Integrated Marketing Analytics & Data-Driven Intelligence

2,732 views

Published on

Published in: Technology, Business
0 Comments
5 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
2,732
On SlideShare
0
From Embeds
0
Number of Embeds
138
Actions
Shares
0
Downloads
120
Comments
0
Likes
5
Embeds 0
No embeds

No notes for slide
  • high-impact recommendation from a trusted friend conveying a relevant message is up to 50 times more likely to trigger a purchase than a low-impact recommendation.Mobile internet users will reach 113.9 million in 2012, up 17.1% from 97.3 million in 2011. About 2/3 of web users will use social networks in 201288.1% of US internet users will browse or research products online in 2012.
  • ….However…while it’s great to understand the overall there has definitely been a shift in recent years to do more micro-segmentation or targeting segmentation in each of the marketing communications we have.
  • How do I make these ‘Email Segments / Clusters’ Actionable? (EXAMPLES)
  • Integrated Marketing Analytics & Data-Driven Intelligence

    1. 1. Integrated Marketing Analytics & Data-Driven Intelligence
    2. 2. • Bruce Swann • Manager, CI / Integrated Marketing, SAS • Scott Briggs • Principal Solutions Architect, Customer Intelligence, SAS • Suneel Grover • Sr. Solutions Architect, Integrated Marketing Analytics, SAS • Adjunct Professor, The George Washington University (GWU)
    3. 3. Module 2: Omni Channel Orchestration & Interaction
    4. 4. Agenda I. Operationalize digital data capture and integration II. Data-driven, multichannel outbound marketing III. Real-time inbound marketing and dynamic analytics IV. Creating adaptive customer experiences
    5. 5. Video (Time: 0:00 – 0:51) http://youtu.be/Y7hekVg4OwU
    6. 6. Think Big…
    7. 7. Customer Journey RESEARCH ARRIVE AIRPORT CHECK-IN BAG DROP WAITING TO BOARD BOARDING INFLIGHTARRIVAL COLLECT BAGGAGE DEPART AIRPORT PURCHASE THE CAPTIVE CUSTOMER
    8. 8. The Marketers Mandate Responsibilities The Marketing Campaign Where are Customer Engaging? Integrated, multi-channel inbound/outbound conversations in real-time Expectation Unearth and dynamically manage insights to drive action Expectation Deliver relevant and engaging customer experience, across all interactions…every time. Expectation Identify where customers are engaging…what marketing tactics are working and driving revenue Expectation The Customer Experience Analytics
    9. 9. Today’s Landscape EMPOWERED CONSUMERS: Proliferation of Digital Channels EXPLOSION OF DATA: Big Data vs. Right Data EVOLVING CMO ROLE: Dual Mindset analytical creative
    10. 10. Valued Customer Meet Jack… Valued Guest • 44 Years Old • Wife, 2 Kids • Has a MAC, iPad and PC • Loyalty Club for 6 months • Avg. 1.5 Stays a Month • Often does research on website • Visits Site Once a Month • Active in Social Media (influencer) • Active in online forums
    11. 11. Customer Journey Guest Lifecycle Identify Create Interest Create Desire Win-Back Marketing No Longer Dictates the Path to Purchase… …Being Relevant and Engaging is key!
    12. 12. Challenge = Opportunity • Big Data • Silos • Empowered Customer(s) • Channel Proliferation
    13. 13. The Marketers Mission • Removal and consolidation of customer data silos • Bringing together offline and online data sources • Doing more marketing with less money • Enabling marketing and communications to do more with data • Less reliance on IT department to perform data extracts • Marketing complexity expanding faster than budget
    14. 14. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. Example Guest Path CustomerValue Time Interest Engage Monitor & RetainGrow and Delight!
    15. 15. Integrated Marketing Analytics #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS
    16. 16. Consolidate Information #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS
    17. 17. Drive Value of Data #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS
    18. 18. Orchestrate Next Best Action #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS
    19. 19. Measure and Improve #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS
    20. 20. The Messaging Mandate Relevant and Engaging Every time! Customer-Focused Messaging Cross-channel Incorporate Analytics Unified View
    21. 21. Data Management #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS Createa Complete View of the Customer: • Record Customer Contact History • Incorporate Cross-Channel Usage • Add Cross Product Behavior Include a Complete View of the Business: • Add Key Business Unit Activity • Layer in Relevant Market Data #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS
    22. 22. Solution Silos Email Web Mobile Social
    23. 23. DIGITAL DATA IS THERE UNTAPPED OPPORTUNITY?
    24. 24. Video (Time: 0:00 – 1:22) http://youtu.be/N5WurXNec7E
    25. 25. The Challenge Brands Are Addressing “Thecapture, management, and analysis of data to provide a holistic view of the digital customer experience that drives the measurement, optimization, and execution of marketing tactics and business strategies.” Progressing Towards Digital Intelligence…
    26. 26. How Did We Get Here?
    27. 27. BIG Data
    28. 28. Ability To Collect, Integrate, & Prepare Digital & Offline Data With Business Context
    29. 29. Challenges? We Think Not… Opportunity #1 Ability To Collect & Immediately Access Granular Data [Addressing Latency & Tag Management] Opportunity #2 Pre-Processing Data For Marketing & Analytics [Adding Business Context] Opportunity #3 Leveraging Advanced Analytics & Marketing Automation [Relevance & Personalization]
    30. 30. Where We Want To Get To…
    31. 31. Digital Analytic Data Mart Digital Visitor Dynamic Data Collection Data Transformation Visual Analytics & Reporting Predictive Marketing Analytics Outbound MarketingInbound Marketing Personalization “Other” Data
    32. 32. DIGITAL DATA IS THERE A BETTER WAY TO COLLECT IT?
    33. 33. Traditional Web Analytic Tagging 1. Involves IT skills 2. Requires getting involved with your CMS/Website rollout 3. Adds weight to the page 4. Business meaning is applied at capture 5. You need to know what you want upfront 6. Missed tag, missed data
    34. 34.  Digital Data Collection at the browser level by adding one insert, that never changes, to each page of your site, mobile app, or social media brand page: <SCRIPT language="JavaScript"type="text/javascript“src=“…………….."></SCRIPT>  Dynamic content recognition  Automatic collection of ALL activity (Subscriber or Anonymous)  Highly accurate granular data - timed to the millisecond  Choose to collect the level of detail to meet your PII Policy GLOBAL TAGGING
    35. 35. DATA COLLECTION DEMONSTRATION Demo:Browser Demo:Events
    36. 36. DIGITAL DATA TRANSFORMATION INTO BUSINESS- READY INFORMATION
    37. 37. DEALING WITH DIGITAL DATA COMPLEXITY Automated Data Preparation Data Integration Data Quality Data Governance
    38. 38. CUSTOMER LEVEL BUSINESS CONTEXT Customer (name, email, account id, etc.) Visitor (Online ID) Visitor (Online ID) Session 1 Session 2 Session 3Session 2Session 1 Session 3 Session 4 Session 5
    39. 39. THE DECISION YOU MAKE AS AN ORGANIZATION
    40. 40. Digital Analytic Data Mart Digital Visitor Dynamic Data Collection Data Transformation “Other” Data If you succeed, you land here…and that opens the door to:
    41. 41. Data Management Silos Structure & Unstructured Big Data Who knows how to leverage? • Last open date • Forward to a friend • Visit recency • Abandoned cart • SMS response • Poll response • Network membership • Number of friends Data Hub 1 2 3Source Data Merge/Purge/Cleanse Make accessible/actionable
    42. 42. Consumer Data Ann and Jack Smith 280 King St., Denver, CO btSmith@outdoor.comCutID= 99-1234567 P&O_ID=977, CMR_ID=334, DW_ID=99, CC_ID=55 CRM CMS Data WarehouseCall Center Data Hub Ann Smith (ID=55) 280 King St., Denver, CO (406) 363 4424 Jack T. Smith (PatronID=99) Denver, CO btSmith@outdoor.com Jack Thomas Smith (ID=334) 280 King St., Denver, CO 80236 bSmith@outdoor.com Jack Smith(ID=99) Denver, CO PatronID = 99-1234567 Ann Smith (CookieID=99xyz123) annie@outdoor.com Web
    43. 43. Online/Offline Profile Anon Time E-MailBehavior Account Attract visitor Track across sessions Browsing Behavior Clustering Product linking (nearness) Customer Insight CustomerValue Browse Segmentation More effective targeting Frequency of visits Engagement Level Shopping Behavior Customer Behavior Customer Value (RFM) Predictive Insight (Churn/Opportunity) Lifecycle and Lifestyle Customer as partner/promoter 76013 Cookie Sessions Online Segment User IDGeo 1 2 . . . n Browser owencox01 Visitor Profile Young Prof Owen.Cox@sas.com Email Product Affinity Sale Gadgets Value Hi/Promo
    44. 44. Better Informed Decisions  Aligns marketing strategy with how online consumer behave • Ex. Stop sending a winback email to a customer who was just onsite  Reveals behaviors across online media to discover new marketing opportunities • Ex. Expand marketing reach by tapping into customers with large social influence  Enables more efficient marketing processes by leveraging technology to execute messaging • Ex. Use triggered messaging functionality to reduce dependency on human resources
    45. 45. Starting Point  Start with a simple, phased approach: • Digital • Email • Mobile CRM • Social Media  Use marketing initiatives to encourage “identifiable” customer integration events • Integrate Social/Mobile/Web • Onsite registration/Opt-in • Email click • Online purchase • Product review
    46. 46. Integrated Analytics #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS Create Insight • Define Business Value Drivers • Profile and Model Customers • Calculate Customer Profitability • Forecast Customer Behavior and Potential Visualize and Act on Insights • Segment Customers and Markets • Isolate Trends and Opportunities • Layer in Relevant Market Data #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS
    47. 47. Operationalizing Analytics Time Introduction Growth Maturity Fader Significant Opportunity PatronValue Inform Persuade Remind Retain
    48. 48. Acquisition Development Retention Churn/ Win-back NetMargin Decisions points during Acquisition: •Customer looking at website, exploring, comparing pricing, etc… • Predict next best action, likelihood to respond •Evaluate product Bundles Operationalizing Analytics The Life Cycle
    49. 49. Acquisition Development Retention Churn/ Win-back NetMargin Decisions points during Relationship Development: •Customer engages, books initial trip, customer service, call routing, inquiry handling • Recency, Frequency, Monetary • Cross & Up-sell, Next Best Offer • Dynamic website experience & web self-service Operationalizing Analytics The Life Cycle
    50. 50. Acquisition Development Retention Churn/ Win-back NetMargin Decisions points during Retention: •Customer comparing options, complaints, less frequent/recent, value dropping • Targeted Retention Activities • Next Trip Pricing, Discounting & Bundling • Reactive Retention Operationalizing Analytics The Life Cycle
    51. 51. Acquisition Development Retention Churn/ Win-back NetMargin Decisions points during Churn/Win-back: •Customer activity drops (web, play, visits, stays, etc.) • Win-back Discount and Bundling Pricing • Trigger campaigns for future reacquisition Operationalizing Analytics The Life Cycle
    52. 52. HARNESSING DATA USING PREDICTIVE ANALYTICS
    53. 53. Video (Time: 0:00 – 2:20) http://youtu.be/BjznLJcgSFI
    54. 54. Predictive Marketing Analytics OtherEDW SocialCRM Digital Mobile Data Sources Data Management Analytics Data Quality Data Integration Data Model Data Governance Analytic Segmentation Predictive Modeling Analytic Data Visualization Forecasting
    55. 55. Advancing Predictive Marketing Decision Trees, Regressions, and Neural Networks Association / Sequence (Acquisition / Cross-sell) Survival Analysis (Retention / Churn) Two-Step & Ensemble Models Clustering (Proprietary Segmentation)
    56. 56. HARNESSING ONLINE & OFFLINE DATA RAPID PREDICTIVE MODELING DEMO
    57. 57. Analytics on Email Data #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS High Level Segmentation % New 5 % Inactive 30 % Opener 30 % Clicker 15 % Social Email Sharer 5 % Total 100% Email Behavioral Segmentation Definition % Potential New and Inactive (<1 Month) 5% Newly Engaged New opener and clicker (< 1 Month) 7% Up and Comers Consistent opener and clicker (2-4 Months) 18% Highly Active Long term opener and clicker (6+ months) 10% Declining Interest Lapsed opener, clicker (2-4 Months) 10% Bored Inactive (2-4 Months), Lapsed opener 20% Asleep Inactive (6+ Months) 30% Basic Behaviorial Segmentation
    58. 58. Objectives: To identify customer segments which are most likely to response positively to a specific campaign or marketing actions. Considerations:  Not all customers may be considered for a specific marketing program/campaign  Each customer may fall into many different segments  Useful and actionable segments Key Attributes:  Trip Data  Play Data  Value Tier  RFM  Age  Gender  Demographics  Mobile Subscriber  Socially Active  Online RFM Targeting Segmentation
    59. 59. Online/Offline Profile Anon Time E-MailBehavior Account Attract visitor Track across sessions Browsing Behavior Clustering Product linking (nearness) Customer Insight CustomerValue Browse Segmentation More effective targeting Frequency of visits Engagement Level Shopping Behavior Customer Behavior Customer Value (RFM) Predictive Insight (Churn/Opportunity) Lifecycle and Lifestyle Customer as partner/promoter 76013 Cookie Sessions Online Segment User IDGeo 1 2 . . . n Browser owencox01 Visitor Profile Young Prof Owen.Cox@sas.com Email Product Affinity Sale Gadgets Value Hi/Promo
    60. 60. Analytical Marketing Strategy 1 2 3 4 5 6 Define Marketing Objectives and Strategy Develop Segmentation Predictive Modeling Customer Contact Strategy Optimization Strategy Marketing Performance
    61. 61. Omni Channel Marketing #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS Create and Automate Customer Interactions • Define Desired Customer Experience • Set Business & Customer Thresholds • Identify Relevant Service and Offers • Execute Outbound Multi-channel Interactions Integrate Predictive Analytics Into Campaigns • Optimize Interactions and Investments • Score for predicted behavior within a campaign #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS
    62. 62. Multi-channel Marketing “Marketers are aggressively shifting budget to digital media and seeing interactive as more effective than traditional efforts. They look now to campaign management applications that enable them to act on and react to empowered customers rather than just integrate more channels.”
    63. 63. Messaging Mandate Customer-focused messaging drives more customer engagement, more relevant messages/content and higher marketing ROI. Key enablers: • Single View • Reduce Silos • Real-time Decisions • Analytics
    64. 64. Is Outbound Still Relevant?
    65. 65. Example Re-Engagement Email Message 1 Engage? Yes Website Message 1 No Site Visit? Mobile? Mobile Message 1 Email Message 2 Engage? Email Trigger Email Trigger Engage? Mobile Trigger No No Yes Email Trigger Yes No No Yes Yes Email Message Mobile Message Website Message
    66. 66. Multi-channel Marketing 2. Request sent via web service Applications Web sites, call center, terminals 1. Request a decision utilizing data about the customer captured in real- time 6. Receive decision and take action while the customer is still engaged 5. Return decision Decisions in Real- time… 3. Execute a decision process 4. Make a decision using business rules, analytics, & a comprehensive view Other data Real-time analytics
    67. 67. DecisioningDelimma Offer1 Offer2 Offer3 Offers and Marketing Content Channels Customers Maximize economic outcomes by solving the offer assignment problem while considering… Call Center Capacity Minimum Mail Run Budgets Contact Preferences Lifestyle Needs Product Targets Rules & Constraints I just moved up a tier… I just booked a trip… Can only make 1 contact a month… Email Only Please!
    68. 68. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. Guest Intelligence Lifecycle Welcome Message(s) Re-Activation SMS/Email Win Back Message/Offers Acquisition Development & Engage Retention GuestValue Win Opt-in & Conversion Promotional Messages Time Interaction Analyze Analyze AnalyzeInteraction Interaction
    69. 69. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. Guest Intelligence Lifecycle Acquisition Development & Engage Retention GuestValue Time
    70. 70. Improve Impact and Performance #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS Measure Results • Automate the measurement process • Track the performance of campaigns • Monitor key performance indicators around customers Leverage Knowledge Across Business • Refine business actions using automated tools based • Distribute information in a consumable manner to stakeholders
    71. 71. How Do We Get There? #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS #1. DATA INTEGRATION #3. OMNI CHANNEL MARKETING #4. MEASURE/ ATTRIBUTE #2. INTEGRATED ANALYTICS 2. Integrated Marketing Analytics 1.Data Management 3. Multi-Channel Marketing 4. Customer Optimization 5. Real-Time & Digital Analytics • Consolidating robust information • Collect guest behaviors & preferences • Single version of truth • 360 view of customers, brands & market • Unearthing insights & patterns • Data mining & analytics • Understanding customer & business value drivers • Measuring outcomes • Leveraging insights • Developing & executing customer- centric interactions • Outbound & Inbound • Automate • Planning optimal customer interactions • Maximizing customer engagement and biz ROI • The next best offer by channel for each customer • Real-time Decisions • Collect, analyze and leverage online behavior • Social Media Analytics • Create online profile
    72. 72. LET’S IMAGINE A FEW BUSINESS USE CASES…
    73. 73. Prospect Visits Website Explores Pricing Explores Registration CONFERENCE PROSPECT: ACQUISITION
    74. 74. CONFERENCE PROSPECT: OUTBOUNDMARKETING AUTOMATION Explores Registration Trigger Outbound Marketing Campaign CRM Data On Line Behavior Enrichment Data Advanced Analytics & Scoring
    75. 75. Prospect Visits Website Explores Pricing Explores Registration CONFERENCE PROSPECT: UPSELL / CROSS SELL Prospect Returns Two Hours Later
    76. 76. CONFERENCE PROSPECT: INBOUNDMARKETING AUTOMATION ProspectEnds Session Create Real-Time Trigger (Inbound) CRM Data On Line Behavior Enrichment Data Advanced Analytics & Scoring Prospect Returns Two Hours Later Personalize Website Experience Targeted Personalization
    77. 77. CASE STUDIES
    78. 78. Video (Time: 0:00 – 1:22) http://youtu.be/8mzF7DOzmiA
    79. 79. Video (Time: 0:00 – 1:22) http://youtu.be/8mzF7DOzmiA
    80. 80. Video (Time: 0:00 – 1:22) http://youtu.be/8mzF7DOzmiA
    81. 81. Questions?
    82. 82. Sunday Morning Preview Marketing Operations Management 1. Integrate your marketing process management activities 2. Control the management and distribution of marketing programs and workflows

    ×