An AI-Based Customer Relationship Management Framework for Business Applications (original) (raw)
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Donald Tedom, 2023
In recent years, ecommerce applications in Cameroon have grown significantly, resulting in a wide array of products. However, improving user experience remains a major challenge for existing platforms, particularly with their categorical-based recommendation systems. These systems recommend products based on user purchases within a specific category, which has proven to be inefficient. With the rise of machine learning, alternative solutions offering improved performance have emerged. This report presents the development and evaluation of an ecommerce application with a machine learning-based recommendation system. The project aims to enhance personalized shopping experiences, increase company revenue, and improve customer satisfaction through accurate and relevant product recommendations. The recommendation system employs collaborative filtering, neural networks, content-based, and knowledge-based methods, trained on a dataset of 10,000 records from GroupLens capturing product ratings by hundreds of users in Cameroon. The evaluation indicates promising accuracy (60%), mean reciprocal rank (MRR) of 0.26, mean absolute error (MAE) of 0.214, root mean squared error (RMSE) of 0.370, precision of 0.8, and recall of 0.8. Recently, the ecommerce app was hosted at https://hooyia-market.onrender.com, attracting 13 new user registrations within a week. Where 45 products were added to the cart, with 15 directly influenced by the recommendation system, representing 33.3% of total cart additions. Additionally, the recommendation-driven purchases may lead to a 33.3% increase in sales. The findings demonstrate the hybrid recommendation system's effectiveness in providing accurate and relevant recommendations, significantly enhancing shopping experiences and user engagement in Cameroon. Overall, this project contributes to the field of ecommerce and machine learning, showcasing a successful implementation and evaluation of a hybrid recommendation system tailored for Cameroon. The results serve as a valuable reference for ecommerce companies seeking similar approaches to enhance customer experiences, increase revenue, and foster business growth. Keywords: Ecommerce, Recommendation System, Hybrid Models, Cameroon, Sales Impact, Customer Satisfaction.
Tedom Noutchogouin Donald , 2023
In recent years, ecommerce applications in Cameroon have grown significantly, resulting in a wide array of products. However, improving user experience remains a major challenge for existing platforms, particularly with their categorical-based recommendation systems. These systems recommend products based on user purchases within a specific category, which has proven to be inefficient. With the rise of machine learning, alternative solutions offering improved performance have emerged. This report presents the development and evaluation of an ecommerce application with a machine learning-based recommendation system. The project aims to enhance personalized shopping experiences, increase company revenue, and improve customer satisfaction through accurate and relevant product recommendations. The recommendation system employs collaborative filtering, neural networks, content-based, and knowledge-based methods, trained on a dataset of 10,000 records from GroupLens capturing product ratings by hundreds of users in Cameroon. The evaluation indicates promising accuracy (60%), mean reciprocal rank (MRR) of 0.26, mean absolute error (MAE) of 0.214, root mean squared error (RMSE) of 0.370, precision of 0.8, and recall of 0.8. Recently, the ecommerce app was hosted at https://hooyia-market.onrender.com, attracting 13 new user registrations within a week. Where 45 products were added to the cart, with 15 directly influenced by the recommendation system, representing 33.3% of total cart additions. Additionally, the recommendation-driven purchases may lead to a 33.3% increase in sales. The findings demonstrate the hybrid recommendation system's effectiveness in providing accurate and relevant recommendations, significantly enhancing shopping experiences and user engagement in Cameroon. Overall, this project contributes to the field of ecommerce and machine learning, showcasing a successful implementation and evaluation of a hybrid recommendation system tailored for Cameroon. The results serve as a valuable reference for ecommerce companies seeking similar approaches to enhance customer experiences, increase revenue, and foster business growth. Keywords: Ecommerce, Recommendation System, Hybrid Models, Cameroon, Sales Impact, Customer Satisfaction.
Artificial Intelligence for Customer Relationship Management: Personalization and Automation
Businesses manage and assess client interactions and data using a collection of procedures, strategies, and tools referred to as customer relationship management (CRM). The dynamics of the commercial world have altered as a result of artificial intelligence. As AI solutions change the way marketers do business, customers are discovering it more and more impossible to ignore the significance of employing and investing in them. To successfully react to client inquiries and help increase customer loyalty, businesses have used AI-based CRM. This study employs machine-learning algorithms to analyze personalized and behavioral data from customers to provide companies with an upper hand through rising customer retention rates. When a company attempts to CRM, various stages occur, such as identifying customers depending on geography or financial benefit, retaining the customer, and attracting more customers. A CRM system that can analyze various types of customer data for the benefit of companies has been effectively constructed.
Machine learning based recommender system for e-commerce
IAES International Journal of Artificial Intelligence, 2023
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E-Commerce Product Recommendation System Using Machine Learning Algorithms
International Journal of Computer Science and Information Security (IJCSIS), Vol. 22, No. 3, June 2024, 2024
Algorithms are used in e-commerce product recommendation systems. These systems just recently began utilizing machine learning algorithms due to the development and growth of the artificial intelligence research community. This project aspires to transform how ecommerce platforms communicate with their users. We have created a model that can customize product recommendations and offers for each unique customer using cutting-edge machine learning techniques, we used PCA to reduce features and four machine learning algorithms like Gaussian Naive Bayes (GNB), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), the Random Forest algorithms achieve the highest accuracy of 99.6% with a 96.99 r square score, 1.92% MSE score, and 0.087 MAE score. The outcome is advantageous for both the client and the business. In this research, we will examine the model's development and training in detail and show how well it performs using actual data. Learning from machines can change of ecommerce world. Keywords: Machine Learning, Random Forest, Recommendations System, Decision Tree, PCA, E-commerce.
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International Journal Of Engineering And Computer Science, 2017
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In today's digital landscape and dynamic business needs, deeper customer engagement is imperative for organizational success. In-order to achieve that Customer Relationship Management (CRM) stands as an integral part of any organizational strategies. With the advent of Artificial Intelligence (AI), the way business interacts with customers is up for a giant leap. Integration of AI with CRM empowers businesses to forge deeper customer engagement, harness the potential of predictive analytics and offer personalized customer experiences. This article explores the study on how AI technologies such as machine learning , natural language processing (NLP) and sentiment analysis would revolutionize customer interactions along with sales forecasting and marketing strategies recommendations. Furthermore, the article discusses the importance & usage of AI driven Chatbots and Virtual Assistant with CRM and how it can improve efficiency of customer support processes and improve customer satisfaction.
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The study of customer behavior both in online and offline purchases plays a very important role for the seller. The aim of this study is to identify customers on various parameters and thus re-define policies based on the behavior of customers. This paper works on churn analytics for retaining customers, a market-based analysis for identifying the support and confidence among products and a recommendation system built on the IBCF approach. Churn Analytics helps the seller to answer about whether the customers are leaving there products or services. The goal of every seller is to maintain a low churn rate and thus have large margins and bigger profits. Further, performing a marketbased analysis can be very fruitful for a supermart. This approach helps in organizing the items in a store in an efficient and scientific manner. This paper uses different machine learning algorithms techniques to conduct churn for the given data. It then calculates the accuracy and precision of each model ...
Analysis on Prediction of Customer Purchasing Decisions using Machine Learning
International Journal of Engineering and Advanced Technology, 2021
In our day-to-day life, everyone settles on choices on whether to purchase an item or not. In a couple of cases, the choice depends on cost however on numerous occasions the buying choice is more intricate, still, numerous other reasons may be cogitated prior to the last decision is take. Within large-scale industries, understanding existing consumer’s purchasing behavior towards the product is more important to drive a business to the next level. In the context to expand the business on a large scale understanding, the customer interest is more important. To understand the behavior of customers and their interest in the products we need some new technologies and a large amount of data. In this paper we present a progression of examinations, investigate and analyze the exhibitions of various ML strategies, and talk about the meaning of the discoveries with regards to public arrangement and purchaser buying choice. Utilizing an enormous certifiable informational collection (which wil...
Machine Learning Techniques to Recommend Products in E Commerce A Systematic Review
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Product recommender systems have been an effective approach to overcoming information overload on the Web, with the growing size of online statistics, as recommending the right product based on consumer liking became challenging for e-commerce businesses. The machine learning techniques can be applied to solve it. However, due to the large number of algorithms available in the literature, it is quite difficult to select a suitable machine learning algorithm. Researchers have little information about the best approaches to develop recommender systems for e-commerce using machine learning. Here, we have presented our work as a systematic review of the literature, which surveys to choose machine learning algorithms to recommend products in e-commerce and recognise research opportunities for the researchers in developing recommender systems. The survey concluded that deep learning and neural networks techniques are widely used to predict the right products for recommendation to the customers in e-commerce, because they can be very good at recognising patterns in a way similar to the human brain.