Lecture on "A Practical Exposition of Data Science in the Retail Marketing and Financial Services" delivered by Delali Agbenyegah, Director of Data Science and Analytics, Express, Columbus OH, USA.
Presentation on "A Complete Overview of Data Driven Decision Making in a Quickly Changing Business Environment" given by Isaac Aidoo, Head of Data Analytics, Zoona.
Day 1 Keynote address by Winifred Kotin, Country Director of Superfluid Labs, Ghana on the theme: "The promise of Data Science for Economic Transformation".
This is a presentation in a meetup called "Business of Data Science". Data science is being leveraged extensively in the field of Banking and Financial Services and this presentation will give a brief and fundamental highlight to the evergreen field.
A refresher guide about Data Science and Statistics in the domain of Business Intelligence. This presentation not only covers the data science basics but also the way business intelligence industry is headed.
How is Big Data extending the life of the banking sector?NexSoftsys
Big data is increasingly important in the banking sector due to the large volumes of customer data being handled. Billions have been invested in big data by banks in order to gain insights from customer data. This allows banks to provide personalized customer service, more efficiently detect fraud and errors, improve regulatory compliance, and better analyze customer feedback. Adopting big data solutions helps banks promote better performance and customer loyalty.
This whitepaper is geared to help
bank marketing professionals
understand the scope of marketing
analytics and also on how it can
contribute value to the various
factions of a bank’s marketing
activities.
Big Data presence in the high volume in the data storages can help in various ways to learn more about the need and trends of the current market which will be useful for all type of organizations. Modern information technology used to analyze the relationship between social trends and market insights is a useful way to have indirectly interlinked to customers and their interests from unstructured and semi-structured data. Such analysis will give organizations a broader view towards the practical needs of customers and once banking industry or any industry could know the customers, they can serve better and with more flexibility. In this presentation, team has primarily created the platform and designed the architecture in big data technology for banking industry to maximize the users of credit card.
Role of business analytics in the banking industryVaisakh Nambiar
Banking analytics can help banks improve customer segmentation, acquisition, and retention. It also enhances risk management, customer understanding, and fraud prevention. Examples show how analytics helped a bank reduce customer churn by 15% through targeted campaigns, increase bank revenues by 8% by correcting unnecessary discounts, and increase products per customer three times over through microsegmentation. In conclusion, analytics provides banks marketing advantages and helps optimize risk, compliance, and decision-making.
Presentation on "A Complete Overview of Data Driven Decision Making in a Quickly Changing Business Environment" given by Isaac Aidoo, Head of Data Analytics, Zoona.
Day 1 Keynote address by Winifred Kotin, Country Director of Superfluid Labs, Ghana on the theme: "The promise of Data Science for Economic Transformation".
This is a presentation in a meetup called "Business of Data Science". Data science is being leveraged extensively in the field of Banking and Financial Services and this presentation will give a brief and fundamental highlight to the evergreen field.
A refresher guide about Data Science and Statistics in the domain of Business Intelligence. This presentation not only covers the data science basics but also the way business intelligence industry is headed.
How is Big Data extending the life of the banking sector?NexSoftsys
Big data is increasingly important in the banking sector due to the large volumes of customer data being handled. Billions have been invested in big data by banks in order to gain insights from customer data. This allows banks to provide personalized customer service, more efficiently detect fraud and errors, improve regulatory compliance, and better analyze customer feedback. Adopting big data solutions helps banks promote better performance and customer loyalty.
This whitepaper is geared to help
bank marketing professionals
understand the scope of marketing
analytics and also on how it can
contribute value to the various
factions of a bank’s marketing
activities.
Big Data presence in the high volume in the data storages can help in various ways to learn more about the need and trends of the current market which will be useful for all type of organizations. Modern information technology used to analyze the relationship between social trends and market insights is a useful way to have indirectly interlinked to customers and their interests from unstructured and semi-structured data. Such analysis will give organizations a broader view towards the practical needs of customers and once banking industry or any industry could know the customers, they can serve better and with more flexibility. In this presentation, team has primarily created the platform and designed the architecture in big data technology for banking industry to maximize the users of credit card.
Role of business analytics in the banking industryVaisakh Nambiar
Banking analytics can help banks improve customer segmentation, acquisition, and retention. It also enhances risk management, customer understanding, and fraud prevention. Examples show how analytics helped a bank reduce customer churn by 15% through targeted campaigns, increase bank revenues by 8% by correcting unnecessary discounts, and increase products per customer three times over through microsegmentation. In conclusion, analytics provides banks marketing advantages and helps optimize risk, compliance, and decision-making.
The document discusses business analytics and decision making. It defines key concepts like data warehousing, data mining, business intelligence, descriptive analytics, predictive analytics, and prescriptive analytics. It explains how these concepts are used to extract insights from data to support decision making in organizations. Examples of how different types of analytics can be applied in a retail context are provided.
Predictive and prescriptive analytics: Transform the finance function with gr...Grant Thornton LLP
As all businesses continue to collect, store and analyze more data than ever before, they face growing data challenges to support decision-making. Those who can leverage predictive and prescriptive analytics will differentiate themselves in the marketplace and gain a competitive advantage. In this report by Financial Executives Research Foundation Inc. and Grant Thornton LLP, we highlight insights from in-depth interviews with senior-level executives. These organizations use advanced analytics in their businesses to gain significant profit improvements. See more at - http://gt-us.co/1vv2KU9
DatKnoSys provides business analytics services including customer intelligence, marketing analytics, risk analysis, web analytics, and social media analytics. They help companies make better business decisions by merging internal company data with external market data. Their value proposition is that they are experts in data integration and analysis who can provide clients a clear overview of what is happening in their business and sector, along with actionable recommendations.
This document provides an introduction to predictive analytics. It defines analytics and predictive analytics, comparing their purposes and differences. Analytics uses past data to understand trends while predictive analytics anticipates the future. Business intelligence involves using data to support decision making and aims to provide historical, current and predictive views of business. As technologies advanced, business intelligence evolved from being organized under IT to potentially being aligned under strategy management. Effective communication between business and analytics professionals is important for organizations to benefit from predictive analytics. The business case for predictive analytics includes enabling strategic planning, competitive analysis, and improving business processes to work smarter.
The document describes case studies of various organizations using IBM analytics solutions to address challenges and gain benefits. It provides examples of how Arad Group used analytics to reduce water loss and costs by detecting leaks, how ASTRON analyzed exabytes of astronomical data to accelerate insights, and how Wimbledon used analytics to enhance the fan experience with real-time data and sentiment analysis.
Business analytics is used by industries to maximize operations and is applied in many fields including marketing, sales, finance, and human resources. Companies study consumer behavior patterns through social media, spending habits, and lifestyles to segment markets and identify target audiences. Business analytics tools help marketing and sales teams optimize their strategies, perform competitor analysis, and assess sales performance.
Business intelligence systems are also unable to deal with market volatiles. Infosys' business analytics offerings provide the processes, tools and expertise to extract the most from information investments description.
An introduction to BRIDGEi2i - Analytics Solutions company focused on solving complex based problems based on data mining and advanced analytics on big data. Visit http://www.bridgei2i.com
In this presentation Juan M. Huerta talks about big data adoption process at Citi, realising the technical value of big data and global solutions. Huerta goes on to talk about following a hybrid approach, and the future of analytics, expensive algorithms applied to large datasets. With Citi using these approaches in hopes of getting even wider global recognition.
Predictive Analytics for Customer Targeting: A Telemarketing Banking ExamplePedro Ecija Serrano
This document discusses using predictive analytics and machine learning models to identify customers likely to purchase bank deposits. It tests various techniques including oversampling, undersampling, and generating synthetic data to address class imbalance in the dataset. Models tested include naive Bayes, support vector machines, decision trees, and ensembles. The best performing techniques were under sampling naive Bayes and support vector machines, predicting over 60% of buyers with around 25% of calls. Key factors identified for predicting purchases included customer contact history, economic conditions, time of year, and demographics.
Pi cube banking on predictive analytics151Cole Capital
Predictive analytics can help banks in several key areas:
1) Predictive models can analyze customer data to better understand customers, identify new customers, estimate lifetime value, maximize spending, and reduce attrition.
2) Risk management models can assess default risk, optimize lending policies, and proactively restructure loans to manage credit risk.
3) Revenue models can help target marketing, make customized offers, and increase sales and loyalty by anticipating customer needs.
BI & Big data use case for banking - by rully feranataRully Feranata
Big Data and all about its business case in banking industry - how it will change the landscape and how it can be harness in order organization to stay ahead of the game
The document discusses analytics with big data, describing how businesses are using analytics to gain insights from large datasets. It provides examples of common business questions and the types of analytics that can help answer them, such as forecasting, recommendations, and predictive modeling. The document also introduces Robust Designs, a software company that specializes in business intelligence solutions using their CUBOT product.
Business intelligence (BI) refers to technologies and processes used to gather, store, analyze and provide access to data to help business users make better decisions. BI systems aggregate data from various sources, enrich it with context and analysis, and present it to inform fact-based decisions. Advanced analytics can also be used to predict customer behavior and business trends. BI is important because it provides timely, reliable data to support decision making rather than relying solely on opinions. Major BI trends include mobile, cloud, social media and advanced analytics. BI systems are used across industries for applications like customer segmentation, inventory forecasting, and predicting customer churn.
Highlights of the Business Analytics seminar by Gary Cokins from October 21, 2014 presentation with Illinois CPA Society.
Gary Cokins is an internationally recognized expert, speaker, and author in performance improvement systems and cost management.
http://www.GaryCokins.com
This document discusses how data and advanced analytics are transforming businesses. It notes that $1.6 trillion in value could be created for businesses that embrace data over the next four years. It then provides overviews of different types of analytics (descriptive, diagnostic, predictive, prescriptive) and how analytics are being applied in areas like the Internet of Things, machine learning, and establishing effective data science practices. Machine learning applications discussed include hospital readmissions, stock price prediction, and fraud detection. The document emphasizes that Azure ML can help streamline the challenging data science process by providing tools for collaboration, scaling, and easy model deployment.
What we do; predictive and prescriptive analyticsWeibull AS
Prescriptive Analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision.
Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it.
Prescriptive analytics will also tell what probably will happen, but in addition: when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.
The main task of this talk is to see how Data Science can influence big companies to generate new revenue and more profit.
Subjects that will be addressed in this talk are:
• Understanding a value it brings to corporations on long-term (direct revenue generation not only cost reduction);
• Data Science is important part of digital transformation. Are corporations ready?
• Management dedication on investment;
• Lack of Data Science managers acting as a link between Data Scientists and Business managers. Provide motivation/interesting tasks for Data Scientists while validating investments in business environment;
• Lack of skillful Data scientists;
• Compensation of Data Scientists among other Employees (obviously a different scales needs to be applied);
• Examples of Applied Data Science as revenue generators in Telenor Serbia;
This document discusses big data in the finance industry. It notes that payment, insurance, lending, mortgages, and business capital will all be disrupted by big data. The finance sector will become leaner and less lucrative. Examples are given of how big data is already being used, such as by analyzing customer data to find overlapping insurance policies or predict customer churn. The document emphasizes that big data should be used to improve customer experience and engagement while respecting privacy. Boardrooms must discuss digitization and focus on implementing big data solutions where it can address major pains or opportunities.
Big data analytics can provide acquirers with revenue advantages, improved knowledge of customer needs, and greater operational efficiencies. It allows for enhanced fraud management, loyalty programs, and merchant services through analysis of large, diverse transaction datasets. Realizing these benefits requires integrating multiple data sources and deploying analytical tools to glean insights from both structured and unstructured payment information.
The document discusses business analytics and decision making. It defines key concepts like data warehousing, data mining, business intelligence, descriptive analytics, predictive analytics, and prescriptive analytics. It explains how these concepts are used to extract insights from data to support decision making in organizations. Examples of how different types of analytics can be applied in a retail context are provided.
Predictive and prescriptive analytics: Transform the finance function with gr...Grant Thornton LLP
As all businesses continue to collect, store and analyze more data than ever before, they face growing data challenges to support decision-making. Those who can leverage predictive and prescriptive analytics will differentiate themselves in the marketplace and gain a competitive advantage. In this report by Financial Executives Research Foundation Inc. and Grant Thornton LLP, we highlight insights from in-depth interviews with senior-level executives. These organizations use advanced analytics in their businesses to gain significant profit improvements. See more at - http://gt-us.co/1vv2KU9
DatKnoSys provides business analytics services including customer intelligence, marketing analytics, risk analysis, web analytics, and social media analytics. They help companies make better business decisions by merging internal company data with external market data. Their value proposition is that they are experts in data integration and analysis who can provide clients a clear overview of what is happening in their business and sector, along with actionable recommendations.
This document provides an introduction to predictive analytics. It defines analytics and predictive analytics, comparing their purposes and differences. Analytics uses past data to understand trends while predictive analytics anticipates the future. Business intelligence involves using data to support decision making and aims to provide historical, current and predictive views of business. As technologies advanced, business intelligence evolved from being organized under IT to potentially being aligned under strategy management. Effective communication between business and analytics professionals is important for organizations to benefit from predictive analytics. The business case for predictive analytics includes enabling strategic planning, competitive analysis, and improving business processes to work smarter.
The document describes case studies of various organizations using IBM analytics solutions to address challenges and gain benefits. It provides examples of how Arad Group used analytics to reduce water loss and costs by detecting leaks, how ASTRON analyzed exabytes of astronomical data to accelerate insights, and how Wimbledon used analytics to enhance the fan experience with real-time data and sentiment analysis.
Business analytics is used by industries to maximize operations and is applied in many fields including marketing, sales, finance, and human resources. Companies study consumer behavior patterns through social media, spending habits, and lifestyles to segment markets and identify target audiences. Business analytics tools help marketing and sales teams optimize their strategies, perform competitor analysis, and assess sales performance.
Business intelligence systems are also unable to deal with market volatiles. Infosys' business analytics offerings provide the processes, tools and expertise to extract the most from information investments description.
An introduction to BRIDGEi2i - Analytics Solutions company focused on solving complex based problems based on data mining and advanced analytics on big data. Visit http://www.bridgei2i.com
In this presentation Juan M. Huerta talks about big data adoption process at Citi, realising the technical value of big data and global solutions. Huerta goes on to talk about following a hybrid approach, and the future of analytics, expensive algorithms applied to large datasets. With Citi using these approaches in hopes of getting even wider global recognition.
Predictive Analytics for Customer Targeting: A Telemarketing Banking ExamplePedro Ecija Serrano
This document discusses using predictive analytics and machine learning models to identify customers likely to purchase bank deposits. It tests various techniques including oversampling, undersampling, and generating synthetic data to address class imbalance in the dataset. Models tested include naive Bayes, support vector machines, decision trees, and ensembles. The best performing techniques were under sampling naive Bayes and support vector machines, predicting over 60% of buyers with around 25% of calls. Key factors identified for predicting purchases included customer contact history, economic conditions, time of year, and demographics.
Pi cube banking on predictive analytics151Cole Capital
Predictive analytics can help banks in several key areas:
1) Predictive models can analyze customer data to better understand customers, identify new customers, estimate lifetime value, maximize spending, and reduce attrition.
2) Risk management models can assess default risk, optimize lending policies, and proactively restructure loans to manage credit risk.
3) Revenue models can help target marketing, make customized offers, and increase sales and loyalty by anticipating customer needs.
BI & Big data use case for banking - by rully feranataRully Feranata
Big Data and all about its business case in banking industry - how it will change the landscape and how it can be harness in order organization to stay ahead of the game
The document discusses analytics with big data, describing how businesses are using analytics to gain insights from large datasets. It provides examples of common business questions and the types of analytics that can help answer them, such as forecasting, recommendations, and predictive modeling. The document also introduces Robust Designs, a software company that specializes in business intelligence solutions using their CUBOT product.
Business intelligence (BI) refers to technologies and processes used to gather, store, analyze and provide access to data to help business users make better decisions. BI systems aggregate data from various sources, enrich it with context and analysis, and present it to inform fact-based decisions. Advanced analytics can also be used to predict customer behavior and business trends. BI is important because it provides timely, reliable data to support decision making rather than relying solely on opinions. Major BI trends include mobile, cloud, social media and advanced analytics. BI systems are used across industries for applications like customer segmentation, inventory forecasting, and predicting customer churn.
Highlights of the Business Analytics seminar by Gary Cokins from October 21, 2014 presentation with Illinois CPA Society.
Gary Cokins is an internationally recognized expert, speaker, and author in performance improvement systems and cost management.
http://www.GaryCokins.com
This document discusses how data and advanced analytics are transforming businesses. It notes that $1.6 trillion in value could be created for businesses that embrace data over the next four years. It then provides overviews of different types of analytics (descriptive, diagnostic, predictive, prescriptive) and how analytics are being applied in areas like the Internet of Things, machine learning, and establishing effective data science practices. Machine learning applications discussed include hospital readmissions, stock price prediction, and fraud detection. The document emphasizes that Azure ML can help streamline the challenging data science process by providing tools for collaboration, scaling, and easy model deployment.
What we do; predictive and prescriptive analyticsWeibull AS
Prescriptive Analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision.
Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it.
Prescriptive analytics will also tell what probably will happen, but in addition: when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.
The main task of this talk is to see how Data Science can influence big companies to generate new revenue and more profit.
Subjects that will be addressed in this talk are:
• Understanding a value it brings to corporations on long-term (direct revenue generation not only cost reduction);
• Data Science is important part of digital transformation. Are corporations ready?
• Management dedication on investment;
• Lack of Data Science managers acting as a link between Data Scientists and Business managers. Provide motivation/interesting tasks for Data Scientists while validating investments in business environment;
• Lack of skillful Data scientists;
• Compensation of Data Scientists among other Employees (obviously a different scales needs to be applied);
• Examples of Applied Data Science as revenue generators in Telenor Serbia;
This document discusses big data in the finance industry. It notes that payment, insurance, lending, mortgages, and business capital will all be disrupted by big data. The finance sector will become leaner and less lucrative. Examples are given of how big data is already being used, such as by analyzing customer data to find overlapping insurance policies or predict customer churn. The document emphasizes that big data should be used to improve customer experience and engagement while respecting privacy. Boardrooms must discuss digitization and focus on implementing big data solutions where it can address major pains or opportunities.
Big data analytics can provide acquirers with revenue advantages, improved knowledge of customer needs, and greater operational efficiencies. It allows for enhanced fraud management, loyalty programs, and merchant services through analysis of large, diverse transaction datasets. Realizing these benefits requires integrating multiple data sources and deploying analytical tools to glean insights from both structured and unstructured payment information.
This document summarizes a panel discussion on data-driven marketing. The panelists were from various companies and included Wim Stoop, Jason Foster, Nick Muir, and Michael Whitelegge. They discussed how customer 360 views can help understand customers by combining data from various sources. Traditional approaches kept customer data in silos and had limitations. New approaches use enterprise data hubs to ingest both structured and unstructured data in real-time. Case studies from RBS and M&S showed how customer insights helped improve marketing campaigns and the customer experience. The panel then discussed how customer 360 is now a requirement, ideal first projects, the impact of GDPR regulations, and the importance of culture change for success.
Connaizen is a Personalization Platform to optimize customer communication and increase lifetime value of customers.
Connaizen works with players with rich and scarce customer data including banks and retailers.
This deck is a an open pitch for banking customers
This document provides an overview of Intelli-Global, a direct marketing services agency. It discusses Intelli-Global's capabilities including advanced analytics, proprietary marketing platforms, and performance-based multi-channel campaigns. It also outlines Intelli-Global's typical working relationship with clients, including account management, analytic services, and performance reporting. Finally, it provides examples of Intelli-Global's experience with targeted marketing models and databases to optimize customer acquisition, retention, and profitability.
5 Key Steps to Drive with Fintech Customer JourneysDouglas Karr
Customer loyalty is waning in the financial industry as consumers are presented with a vast array of alternative offerings both on and offline. Careful research and design of customer journeys is having a positive impact on organizations to win, keep, and increase the value of prospects and customers. This is a webinar that I did on behalf of Salesforce.
This document discusses big data analytics and its impact on e-commerce. It begins with background on how data analysis motivates human actions and helps businesses understand customer expectations. It then defines big data as the collection of traditional and digital data used to discover insights. The document outlines how e-commerce businesses can apply big data analytics to identify customer segments, make recommendations, optimize operations, and contact customers at the right time. It also discusses the impact of big data on increasing sales and margins. Finally, it covers methods used in data analysis, benefits of big data for e-commerce, challenges faced in the author's experience with e-commerce projects, and future challenges around privacy and costs.
This document discusses big data analytics trends across various industries. It begins with an overview of key trends driving big data analytics in industries like financial services, travel/hospitality/retail, life sciences, healthcare, communications/media/entertainment, manufacturing, and hi-tech. Specific examples are provided of how big data is used in areas like fraud detection, customer churn analysis, planogram compliance, intelligent item search, predictive sciences, energy management, and telecommunications. Challenges and opportunities of big data analytics are also addressed.
This white paper discusses the importance of credit card customer segmentation for marketing purposes. It describes micro-segmentation, an approach that groups customers into multiple models based on different behaviors and perspectives. Common models include lifestyle, purchase behavior, cash usage, installment preferences, and revolving balances. Segmentation is used to increase customer value, cross-sell other products, optimize risk, and improve communications. The document also promotes two products from Forte Wares: QIWare for general customer analytics and segmentation, and ReadyWare, a specialized tool for credit card data analysis and segmentation.
The Secret to Acquiring and Retaining Customers in Financial ServicesPerficient, Inc.
The document discusses customer intelligence in the financial services industry. It defines customer intelligence as the strategic combination of data, analytics, technology, and operations to acquire and retain customers through data-driven insights. It outlines challenges such as legacy systems, lack of skills, and no coherent strategy. It provides examples of how insurers and banks are using customer data for personalization. It also presents a customer intelligence framework and discusses measures for customer engagement, acquisition, and retention.
U.S. Consumer Banks and the Potential of Location-Based OffersCognizant
The document discusses how location-based offers (LBOs) present an opportunity for U.S. retail banks. As mobile device usage increases and location technologies advance, LBOs are growing across industries like retail and healthcare. However, retailers lack customer spending data to make offers truly relevant. Banks have this valuable customer data from transaction histories. By partnering with retailers and using location data, banks can create highly customized LBOs that drive customer loyalty and sales for both banks and retailers. Success requires strategies that leverage mobile technologies, location data, and analytics to deliver the right offers at the right time based on a customer's unique "Code Halo" of digital data.
Network Conference LMS Big Data Final 1.24.14LMSmith361
This document discusses how non-profits can leverage big data and analytics to improve fundraising. It begins by providing background on big data, defining it as vast volumes of unstructured and fast-moving data from many sources. It then discusses how big data is being used by large companies like UPS and IBM to optimize operations and make data-driven decisions. While non-profits currently rely mostly on smaller, structured data, the document advocates for creatively using even small amounts of data to personalize communications and engage donors across multiple channels. It outlines strategies non-profits can take to clean up data, understand donor behaviors and relationships, target younger audiences, and optimize fundraising efforts over the long term.
Customer Analytics – Win Your Customers and Increase RevenueKavika Roy
https://www.datatobiz.com/blog/customer-analytics/
Corporations across the globe are trying their best to look at the business from a customer-centric view. This exercise opens for them a window to peek into the interests of their clientele and create policies accordingly. But in today’s volatile business environment judgments built simply from past experience or intuition is increasingly unreliable. Customers today are more connected and empowered. Access to the internet all the time has allowed them to become more specific about their needs. They are aware of everything that is trending in the market.
In such a scenario it becomes important for a business owner to predict a customer’s response with respect to his organization. The deeper businesses understand their customers’ preferences and lifestyle habits, the more they are able to attract potential buyers. However, it is not as simple as it seems. It is a big challenge for organizations to understand customer feedback, behavior and needs, well enough so as to make data-driven decisions about what customers are likely to respond to or what they are likely to purchase.
Customer analytics or customer data analytics is that significant insight gained with the help of data science, that allows businesses to use customer data in order to make key business decisions. The information obtained from the process is used for designing effective marketing campaigns, site selection, customer relationship management, and secure decisions for the future.
Insights pertaining to the customer’s feedbacks and responses drive the organizations to directions that help them outperform their competitors. Strategizing everything beginning from their production to their supply far before the demand arises, helps them improve their key performing metrics.
This document presents a business model for attracting the unbanked population in Nigeria to the formal banking system through a new debit card product. It discusses developing a cost-effective product that meets the financial, emotional and aspirational needs of the target market of 18-45 year olds in socioeconomic classes D and E with incomes of 5,000-50,000 naira. The product will work on ATMs, POS, web and mobile channels. It also outlines plans for the business, financials, marketing and assessing the product.
The document discusses how credit card issuers can leverage customer transaction data to optimize profits through targeted customer engagement initiatives. It recommends a 7 step approach: 1) Implement customer analytics to understand purchase trends, 2) Design reports on spending patterns and profitability, 3) Customize campaigns based on customer insights, 4) Identify upselling/cross-selling opportunities, 5) Enhance individual customer profitability, 6) Improve retention through incentives, 7) Redesign value propositions for customer segments. Implementing this approach can potentially triple customer profitability through higher spend and superior retention.
Adtelligence best practise use case guide for automated customer lifecycle ma...ADTELLIGENCE GmbH
The document provides guidance on leveraging customer data with AI across the customer lifecycle for payment and credit card issuers. It discusses challenges they face from changes in regulation and competition. It then outlines how data-driven customer lifecycle management using AI can increase engagement, transactions, cross-selling and retention. The document describes data sources issuers can utilize and provides examples of applying machine learning models at each stage of the customer lifecycle.
The Mindset of Decision-Making: Best Practices to Increase Agility and Visibi...Prolifics
The document discusses operational decision management (ODM) and best practices for increasing agility and visibility with decision automation. It describes how decisions are found throughout business processes in areas like insurance, banking, healthcare, and more. Automating decisions can simplify processes by extracting business rules and encapsulating them in decision models. This allows for centralized management of decisions independent of processes. The document advocates using analytics to continuously improve decisions and processes over time through predictive analysis and optimization. It provides an example reference architecture and use cases for automating customer-related decisions to improve cross-selling based on behaviors and external events.
François Protopapa gave a presentation on how data is changing consumer experiences. He discussed how the amount of data being created is growing exponentially and how businesses can collect consumer data as their most valuable asset. However, he emphasized that data must be properly analyzed and used to make good decisions in order to improve customer experiences and business performance. Protopapa proposed that companies implement a customer intelligence hub and platform to better integrate data across silos, generate insights, and provide an omnichannel customer experience.
Big data has been hyped so heavily that CMO’s are expecting it to be ‘the’ miracle solution in today’s complex marketing environment. However, what we’re actually seeing, is that most companies are already struggling with the small amount of data they already have accumulated. The trouble is most obvious on these three levels: companies don’t know how to manage the data, companies don’t know how to analyze the data so as to yield insights, companies don’t know how to act upon the new insights. Of course technology is needed. But even more so, a cultural shift in how CMO’s run their daily marketing operations is definitely required. The good news is that, once CMO’s have accomplished this cultural change, they usually don’t go back. Because they realize they now have a huge competitive advantage. Now, those forward-thinking CMO’s are able to use customer data to their advantage by delivering more targeted and relevant messages to people. During this session, you will discover how to embrace the power of data and turn it into gold for your company.
Similar to Day 1 (Lecture 4): Data Science in the Retail Marketing and Financial Services (20)
Day 2 (Lecture 1): Introduction to Statistical Machine Learning and ApplicationsAseda Owusua Addai-Deseh
Shakir Mohamed discusses building machine learning and AI capacity in Africa through IndabaX Ghana. The document outlines Shakir's work since 2017 strengthening ML/AI through community partnerships and local leadership development across the continent. It also summarizes a talk given in April 2019 on statistical machine learning principles and their application in areas like science, healthcare, and fairness.
Day 2 (Lecture 3): Deep Learning Fundamentals - Architecture and ApplicationsAseda Owusua Addai-Deseh
Presentation on "Deep Learning Fundamentals - Architecture and Applications" delivered by Kwadwo Agyapon-Ntra, Entrepreneur in Training, Meltwater Entrepreneurial School of Technology.
Workshop on "Data Management - The Foundation of all Analytics" given by John Aidoo, Data Analytics Manager at Central Insurance Company, Van Wert, Ohio.
Lecture on "Machine Learning Applications in Healthcare" delivered by Darlington Akogo, Founder, CEO, and Director of Artificial Intelligence, minoHealth AI Labs.
Workshop on "Building Successful Pipelines for Predictive Analytics in Healthcare" delivered by Danielle Belgrave, PhD, Researcher at Microsoft Research, Cambridge, UK.
This is the welcome address presentation of the maiden Ghana Data Science Summit 2019 (IndabaX Ghana) delivered by Delali Agbenyegah, Chairman of the organizing team.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
7. Data Science ≠ Business Intelligence
Concepts
Business
Intelligence/Reporting Data Science
Questions What happened? What will happened? What if?
Actions Slice and Dice Interact
Data Warehouse, siloed Distributed, real time
Perspective Looking backwards Understanding past but looking forwards
Scope Unlimited, general Specific business question(s)
Output Reports,Tables Model scores, integrated applications
Tools SAP,Cognos,Microstrategy,etc.
Python,R,H20,SAS Enterprise
Miner,Viya,etc.
Applicability Historic,possible confoundings Future,correcting for influences
Sexy? I don't know Very Sexy