Module 2: Exploring the Idea & Value of   Marketing Analytic Techniques   2.1 Introduction   2.2 Data Mining Techniques Fo...
• Debbie Mayville  – Sr. Solutions Architect, Communications & Marketing    Analytics, SAS• David Kelley  – Sr. Solutions ...
Module 2: Exploring the Idea & Value of   Marketing Analytic Techniques   2.1 Introduction   2.2 Data Mining Techniques Fo...
The Marketing Process                             Mobile Online Finance Risk                   Call                       ...
The Customer Lifecycle• The business relationship with a customer  evolves over time• Five phases  1.   Prospects  2.   Re...
Event-Based Relationships• Primarily based on transactions• Customer may or may not return   – Tracking customers over tim...
Subscription-Based Relationships• Provide more natural opportunities  for understanding customers   – Offers opportunity f...
Customer Acquisition• The process of attracting prospects and turning them into  customers   – Advertising   – Word-of-mou...
Who Are the Prospects?• Understanding prospects is important because messages  should be targeted to the appropriate audie...
Prospecting Incorrectly• NYC-based direct marketing company   – Large customer base in Manhattan      • Looking to expand ...
Prospecting: What Is The Role Of Data Mining?• Available data limits the role that data mining can play• The goal is to ta...
Prospecting: What Is The Role Of Data Mining?• Identifying good prospects   – The need to define what it means     to be a...
Customer Activation• Provides a view of new customers at the point when they start   – This perspective is an important da...
Customer Relationship Management• The primary goal of CRM is to increase  the customer’s value   1. Up-selling   2. Cross-...
Using Current Customers To            Learn About Future Prospects• How to identify your best customers   – Start tracking...
CRM: What Is The Role Of Data Mining?• Customers provide the richest source of data for mining• Behavioral data provides t...
Retention• Attrition is a major application of data mining• Challenges   1. Recognition       What it is & when it occurs...
Win-back• Even after customers have left, they can still  be lured back   – Data mining can explain why customers left• Ca...
Why Operationalize Analytics?• Increase customer lifetime value with relevancy• Maintain customer satisfaction proactively...
Operationalizing Analytics – The Life Cycle    Acquisition        Development   Retention   Churn/ Win-                   ...
Operationalizing Analytics – The Life Cycle    Acquisition        Development        Retention             Churn/ Win-    ...
Operationalizing Analytics – The Life Cycle    Acquisition        Development            Retention         Churn/ Win-    ...
Operationalizing Analytics – The Life Cycle    Acquisition             Development             Retention   Churn/ Win-    ...
Operationalizing Analytics – The Life Cycle    Acquisition           Development           Retention          Churn/ Win- ...
Execute on Actionable Insights
Applying Predictive Models to Marketing               Strategy
Proactively Manage the Customer ExperiencePreventive Actions   Predictive Actions    Reactive Actions                     ...
Define Customer Value  A smaller percentage of your customer base is driving the  majority of the profit.                 ...
Achieving Success With Business Analytics                                         What’s the best that can happen?        ...
Module 2: Exploring the Idea & Value of   Marketing Analytic Techniques   2.1 Introduction   2.2 Data Mining Techniques Fo...
Set-top Box Analytics Situation Slide               • Marketing: How can I increase revenue and lower churn?  Critical    ...
Your Set-top Box Data Who is           What & when            What kind ofwatching?           are they          customer a...
Set-top Box Analytics BenefitsAnalytic Insights Provide Value for Multiple Departments                          Set-top Bo...
Audience Intelligence                 Audience                Viewership   Media                      Audience  Planning  ...
Audience Intelligence                   Audience                  Viewership   Media        When To          Audience  Pla...
Data Process
Set-top Box DataRaw Set-top box Data                                                    duration                          ...
Marketing Segmentation    Premium Couch                                          Price Conscious    Potatoes              ...
Programming Segmentation                     Only a Network A                     Weekend Watcher            Network A    ...
STB Data - Advertising SegmentationAutomotive                     Wireless                        7%   5%                 ...
Audience Insight
Dashboards
Segmentation
Segmentation Details
Network Analysis
Mapping Visualization
Path Analysis
What-if Analysis
Content Analytics Situation Slide Critical    • As a digital publisher, do you provide the most               engaging, re...
Case Study: Tribune Company• Business Issue: To accurately define and categorize  content efficiently to deliver highly re...
Text Analytics                Text Mining                  Natural Ontology                       Sentiment               ...
Text Analytics        Natural Language Processing (NLP)              Support for multiple languages              Stemmin...
Text Analytics                Text Mining                  Natural Ontology                       Sentiment               ...
Text Analytics                                          Insight                            Text                           ...
Usage Example: Chicago Tribune
Usage Example: New York Times                                        Real-Time                                       Deplo...
Text Analytics                                           Insight                            Text                          ...
Potential Data Sources        Data     Management
Data Cleansing•   Unstructured data, in the form of text, when captured, presents    unique challenges    –   Correctly st...
Sentiment Analysis• The action of identifying the expressed sentiments by customers,  partners, suppliers and employees   ...
Overall vs. Granular/Feature-level Sentiment   Good, but a little outdated. I bought the Nikon Coolpix L10 as my    first...
Case Study: Yogurt Brand• Business Issue: Search sources of consumer-generated  content and social media activity to find ...
Text Analytics                                           Insight                            Text                          ...
Discover vs. Define• How does an organization proactively identify new  topics, new terms, and new information being  gene...
Text Mining• The process of discovering and extracting  meaningful patterns and relationships from  text collections      ...
Case Study: University of Louisville• Business Issue: Analyze text-based medical records  and healthcare reporting• Outcom...
Web Analytics Situation Slide Critical    • How do I increase my understanding of anonymous,               digital visitat...
Advancing Web Analytics    Pull Web analytic data (e.g.                Create Customer State                    Develop “l...
Module 2: Exploring the Idea & Value of   Marketing Analytic Techniques   2.1 Introduction   2.2 Data Mining Techniques Fo...
The Marketing Process                             Mobile Online Finance Risk                   Call                       ...
The Marketing Process                             Mobile Online Finance Risk                   Call                       ...
Module 2: Exploring the Idea & Value of   Marketing Analytic Techniques   2.1 Introduction   2.2 Data Mining Techniques Fo...
Customer Intelligence & Analytics - Part II: Exploring the Idea & Value of Marketing Analytic Techniques Exploring the Ide...
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Customer Intelligence & Analytics - Part II: Exploring the Idea & Value of Marketing Analytic Techniques Exploring the Idea & Value of Marketing Analytic Techniques

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Customer Intelligence & Analytics - Part II: Exploring the Idea & Value of Marketing Analytic Techniques Exploring the Idea & Value of Marketing Analytic Techniques

  1. 1. Module 2: Exploring the Idea & Value of Marketing Analytic Techniques 2.1 Introduction 2.2 Data Mining Techniques For Marketing, Sales, & CRM 2.3 The Power of Analyzing Structured & Unstructured Data 2.4 The Competitive Advantage of an Integrated, Analytic Marketing Platform 2.5 Questions
  2. 2. • 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)
  3. 3. Module 2: Exploring the Idea & Value of Marketing Analytic Techniques 2.1 Introduction 2.2 Data Mining Techniques For Marketing, Sales, & CRM 2.3 The Power of Analyzing Structured & Unstructured Data 2.4 The Competitive Advantage of an Integrated, Analytic Marketing Platform 2.5 Questions
  4. 4. The Marketing Process Mobile Online Finance Risk Call Customer Center Service In Person Merchandising Social Corporate AffairsDirect Mail Marketing Operations Optimization Marketing Marketing Marketing Strategy Processes Campaigns Analytics Data IntegrationERP CRM EDW Online Social Campaign
  5. 5. The Customer Lifecycle• The business relationship with a customer evolves over time• Five phases 1. Prospects 2. Responders 3. New customers 4. Established customers 5. Former customers
  6. 6. Event-Based Relationships• Primarily based on transactions• Customer may or may not return – Tracking customers over time may be difficult or impossible• Prospect communications focused on message broadcasting – Advertising – Web ads – Viral marketing• Targeted, 1:1 messaging is challenging• Analytic work focused on product, geography, and time
  7. 7. Subscription-Based Relationships• Provide more natural opportunities for understanding customers – Offers opportunity for future cash flow and customer interactions• Can take many forms – Billing relationships – Affinity cards – Website registrations• The beginning and end of the relationship are two key events When these events are well-defined, survival analysis is a good candidate for understanding the relationship duration
  8. 8. Customer Acquisition• The process of attracting prospects and turning them into customers – Advertising – Word-of-mouth – Targeted marketing• Data mining can play an important role – Three questions 1. Who are the prospects? 2. When is a customer acquired? 3. What is the role of data mining?
  9. 9. Who Are the Prospects?• Understanding prospects is important because messages should be targeted to the appropriate audience• Challenges  Geographic expansion  Changes to products, services, and pricing  Competition• Will the past be a good predictor of the future? – In most cases, the answer is “yes” – The past has to be used intelligently
  10. 10. Prospecting Incorrectly• NYC-based direct marketing company – Large customer base in Manhattan • Looking to expand into the suburbs • DM campaigns have always been targeted to Manhattan – Data mining model built from campaign responders • Manhattan - high concentration of wealthy residents (model bias) • Responders wealthier than most prospects in surrounding areas• When the model was extended to areas outside of Manhattan, what areas did the model choose?
  11. 11. Prospecting: What Is The Role Of Data Mining?• Available data limits the role that data mining can play• The goal is to target prospects that are: – More likely to respond – Become good customers• Data availability falls into three categories 1. Source of prospect 2. Appended individual/household data 3. Appended demographic data at a geographic level• Challenge: The echo (“halo”) effect
  12. 12. Prospecting: What Is The Role Of Data Mining?• Identifying good prospects – The need to define what it means to be a “good prospect” – Identify rules that allow for this type of targeting • Example: Response modeling• Choosing a communications channel – Mass media vs. direct-response media?• Picking appropriate messages for different segments – Price vs. convenience?
  13. 13. Customer Activation• Provides a view of new customers at the point when they start – This perspective is an important data source – Often a useful predictor of long-term customer behavior• The activation funnel 1. The sales lead 2. The order 3. The subscription 4. The paid subscription• Data mining can play a role in understanding whether or not customers are migrating the way they should be
  14. 14. Customer Relationship Management• The primary goal of CRM is to increase the customer’s value 1. Up-selling 2. Cross-selling 3. Usage stimulation 4. Customer value calculation• CRM is successful when customer messaging is highly relevant – Data mining plays a key role in identifying relevant affinities• Potentially, the single most important part of CRM is retaining customers – Predictive modeling is heavily applicable
  15. 15. Using Current Customers To Learn About Future Prospects• How to identify your best customers – Start tracking customers before they become customers • Marketing campaign data • Cookie data – Gather information from new customers at time of acquirement • Golden opportunity - prospect to customer transition • Geographic and demographic – Model the relationship • Customer longevity • Customer value • Default risk
  16. 16. CRM: What Is The Role Of Data Mining?• Customers provide the richest source of data for mining• Behavioral data provides the following opportunities: 1. Matching campaigns to customers 2. Reducing exposure to risk 3. Determining customer value 4. Cross-selling, up-selling, and making recommendations
  17. 17. Retention• Attrition is a major application of data mining• Challenges 1. Recognition  What it is & when it occurs 2. Why it matters 3. Different kinds of attrition  Two approaches  Predicting who will leave  Predicting how long customers will stay
  18. 18. Win-back• Even after customers have left, they can still be lured back – Data mining can explain why customers left• Case Study: Media product boycott – What do you do when the unexpected happens? – Consumer backlash to end customer subscriptions • How many stops can be attributed to the boycott? • Who is stopping? • Are they coming back? – Challenges in tracking – Manual investigation vs. text mining
  19. 19. Why Operationalize Analytics?• Increase customer lifetime value with relevancy• Maintain customer satisfaction proactively• Interact with precise offers, messages, and communications• Current/recent interaction may be the tipping point to negative sentiment• Significant events: sentiment/social media, interaction points
  20. 20. Operationalizing Analytics – The Life Cycle Acquisition Development Retention Churn/ Win- backNet Margin
  21. 21. Operationalizing Analytics – The Life Cycle Acquisition Development Retention Churn/ Win- backNet Margin Decisions points during acquisition: • Looking at products and offers • Comparing pricing • Company can be scoring - credit worthiness
  22. 22. Operationalizing Analytics – The Life Cycle Acquisition Development Retention Churn/ Win- back Decisions points during relationship development:Net Margin • Service & product usage • Customer user experience • Cross & up-sell • Bad debt detection and collection • Customer service
  23. 23. Operationalizing Analytics – The Life Cycle Acquisition Development Retention Churn/ Win- backNet Margin Decisions points during retention: • Targeted retention activities • Complaint handling • Renewal pricing, discounting & bundling • Reactive retention
  24. 24. Operationalizing Analytics – The Life Cycle Acquisition Development Retention Churn/ Win- backNet Margin Decisions points during churn/win-back: • Win-back discount and bundle pricing • Trigger campaigns for future reacquisition
  25. 25. Execute on Actionable Insights
  26. 26. Applying Predictive Models to Marketing Strategy
  27. 27. Proactively Manage the Customer ExperiencePreventive Actions Predictive Actions Reactive Actions Action is identified
  28. 28. Define Customer Value A smaller percentage of your customer base is driving the majority of the profit. Migrate / Spend Keep & Shift to to keep migrate lower cost May be some of your largest customersSource: Gartner
  29. 29. 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?
  30. 30. Module 2: Exploring the Idea & Value of Marketing Analytic Techniques 2.1 Introduction 2.2 Data Mining Techniques For Marketing, Sales, & CRM 2.3 The Power of Analyzing Structured & Unstructured Data 2.4 The Competitive Advantage of an Integrated, Analytic Marketing Platform 2.5 Questions
  31. 31. Set-top Box Analytics Situation Slide • Marketing: How can I increase revenue and lower churn? Critical • Programming: How do I know viewership across Business programs? Issue • Advertising: How can I drive up better yields on my ad units? Current • Using 3rd party dataCapabilities • Difficulty mining vast amount of viewing information • Capabilities for sourcing and preparing the set-top box New dataCapabilities • Analytics for uncovering insight and unknown patterns • Interactive dashboard solution for executive decisioning
  32. 32. Your Set-top Box Data Who is What & when What kind ofwatching? are they customer are they? watching? Valuable Resource Augmenting Existing Data Smarter, More Accurate, Timely, Control
  33. 33. Set-top Box Analytics BenefitsAnalytic Insights Provide Value for Multiple Departments Set-top Box Analytics Audience Intelligence Marketing Programming Advertising1. Churn prevention 1. Insights for 1. Higher ROI on2. Up-sell / cross-sell program addressable3. Optimize negotiations advertising packaging 2. Uncover 2. Uncover unknown4. Drive engagement replacement targets for across channels programming addressable 3. Identify new advertising program targets 3. Optimize 4. Produce Tier 2 advertising viewer insights inventory
  34. 34. Audience Intelligence Audience Viewership Media Audience Planning ForecastingLikelihood to Audience Watch Behavior Audience Discovery
  35. 35. Audience Intelligence Audience Viewership Media When To Audience Planning Target Forecasting What To Who To Target TargetLikelihood to How To Audience Watch Behavior Target Audience Discovery
  36. 36. Data Process
  37. 37. Set-top Box DataRaw Set-top box Data duration + Transform Data duration + Incorporate Other DataHH_ID device_id Timeframe channel program (secs) Timeframe (min) Income LOB Plan 123 4567 5/2/11 9:00 17 22 1200 week1_9-9:30am 20 150000 3 Triple-play bundle A 123 4567 5/2/11 9:20 15 45 300 week1_9-9:30am 5 150000 3 Triple-play bundle A 123 4567 5/2/11 9:25 3 55 300 week1_9-9:30am 5 150000 3 Triple-play bundle A 123 4567 5/2/11 9:30 17 66 900 week1_9:30-10am 15 150000 3 Triple-play bundle A 123 4567 5/2/11 9:45 15 77 900 week1_9:30-10am 15 150000 3 Triple-play bundle A 1. Source Set-top Box Data 2. Append Data • HD vs. Non-HD • Billing • Weekday vs. Weekend • Account • Time of Day (Morning, Night) • Calls to Care • Day of the Week (Mon, Tues, etc.) • 3rd Party (demographic, Axciom, • Channels Experian) • Channel Category • Tribune • Programs • Social Media (Twitter, Facebook) • Program Category (Genre, 1st run/2nd run) • Series Usage Levels (Avid 3. Aggregate & Build Viewing Watchers, Fly-bys) Categories • Last Tuning Event • Daily, Weekly, Monthly, Series • Combination of Watching • Sums & Averages of Durations • Tune-aways • Viewing rates & Change in • Time Slot Viewing rates • Geographic
  38. 38. Marketing Segmentation Premium Couch Price Conscious Potatoes Families Family Viewers with PremiumsStay HomeMoms & Kids Price is not an Object Multi-cultural Programming
  39. 39. Programming Segmentation Only a Network A Weekend Watcher Network A Weekend News Crazy Network A Network A Sampler Movie Watcher 7% 12% Network 22% 8%Not a Network A A Fly- 7%Watcher bys 12% 13% 6% 2% 11% Network A Sports FanNetwork A Weekday Fan Weekday Network A Network A Devoted Fan Frequent Watcher
  40. 40. STB Data - Advertising SegmentationAutomotive Wireless 7% 5% 40% 25%Movie Studios Financial Services 23% Healthcare
  41. 41. Audience Insight
  42. 42. Dashboards
  43. 43. Segmentation
  44. 44. Segmentation Details
  45. 45. Network Analysis
  46. 46. Mapping Visualization
  47. 47. Path Analysis
  48. 48. What-if Analysis
  49. 49. Content Analytics Situation Slide Critical • As a digital publisher, do you provide the most engaging, relevant content possible?Business • Is your content management strategy driven by a deep Issue understanding of your audience’s evolving behavior? • Lose audience share to competitorsImportance • Reduced halo effect around other revenue streams • Flat or decreasing marketing performance metrics • How to organize content for dynamic categorization?Challenges • How to analyze the data for actionable insight? • How to become more proactive vs. reactive?
  50. 50. Case Study: Tribune Company• Business Issue: To accurately define and categorize content efficiently to deliver highly relevant information to its online readership• Outcome: Analytic approaches enabled the ability to define, apply and push the right content, in the right context, to the right audience in the most optimized way• Usage Examples – Repurposing content – Driving ad revenue – Improving search performance
  51. 51. Text Analytics Text Mining Natural Ontology Sentiment LanguageManagement Analysis Processing Content Categorization
  52. 52. Text Analytics Natural Language Processing (NLP)  Support for multiple languages  Stemming to locate the various forms of an input  Part-of-speech recognition and tagging NaturalLanguage to recognize nouns, verbs, adjectives,Processing etc.  Word and sentence tokenization: Identify distinct words or expressions  Information extraction: Facts and events, people, dates, places, sentiment, emotion, etc…
  53. 53. Text Analytics Text Mining Natural Ontology Sentiment LanguageManagement Analysis Processing Content Categorization
  54. 54. Text Analytics Insight Text Discovery MiningTop Up Natural Ontology Sentiment Language Management Analysis ProcessingDown Bottom Content Information Categorization Organization
  55. 55. Usage Example: Chicago Tribune
  56. 56. Usage Example: New York Times Real-Time DeploymentTopicsAutomatic EntitiesExtraction Automatic Categorization
  57. 57. Text Analytics Insight Text Discovery MiningTop Up Natural Ontology Sentiment Language Management Analysis ProcessingDown Bottom Content Information Categorization Organization
  58. 58. Potential Data Sources Data Management
  59. 59. Data Cleansing• Unstructured data, in the form of text, when captured, presents unique challenges – Correctly structure the data and clean it is a priority – Technology needs to have the ability to: » Eliminate irrelevant information » Quantity ≠ Quality » Miss-spelings » Treat acronyms and abbreviations (e.g. “LOL”) » Pr☺f@nity » *Punctuation*
  60. 60. Sentiment Analysis• The action of identifying the expressed sentiments by customers, partners, suppliers and employees • Three levels – Polarity indicator: Positive, negative, neutral• Why is it important to measure sentiment? • Public perception• Traditional methodologies • Statistical and rules-based • Typically use one or the other – Common issues with measuring polarity accurately – Hybrid approach advantages• Overall vs. granular/feature-level sentiment
  61. 61. Overall vs. Granular/Feature-level Sentiment Good, but a little outdated. I bought the Nikon Coolpix L10 as my first digital compact P&S camera. I had it for a couple of weeks, until mine had a lens error that basically made the camera inoperable (it was stuck open). It mightve been due to batteries running low, but I tried another set. The picture quality from the L10 was very good, a bit of barrel distortion was noticed in the wide angle and shooting tall skyscrapers (noticed by the curve along the side of the frame where the buildings are supposed to be straight).Another gripe I had with the camera was how slow the auto-focus was. It would basically go through the whole range of focus every time I pressed the shutter half-way and then some Eventually a lot of my pictures came out blurry, including outdoor overcast days with 3x optical zoom. Basically anytime theres zoom & less than ideal lighting, I would have to have rock steady hands to get non-blurry pictures. Overall its a good camera if you can overlook the issues I mentioned. Product: Nikon Coolpix L10, Polarity: mixed Feature: Picture Quality, Polarity: positive Feature: Autofocus, Polarity: negative
  62. 62. Case Study: Yogurt Brand• Business Issue: Search sources of consumer-generated content and social media activity to find and analyze opinions about brand and products• Outcome: Sentiment analysis technology enabled the ability to: – Take targeted measures based on Web feedback – Align with customers needs by analyzing indicators that reveal strengths and weaknesses – Define new products – Discover innovative uses for existing products
  63. 63. Text Analytics Insight Text Discovery MiningTop Up Natural Ontology Sentiment Language Management Analysis ProcessingDown Bottom Content Information Categorization Organization
  64. 64. Discover vs. Define• How does an organization proactively identify new topics, new terms, and new information being generated by the target consumer? – Text mining: Let the data speak for itself! UP TOP BOTTOM DOWN
  65. 65. Text Mining• The process of discovering and extracting meaningful patterns and relationships from text collections Text Data Natural Language Mining Mining Processing• Text Mining is not searching, but the concepts are related Mine Discover Search
  66. 66. Case Study: University of Louisville• Business Issue: Analyze text-based medical records and healthcare reporting• Outcome: – Extract and explore information from thousands of medical records - improving patient outcomes – Examine relationships between physician practices and patient outcome records – Pull relevant information from patient charts and easily look at patterns in patient treatments and patient outcomes
  67. 67. Web Analytics Situation Slide Critical • How do I increase my understanding of anonymous, digital visitation?Business • How can I increase the value of my digital property’s Issue advertising inventory? • Inability to accurately segment digital visitationImportance • Lose advertiser share to competitors • Lose revenue • How do I improve targeting strategies at anonymous visitors?Challenges • How do I improve my ad inventory performance? • How to analyze the data for proactive insight?
  68. 68. Advancing Web Analytics Pull Web analytic data (e.g. Create Customer State Develop “look-a-like” models to1 Omniture) and load into 2 Vector to record customer 3 gain intelligence on registered advanced analytic platform web behavior across time visitors, and apply insights to the unregistered CSV Customers Dimensions Utilize “look-a-like” model Perform analysis of results 4 results to offer demographic 5 and reiterate the process and behavioral ad targeting to all digital visitors
  69. 69. Module 2: Exploring the Idea & Value of Marketing Analytic Techniques 2.1 Introduction 2.2 Data Mining Techniques For Marketing, Sales, & CRM 2.3 The Power of Analyzing Structured & Unstructured Data 2.4 The Competitive Advantage of an Integrated, Analytic Marketing Platform 2.5 Questions
  70. 70. The Marketing Process Mobile Online Finance Risk Call Customer Center Service In Person Merchandising Social Corporate AffairsDirect Mail Marketing Operations Optimization Marketing Marketing Marketing Strategy Processes Campaigns Analytics Data IntegrationERP CRM EDW Online Social Campaign
  71. 71. The Marketing Process Mobile Online Finance Risk Call Customer Center Service In Person Merchandising Social Corporate AffairsDirect Mail Marketing OperationsMarketing Mix Real-Time Campaign Optimization Management Analysis Decisioning Marketing MarketingPerformance Operations Online Customer Social MediaManagement Management BehaviourData Mining & Sentiment & Customer Customer Analytics Unstructured Profitability Analytics Data Analysis & Forecasting Data IntegrationERP CRM EDW Online Social Campaign
  72. 72. Module 2: Exploring the Idea & Value of Marketing Analytic Techniques 2.1 Introduction 2.2 Data Mining Techniques For Marketing, Sales, & CRM 2.3 The Power of Analyzing Structured & Unstructured Data 2.4 The Competitive Advantage of an Integrated, Analytic Marketing Platform 2.5 Questions

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