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
This document discusses using critical thinking and analytics to transform data science into actionable insights. It provides examples of using customer segmentation, predictive modeling, and optimization to personalize marketing campaigns. The document outlines a framework for developing analytical solutions, implementing predictive models, and measuring their impact on business goals like increasing customer lifetime value. Key aspects include generating segment-level forecasts, modeling customer behaviors, and optimizing variables like marketing spend.
1. Adiyanth Analytics is being set up to provide analytical capabilities and data-driven competitive advantages to organizations.
2. It focuses on information and knowledge management services for organizations that have experienced growth and are at risk of losing their competitive positions.
3. Adiyanth provides analytics services through outsourcing, data solutions, and professional services to help clients make knowledge-driven decisions.
Marketing Technology, including Big Data capabilities now drive most of the marketing organization's drive to build a power base needed to bring about the "right" segmentation to achieve sharper positioning and precise targeting.
This document discusses a framework for datafication of e-commerce prospecting. It considers using big data from sources like social media, website logs, and customer feedback to build a proprietary analytics architecture. The goals are to conduct big data discovery based on the business model and create methodologies to harness existing processes. Architectural considerations include data linking, text mining, and generating insights from structured and unstructured data. The framework would enable e-commerce decisions by providing an integrated customer lifetime value view and addressing data quality.
1) The document proposes using advanced data analytics to build knowledge of customer behavior, preferences, and aspirations in order to maximize revenue.
2) A case study uses data from an online beauty/personal care subsidiary to demonstrate how clustering, classification, and regression analyses can provide insights.
3) The analyses identify customer subgroups, predict which customers will churn, and forecast spending amounts. This knowledge can then be used to target marketing and improve customer retention and spending.
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
This document discusses using critical thinking and analytics to transform data science into actionable insights. It provides examples of using customer segmentation, predictive modeling, and optimization to personalize marketing campaigns. The document outlines a framework for developing analytical solutions, implementing predictive models, and measuring their impact on business goals like increasing customer lifetime value. Key aspects include generating segment-level forecasts, modeling customer behaviors, and optimizing variables like marketing spend.
1. Adiyanth Analytics is being set up to provide analytical capabilities and data-driven competitive advantages to organizations.
2. It focuses on information and knowledge management services for organizations that have experienced growth and are at risk of losing their competitive positions.
3. Adiyanth provides analytics services through outsourcing, data solutions, and professional services to help clients make knowledge-driven decisions.
Marketing Technology, including Big Data capabilities now drive most of the marketing organization's drive to build a power base needed to bring about the "right" segmentation to achieve sharper positioning and precise targeting.
This document discusses a framework for datafication of e-commerce prospecting. It considers using big data from sources like social media, website logs, and customer feedback to build a proprietary analytics architecture. The goals are to conduct big data discovery based on the business model and create methodologies to harness existing processes. Architectural considerations include data linking, text mining, and generating insights from structured and unstructured data. The framework would enable e-commerce decisions by providing an integrated customer lifetime value view and addressing data quality.
1) The document proposes using advanced data analytics to build knowledge of customer behavior, preferences, and aspirations in order to maximize revenue.
2) A case study uses data from an online beauty/personal care subsidiary to demonstrate how clustering, classification, and regression analyses can provide insights.
3) The analyses identify customer subgroups, predict which customers will churn, and forecast spending amounts. This knowledge can then be used to target marketing and improve customer retention and spending.
The consumer has been the king for quite a while now. Why then are organizations struggling to engage the consumer, personalize its offering and maximize the value that they can realize.
BRIDGEi2i presents a comprehensive, end to end Consumer Analytics solution that helps you know your consumer better, predict purchasing decisions and personalize recommendations
Introduction to Decision Strategy Manager, the tool used to create Decision Strategies.
Introduction to the Decisioning Components, the building blocks of Decision Strategies
This document discusses the need to rethink traditional marketing analytics approaches and leverage big data solutions. It notes that while many firms want to be data-driven, few are good at taking action on data. Traditional approaches have limitations in scaling and real-time processing across new data sources like mobile and apps. A big data approach allows for a 360-degree customer view, real-time campaign adjustments, accurate customer value scoring, and understanding customer behavior patterns. It presents architectures for ingesting diverse customer data, building customer profiles, modeling to gain insights, and optimizing marketing based on those insights.
Acquire, grow and retain customers with IBM Big Data & Analytics - Client Exa...David Pittman
Are you effectively converting your audience insights into added value for your consumers? 80% of CEOs believe they deliver a superior customer experience, but only 8% of their customers agree. With IBM Big Data & Analytics solutions you can acquire, grow, and retain customers by improving customer interaction, building long term relationships and realizing value from customer sentiment.
Acquire, grow and retain customers with IBM Big Data & Analytics - Client Exa...IBM Analytics
Are you effectively converting your audience insights into added value for your consumers? 80% of CEOs believe they deliver a superior customer experience, but only 8% of their customers agree. With IBM Big Data & Analytics solutions you can acquire, grow, and retain customers by improving customer interaction, building long term relationships and realizing value from customer sentiment.
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.
The document provides an overview of marketing analytics, including defining marketing analytics, key elements and capabilities, impact, and getting started with analytics. Some key points:
- Marketing analytics is the process of identifying valid performance metrics, tracking them over time, and using the results to improve marketing. The goal is to measure progress toward objectives.
- Key elements include people, steps, tools/technology, inputs and outputs. Capabilities include understanding performance and reporting it externally.
- Impacts can include optimizing brand recognition, content, channels, customer understanding, and predictive intelligence.
- Getting started involves assessing readiness, reviewing objectives, and establishing metrics like website, social media, email, and digital advertising metrics.
business analytics and its importance, marketing analytics definition and its importance, how marketing analytics helps to run the organization in effective and efficient manner.
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.
Next-best offer refers to the use of predictive analytics solutions to identify the products or services your customers are most likely to be interested in for their next purchase.
Facing this topic I have made a personal research, and realize a synthesis, which has helped me to clarify some ideas. This presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights.
Loyalytics is a boutique analytics consulting company helping businesses improve their customer loyalty across industries through the use of data and analytics. We help businesses convert data into an enterprise asset and leverage it to shape their customer strategy. Our mission is to be the “Voice of customers” for all our business partners.
Our capabilities span across the entire gamut of Analytics service offerings from Enterprise BI solutions for tracking and monitoring KPI’s to advanced data science solutions like Price optimization, Customer segmentations etc. Our Solutions can be clubbed into the following 5 verticals:
• Business Dashboards
• Customer Analytics
• Digital Analytics
• Merchandising Analytics
• Market Research
Our engagement model is very flexible and can be customized to the specific needs of the organization. From pure consulting assignment (Analytics Maturity assessment for business) to E2E deployment of specific analytics solutions, we do it all. For more information regarding our solutions/offerings please visit our website at www.loyalytics.in
Intelibiz is a highly scalable cloud based BI platform that provides real time time actionable insights to business across all important functions like Merchandising, Finance, Operations, Supply chain etc. Unlike traditional ERP reporting solutions that are siloed in their architecture, Intelibiz provides businesses with the ability to generate cross functional insights in a matter of seconds. The platform is powered by Industry renowned tableau software leading to highly efficient data architecture and world class security standards. Its cloud based architecture removes the need of having extensive IT hardware on premise leading to better ROI for the business. All the reports and dashboards can be accessed from anywhere in the world across multiple devices enabling faster insights. The entire platform is highly configurable and can be customized as per the specific needs of the business. Deployment takes anywhere between 2 weeks to 6 depending upon the specific need and existing data architecture at the business.
- The RBGE is a modular suite of advanced customer analytics models developed by Deloitte to help retail banks gain deeper insights from customer data and address growth challenges throughout the customer lifecycle.
- The core of RBGE is a comprehensive customer database combining internal and external customer data. Predictive analytical models are built on this database, including propensity to buy, segmentation, customer lifetime value, and churn models.
- Model outputs can be used in CRM systems and sales interactions to select the most appropriate offerings for customers. RBGE can deliver increases in up-sell by 15-25%, cross-sell by up to 45%, and marketing campaign success up to 30%. It also improves the customer experience.
This document discusses how a Data Ninja can help businesses address the growing complexity of data and shrinking budgets. A Data Ninja would define the target market and key drivers, build a comprehensive list of market drivers and data sources, and leverage statistical techniques to create an accurate forecasting model that clearly explains the relationships between drivers and the market. Additionally, a Data Ninja would add interactive visualizations to simplify and bring the data to life, providing transparency and a single version of truth to gain user trust.
The document provides guidance on building permission-based email marketing lists. It defines permission-based marketing as requiring formal consent from recipients to receive emails. There are various methods for gaining permission, including opt-in, confirmed opt-in, and double opt-in forms. Lists can be built in-house from customer data or rented from external sources, following best practices like incentivizing opt-ins, collecting subscriber interests, and maintaining CAN-SPAM compliance.
Accelerating Personalization to Cut Through Digital NoisePrecisely
Your customers’ inboxes are overflowing with unread email. Even social media and text messages are ignored. There is simply too much digital content and too little time to consume it.
So how do you stand out and get your customers’ attention? You need to go beyond traditional Customer 360 efforts. To get your customers’ attention, you need to maximize personalization based on context.
This document discusses using predictive analytics for retail businesses. It outlines using store clustering and RFM (recency, frequency, monetary) analysis to develop predictive models. Store clusters would be used to design customized planograms and segmentation strategies. RFM would analyze active and expired customers to develop targeted strategies like offering discounts or new products to high value customers or win back lower value, expired customers. The overall goal is to use predictive models to improve planogram performance, customer retention and reactivation, and sales.
Hospital CRM focuses on understanding customers through continual feedback capture and improved communication and personalization to drive loyalty. Key goals include increasing customer acquisition, retention and evangelism through improved profiling, targeting, and conversion. Hospitals should select a CRM system that best meets current and future needs to increase revenue, efficiency and customer value while decreasing costs and response times through a customer-centric approach.
Cloudway Consulting Pvt Ltd Is a SAP Strategic Sourcing Consulting Company For SAP, SAP S4 Hana, SAP Ariba, SAP C4C, Success Factor and Business By Design for More Call us at +0120-4226511
Sanjiv Bhatia “Critical Mass Makes Magic Happen”Elemica
Critical Mass Makes Magic Happen discusses how focusing resources on high-value initiatives through a value creation framework can provide benefits. The framework involves 4 steps: 1) identifying value through supply chain analysis, 2) prioritizing partners based on business value and technical complexity, 3) developing an onboarding roadmap, and 4) implementing projects. When applied to a company, the framework could generate $65 million in annual benefits and $43 million in one-time benefits through strategies like improved procurement and inventory management.
The consumer has been the king for quite a while now. Why then are organizations struggling to engage the consumer, personalize its offering and maximize the value that they can realize.
BRIDGEi2i presents a comprehensive, end to end Consumer Analytics solution that helps you know your consumer better, predict purchasing decisions and personalize recommendations
Introduction to Decision Strategy Manager, the tool used to create Decision Strategies.
Introduction to the Decisioning Components, the building blocks of Decision Strategies
This document discusses the need to rethink traditional marketing analytics approaches and leverage big data solutions. It notes that while many firms want to be data-driven, few are good at taking action on data. Traditional approaches have limitations in scaling and real-time processing across new data sources like mobile and apps. A big data approach allows for a 360-degree customer view, real-time campaign adjustments, accurate customer value scoring, and understanding customer behavior patterns. It presents architectures for ingesting diverse customer data, building customer profiles, modeling to gain insights, and optimizing marketing based on those insights.
Acquire, grow and retain customers with IBM Big Data & Analytics - Client Exa...David Pittman
Are you effectively converting your audience insights into added value for your consumers? 80% of CEOs believe they deliver a superior customer experience, but only 8% of their customers agree. With IBM Big Data & Analytics solutions you can acquire, grow, and retain customers by improving customer interaction, building long term relationships and realizing value from customer sentiment.
Acquire, grow and retain customers with IBM Big Data & Analytics - Client Exa...IBM Analytics
Are you effectively converting your audience insights into added value for your consumers? 80% of CEOs believe they deliver a superior customer experience, but only 8% of their customers agree. With IBM Big Data & Analytics solutions you can acquire, grow, and retain customers by improving customer interaction, building long term relationships and realizing value from customer sentiment.
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.
The document provides an overview of marketing analytics, including defining marketing analytics, key elements and capabilities, impact, and getting started with analytics. Some key points:
- Marketing analytics is the process of identifying valid performance metrics, tracking them over time, and using the results to improve marketing. The goal is to measure progress toward objectives.
- Key elements include people, steps, tools/technology, inputs and outputs. Capabilities include understanding performance and reporting it externally.
- Impacts can include optimizing brand recognition, content, channels, customer understanding, and predictive intelligence.
- Getting started involves assessing readiness, reviewing objectives, and establishing metrics like website, social media, email, and digital advertising metrics.
business analytics and its importance, marketing analytics definition and its importance, how marketing analytics helps to run the organization in effective and efficient manner.
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.
Next-best offer refers to the use of predictive analytics solutions to identify the products or services your customers are most likely to be interested in for their next purchase.
Facing this topic I have made a personal research, and realize a synthesis, which has helped me to clarify some ideas. This presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights.
Loyalytics is a boutique analytics consulting company helping businesses improve their customer loyalty across industries through the use of data and analytics. We help businesses convert data into an enterprise asset and leverage it to shape their customer strategy. Our mission is to be the “Voice of customers” for all our business partners.
Our capabilities span across the entire gamut of Analytics service offerings from Enterprise BI solutions for tracking and monitoring KPI’s to advanced data science solutions like Price optimization, Customer segmentations etc. Our Solutions can be clubbed into the following 5 verticals:
• Business Dashboards
• Customer Analytics
• Digital Analytics
• Merchandising Analytics
• Market Research
Our engagement model is very flexible and can be customized to the specific needs of the organization. From pure consulting assignment (Analytics Maturity assessment for business) to E2E deployment of specific analytics solutions, we do it all. For more information regarding our solutions/offerings please visit our website at www.loyalytics.in
Intelibiz is a highly scalable cloud based BI platform that provides real time time actionable insights to business across all important functions like Merchandising, Finance, Operations, Supply chain etc. Unlike traditional ERP reporting solutions that are siloed in their architecture, Intelibiz provides businesses with the ability to generate cross functional insights in a matter of seconds. The platform is powered by Industry renowned tableau software leading to highly efficient data architecture and world class security standards. Its cloud based architecture removes the need of having extensive IT hardware on premise leading to better ROI for the business. All the reports and dashboards can be accessed from anywhere in the world across multiple devices enabling faster insights. The entire platform is highly configurable and can be customized as per the specific needs of the business. Deployment takes anywhere between 2 weeks to 6 depending upon the specific need and existing data architecture at the business.
- The RBGE is a modular suite of advanced customer analytics models developed by Deloitte to help retail banks gain deeper insights from customer data and address growth challenges throughout the customer lifecycle.
- The core of RBGE is a comprehensive customer database combining internal and external customer data. Predictive analytical models are built on this database, including propensity to buy, segmentation, customer lifetime value, and churn models.
- Model outputs can be used in CRM systems and sales interactions to select the most appropriate offerings for customers. RBGE can deliver increases in up-sell by 15-25%, cross-sell by up to 45%, and marketing campaign success up to 30%. It also improves the customer experience.
This document discusses how a Data Ninja can help businesses address the growing complexity of data and shrinking budgets. A Data Ninja would define the target market and key drivers, build a comprehensive list of market drivers and data sources, and leverage statistical techniques to create an accurate forecasting model that clearly explains the relationships between drivers and the market. Additionally, a Data Ninja would add interactive visualizations to simplify and bring the data to life, providing transparency and a single version of truth to gain user trust.
The document provides guidance on building permission-based email marketing lists. It defines permission-based marketing as requiring formal consent from recipients to receive emails. There are various methods for gaining permission, including opt-in, confirmed opt-in, and double opt-in forms. Lists can be built in-house from customer data or rented from external sources, following best practices like incentivizing opt-ins, collecting subscriber interests, and maintaining CAN-SPAM compliance.
Accelerating Personalization to Cut Through Digital NoisePrecisely
Your customers’ inboxes are overflowing with unread email. Even social media and text messages are ignored. There is simply too much digital content and too little time to consume it.
So how do you stand out and get your customers’ attention? You need to go beyond traditional Customer 360 efforts. To get your customers’ attention, you need to maximize personalization based on context.
This document discusses using predictive analytics for retail businesses. It outlines using store clustering and RFM (recency, frequency, monetary) analysis to develop predictive models. Store clusters would be used to design customized planograms and segmentation strategies. RFM would analyze active and expired customers to develop targeted strategies like offering discounts or new products to high value customers or win back lower value, expired customers. The overall goal is to use predictive models to improve planogram performance, customer retention and reactivation, and sales.
Hospital CRM focuses on understanding customers through continual feedback capture and improved communication and personalization to drive loyalty. Key goals include increasing customer acquisition, retention and evangelism through improved profiling, targeting, and conversion. Hospitals should select a CRM system that best meets current and future needs to increase revenue, efficiency and customer value while decreasing costs and response times through a customer-centric approach.
Cloudway Consulting Pvt Ltd Is a SAP Strategic Sourcing Consulting Company For SAP, SAP S4 Hana, SAP Ariba, SAP C4C, Success Factor and Business By Design for More Call us at +0120-4226511
Sanjiv Bhatia “Critical Mass Makes Magic Happen”Elemica
Critical Mass Makes Magic Happen discusses how focusing resources on high-value initiatives through a value creation framework can provide benefits. The framework involves 4 steps: 1) identifying value through supply chain analysis, 2) prioritizing partners based on business value and technical complexity, 3) developing an onboarding roadmap, and 4) implementing projects. When applied to a company, the framework could generate $65 million in annual benefits and $43 million in one-time benefits through strategies like improved procurement and inventory management.
The low-interest period is forcing most insurers to control and monitor their financial investments. In contrast to a risk focused
approach seen in recent years, yield controlling and monitoring will have top priority. In order to reach this goal, many
insurers are modernizing and enhancing their data warehouses. BearingPoint is offering a predefined investment data warehouse comprising the most required KPIs, reports and the underlying data model.
Predictive Analytics & Decision Solutions [PrADS], a subsidiary of Dun & Bradstreet provides cutting edge analytics solutions and actionable insights to leading organizations globally , The following presentation provides an overview of the services offered
Shared services - A Strategic Cost Management PlatformSanjay Chaudhuri
Shared Services Platform (as self defining as it can be) promotes the idea of 'sharing' within an organization or group or may also be provided as 3rd party SBU services.
Creating a Single point of contact for all service deliveries, enabling Cost effective solutions, leverage Automation, optimize workforce and the Speed to fulfillment is the key to success of such organizations.
More and more companies are moving to such platforms and the success rate is very high.
This document discusses BRIDGEi2i, an analytics solutions company that helps businesses achieve accelerated outcomes through data-driven decisions. It outlines BRIDGEi2i's vision of transforming big data into meaningful business metrics and insights to realize sustainable business value. The document also describes BRIDGEi2i's core capabilities in areas like behavior modeling, segmentation, and forecasting using advanced analytics techniques. It provides examples of how these capabilities can be applied across various business functions such as marketing, sales, customer support, and operations.
Account Based Sales for Key Account GrowthRevegy, Inc.
According to SiriusDecisions, the recent buzz around account based sales reflects a long overdue need to enhance the way companies do account management...by breaking out of the status quo and exploring new, innovative approaches to the age-old concept of account planning, companies like Oracle are driving immediate pipeline growth from their most strategic customers.
Learn how world-class sales organizations are applying modern, account-based selling techniques to grow existing revenues:
The critical missing element that prevent account teams from discovering more immediate revenue opportunities
Why traditional approaches to account planning fail and what the most successful programs have in common
The best practices framework that companies like Oracle, JDA and BlackLine use to drive organic growth
Product Strategy to increase the incoming leads ROI.pdfPrasanna Hegde
Problem Statement: Design a product strategy to increase the return on investment (ROI) for incoming leads at a technology company.
Case Study: A technology company faced challenges in maximizing the ROI from their incoming leads. To address this, they implemented a comprehensive product strategy that involved optimizing lead conversion flow, integrating relevant applications, leveraging analytics, AI, and automation, and defining key metrics/KPIs to measure the effectiveness of the strategy. The company's data-driven approach and continuous improvement efforts resulted in a significant increase in lead ROI, driving business growth and success.
This document discusses finance transformation and becoming a strategic business partner. It provides an overview of finance challenges, objectives of high-performing finance organizations, and a maturity model assessment tool. The key aspects of a successful transformation include having a clear business case, executive support, program management, addressing cultural issues, and effective communication throughout the process. The payoff is shifting from transactional to more analytical/strategic work, optimizing resources, and enhancing business competitiveness over time.
Moving to the Front of the Pack: How to Achieve Digital Transformation with M...Apttus
Digital transformation is achieved by engaging your clients, empowering your employees, optimizing your operations and transforming your products. Join this session to learn how Microsoft and Hitachi Solutions give Financial Advisors unprecedented visibility into client, company, and investment data so you can use it to make better-informed decisions and execute on new business opportunities, so you can stay in front of the pack.
This document summarizes a presentation about how retail industry leaders are driving growth through agile analytics. The presentation discusses how leaders extensively utilize different types of data and look outside their industry for innovative analytics solutions. It provides examples of innovative analytics applications in different industries. The presentation emphasizes that leaders operationalize analytics by embedding predictive models into business processes and applications. It discusses key steps in the operational analytics journey and assessing an organization's analytic maturity.
This document summarizes National Financial's offerings for broker-dealers, including superior services, comprehensive product solutions, value-added services, and technology solutions to help firms grow their business. It highlights National Financial's dedicated relationship managers, flexible technology platform Streetscape, and the strength and experience of working with a leading clearing and custody provider of Fidelity Investments, which has $5 trillion in assets under administration.
FundQuest is a $40 billion asset management firm that offers outsourced investment management and support services to RIAs and other financial advisory firms. Its platform provides proposal generation tools, portfolio accounting, client reporting, and other services to supplement an RIA's core functions. It has partnerships with major custodians and offers both actively and passively managed model portfolios, including its own ActivePassive portfolios that combine both strategies. RIAs can also offer their own proprietary products through FundQuest's platform.
This document outlines a course on utilizing ProfitCents, a web-based business analytics software. It introduces ProfitCents and discusses how accounting firms can use it to enhance audit and review engagements, differentiate themselves to potential clients, and provide additional consulting services. The document covers best practices for implementation, leveraging industry data in various applications, and using ProfitCents for analytical procedures and risk assessment in audit engagements.
The document discusses business analytics frameworks and trends. It provides Gartner's key findings that enterprises will use a combination of products and services to support diverse analytics needs. A strategic view requires defining decision-making and analytical processes. Gartner recommends using a framework to develop an implementation plan and portfolio of capabilities. The business analytics framework defines the people, processes, and platforms needed to take a strategic approach to business intelligence and analytics initiatives. Major focus areas for analytics include healthcare, finance, and supply chain.
Data Analytics 201: Adding Value with Modeling TechniquesNICSA
Take a deeper dive into Data Analytics and better understand what it takes to develop useful algorithms.
This webinar will cover demonstrated use cases and applications for three different data analysis approaches. Panelists will discuss different customer segmentation approaches, as well as the development of scoring models. Participants will benefit from engaging discussion surrounding the value of analytics, incremental data acquisition, and the development of simple modeling techniques that can help financial firms succeed.
Expert strategies for your loyalty programArun Krishnan
The document provides an overview of strategies for implementing a successful loyalty program using SAP Loyalty Management. It discusses the current state of customer loyalty, an overview of SAP Loyalty Management capabilities, a case study of a loyalty program implementation, and 8 leading practices for a loyalty program. The leading practices include focusing on acquiring customer data, aligning the loyalty experience to the brand, engaging members with personalized content, excelling at core program benefits, harnessing partner networks, empowering brand advocates, increasing member wallet share, and reducing marketing costs.
As a product manager specialize in monetization for software, I like to share my concepts and techniques with my colleagues to help them understand my approach.
The document discusses various types of information systems that support decision making. It describes management information systems that provide routine operational reports, decision support systems that help with semi-structured tactical decisions through modeling and analysis, and executive information systems that provide customized insights to top executives. The document also covers data warehousing, data mining, expert systems, and emerging trends like personalized decision support and what-if scenario analysis.
People-as-a-Asset is a live, breathing strategy that need constant vigilance to prevent entropy.
Management 3.0 is all about personalization, configuration & Business of One. This requires systematic monitoring of practices and mindset to enable the most effective usage of human Intelligence towards building sustainable Social Capital & Network Effects for the Organization
Algorithms drive delivery of "Moments of Truth by
1) helping with Search, Negotiate & Deliver processes
2) providing energy for digital transformation
3) enabling assetization of customer journey
4) ensuring Digital Presence is the Outcome
Omnichannel Conversations are key to successful execution of Digitalization framework along with technocratization of decision making, realtime interventions & identification of Imposters
Analytics is important to understand & appreciate the career pathing choices made by various employees. This will also aid in better sculpting and engagement choices organizations can make
Case study gamification approach to analytics deploymentAditya Madiraju
The document describes a case study of introducing and promoting analytics services at a global marketing operations hub through gamification. It faced challenges of being seen as just an operations team and lack of funding. A diagnosis found high client focus led to individualism over process. An action plan used gamification to improve processes, train staff, and promote analytics. Gamification helped socialize changes by altering behaviors and understanding reactions through games and platforms like social media. It identified behavioral factors and created a unified customer experience program around commitment and involvement principles.
Analytics @ Marketing Service Center - discussion documentAditya Madiraju
Modern Marketing Ops have a unique challenge of deploying campaigns that are targeted based on specificity of Data. That means being adroit not only in Digital capabilities, but also, in Data Engineering
Digital Marketing Enablement starts with Web Analytics. This is a presentation used to capture different facets of web analytics & how it helps in enablement of Digital Marketing
Analytics led transformation of marketing functionAditya Madiraju
Marketing plays a critical role of providing forward looking experiences at an optimal cost. Hence, the department/function needs establish strong linkage to experiences & value capturing strategies.
Here is an approach, I implemented that was found useful....
Any business having unsecured revolving balances have to worry about potential fraud. Why? Fraud occurences typically show an inverse proportion to an organization's customer centricity, ie, more customer centricity higher the likelihhod of a fraud occurring.
The document discusses customer relationship management (CRM) analytics and how it is evolving from simply tracking customer data to using data mining and predictive modeling to gain insights. It provides an overview of developing CRM analytics capabilities, including establishing data governance, building analytical models, prioritizing custom vs generic models, and monitoring metrics. The document also outlines how CRM analytics engagements are structured and operated, with strategists, statisticians, and analysts working on pilot programs, opportunity matrices, and engagement styles.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
4. Page 4
Wealth Management Business Model is based on Customer-Centricity, Service
Offering & IT Platform
Wealth Management Value Chain
Technology – Direct Banking /
CRM / Personalized View
Bouquet of Offers – Partnership
Tie-ups / Investment Services /
Portfolio Management
Trusted Advisors – Qualified
Financial Planners, Relationship
Managers, Branch Managers
5. Page 5
Wealth Management by Objectives – Primary Performance Metrics
Relationship Manager’s KPI
Length of Relationship
Beginner Seasoned
FinancialObjectives
Share of Mind
Share of Wallet
1. Number of Sales Contacts
2. Enrollment in Concierge Service
3. Product Holding Ratio
4. Size of Loan Portfolio
5. Assets Under Management
6. Page 6
Critical to Success – Managing Changing Client Expectations (Alignment of Interests)
3. Activation Efforts
- High Value Proposition
- Unique and customized
1. Solicitation Efforts
- Open & Honest
- Setting realistic expectations
- Upfront on Fee Structure
2. Account Set UP
- On-time
- First Time Right
- Welcome Kit
6. Retention Efforts
- Value Matching
- Highly Subsidized
“White Goods” offers
4. Value Enhancement Efforts
- Cross-Sell /Up-Sell
- Add-on: Family/Business Associates
- International Offerings
5. Loyalty Building Efforts
- Speaking Opportunities
- Access to “Page 3” events
- Personalized Attention
Impacts on
Life Time Value
Solution should enable a Bank’s Wealth Management team to “Keep Eye on the
Ball” across stages
7. Page 7
Achieving Targets - Focusing on Pipeline and Efficient “Closing”
Opinions – Favorability in Usage
Positive Neutral Negative
1. Loyalty Programs 2. Value Enhancement Programs
3. Retention Program 4. Winback Programs
5. Courtesy Calls
Behavior-SpeedtoDecision
Aggressive
Fast
Slow
2
2
2
51
1
44
3
Objective: Increase efficiency of Relationship Manager
Potential Attrition
10. Page 10
Analytics as Enabler – Overview
Marketing Mix
Models
Promotion Response
Models
Forecasting Models
Personal
Visits
Campaigns
Programs
Execution Planning
Analytics will increase Relationship Manager’s Effectiveness and Efficiency
11. Page 11
Analytics as Enabler (Effectiveness) - Identifying my Wealth Management Candidate
Segmentation framework follows three-step segmentation approach evaluation
process, centering around the solution’s business objectives.
1. Both attitudinal and behavioral segmentation approaches are equally well-suited for usage as
customer classification and/or description mechanisms – Identify desired functionalities
• Understanding the structure of the market
• Derive preference/affinity segments
• Use consumer attitude/preference-driven descriptive segmentation
• Require segmentation schema capable of driving cross-sell / up-sell programs
• Require assigning segments to the newly acquired customers
2. Leverage your current informational assets, but do not lose sight of the marketing need at
hand – Investigate data availability
• Rich in transactional data - use behavioral segmentation
• Rich in abounding reservoir of customer preferences – use attitudinal data
3. Differences between population and customer samples – Evaluate spatial population drift
12. Page 12
Analytics as Enabler (Efficiency) - Dashboards
INR 0
INR 1,000
INR 2,000
INR 3,000
INR 4,000
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INR 10,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
(INR 5,000,000)
INR 0
INR 5,000,000
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1 2 3 4 5
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INR 80,000,000
East West North South Central
Assets by RegionDaily Average in Flow
Net Flows by Product Net Flows by Region
Example – Advisor’s home page providing overview of performance
13. Page 13
Analytics as Enabler - Dashboards
Daily Average in Flow
2%
9%
30%
20%
8%
10%
21% Cash
International
Large Cap
Fixed Income
Other
Small Cap
Mid Cap
INR 0
INR 100,000
INR 200,000
INR 300,000
INR 400,000
INR 500,000
INR 600,000 Top Advisor Opportunities
Asset Allocation Advisorswiththegreatestriskfactors
Advisor AUM RollingProduction
ShyamSundar INR3.5Crores INR12Lakhs
AmeyaJoshi INR1.45Crores INR20Lakhs
RahulBhagat INR4.34Crores INR0.48Lakhs
SureshPatwardhan INR0.76Crores INR0.12Lakhs
ShyamalSurana INR0.54Crores INR0.23Lakhs
0
50
100
150
200
250
INR 0
INR 2,000,000
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INR 18,000,000
UGDC AHDF DSER FGHU HJIU
Top Recruitment Profiles
Example – Manager’s home page providing overview of business and opportunities.
14. Page 14
Analytics as Enabler - Dashboards
Example – Advisor’s home page providing further drilldown of customers.
Sharukh Salman Preity Nagesh Chiranjeevi Nagarjuna Mahesh Firoz Philip
AUM
Days Since Last Contact
Days Since Last Service
Service Pipeline Eligible For
Attrition Risk
Potential Score
Cross-Sell Score
Satisfaction Index
Next Revision Date
Age
Income
Prescence of Children
Occupation
Education
Lifestyles
Opinions
Brand Loyalty
Shopping Pattern
Distribution Channel Preferences
Product Preferences
Perception of Product
Needs to be fulfilled
Age
Occupation
Education
Gender
Geography
Nature of Employment
Address
Industry
Ownership
Sales Turnover / Profit
Year of establishment
Number of Locations
Geography
Export / Import
IT Budget
II. Pscychographic Information
III. Demographic Information
IV. Firmographic Data
I. Financial Drivers
16. Page 16
Structuring Analytics Solution Engagement – 3 Year Timeline
Year 1
Define Analytics Objectives:
MIS/Decision Analytics /
Predictive Modeling/ Strategy
Design
Contact Strategy:
Developing Contact Intensity
Cross-Sell Framework:
Identifying Eligible Customers
Year 2
Refine Analytics Objectives:
MIS/Decision Analytics /
Predictive Modeling/ Strategy
Design
Automating data preparation &
Improving campaign processes
Year 3
Imbibe Analytics Objectives:
MIS/Decision Analytics /
Predictive Modeling/ Strategy
Design
Automating data preparation &
Improving campaign processes
Cross-Sell Framework:
Evaluating Product/Service
Sequencing based on LTV
Contact Strategy: Developing
contact schema based on
contact channels and messages
Automating data preparation &
Improving campaign processes
Cross-Sell Framework:
Integrating LTV metric across
service lines and products
Contact Strategy: Instituting
organization-wide contact rules
including DNC Management
• Agree on timeline for start of engagement.
• This will help plan the appropriate team to work with Wealth Management team
• Agree on sequence of projects
• This will help identify the type of data needed; and the teams can work to get this ready in
the background, while other paperwork is being worked on. This will ensure a quick start.
19. Page 19
Solution Considerations – Data Stream
C-Sat Data
Agent Logs
CRM Data
Call Transcripts
Payment Data
Data Linking
& Cleaning
Text Mining
Framework
Derived
Attributes
Framework
Common Text
Representation
Indexed XML/
CSV files
Data
warehouse
Data Sources
Data Processing & Conversion
Stage
Data Storage Stage Analysis & Reporting Stage
Assisted Insight
generation
Decision Matrix
Reporting &
Automation
Social Signals
Digital Pathways
Enabling highest data quality and governance