. The interaction between the customer persona and customer experience directly influences the range of choices, often referred to as the "choice set," that a customer considers before making a purchase.
[0066] As customer expectations and preferences evolve, they are shaped into personal perceptions and attitudes, which serve as an intermediary bridge between customer identity and brand interaction. This phase highlights how customers interpret their own needs, aspirations, and past experiences, aligning them with the external signals they receive from brands. These perceptions are central in determining whether a customer feels aligned with a brand or perceives a disconnect that may deter engagement.
[0067] This leads into the brand selection stage, where customers actively decide which brand best meets their expectations. At this point, perceived brand fit plays a critical role, as customers assess whether the brand aligns with their self-image, lifestyle, or requirements. Alongside this, purchase intent emerges, reflecting the psychological readiness to buy. This intent then transitions into an actual buying decision, followed by post-purchase evaluation where customers judge whether the product or service met their expectations. Positive evaluations reinforce loyalty, while negative ones can reduce trust or discourage future purchases.
[0068] Finally, the system incorporates a feedback loop, which is essential for continuous improvement and adaptation. The insights gained from post-purchase evaluations feed back into both customer personas and brand strategies. For instance, a customer’s satisfaction or dissatisfaction with a purchase may alter their attitudes, preferences, and expectations, which in turn influence their future interactions with the brand. Similarly, companies can use this feedback to refine brand messaging, improve products, or strengthen customer relationships.
[0069] 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.
[0070] 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.
[0071] 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.
[0072] 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.
[0073] 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. An integrated system for linking customer persona and experience to brand-based purchase decisions (100) comprising:
a data collection module (102) configured to gather customer information from multiple sources;
a dynamic persona modeling module (104) configured to generate adaptive customer personas based on the collected data and continuously update the personas in real-time as new customer interactions and behavioral inputs are received;
a customer experience analysis module (106) configured to capture and process customer experience data;
a predictive decision intelligence module (108) configured to apply machine learning algorithms to analyze patterns, emotional cues, and behavior from the integrated customer persona and experience data to predict and influence brand-based purchase decisions;
a feedback loop module (110) configured to receive post-purchase evaluations and updated customer interactions to refine and adapt the dynamic personas and predictive decision models continuously;
an output interface module (112) configured to generate personalized brand engagement, product recommendations, and marketing insights based on the predictive decision intelligence for enhancing purchase decisions.
2. The system (100) as claimed in claim 1, wherein the data collection module (102) gathers customer information from sources including web and mobile applications, customer relationship management (CRM) systems, third-party databases, social media platforms, and in-store sensors.
3. The system (100) as claimed in claim 1, wherein the dynamic persona modeling module (104) generates adaptive customer personas by integrating demographic, psychographic, behavioral, and geo-demographic attributes, and updates the personas in real-time based on live customer interactions and behavioral inputs.
4. The system (100) as claimed in claim 1, wherein the customer experience analysis module (106) captures data related to brand awareness, brand image and identity, brand loyalty, perceived brand value, and social proof, and correlates the experience data with the dynamic customer personas to form a comprehensive customer profile.
5. The system (100) as claimed in claim 1, wherein the predictive decision intelligence module (108) applies machine learning algorithms including supervised, unsupervised, and reinforcement learning techniques to analyze patterns, emotional cues, and customer behaviors for predicting purchase intent, perceived brand fit, and brand selection.
6. The system (100) as claimed in claim 1, wherein the feedback loop module (110) continuously receives post-purchase evaluations, customer feedback, and subsequent interaction data to refine and adapt the dynamic persona models and predictive decision algorithms.
7. The system (100) as claimed in claim 1, wherein the output interface module (112) generates personalized brand messaging, product recommendations, promotional offers, and marketing insights, and delivers the outputs via digital platforms including websites, mobile applications, email, and social media channels.
8. The system (100) as claimed in claim 1, wherein the predictive decision intelligence module (108) leverages natural language processing techniques to analyze textual customer feedback and social media interactions for sentiment and behavioral insights.
9. The system (100) as claimed in claim 1, wherein the system further comprising integration with cloud-based computing infrastructure to provide scalable processing, storage, and analytics capabilities for handling large volumes of real-time customer data.
10. The system (100) as claimed in claim 1, wherein the data collection module (102) additionally receives inputs from in-store edge devices, IoT sensors, beacons, and NFC-enabled devices to capture offline customer behavior for inclusion in the dynamic persona models.