Data analytics is used in the banking sector in several ways: (1) To better understand customer priorities and tailor offers accordingly, (2) To detect fraud by monitoring for imbalances in customer account usage patterns, and (3) To strengthen the customer base by using analytics to identify reasons for customer dissatisfaction and make improvements.
This document discusses customer segmentation and provides details on its various phases and processes. It is divided into the following key sections:
1. It outlines a three phase customer segmentation framework: customer segmentation, planning and execution, and institutionalization.
2. It then provides more details on the customer segmentation analytics process, including defining objectives, identifying relevant variables, data preparation, modeling, scoring, profiling segments, and identifying segment strategies.
3. Various statistical tools for segmentation like cluster analysis and CHAID are mentioned. Example attributes for segmenting banking customers and IT company customers are also listed.
This document discusses consumer buyer behavior and the factors that influence purchasing decisions. It outlines the consumer decision process, which begins with need recognition and information search. The consumer then evaluates alternatives and makes a purchase decision. After purchasing, the consumer experiences post-purchase satisfaction or dissonance based on whether expectations match actual product performance. Key factors like culture, social class, personality, motivation, and perception shape how consumers move through the decision stages.
The document discusses several models of consumer behaviour, including Lawson's model of buying behaviour, factors that influence consumer behaviour, and the buyer decision process. It also covers behaviourist and cognitivist theories of consumer behaviour, and discusses models like the Engel-Kollat-Blackwell model, Howard & Sheth model, and Maslow's hierarchy of needs model.
Introduction to Sales Management – The Sales Organization
– Determining Sales Related Marketing Policies – Sales
Functions and Policies – International Sales Management
– Personal Selling.
Sales Planning – Sales Budgets – Estimating Market
Potential and Forecasting Sales – Sales Quotes – Sales &
Cost Analysis, Sales Force Management: Hiring and Training Sales
Personnel – Time and Territory Management –Compensating Sales Personnel – Motivating the Sales Force
– Leading the Sales Force – Evaluating Sales Force
Performance.
Marketing Logistics - Distribution as Marketing Mix
Element – Distribution Resource Planning – Marketing
Channel Integration – Channel Management – Nature of
Marketing Channels – Evaluating Channel Performance-
Specialized Techniques in selling – Tele Marketing – Web
Marketing
Distribution Cost Analysis: Managing Channel Conflicts –
Channel Information Systems – Wholesaling – Retailing –
Ethical And Social Issues in Sales and Distribution
Management.
1. The document discusses effective customer segmentation for understanding website traffic and properly evaluating marketing campaigns.
2. It recommends starting with a simple segmentation of logged-in visitors to change one's view of web analytics data. Calculating conversion rates from potential customers only is also recommended.
3. Customer cohorts based on acquisition date can provide insights about customer activation and retention. The document recommends not limiting oneself to what web analytics tools offer and making tools analyze customized segments.
Data analytics is used in the banking sector in several ways: (1) To better understand customer priorities and tailor offers accordingly, (2) To detect fraud by monitoring for imbalances in customer account usage patterns, and (3) To strengthen the customer base by using analytics to identify reasons for customer dissatisfaction and make improvements.
This document discusses customer segmentation and provides details on its various phases and processes. It is divided into the following key sections:
1. It outlines a three phase customer segmentation framework: customer segmentation, planning and execution, and institutionalization.
2. It then provides more details on the customer segmentation analytics process, including defining objectives, identifying relevant variables, data preparation, modeling, scoring, profiling segments, and identifying segment strategies.
3. Various statistical tools for segmentation like cluster analysis and CHAID are mentioned. Example attributes for segmenting banking customers and IT company customers are also listed.
This document discusses consumer buyer behavior and the factors that influence purchasing decisions. It outlines the consumer decision process, which begins with need recognition and information search. The consumer then evaluates alternatives and makes a purchase decision. After purchasing, the consumer experiences post-purchase satisfaction or dissonance based on whether expectations match actual product performance. Key factors like culture, social class, personality, motivation, and perception shape how consumers move through the decision stages.
The document discusses several models of consumer behaviour, including Lawson's model of buying behaviour, factors that influence consumer behaviour, and the buyer decision process. It also covers behaviourist and cognitivist theories of consumer behaviour, and discusses models like the Engel-Kollat-Blackwell model, Howard & Sheth model, and Maslow's hierarchy of needs model.
Introduction to Sales Management – The Sales Organization
– Determining Sales Related Marketing Policies – Sales
Functions and Policies – International Sales Management
– Personal Selling.
Sales Planning – Sales Budgets – Estimating Market
Potential and Forecasting Sales – Sales Quotes – Sales &
Cost Analysis, Sales Force Management: Hiring and Training Sales
Personnel – Time and Territory Management –Compensating Sales Personnel – Motivating the Sales Force
– Leading the Sales Force – Evaluating Sales Force
Performance.
Marketing Logistics - Distribution as Marketing Mix
Element – Distribution Resource Planning – Marketing
Channel Integration – Channel Management – Nature of
Marketing Channels – Evaluating Channel Performance-
Specialized Techniques in selling – Tele Marketing – Web
Marketing
Distribution Cost Analysis: Managing Channel Conflicts –
Channel Information Systems – Wholesaling – Retailing –
Ethical And Social Issues in Sales and Distribution
Management.
1. The document discusses effective customer segmentation for understanding website traffic and properly evaluating marketing campaigns.
2. It recommends starting with a simple segmentation of logged-in visitors to change one's view of web analytics data. Calculating conversion rates from potential customers only is also recommended.
3. Customer cohorts based on acquisition date can provide insights about customer activation and retention. The document recommends not limiting oneself to what web analytics tools offer and making tools analyze customized segments.
This document discusses factors retailers consider when selecting store locations and evaluating trade areas. Key factors include economic conditions, competition, fit with the target market, and operating costs of the area. When evaluating a specific site, retailers examine characteristics like traffic flow, location, and costs. They also analyze the trade area to understand customer demographics, size of the primary trading zone, and sales potential. Common tools used include census data, GIS mapping, and models like Huff's gravity model or regression analysis.
This document provides an overview of key concepts in services marketing. It introduces the objectives of Module 1 on services, which are to explain what services are, identify service trends, and outline differences between goods and services. Challenges for services are discussed, including defining quality and ensuring consistent delivery. The services marketing triangle and expanded 7 Ps marketing mix are introduced as frameworks. Gaps in service quality are explained using the gaps model. Characteristics of services like intangibility and simultaneous production/consumption are reviewed.
The Engel, Blackwell and Kollat's (EKB) model proposes that consumers go through five stages in any purchase decision: need recognition, information search, evaluation of alternatives, purchase decision, and post-purchase behavior. It was developed to organize the growing body of research on consumer decision making and shows the relationships between the different components involved in the consumer choice process.
"Consumer behaviour may be defined as the behaviour that consumers display in searching for, purchasing, evaluating and disposing of produces, services and ideas which they expect will satisfy their needs."
The document discusses buyer-seller dyads and different types of personal selling situations. It describes the relationship between a salesperson and prospect as a buyer-seller dyad. Personal selling situations are divided into two groups: service selling which includes order takers, delivery salespeople, route salespeople, missionaries, and technical salespeople; and developmental selling which includes creative salespeople of tangibles and intangibles. Recent trends in selling discussed include relationship selling, consultative selling, team selling, and sales force automation.
This ppts describes the application of traditional method of segmentation, targeting & positioning in the digital arena and tools that are used for STP
The consumer has been the king for quite a while now. Why then are organizations struggling to engage the consumer, personalize its offering and maximize the value that they can realize.
BRIDGEi2i presents a comprehensive, end to end Consumer Analytics solution that helps you know your consumer better, predict purchasing decisions and personalize recommendations
This document outlines three main theories of retail development: environmental, cyclical, and conflictual. The environmental theory states that retailers must adapt to changes in their operating environment like technology, the economy, and demographics in order to survive. The cyclical theory includes the wheel of retailing and accordion theory, which propose that retailers go through different phases or change formats over time. Finally, the conflict theory suggests that new retail formats emerge through a dialectic process as different types of retailers compete with each other and eventually blend to create innovative new formats.
Consumer research consists of systematically collecting and analyzing data to aid decision makers in developing goods, services, and ideas. There are two main types of research: qualitative exploratory research that attempts to understand a phenomenon and provide initial insights, and quantitative conclusive research that confirms preliminary insights and informs appropriate actions. The research process involves defining objectives, designing the study, collecting data through various methods like surveys and experiments, analyzing the results, and presenting findings. Ethics require research to maintain confidentiality, be unbiased, and relevant to its context.
The document discusses how companies manage sales territories and quotas, including how they design sales territories to optimize coverage and evaluate performance, plan efficient routes and schedules for salespeople, and set quotas to motivate salesforce and identify strengths and weaknesses. Territories are assigned based on geographic areas and customers, while quotas are set using various methods like past sales, market estimates, and salesperson input to be realistic goals.
The document discusses trading area analysis, which involves analyzing geographic areas containing potential customers for a firm. Key benefits include uncovering customer demographics, identifying opportunities and strategies, and determining promotional focus. Important factors to analyze include population characteristics, labor availability, proximity to suppliers, competition, and regulations. The goal is to evaluate and select the most desirable trading area for a retail store location.
This Module discuss the topic related to Understanding CRM, Need for CRM, CRM Objectives, Goals of CRM, How CRM helps Business, Essentials for CRM strategies, Acquisition Strategies, Referrals Programs, Retention Strategies, Why should you care about existing customers, Welcoming the Customers, Customer Responsiveness for CRM initiative, How to be Responsive to Customer, Customer Recognition, and Personalization and various Cases on Retention Strategies and Personalization.
This document discusses marketing policies related to sales, including product, distribution, and pricing policies. It provides details on key considerations for each type of policy, such as determining product lines, distribution intensity strategies, approaches to pricing relative to competition and costs. The document also discusses sales strategies, including account targeting, relationship strategies, selling strategies, and sales channel strategies. Overall, the document provides an overview of various sales and marketing policies and strategies that can guide a company's sales efforts.
This document provides an overview of customer segmentation techniques and applications for telecommunications. It defines customer segmentation as splitting a customer database into meaningful groups based on specific criteria. The goals are to gain customer insights, enable targeted marketing, and achieve competitive advantages. Various types of segmentation are described, including structural, categorical, and behavioral. Examples are given using dimensions like tenure, profitability, and risk. Effective customer metrics, technologies, infrastructure, and the segmentation lifecycle are also outlined.
BB Chapter Seven : Post Purchase Processes, Customer Satisfaction and LoyaltyBBAdvisor
The document discusses post-purchase processes, customer satisfaction, and consumer loyalty. It covers topics like post-purchase dissonance, product use and non-use, product disposal, purchase evaluation, customer satisfaction, and repeat purchase behavior. The key aspects of each topic are explained through examples and frameworks. Customer satisfaction is influenced by expectations, perceived performance across instrumental, symbolic, and affective dimensions. Dissatisfaction can lead to actions like taking no action, switching brands, or warning others.
Chadwick Martin Bailey’s Brant Cruz and Jeff McKenna presented best practices of market segmentation based on their years of experience working with clients like eBay, Electronic Arts, Plantronics, and Microsoft.
The document discusses management of sales territories and quotas. It covers procedures for designing sales territories using build-up and breakdown methods. Factors considered when assigning salespeople to territories include ability and effectiveness in the territory. Managing territorial coverage involves planning routes, scheduling time, and using time management tools. Sales quotas are set as goals for sales units and types include sales volume, financial, activity, and combination quotas. Methods used to set quotas include total market estimates, territory potential, past sales experience, executive judgement, salespeople's estimates, and compensation plans.
• This Module discuss the topic related to Type of CRM, The Strategic Framework for CRM, Strategic CRM, Analytical CRM, Analytical CRM answers these questions, Successful analytical CRM solution, Benefits of Analytical CRM, Case on Analytical CRM, Collaborative CRM, Case on Collaborative CRM, Social CRM, Types of Social Media, Understanding Social CRM, Difference Between Traditional and Social CRM, Benefits of SCRM, Risk Associated with SCRM, Steps towards effective SCRM, Critical Success Factors for SCRM.
Banks can leverage machine learning models to increase value through stronger customer acquisition, higher customer lifetime value, and lower operating costs. AI-powered decision making allows for personalized experiences, continuous customer engagement, automated document processing, and early risk detection. Advanced analytical models can be organized around significant elements like the customer lifecycle to benefit banks.
Applications of Data Science in Banking Sector.pptxjojikriparachel
This presentation tells you about the applicability of data science in the banking sector. This will make you understand about the general aspects of the topic.
This document discusses factors retailers consider when selecting store locations and evaluating trade areas. Key factors include economic conditions, competition, fit with the target market, and operating costs of the area. When evaluating a specific site, retailers examine characteristics like traffic flow, location, and costs. They also analyze the trade area to understand customer demographics, size of the primary trading zone, and sales potential. Common tools used include census data, GIS mapping, and models like Huff's gravity model or regression analysis.
This document provides an overview of key concepts in services marketing. It introduces the objectives of Module 1 on services, which are to explain what services are, identify service trends, and outline differences between goods and services. Challenges for services are discussed, including defining quality and ensuring consistent delivery. The services marketing triangle and expanded 7 Ps marketing mix are introduced as frameworks. Gaps in service quality are explained using the gaps model. Characteristics of services like intangibility and simultaneous production/consumption are reviewed.
The Engel, Blackwell and Kollat's (EKB) model proposes that consumers go through five stages in any purchase decision: need recognition, information search, evaluation of alternatives, purchase decision, and post-purchase behavior. It was developed to organize the growing body of research on consumer decision making and shows the relationships between the different components involved in the consumer choice process.
"Consumer behaviour may be defined as the behaviour that consumers display in searching for, purchasing, evaluating and disposing of produces, services and ideas which they expect will satisfy their needs."
The document discusses buyer-seller dyads and different types of personal selling situations. It describes the relationship between a salesperson and prospect as a buyer-seller dyad. Personal selling situations are divided into two groups: service selling which includes order takers, delivery salespeople, route salespeople, missionaries, and technical salespeople; and developmental selling which includes creative salespeople of tangibles and intangibles. Recent trends in selling discussed include relationship selling, consultative selling, team selling, and sales force automation.
This ppts describes the application of traditional method of segmentation, targeting & positioning in the digital arena and tools that are used for STP
The consumer has been the king for quite a while now. Why then are organizations struggling to engage the consumer, personalize its offering and maximize the value that they can realize.
BRIDGEi2i presents a comprehensive, end to end Consumer Analytics solution that helps you know your consumer better, predict purchasing decisions and personalize recommendations
This document outlines three main theories of retail development: environmental, cyclical, and conflictual. The environmental theory states that retailers must adapt to changes in their operating environment like technology, the economy, and demographics in order to survive. The cyclical theory includes the wheel of retailing and accordion theory, which propose that retailers go through different phases or change formats over time. Finally, the conflict theory suggests that new retail formats emerge through a dialectic process as different types of retailers compete with each other and eventually blend to create innovative new formats.
Consumer research consists of systematically collecting and analyzing data to aid decision makers in developing goods, services, and ideas. There are two main types of research: qualitative exploratory research that attempts to understand a phenomenon and provide initial insights, and quantitative conclusive research that confirms preliminary insights and informs appropriate actions. The research process involves defining objectives, designing the study, collecting data through various methods like surveys and experiments, analyzing the results, and presenting findings. Ethics require research to maintain confidentiality, be unbiased, and relevant to its context.
The document discusses how companies manage sales territories and quotas, including how they design sales territories to optimize coverage and evaluate performance, plan efficient routes and schedules for salespeople, and set quotas to motivate salesforce and identify strengths and weaknesses. Territories are assigned based on geographic areas and customers, while quotas are set using various methods like past sales, market estimates, and salesperson input to be realistic goals.
The document discusses trading area analysis, which involves analyzing geographic areas containing potential customers for a firm. Key benefits include uncovering customer demographics, identifying opportunities and strategies, and determining promotional focus. Important factors to analyze include population characteristics, labor availability, proximity to suppliers, competition, and regulations. The goal is to evaluate and select the most desirable trading area for a retail store location.
This Module discuss the topic related to Understanding CRM, Need for CRM, CRM Objectives, Goals of CRM, How CRM helps Business, Essentials for CRM strategies, Acquisition Strategies, Referrals Programs, Retention Strategies, Why should you care about existing customers, Welcoming the Customers, Customer Responsiveness for CRM initiative, How to be Responsive to Customer, Customer Recognition, and Personalization and various Cases on Retention Strategies and Personalization.
This document discusses marketing policies related to sales, including product, distribution, and pricing policies. It provides details on key considerations for each type of policy, such as determining product lines, distribution intensity strategies, approaches to pricing relative to competition and costs. The document also discusses sales strategies, including account targeting, relationship strategies, selling strategies, and sales channel strategies. Overall, the document provides an overview of various sales and marketing policies and strategies that can guide a company's sales efforts.
This document provides an overview of customer segmentation techniques and applications for telecommunications. It defines customer segmentation as splitting a customer database into meaningful groups based on specific criteria. The goals are to gain customer insights, enable targeted marketing, and achieve competitive advantages. Various types of segmentation are described, including structural, categorical, and behavioral. Examples are given using dimensions like tenure, profitability, and risk. Effective customer metrics, technologies, infrastructure, and the segmentation lifecycle are also outlined.
BB Chapter Seven : Post Purchase Processes, Customer Satisfaction and LoyaltyBBAdvisor
The document discusses post-purchase processes, customer satisfaction, and consumer loyalty. It covers topics like post-purchase dissonance, product use and non-use, product disposal, purchase evaluation, customer satisfaction, and repeat purchase behavior. The key aspects of each topic are explained through examples and frameworks. Customer satisfaction is influenced by expectations, perceived performance across instrumental, symbolic, and affective dimensions. Dissatisfaction can lead to actions like taking no action, switching brands, or warning others.
Chadwick Martin Bailey’s Brant Cruz and Jeff McKenna presented best practices of market segmentation based on their years of experience working with clients like eBay, Electronic Arts, Plantronics, and Microsoft.
The document discusses management of sales territories and quotas. It covers procedures for designing sales territories using build-up and breakdown methods. Factors considered when assigning salespeople to territories include ability and effectiveness in the territory. Managing territorial coverage involves planning routes, scheduling time, and using time management tools. Sales quotas are set as goals for sales units and types include sales volume, financial, activity, and combination quotas. Methods used to set quotas include total market estimates, territory potential, past sales experience, executive judgement, salespeople's estimates, and compensation plans.
• This Module discuss the topic related to Type of CRM, The Strategic Framework for CRM, Strategic CRM, Analytical CRM, Analytical CRM answers these questions, Successful analytical CRM solution, Benefits of Analytical CRM, Case on Analytical CRM, Collaborative CRM, Case on Collaborative CRM, Social CRM, Types of Social Media, Understanding Social CRM, Difference Between Traditional and Social CRM, Benefits of SCRM, Risk Associated with SCRM, Steps towards effective SCRM, Critical Success Factors for SCRM.
Banks can leverage machine learning models to increase value through stronger customer acquisition, higher customer lifetime value, and lower operating costs. AI-powered decision making allows for personalized experiences, continuous customer engagement, automated document processing, and early risk detection. Advanced analytical models can be organized around significant elements like the customer lifecycle to benefit banks.
Applications of Data Science in Banking Sector.pptxjojikriparachel
This presentation tells you about the applicability of data science in the banking sector. This will make you understand about the general aspects of the topic.
1. Analytics is increasingly important in the banking industry for applications like risk management, fraud detection, and customer segmentation. Tools like data mining and predictive analytics help banks understand customer behavior and mitigate risks.
2. Analytics supports decision making to increase revenue, reduce costs, and manage risks. This improves customer retention and understanding. Popular analytics tools in banking include R, SAS, and Python.
3. Use cases for banking analytics include customer analytics, fraud analysis, big data analytics, and risk analytics. Analytics provides insights into areas like marketing, compliance, and optimal performance.
This document discusses how financial services companies are using data analytics. It defines data analytics as using new applications and processes derived from independent service providers and banks/insurers to automate insurance, trading, and risk management. Some key points made include:
- Financial services companies are using data science techniques like customer profiling and segmentation to increase cross-selling success rates and improve fraud detection.
- Case studies show how banks like Capital One and Citi are using customer transaction data to develop new products and identify business patterns.
- Data analytics can help financial institutions optimize processes, improve customer service, and inform product and pricing decisions.
- Risks of data analytics include loss of customer focus, diminished margins from new
Marcia Tal’s latest video presentation – Omnichannel Banking: Embedding Banking in Consumers’ Daily Lives – explains why and how the future of retail banking lies in the Omnichannel customer experience.
Business analytics has become prominent in the banking sector as banks deal with large amounts of customer data. Analytics helps banks meet strategic goals beyond basic operations by providing insights into customer behavior from their data. This allows banks to better manage risks, identify fraud, improve customer retention through personalized offers, and place assets like ATMs more efficiently. Major banks in India like HDFC, Axis, and SBI are using analytics for applications like complete customer profiling, risk management, and incorporating social media data to make more informed decisions.
Big Data solution for multi-national BankRitu Sarkar
This document discusses AMZ Bank's plans to implement a big data analytics system using R to maximize the number of active credit card customers and reduce customer churn. It proposes a two-layered data warehouse with a MPP database and HDFS to centralize and democratize customer data. Alpine Miner predictive analytics software would be used on the MPP database to conduct modeling and scoring. Key challenges include transforming the business with big data and aligning the organization for analytics.
Data science is transforming the banking industry by helping banks better understand customers to increase loyalty and operational efficiency. Banks are utilizing large amounts of customer transaction, history, communication and loyalty data to extract insights through various data analysis methods like machine learning, natural language processing and more. This allows banks to perform important tasks like fraud detection, customer segmentation, risk management, marketing and sales, real-time analytics and automating communication channels. Data science is proving critical for banks to stay competitive by improving accuracy, customer service and automating processes for increased efficiency.
Coman Bank of India is seeking to adopt analytics to improve customer retention, cross-selling, and risk assessment. The bank currently has issues with high customer churn of 4% and single-product customers comprising 50% of its base. It aims to reduce churn to less than 2% and increase cross-selling to 40% of customers. The bank also aims to reduce fraud and loan defaults from the current 4% loss level. It has data available in its FINACLE core banking system and enterprise data warehouse. The analytics vendor's scope of work includes customer segmentation, identifying reasons for churn, and risk/fraud analysis. Success will be measured based on ROI, data loading time, scalability, integrity, complexity, and
Coman Bank of India is seeking to adopt analytics to improve customer retention, cross-selling, and risk assessment. The bank currently has issues with high customer churn of 4% and single-product customers comprising 50% of its base. It aims to reduce churn to less than 2% and increase cross-selling to 40% of customers. The bank also aims to reduce fraud and loan defaults from the current 4% loss level. It has data available in its FINACLE banking system and enterprise data warehouse. The proposed scope of work includes customer segmentation, identifying reasons for churn, and risk/fraud analysis. Success will be measured based on ROI, data loading time, scalability, integrity, complexity, and security. The
Business analytics uses statistical analysis of a company's data to gain insights and make data-driven decisions. It aims to identify useful datasets and increase revenue, productivity, and efficiency. Examples of using analytics in business include churn prevention to identify at-risk customers, e-commerce personalization to increase sales, predictive maintenance to avoid downtime, insurance fraud detection to save billions, and automated candidate placement to improve hiring.
Uses of analytics in the field of BankingNiveditasri N
Analytics refers to the systematic analysis of data to derive meaningful conclusions and insights. In banking, analytics is used for applications like customer segmentation, risk modeling, fraud prevention, identifying transaction channels, and predicting customer lifetime value. It allows banks to better understand customers, assess risks, prevent fraud, optimize operations, and increase customer loyalty and profits.
Business Intelligence and Analytics for ICICI BankPrajakta Talathi
ICICI Bank uses business intelligence and analytics for various internal and external processes. Internally, it uses techniques like customer profitability analysis and segmentation to identify profitable vs unprofitable customers. Externally, it uses credit card fraud detection analyzing spending patterns to detect fraudulent transactions. The bank analyzes customer data from transactions using tools like Excel, SPSS to generate insights and make recommendations like personalized offers, cross-selling, retention strategies to increase revenues and profitability.
Introduction to Analytic fields. Data Analytics. What is Analytics. What it takes to be a Analyst, Different Profiles in Analytics fileds, Data science, data analytics, big data profiles, etc
Future and scope of big data analytics in Digital Finance and banking.VIJAYAKUMAR P
Big data analytics is a powerful tool for banking and finance that can increase revenue, enhance customer engagement, and optimize risk. For example, Reliance Jio was able to gain 100 million users in a short time by collecting customer data to design profitable plans. Banks like ICICI have used analytics to improve debt collection, reduce turnaround time, and automate loan allocation. Leading banks now use analytics to personalize customer service, connect with customers on important dates, and provide a unified customer view across channels. As big data applications and analytics continue to grow, it presents career opportunities for finance professionals to adopt these new skills.
- The document discusses recommendations for digitizing banking services based on a comparative study of digital and branch banking.
- A survey found customers prefer digital banking over branches due to convenience and time savings. Key implementation factors are infrastructure, data management, analytics, and user interfaces.
- The recommendations include creating an integrated customer database, origination systems, independent processing support, and data repository to power customized digital products and services.
1. The document discusses how analytics can help microfinance institutions (MFIs) in Kenya sustain themselves given recent regulatory constraints, including by reducing operational costs, improving portfolio management and monitoring, and enhancing regulatory reporting.
2. It notes that MFIs face challenges like maintaining high credit quality and low costs given stricter regulations and macroeconomic conditions in Sub-Saharan Africa. Analytics solutions can help address these by providing insights to optimize processes, products, and resource allocation.
3. Implementing analytics tools could significantly reduce MFIs' operational costs through increased efficiencies, while also decreasing delinquency through better portfolio monitoring and forecasting of trends. This would directly impact their income.
Business intelligence (BI) provides tools for exploring, analyzing, and modeling large amounts of complex data. It consists of statistical modeling, data mining, and multidimensional data exploration technologies. BI is built on well-defined data marts and models customer data to provide customer intelligence. It uses several technologies to support decision making, CRM, customer loyalty, campaign management, and marketing. BI requires integrating data from various sources into a data warehouse where advanced analytics can be performed to generate insights.
This document discusses 10 applications of data mining in the banking industry:
1) Fraud detection to identify patterns of fraudulent activity from internal and external data.
2) Investment banking to analyze customer profiles and historic asset prices to predict returns and prices.
3) Marketing to analyze customer behavior and segment customers to target profitable segments.
4) Risk management to analyze customer profiles and histories to identify and minimize risk in credit approval.
5) Money laundering detection by analyzing transaction patterns and behaviors.
6) Customer experience management by analyzing customer data to personalize offers.
7) Analyzing channel performance like branches and online to optimize strategies.
8) Identifying at-risk customers
Business intelligence (BI) is a broad set of technologies used to gather, store, analyze and provide access to data to help business users make better decisions. BI technologies include reporting, dashboards, data mining, etc. Business analytics (BA) focuses more on predictive analytics using statistical modeling and machine learning to predict future outcomes and optimize decisions. While BI and BA overlap, BI answers questions about past performance, while BA answers questions about why things are happening, what will happen next, and how to optimize outcomes.
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2. MEANING
• By applying data mining and predictive analytics to extract actionable
intelligent insights and quantifiable predictions, banks can gain
insights that encompass all types of customer behavior.
• It can help improve how banks segment, target, acquire and retain
customers.
• Improvements to risk management, customer understanding, risk and
fraud enable banks to maintain and grow a more profitable customer
base.
3. USE OF ANALYTICS
• Data analytics in banking and financial sector play a crucial role in
Informed decision making to drive organizations forward
Improve efficiency
Increase returns
Achieve business goals.
• Monitor and Assess large amounts of customer data
• Create personalized/customized products and services specific to
individual consumers.
4.
5. AREAS OF USE
Fraud detection Managing customer data
Risk modelling for investment banks Personalized marketing
Lifetime value prediction Real-time and predictive analytics
Customer segmentation Customer spending patterns
Transaction channel identification Customer feedback analysis and application
7. Operational Risk Dashboard
• An Operational risk dashboard offers a web-based view of the risk
exposures to the client.
• The solution leverages descriptive analytics, providing latest insights
into risk data and features tools to slice and dice, drill down, filtering
and more, for the risk leadership to make informed decisions.
• Banks can consolidate and refresh the risk dashboard periodically
8. Forensic analytics
• Employing Advanced analytics techniques, Banks and Finance
organizations can learn, understand and analyse fraud transactions
that occurred in the past along with its trends, patterns and other
parameters.
• Advanced modelling techniques could be used to build a machine
learning based predictive model that predicts the probability of any
fraudulent transactions
9. Predictive Maintenance
• To detect the probability of ATM failures
• Enabling better utilization of maintenance staff
• Significantly reducing operational expenses.
10. Customer Analytics
• Advanced analytics techniques can be leveraged to combine big data
sets such as customer demographics, key characteristics, products
held, credit card statements, among others to classify the customer
base and identify similarities and create micro segments among the
customer base.
• Helps to customize marketing campaigns for each individual micro
segments by defining “next-best-product-to-buy” models, improving
the effectiveness of such campaigns.
11. Application screening
• Predictive modelling and machine learning techniques can be utilized
to create a model which accepts the customer details and predicts
the probability of the customer defaulting.
• Bigdata technologies can help in building an efficient screening
process.
• The solution can also assess repayment capability of a customer by
looking at various parameters which is usually impossible via manual
screening.
• It also reduces the probability of an asset turning into a Non-
Performing Assets (NPA).