Predictive Churn Modeling and Proactive Service Using Customer Interaction Data Objectives: 1. To provide a comprehensive understanding of cloud-native architectures and middleware technologies used for designing scalable, resilient, and high-performance financial trading systems. 2. To explain the core concepts of microservices, containerization, orchestration, distributed messaging, and data management that power modern financial platforms and digital banking ecosystems. 3. To demonstrate the practical implementation of advanced technologies such as Kubernetes, Apache Kafka, Redis, gRPC, and AI-driven solutions for real-time trading and financial service delivery. 4. To equip software engineers, solution architects, researchers, and FinTech professionals with the knowledge required to build secure, fault-tolerant, low-latency, and highly observable trading infrastructures. 5. To explore emerging trends in financial technology, including serverless computing, WebAssembly, Artificial Intelligence, Machine Learning, and Decentralized Finance (DeFi), preparing readers for the next generation of cloud-native financial systems. Table of Contents CHAPTER 1 The Foundation of Customer Retention: Concepts and Definitions CHAPTER 2 The Business Value of Predicting Churn: Impact on ROI CHAPTER 3 Sources of Customer Interaction Data: CRM, Logs, and Beyond CHAPTER 4 The Architecture of a Churn Prediction System CHAPTER 5 Data Acquisition and Quality Assessment CHAPTER 6 Preprocessing High-Dimensional Interaction Data CHAPTER 7 Feature Engineering: Creating Meaningful Indicators from Raw Data CHAPTER 8 Exploratory Data Analysis for Churn Patterns CHAPTER 9 Traditional Statistical Methods in Churn Modeling CHAPTER 10 Machine Learning Approaches: From Random Forests to XGBoost CHAPTER 11 Deep Learning for Temporal Interaction Sequences CHAPTER 12 Natural Language Processing for Sentiment-Based Churn Analysis CHAPTER 13 Handling Class Imbalance in Churn Datasets CHAPTER 14 Evaluating Model Performance: Beyond Accuracy CHAPTER 15 Interpreting Black-Box Models for Stakeholder Trust CHAPTER 16 Real-Time Churn Scoring and Pipeline Automation CHAPTER 17 Designing Proactive Service Interventions CHAPTER 18 Personalized Marketing and Customer Success Strategies CHAPTER 19 Ethical Considerations and Data Privacy in Churn Modeling CHAPTER 20 Case Studies and Future Trends in Predictive Analytics
In the dynamic landscape of e-commerce, fostering customer loyalty is critical for sustainable growth and profitability, given the ease with which consumers can switch platforms and the high cost of acquiring new customers. This study explores multifaceted strategies for enhancing customer retention, including loyalty programs, gamification, customer lifetime value (CLV) and churn analytics, and community-based approaches. It examines how data-driven personalization, psychological reward systems, and emotional connections through brand communities drive loyalty. Examples such as Amazon Prime, Sephora’s Beauty Insider, and Nike Run Club illustrate the effectiveness of tailored rewards, gamification, and social engagement. The integration of CLV and churn analytics enables businesses to optimize resources by targeting high-value customers and predicting churn risk. Community strategies, leveraging social media, user-generated content, and events, foster a sense of belonging, particularly among younger demographics. Ethical considerations, including data privacy and transparency, are highlighted as essential for maintaining trust. The study underscores the evolving role of technology, such as AI and Web3, in shaping innovative, customer-centric loyalty strategies for both large and small e-commerce businesses.
As Decentralized Finance (DeFi) develops, understanding user intent behind DeFi transactions is crucial yet challenging due to complex smart contract interactions, multifaceted on-/off-chain factors, and opaque hex logs. Existing methods lack deep semantic insight. To address this, we propose the Transaction Intent Mining (TIM) framework. TIM leverages a DeFi intent taxonomy built on grounded theory and a multi-agent Large Language Model (LLM) system to robustly infer user intents. A Meta-Level Planner dynamically coordinates domain experts to decompose multiple perspective-specific intent analyses into solvable subtasks. Question Solvers handle the tasks with multi-modal on/off-chain data. While a Cognitive Evaluator mitigates LLM hallucinations and ensures verifiability. Experiments show that TIM significantly outperforms machine learning models, single LLMs, and single Agent baselines. We also analyze core challenges in intent inference. This work helps provide a more reliable understanding of user motivations in DeFi, offering context-aware explanations for complex blockchain activity.
Smart contracts play a pivotal role in blockchain ecosystems, and fuzzing remains a critical approach to securing them. However, existing smart contract fuzzers often optimize either seed generation or mutation scheduling in isolation and rely on narrow, fragmented feedback signals, leaving multi-transaction reasoning and stagnation recovery under-explored. In this work, we propose aLarge Language Models(LLMs)-based Multi-feedback Smart Contract Fuzzing framework (LLAMA). Key components of the proposed LLAMA include: (i) a hierarchical prompting strategy that guides LLMs to generate structurally valid, context-aware multi-transaction initial seeds, together with a lightweight pre-fuzzing phase that validates and prioritizes high-potential LLM-generated candidates; (ii) a multi-feedback-guided evolutionary optimization module that jointly optimizes seed selection and mutation scheduling by a group of constraints for driving an LLM-bootstrapped bandit scheduler. (iii) an LLM-guided hybrid fuzzing module that integrates evolutionary fuzzing with a dual-channel recovery mechanism, which concurrently employs asynchronous coverage-stagnation- based LLM reseeding and selective symbolic execution to resolve complex path constraints. Our extensive experiments demonstrate that LLAMA outperforms state-of-the-art fuzzers in both coverage and vulnerability detection. Specifically, it achieves 92% instruction coverage on small contracts and 81% on large contracts, while detecting 132 out of 148 known vulnerabilities across diverse categories. Ablation studies further evidence that the proposed multi-feedback and hybrid recovery strategies have strong impact on LLAMA’s performance. The results explain LLAMA’s effectiveness, adaptability, and practicality in complex smart contract scenarios.
With the rise of decentralized finance (DeFi), liquidity pools have become essential components in token exchange processes on platforms like Uniswap and Sushiswap. These pools enable users to earn returns through fees and encourage active participation in decentralized markets. This study collects historical data from various liquidity pools. These data are examined to derive insights into investor behavior within these pools. Additionally, the analysis focuses on assessing the returns and risks associated with such investments. By providing a comprehensive perspective on these critical aspects, this research aims to guide liquidity providers in making informed decisions while promoting the growth of sustainable and user-centric liquidity pool ecosystems in the DeFi space.
The recent surge in Non-Fungible Tokens (NFTs) has changed the way digital assets have been valued. Of late, market sentiment has become essential in price determination. This study provides insights into this intricate nexus between NFT prices and market sentiments by use of data analytics. We convert categorical NFT variables into sentiment proxies using Pythonbased data preprocessing, visualization, and machine learning models to gauge their effect on price variability. Using Random Forest and Linear Regression as models, the comparison shows that market sentiment affects price variability considerably. Our work advances a noble cause of transparency in decision-making among investors by empowering those who navigate the unstable NFT ecosystem with data-developed perspectives.
Customer Lifetime Value (CLV) is an important metric that measures the total value a customer will bring to a business over their lifetime. The Beta Geometric Negative Binomial Distribution (BGNBD) and Gamma Gamma Distribution are two models that can be used to calculate CLV, taking into account both the frequency and value of customer transactions. This article explains the BGNBD and Gamma Gamma Distribution models, and how they can be used to calculate CLV for NFT (Non-Fungible Token) transaction data in a blockchain setting. By estimating the parameters of these models using historical transaction data, businesses can gain insights into the lifetime value of their customers and make data-driven decisions about marketing and customer retention strategies.
With the evolution of Web 3.0 and the rise of the DeFi ecosystems it is only natural that the paradigms of customer relationship management have changed, and are in urgent need of novel and real-time ways of gaining insights into portfolios and predicting engagement. In this work, we explore the fusion of Salesforce CRM frameworks with machine learning methodologies such as DeFi portfolio management and customer engagement prediction in the context of Web3. The study uses a quantitative research method that targets a sample of 250 users of DeFi platforms among Indian cryptocurrency exchanges. The questionnaires were structured and dealt with portfolio performance metrics, customer engagement scores, and ML model accuracy indicators for the primary data collection. It includes some predictive analytics methodology such as decision tree algorithms, K-Nearest Neighbors (KNN), and hybrids between deep learning models. Their results show that Salesforce CRM systems integrated with ML achieve 87.3 percent accuracy in forecasting the portfolio and achieve 82.6 percent precision in predicting their customer engagement factors. Results show interesting, statistically relevant correlations between the ability to perform real-time data processing technologies and user satisfaction. The results suggest that blockchain-based CRM models improve transparency, security, and personalization within DeFi ecosystems. We add to the ongoing Web3 customer relationship management dialogue and offer guidance in bringing intelligent CRM solutions to existence within decentralized financial platforms
Because block chain technology can improve distributed systems' security, dependability, and resilience, it has been gaining popularity. Research based on this technique has helped a number of fields, including data analysis, finance, remote sensing, and healthcare. The primary characteristics that make block chain technology appealing include distributed ledgers, decentralization, privacy, transparency, and data immutability. However, because there is a chance of a privacy breach, medical records that hold private patient information make this system extremely complex. The purpose of this project is to investigate block chain applications in the healthcare sector. We also include articles that touch on other topics, like the Internet of Things, information management, medicine supply chain tracking, and privacy and security issues. Lastly, we aim to investigate block chain concepts in the medical field by evaluating their advantages and disadvantages and providing direction to other studies in the field. We also provide a summary of the Block chain's techniques. Index Terms—IoT, Block Chain, Healthcare, Smart Contract, Etherum.
Distributed ledgers are common in the industry. Some of them can use blockchains as their underlying infrastructure. A blockchain requires participants to agree on its contents. This can be achieved via a consensus protocol, and several BFT (Byzantine Fault Tolerant) protocols have been proposed for this purpose. How do these protocols differ in performance? And how is this difference affected by the communication network? Moreover, such a protocol would need a timer to ensure progress, but how should the timer be set? This paper presents an analytical model to address these and related issues in the case of crash faults. Specifically, it focuses on two consensus protocols (Istanbul BFT and HotStuff) and two network topologies (Folded-Clos and Dragonfly). The model provides closed-form expressions for analyzing how the timer value and number of participants, faults and switches affect the consensus time. The formulas and analyses are validated with simulations. The conclusion offers some tips for analytical modeling of such protocols.
Rahul Diliprao Tamhane, Aman Grewal, Ishan Sandhu, Prof. Vivek Rastogi · 6 authors
The integration of Artificial Intelligence (AI) and blockchain technologies into Customer Relationship Management (CRM) systems has the potential to revolutionize how organizations engage with their customers, streamline business processes, and ensure data integrity. This review synthesizes recent literature and market reports to highlight the emerging trends, benefits, and challenges of using AI and blockchain in CRM. We explore the capabilities of AI-driven data analytics, predictive modeling, and personalized customer engagement features, as well as the trust and transparency offered by distributed ledger technology. Furthermore, we identify technical, regulatory, and organizational hurdles that must be overcome for successful adoption. The review concludes by suggesting avenues for future research, including the development of standardized protocols, improved interoperability, and ethical frameworks for responsible AI and blockchain use in CRM ecosystems.
With the gradual development and integration of artificial intelligence into various industries, there is also a great range of integration in the financial industry. Therefore, this article focuses on the trend prediction model and financial risk management problems of deep reinforcement learning (DRL), one of the largest branches of artificial intelligence, in the cryptocurrency market. In addition, in the experimental part of this paper, the artificial intelligence machine learning Long short-term memory network (LSTM) model is used to make effective time series prediction and analysis on the relevant data of the cryptocurrency market, so as to make a large-scale analysis to improve the accuracy of market trend prediction and the effectiveness of risk management. In addition, in this experiment, technology-related indicators, emotional states of financial market customers and other content related to large language models are combined. While optimizing investment strategy by using deep reinforcement learning algorithm, machine learning prediction model is also used to capture the time dependence of financial market. The experimental results also show that the predicted results are consistent with the actual value. Therefore, the model has high practical application value in predicting the time series price trend of cryptocurrency in the financial market and indicates that the integrated DRL model framework can further optimize and manage the price and trading strategy of the financial market. Future research should focus on improving the LSTM model and incorporating more features to improve prediction accuracy and adapt to market changes.
The emergence of cryptocurrencies as a form of digital payments has contributed to the emergence of numerous opportunities for the implementation of effective and efficient financial transactions, however, new fraud and money laundering schemes have emerged, as the anonymity and decentralization inherent in cryptocurrencies complicate the process of monitoring transactions and control by governments and law enforcement agencies. This study aims to develop a mechanism for analyzing transactions in the Ethereum cryptocurrency using a Bayesian classifier to identify potentially suspicious transactions that may be related to terrorist financing and money laundering. The Bayesian approach makes it possible to consider the probabilistic characteristics of transactions and their interrelationships to increase the accuracy of detecting anomalous and potentially illegal transactions. For the analysis, data on transactions of the Ethereum currency from June 2020 to December 2022 were taken. The developed mechanism involves determining a set of characteristics of transaction graph nodes that identify the potential for their use in illegal financial transactions and forming intervals of their permissible values. The article presents cryptocurrency transactions as an oriented graph, with the nodes being the entities conducting transactions and the arcs being the transactions between the nodes. In assessing the risks of using cryptocurrencies in money laundering, the number/amount of transactions to and from the respective node, the balance of these transactions (absolute value), and the type of node were considered. The analysis showed that among the 100 largest nodes in the network, 11 were identified as having a «critical» risk level, and the most closely connected nodes were identified. This methodology can be used not only to analyze the Ethereum cryptocurrency but also for other cryptocurrencies and similar networks.
S. Kiruthiga, R. Balamanigandan, R Mahaveerakannan, A. Mary Jenifer
The effectiveness of the Novel Random Forest (RF) Algorithm for predicting cryptocurrency prices was evaluated and compared to the K-Nearest Neighbor (KNN) Algorithm. Machine learning methods were used to develop the two algorithms, and a pretest power analysis was conducted using two groups with the iteration of 10 at 85% of G-power and the setup parameters are alpha = 0.05 and beta = 0.85. Hence the P value is less than 0.005 (P<0.05) there is a statistical significance (p=0.007) between these two algorithms. The Novel RF Algorithm achieved an accuracy of 87.7330%, while the KNN Algorithm achieved an accuracy of 72.1250%. When the two algorithms were compared using an independent sample t-test, the difference in accuracy was found to be statistically significant at 0.760.
Cryptocurrency is a digital asset created using blockchain technology and it has different properties from money that we have known such as US Dollar, Rupiah, Yen, etc. Right now, people use cryptocurrency as an investment instrument. However, choosing a cryptocurrency as an investment instrument is challenging because investors need to consider many attributes of a cryptocurrency project. Also, the number of cryptocurrencies is growing each day. Currently, there are at least 20,000 cryptocurrencies to choose from. Developing a recommender system can shorten investors' cryptocurrency screening process. The recommendation system uses Knowledge Graph Convolutional Network (KGCN). It is a model that effectively reveals the relationship between items by mining the attributes of the items in the knowledge graph. Thus, KGCN is suitable for creating a personalized recommendation. The cryptocurrency knowledge graph used in the study is based on the cryptocurrency dataset on the CoinGecko website crawled using its API. The output from the system is in the form of the top 3 cryptocurrency recommendations for specific users and AUC evaluation scores. The research results show that KGCN can provide relevant recommendation results based on investor's preferences up to 17.55% better evaluation scores compared to other models such as RippleNet.
Considering that cryptocurrencies are now present in practically every financial transaction because they are widely accepted as an alternate means of making payments and exchanging currencies, academics and economists have more opportunities to study cryptocurrency prices. Over the years, investors, traders and investment banks have found it difficult to predict the closing daily price of Ethereum due to its rapid price fluctuation. The daily closing price of cryptocurrency is essential to consider when trading or investing in Ethereum. This report focuses on carrying out a comparative study of the predictive capabilities of deep machine learning algorithms with a stacking ensemble modelling framework using daily historical observations of the price of Ethereum obtained from Coindesk, tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022 and other five covariates (closing price lag1, closing price lag2, noltrend, daytype and month) engineered from the closing price of Ethereum. Seven models are used to compute the forecasts for the daily closing price of Ethereum; these are the recurrent neural network, ensemble stacked recurrent neural network, gradient boosting machine, generalized linear model, distributed random forest, deep neural networks and stacked ensemble for gradient boosting machine, generalized linear model, distributed random forest and deep neural networks. The main evaluation metric used is the mean absolute error. According to MAE, RNN forecasts outperform the other model’s forecasts in this study, producing an MAE of 0.0309.
Websites are a great place for marketers to use new technologies to optimize the price of their products. The idea is to increase the conversion rate of users into customers. With the help of our Shoppers Delight, the owner has a better understanding of its rivals and can set variable rates depending on their costs. Paper aims to provide solutions for vendors to sell products at a good deal and save their valuable time, effort, and money. The best deals will be clearly highlighted on the websites. Web3.0 technologies are an emerging set of technologies that have the potential to impact the way we interact with the internet. These technologies focus on creating a user-friendly interface that users could operate at their own pace, with a wider range of functionality in a couple of clicks. Websites will be more interactive, 3D and seamless. They will have built-in payment gateways and offer online services. The web3.0 is a new internet protocol that is decentralized and peer-to-peer.
Non-fungible tokens (NFTs) are becoming increasingly popular in Play-to-Earn (P2E) Web3 applications as a means of incentivizing user engagement. In Web3, users with NFTs ownership are entitled to monetize them. However, due to lack of objective NFT valuation, which makes NFT value determination challenging, P2E applications ecosystems have experienced inflation. In this paper, we propose a method that enables NFT inflation value management in P2E applications. Our method leverages the contribution-rewards model proposed by Curve Finance and the automated market maker (AMM) of decentralized exchanges. In decentralized systems, P2E Web3 applications inclusive, not all participants contribute in good faith. Therefore, rewards are provided to incentivize contribution. Our mechanism proves that burning NFTs, indicating the permanent removal of NFTs, contributes to managing inflation by reducing the number of NFTs in circulation. As a reward for this contribution, our method mints a compensation (CP) token as an ERC-20 token, which can be exchanged for NFTs once enough tokens have been accumulated. To further increase the value of the CP token, we suggest using governance tokens and CP tokens to create liquidity pools for AMM. The value of the governance token is determined by the market, and the CP token derives its value from the governance token in AMM. The CP token can determine its worth based on the market value of the governance token. Additionally, since CP tokens are used for exchanging NFTs, the value of the NFT is ultimately determined by the value of the CP token. To further illustrate our concept, we show how to adjust burning rewards based on factors such as the probability of upgrading NFTs' rarity or the current swap ratio of governance and CP tokens in AMM.
In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation, at the smart contract level of the blockchain, a customer churn prediction framework based on situational awareness and integrating customer attributes, the impact of project hotspots on customer interests, and customer satisfaction with the project has been built. This framework introduces the background factors in the financial customer environment, and further discusses the relationship between customers, the background of customers and the characteristics of pre-lost customers. The improved Singular Value Decomposition (SVD) algorithm and the time decay function are used to optimize the search and analysis of the characteristics of pre-lost customers, and the key index combination is screened to obtain the data of potential lost customers. The framework will change with time according to the customer’s interest, adding the time factor to the customer churn prediction, and improving the dimensionality reduction and prediction generalization ability in feature selection. Logistic regression, naive Bayes and decision tree are used to establish a prediction model in the experiment, and it is compared with the financial customer churn prediction framework under situational awareness. The prediction results of the framework are evaluated from four aspects: accuracy, accuracy, recall rate and F-measure. The experimental results show that the context-aware customer churn prediction framework can be effectively applied to predict customer churn trends, so as to obtain potential customer data with high churn probability, and then these data can be transmitted to the company’s customer service department in time, so as to improve customer churn rate and customer loyalty through accurate service.