Vivastream, profile picture
Uploaded byVivastream
PDF, PPTX5,258 views

Customer Intelligence & Analytics - Part I

This document discusses how the field of marketing analytics is evolving due to the explosion of available data and increased use of analytics. It describes how companies now use analytics throughout their operations to gain insights from data and make better decisions. The document also outlines some common areas where companies apply analytics, such as customer acquisition, pricing, and forecasting. It cautions that analytics must be implemented carefully and should be guided by the data rather than preconceptions to avoid bias.

Embed presentation

Download as PDF, PPTX
Module 1: The World of Marketing Is Changing - Are You Being Left Behind? 1.1 Introduction 1.2 Competing On Analytics 1.3 The Data Explosion 1.4 Evolving Context of Marketing Analytics & Research 1.5 Questions
• Debbie Mayville – Sr. Solutions Architect, Communications & Marketing Analytics, SAS • David Kelley – Sr. Solutions Architect, Customer Intelligence, SAS • Suneel Grover – Solutions Architect, Integrated Marketing Analytics, SAS – Adjunct Professor, Integrated Marketing Analytics, New York University (NYU)
Module 1: The World of Marketing Is Changing - Are You Being Left Behind? 1.1 Introduction 1.2 Competing On Analytics 1.3 The Data Explosion 1.4 Evolving Context of Marketing Analytics & Research 1.5 Questions
„Old Spice‟ Campaign Case Study
Achieving Success With Business Analytics What’s the best that can happen? Optimization What will happen next? Predictive Modeling What if these trends continue? Forecasting Why is this happening? Statistical Analysis Alerts Query What actions are needed? Drilldown Ad hoc Where exactly is the problem? Reports Std. How many, how often, where? Reports What happened?
Business Analytics “The extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact- based management to drive decisions and actions.” Davenport and Harris (2007) Competing on Analytics: The New Science of Winning
Data Deluge
Three Consequences Of The Data Deluge 1. Every problem will generate data eventually. 2. Every company will need analytics eventually. 3. Everyone will need analytics eventually. ...
Three Consequences Of The Data Deluge 1. Every problem will generate data eventually. Proactively defining a data collection protocol will result in more useful information, leading to more useful analytics. 2. Every company will need analytics eventually. 3. Everyone will need analytics eventually. ...
Three Consequences Of The Data Deluge 1. Every problem will generate data eventually. Proactively defining a data collection protocol will result in more useful information, leading to more useful analytics. 2. Every company will need analytics eventually. Proactively analytical companies will compete more effectively. 3. Everyone will need analytics eventually. ...
Three Consequences Of The Data Deluge 1. Every problem will generate data eventually. Proactively defining a data collection protocol will result in more useful information, leading to more useful analytics. 2. Every company will need analytics eventually. Proactively analytical companies will compete more effectively. 3. Everyone will need analytics eventually. Proactively analytical people will be more marketable and more successful in their work.
The Business Analytics Challenge Getting anything useful out of tons and tons of data
Hope For The Data Deluge + analytical tools = actionable knowledge
Changes In The Analytical Landscape Historically… Models Analytical Modelers Management Historically, analytics have typically been handled in the “back office,” and information was shared only by a few individuals.
Changes In The Analytical Landscape Historical Changes – Executive Dashboards • Static reports about business processes – Customer Relationship Management (CRM) • The right offer to the right person at the right time – 360-degree customer view
Changes In The Analytical Landscape Relational Databases Enterprise Resource Planning (ERP) Point of Sale (POS) Systems Decision Support Systems – Reporting and Ad Hoc Queries – Online Analytical Processing (OLAP) Performance Management Systems – Executive Information Systems (EIS) – Balanced Scorecard Business Intelligence
CRM Evolution • Total Quality Management (TQM) – Product-Centric • Quality: Six Sigma • Total Customer Satisfaction • Mass Marketing • One-to-One Marketing – Customer Relationship • Wallet Share of Customer • Customer Retention • Customer Relationship Management (CRM) – Customer-Centric • Strategy • Process • Technology
Changes In The Analytical Landscape Now… Operations Targeting Proliferation of Models Customer Customers Analytical Modelers Service Retail Suppliers Now analytics are being pushed out to the “front office”. There are clear, tangible benefits that management will track. Data mining is a critical part of business analytics. Promotions Employees
Idiosyncrasies Of Business Analytics 1. The Data - Massive, operational, and opportunistic 2. The Users and Sponsors - Business decision support 3. The Methodology - Computer-intensive adhockery - Multidisciplinary Data mining can be defined as advanced methods for exploring and modeling relationships in large amounts of data.
The Data Experimental Opportunistic Purpose Research Operational Value Scientific Commercial Generation Actively controlled Passively observed Size Small Massive Hygiene Clean Dirty State Static Dynamic
The Data: Disparate Business Units Marketing Invoicing Risk Acquisitions Operations Sales
Opportunistic Data – Operational data • Typically not collected with data analysis in mind – Multiple business units • Silo-based data environment  This makes business analytics different from experimental statistics and especially challenging
The Methodology: What We Learned Not to Do • Prediction is more important than inference 1. Metrics are used “because they work” 2. p-values are directional guides 3. Interpretation of a model might be irrelevant 4. The preliminary value of a model is determined by its ability to predict a holdout sample 5. The long-term value of a model is determined by its ability to continue to perform well over time 6. Models are retired as behavior and trends shifts
Using Analytics Intelligently • Intelligent use of analytics 1. Understanding of how marketplace shifts affect business performance 2. Ability to distinguish between effective and ineffective interventions 3. Efficient use of assets, reduced waste 4. Risk reduction via measurable outcomes 5. Early detection of trends hidden in massive data 6. Continuous improvement in decision making
Simple Reporting Examples: OLAP, RFM, descriptive statistics, extrapolation Answer questions such as: 1. Where are my key indicators now? 2. Where were my key indicators last week? 3. Is the current process behaving like normal? 4. What’s likely to happen tomorrow?
Proactive Analytical Investigation Examples: Data mining, experimentation, empirical validation, predictive modeling, optimization Answer questions such as: 1. What does a change in the market mean for my targets? 2. What do other factors tell me about my target? 3. What is the best combination of factors for maximum profit? 4. What is the highest price the market will tolerate?
Data Stalemate • Many companies have data that they do not use or sell to third parties. These third parties might even resell the data and any derived metrics back to the original company! • Story: Retail grocery POS card
Every Little Bit… Taking an analytical approach to only a few key business problems with reliable metrics  tangible benefit The benefits and savings derived from early analytical successes  managerial support for more analytics 1. Everyone has data 2. Analytics can connect data to smart decisions 3. Proactively analytical companies outpace competition
Areas Where Analytics Are Often Used • New customer acquisition Which residents in a ZIP • Customer loyalty code should receive a • Cross-sell / up-sell coupon in the mail for a new store location? • Pricing tolerance • Supply optimization • Staffing optimization • Financial forecasting • Product placement • Churn • Insurance rate setting • Fraud detection • …
Areas Where Analytics Are Often Used • New customer acquisition • Customer loyalty What advertising strategy • Cross-sell / up-sell best elicits positive sentiment toward the • Pricing tolerance brand? • Supply optimization • Staffing optimization • Financial forecasting • Product placement • Churn • Insurance rate setting • Fraud detection • …
Areas Where Analytics Are Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell What is the best next • Pricing tolerance product for this customer? • Supply optimization • Staffing optimization • Financial forecasting • Product placement • Churn • Insurance rate setting • Fraud detection • …
Areas Where Analytics Are Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance What is the highest price • Supply optimization that the market will bear • Staffing optimization without substantial loss of demand? • Financial forecasting • Product placement • Churn • Insurance rate setting • Fraud detection • …
Areas Where Analytics Are Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance • Supply optimization How many 60-inch HDTVs • Staffing optimization should be in stock? • Financial forecasting • Product placement • Churn • Insurance rate setting • Fraud detection • …
Areas Where Analytics Are Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance • Supply optimization • Staffing optimization What are the best times • Financial forecasting and best days to have technical experts on the • Product placement showroom floor? • Churn • Insurance rate setting • Fraud detection • …
Areas Where Analytics Are Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance • Supply optimization • Staffing optimization • Financial forecasting What weekly revenue • Product placement increase can be expected • Churn after the Mother’s Day sale? • Insurance rate setting • Fraud detection • …
Areas Where Analytics Are Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance • Supply optimization • Staffing optimization • Financial forecasting • Product placement Will oatmeal sell better • Churn near granola bars or near • Insurance rate setting baby food? • Fraud detection • …
Areas Where Analytics Are Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance • Supply optimization • Staffing optimization • Financial forecasting • Product placement • Churn Which customers are most • Insurance rate setting likely to switch to a • Fraud detection different wireless provider in the next six months? • …
Areas Where Analytics Are Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance • Supply optimization • Staffing optimization • Financial forecasting • Product placement • Churn • Insurance rate setting How likely is it that this • Fraud detection individual will have a claim? • …
Areas Where Analytics Are Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance • Supply optimization • Staffing optimization • Financial forecasting • Product placement • Churn • Insurance rate setting • Fraud detection How can I identify a fraudulent • … purchase?
When Analytics Are Not Helpful • Snap decisions required Deciding when to run • Novel approach (no previous from danger data possible) • Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) • Expert analysis suggests a particular path • Metrics are inappropriate • Naïve implementation of analytics • Confirming what you already know
When Analytics Are Not Helpful • Snap decisions required • Novel approach (no previous Predicting the adoption of data possible) a new technology • Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) • Expert analysis suggests a particular path • Metrics are inappropriate • Naïve implementation of analytics • Confirming what you already know
When Analytics Are Not Helpful • Snap decisions required • Novel approach (no previous data possible) • Most salient factors are rare Planning contingencies (making decisions to work for employees winning around unlikely obstacles or the lottery miracles) • Expert analysis suggests a particular path • Metrics are inappropriate • Naïve implementation of analytics • Confirming what you already know
When Analytics Are Not Helpful • Snap decisions required • Novel approach (no previous data possible) • Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) • Expert analysis suggests a The seasoned art critic particular path can recognize a fake • Metrics are inappropriate • Naïve implementation of analytics • Confirming what you already know
When Analytics Are Not Helpful • Snap decisions required • Novel approach (no previous data possible) • Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) • Expert analysis suggests a particular path Predicting athletes’ • Metrics are inappropriate salaries or quantifying • Naïve implementation of love analytics • Confirming what you already know
When Analytics Are Not Helpful • Snap decisions required • Novel approach (no previous data possible) • Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) • Expert analysis suggests a particular path • Metrics are inappropriate • Naïve implementation of Only looking at one analytics variable at a time • Confirming what you already know
When Analytics Are Not Helpful • Snap-decisions required • Novel approach (no previous data possible) • Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) • Expert analysis suggests a particular path • Metrics are inappropriate • Naïve implementation of analytics • Confirming what you already Ignoring variables that know might be important
The Fallacy Of Univariate Thinking What is the most important cause of churn? Prob(churn) International Daytime Usage Usage
Expectations Leading The Analysis • Sophisticated analytics are not immune to personal bias – Selectively fitting models because they place an opinion or agenda in a positive light – Ignoring information that might disprove a hypothesis • Personal bias, whether intentional or not, can diminish the usefulness of analytics
Trustworthy Analytics Let the data guide your conclusions – Are my assumptions about the causes of the data patterns warranted? – Should I be trying something different? Assign a cynic to the analytical team whose purpose is to question the assumptions
Idea Exchange  Identify several business problems that you could address with analytics  Describe the goal, whether the variables can be measured, how the data could be obtained, and what types of specific questions you would like to address with analytics
Case Study – US Telco • Data Deluge: Just Get Started – Low hanging fruit – Continue to build and get smarter – 360 degree view of the customer • Tools: Efficiency & Effectiveness – Data management tools – Analytic tools • Move to data driven insights versus gut reactions • Establish measurement system – Test & Learn Environment
Customer Lifecycle – Touch Points
Obtaining 360 Degree View Of The Customer Activ- Social ation Firmo- Network graphics Usage Demo- graphics Care Point of 360 Sale Degree Customer Hard- View Service, ware Repair VOD, Network Games Commu- Billing ni- Collect- cations ions
Large Telco With Industry-leading Churn Rate Churn Churn Reduction By Reduction Value ($) Reason Equipment 9 bps $121M Usage 16 bps $163M Network 15 bps $158M Active Issue Resolution 11 bps $110M Contract Renewal 25 bps $273M Sales Channel / Credit & 6 bps $87M Collections Total 82 bps $912M
Case Study US Telco Business Issue • Company-wide initiative to lower the churn rate among customers • Focus on “high value” or “high value potential” customers • Improve treatment strategy and relevance Solution • Data management • Advanced analytics Results/Benefits • Reduced churn by 40% • Increased customer loyalty and lifetime value • Increase of operational revenues by $1B over 3 years • Ability to uncover dissatisfaction drivers and tailor proactive churn treatments
Module 1: The World of Marketing Is Changing - Are You Being Left Behind? 1.1 Introduction 1.2 Competing On Analytics 1.3 The Data Explosion 1.4 Evolving Context of Marketing Analytics & Research 1.5 Questions
Key BUSINESS Trends Affecting Marketing From Product to Customer • Customer-centric business strategy • The customer experience • 360-degree customer view Finding the Next Origin of Business Growth • Consolidation/mergers/acquisitions • Market expansion • Efficiency & optimization The Regulatory Rise • Increased disclosure and transparency • Privacy and information sharing • Consumer contact rules • Regulatory reform
Key CONSUMER Forces Affecting Marketing Consumer in Charge • Rising expectations and more choice • From right time to “real time” • Demographic divide Channel Adoption • Mobile devices and consumer adoption • Web 2.0 and the digital age • Cross-channel usage Huge Online and Social Adoption • Social networking • Consumer-controlled content and channels • Consumer engagement
A Broadened Definition of “The Customer”  The Consumer  The Citizen  The Subscriber  The Plan Member  The Patient  The Patron ...applicable across B2C & B2B
Customer Intelligence Is Relevant Across Industries Financial Services Insurance Retail Hospitality & Telco & Cable Manufacturing Gaming Government Marketing Service Health & Providers Life Sciences Utilities
The Marketer Has An Evolving Mandate Expectation Expectation Deliver a branded Integrated, multi-channel customer experience in/outbound conversations in and outside of in real-time marketing The Marketing Campaign The The Customer Brand Experience Responsibilities Expectation Sustain brand health in a rapidly changing virtual world Insights and Analytics Expectation Unearth and dynamically manage insights to drive action
Key Forces Affecting Marketers Huge Online and Social Consumer Adoption 2B people online,100B monthly searches and 600MM people on social networks globally
Key Forces Affecting Marketers Huge Online and Social Consumer Adoption Ever-Growing and Converging Marketing Channels Technology advances and consumer preferences driving new channels at unprecedented rates
Key Forces Affecting Marketers Huge Online and Social Ever-Growing and Converging Consumer Adoption Marketing Channels Information Explosion Business information doubling every 18 months with unstructured data representing 70% of it.
Key Forces Affecting Marketers Huge Online and Social Ever-Growing and Converging Information Explosion Consumer Adoption Marketing Channels The Speed of Business Information traveling at unprecedented rates, compounded by rising consumer expectations.
Key Forces Affecting Marketers Huge Online and Social Ever-Growing and Converging Information Explosion Consumer Adoption Marketing Channels The Speed of Business Accountability and Need to do More with Less Economic and competitive pressures putting focus on marketing budgets and returns.
Key Forces Affecting Marketers Huge Online and Social Ever-Growing and Converging Information Explosion Consumer Adoption Marketing Channels The Speed of Business Increasingly Competitive & Converging Markets Accountability and Need to do Parity markets with limited differentiation . More with Less Fight for share of wallet.
Key Forces Affecting Marketers Huge Online and Social Ever-Growing and Converging Information Explosion Consumer Adoption Marketing Channels Brand Health Less corporate trust compounded by The Speed of Business brands being publicly scrutinized. Traditional mass marketing proving less impactful. Increasingly Competitive & Converging Markets Accountability and Need to do More with Less
The Marketing Process Mobile Online Finance Risk Call Customer Center Service In Person Merchandising Social Corporate Affairs Direct Mail Marketing Operations Optimization Marketing Marketing Marketing Strategy Processes Campaigns Analytics Data Integration ERP CRM EDW Online Social Campaign
The Data Integration & Management Challenge
The Flood Of Data • Customer data continues to flood the organization exponentially • Progressing from functional to strategic – Namely how to capture, integrate, manage, analyze, and apply knowledge/insight about customers – Google Executive Chairman Eric Schmidt: “We create as much information in two days now as we did from the dawn of man through 2003.”
Structured & Unstructured Data • Company data: billing, usage, collections, set-top box, customer, web interactions, campaign, and more! • Consumer-generated data: Social media, blogs, product reviews, and more! Structured data 25% 70% 5% Unstructured data Semistructured data
“Big Data” Myths • Data Volumes are “Exploding” – Did Wal-Mart suddenly sell more stuff? – Did NYSE suddenly do more stock trades? – Did Netflix suddenly rent more movies? – Did Amazon suddenly sell more books? • This is existing data that previously went un-analyzed: A. Too large to manage B. Too costly to store C. Lack of “analytic chops” to capitalize
“Big Data” - Why Now? Three Vs? Complex, Unstructured 1. Volume 2. Velocity 3. Variety Relational The primary driver is Value… Source: IDC .
“Big Data” - Why Now? • Cost of storage dropping
Data - Prerequisite For Everything Analytical “You can’t be analytical without data, and you can’t be really good at analytics without really good data” • Structure • Uniqueness • Integration • Quality • Access • Privacy • Governance Davenport, Harris, Morison (2010) Analytics at Work: Smarter Decision Better Results
Data Structure / Uniqueness / Integration Structure • Data structure affects analysis performance • Transaction systems (tables), data cubes (limitations) • Data arrays • Unstructured data Uniqueness • Data only your company has access – proprietary • Commercially available data – be the industry 1st • Create new metrics and data fields Integration • Aggregate data from inside/outside your organization • Consolidate silos across departments • Data has to be sourced, cleaned, integrated • Evolve to “one version of the truth”
Data Quality / Access / Privacy Quality • Flawed data causes misleading results • To fix problems - look at the data source • Continuous process – data will never be perfect • Start based on business objectives Access • Source data and load in a form for analytics • Size or complexities can cause user issues • Speed needs require data warehouse appliances • Sample populations Privacy • Guard the information collected • Well defined policies • Privacy laws within territories or industries • Don’t sell information without permission (opt-in)
Data Governance Governance • Ensure data is useful for analysis • Consistent, defined, sufficient quality, standardized, integrated, accessible • Standard definitions and terminology • Decide on investments • Owners and stewards • Analytical data advocates • Business intelligence competency centers, analytical data advocate group, information management
Module 1: The World of Marketing Is Changing - Are You Being Left Behind? 1.1 Introduction 1.2 Competing On Analytics 1.3 The Data Explosion 1.4 Evolving Context of Marketing Analytics & Research 1.5 Questions
„The Greatest Job In The World‟
The Challenge of Digital Marketing • As digital marketing continues to grow more significant, new channels add complexity to the design of a successful integrated campaign. – It’s both a blessing and a curse for when an integrated campaign goes viral – Key Challenge: How do we do it again? – No repeatable formulas or clear attribution metrics
Reactive Business Analytics What’s the best that can happen? Optimization What will happen next? Predictive Modeling What if these trends continue? Forecasting Why is this happening? Statistical Analysis Alerts Query What actions are needed? Drilldown Ad hoc Where exactly is the problem? Reports Std. How many, how often, where? Reports What happened?
Proactive Business Analytics What’s the best that can happen? Optimization What will happen next? Predictive Modeling What if these trends continue? Forecasting Why is this happening? Statistical Analysis Alerts Query What actions are needed? Drilldown Ad hoc Where exactly is the problem? Reports Std. How many, how often, where? Reports What happened?
Afternoon Workshop Preview • What if I could? – Automate the measurement of sentiment relevant to my business goals from digital channels – Capitalize on the hidden value in vast amounts of available structured/unstructured data associated with my brand – Become strategically more proactive to shifting (dynamic) consumer trends
The Marketing Process Mobile Online Finance Risk Call Customer Center Service In Person Merchandising Social Corporate Affairs Direct Mail Marketing Operations Optimization Marketing Marketing Marketing Strategy Processes Campaigns Analytics Data Integration ERP CRM EDW Online Social Campaign
Module 1: The World of Marketing Is Changing - Are You Being Left Behind? 1.1 Introduction 1.2 Competing On Analytics 1.3 The Data Explosion 1.4 Evolving Context of Marketing Analytics & Research 1.5 Questions

More Related Content

PPTX
Regression and correlation
byVrushaliSolanke
 
PDF
Predictive and prescriptive analytics: Transform the finance function with gr...
byGrant Thornton LLP
 
PDF
Predictive Modelling
byRajib Kumar De
 
PDF
From Business Intelligence to Predictive Analytics
byDecision Management Solutions
 
PPTX
Baworld adapting to whats happening
byDave Davis PMP, PgMP, PBA
 
PDF
Predictive analytics in action real-world examples and advice
byThe Marketing Distillery
 
PDF
1120 track2 bennett
byRising Media, Inc.
 
PPTX
Analytics in business
byNiko Vuokko
 
Regression and correlation
byVrushaliSolanke
 
Predictive and prescriptive analytics: Transform the finance function with gr...
byGrant Thornton LLP
 
Predictive Modelling
byRajib Kumar De
 
From Business Intelligence to Predictive Analytics
byDecision Management Solutions
 
Baworld adapting to whats happening
byDave Davis PMP, PgMP, PBA
 
Predictive analytics in action real-world examples and advice
byThe Marketing Distillery
 
1120 track2 bennett
byRising Media, Inc.
 
Analytics in business
byNiko Vuokko
 

What's hot

PPTX
Machine learning101 v1.2
byCCG
 
PDF
Marketing analytics for the Banking Industry
bySashindar Rajasekaran
 
PPTX
Ml in a Day Workshop 5/1
byCCG
 
PDF
Machine Learning for Business - Eight Best Practices for Getting Started
byBhupesh Chaurasia
 
PPTX
Introduction to Business Data Analytics
byVadivelM9
 
PDF
Data Analytics in Azure Cloud
byMicrosoft Canada
 
PPTX
Predictive analytics and new CRM systems
byValgen, Inc.
 
PDF
1140 track 3 ramirez_using our laptop
byRising Media, Inc.
 
PPTX
Application of predictive analytics
byPrasad Narasimhan
 
PDF
Business Analytics and Optimization Introduction (part 2)
byRaul Chong
 
DOCX
Business Intelligence
byAbu Bashar
 
PPTX
Causal Inference in Marketing
byTa-Wei (David) Huang
 
PPTX
Create Success with Analytics: Predictive Analytics 101: Your Roadmap to Driv...
byHannah Flynn
 
PDF
De-Mystefying Predictive Analytics
byGalit Shmueli
 
PDF
segmentda
bySteven Cosgrove, Ph.D.
 
PPTX
Disscusion - a crm final
bysamuelox
 
PDF
1645 track 3 porter
byRising Media, Inc.
 
PDF
1120 track 3 prendki_using our laptop
byRising Media, Inc.
 
PPTX
From BI to Predictive Analytics
byDecision Management Solutions
 
Machine learning101 v1.2
byCCG
 
Marketing analytics for the Banking Industry
bySashindar Rajasekaran
 
Ml in a Day Workshop 5/1
byCCG
 
Machine Learning for Business - Eight Best Practices for Getting Started
byBhupesh Chaurasia
 
Introduction to Business Data Analytics
byVadivelM9
 
Data Analytics in Azure Cloud
byMicrosoft Canada
 
Predictive analytics and new CRM systems
byValgen, Inc.
 
1140 track 3 ramirez_using our laptop
byRising Media, Inc.
 
Application of predictive analytics
byPrasad Narasimhan
 
Business Analytics and Optimization Introduction (part 2)
byRaul Chong
 
Business Intelligence
byAbu Bashar
 
Causal Inference in Marketing
byTa-Wei (David) Huang
 
Create Success with Analytics: Predictive Analytics 101: Your Roadmap to Driv...
byHannah Flynn
 
De-Mystefying Predictive Analytics
byGalit Shmueli
 
segmentda
bySteven Cosgrove, Ph.D.
 
Disscusion - a crm final
bysamuelox
 
1645 track 3 porter
byRising Media, Inc.
 
1120 track 3 prendki_using our laptop
byRising Media, Inc.
 
From BI to Predictive Analytics
byDecision Management Solutions
 

Similar to Customer Intelligence & Analytics - Part I

PPTX
Introductions to Business Analytics
byVenkat .P
 
PPTX
Business Analytics, Types of Business Analytics
byAce Institute of Management (Faculty Member) , Institute of Management Studies (Executive Director)
 
PPTX
Making a Systematic Business Case for Analytics
byAshwin Malshe
 
PPTX
Introduction to data analytics and data analysis.pptx
bysheddybro4318
 
PPTX
HR analytics
bypreksha1185
 
PPTX
Business Analytics (2).pptx
byrachikar
 
PPTX
Unit 1 pptx.pptx
byrekhabawa2
 
PPTX
727325165-Unit-1-Data-Analytics-PPT-1.pptx
byrevathi148366
 
PDF
Introduction to Business Analytics.pdf
bySuku Thomas Samuel
 
DOC
BIG DATA & BUSINESS ANALYTICS
byVikram Joshi
 
PDF
Marketing in crisis: using analytics, data and intelligence
byThe House of Marketing
 
PPTX
Business Analytics models, measuring scales etc.pptx
byfaizhasan406
 
PPTX
Business Analytics unit 1 prof dr kanchan.pptx
byDr. Kanchan Kumari
 
PDF
How to make data actionable for business
byRavi Padaki
 
PDF
The synthesis journal 2011 business analytics sas
bySAS Singapore Institute Pte Ltd
 
PDF
Accenture white bim trichy
byNikhil Kansari
 
PDF
EMC Perspective: Big Data Transforms the Life Science Commercial Model
byEMC
 
PDF
Pivotal CRM - Analytics
byPivotal CRM
 
PDF
The New Normal: Predictive Power on the Front Lines
byInside Analysis
 
PDF
A case for business analytics learning
byMark Tabladillo
 
Introductions to Business Analytics
byVenkat .P
 
Business Analytics, Types of Business Analytics
byAce Institute of Management (Faculty Member) , Institute of Management Studies (Executive Director)
 
Making a Systematic Business Case for Analytics
byAshwin Malshe
 
Introduction to data analytics and data analysis.pptx
bysheddybro4318
 
HR analytics
bypreksha1185
 
Business Analytics (2).pptx
byrachikar
 
Unit 1 pptx.pptx
byrekhabawa2
 
727325165-Unit-1-Data-Analytics-PPT-1.pptx
byrevathi148366
 
Introduction to Business Analytics.pdf
bySuku Thomas Samuel
 
BIG DATA & BUSINESS ANALYTICS
byVikram Joshi
 
Marketing in crisis: using analytics, data and intelligence
byThe House of Marketing
 
Business Analytics models, measuring scales etc.pptx
byfaizhasan406
 
Business Analytics unit 1 prof dr kanchan.pptx
byDr. Kanchan Kumari
 
How to make data actionable for business
byRavi Padaki
 
The synthesis journal 2011 business analytics sas
bySAS Singapore Institute Pte Ltd
 
Accenture white bim trichy
byNikhil Kansari
 
EMC Perspective: Big Data Transforms the Life Science Commercial Model
byEMC
 
Pivotal CRM - Analytics
byPivotal CRM
 
The New Normal: Predictive Power on the Front Lines
byInside Analysis
 
A case for business analytics learning
byMark Tabladillo
 

More from Vivastream

PDF
EY Global Consumer Banking Survey
byVivastream
 
PDF
Automation Services
byVivastream
 
PDF
Accura XV
byVivastream
 
PPT
SQA
byVivastream
 
PDF
Automation for RDC and Mobile
byVivastream
 
PDF
Tcap
byVivastream
 
PDF
Next Generation Recognition Solutions
byVivastream
 
PDF
Exchange Solutions Datasheet_Ecommerce
byVivastream
 
PDF
Serano
byVivastream
 
PDF
Exchange Solutions Datasheet_Customer Engagement Roadmap
byVivastream
 
PDF
EY Global Consumer Banking Survey 2014
byVivastream
 
PDF
EY Smart Commerce Report
byVivastream
 
PDF
Breaking Up is Hard to Do: Small Businesses’ Love Affair with Checks
byVivastream
 
PDF
Company Overview
byVivastream
 
PDF
Healthcare Payments Automation Center
byVivastream
 
PDF
APEX
byVivastream
 
PPTX
Jeeva jessf
byVivastream
 
PDF
Vivastream Poster
byVivastream
 
PDF
Vivastream Poster
byVivastream
 
TXT
Test
byVivastream
 
EY Global Consumer Banking Survey
byVivastream
 
Automation Services
byVivastream
 
Accura XV
byVivastream
 
SQA
byVivastream
 
Automation for RDC and Mobile
byVivastream
 
Tcap
byVivastream
 
Next Generation Recognition Solutions
byVivastream
 
Exchange Solutions Datasheet_Ecommerce
byVivastream
 
Serano
byVivastream
 
Exchange Solutions Datasheet_Customer Engagement Roadmap
byVivastream
 
EY Global Consumer Banking Survey 2014
byVivastream
 
EY Smart Commerce Report
byVivastream
 
Breaking Up is Hard to Do: Small Businesses’ Love Affair with Checks
byVivastream
 
Company Overview
byVivastream
 
Healthcare Payments Automation Center
byVivastream
 
APEX
byVivastream
 
Jeeva jessf
byVivastream
 
Vivastream Poster
byVivastream
 
Vivastream Poster
byVivastream
 
Test
byVivastream
 

Recently uploaded

PPTX
Why Most GenAI Projects Fail to Scale and How to Become One of the Success St...
byEarley Information Science
 
PPTX
From Backup to Resilience: How MSPs Are Preparing for 2026
byMSP360
 
PDF
10 Things AI-First Apps Do Differently by iProgrammer Solutions
byiProgrammer Solutions Private Limited
 
PPTX
Top _HR Technology Trends _for 2026_.pptx
byAutomationEdge Technologies
 
PDF
What Is a Private LLM and Why Enterprises Need It
byAivada
 
PDF
Workshop on Sustaining & Growing Open Source Communities - GAS2025
bySammy Fung
 
PDF
Security Forum Sessions from Houston 2025 Event
byMark Simos
 
PDF
100 Insights After the 200th Issue of NewMind AI Journal
byNewMind AI
 
PDF
API-First Architecture in Financial Systems
bycyy111112
 
PDF
DevFest El Jadida 2025 - Product Thinking
byElmehdi AMLOU
 
PPTX
Emancipatory Information Retrieval: Radically Reorienting Information Retriev...
byBhaskar Mitra
 
PPTX
communication-skills-with-technology tools
byJaleto Sunkemo
 
PDF
Data Virtualization in Action: Scaling APIs and Apps with FME
bySafe Software
 
PDF
WHITE PAPER_ Ransomware Payment Policy and Resilience Strategy_ A Framework f...
byssuser0d171c
 
PDF
Cross-Cultural Agile Development -Challenges and Strategies for Overcoming Them-
byTakashi Makino
 
PDF
Day 4 - Access, Deployments, and Monitoring - 2nd Sight Lab Cloud Security Class
by2nd Sight Lab
 
PDF
Our Digital Tribe_ Cultivating Connection and Growth in Our Slack Community 🌿...
bysanjeetmishra30
 
PPTX
MGw_MRS Benfits seu beficios de redes 4g
byJoaquimBarros18
 
PDF
CompTIA Cybersecurity Analyst (CySA+) CS0-003: Unit 5
byVICTOR MAESTRE RAMIREZ
 
PDF
Day 3 - Data and Application Security - 2nd Sight Lab Cloud Security Class
by2nd Sight Lab
 
Why Most GenAI Projects Fail to Scale and How to Become One of the Success St...
byEarley Information Science
 
From Backup to Resilience: How MSPs Are Preparing for 2026
byMSP360
 
10 Things AI-First Apps Do Differently by iProgrammer Solutions
byiProgrammer Solutions Private Limited
 
Top _HR Technology Trends _for 2026_.pptx
byAutomationEdge Technologies
 
What Is a Private LLM and Why Enterprises Need It
byAivada
 
Workshop on Sustaining & Growing Open Source Communities - GAS2025
bySammy Fung
 
Security Forum Sessions from Houston 2025 Event
byMark Simos
 
100 Insights After the 200th Issue of NewMind AI Journal
byNewMind AI
 
API-First Architecture in Financial Systems
bycyy111112
 
DevFest El Jadida 2025 - Product Thinking
byElmehdi AMLOU
 
Emancipatory Information Retrieval: Radically Reorienting Information Retriev...
byBhaskar Mitra
 
communication-skills-with-technology tools
byJaleto Sunkemo
 
Data Virtualization in Action: Scaling APIs and Apps with FME
bySafe Software
 
WHITE PAPER_ Ransomware Payment Policy and Resilience Strategy_ A Framework f...
byssuser0d171c
 
Cross-Cultural Agile Development -Challenges and Strategies for Overcoming Them-
byTakashi Makino
 
Day 4 - Access, Deployments, and Monitoring - 2nd Sight Lab Cloud Security Class
by2nd Sight Lab
 
Our Digital Tribe_ Cultivating Connection and Growth in Our Slack Community 🌿...
bysanjeetmishra30
 
MGw_MRS Benfits seu beficios de redes 4g
byJoaquimBarros18
 
CompTIA Cybersecurity Analyst (CySA+) CS0-003: Unit 5
byVICTOR MAESTRE RAMIREZ
 
Day 3 - Data and Application Security - 2nd Sight Lab Cloud Security Class
by2nd Sight Lab
 

Customer Intelligence & Analytics - Part I

  • 2.
    Module 1: TheWorld of Marketing Is Changing - Are You Being Left Behind? 1.1 Introduction 1.2 Competing On Analytics 1.3 The Data Explosion 1.4 Evolving Context of Marketing Analytics & Research 1.5 Questions
  • 3.
    • Debbie Mayville – Sr. Solutions Architect, Communications & Marketing Analytics, SAS • David Kelley – Sr. Solutions Architect, Customer Intelligence, SAS • Suneel Grover – Solutions Architect, Integrated Marketing Analytics, SAS – Adjunct Professor, Integrated Marketing Analytics, New York University (NYU)
  • 4.
    Module 1: TheWorld of Marketing Is Changing - Are You Being Left Behind? 1.1 Introduction 1.2 Competing On Analytics 1.3 The Data Explosion 1.4 Evolving Context of Marketing Analytics & Research 1.5 Questions
  • 5.
  • 6.
    Achieving Success WithBusiness Analytics What’s the best that can happen? Optimization What will happen next? Predictive Modeling What if these trends continue? Forecasting Why is this happening? Statistical Analysis Alerts Query What actions are needed? Drilldown Ad hoc Where exactly is the problem? Reports Std. How many, how often, where? Reports What happened?
  • 7.
    Business Analytics “The extensiveuse of data, statistical and quantitative analysis, explanatory and predictive models, and fact- based management to drive decisions and actions.” Davenport and Harris (2007) Competing on Analytics: The New Science of Winning
  • 8.
  • 9.
    Three Consequences OfThe Data Deluge 1. Every problem will generate data eventually. 2. Every company will need analytics eventually. 3. Everyone will need analytics eventually. ...
  • 10.
    Three Consequences OfThe Data Deluge 1. Every problem will generate data eventually. Proactively defining a data collection protocol will result in more useful information, leading to more useful analytics. 2. Every company will need analytics eventually. 3. Everyone will need analytics eventually. ...
  • 11.
    Three Consequences OfThe Data Deluge 1. Every problem will generate data eventually. Proactively defining a data collection protocol will result in more useful information, leading to more useful analytics. 2. Every company will need analytics eventually. Proactively analytical companies will compete more effectively. 3. Everyone will need analytics eventually. ...
  • 12.
    Three Consequences OfThe Data Deluge 1. Every problem will generate data eventually. Proactively defining a data collection protocol will result in more useful information, leading to more useful analytics. 2. Every company will need analytics eventually. Proactively analytical companies will compete more effectively. 3. Everyone will need analytics eventually. Proactively analytical people will be more marketable and more successful in their work.
  • 13.
    The Business AnalyticsChallenge Getting anything useful out of tons and tons of data
  • 14.
    Hope For TheData Deluge + analytical tools = actionable knowledge
  • 15.
    Changes In TheAnalytical Landscape Historically… Models Analytical Modelers Management Historically, analytics have typically been handled in the “back office,” and information was shared only by a few individuals.
  • 16.
    Changes In TheAnalytical Landscape Historical Changes – Executive Dashboards • Static reports about business processes – Customer Relationship Management (CRM) • The right offer to the right person at the right time – 360-degree customer view
  • 17.
    Changes In TheAnalytical Landscape Relational Databases Enterprise Resource Planning (ERP) Point of Sale (POS) Systems Decision Support Systems – Reporting and Ad Hoc Queries – Online Analytical Processing (OLAP) Performance Management Systems – Executive Information Systems (EIS) – Balanced Scorecard Business Intelligence
  • 18.
    CRM Evolution • TotalQuality Management (TQM) – Product-Centric • Quality: Six Sigma • Total Customer Satisfaction • Mass Marketing • One-to-One Marketing – Customer Relationship • Wallet Share of Customer • Customer Retention • Customer Relationship Management (CRM) – Customer-Centric • Strategy • Process • Technology
  • 19.
    Changes In TheAnalytical Landscape Now… Operations Targeting Proliferation of Models Customer Customers Analytical Modelers Service Retail Suppliers Now analytics are being pushed out to the “front office”. There are clear, tangible benefits that management will track. Data mining is a critical part of business analytics. Promotions Employees
  • 20.
    Idiosyncrasies Of BusinessAnalytics 1. The Data - Massive, operational, and opportunistic 2. The Users and Sponsors - Business decision support 3. The Methodology - Computer-intensive adhockery - Multidisciplinary Data mining can be defined as advanced methods for exploring and modeling relationships in large amounts of data.
  • 21.
    The Data Experimental Opportunistic Purpose Research Operational Value Scientific Commercial Generation Actively controlled Passively observed Size Small Massive Hygiene Clean Dirty State Static Dynamic
  • 22.
    The Data: DisparateBusiness Units Marketing Invoicing Risk Acquisitions Operations Sales
  • 23.
    Opportunistic Data – Operationaldata • Typically not collected with data analysis in mind – Multiple business units • Silo-based data environment  This makes business analytics different from experimental statistics and especially challenging
  • 24.
    The Methodology: WhatWe Learned Not to Do • Prediction is more important than inference 1. Metrics are used “because they work” 2. p-values are directional guides 3. Interpretation of a model might be irrelevant 4. The preliminary value of a model is determined by its ability to predict a holdout sample 5. The long-term value of a model is determined by its ability to continue to perform well over time 6. Models are retired as behavior and trends shifts
  • 25.
    Using Analytics Intelligently •Intelligent use of analytics 1. Understanding of how marketplace shifts affect business performance 2. Ability to distinguish between effective and ineffective interventions 3. Efficient use of assets, reduced waste 4. Risk reduction via measurable outcomes 5. Early detection of trends hidden in massive data 6. Continuous improvement in decision making
  • 26.
    Simple Reporting Examples: OLAP,RFM, descriptive statistics, extrapolation Answer questions such as: 1. Where are my key indicators now? 2. Where were my key indicators last week? 3. Is the current process behaving like normal? 4. What’s likely to happen tomorrow?
  • 27.
    Proactive Analytical Investigation Examples:Data mining, experimentation, empirical validation, predictive modeling, optimization Answer questions such as: 1. What does a change in the market mean for my targets? 2. What do other factors tell me about my target? 3. What is the best combination of factors for maximum profit? 4. What is the highest price the market will tolerate?
  • 28.
    Data Stalemate • Manycompanies have data that they do not use or sell to third parties. These third parties might even resell the data and any derived metrics back to the original company! • Story: Retail grocery POS card
  • 29.
    Every Little Bit… Takingan analytical approach to only a few key business problems with reliable metrics  tangible benefit The benefits and savings derived from early analytical successes  managerial support for more analytics 1. Everyone has data 2. Analytics can connect data to smart decisions 3. Proactively analytical companies outpace competition
  • 30.
    Areas Where AnalyticsAre Often Used • New customer acquisition Which residents in a ZIP • Customer loyalty code should receive a • Cross-sell / up-sell coupon in the mail for a new store location? • Pricing tolerance • Supply optimization • Staffing optimization • Financial forecasting • Product placement • Churn • Insurance rate setting • Fraud detection • …
  • 31.
    Areas Where AnalyticsAre Often Used • New customer acquisition • Customer loyalty What advertising strategy • Cross-sell / up-sell best elicits positive sentiment toward the • Pricing tolerance brand? • Supply optimization • Staffing optimization • Financial forecasting • Product placement • Churn • Insurance rate setting • Fraud detection • …
  • 32.
    Areas Where AnalyticsAre Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell What is the best next • Pricing tolerance product for this customer? • Supply optimization • Staffing optimization • Financial forecasting • Product placement • Churn • Insurance rate setting • Fraud detection • …
  • 33.
    Areas Where AnalyticsAre Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance What is the highest price • Supply optimization that the market will bear • Staffing optimization without substantial loss of demand? • Financial forecasting • Product placement • Churn • Insurance rate setting • Fraud detection • …
  • 34.
    Areas Where AnalyticsAre Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance • Supply optimization How many 60-inch HDTVs • Staffing optimization should be in stock? • Financial forecasting • Product placement • Churn • Insurance rate setting • Fraud detection • …
  • 35.
    Areas Where AnalyticsAre Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance • Supply optimization • Staffing optimization What are the best times • Financial forecasting and best days to have technical experts on the • Product placement showroom floor? • Churn • Insurance rate setting • Fraud detection • …
  • 36.
    Areas Where AnalyticsAre Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance • Supply optimization • Staffing optimization • Financial forecasting What weekly revenue • Product placement increase can be expected • Churn after the Mother’s Day sale? • Insurance rate setting • Fraud detection • …
  • 37.
    Areas Where AnalyticsAre Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance • Supply optimization • Staffing optimization • Financial forecasting • Product placement Will oatmeal sell better • Churn near granola bars or near • Insurance rate setting baby food? • Fraud detection • …
  • 38.
    Areas Where AnalyticsAre Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance • Supply optimization • Staffing optimization • Financial forecasting • Product placement • Churn Which customers are most • Insurance rate setting likely to switch to a • Fraud detection different wireless provider in the next six months? • …
  • 39.
    Areas Where AnalyticsAre Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance • Supply optimization • Staffing optimization • Financial forecasting • Product placement • Churn • Insurance rate setting How likely is it that this • Fraud detection individual will have a claim? • …
  • 40.
    Areas Where AnalyticsAre Often Used • New customer acquisition • Customer loyalty • Cross-sell / up-sell • Pricing tolerance • Supply optimization • Staffing optimization • Financial forecasting • Product placement • Churn • Insurance rate setting • Fraud detection How can I identify a fraudulent • … purchase?
  • 41.
    When Analytics AreNot Helpful • Snap decisions required Deciding when to run • Novel approach (no previous from danger data possible) • Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) • Expert analysis suggests a particular path • Metrics are inappropriate • Naïve implementation of analytics • Confirming what you already know
  • 42.
    When Analytics AreNot Helpful • Snap decisions required • Novel approach (no previous Predicting the adoption of data possible) a new technology • Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) • Expert analysis suggests a particular path • Metrics are inappropriate • Naïve implementation of analytics • Confirming what you already know
  • 43.
    When Analytics AreNot Helpful • Snap decisions required • Novel approach (no previous data possible) • Most salient factors are rare Planning contingencies (making decisions to work for employees winning around unlikely obstacles or the lottery miracles) • Expert analysis suggests a particular path • Metrics are inappropriate • Naïve implementation of analytics • Confirming what you already know
  • 44.
    When Analytics AreNot Helpful • Snap decisions required • Novel approach (no previous data possible) • Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) • Expert analysis suggests a The seasoned art critic particular path can recognize a fake • Metrics are inappropriate • Naïve implementation of analytics • Confirming what you already know
  • 45.
    When Analytics AreNot Helpful • Snap decisions required • Novel approach (no previous data possible) • Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) • Expert analysis suggests a particular path Predicting athletes’ • Metrics are inappropriate salaries or quantifying • Naïve implementation of love analytics • Confirming what you already know
  • 46.
    When Analytics AreNot Helpful • Snap decisions required • Novel approach (no previous data possible) • Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) • Expert analysis suggests a particular path • Metrics are inappropriate • Naïve implementation of Only looking at one analytics variable at a time • Confirming what you already know
  • 47.
    When Analytics AreNot Helpful • Snap-decisions required • Novel approach (no previous data possible) • Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) • Expert analysis suggests a particular path • Metrics are inappropriate • Naïve implementation of analytics • Confirming what you already Ignoring variables that know might be important
  • 48.
    The Fallacy OfUnivariate Thinking What is the most important cause of churn? Prob(churn) International Daytime Usage Usage
  • 49.
    Expectations Leading TheAnalysis • Sophisticated analytics are not immune to personal bias – Selectively fitting models because they place an opinion or agenda in a positive light – Ignoring information that might disprove a hypothesis • Personal bias, whether intentional or not, can diminish the usefulness of analytics
  • 50.
    Trustworthy Analytics Let thedata guide your conclusions – Are my assumptions about the causes of the data patterns warranted? – Should I be trying something different? Assign a cynic to the analytical team whose purpose is to question the assumptions
  • 51.
    Idea Exchange  Identifyseveral business problems that you could address with analytics  Describe the goal, whether the variables can be measured, how the data could be obtained, and what types of specific questions you would like to address with analytics
  • 52.
    Case Study –US Telco • Data Deluge: Just Get Started – Low hanging fruit – Continue to build and get smarter – 360 degree view of the customer • Tools: Efficiency & Effectiveness – Data management tools – Analytic tools • Move to data driven insights versus gut reactions • Establish measurement system – Test & Learn Environment
  • 53.
  • 54.
    Obtaining 360 DegreeView Of The Customer Activ- Social ation Firmo- Network graphics Usage Demo- graphics Care Point of 360 Sale Degree Customer Hard- View Service, ware Repair VOD, Network Games Commu- Billing ni- Collect- cations ions
  • 55.
    Large Telco WithIndustry-leading Churn Rate Churn Churn Reduction By Reduction Value ($) Reason Equipment 9 bps $121M Usage 16 bps $163M Network 15 bps $158M Active Issue Resolution 11 bps $110M Contract Renewal 25 bps $273M Sales Channel / Credit & 6 bps $87M Collections Total 82 bps $912M
  • 56.
    Case Study US Telco Business Issue • Company-wide initiative to lower the churn rate among customers • Focus on “high value” or “high value potential” customers • Improve treatment strategy and relevance Solution • Data management • Advanced analytics Results/Benefits • Reduced churn by 40% • Increased customer loyalty and lifetime value • Increase of operational revenues by $1B over 3 years • Ability to uncover dissatisfaction drivers and tailor proactive churn treatments
  • 57.
    Module 1: TheWorld of Marketing Is Changing - Are You Being Left Behind? 1.1 Introduction 1.2 Competing On Analytics 1.3 The Data Explosion 1.4 Evolving Context of Marketing Analytics & Research 1.5 Questions
  • 58.
    Key BUSINESS TrendsAffecting Marketing From Product to Customer • Customer-centric business strategy • The customer experience • 360-degree customer view Finding the Next Origin of Business Growth • Consolidation/mergers/acquisitions • Market expansion • Efficiency & optimization The Regulatory Rise • Increased disclosure and transparency • Privacy and information sharing • Consumer contact rules • Regulatory reform
  • 59.
    Key CONSUMER ForcesAffecting Marketing Consumer in Charge • Rising expectations and more choice • From right time to “real time” • Demographic divide Channel Adoption • Mobile devices and consumer adoption • Web 2.0 and the digital age • Cross-channel usage Huge Online and Social Adoption • Social networking • Consumer-controlled content and channels • Consumer engagement
  • 60.
    A Broadened Definitionof “The Customer”  The Consumer  The Citizen  The Subscriber  The Plan Member  The Patient  The Patron ...applicable across B2C & B2B
  • 61.
    Customer Intelligence IsRelevant Across Industries Financial Services Insurance Retail Hospitality & Telco & Cable Manufacturing Gaming Government Marketing Service Health & Providers Life Sciences Utilities
  • 62.
    The Marketer HasAn Evolving Mandate Expectation Expectation Deliver a branded Integrated, multi-channel customer experience in/outbound conversations in and outside of in real-time marketing The Marketing Campaign The The Customer Brand Experience Responsibilities Expectation Sustain brand health in a rapidly changing virtual world Insights and Analytics Expectation Unearth and dynamically manage insights to drive action
  • 63.
    Key Forces AffectingMarketers Huge Online and Social Consumer Adoption 2B people online,100B monthly searches and 600MM people on social networks globally
  • 64.
    Key Forces AffectingMarketers Huge Online and Social Consumer Adoption Ever-Growing and Converging Marketing Channels Technology advances and consumer preferences driving new channels at unprecedented rates
  • 65.
    Key Forces AffectingMarketers Huge Online and Social Ever-Growing and Converging Consumer Adoption Marketing Channels Information Explosion Business information doubling every 18 months with unstructured data representing 70% of it.
  • 66.
    Key Forces AffectingMarketers Huge Online and Social Ever-Growing and Converging Information Explosion Consumer Adoption Marketing Channels The Speed of Business Information traveling at unprecedented rates, compounded by rising consumer expectations.
  • 67.
    Key Forces AffectingMarketers Huge Online and Social Ever-Growing and Converging Information Explosion Consumer Adoption Marketing Channels The Speed of Business Accountability and Need to do More with Less Economic and competitive pressures putting focus on marketing budgets and returns.
  • 68.
    Key Forces AffectingMarketers Huge Online and Social Ever-Growing and Converging Information Explosion Consumer Adoption Marketing Channels The Speed of Business Increasingly Competitive & Converging Markets Accountability and Need to do Parity markets with limited differentiation . More with Less Fight for share of wallet.
  • 69.
    Key Forces AffectingMarketers Huge Online and Social Ever-Growing and Converging Information Explosion Consumer Adoption Marketing Channels Brand Health Less corporate trust compounded by The Speed of Business brands being publicly scrutinized. Traditional mass marketing proving less impactful. Increasingly Competitive & Converging Markets Accountability and Need to do More with Less
  • 70.
    The Marketing Process Mobile Online Finance Risk Call Customer Center Service In Person Merchandising Social Corporate Affairs Direct Mail Marketing Operations Optimization Marketing Marketing Marketing Strategy Processes Campaigns Analytics Data Integration ERP CRM EDW Online Social Campaign
  • 71.
    The Data Integration& Management Challenge
  • 72.
    The Flood OfData • Customer data continues to flood the organization exponentially • Progressing from functional to strategic – Namely how to capture, integrate, manage, analyze, and apply knowledge/insight about customers – Google Executive Chairman Eric Schmidt: “We create as much information in two days now as we did from the dawn of man through 2003.”
  • 73.
    Structured & UnstructuredData • Company data: billing, usage, collections, set-top box, customer, web interactions, campaign, and more! • Consumer-generated data: Social media, blogs, product reviews, and more! Structured data 25% 70% 5% Unstructured data Semistructured data
  • 74.
    “Big Data” Myths •Data Volumes are “Exploding” – Did Wal-Mart suddenly sell more stuff? – Did NYSE suddenly do more stock trades? – Did Netflix suddenly rent more movies? – Did Amazon suddenly sell more books? • This is existing data that previously went un-analyzed: A. Too large to manage B. Too costly to store C. Lack of “analytic chops” to capitalize
  • 75.
    “Big Data” -Why Now? Three Vs? Complex, Unstructured 1. Volume 2. Velocity 3. Variety Relational The primary driver is Value… Source: IDC .
  • 76.
    “Big Data” -Why Now? • Cost of storage dropping
  • 77.
    Data - PrerequisiteFor Everything Analytical “You can’t be analytical without data, and you can’t be really good at analytics without really good data” • Structure • Uniqueness • Integration • Quality • Access • Privacy • Governance Davenport, Harris, Morison (2010) Analytics at Work: Smarter Decision Better Results
  • 78.
    Data Structure /Uniqueness / Integration Structure • Data structure affects analysis performance • Transaction systems (tables), data cubes (limitations) • Data arrays • Unstructured data Uniqueness • Data only your company has access – proprietary • Commercially available data – be the industry 1st • Create new metrics and data fields Integration • Aggregate data from inside/outside your organization • Consolidate silos across departments • Data has to be sourced, cleaned, integrated • Evolve to “one version of the truth”
  • 79.
    Data Quality /Access / Privacy Quality • Flawed data causes misleading results • To fix problems - look at the data source • Continuous process – data will never be perfect • Start based on business objectives Access • Source data and load in a form for analytics • Size or complexities can cause user issues • Speed needs require data warehouse appliances • Sample populations Privacy • Guard the information collected • Well defined policies • Privacy laws within territories or industries • Don’t sell information without permission (opt-in)
  • 80.
    Data Governance Governance • Ensuredata is useful for analysis • Consistent, defined, sufficient quality, standardized, integrated, accessible • Standard definitions and terminology • Decide on investments • Owners and stewards • Analytical data advocates • Business intelligence competency centers, analytical data advocate group, information management
  • 81.
    Module 1: TheWorld of Marketing Is Changing - Are You Being Left Behind? 1.1 Introduction 1.2 Competing On Analytics 1.3 The Data Explosion 1.4 Evolving Context of Marketing Analytics & Research 1.5 Questions
  • 82.
    „The Greatest JobIn The World‟
  • 83.
    The Challenge ofDigital Marketing • As digital marketing continues to grow more significant, new channels add complexity to the design of a successful integrated campaign. – It’s both a blessing and a curse for when an integrated campaign goes viral – Key Challenge: How do we do it again? – No repeatable formulas or clear attribution metrics
  • 84.
    Reactive Business Analytics What’s the best that can happen? Optimization What will happen next? Predictive Modeling What if these trends continue? Forecasting Why is this happening? Statistical Analysis Alerts Query What actions are needed? Drilldown Ad hoc Where exactly is the problem? Reports Std. How many, how often, where? Reports What happened?
  • 85.
    Proactive Business Analytics What’s the best that can happen? Optimization What will happen next? Predictive Modeling What if these trends continue? Forecasting Why is this happening? Statistical Analysis Alerts Query What actions are needed? Drilldown Ad hoc Where exactly is the problem? Reports Std. How many, how often, where? Reports What happened?
  • 86.
    Afternoon Workshop Preview •What if I could? – Automate the measurement of sentiment relevant to my business goals from digital channels – Capitalize on the hidden value in vast amounts of available structured/unstructured data associated with my brand – Become strategically more proactive to shifting (dynamic) consumer trends
  • 87.
    The Marketing Process Mobile Online Finance Risk Call Customer Center Service In Person Merchandising Social Corporate Affairs Direct Mail Marketing Operations Optimization Marketing Marketing Marketing Strategy Processes Campaigns Analytics Data Integration ERP CRM EDW Online Social Campaign
  • 88.
    Module 1: TheWorld of Marketing Is Changing - Are You Being Left Behind? 1.1 Introduction 1.2 Competing On Analytics 1.3 The Data Explosion 1.4 Evolving Context of Marketing Analytics & Research 1.5 Questions