In the digital age we are living in today, organizations are required to be able to make decisions based upon the information they have. This enables businesses to make decisions based on facts as opposed to gut feelings. Customer relationship management (CRM) technology is at the heart of this transformation. They are venues when businesses can get, assemble together, and study client data from a lot of various areas. Companies trying to figure out how customers usually responded and what their bodies really need have had their work cut out for these individuals since they started employing advanced AI methods like supervised learning, natural language processing, and standard analytics for prediction. This study looks at how AI can be used with typical CRM systems to strengthen how people conduct decisions in regular organizations, making them superior, faster, and tailored to each person. It explains how AI makes CRM more accurate by finding patterns of behavior that aren't visible, automating operations that are done countless times, and offering organizations resources to help them predict what will happen in the future so they are more capable of helping their customers and make things run easier and more efficiently. The study utilizes a pragmatic approach, utilizing an authentic case study to illustrate the procedure's functionality, the problems presented, and the methods put in place to ensure a successful implementation. This method demonstrates how a company can turn unstructured into information that is useful. The most significant outcomes show that CRM apps enabled by AI make businesses much easier to divide clients into groups, guess sales, and help customers with customer service questions. This gives the organization greater standards and an increased probability of beating other companies. The integration also assists in establishing a culture of decision-making that is more forward-thinking, where insights are mainly descriptive but also predictive as well as prescriptive. This article examines how crucial it is to link AI developments to CRM platforms in increasing numbers.
Satyendra Kumar Vanapalli· International Journal of App...· 0 citations
The concept of a business analyst has changed drastically, and the business analyst has now become a 'strategic' 'data-driven' role that supports decision-making at the highest level of the organization. Business analysts have been traditionally understood to be limited in roles, inhale channels, analyzed typified, even recording data growth, and taking long trips to hear what customers have to say. In short, business analysts were seen as passive recipients of customer insight through time-consuming data analysis. However, contemporary business analysts operate within the framework of customer relationship management (CRM) systems that incorporate AI, smart analytics, and process automation tools. In other words, business analysts who work with intelligent CRM systems are active in usage, extracting and applying insights in real-time to formulate strategies driven by customer needs, operational excellence, etc. So, these analysts, together with the AI-powered CRM platforms, herald moving towards more forward-looking, value-creation-oriented modes of analysis through predictive and prescriptive analytics, respectively. This paper characterizes the evolution of business analysis in the set-up of intelligent CRM systems and discusses how the introduction of new technologies along with other factors is influencing the changing roles, methods, and organizational significance of business analysts. This study is qualitative and conceptually based; it does a literature survey on the topic and also an analysis of the current industry practices to identify trends and catalysts for changes. The results show that intelligent CRM systems through features such as task automation, data cleansing, and better customer understanding greatly contribute to making business analysis more efficient, accurate and strategically relevant. Additionally the analysis brings out that business analysts will need increased capabilities to work across departments, continually learn, and be proficient with technology to meet the challenges of the evolving environment.
Satyendra Kumar Vanapalli· International Journal of Eme...· 0 citations
Financial fraud is moving very swiftly in today’s technologically advanced where everything is connected. This makes it extremely challenging for banks and other institutions of finance to follow the rules, preserve their customer trust, and run their businesses with integrity. Traditionally based on rules, detection systems can miss both small and big dangers when the number of transactions goes up and schemes for fraud get more intricate. Combining Machine Learning (ML) with Customer Relationship Management (CRM) data models is a powerful and versatile technique to stop fraud in this instance. Machine learning algorithms can identify hidden problems and anticipate fraud faster and more precisely by integrating information from CRM systems about past interactions with customers, behavior, and transactions in general. This work investigates a methodology that incorporates supervised and unsupervised methods of learning with enhanced CRM datasets in order to create sophisticated detection of fraud models. The methodology demonstrates data preprocessing, standardized feature engineering, and model training based on real financial parameters, including transaction frequency, alterations in typical customer behavior, and assessment of risk ratings. It also says that integrating CRM makes it less difficult for businesses to see the big picture of their customers, which enables these individuals to go from checking transactions by themselves to making decisions according to the situation. The suggested technique is to continually acquire knowledge and enhance the model so that it can keep up with the latest fraud strategies while minimizing the number of false positives that might adversely affect actual customers. This paper demonstrates the fact that banks may find fraud, minimize risks before they happen, and make their clients happy by combining ML and CRM standard data models together in an effective manner.
Satyendra Kumar Vanapalli· International Journal of Mac...· 0 citations
It has been found that CRM in combination with BI could lead to deeper customer understanding, forecasting capabilities, and making marketing strategies customer-specific and has practical value for enterprises in that they should consider not only sophisticated CRM equipment but also data management and human resources to be able to fully benefit from BI interfacing.
Satyendra Kumar Vanapalli· International Journal of App...· 0 citations
This paper describes a system for the detection of fraud, which is both dynamic and adaptable and which is obtained through the synthesis of machine learning techniques and the CRM data streams and shows how this unified method can lead to an increase in detection performance, shortening of the time for the response, and higher customer confidence in comparison to the existing systems.
Satyendra Kumar Vanapalli· International Journal of Mac...· 0 citations
