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Machine learning for pain point identification based on outside-in analysis of data
Abstract
In an example embodiment, machine learning is utilized to automatically identify pain points of an entity. More particularly, a random forest search algorithm receives inputs from a number of different outside-in data engines. These inputs are segregated in terms of key performance indexes (KPIs), which is then fed to a random forest decision tree. The random forest search algorithm calculates a p-value for each KPI. This p-value may then be used to identify the most relevant KPIs (such as by ranking the KPIs by their respective p-values). Rules are utilized not to identify the pain points themselves, but instead to define sorting preferences (refine the ranking). Based on a combination of the p-values and the evaluation of the rules, the top-N pain points may be identified.
... providing personalized services, predictive insights, and seamless interactions across multiple channels. From AI-powered chatbots to personalized recommendations, banks leverage AI to deliver hyper-personalized experiences that enhance customer satisfaction and loyalty [13], [14]. ...
The advent of artificial intelligence (AI) has revolutionized traditional banking and financial services by leveraging advanced data analytics and machine learning algorithms. This paper explores how AI disrupts conventional banking paradigms, offering institutions a competitive edge through enhanced customer experiences, risk management, and operational efficiency. By harnessing vast amounts of data, AI algorithms can predict customer behavior, personalize services, and detect fraudulent activities in real-time. Moreover, machine learning models optimize lending decisions, streamline back-office operations, and automate routine tasks, reducing costs and improving scalability. However, AI implementation comes with challenges such as data privacy concerns, algorithmic biases, and regulatory compliance. Successful integration requires a strategic approach encompassing talent acquisition, infrastructure development, and stakeholder collaboration. Overall, AI represents a transformative force in the financial sector, reshaping business models and redefining industry standards. Organizations that embrace AI technologies stand to gain a sustainable competitive advantage in the evolving landscape of banking and financial services.
... Reforms that impose higher capital and liquidity requirements may enhance banks' resilience to financial shocks, thereby improving profitability in the long run. However, overly stringent regulations may also impose significant compliance costs on banks, which can offset the benefits of improved stability and weigh on profitability [13], [14]. ...
In the aftermath of the 2008 financial crisis, regulatory reforms have significantly reshaped the banking landscape, aiming to enhance financial stability and mitigate systemic risks. This study investigates the implications of these reforms on bank profitability across global banking institutions. Utilizing a comprehensive dataset spanning the post-crisis era, our analysis reveals nuanced insights into the relationship between regulatory changes and bank profitability metrics. We employ a combination of quantitative methodologies, including regression analysis and comparative assessments, to discern the effects of regulatory measures on key profitability indicators such as return on assets (ROA) and return on equity (ROE). Our findings suggest a complex interplay between regulatory reforms and bank profitability, highlighting both positive and negative impacts across different institutional contexts. Furthermore, we identify specific regulatory factors that significantly influence profitability outcomes, offering valuable insights for policymakers, regulators, and banking practitioners navigating the evolving regulatory landscape. Overall, this research contributes to a deeper understanding of the dynamics between regulatory interventions and financial performance in the post-crisis era, informing strategic decision-making and fostering stability in the global banking sector.
Artificial intelligence (AI) and machine learning are reshaping customer relationship management (CRM) strategies, leveraging advanced capabilities such as predictive analytics, personalized engines, and customer segmentation within cloud computing environments. This paper provides an in-depth exploration of how AI-driven functionalities, hosted on cloud computing platforms, are enabling tailored and relevant experiences, enhancing customer relationships, and fostering loyalty over time. Modern CRM systems harness vast datasets on customer interactions and behaviors stored in the cloud, which fuel AI algorithms to unveil concealed insights regarding individual preferences, future behaviors, and optimal cross-selling recommendations tailored to each customer. Key AI methodologies powering next-generation CRM systems within cloud environments, including reinforcement learning, neural networks, natural language processing, and computer vision, are analyzed. The paper delves into sample use cases and real-world examples of AI-driven CRM initiatives from industry leaders, showcasing the seamless integration of AI capabilities with cloud-based CRM solutions. Additionally, emerging technologies on the horizon such as affective computing, virtual reality, and the metaverse are explored, presenting novel opportunities to comprehend customers and fulfill their needs in highly personalized, emotionally intelligent ways within cloud environments. Critical considerations for firms implementing AI-enabled CRM in the cloud, such as data privacy, transparent AI, and mitigating algorithmic bias, are also examined. With responsible implementation, AI hosted on cloud computing platforms stands poised to revolutionize CRM, delivering unprecedented levels of personalized relevance at scale and ultimately driving growth in customer lifetime value.
Aim: This study aims to explore the implementation of artificial intelligence-driven customer relationship management (CRM) within the framework of PK Salon Management, investigating both the practical aspects and the challenges and opportunities associated with integrating artificial intelligence into the salon business. Methods: A qualitative research approach, primarily relying on secondary research methods, was employed to gather insights into the adoption of artificial intelligence in PK salons. Literature reviews and case studies related to artificial intelligence in salon management were examined and synthesized. Additionally, interviews with salon partners provided valuable perspectives on the practical implications of artificial intelligence adoption. Results: Evaluation findings indicate that PK Salons are actively embracing automation in appointment scheduling and marketing, alongside increased investment in artificial intelligence applications. However, challenges such as data security concerns and workforce readiness have been identified as barriers to effective integration. Qualitative experiences underscore the importance of overcoming implementation challenges while harnessing artificial intelligence's potential to enhance operations and improve customer engagement. Conclusion: Artificial intelligence-powered CRM systems hold significant potential to revolutionize salon management within the PK Salon context. Addressing challenges such as cost assessment and data security requires proactive measures and collaboration among stakeholders. By fostering a culture of innovation and investing in workforce training, salon establishments can leverage artificial intelligence technology to deliver enhanced customer experiences and gain a competitive edge in the market.
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