Description:FIELD OF INVENTION

[0001] Embodiments of the present disclosure relates to the field of digital privacy and behavioural analysis of a user and more particularly a system and a method for analysing audience characteristics utilizing anonymized data from digital platforms.
BACKGROUND
[0002] The concept of audience analysis has been a cornerstone of marketing, consumer research, and business expansion for decades. Traditionally, businesses and researchers would segment audiences based on demographic and behavioural data, often using personal information such as age, gender, and location to create targeted groups. This helped them to better understand customer behaviour and target their products and endorsements.

[0003] The need for audience analysis has grown significantly as more organizations are relying on large-scale data collection and analysis to make informed business decisions. Today, businesses must segment and analyse customer behaviour to tailor their products, services, and marketing efforts effectively. However, with the rise of digital technologies and the increasing amount of personal data being collected, concerns over privacy began to emerge in the late 20th and early 21st centuries. In response to these concerns, a plurality of regulatory frameworks across the continents were introduced and force an organization to adopt more responsible data practices. The challenge then became how to continue meaningful and segmented analysis while ensuring compliance with privacy laws and maintaining user trust.
[0004] However, with privacy regulations in place, it is critical that this data is collected and used in ways that do not infringe upon individual privacy rights. The consumers are increasingly concerned about how their data is being used, leading to a demand for greater transparency, control, and security. There are a plurality of privacy-compliant approaches, such as anonymization, differential privacy, federated learning and the like. These existing methods allow for audience analysis without personal data ever leaving user devices and keeping it secure.

[0005] Although there is a notable progress in integrating privacy compliance norms with digital technology , there remains a significant gap and a few drawbacks in the application of existing methods with digital privacy. Firstly, these existing methods offer a way to comply with privacy regulations, they also require specialized skills and infrastructure, which can make them challenging for smaller organizations to implement effectively. Additionally, existing methods while effective in preserving individual privacy, can sometimes result in the loss of data granularity, leading to less precise insights. This can make it harder to identify nuanced consumer behaviours and predict trends with the same level of accuracy as traditional methods. Furthermore, the rapidly evolving landscape of privacy laws means that compliance is an ongoing challenge, requiring continuous adaptation to new regulations and legal requirements.

[0006] In response to these challenges, there exists a need to address these challenges and develop a privacy-compliant cohort-based audience analysis which looks promising, with several technological advancements on the horizon that could address current limitations. The need to evolve the technological advancement will not only improve the effectiveness of cohort-based audience analysis but also help create a more privacy-respecting digital ecosystem where both businesses and consumers can thrive.

[0007] Hence, there is a need for an improved a system and a method for analysing audience characteristics utilizing anonymized data from digital platforms.
OBJECTIVES OF THE INVENTION
[0008] The primary objective of the invention is to provide a privacy protecting, a segregated behavioural segmentation for analysing customer traits without the need to access or handle personally identifiable information. The data from digital intermediates including aggregated interactions across third-party platforms and anonymized behavioural data. This approach uncovers a comprehensive view of customer characteristics, behaviours and enables businesses to understand customer preferences and trends without violating privacy guidelines or relying on sensitive personal data.

[0009] Another objective of the invention is to analyse customer segments based on patterns while maintaining privacy regulations with respect to digital compliance. By focusing on behaviour and characteristics at a cohort level, rather than individual data, this approach provides valuable insights into how different customer groups engage with products, services, or content.

[0010] Yet another objective of the invention is to offer a more holistic view of customer profiles, allowing businesses to make informed decisions about marketing, product development, and customer engagement. It removes the need for invasive data collection practices while still delivering actionable insight.

BRIEF DESCRIPTION
[0011] In accordance with an embodiment of the present disclosure, a system for analysing audience characteristics utilizing anonymized data from digital platforms is provided. The system includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a data collection module configured to collect data of a plurality of users associated to an entity from a plurality of digital platforms, wherein the data is uploaded by the entity based on interactions between the plurality of users and the entity wherein the entity comprises one or a merchant, provider, retailer and shopkeeper. The data collection module is also configured to obtain a matched data from the plurality of digital platforms, wherein the matched data comprises a plurality of phone numbers and email addresses corresponding to the plurality of user to ensure that the data is validated. The processing subsystem includes an analysis engine operatively coupled to the data collection module wherein the analysis engine is configured to combine and aggregate the data by utilizing a statistical model. The analysis engine is also configured to process the data to identify a plurality of behavioural patterns comprising traits and trends associated to the plurality of users. The analysis engine is also configured to group and segment the data into a plurality of cohorts, attributes and group the plurality of users with similar traits into distinct segments. The processing subsystem includes a dashboard interface operatively coupled to the analysis engine wherein the dashboard interface is configured to display the data and enable the plurality of digital platforms to view and interpret the plurality of behavioural patterns.

[0012] In accordance with another embodiment of the present disclosure, a method for analysing audience characteristics utilizing anonymized data from digital platforms is provided. The method includes collecting, by a data collection module, data of a plurality of users associated to an entity from a plurality of digital platforms, wherein the data is uploaded by the entity based on interactions between the plurality of users and the entity wherein the entity comprises one or a merchant, provider, retailer and shopkeeper. The method includes obtaining, by the data collection module, the matched data from the plurality of digital platforms, wherein the matched data comprises a plurality of phone numbers and email addresses corresponding to the plurality of user to ensure that the data is validated. The method includes combining and aggregating, by an analysis engine, the data by utilizing a statistical model. The method includes processing, by the analysis engine, the data to identify a plurality of behavioural patterns comprising traits and trends associated to the plurality of users. The method includes grouping and segmenting, by the analysis engine, the data into a plurality of cohorts, attributes and group the plurality of users with similar traits into distinct segments. The method includes displaying the data , by the dashboard interface, and enable the plurality of digital platforms to view and interpret the plurality of behavioural patterns.

[0013] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

[0015] FIG. 1 is a block diagram representation of a system for analysing audience characteristics utilizing anonymized data from digital platforms in accordance with an embodiment of the present disclosure;

[0016] FIG. 2 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure; and

[0017] FIG. 3 illustrates a flow chart representing the steps involved in a method for analysing audience characteristics utilizing anonymized data from digital platforms in accordance with an embodiment of the present disclosure.

[0018] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

DETAILED DESCRIPTION
[0019] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

[0020] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or subsystems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.

[0022] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

[0023] In accordance with an embodiment of the present disclosure, a system for analysing audience characteristics utilizing anonymized data from digital platforms is provided. The system includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a data collection module configured to collect data of a plurality of users associated to an entity from a plurality of digital platforms, wherein the data is uploaded by the entity based on interactions between the plurality of users and the entity wherein the entity comprises one or a merchant, provider, retailer and shopkeeper. The data collection module is also configured to obtain a matched data from the plurality of digital platforms, wherein the matched data comprises a plurality of phone numbers and email addresses corresponding to the plurality of user to ensure that the data is validated. The processing subsystem includes an analysis engine operatively coupled to the data collection module wherein the analysis engine is configured to combine and aggregate the data by utilizing a statistical model. The analysis engine is also configured to process the data to identify a plurality of behavioural patterns comprising traits and trends associated to the plurality of users. The analysis engine is also configured to group and segment the data into a plurality of cohorts, attributes and group the plurality of users with similar traits into distinct segments. The processing subsystem includes a dashboard interface operatively coupled to the analysis engine wherein the dashboard interface is configured to display the data and enable the plurality of digital platforms to view and interpret the plurality of behavioural patterns.
[0024] FIG. 1 is a block diagram representation of a system for analysing audience characteristics utilizing anonymized data from digital platforms in accordance with an embodiment of the present disclosure. The system (100) includes a processing subsystem (105) hosted on a server (108), wherein the processing subsystem (105) is configured to execute on a network (160) to control bidirectional communications among a plurality of modules. In one embodiment, the server (108) may include a cloud-based server. In one example, the network (160) may be a private or public local area network (LAN) or Wide Area Network (WAN), such as the Internet. In another embodiment, the network (160) may include both wired and wireless communications according to one or more standards and/or via one or more transport mediums. In one example, the network (160) may include wireless communications according to one of the 802.11 or Bluetooth specification sets, or another standard or proprietary wireless communication protocol. In yet another embodiment, the network (160) may also include communications over a terrestrial cellular network, including, a global system for mobile communications (GSM), code division multiple access (CDMA), and/or enhanced data for global evolution (EDGE) network.

[0025] The plurality of modules includes a data collection module (110), an analysis engine (120), and a dashboard interface (130) ,wherein the plurality of modules are operatively coupled to allow a flow of data into the system (100).

[0026] The data collection module (110) is configured to collect data (150) of a plurality of users (145) associated to an entity from a plurality of digital platforms (140). The data (150) is uploaded by the entity based on their interactions between the plurality of users (145). Examples of the entity includes, but is not limited to, one or a merchant, provider, retailer and shopkeeper. The data collection module (110) is adapted to fetch data from multiple digital platforms, wherein multiple digital platforms includes a variety of segments including social media platforms, entertainment platforms, content creating and sharing platforms and the like. The data collection module (110) enables the entity to gain a holistic view of plurality of user (145) behaviour and engagement across different touchpoints. By aggregating interactions between the plurality of users (145) and the entity, the system (100) provides insights into user preferences, needs, and trends. This data-driven approach helps the entity optimize marketing strategies, personalize offerings, and improve customer experiences. The entity refers to any business or organization that interacts with the users (145) including a merchant, provider, retailer, or shopkeeper. These entities engage with the users (145) to offer products, services, or experiences. The data collected helps the entity to understand user (145) behaviour and optimize its offerings or marketing strategies. Additionally, the plurality of user (145) refers to individuals or a group interacting with the entity, who can be categorized including a customer, consumer, audience, based on their engagement or purchase behaviour. These users (145) may represent different segments of the entity's target audience.

[0027] Additionally, the data collection module (110) utilizes the data (150) fetched from the plurality of digital platforms (140), wherein the plurality of digital platforms perform as a third-party interface for the data (150) associated with the plurality of users (145) . The data (150) utilized here is an anonymized data that has been processed to remove and obscure personally identifiable information associated with the plurality of user (145) and adapt to hide an identity of the plurality of user (145) without additional information. Furthermore, the third-party interface includes the plurality of digital platforms including meta, snapchat, tik tok, instagram and the like adapted to fetch the user data (150) .

[0028] Additionally, the data collection module (110 ) is configured to obtain a matched data (150) from the plurality of digital platforms (140), wherein the matched data (150) comprises a plurality of phone numbers and email addresses corresponding to the plurality of user (145) to ensure that the data (150) is validated. The data (150) is intended to be verified and authenticated by the plurality of digital platforms (140) during the user (145) registration . Further, the plurality of user (145) registration is authenticated, wherein the authentication includes validation of a plurality of phone numbers and email addresses and ensures that the user (145) information is accurate and validated .Furthermore, the data collection module utilizes this matched data (145) and forward it for further processing and analysis. Furthermore, the data collection module (110 ) also ensures that the data (150) can be reliably used for segmentation and analysis.

[0029] In one embodiment, the data collection module (110) is adapted to utilize a public application programming interface to download a plurality of data signal and convert it into an insight report, wherein the plurality of data signals are fetched from the plurality of digital platforms (140). The public application programming interface is an accessible interface that enables the third-party including plurality of digital platforms (140) to interact and allowing external applications to integrate functionality and services. Additionally, the data signals are utilized from the public application programming interface and further processed to prepare an insight report , wherein the insight report is adapted to provide an insight about a plurality of behavioural patterns associated with the plurality of users (145).

[0030] The analysis engine (120) is operatively coupled to the data collection module (110). The analysis engine (120) is configured to combine and aggregates the data (150) by applying statistical models to analyse large datasets from a plurality of sources. The statistical models are utilized to identify patterns, correlations, and trends within the data (150). This aggregation merges relevant information into cohesive groups, making it easier to derive actionable insights. The statistical model helps to ensure that the data (150) is analysed accurately and efficiently for decision-making.

[0031] Additionally, the analysis engine (120) is configured to process the data (150) to identify a plurality of behavioural patterns comprising traits and trends associated to the plurality of users (145). The analysis engine (120) by analysing interactions, purchase history, and engagement, detects recurring behaviours that reveal the user (145) interests and habits. The analysis engine (120) utilizes statistical models and machine learning concepts to uncover these patterns and ensure a data-driven decision-making. These insights are crucial for targeted marketing, personalized offerings, and improving the overall user (145) experiences.

[0032] Additionally, the analysis engine (120) is configured to group and segment the data (150) into a plurality of cohorts, attributes and group the plurality of users (145) with similar traits into distinct segments. The grouping and segmenting the data (150) into cohorts and attributes allows for more precise targeting of users (145) based on shared traits and behaviours. This is done by applying clustering and statistical methods to categorize the plurality of users (145) into distinct segments. The plurality of segments and groups includes a range of categories based on the entity and the user (145) base where it is applied. Traditionally, the segments and groups includes age , gender , region, choices, price range and the like.

[0033] In one embodiment, the analysis engine (120) is adapted to utilize display insights from a cohort analysis by organizing the data (150) into visual segments that highlight key trends and behaviours over time and allow for easily interpretation. The analysis engine (120) utilizes the organized data into visual formats including graphs, charts, pie charts, and enables quicker identification of patterns and decision-making. This visualization simplifies the user (145) analysis and helps the entities to tailor their strategies based on clear, actionable insights.

[0034] In one embodiment, the analysis engine (120) is adapted to utilize the visual segments for a creation of targeted creative and media plans by aligning strategies with identified plurality of user (145) preferences and behaviours. The analysis engine (120) examines visual segments to understand the user (145) preferences and behaviours and subsequently applies this data to develop targeted creative and media strategies.

[0035] The dashboard interface (130) is operatively coupled to the analysis engine (120). The dashboard interface (130) is configured to display the data (150) and enable the plurality of digital platforms (140) to view and interpret the plurality of behavioural patterns. The dashboard interface (130) serves as a centralized platform for visualizing and interpreting data (150) from multiple digital platforms (140). The dashboard interface (130) aggregates behavioural patterns and offer the users (145) an intuitive view of trends and insights. In a nutshell the dashboard interface (130) consolidates data (150) from a plurality of digital platforms (140) for easy viewing and analysis.

[0036] In one embodiment, the dashboard interface (130) is a user-friendly dashboard adapted to translate a complex data analysis into an actionable insight without exposing the data. The simplified version of the dashboard interface (130) mitigates the need of additional skilled analysis and can be operated and utilized by someone with basic skillset. The user-friendly dashboard interface (130) is also adapted to retrieve complex data set into simplified form.

[0037] In one embodiment, the system (100) utilizes the statistical model, wherein the statistical model is adapted to utilize a plurality of techniques to safeguard privacy. The statistical model employs a plurality of techniques including data anonymization, encryption, and differential privacy, to protect sensitive information. These methods ensure that individual data (150) points cannot be traced back to specific users (145). By combining these techniques, the statistical model balances privacy protection with the accuracy of analysis.

[0038] In one embodiment, the system (100) ensures compliance with privacy regulations and adhere to digital standard norms by securely handling the user data (150) without compromising privacy. The system (100) is adapted to utilize and strictly adhere to the digital privacy norms and compliance across the globe. The system (100) provides a unified solution to privacy concerns, prevent data violation and adhere to data protection regulations including general data protection regulation , California consumer privacy act and the like.

[0039] Further, the processing subsystem (105) is configured to store data in a database (170) to control data integrity and prevent security breaches. The database (170) is a structured collection of data organized to facilitate efficient access, management, and updating. It serves as a central repository for storing and retrieving information, enabling applications to store, retrieve, and manipulate data easily. The database (170) can range from simple flat file systems to complex relational databases like Structured query language (SQL), which use tables to store data in rows and columns. They are crucial in modern applications for maintaining data integrity, ensuring scalability, and supporting transactions. Common database management systems include MySQL, Oracle, and MongoDB, each offering unique features suited to different use cases and scale requirements.

[0040] FIG. 2 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure. The server (300) includes a processor(s) (330), and memory (310) and a dashboard interface (130). The processor(s) (330), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.

[0041] The memory (310) includes several subsystems stored in the form of executable program which instructs the processor (330) to perform the method steps illustrated in FIG. 1. The memory (310) includes a processing subsystem (105) of FIG.1. The processing subsystem (105) further has following modules: a data collection module (110), an analysis engine (120), and a dashboard interface (130).

[0042] The data collection module (110) configured to collect data of a plurality of users associated to an entity from a plurality of digital platforms, wherein the data is uploaded by the entity based on interactions between the plurality of users and the entity wherein the entity comprises one or a merchant, provider, retailer and shopkeeper. The data collection module (110) is also configured to match the data with a plurality of phone numbers and email addresses corresponding to the plurality of user to ensure that the data is validated. The analysis engine (120) is operatively coupled to the data collection module wherein the analysis engine is configured to combine and aggregate the data by utilizing a statistical model. The analysis engine (120) is also configured to process the data to identify a plurality of behavioural patterns comprising traits and trends associated to the plurality of users. The analysis engine (120) is also configured to group and segment the data into a plurality of cohorts, attributes and group the plurality of users with similar traits into distinct segments. The dashboard interface (130) is operatively coupled to the analysis engine wherein the dashboard interface (130) is configured to display the data and enable the plurality of digital platforms to view and interpret the plurality of behavioural patterns.

[0043] For example, consider a scenario , a retail company, “Z” wants to analyse its customer base across multiple digital platforms to better understand purchasing behaviours and preferences. To do this, the company Z implements a system designed to analyse audience characteristics using anonymized data. The system’s (100) data collection module (110) gathers user data (150) from various touchpoints, including the Z company’s website, mobile app, and social media. It collects anonymized data on browsing history, product preferences, and purchase behaviour, ensuring privacy. The data collection module (110) obtains the validated data (150) associated with the plurality of users (145), wherein the plurality of digital platforms (140) are accountable for the user data (150) validation. The collected data (150) is then passed to the analysis engine (120), which uses a statistical model to aggregate and process the information. The analysis engine (120) identifies key behavioural patterns including peak purchasing times, popular product categories, and recurring customer preferences. These insights allow Z company to understand trends across different user (145) segments. The analysis engine (120) further segments users (145) into cohorts, including high-value customers, frequent buyers, and bargain hunters, based on specific behaviours. For instance, one cohort may consist of weekend shoppers, while another focuses on customers who primarily buy during sales event. All this data (150) is displayed through a dashboard interface (130), allowing the Z company marketing and sales teams to interpret behavioural trends easily. The dashboard interface (130) shows which products are trending, which user (145) segments are most likely to convert, and the overall effectiveness of current marketing efforts. With this information, the Z company can create more targeted campaigns and personalized product recommendations, enhancing customer engagement. The system (100) allows the Z company to optimize its marketing strategies, increase conversion rates, and fine-tune inventory management based on real-time insights. By using this system (100), the Z company can allocate resources more efficiently, tailoring promotions to the specific user (145) groups while ensuring data (150) privacy utilizing anonymization. Ultimately, the integration of plurality of modules helps the Z company to provide a more personalized shopping experience, boosting business growth while maintaining trust and privacy.

[0044] FIG. 3 illustrates a flow chart representing the steps involved in a method for analysing audience characteristics utilizing anonymized data from digital platforms in accordance with an embodiment of the present disclosure. The method (200) includes collecting, by a data collection module, data of a plurality of users associated to an entity from a plurality of digital platforms, wherein the data is uploaded by the entity based on interactions between the plurality of users and the entity wherein the entity comprises one or a merchant, provider, retailer and shopkeeper in the step (205).

[0045] The method (200) includes obtaining, by the data collection module, the matched data from the plurality of digital platforms , wherein the matched data comprises a plurality of phone numbers and email addresses corresponding to the plurality of user to ensure that the data is validated in the step (210).The data collection module utilizes the matched data obtained from the plurality of digital platforms. The plurality of digital platforms verifies the associated user data during an initial registration. This validation ensures that the data corresponds to the correct individuals, improving data reliability.

[0046] The method (200) includes combining and aggregating, by an analysis engine, the data by utilizing a statistical model in the step (215). The analysis engine combines and aggregates data by applying the statistical model to identify patterns and relationships. This process helps in generating insights from diverse data sources for informed decision-making.

[0047] The method (200) includes processing, by the analysis engine, the data to identify a plurality of behavioural patterns comprising traits and trends associated to the plurality of users in the step (220). The analysis engine processes the data by examining user interactions and behaviours to identify recurring traits and trends. This helps in recognizing distinct behavioural patterns associated with different user groups.

[0048] The method (200) includes grouping and segmenting, by the analysis engine, the data into a plurality of cohorts, attributes and group the plurality of users with similar traits into distinct segments in the step (225). The analysis engine groups and segments the data by clustering users with similar traits into distinct cohorts. This segmentation helps in identifying patterns and creating targeted strategies for each user group.

[0049] The method (200) includes displaying the data , by the dashboard interface, and enable the plurality of digital platforms to view and interpret the plurality of behavioural patterns in the step (230). The dashboard interface displays the aggregated data visually, allowing users to explore and interpret behavioural patterns. It integrates insights from multiple digital platforms, making the data accessible and actionable for decision-making.

[0050] Various embodiments of the system and the method and a method for analysing audience characteristics utilizing anonymized data from digital platforms described above enables various advantages. The system (100) for analysing audience characteristics offers significant advantages by leveraging anonymized data from various digital platforms while ensuring user privacy. The data collection module (110) efficiently gathers interactions from multiple platforms, providing businesses with a comprehensive view of customer behaviours without compromising personal identities. The matched data(150) is utilized by the data collection module (110) and progressed for further processing and analysis. The analysis engine (120) processes this data (150) using statistical models to uncover meaningful behavioural patterns and trends, such as purchasing habits or preferred product categories. The system’s (100) ability to segment users (145) into cohorts based on shared traits allows businesses to target specific customer groups more effectively, optimizing marketing campaigns and promotions. The dashboard interface (130) makes it easy for organizations to visualize and interpret the data (150), providing actionable insights in real-time. This enables businesses to make data-driven decisions quickly, improving customer engagement, increasing conversion rates, and enhancing overall operational efficiency. By automating data(150) aggregation and analysis, the system (100) reduces manual effort and resource allocation, allowing businesses to focus on strategic goals. Overall, the system (100) enhances customer understanding, fosters personalized experiences, and drives business growth while adhering to privacy standards.

[0051] The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, various aspects of the described techniques may be implemented within one or more processors, including one or more microprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components. The term “processor” or “processing subsystem” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry. A control unit including hardware may also perform one or more of the techniques of this disclosure.

[0052] Such hardware, software, and firmware may be implemented within the same device or within separate devices to support the various techniques described in this disclosure. In addition, any of the described units, modules, or components may be implemented together or separately as discrete but interoperable logic devices. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware, firmware, or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware, firmware, or software components, or integrated within common or separate hardware, firmware, or software components.

[0053] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.

[0054] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

[0055] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.
, Claims:1. A system (100) for analyzing audience characteristics utilizing anonymized data from digital platforms comprising:
characterized in that,
a processing subsystem (105) hosted on a server (108), wherein the processing subsystem (105) is configured to execute on a network (160) to control bidirectional communications among a plurality of modules comprising:
a data collection module (110) configured to:
collect data (150) of a plurality of users (145) associated to an entity from a plurality of digital platforms (140), wherein the data (150) is uploaded by the entity based on interactions between the plurality of users (145) and the entity wherein the entity comprises one or a merchant, provider, retailer and shopkeeper; and
obtain a matched data (150) from the plurality of digital platforms (140), wherein the matched data (150) comprises a plurality of phone numbers and email addresses corresponding to the plurality of user (145) to ensure that the data (150) is validated;
an analysis engine (120) operatively coupled to the data collection module (110) wherein the analysis engine (120) is configured to:
combine and aggregate the data (150) by utilizing a statistical model;
process the data (150) to identify a plurality of behavioural patterns comprising traits and trends associated to the plurality of users (145); and
group and segment the data (150) into a plurality of cohorts, attributes and group the plurality of users (145) with similar traits into distinct segments; and
a dashboard interface (130) operatively coupled to the analysis engine (120) wherein the dashboard interface (130) is configured to display the data (150) and enable the plurality of digital platforms (140) to view and interpret the plurality of behavioural patterns.

2. The system (100) as claimed in claim 1, wherein the data collection module (110) is configured to utilize a public application programming interface to download a plurality of data signal and convert it into an insight report, wherein the plurality of data signals are fetched from the plurality of digital platforms (140).

3. The system (100) as claimed in claim 1, wherein the statistical model utilizes a plurality of techniques to safeguard privacy.

4. The system (100) as claimed in claim 1, ensures compliance with privacy regulations and adhere to digital standard norms by securely handling the user data (150) without compromising privacy.

5. The system (100) as claimed in claim 1, wherein the analysis engine (120) is adapted to utilize display insights from a cohort analysis by organizing the data (150) into visual segments that highlight key trends and behaviours over time and allow for easily interpretation.

6. The system (100) as claimed in claim 4, wherein the visual segments is utilized for a creation of targeted creative and media plans by aligning strategies with identified plurality of user (145) preferences and behaviours.

7. The system (100) as claimed in claim 1, wherein the dashboard interface (130) is a user-friendly dashboard adapted to translate a complex data analysis into an actionable insight without exposing the data.

8. A method (200) for analysing audience characteristics utilizing anonymized data from digital platforms comprising:
collecting, by a data collection module, data of a plurality of users associated to an entity from a plurality of digital platforms, wherein the data is uploaded by the entity based on interactions between the plurality of users and the entity wherein the entity comprises one or a merchant, provider, retailer and shopkeeper; (205)
obtaining, by the data collection module, the matched data from the plurality of digital platforms, wherein the matched data comprises a plurality of phone numbers and email addresses corresponding to the plurality of user to ensure that the data is validated; (210)
combining and aggregating, by an analysis engine, the data by utilizing a statistical model; (215)
processing, by the analysis engine, the data to identify a plurality of behavioural patterns comprising traits and trends associated to the plurality of users; (220)
grouping and segmenting, by the analysis engine, the data into a plurality of cohorts, attributes and group the plurality of users with similar traits into distinct segments; (225) and
displaying the data , by the dashboard interface, and enable the plurality of digital platforms to view and interpret the plurality of behavioural patterns. (230)

Dated this 17th day of January 2025
Signature

Gokul Nataraj E
Patent Agent (IN/PA-5309)
Agent for the Applicant