1. A system for evaluating buying behaviour and satisfaction of customers towards online shopping, the system comprising: a data collection module configured to gather the transaction data related to the customer purchases from an online shopping platform; a customer feedback module configured to solicit and receive a customer feedback related to their online shopping experiences; a data analysis module configured to analyze the transaction data and the customer feedback to assess customer buying behaviour and satisfaction; and a report generation module configured to generate the reports based on the analyzed data, said reports indicating customer buying behaviour and satisfaction trends.

2. The system of claim 1, wherein the data collection module is further configured to gather demographic data of the customers.

3. The system of claim 1, wherein the customer feedback module uses an interactive user interface for soliciting customer feedback.

4. The system of claim 1, wherein the customer feedback module includes an automatic sentiment analysis algorithm to evaluate customer satisfaction from received feedback.

5. The system of claim 1, wherein the data analysis module uses machine learning algorithms to assess customer buying behaviour and satisfaction.

6. The system of claim 1, wherein the report generation module is further configured to generate customizable reports.

7. The system of claim 1, wherein the report generation module is further configured to automatically send the generated reports to pre-determined recipients.

8. The system of claim 1, wherein the transaction data includes at least one of: item purchased, time of purchase, price, payment method, and customer location.

9. The system of claim 1, wherein the customer feedback module solicits feedback at various stages of the customer's shopping experience.

10. A method for evaluating buying behaviour and satisfaction of customers towards online shopping, the method comprising the steps of: collecting transaction data related to the customer purchases from an online shopping platform; soliciting and receiving a customer feedback related to their online shopping experiences; analyzing the transaction data and the customer feedback to assess customer buying behaviour and satisfaction; and generating reports based on the analyzed data, said reports indicating customer buying behaviour and satisfaction trends. A SYSTEM AND METHOD FOR EVALUATING BUYING BEHAVIOUR AND SATISFACTION OF CUSTOMERS TOWARDS ONLINE SHOPPING Abstract The present invention introduces a system and method for evaluating customer buying behaviour and satisfaction in online shopping. The system includes a data collection module for gathering transaction and demographic data, a customer feedback module for soliciting and receiving feedback, a data analysis module using machine learning algorithms to assess buying behaviour and satisfaction, and a report generation module for creating detailed, customizable reports. The invention provides a comprehensive understanding of customer behaviour, aiding online shopping platforms to make informed, data-driven decisions and enhancing the overall customer experience. , Claims:Claims :

1. A system for evaluating buying behaviour and satisfaction of customers towards online shopping, the system comprising: a data collection module configured to gather the transaction data related to the customer purchases from an online shopping platform; a customer feedback module configured to solicit and receive a customer feedback related to their online shopping experiences; a data analysis module configured to analyze the transaction data and the customer feedback to assess customer buying behaviour and satisfaction; and a report generation module configured to generate the reports based on the analyzed data, said reports indicating customer buying behaviour and satisfaction trends.

2. The system of claim 1, wherein the data collection module is further configured to gather demographic data of the customers.

3. The system of claim 1, wherein the customer feedback module uses an interactive user interface for soliciting customer feedback.

4. The system of claim 1, wherein the customer feedback module includes an automatic sentiment analysis algorithm to evaluate customer satisfaction from received feedback.

5. The system of claim 1, wherein the data analysis module uses machine learning algorithms to assess customer buying behaviour and satisfaction.

6. The system of claim 1, wherein the report generation module is further configured to generate customizable reports.

7. The system of claim 1, wherein the report generation module is further configured to automatically send the generated reports to pre-determined recipients.

8. The system of claim 1, wherein the transaction data includes at least one of: item purchased, time of purchase, price, payment method, and customer location.

9. The system of claim 1, wherein the customer feedback module solicits feedback at various stages of the customer's shopping experience.

10. A method for evaluating buying behaviour and satisfaction of customers towards online shopping, the method comprising the steps of: collecting transaction data related to the customer purchases from an online shopping platform; soliciting and receiving a customer feedback related to their online shopping experiences; analyzing the transaction data and the customer feedback to assess customer buying behaviour and satisfaction; and generating reports based on the analyzed data, said reports indicating customer buying behaviour and satisfaction trends.

Field of the Invention
[0001] This invention relates to the field of e-commerce and customer analytics, specifically systems and methods for evaluating customer buying behaviour and satisfaction towards online shopping.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] In the era of digital commerce, understanding customer buying behaviour and satisfaction is crucial for the success of online shopping platforms. The rising competition in the e-commerce industry makes it imperative for businesses to offer superior customer experiences to stand out from the crowd and retain their customer base.
[0004] Traditionally, businesses have relied on surveys, interviews, and focus groups to gauge customer satisfaction. While these methods can provide valuable insights, they are often time-consuming, expensive, and potentially biased due to self-reporting. Furthermore, these conventional methods typically fail to capture the complete picture of customer behaviour, as they often overlook transaction data such as the type of products purchased, purchase frequency, and payment methods.
[0005] In the context of customer buying behaviour, many businesses lack the resources to effectively analyze transaction data. This data holds invaluable insights into customer preferences, habits, and satisfaction, which can guide business strategies, product development, and marketing campaigns. However, analyzing such vast amounts of data requires advanced analytics capabilities, which many businesses do not possess.
[0006] To address these challenges, businesses have increasingly turned to data-driven approaches. By leveraging technologies such as data analytics and machine learning, businesses aim to gain a deeper understanding of their customers. However, many existing solutions tend to focus either on analyzing transaction data or on collecting customer feedback. Few offer a comprehensive solution that integrates both aspects into a single system, resulting in a fragmented understanding of customer behaviour and satisfaction.
[0007] Moreover, existing solutions often lack the capability to present the analysis results in an easily understandable manner. Even when businesses successfully gather and analyze data, the insights derived are of little use if they cannot be effectively communicated to stakeholders. Businesses need intuitive and customizable reports that highlight the key trends and insights in a visually appealing and understandable manner.
[0008] Lastly, most existing solutions fail to offer real-time or near-real-time insights. The dynamic nature of customer behaviour and the highly competitive e-commerce landscape necessitate a system that can provide timely insights to enable prompt business decisions.
[0009] In view of these challenges, there is a need for a system that can not only collect and analyze both transaction data and customer feedback but also generate customizable, understandable reports, providing a comprehensive and real-time understanding of customer buying behaviour and satisfaction. Such a system would help businesses make data-driven decisions, enhance customer satisfaction, and ultimately improve business performance. This invention addresses these needs in the industry by providing a comprehensive system and method for evaluating buying behaviour and satisfaction of customers towards online shopping..
[00010] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
[00011] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00012] This invention relates to the field of e-commerce and customer analytics, specifically systems and methods for evaluating customer buying behaviour and satisfaction towards online shopping.
[00013] This invention presents a system and accompanying method to analyze and evaluate buying behaviour and satisfaction of customers towards online shopping. With the evolution of e-commerce and the digital marketplace, understanding customer behaviour and ensuring satisfaction has become critical for businesses. The invention is designed to provide a comprehensive solution to this need.
[00014] The system consists of four major components: a data collection module, a customer feedback module, a data analysis module, and a report generation module.
[00015] The data collection module gathers transaction data related to customer purchases from an online shopping platform. This includes the item purchased, time of purchase, price, payment method, and customer location. Additionally, the module is configured to gather demographic data of the customers, providing a more nuanced understanding of customer behaviour patterns and preferences.
[00016] The customer feedback module solicits and receives feedback related to the customers' online shopping experiences. This is accomplished through an interactive user interface that provides an engaging and easy way for customers to share their thoughts and feelings. Furthermore, the feedback module includes an automatic sentiment analysis algorithm, which evaluates the overall satisfaction of customers from the received feedback, transforming qualitative feedback into quantifiable data.
[00017] The data analysis module utilizes machine learning algorithms to analyze the gathered transaction data and the received customer feedback. These algorithms can discern complex patterns and trends in the data, providing a holistic assessment of customer buying behaviour and satisfaction. This allows the system to understand not only what customers are buying, but also how and why they make their purchasing decisions.
[00018] Finally, the report generation module is designed to generate comprehensive reports based on the analyzed data. These reports visually indicate customer buying behaviour and satisfaction trends, making complex data more accessible and understandable. The report generation module also allows for customization of reports, ensuring the generated insights meet specific needs and preferences. It is further configured to automatically send these generated reports to pre-determined recipients, facilitating efficient communication and prompt action based on the insights.
[00019] The invention also includes a method for evaluating buying behaviour and satisfaction of customers towards online shopping. The method mimics the functionality of the system, encompassing the steps of collecting transaction data, soliciting and receiving customer feedback, analyzing the data, and generating reports based on the analyzed data.
[00020] In summary, the proposed system and method offer a novel approach to understanding customer behaviour and satisfaction in online shopping. By integrating data collection, customer feedback, data analysis, and report generation into one coherent system, the invention provides businesses with a powerful tool to enhance their customer understanding, improve their service, and ultimately increase their success in the online marketplace.
Brief Description of the Drawings
[00021] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00022] Fig. 1 illustrates a system 100 for evaluating buying behaviour and satisfaction of customers towards online shopping, in accordance with an embdoiment of the present disclosure.
[00023] Fig. 2 illustrates a method 200 for evaluating buying behaviour and satisfaction of customers towards online shopping, in accordance with an embdoiment of the present disclosure.
Detailed Description
[00024] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00025] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00026] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00027] This invention relates to the field of e-commerce and customer analytics, specifically systems and methods for evaluating customer buying behaviour and satisfaction towards online shopping.
[00028] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00029] Fig. 1 illustrates a system 100 for evaluating buying behaviour and satisfaction of customers towards online shopping, in accordance with an embdoiment of the present disclosure. The system 100 incorporates a sophisticated amalgamation of interconnected modules: a data collection module 102, a customer feedback module 104, a data analysis module 106, and a report generation module 108.
[00030] In an embodiment, the data collection module can be a crucial module in the system, wherein the data collection module is designed to gather transaction data related to customer purchases from various online shopping platforms. The data collection module retrieves essential data including the type of product purchased, time of purchase, purchase frequency, price, and the mode of payment among other transaction-related details. The data collection module also collects demographic data about the customers, including age, gender, location, and shopping preferences. All these details are pivotal to building a comprehensive customer profile that provides an understanding of customers' buying habits.
[00031] For instance, in one embodiment, the data collection module can be interfaced with a large e-commerce platform like Amazon, Ebay, etc. This allows for a seamless flow of real-time data from the platform into the system for prompt analysis and evaluation. The gathered data can be used for trend analysis, prediction of future buying patterns, and implementation of targeted marketing strategies.
[00032] In an embodiment, the customer feedback module is instrumental in soliciting and receiving customer feedback related to their online shopping experiences. It provides an interactive user interface, enabling customers to conveniently share their experiences, opinions, and suggestions. The customer feedback module employs multiple methods to gather feedback such as online surveys, feedback forms, and even social media analysis. The feedback can be about the shopping experience, product quality, delivery speed, customer service, and any other aspect that impacts customer satisfaction.
[00033] An example of the customer feedback module’s application could involve sending a follow-up email or in-app notification soliciting feedback after a customer makes a purchase or receives their order. The feedback form might include questions about the ease of finding the product, checkout process, payment options, and overall shopping experience. Such feedback provides invaluable qualitative data which, combined with the quantitative transaction data, leads to a holistic understanding of customer behaviour and satisfaction.
[00034] In an embodiment, the data analysis module plays a critical role in making sense of the vast amounts of data collected. The data analysis module employs sophisticated algorithms to analyze both the transaction data and customer feedback to assess buying behaviour and customer satisfaction. In certain embodiments, machine learning algorithms are used, enabling the system to learn and improve its understanding of customer behaviour over time.
[00035] One practical implementation of the data analysis module might involve the application of sentiment analysis algorithms to customer feedback data. This would allow the system to automatically gauge the general sentiment of the customer feedback and correlate it with transaction data. For example, the module could analyze a large volume of feedback and identify patterns, such as high satisfaction rates correlating with repeat purchases or particular delivery methods being associated with negative feedback.
[00036] In an embodiment, the final stage of our system involves the report generation module that is configured to generate comprehensive and insightful reports based on the analyzed data. These reports provide a graphical representation of customer buying behaviour and satisfaction trends. They can be customized to present specific data sets and can be automatically sent to predetermined recipients such as marketing managers, product development teams, or executive leadership.
[00037] For example, a report could visually represent the frequency of purchases over a specified time period, popular purchase times, highest-selling products, or customer satisfaction levels across different product categories. The data can be instrumental in decision-making processes, guiding strategies for product development, marketing, and customer service.
[00038] In an embodiment, the ability to have such an extensive understanding of customer behaviour and satisfaction makes the system invaluable for online shopping platforms. It allows them to provide an optimized shopping experience, foster customer loyalty, and improve overall business performance.
[00039] In conclusion, the disclosed system provides a comprehensive solution to understand and enhance the online shopping experience. It captures a 360-degree view of the customer's journey by gathering transaction data, soliciting feedback, and combining these through sophisticated analysis. The insights gained are then presented in an accessible format, enabling businesses to optimize their strategies and operations.
[00040] In another embodiment, the system can be scaled to integrate with multiple e-commerce platforms. By doing this, the data collection module can gather transaction data from a variety of sources, leading to a more comprehensive dataset. Such multi-platform approach can provide insights into buying behaviour and customer satisfaction across different platforms, enabling comparisons and competitive analysis. For example, the system could integrate with Amazon, eBay, and Alibaba simultaneously, gathering and comparing data from these diverse platforms.
[00041] A further embodiment could incorporate real-time analytics into the data analysis module. Using advanced algorithms and computing power, the system could analyze incoming transaction data and customer feedback as it arrives. This would enable real-time tracking of customer behaviour and satisfaction, allowing for immediate actions or responses by the e-commerce platform if needed. For instance, if there's an uptick in negative feedback related to a specific product, the system can notify the platform to investigate and possibly halt sales of the product immediately.
[00042] Another embodiment of the system could utilize predictive modelling within the data analysis module. Here, based on historical transaction data and customer feedback, the system can predict future buying trends and customer satisfaction levels. These predictions can be used to forecast sales, manage inventory, and even tailor future marketing campaigns. For example, if the system predicts an increase in demand for a certain product, the e-commerce platform can proactively stock up and even ramp up marketing for that product.
[00043] An embodiment of the customer feedback module might implement personalized feedback solicitation. Using customer transaction data, the system can customize feedback forms for each customer based on their purchase history and behavior. Such personalized approach can lead to higher response rates and more detailed feedback. For instance, a customer who frequently purchases books may be asked specific questions about the range of titles available, pricing, and the recommendation system.
[00044] In yet another embodiment, the report generation module could generate reports contextually based on specific events or triggers. For instance, after a new product launch or a major sales event (like Black Friday), the system could generate a specialized report focusing on customer buying behaviour and satisfaction related to these specific events. Such reports can offer focused insights that can guide future event planning and product development.
[00045] By tailoring the system to meet the unique needs of different scenarios, the system can provide more comprehensive and nuanced insights, making it a highly valuable tool for any e-commerce platform seeking to understand and enhance its customers' online shopping experiences.
[00046] In an exemplary aspect, Sarah, the owner of an online retail store, is interested in understanding her customers' buying behavior and satisfaction levels to improve her business strategies. She decides to implement the system for evaluating customer behavior and satisfaction towards online shopping. Firstly, the data collection module of the system gathers transaction data from the online shopping platform used by Sarah's customers. This includes information such as the products purchased, purchase amounts, order frequencies, and other relevant transaction details. Secondly, the customer feedback module is utilized to solicit feedback from customers regarding their online shopping experiences. Sarah sets up a feedback mechanism where customers can provide ratings, reviews, and comments about their satisfaction levels, delivery experience, product quality, and overall shopping experience. Once the data collection and feedback gathering processes are complete, the data analysis module comes into play. It analyzes the transaction data and customer feedback to assess customer buying behavior and satisfaction. The module employs various analytical techniques, such as data mining and statistical analysis, to derive insights from the collected data. Based on the analyzed data, the report generation module generates comprehensive reports that highlight customer buying behavior and satisfaction trends. These reports provide Sarah with valuable insights, including popular products, customer preferences, peak shopping periods, satisfaction levels, and areas for improvement. The reports are presented in an easily understandable format, enabling Sarah to make informed decisions and tailor her strategies accordingly. With the system in place, Sarah can now gain a deeper understanding of her customers' preferences, behaviors, and satisfaction levels. This knowledge allows her to optimize her inventory, personalize marketing campaigns, enhance the customer experience, and address any areas that may be causing dissatisfaction. For example, if the analysis reveals that customers are often dissatisfied with the delivery process, Sarah can focus on improving the logistics and shipping methods to ensure faster and more reliable deliveries. Similarly, if certain products consistently receive positive feedback, Sarah can prioritize promoting and expanding those product lines. Over time, Sarah can track the changes in customer buying behavior and satisfaction trends through the system's reports. By regularly analyzing the data and adapting her strategies based on the insights gained, Sarah can continuously improve her online retail business, enhance customer satisfaction, and drive long-term success.
[00047] In this embodiment, the data collection module of the system is further configured to gather demographic data of the customers. The gathered data includes, but is not limited to, age, gender, location, and shopping preferences. This enhancement enables a more nuanced understanding of customer behaviour as demographic factors greatly influence buying patterns. For instance, young adults may have different shopping preferences and habits compared to older adults.
[00048] In this implementation, the customer feedback module employs an interactive user interface for soliciting customer feedback. This may involve an intuitive web form or in-app pop-up that the customer interacts with to provide their feedback. The interface can be designed to encourage the customer to share their opinions about their shopping experience, product quality, delivery service, and customer support among others. Interactive elements such as rating scales, checkboxes, and open text fields can be utilized to make the feedback process engaging and efficient.
[00049] In this embodiment, the customer feedback module includes an automatic sentiment analysis algorithm to evaluate customer satisfaction from received feedback. This advanced feature allows the system to analyze textual feedback and assign sentiment scores (positive, negative, or neutral) based on the language used by the customer. For example, a feedback saying "I loved the quick delivery and excellent product quality!" would be assigned a positive sentiment score.
[00050] In this variant, the data analysis module uses machine learning algorithms to assess customer buying behaviour and satisfaction. By leveraging machine learning, the system can identify patterns and correlations in the data that may not be readily apparent through conventional analysis. For instance, a machine learning model might reveal that customers who purchase certain types of products are more likely to leave positive feedback, indicating a higher level of satisfaction.
[00051] In this embodiment, the report generation module generates customizable reports. Users can specify the parameters and data they wish to include in the reports, enabling them to focus on the insights most relevant to their needs. For example, a marketing team may wish to

generate a report focusing on the most purchased items in a particular demographic, to guide their promotional strategies.
[00052] In this variant, the report generation module automatically sends the generated reports to predetermined recipients. This could include business stakeholders, team leads, or department heads. For example, a weekly report on customer satisfaction could be automatically generated and emailed to the customer service team lead, providing them with regular insights to improve their strategies and actions.
[00053] In this embodiment, the transaction data collected by the system includes item purchased, time of purchase, price, payment method, and customer location, among other data. This provides a detailed view of the customer's shopping behaviour. For example, the system can identify peak shopping times, popular products, preferred payment methods, or regional shopping trends.
[00054] In this embodiment, the customer feedback module solicits feedback at various stages of the customer's shopping experience. Feedback can be requested post-purchase, post-delivery, or even during the browsing stage. This allows the system to gather feedback on a wide array of aspects, from website navigation and product selection to delivery speed and product satisfaction. By gathering feedback at multiple stages, the system can capture a more comprehensive picture of the overall customer experience.
[00055] Fig. 2 illustrates a method 200 for evaluating buying behaviour and satisfaction of customers towards online shopping, in accordance with an embdoiment of the present disclosure. The method 200 involves several interlinked steps including collecting transaction data, soliciting and receiving customer feedback, analyzing this data, and generating reports to represent customer behaviour and satisfaction trends. At step 202, the first step of this method involves collecting transaction data related to customer purchases from an online shopping platform. This data encompasses several details such as the type of product purchased, the time of purchase, the frequency of purchases, the price of the product, and the method of payment used by the customer. It can also include customer demographic data like age, gender, location, and other customer-specific details. This information provides a comprehensive understanding of the customer's purchasing habits and preferences. For example, the method might begin by interfacing with a large e-commerce platform like Amazon to extract real-time data related to customer purchases. It retrieves data about each transaction, building a rich dataset to be further analyzed for insight into buying patterns and behaviour. At step 204, the next step involves soliciting and receiving customer feedback related to their online shopping experiences. This process includes gathering customer responses about their experiences with the platform, the product, the delivery service, and any other element that may influence customer satisfaction. In practical application, the method might involve sending follow-up emails to customers post-purchase, encouraging them to provide feedback about their shopping experience. An interactive user interface can be used for this purpose, prompting the customer to provide their opinions and suggestions about their experience. At step 206, in the third step, the method calls for analyzing the collected transaction data and customer feedback to assess customer buying behaviour and satisfaction. Advanced algorithms, potentially including machine learning models, are used to derive insights from this data, drawing correlations and uncovering patterns. In practice, this might involve running a sentiment analysis algorithm on the customer feedback data to gauge overall customer sentiment. This data is then cross-referenced with the transaction data to draw insights, such as identifying a correlation between positive feedback and repeat purchases. At step 208, the final step of the method involves generating reports based on the analyzed data. These reports visually represent customer buying behaviour and satisfaction trends, providing stakeholders with clear and concise insights into customer behavior. In a practical sense, this step might involve the creation of various visualizations such as charts, graphs, and heat maps. These visualizations might depict various trends such as most popular products, peak shopping times, and levels of customer satisfaction across different product categories. These reports can be customized to highlight specific data and can be automatically sent to predetermined recipients.
[00056] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00057] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
[00058] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00059] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00060] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.

Claims
I/We Claim:
1. A system for evaluating buying behaviour and satisfaction of customers towards online shopping, the system comprising:
a data collection module configured to gather the transaction data related to the customer purchases from an online shopping platform;
a customer feedback module configured to solicit and receive a customer feedback related to their online shopping experiences;
a data analysis module configured to analyze the transaction data and the customer feedback to assess customer buying behaviour and satisfaction; and
a report generation module configured to generate the reports based on the analyzed data, said reports indicating customer buying behaviour and satisfaction trends.
2. The system of claim 1, wherein the data collection module is further configured to gather demographic data of the customers.

3. The system of claim 1, wherein the customer feedback module uses an interactive user interface for soliciting customer feedback.
4. The system of claim 1, wherein the customer feedback module includes an automatic sentiment analysis algorithm to evaluate customer satisfaction from received feedback.
5. The system of claim 1, wherein the data analysis module uses machine learning algorithms to assess customer buying behaviour and satisfaction.
6. The system of claim 1, wherein the report generation module is further configured to generate customizable reports.
7. The system of claim 1, wherein the report generation module is further configured to automatically send the generated reports to pre-determined recipients.
8. The system of claim 1, wherein the transaction data includes at least one of: item purchased, time of purchase, price, payment method, and customer location.
9. The system of claim 1, wherein the customer feedback module solicits feedback at various stages of the customer's shopping experience.
10. A method for evaluating buying behaviour and satisfaction of customers towards online shopping, the method comprising the steps of:
collecting transaction data related to the customer purchases from an online shopping platform;
soliciting and receiving a customer feedback related to their online shopping experiences;
analyzing the transaction data and the customer feedback to assess customer buying behaviour and satisfaction; and
generating reports based on the analyzed data, said reports indicating customer buying behaviour and satisfaction trends.

A SYSTEM AND METHOD FOR EVALUATING BUYING BEHAVIOUR AND SATISFACTION OF CUSTOMERS TOWARDS ONLINE SHOPPING
Abstract
The present invention introduces a system and method for evaluating customer buying behaviour and satisfaction in online shopping. The system includes a data collection module for gathering transaction and demographic data, a customer feedback module for soliciting and receiving feedback, a data analysis module using machine learning algorithms to assess buying behaviour and satisfaction, and a report generation module for creating detailed, customizable reports. The invention provides a comprehensive understanding of customer behaviour, aiding online shopping platforms to make informed, data-driven decisions and enhancing the overall customer experience. , Claims:Claims
I/We Claim:
1. A system for evaluating buying behaviour and satisfaction of customers towards online shopping, the system comprising:
a data collection module configured to gather the transaction data related to the customer purchases from an online shopping platform;
a customer feedback module configured to solicit and receive a customer feedback related to their online shopping experiences;
a data analysis module configured to analyze the transaction data and the customer feedback to assess customer buying behaviour and satisfaction; and
a report generation module configured to generate the reports based on the analyzed data, said reports indicating customer buying behaviour and satisfaction trends.
2. The system of claim 1, wherein the data collection module is further configured to gather demographic data of the customers.

3. The system of claim 1, wherein the customer feedback module uses an interactive user interface for soliciting customer feedback.
4. The system of claim 1, wherein the customer feedback module includes an automatic sentiment analysis algorithm to evaluate customer satisfaction from received feedback.
5. The system of claim 1, wherein the data analysis module uses machine learning algorithms to assess customer buying behaviour and satisfaction.
6. The system of claim 1, wherein the report generation module is further configured to generate customizable reports.
7. The system of claim 1, wherein the report generation module is further configured to automatically send the generated reports to pre-determined recipients.
8. The system of claim 1, wherein the transaction data includes at least one of: item purchased, time of purchase, price, payment method, and customer location.
9. The system of claim 1, wherein the customer feedback module solicits feedback at various stages of the customer's shopping experience.
10. A method for evaluating buying behaviour and satisfaction of customers towards online shopping, the method comprising the steps of:
collecting transaction data related to the customer purchases from an online shopping platform;
soliciting and receiving a customer feedback related to their online shopping experiences;
analyzing the transaction data and the customer feedback to assess customer buying behaviour and satisfaction; and
generating reports based on the analyzed data, said reports indicating customer buying behaviour and satisfaction trends.