Module 4: An Evolutionary Process - Moving  Toward Analytically Driven Marketing     3.1 Introduction     3.2 Marketing op...
• Debbie Mayville  – Sr. Solutions Architect, Communications & Marketing    Analytics, SAS• David Kelley  – Sr. Solutions ...
Module 4: An Evolutionary Process - Moving  Toward Analytically Driven Marketing     3.1 Introduction     3.2 Marketing op...
The Marketing Process                             Mobile Online Finance Risk                   Call                       ...
The Marketing Process                             Mobile Online Finance Risk                   Call                       ...
Optimization Defined      OptimizationA computational problem in  which the objective is to    find the best of all    fea...
The Relationship Marketing Context        • Many customers, offers, channels        • Managing the contact strategy       ...
Marketing Optimization                                        Marketing Optimization                “What should I do to a...
Massive Problem - Potential ChoicesProduct AProduct BProduct C
Marketing Optimization Applications• Financial Services   – Insurance policy offers   – Credit line increase/decrease   – ...
Do All Marketing Approaches             Yield The Same Results?                                                  10–100+ %...
Optimization Techniques Example•       Lines of business = 3•       Return = expected value (probability*expected revenue)...
Optimization Techniques –                     Campaign Prioritization• Campaigns assigned a priority• Customers allocated ...
Cross-channel Optimisation                      Campaign PrioritizationConstraints:                           Expected Ret...
Campaign PrioritizationConstraints:                           Expected       485                                          ...
Campaign PrioritizationConstraints:                           Expected Return: 655                                        ...
Optimization Techniques - Customer Rules• Customers assigned a priority• Campaigns allocated to customers by expected cust...
Customer RulesConstraints:                            Expected Return: 120                                                ...
Customer RulesConstraints:                            Expected Return: 1951 customer - 1 campaign1 campaign - 3 customersC...
Customer RulesConstraints:                            Expected Return: 195                                                ...
Customer RulesConstraints:                            Expected Return: 270                                                ...
Customer RulesConstraints:                            Expected Return: 4251 customer - 1 campaign1 campaign - 3 customersC...
Customer RulesConstraints:                            Expected Return: 500                                                ...
Customer RulesConstraints:                            Expected Return: 500                                                ...
Customer RulesConstraints:                            Expected Return: 6401 customer - 1 campaign1 campaign - 3 customersC...
Customer RulesConstraints:                            Expected Return: 715 +601 customer - 1 campaign1 campaign - 3 custom...
Optimization Techniques - Optimization  Business objectives, constraints, contact policies define ‘priority’  Optimizati...
OptimizationConstraints:                               Expected Return: 745   +301 customer - 1 campaign1 campaign - 3 cus...
Marketing Optimization: Process Flow PlannedCampaigns EligibleCustomers                   Marketing Optimization          ...
Case Study: Commerzbank, Germany   Challenges                                                 Business Impact   • 4 millio...
More Case Studies…        Client Name                              BenefitsVodafone (Australia)        • 3-10x Response Ra...
Module 4: An Evolutionary Process - Moving  Toward Analytically Driven Marketing     3.1 Introduction     3.2 Marketing op...
The Marketing Process                             Mobile Online Finance Risk                   Call                       ...
Increased Complexity With MarketingHow do you decide the right mix across all channels?  Web             Web         Socia...
Above The Line…Below The Line…» Above the Line             » Below the Line
Above the Line…Below the Line…Media Planner/Buyer           •   How did we perform across products, geographies, campaign ...
Marketing Challenge: Financial Pressures               • Aggressive corporate goals & objectives               • Increased...
Questions Marketing Mix can Address• How can I still achieve my marketing goals while facing  budget cuts?• I am below tar...
What is Marketing Mix Modeling?A data driven analytic process that quantifies therelationship between drivers/influencers ...
Marketing Mix TechnologyAnalytic Dashboards
Technology CapabilitiesAnalytic dashboards•   Analytic data warehouse surfaced through    interactive dashboards•   All me...
Technology CapabilitiesEconometric response models•   Build and test time series and causal modelsElasticity reports      ...
Technology CapabilitiesWhat‐if analysis & scenario planning•   Ability to simulate expenditures over different      Report...
Case Study: Large Insurance Company  Business Issue                    Solution                    BenefitsQuantify effect...
Module 4: An Evolutionary Process - Moving  Toward Analytically Driven Marketing     3.1 Introduction     3.2 Marketing op...
Customer Intelligence & Analytics - Part IV: An Evolutionary Process: Moving Toward Analytically Driven Marketing
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Customer Intelligence & Analytics - Part IV: An Evolutionary Process: Moving Toward Analytically Driven Marketing

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Customer Intelligence & Analytics - Part IV: An Evolutionary Process: Moving Toward Analytically Driven Marketing

  1. 1. Module 4: An Evolutionary Process - Moving Toward Analytically Driven Marketing 3.1 Introduction 3.2 Marketing optimization 3.3 The art and science of the marketing mix 3.4 Real-world, success case studies 3.5 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 4: An Evolutionary Process - Moving Toward Analytically Driven Marketing 3.1 Introduction 3.2 Marketing optimization 3.3 The art and science of the marketing mix 3.4 Real-world, success case studies 3.5 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 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 Behaviour Analytics Data IntegrationERP CRM EDW Online Social Campaign
  6. 6. Optimization Defined OptimizationA computational problem in which the objective is to find the best of all feasible solutions
  7. 7. The Relationship Marketing Context • Many customers, offers, channels • Managing the contact strategy • Looking ahead and behind • How do you allocate offers effectively to maximize return? • Many constraints impact decisions  Budgets, resources, policies • How to respect constraints? • How to reconcile competing goals? • How to plan effectively for change?
  8. 8. Marketing Optimization Marketing Optimization “What should I do to achieve the best results?“ Marketing Simulation “What would happen?"Business Value Predictive Modeling “How likely are my customers to respond to an offer?” Marketing Dashboard “How many new customers did we get last Data Quality, month? How much customer attrition?" Integration “How can we trust analysis if we don’t trust the data?” Data Access “What measures are available to better understand our business?” Reactive Proactive Predictive Strategic Intelligence
  9. 9. Massive Problem - Potential ChoicesProduct AProduct BProduct C
  10. 10. Marketing Optimization Applications• Financial Services – Insurance policy offers – Credit line increase/decrease – APR to offer on balance transfer offers• Telecom – Complex cell phone plan offers – Bundled services – Cross channel offers with different execution costs• Hospitality (Hotels, Casinos) • Loyalty offers• Retail • Personalized coupons (POS) • Offer prioritization and collisions • Contact stream optimization
  11. 11. Do All Marketing Approaches Yield The Same Results? 10–100+ % Optimization - Solves by holistic 5-10 % approach - Factors all constraints Customer Rules - Determines the best result - First In, First Out ? - Prioritized by Customer/CampaignPrioritization - Fails in the face of constraints- First In, First Out- Prioritized byCampaign- Does not providebest combination
  12. 12. Optimization Techniques Example• Lines of business = 3• Return = expected value (probability*expected revenue)• Business objective = maximise value• Constraints: Each customer is assigned to at most 1 campaign Each campaign can have at most 3 customersClient Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 Campaign C 3 60 75 65 4 55 80 75 5 75 60 50 6 75 65 60 7 80 70 75 Campaign B Campaign A 8 65 60 60 9 80 110 75
  13. 13. Optimization Techniques – Campaign Prioritization• Campaigns assigned a priority• Customers allocated to campaigns by expected customer value Client Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 3 60 75 65 Campaign C 4 55 80 75 5 75 60 50 6 75 65 60 7 80 70 75 Campaign B Campaign A 8 65 60 60 9 80 110 75
  14. 14. Cross-channel Optimisation Campaign PrioritizationConstraints: Expected Return: 260 ???1 customer - 1 campaign1 campaign - 3 customersClient Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 Campaign C 3 60 75 65 4 55 80 75 5 75 60 50 6 75 65 60 Campaign B Campaign A 7 80 70 75 8 65 60 60 9 80 110 75
  15. 15. Campaign PrioritizationConstraints: Expected 485 2601 customer - 1 campaign Return:1 campaign - 3 customersClient Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 Campaign C 3 60 75 65 4 55 80 75 5 75 60 50 6 75 65 60 Campaign B Campaign A 7 80 70 75 8 65 60 60 9 80 110 75
  16. 16. Campaign PrioritizationConstraints: Expected Return: 655 4851 customer - 1 campaign1 campaign - 3 customersClient Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 Campaign C 3 60 75 65 4 55 80 75 5 75 60 50 6 75 65 60 Campaign B Campaign A 7 80 70 75 8 65 60 60 9 80 110 75
  17. 17. Optimization Techniques - Customer Rules• Customers assigned a priority• Campaigns allocated to customers by expected customer value Client Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 Campaign C 3 60 75 65 4 55 80 75 5 75 60 50 6 75 65 60 Campaign B Campaign A 7 80 70 75 8 65 60 60 9 80 110 75
  18. 18. Customer RulesConstraints: Expected Return: 120 ???1 customer - 1 campaign1 campaign - 3 customersClient Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 Campaign C 3 60 75 65 4 55 80 75 5 75 60 50 6 75 65 60 Campaign B Campaign A 7 80 70 75 8 65 60 60 9 80 110 75
  19. 19. Customer RulesConstraints: Expected Return: 1951 customer - 1 campaign1 campaign - 3 customersClient Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 Campaign C 3 60 75 65 4 55 80 75 5 75 60 50 6 75 65 60 7 80 70 75 Campaign B Campaign A 8 65 60 60 9 80 110 75
  20. 20. Customer RulesConstraints: Expected Return: 195 2701 customer - 1 campaign1 campaign - 3 customersClient Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 Campaign C 3 60 75 65 4 55 80 75 5 75 60 50 6 75 65 60 Campaign B Campaign A 7 80 70 75 8 65 60 60 9 80 110 75
  21. 21. Customer RulesConstraints: Expected Return: 270 3501 customer - 1 campaign1 campaign - 3 customersClient Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 Campaign C 3 60 75 65 4 55 80 75 5 75 60 50 6 75 65 60 7 80 70 75 Campaign B Campaign A 8 65 60 60 9 80 110 75
  22. 22. Customer RulesConstraints: Expected Return: 4251 customer - 1 campaign1 campaign - 3 customersClient Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 Campaign C 3 60 75 65 4 55 80 75 5 75 60 50 6 75 65 60 7 80 70 75 Campaign B Campaign A 8 65 60 60 9 80 110 75
  23. 23. Customer RulesConstraints: Expected Return: 500 4251 customer - 1 campaign1 campaign - 3 customersClient Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 Campaign C 3 60 75 65 4 55 80 75 5 75 60 50 6 75 65 60 7 80 70 75 Campaign B Campaign A 8 65 60 60 9 80 110 75
  24. 24. Customer RulesConstraints: Expected Return: 500 5801 customer - 1 campaign1 campaign - 3 customersClient Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 Campaign C 3 60 75 65 4 55 80 75 5 75 60 50 6 75 65 60 7 80 70 75 Campaign B Campaign A 8 65 60 60 9 80 110 75
  25. 25. Customer RulesConstraints: Expected Return: 6401 customer - 1 campaign1 campaign - 3 customersClient Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 Campaign C 3 60 75 65 4 55 80 75 5 75 60 50 6 75 65 60 7 80 70 75 Campaign B Campaign A 8 65 60 60 9 80 110 75
  26. 26. Customer RulesConstraints: Expected Return: 715 +601 customer - 1 campaign1 campaign - 3 customersClient Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 Campaign C 3 60 75 65 4 55 80 75 5 75 60 50 6 75 65 60 7 80 70 75 Campaign B Campaign A 8 65 60 60 9 80 110 75
  27. 27. Optimization Techniques - Optimization  Business objectives, constraints, contact policies define ‘priority’  Optimization decides allocationClient Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 3 60 75 65 Campaign C 4 55 80 75 5 75 60 50 6 75 65 60 7 80 70 75 Campaign B Campaign A 8 65 60 60 9 80 110 75
  28. 28. OptimizationConstraints: Expected Return: 745 +301 customer - 1 campaign1 campaign - 3 customersClient Camp’ A Camp’ B Camp’ C 1 100 120 90 2 50 70 75 Campaign C 3 60 75 65 4 55 80 75 5 75 60 50 6 75 65 60 7 80 70 75 Campaign B Campaign A 8 65 60 60 9 80 110 75
  29. 29. Marketing Optimization: Process Flow PlannedCampaigns EligibleCustomers Marketing Optimization Identify & Define Review Model Execute Optimization Optimization Scores Optimal Scenarios Results Outcome Contact “What-If Analysis” Policy Optimization Parameters: • Objective • Suppression Rules • Constraints: • Budget • Capacity • Contact / Blocking Policies
  30. 30. Case Study: Commerzbank, Germany Challenges Business Impact • 4 million customers, 20 offer types • POV: Up to 80% ROI improvement • Optimize utilization of consultants • Production: 50% yield with the same budget • Optimize Yield vs. Budget • ROI increased by 407% • Optimize Marketing ROI (revenue / cost)"We have compared SAS intensively with other manufacturers offerings. The result wasimpressive: SAS Marketing Optimization is exactly the solution we were looking for. We are +407%setting an industry Benchmark” ROI Heiko Güthenke, Department Director Customer & Business Analysis
  31. 31. More Case Studies… Client Name BenefitsVodafone (Australia) • 3-10x Response Rate increase • Improve campaign ROI by 4x • 30% reduction in campaign costsScotiabank • 50% Campaign ROI improvementMajor Insurer • 12% increase in revenue; 52% in earnings • Savings of >$4 million per yearU.S. Regional Telco • $6 million incremental LTV in the 1st monthGlobal Telco • Reduced call center contacts by 25% without decreasing effectiveness#1 Market Share European • Individualized targeting of monthly couponRetailer mailers • Increased offer response rates • Decrease mailing costs
  32. 32. Module 4: An Evolutionary Process - Moving Toward Analytically Driven Marketing 3.1 Introduction 3.2 Marketing optimization 3.3 The art and science of the marketing mix 3.4 Real-world, success case studies 3.5 Questions
  33. 33. 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 Behaviour Analytics Data IntegrationERP CRM EDW Online Social Campaign
  34. 34. Increased Complexity With MarketingHow do you decide the right mix across all channels? Web Web Social Media & Direct Word of Customer Sales (Corp) (eCommerce) Media Ads Mail Mouth Service Advertising Email & Social RetailInteractive Direct 1:1 & Mobile Marketing Marketing Promotions
  35. 35. Above The Line…Below The Line…» Above the Line » Below the Line
  36. 36. Above the Line…Below the Line…Media Planner/Buyer • How did we perform across products, geographies, campaign types? Brand Manager • What marketing activities drove our new sales? • What if we move funds from traditional to online marketing?Interactive Marketing • What actions/decisions to we make for various scenarios? • MOST of my marketing data is in silos…can I leverage it for analysis? Marketing Planning» Above the Line » Below the Line IT• How can I get the right offer, to the right person via the right channel? Interactive Marketing• Can I coordinate my multi-channel campaign efforts? Director of Database Mkt• Can I be relevant with EVERY interaction, every time? Campaign Planner/Designer Marketing Operations
  37. 37. Marketing Challenge: Financial Pressures • Aggressive corporate goals & objectives • Increased accountability and scrutiny into marketing budgets • Reductions in budgets
  38. 38. Questions Marketing Mix can Address• How can I still achieve my marketing goals while facing budget cuts?• I am below target, how do I re-allocate my marketing budget to hit targets?• How do I decide where to invest my marketing budget to support a product portfolio?• How and where do I invest in social media to maximize business impacts?• Where do I increase marketing investments to achieve higher returns?
  39. 39. What is Marketing Mix Modeling?A data driven analytic process that quantifies therelationship between drivers/influencers of sales and theresulting sales across channels • Understand the past performance of sales & marketing activities • Analyze and assess average ROI and marginal ROI • Evaluate marketing investment among ever increasing media options • Compare and assess different future marketing spending plans
  40. 40. Marketing Mix TechnologyAnalytic Dashboards
  41. 41. Technology CapabilitiesAnalytic dashboards• Analytic data warehouse surfaced through interactive dashboards• All media and promotions display in one location with prebuilt reports delivering summary and Analytic Dashboards detailed resultsPowerful analytic tools• Understand the impact of advertising on sales and incorporate into response models Adstock Analysis• Ability to explore product interactions to understand and uncover halo and cannibalization effects across your product portfolio Halo / Cannibalization Analysis
  42. 42. Technology CapabilitiesEconometric response models• Build and test time series and causal modelsElasticity reports Response Model Diagnostics• Objectively quantify the relative responsiveness of each driver of sales• Decompose sales into its various components.Diminishing returns• Capture changes in marginal ROI as spending Elasticity Reports levels increase through diminishing returns curve for each channel• Determine the threshold point beyond which Sensitivity Report marketing expenditures would not yield any additional benefits Diminishing Returns
  43. 43. Technology CapabilitiesWhat‐if analysis & scenario planning• Ability to simulate expenditures over different Report Dashboard media and analyze the impact on products/brands/channels/geo’s Simulate/Forecast• Compare competing spending plans to understand the differences in salesMarketing mix optimization Decomposition Reports• Optimal media expense allocation for selected p roduct, channel & geography combination over a defined period of time.• Define different sets of business constraints to Compare scenarios explore the impact on the optimal solution Marketing Mix Analytics Optimization “Leave less up to chance and make data driven, evidence-based decisions”
  44. 44. Case Study: Large Insurance Company Business Issue Solution BenefitsQuantify effectiveness of Marketing mix analytics Even though they areall marketing mix elements allows them to share consistently outspent by• Direct-response assumptions about their competitors, became• TV marketing analysis across more competitive by• Direct marketing all types of marketing determining which media• Web marketing and channels worked the• Retail channel Data is integrated from best across products and communications multiple sources and regions. analyzed to ensure accurate short-term and long-term forecasts across marketing and operations “The technology help us develop a “strategic” tool that enables us to lower risk in decision-making as we integrate all marketing disciplines with an eye toward better forecasting, budgeting, and collaboration.” Director of Strategy
  45. 45. Module 4: An Evolutionary Process - Moving Toward Analytically Driven Marketing 3.1 Introduction 3.2 Marketing optimization 3.3 The art and science of the marketing mix 3.4 Real-world, success case studies 3.5 Questions

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