The document discusses several case studies of analytics solutions developed by Valiance Solutions for various clients. It describes predictive models developed for product recommendation and customer retention in the insurance industry, sales forecasting for a retailer, and fraud detection for an online lending company. Specific case studies are presented on developing a propensity model for cross-selling insurance policies, a churn scoring algorithm to identify at-risk insurance customers, and a real-time fraud scoring system for consumer loans. The solutions achieved benefits like increased revenues, lower customer retention costs, and decreased fraudulent loan disbursements.
Valiance Solutions provides data-driven solutions to business problems using a team of experienced consultants, data scientists, and developers. They have nearly 30 years of combined experience in analytics and technology. Their services include decision analytics, decision products, data visualization, and they have expertise in domains like retail, financial services, education, and ad tech. They engage with clients through proof of concepts, full projects, or retainer models. Their solutions help clients with problems like customer churn, cross-sell opportunities, and have achieved outcomes like increased revenue and lower costs.
While customer experience is a top priority among business leaders, very few are successful in building a financial business case for their customer-focused efforts. As a result, customer experience improvement initiatives may not get the attention they deserve in the boardroom. In the presentation, “Connect the ROI Dots with a Customer Experience Value Strategy”, Don Ryan, senior partner, iKnowtion, a TeleTech Company, and Elizabeth Glagowski, editor-in-chief, Customer Strategist Journal, discuss concrete steps companies can take to tie customer experience to financial impact. Learn how to:
- Make the quantifiable business case for customer experience initiatives
- Translate NPS, customer experience scores, and other measurements into corporate financial terms using advanced analytics
- Understand which metrics lead to financial outcomes, so you know which levers to pull
Becoming a Psychic Brand: Moving from Concept to Reality to Grow ValuePeppers & Rogers Group
A psychic brand is one that goes beyond simply understanding customer insight and customizing interactions based on that insight. It means sensing and acting on all of the physical and digital signals customers send out – before customers even realize what they need. In the presentation, "Becoming a Psychic Brand: Moving from Concept to Reality to Grow Value," Elizabeth Glagowski, Customer Strategist Journal Editor-in-Chief, and Ron Wince, Peppers & Rogers Group President and General Manager, explore the findings of TeleTech’s Psychic Brands study. Learn:
- Why it’s important to be a psychic brand, and the financial and customer benefits that can be achieved
- Details of the TeleTech Psychic Brands study and the gaps that exist between customer and brand perception
- Which strategies, capabilities, and resources will bridge the gap between wanting to be psychic and actually being psychic – and the roadmap to get there
Digital Demand Generation for Credit Unionsedynamic
This document discusses digital demand generation strategies for credit unions. It covers how the digital landscape and customer experience has changed, with buyers now using multiple devices and channels. It emphasizes the importance of customer satisfaction and retention. The document then discusses developing a digital customer experience plan, including creating buyer personas, mapping the customer journey, and how this can help with acquisition and retention. Finally, it addresses developing a demand generation strategy, including data and content management strategies, and choosing appropriate marketing channels. The overall goal is helping credit unions integrate digital and physical channels into a seamless customer experience.
Pega Next-Best-Action Marketing White PaperVivastream
N-B-A (Next-Best-Action) marketing is an approach that uses real-time customer data and analytics to determine the optimal next action or communication for each individual customer across marketing channels. It aims to improve profitability through more customer-centric interactions. When implemented by O2, an early adopter, N-B-A resulted in a 9% increase in bill value, 75% response rate, and reduced customer retention costs in the first month. N-B-A marketing considers each customer's unique profile and preferences to identify the single best offer or message to provide at any given time, avoiding issues like campaign collisions seen in traditional marketing.
The advent of ‘big data’ has completely changed the way businesses can harness the information about customers to make powerful business decisions. Data could be of any type – campaign information, customer demographics, individual transaction behavior, interactions on social networks, web usage, or satisfaction surveys etc. BRIDGEi2i has the ability and experience to mine this wealth of unstructured and structured information to help businesses identify prospects, target them through the right channel, maximize cross sell and up-sell opportunities and thereby enhance the life time value of customer relationships. To know more visit: http://www.bridgei2i.com/customer-intelligence.html
Case Study: Transforming a Business Through Individual Customer AttentionPeppers & Rogers Group
The document summarizes a case study of a financial services company that underwent an organization-wide transformation to focus on individual customers. As a first step, the company hired Peppers & Rogers Group to identify different customer groups and develop value propositions for each. Peppers conducted quantitative and qualitative research, including customer surveys, interviews, and data analysis to understand customers. They identified customer segments and created profiles for each, along with sales and service models. Based on Peppers' recommendations, the company is now implementing a new approach to uniquely identify and treat individual customers.
The Personalization Revolution: Policyholder Acquisition & Retention in a Di...Peppers & Rogers Group
Service expectations in the insurance industry are on the rise. To keep pace, leading insurers are moving away from a traditional product focus to center business around the customer. In particular, there are a number of opportunities to improve customer acquisition and retention ROI by personalizing marketing, sales, and service interactions. In the presentation, “Good Policy: Personalization Drives Customer Acquisition and Retention for Insurers”, Weston McDonald, SVP of Financial Services at TeleTech, Jonathan Gray, VP of Marketing at Revana, and Elizabeth Glagowski, editor-in-chief, Customer Strategist Journal, explore the specific ways that personalization can drive success in customer acquisition and retention activities for the insurance industry. Discover:
- The growing importance of customer focus in the P&C and life insurance industries
- Six areas of personalization strategy that will boost customer acquisition performance
- Five ways in which personalization can immediately deepen customer retention
Valiance Solutions provides data-driven solutions to business problems using a team of experienced consultants, data scientists, and developers. They have nearly 30 years of combined experience in analytics and technology. Their services include decision analytics, decision products, data visualization, and they have expertise in domains like retail, financial services, education, and ad tech. They engage with clients through proof of concepts, full projects, or retainer models. Their solutions help clients with problems like customer churn, cross-sell opportunities, and have achieved outcomes like increased revenue and lower costs.
While customer experience is a top priority among business leaders, very few are successful in building a financial business case for their customer-focused efforts. As a result, customer experience improvement initiatives may not get the attention they deserve in the boardroom. In the presentation, “Connect the ROI Dots with a Customer Experience Value Strategy”, Don Ryan, senior partner, iKnowtion, a TeleTech Company, and Elizabeth Glagowski, editor-in-chief, Customer Strategist Journal, discuss concrete steps companies can take to tie customer experience to financial impact. Learn how to:
- Make the quantifiable business case for customer experience initiatives
- Translate NPS, customer experience scores, and other measurements into corporate financial terms using advanced analytics
- Understand which metrics lead to financial outcomes, so you know which levers to pull
Becoming a Psychic Brand: Moving from Concept to Reality to Grow ValuePeppers & Rogers Group
A psychic brand is one that goes beyond simply understanding customer insight and customizing interactions based on that insight. It means sensing and acting on all of the physical and digital signals customers send out – before customers even realize what they need. In the presentation, "Becoming a Psychic Brand: Moving from Concept to Reality to Grow Value," Elizabeth Glagowski, Customer Strategist Journal Editor-in-Chief, and Ron Wince, Peppers & Rogers Group President and General Manager, explore the findings of TeleTech’s Psychic Brands study. Learn:
- Why it’s important to be a psychic brand, and the financial and customer benefits that can be achieved
- Details of the TeleTech Psychic Brands study and the gaps that exist between customer and brand perception
- Which strategies, capabilities, and resources will bridge the gap between wanting to be psychic and actually being psychic – and the roadmap to get there
Digital Demand Generation for Credit Unionsedynamic
This document discusses digital demand generation strategies for credit unions. It covers how the digital landscape and customer experience has changed, with buyers now using multiple devices and channels. It emphasizes the importance of customer satisfaction and retention. The document then discusses developing a digital customer experience plan, including creating buyer personas, mapping the customer journey, and how this can help with acquisition and retention. Finally, it addresses developing a demand generation strategy, including data and content management strategies, and choosing appropriate marketing channels. The overall goal is helping credit unions integrate digital and physical channels into a seamless customer experience.
Pega Next-Best-Action Marketing White PaperVivastream
N-B-A (Next-Best-Action) marketing is an approach that uses real-time customer data and analytics to determine the optimal next action or communication for each individual customer across marketing channels. It aims to improve profitability through more customer-centric interactions. When implemented by O2, an early adopter, N-B-A resulted in a 9% increase in bill value, 75% response rate, and reduced customer retention costs in the first month. N-B-A marketing considers each customer's unique profile and preferences to identify the single best offer or message to provide at any given time, avoiding issues like campaign collisions seen in traditional marketing.
The advent of ‘big data’ has completely changed the way businesses can harness the information about customers to make powerful business decisions. Data could be of any type – campaign information, customer demographics, individual transaction behavior, interactions on social networks, web usage, or satisfaction surveys etc. BRIDGEi2i has the ability and experience to mine this wealth of unstructured and structured information to help businesses identify prospects, target them through the right channel, maximize cross sell and up-sell opportunities and thereby enhance the life time value of customer relationships. To know more visit: http://www.bridgei2i.com/customer-intelligence.html
Case Study: Transforming a Business Through Individual Customer AttentionPeppers & Rogers Group
The document summarizes a case study of a financial services company that underwent an organization-wide transformation to focus on individual customers. As a first step, the company hired Peppers & Rogers Group to identify different customer groups and develop value propositions for each. Peppers conducted quantitative and qualitative research, including customer surveys, interviews, and data analysis to understand customers. They identified customer segments and created profiles for each, along with sales and service models. Based on Peppers' recommendations, the company is now implementing a new approach to uniquely identify and treat individual customers.
The Personalization Revolution: Policyholder Acquisition & Retention in a Di...Peppers & Rogers Group
Service expectations in the insurance industry are on the rise. To keep pace, leading insurers are moving away from a traditional product focus to center business around the customer. In particular, there are a number of opportunities to improve customer acquisition and retention ROI by personalizing marketing, sales, and service interactions. In the presentation, “Good Policy: Personalization Drives Customer Acquisition and Retention for Insurers”, Weston McDonald, SVP of Financial Services at TeleTech, Jonathan Gray, VP of Marketing at Revana, and Elizabeth Glagowski, editor-in-chief, Customer Strategist Journal, explore the specific ways that personalization can drive success in customer acquisition and retention activities for the insurance industry. Discover:
- The growing importance of customer focus in the P&C and life insurance industries
- Six areas of personalization strategy that will boost customer acquisition performance
- Five ways in which personalization can immediately deepen customer retention
Analytics is a two-sided coin. While on one side, it uses
descriptive and predictive models to gain valuable knowledge from data, i.e. data analysis, on the other side, it provides insight to recommend action or guide decision making, i.e. communication
This document highlights 16 customer successes using SAP BusinessObjects Predictive Analytics. It summarizes how various companies in industries like banking, transportation, manufacturing, and telecommunications were able to improve outcomes such as increasing sales, reducing costs and fraud, improving customer retention, optimizing operations and inventory, and enhancing marketing campaign performance through the use of predictive analytics. Key benefits included higher response and conversion rates, reduced model build and deployment times, improved forecasting accuracy, and increased revenues and profits.
The document summarizes the goals and progress of SunTrust Mortgage's Mortgage Transformation project. It discusses improving processes to better serve customers, becoming a top 5 mortgage originator by 2015, and aligning business goals with the transformation. Associates helped design new standardized processes and technology changes to streamline the loan process and improve customer experience.
Introduction to Decision Strategy Manager, the tool used to create Decision Strategies.
Introduction to the Decisioning Components, the building blocks of Decision Strategies
3 New ways to Improve and Understand your Customers ExperienceVirginia Fernandez
This document discusses new ways for organizations to understand and improve the customer experience. It outlines three key capabilities needed: analyzing customer behavior to understand root causes of issues, visualizing customer journeys across channels, and easily pivoting between different analytics types. The document also discusses challenges like fragmented data, siloed tools and departments. It proposes that a unified analytics solution is needed to provide a holistic view of the customer experience.
The document discusses ideas for cleantech startups and how to develop a business model. It emphasizes that an idea alone is not enough and business plans contain untested hypotheses that need validation. The key is to continually test hypotheses with customers through customer development and be willing to pivot the business model based on what is learned from customers. A good business model diagram shows the flows between a company and customers and should be the focus of startup founders rather than traditional business plans.
Analytics in action how marketelligent helped a card issuer combat transact...Marketelligent
The credit card issuer was facing significant losses from transaction fraud despite having a real-time scoring application. A 5-step analytical process was used to develop optimized authorization rules, improve the transaction scoring mechanism, identify new fraud strategies, and measure fraud operations performance. This resulted in fraud detection rates improving by 70 basis points year-over-year and reductions in false positives, false negatives, and missed opportunities to identify fraud.
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.
Susan Cordts, President/CEO of Adaptive Technologies, Inc. (ATi) provided this presentation to attendees of the American Marketing Association Phoenix meeting on August 27, 2008. The presentation details analytics, customer values and how to target the right customer, at the right time, with the right message and media.
From structuring the plan to execution of the plan, all were shown in this presentation file for a product reviewing platform. This case was assigned in 'Biznation'-an event organized in IUT.
This document discusses how connecting customer engagement and digital process automation can help bridge the digital gap for insurance companies. It highlights how Pega software allows for journey-centric, rapid delivery of digital experiences across omni-channels using AI and end-to-end robotic automation. Common mistakes made are focusing on tasks rather than outcomes, working in silos rather than end-to-end, and prioritizing channels rather than customer journeys. The document provides examples of how companies like CSAA Insurance Group, Royal Bank of Scotland, and General Motors have used Pega's software to improve customer experiences and business outcomes.
Generating insights in a hyperconnected and data riven worldCourse5i
The document discusses how data and analytics have become a new source of competitive advantage. It notes that the C-suite is demanding more business results from insights like customer growth, revenue growth, and share growth. However, research teams often struggle to deliver real-time data and insights, provide a future view, and increase business impact due to limited delivery systems, data silos, and a tactical mindset. The document outlines seven critical steps for research teams, which include cultivating a growth mindset, observing rather than just asking, synthesizing data, democratizing data access, disaggregating the value chain, evolving industry business models, and rethinking skill sets to focus more on business outcomes and consultancy.
Are You Pushing Products, or Connecting Conversations?Pegasystems
This document outlines 5 principles for an always-on customer experience: 1) conversations are always connected across channels, 2) there can only be one centralized decision-making brain, 3) relevance rules relationships by understanding customers, 4) context adds color by determining the right action for each situation, and 5) decisions are based on the math of propensity, value, and leverage (P*V*L) to balance customer and business needs. The document provides examples of companies like Royal Bank of Scotland that have improved customer experience and outcomes by implementing these principles through a centralized customer decision hub and next-best action strategies.
Defining Target Market for Telemarketing CampaignsMelody Ucros
IE Business School MBD Program
Retail Analytics Project O1 Group C:
Annie Pi – Anchal Jaiswal – Cedric Viret – Melody Ucros – Miguel Martin Romero – Pablo Dosal - Victor Kausch
Utilizing Big Data to Optimize Customer Value Management StrategiescVidya Networks
How can big data help us look differently at our customer base? A presentation by Elan Rosenberg, Business Development Director, Marketing Analytics at cVidya
Driving Profitability and Market share in the Indian Non-life Industry, Prese...Nikash Deka
Highlights of the Presentation is mainly scoped around how to engage with the Banks for a more meaningful relationship in the context of Indian Non-life Industry. Also touched upon the lesson learned during this short journey and what is the potential along-with a road draft to Bancassurance success in coming years as Banks are marching towards becoming super-market of financial products to increase affinity of customers towards the banks
Customer Lifecycle Engagement for Insurance Companiesedynamic
This document discusses improving customer engagement and acquisition for insurance companies through digital channels. It begins with an agenda and introduction to eDynamic's expertise in digital solutions for insurers. It then covers key trends in customer acquisition, opportunities for improving engagement through the customer lifecycle. Specifically, it discusses how digital plays a role in each stage from research to claims. It provides eDynamic's perspective on how insurers can respond by understanding the changing customer and providing simplicity, visibility and control. Finally it outlines a approach to improving engagement and acquisition through assessing maturity, creating digital marketing tactics, selecting the right technology elements, and continuous improvement.
- Over 95,000 customers were invited to take a survey about their satisfaction. As of January 23rd, 2,563 customers had responded, a 3% response rate.
- So far, 63% of respondents provided written feedback totaling 1,445 comments. Several customer groups were excluded from the survey.
- A reminder was sent to non-respondents. Additional responses are still needed from certain business areas to achieve statistically valid scores.
- Customer comments and survey results will be analyzed to identify key themes about areas that need improvement and those performing well. Insights from verbatim comments provide additional context.
GIGO - Garbage in Garbage Out dictum is as old as analytics field itself, yet, the relentless focus on improving data quality is a recent phenomenon.
As organizations develop a stronger data orientation, more important this topic is. Here is our approach to keeping data clean.
Modeling Techniques help to bring out the correlations that are predictive in nature. Here I talk about details of modeling statements that has been used to build life cycle management strategies
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.
Analytics is a two-sided coin. While on one side, it uses
descriptive and predictive models to gain valuable knowledge from data, i.e. data analysis, on the other side, it provides insight to recommend action or guide decision making, i.e. communication
This document highlights 16 customer successes using SAP BusinessObjects Predictive Analytics. It summarizes how various companies in industries like banking, transportation, manufacturing, and telecommunications were able to improve outcomes such as increasing sales, reducing costs and fraud, improving customer retention, optimizing operations and inventory, and enhancing marketing campaign performance through the use of predictive analytics. Key benefits included higher response and conversion rates, reduced model build and deployment times, improved forecasting accuracy, and increased revenues and profits.
The document summarizes the goals and progress of SunTrust Mortgage's Mortgage Transformation project. It discusses improving processes to better serve customers, becoming a top 5 mortgage originator by 2015, and aligning business goals with the transformation. Associates helped design new standardized processes and technology changes to streamline the loan process and improve customer experience.
Introduction to Decision Strategy Manager, the tool used to create Decision Strategies.
Introduction to the Decisioning Components, the building blocks of Decision Strategies
3 New ways to Improve and Understand your Customers ExperienceVirginia Fernandez
This document discusses new ways for organizations to understand and improve the customer experience. It outlines three key capabilities needed: analyzing customer behavior to understand root causes of issues, visualizing customer journeys across channels, and easily pivoting between different analytics types. The document also discusses challenges like fragmented data, siloed tools and departments. It proposes that a unified analytics solution is needed to provide a holistic view of the customer experience.
The document discusses ideas for cleantech startups and how to develop a business model. It emphasizes that an idea alone is not enough and business plans contain untested hypotheses that need validation. The key is to continually test hypotheses with customers through customer development and be willing to pivot the business model based on what is learned from customers. A good business model diagram shows the flows between a company and customers and should be the focus of startup founders rather than traditional business plans.
Analytics in action how marketelligent helped a card issuer combat transact...Marketelligent
The credit card issuer was facing significant losses from transaction fraud despite having a real-time scoring application. A 5-step analytical process was used to develop optimized authorization rules, improve the transaction scoring mechanism, identify new fraud strategies, and measure fraud operations performance. This resulted in fraud detection rates improving by 70 basis points year-over-year and reductions in false positives, false negatives, and missed opportunities to identify fraud.
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.
Susan Cordts, President/CEO of Adaptive Technologies, Inc. (ATi) provided this presentation to attendees of the American Marketing Association Phoenix meeting on August 27, 2008. The presentation details analytics, customer values and how to target the right customer, at the right time, with the right message and media.
From structuring the plan to execution of the plan, all were shown in this presentation file for a product reviewing platform. This case was assigned in 'Biznation'-an event organized in IUT.
This document discusses how connecting customer engagement and digital process automation can help bridge the digital gap for insurance companies. It highlights how Pega software allows for journey-centric, rapid delivery of digital experiences across omni-channels using AI and end-to-end robotic automation. Common mistakes made are focusing on tasks rather than outcomes, working in silos rather than end-to-end, and prioritizing channels rather than customer journeys. The document provides examples of how companies like CSAA Insurance Group, Royal Bank of Scotland, and General Motors have used Pega's software to improve customer experiences and business outcomes.
Generating insights in a hyperconnected and data riven worldCourse5i
The document discusses how data and analytics have become a new source of competitive advantage. It notes that the C-suite is demanding more business results from insights like customer growth, revenue growth, and share growth. However, research teams often struggle to deliver real-time data and insights, provide a future view, and increase business impact due to limited delivery systems, data silos, and a tactical mindset. The document outlines seven critical steps for research teams, which include cultivating a growth mindset, observing rather than just asking, synthesizing data, democratizing data access, disaggregating the value chain, evolving industry business models, and rethinking skill sets to focus more on business outcomes and consultancy.
Are You Pushing Products, or Connecting Conversations?Pegasystems
This document outlines 5 principles for an always-on customer experience: 1) conversations are always connected across channels, 2) there can only be one centralized decision-making brain, 3) relevance rules relationships by understanding customers, 4) context adds color by determining the right action for each situation, and 5) decisions are based on the math of propensity, value, and leverage (P*V*L) to balance customer and business needs. The document provides examples of companies like Royal Bank of Scotland that have improved customer experience and outcomes by implementing these principles through a centralized customer decision hub and next-best action strategies.
Defining Target Market for Telemarketing CampaignsMelody Ucros
IE Business School MBD Program
Retail Analytics Project O1 Group C:
Annie Pi – Anchal Jaiswal – Cedric Viret – Melody Ucros – Miguel Martin Romero – Pablo Dosal - Victor Kausch
Utilizing Big Data to Optimize Customer Value Management StrategiescVidya Networks
How can big data help us look differently at our customer base? A presentation by Elan Rosenberg, Business Development Director, Marketing Analytics at cVidya
Driving Profitability and Market share in the Indian Non-life Industry, Prese...Nikash Deka
Highlights of the Presentation is mainly scoped around how to engage with the Banks for a more meaningful relationship in the context of Indian Non-life Industry. Also touched upon the lesson learned during this short journey and what is the potential along-with a road draft to Bancassurance success in coming years as Banks are marching towards becoming super-market of financial products to increase affinity of customers towards the banks
Customer Lifecycle Engagement for Insurance Companiesedynamic
This document discusses improving customer engagement and acquisition for insurance companies through digital channels. It begins with an agenda and introduction to eDynamic's expertise in digital solutions for insurers. It then covers key trends in customer acquisition, opportunities for improving engagement through the customer lifecycle. Specifically, it discusses how digital plays a role in each stage from research to claims. It provides eDynamic's perspective on how insurers can respond by understanding the changing customer and providing simplicity, visibility and control. Finally it outlines a approach to improving engagement and acquisition through assessing maturity, creating digital marketing tactics, selecting the right technology elements, and continuous improvement.
- Over 95,000 customers were invited to take a survey about their satisfaction. As of January 23rd, 2,563 customers had responded, a 3% response rate.
- So far, 63% of respondents provided written feedback totaling 1,445 comments. Several customer groups were excluded from the survey.
- A reminder was sent to non-respondents. Additional responses are still needed from certain business areas to achieve statistically valid scores.
- Customer comments and survey results will be analyzed to identify key themes about areas that need improvement and those performing well. Insights from verbatim comments provide additional context.
GIGO - Garbage in Garbage Out dictum is as old as analytics field itself, yet, the relentless focus on improving data quality is a recent phenomenon.
As organizations develop a stronger data orientation, more important this topic is. Here is our approach to keeping data clean.
Modeling Techniques help to bring out the correlations that are predictive in nature. Here I talk about details of modeling statements that has been used to build life cycle management strategies
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.
Identifying causes of customer risk and churn, and then applying approaches for prospective winback, are tremendously important to any company. The content of this presentation enables organizations to optimize customer loyalty behavior
Predictive analytics. overview of skills and opportunitiesFarid Gurbanov
Predictive Analytics could bring benefits virtually to any data-intensive and knowledge-intensive organization. With integration into existing business processes and applications it gives plenty of powerful opportunities. In this brief presentation I outline my experience and skills in data analytics.
Multichannel Retention Strategies: A Steady Diet of Low Hanging FruitVivastream
The document discusses identifying customer churn and measuring lifetime value. It provides a real-world example of an effective multi-channel retention campaign utilizing analytics and a cost-progressive channel strategy. Specifically, it describes how a wireless provider used business intelligence to target likely churn customers and employed a multi-channel strategy including text, direct mail, and calls to increase retention rates and ROI. The campaign resulted in a 5.6% reduction in churn and a 180% increase in ROI compared to a basic segmentation strategy.
Companies can improve customer retention rates by addressing the root causes of customer attrition through a strategic approach. This involves applying targeted retention strategies across all customer touchpoints in a coordinated effort. The document discusses establishing a "Churn Command Center" to oversee retention efforts across the organization. It also emphasizes the importance of customer analytics to understand why customers churn and tailor retention offers, as well as testing offers across channels to maximize effectiveness and minimize risks. Leading companies see reductions in churn of 10-50% through these integrated, data-driven approaches.
The document discusses how analytics can be used to solve business problems in the retail banking industry. It describes how analytics can be applied to various areas of a bank's profit and loss statement, including acquiring new customers, reducing customer attrition, improving account activation rates, and maximizing revenue from interest, fees, and cross-selling. It also discusses how strategic reporting, marketing analytics, and data-driven insights can be used for segmentation, customer lifetime value analysis, profitability and loyalty analysis, cross-selling strategies, and customer retention programs. The overall aim is to provide a top-down analytical approach to optimize all areas of a bank's operations and financial performance.
This document provides an overview of retailing and customer relationship management (CRM) in the retail sector. It defines retailing as buying products from suppliers and selling directly to consumers. It outlines various forms of retailing and characteristics of the industry. It also describes the types of retail customers and factors influencing business growth. The document then discusses organized versus unorganized retailing and examines CRM penetration and processes in the retail sector. It analyzes operational and analytical CRM and their relationship. Finally, it provides a case study of how Landmark Group implemented a centralized CRM system across its various retail businesses.
The document discusses applying decision science techniques to solve various business problems in customer relationship management. It covers topics like prospect targeting and acquisition, customer segmentation, profitability and loyalty analysis, cross-selling and upselling strategies, campaign management, customer lifetime value analysis, and customer retention through churn management. Decision science helps businesses make targeted decisions at each customer lifecycle stage to optimize acquisition, usage, retention, and customer lifetime value.
Customer Lifetime Value for Insurance AgentsScott Boren
Customer lifetime value for insurance agents was presented by Scott Boren to the BIG Insurance Group in Southern California. The lecture was designed to share insight from his consulting firm and the impact a customer lifetime strategy can have on an insurance agent's service, marketing, and in identifying developing customer personas.
This presentation offers an overview of the Digital Health space, including thematic investment areas, business models, metrics for evaluation, and adoption models for digital health interventions.
This document discusses customer churn, which refers to the rate at which customers leave a company. It states that reducing churn by 5% can boost profits by 75% and that US companies lose $1.6 trillion per year to churn. Common causes of churn include poor customer service, price, functionality, and changing customer needs. The document promotes MECBot, a data analytics product that can detect, prevent, and reduce churn in real-time through capabilities like churn scoring, campaign management, and customer lifetime value maximization.
This document discusses customer retention and churn prediction. It explains that traditional churn prediction models built by data scientists take 6 months to a year to develop and implement, while out-of-the-box solutions like Manthan's Customer360 can identify at-risk customers and execute retention campaigns immediately. Customer360 uses logistic regression to predict churn risk and helps marketers design personalized campaigns to retain profitable customers, like a retailer who stopped $6 million in revenue loss by retaining 22% of at-risk customers in a valuable segment.
Capital One has built its business on customer relationship management strategies:
- The company segments customers into high and low credit risk categories to offer tailored products and pricing.
- It collects extensive customer data to assess individual risk and suggest customized offers.
- Capital One tests marketing strategies on limited groups before broad rollout to optimize customer retention and growth.
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.
The retailer wanted to create a unified customer data platform to provide complete visibility across their customer's omnichannel touchpoints and move from siloed data to a 360-degree view. Tredence helped build a CDP that integrated over 70 data sources, processed 250TB of data weekly, and increased addressable customer data visibility by 14%. This allowed the retailer to put the customer at the center of decisions, optimize their $3B marketing budget, and win a larger share of partners' advertising dollars in a cookie-less world.
This document discusses relationship management and customer relationship management (CRM). It defines CRM as managing customer interactions across the customer lifecycle through information, processes, technology, and people. The document outlines CRM strategies like customer acquisition, retention, loyalty, and evangelism. It discusses tracking customer data and metrics like customer lifetime value to improve the customer experience and business outcomes.
Analytics For Retail Banking - MarketelligentMarketelligent
MarketIntelligent provides analytic services to help clients make better business decisions. They offer expertise in credit risk and marketing analytics across various banking products. Their services include developing scorecards to predict customer behavior, maximize profits from assets and fees, reduce losses, acquire profitable customers, increase activation and cross-sell revenues.
To succeed in the Internet age, insurance marketers have to be more nimble, more innovative and better able to communicate with their customers, both for themselves and through their partner ecosystems. IBM’s Commerce portfolio for Insurance can help insurers achieve their marketing engagement goals. Insurance industry marketing executives need to reach prospective and current policyholders in context at every point of the purchase process by innovating personally relevant and rewarding experiences that draw customers in and keep them engaged. IBM’s Commerce portfolio helps insurance marketing leaders take an integrated approach to customer engagement.
The document discusses how businesses can compete in the digital economy. It covers topics like using big data and analytics to gain insights, delivering superior customer experiences, and the need to act on data insights. It provides examples of how various industries like healthcare, retail, automotive and insurance can leverage digital technologies and data to improve operations and customer value. The key message is that competing in the digital world involves using data and technology to improve quality of service while maintaining operational simplicity and price competitiveness.
2. Work Abstract
www.valiancesolutions.com
Product recommendation
model for prominent Life
Insurer identifying top 2
products existing customers
are likely to purchase.
Analysis was used in email
and direct marketing
campaigns.
Prediction Model for
identifying customers who
are unlikely to pay insurance
premium within 30 days
grace period. Results were
used to formulate pro-active
customer retention
strategies.
Monthly sales forecasting
model for prominent direct
sales retailer in US using
Neural Networks. Achieved
average forecasting
accuracy of 7 percent with 5
to 10 percent error range.
Cross Sell Customer Churn Sales ForecastFraud Prevention
Real time Fraud Detection
algorithm for unsecured
consumer lending.
Substantial decrease in loan
disbursement to fraudulent
cases at Point of Sale
3. Case Study: Product Recommendation
www.valiancesolutions.com
Company Profile
Project Profile
Technology Used
Business Need Solution Benefits to Customer
Location
India
Industry
Insurance
Project type
Propensity Modeling
R
SQL
Excel
To identify the propensity to cross-
sell a policy
• To proactively identify the policy
holders who have high likelihood
to purchase more than one policy
• Use agent characteristics as main
lever to predict cross-sell
propensity
Propensity Algorithm to score
customers using Logistic
Regression
• Cross Sell propensity scores at
product category level for each
customer.
• Scores normalized to recommend
top 2 products customer is likely to
purhacase.
• Recommendations used to power
email and call center campaigns.
Tailored marketing campaigns
across modes of marketing
• Efficient Marketing Campaigns
• Incremental Revenue of USD
100,000 in 3 months
• Lower cost of Marketing
Campaigns
4. Case Study Details
www.valiancesolutions.com
Input
Data
Data
Cleaning
Exploratory
Data Analysis
Data
Enrichment
Propensity
Modeling
Algorithm
Implementation
Customer Attributes
Product Attributes
Transactional
Behavior
Interaction behavior
Missing value
Treatment
Correcting incorrect
values
Removal of duplicate
records
Uni-Variate Analysis
Bi-Variate Analysis
Creation of new
variables
Variable
transformations
Multiple versions of
Models basis
different variable
selection
Model Comparison
Choice of best model
Modify marketing
campaigns.
Feedback monitoring
Algorithm tweaking
(if needed)
Solution: Propensity Algorithm to score customers using Logistic Regression
Objective: To identify the propensity to cross-sell a policy
To proactively identify the policy holders who have
high likelihood to purchase more than one policy
Use agent characteristics as main lever to predict
cross-sell propensity
5. Cross Sell Model
www.valiancesolutions.com
Illustrative
All the customers
acquired in
Analysis Window
Characteristics
Characteristics
Scoring model
Likelihood to Cross-sell
Scoring Algorithm for
Calculation Propensity to
Cross-sell
Identify the Last Agent of a particular customer for Agencies- which
maximize propensity to cross-sell
Customers holding multiple policies in
Analysis window
Customers holding single policy in
Analysis window
If a customer cross-sold more than one policies during analysis window, then each
cross-sell instance will be considered as cross-sell opportunity (one customer might
appear more than once in modeling window)
Identify Best Agent for ARD - which maximize propensity to cross-sell
Orphan Customers of Agencies*
Cross-sell
Campaigning
7. Cross Sell Solution
High Purchase Propensity
Medium Purchase Propensity
Low Purchase Propensity
Tailored marketing
campaigns across
modes of marketing
Efficient Marketing
Campaigns
Incremental Revenue
of USD 100,000 in 3
months
Lower cost of
Marketing Campaigns
Cross Sell
Algorithm
www.valiancesolutions.com
8. Case Study : Customer Retention
www.valiancesolutions.com
Company Profile
Project Profile
Technology Used
Business Need Solution Benefits to Customer
Location
India
Industry
Insurance
Project type
Lapse Modeling
R
SQL
Excel
Logistic Regression
Random Forest
Improving Customer Retention
• To identify policy holders who are
likely to lapse and move out of the
program
• Take proactive measures to keep
them in the program
Quantitative Analysis of Lapsation
• What are the reasons for attrition?
• What are patterns in customer
attrition across different tenure of
policy?
• How does the attrition rates change
by changing factors?
• What is the probability of a customer
to attrite?
• What channel or combination of
channels which will deliver the most
conversion?
Churn Scoring algorithm based on
machine learning.
• Upcoming renewals scored on
monthly basis in a batch mode.
• Customer Segments created on
basis of churn score and Annual
Premium.
• Contact Strategy finalized on basis
on churn score and premium at
stake.
• Customers with higher churn
score and premium >25k pursued
through calls and visits if needed.
• Customers with lower churn score
and lower premium contacted via
sms and emails.
• Frequency of emails, call s to be
adjusted as per segment.
Customer Churn
• Policy Persistency increased by
20% over 1 year
• Incremental Revenue of 3M USD
in 1 year
• Lower cost of retention
Campaigns
9. Case Study Details
www.valiancesolutions.com
What are the
reasons for
attrition?
What are patterns
in customer
attrition across
different tenure of
policy?
How does the
attrition rates
change by
changing factors?
What is the
probability of a
customer to
attrite?
What channel or
combination of
channels which
will deliver the
most conversion?
Quantitative Analysis of Lapsation
Objective : Improving Customer Retention
To identify policy holders who are likely to lapse and
move out of the program
Take proactive measures to keep them in the
program
10. Solution: Lapse Model
www.valiancesolutions.com
Illustrative
Policies for
renewal between
Analysis Window
Characteristics
Characteristics
Scoring model
Likelihood to lapse
Policies lapsed between Analysis
window are bad
Policies lapsed between Analysis
window are good
Retention
Campaigning
Application on policies
coming for renewals in
following month
Scoring Algorithm for
Calculation Propensity
to lapse
Lapsed and Reinstated
Lapsed
Non Lapsed
11. Sample Deliverable: Customer Risk Profiling
www.valiancesolutions.com
Illustrative
Customers were segmented on basis the probability to lapse and APE band
APE BAND
Risk Group <18K
Between 18K
and 25K
>25K Total
High 18% 8% 14% 40%
Medium 15% 8% 7% 30%
Low 10% 7% 13% 30%
Total 43% 23% 34% 100%
Customers were segmented in High,
Medium and Low risk profiles on basis
of Annual Premium and their
probability to lapse.
Cut off probability band for High,
Medium & Low group was identified
from customer deciles. i.e. For High
band probability cut off was based on
top 30 percent of lapsers.
Proactive campaigning to customers
with higher likelihood to lapse
Risk_Group Probability of Lapsation
H >0.18
M 0.03-0.18
L <=0.03
High Risk Priority 1
Medium Risk Priority 2
Low Risk Priority 3
Legend
12. Customer Churn Solution
High Churn Propensity
Medium Churn Propensity
Low Churn Propensity
High risk customers to be
reached pro-actively through
calls and visits if needed.
Medium risk customers to be
reached through calls, emails
and sms’s
Low risk customers to be
reached through sms’s and
emails.
Policy Persistency
increased by 20%
over 1 year
Incremental Revenue
of 3M USD in 1 year
Lower cost of
retention Campaigns
Churn Propensity
Algorithm
www.valiancesolutions.com
13. Case Study : Fraud Modeling for Unsecured Loans
www.valiancesolutions.com
Company Profile
Project Profile
Technology Used
Business Need Solution Benefits to Customer
Develop Credit Risk framework for
POS loan approvals
• To identify customers who are
more likely to commit fraud/default
on consumer durable loans.
• To streamline loan approval
process according to customer
risk profiles.
Real time Fraud Propensity score
at Point of Sale
• Machine Learning based fraud
engine integrated with CRM
• Assigns fraud score for applicant
at point of lending.
• Higher fraud score applications
routed through stringent
verification process.
Substantial decrease in fraud thus
improving the Bottom Line
• Substantial decrease in loan
disbursement to fraudulent cases
at Point of Sale
• Almost 10% of the originations are
referred to ‘Normal process’ in
which the fraud incidence is as
high as 5% which translates into a
gross saving of almost 1.5 million
USD i.e. 50% of the VaR
• Substantial decrease in the third
party cost of loan amount recovery
from the fraudulent cases.
Location
India
Industry
Banking
Project type
Fraud Likelihood Model
SAS
SQL
Java
Excel
14. Case Study Details
www.valiancesolutions.com
Identify attributes of customers who
are most likely to commit fraud?
What are patterns in customer
default across cities/income/
profession segments?
What is the probability of a
customer to default?
Quantitative Analysis of Credit Risk
Objective: Develop Credit Risk framework for POS loan approvals
To identify customers who are more likely to commit
fraud on consumer durable loans.
To streamline loan approval process according to
customer risk profiles.
15. Solution
www.valiancesolutions.com
Text Mining
Fraud Likelihood
Model
Development of
technology solution
Implementation
framework
Strategy roll-out
and testing
• Hypothesis building
• Data cleansing
• Conducting field visits
to understand typical
trends in fraud
patterns
• Profiling patterns
• Algorithm for fraud
prediction
• Build a Java based
algorithm
• Ensure compatibility
with client’s Sales
CRM system
• Host the algorithm on
the client’s system
• Cross-validate the
scores generated by
the system
• Roll-out the algorithm
on the live system
• Continuous monitoring
of through the door
population for any
changes in patterns
16. Fraud Likelihood Model
www.valiancesolutions.com
Illustrative
All account
sourced
Characteristics
Characteristics
Scoring model
Likelihood to Default
Customers identified as not fraud
Customer s identified as Fraud
Loan application coming
for renewal at POS
Scoring Algorithm for
Calculation Propensity
to default
Medium Risk
High Risk
Low Risk
17. Implementation Framework
www.valiancesolutions.com
Customer walks-in to outlet
for purchasing products
Proposal to convert
invoice amount to
EMI’s
Customer Details
fed into the system
The algorithm developed will return fraud score based on inputs
The algorithm developed
will return fraud score
based on inputs
Instant mode
Approvals are made
instantly within 30 min
Normal Mode
Approvals are after
rigorous verification
Medium Risk
Feedback Process
FeedbackLoop
18. ROI of Modeling Exercise
www.valiancesolutions.com
Substantial decrease in loan
disbursement to fraudulent cases at
Point of Sale
Almost 6% of the originations are
referred to ‘Normal process’ in
which the fraud incidence is as high
as 5% which translates into a gross
saving of almost 1.5 million USD i.e.
50% of the VaR
Substantial decrease in the third
party cost of loan amount recovery
from the fraudulent cases.
Fraud Model led to
substantial decrease in
fraud thus improving the
Bottom Line
19. Case Study : Monthly Sales Forecasting for Direct Seller
www.valiancesolutions.com
Company Profile
Project Profile
Technology Used
Business Need Solution Benefits to Customer
Location
United States
Industry
Retail
Project type
Sales Forecasting
R
ARIMA
Linear/Non-Linear
Regression
Neural Networks
Develop Sales Forecasting
Model for Monthly Sales
• To build Monthly forecasting
Model with high degree of
accuracy.
• Forecast Monthly sales for next
9-12 Months
Neural Network based monthly
sales forecasting algorithm.
• Sales in last 1 year plus
external factors as inputs.
Model
Techniques
Error
Moving Average
And Exp
Smoothening
47%
ARIMA 32%
Linear
Regression
20%
**Neural
Networks
6%
20. Case Study Details
www.valiancesolutions.com
To identify Seasonal patterns and
factor affecting monthly sales.
Segment Agent workforce, to
improve forecasting Accuracy.
Forecast Monthly Sales for next 9-
12 months.
Quantitative Analysis of Monthly Sales Trend
Objective: Develop Sales Forecasting Model for Monthly Sales
To build Monthly forecasting Model with high degree
of accuracy.
Forecast Monthly sales for next 9-12 Months
21. Forecasting Solution
www.valiancesolutions.com
Monthly Sales
Raw Data
Sales Lag
Creation for last
12 Months
Train Neural
Network
Forecast for next
6 Months &
Calculate Error
Optimize Network
Weights
Forecast Sales
for Next 12
Months
• Various Forecasting Techniques are
used and best results are selected.
• Neural Network use Single Hidden
Layer Network with 24 Neurons.
Feedback Process