Module 1 Information Management and Analytics Final

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  • Data wasters. These companies underperform financially, and their business and IT functions are not aligned. They collect data, but severely underuse them. Found in every industry, these companies are most likely to put a mid-level manager in charge of their data strategy.* Data collectors. These companies are submerged in data. They recognise the importance of data, but lack the resources to do anything about them, beyond storing them. They suffer from poor IT/business alignment, with nearly one-quarter maintaining that IT does not understand the importance of data; another quarter says the same of the business side. Companies in the healthcare and professional services industries are likely to be found in this category* Aspiring data managers. These companies have fully embraced the importance of big data to the future of their company. They allow data to inform strategic decisions, and invest in them aggressively. But they still lag behind the leaders. Sixty-six percent of them put only about one-half of their data to good use. Companies in the communications and retail industries are most likely to be found in this category.* Strategic data managers. This is the most advanced group of big data managers, with the most mature capabilities. Fifty-three percent of these strategic data managers say they outperformed their peers in the last fiscal year, 44% say they are on even par and only 1% say they underperformed. They are most likely to be found among manufacturing, financial services or technology companies. Strategic data managers first identify specific measurements and data points that align closely with corporate strategic goals.
  • Tie back to IMM and foreshadow the predictive modeling, segmentation & SNA work
  • Customer and product profitability mutually depend on each other, i.e. two customers having exactly the same product and services package may differ heavily with their profitability, also the same product sold to two different customers may cost the operator differentlyTwo flat-access-fee product users may demonstrate different attitude to the available basket of services and generate highly different product costs; Two customers generating similar revenue may utilize different services bundles;Customer, product and service profitability are not universal and transferable across the entire database; more granular profitability calculator is necessary to propose right product to right customer micro-segments
  • Which customers are profitable?Those who keep generate positive Gross Profit*/ Net Profit ** month-after-monthThose, who’s cumulative Gross Profit/ Net Profit is already greater then Acquisition Costs (SAC) and Retention Costs (SRC)* Gross Profit (Return On Sales) = Gross Revenues from Services – Costs of Goods & Services Sold + Revenues from Interconnect and Interworking connections and messaging**Net Profit = Gross Profit – Costs to Serve (call center calls, claims, HLR & Network Costs, etc. directly related to customer activities and services used)
  • Which customers are profitable?Those who keep generate positive Gross Profit*/ Net Profit ** month-after-monthThose, who’s cumulative Gross Profit/ Net Profit is already greater then Acquisition Costs (SAC) and Retention Costs (SRC)* Gross Profit (Return On Sales) = Gross Revenues from Services – Costs of Goods & Services Sold + Revenues from Interconnect and Interworking connections and messaging**Net Profit = Gross Profit – Costs to Serve (call center calls, claims, HLR & Network Costs, etc. directly related to customer activities and services used)
  • Which customers are profitable?Those who keep generate positive Gross Profit*/ Net Profit ** month-after-monthThose, who’s cumulative Gross Profit/ Net Profit is already greater then Acquisition Costs (SAC) and Retention Costs (SRC)* Gross Profit (Return On Sales) = Gross Revenues from Services – Costs of Goods & Services Sold + Revenues from Interconnect and Interworking connections and messaging**Net Profit = Gross Profit – Costs to Serve (call center calls, claims, HLR & Network Costs, etc. directly related to customer activities and services used)
  • Which customers are profitable?Those who keep generate positive Gross Profit*/ Net Profit ** month-after-monthThose, who’s cumulative Gross Profit/ Net Profit is already greater then Acquisition Costs (SAC) and Retention Costs (SRC)* Gross Profit (Return On Sales) = Gross Revenues from Services – Costs of Goods & Services Sold + Revenues from Interconnect and Interworking connections and messaging**Net Profit = Gross Profit – Costs to Serve (call center calls, claims, HLR & Network Costs, etc. directly related to customer activities and services used)
  • Which customers are profitable?Those who keep generate positive Gross Profit*/ Net Profit ** month-after-monthThose, who’s cumulative Gross Profit/ Net Profit is already greater then Acquisition Costs (SAC) and Retention Costs (SRC)* Gross Profit (Return On Sales) = Gross Revenues from Services – Costs of Goods & Services Sold + Revenues from Interconnect and Interworking connections and messaging**Net Profit = Gross Profit – Costs to Serve (call center calls, claims, HLR & Network Costs, etc. directly related to customer activities and services used)
  • Social Network Propensity Score - using centrality and the network structure we can generate highly predictive propensity scores.These propensity scores can be for customer actions such as churn, acquisition, prepaid to postpaid migration etc.Persistent Individual Identification- using links and calling pattern mechanisms to assign similarity scores to individuals. We can track individuals over time, even through number or address changes.The assumption/requirement is that they continue to talk to the same people/telephone numbers over time.Customer, Household, and Life-Stage Segmentation.- Using community detection, in addition to demographics (age, gender etc), location, and address information where available we can allocate customers into family segments.Customer Value - An accurate metric of value is also a product of your influence upon others. If a low value customer is highly connected and a great customer advocate they may be responsible for significant acquisition of many customers (whom may be high value) and reduces churn, and hence marketing costs for retention.Acquisition Of High ARPU Prospects - Refer-a-friend and ‘member-get-member’ offers often yield better results when you are aware of the $ value of off-network friends and the potential market share (number of off-network friends) each customer can bring.Agile Campaigns- By having customer link analytics prepared, it can be matched daily to recent churners. The next day (or even hourly) the friends or any churned customers can be contacted to prevent viral churn.
  • Module 1 Information Management and Analytics Final

    1. 1. Real-Time Analytics & Attribution
    2. 2. • Noah Powers – Principal Solutions Architect, Customer Intelligence, SAS• Patty Hager – Analytics Manager, Content/Communication/Entertainment, SAS• Suneel Grover – Solutions Architect, Integrated Marketing Analytics & Visualization, SAS – Adjunct Professor, Business Analytics & Data Visualization, New York University (NYU)
    3. 3. Video (Time: 1:20-5:00)http://www.colbertnation.com/the-colbert-report-videos/408981/february-22-2012/the-word---surrender-to-a-buyer-power?xrs=share_copy
    4. 4. Module 1Information Management and Analytics
    5. 5. Information Management“There is no better place to start than data, since it is the fuel needed to make insightful decisions that can drive your business forward.” Information Management ERP CRM EDW Online Social Other Data Sources
    6. 6. Information Management & Analytics “Being able to derive insights from data is the key to making smarter, fact-based decisions that will translate into profitable revenue growth.” Customer Social & Predictive Segmentation Profitability & Network Analytics Modeling LTV Analytics Data Data Data Quality Information Management Integration Model Metadata ERP CRM EDW Online Social Other Data Sources
    7. 7. The Business Analytics Challenge
    8. 8. DATADATADATA ANALYSTS
    9. 9. One Perspective…
    10. 10. Marketing Perspective
    11. 11. DECISIONSDATA ANALYTICS INSIGHTSINFORMATION MANAGEMENT
    12. 12. OURPERSPECTIVE Big Data is RELATIVE not ABSOLUTE Big Data When volume, velocity and variety of data exceeds an organization’s storage or compute capacity for accurate and timely decision-making
    13. 13. THRIVING IN THE BIG DATA ERA VOLUME VARIETYDATA SIZE VELOCITY VALUE THE TODAY FUTURE
    14. 14. Which Category Are You? Strategic Data Managers Aspiring Data ManagersCompetitive Advantage Data Collectors • Mature capabilities in data management • Attribute data management Data to C-suite • Embrace importance of Wasters data • 53% outperformed peers • First to identify measurement • Allow data to inform & data points that align with strategic decisions corporate strategic goals • Invest in technology enablement • 60% put 50% of data to • Drowning in data use • Misaligned IT and • Lack resources to • Underperform Business leverage data financially • Lack resources to • Misalign IT and leverage data Business • Underuse data • Mid-levels drive data strategy Degree of Intelligence
    15. 15. Big Data Marketing Challenges (1)Source: 2012 BRITE/NYAMA Marketing in Transition Study
    16. 16. Big Data Marketing Challenges (2)Source: 2012 BRITE/NYAMA Marketing in Transition Study
    17. 17. Unlocking Siloed Operational Data To Understand Customers EDW CRM ANALYST ? BILLING ERP WEB CUSTOMER
    18. 18. Ad Hoc Exploration & Analysis Can Take Weeks ANALYST CUSTOMER
    19. 19. What If We Had A Set Of Master Keys? ANALYST CUSTOMER
    20. 20. Where We Want To Get To… CRM Data Integrated Marketing Enrichment Data Data Table (Customer ID , 12345) (Name , John Smith) (Gender , M) (Age , 42) (Life Stage , FL) (HH Income , 75K-100K) (Children Ind , 1) (HH Education, College) (HH Value Score, Above Avg) (CC Propensity, 0.57) (Visit Recency, 12) (Session Count, 7) (Session Avg. PV, 4) (Engagement, High) (Content Goal, 1) (Sticky Goal, 1)Online History Data (Session Affiliate, Org Search) Current Session Data
    21. 21. Integrated Marketing Data Table Discovery and Marketing Analytic Modeling Reporting Data Queries Acquisition Predictive AnalysisOLAP Cube Discovery CRM Segmentation Analysis Real-Time Model Data Visualization Churn / Attrition ExecutionThe Integrated Marketing Table (also known as “Customer State Vector”) is an analytic approach designed for rapid retrieval of customer-level data from any dimension.
    22. 22. Why Do We Care? Act Orient YOUR Decide Decide COMPETITIVE MARKET ADVANTAGEOPPORTUNITY Orient Act Observe
    23. 23. Big Data - Why Do We Care?Video (Time: 0:00-5:00)http://youtu.be/CrSX97elHDA?hd=1
    24. 24. DECISIONSDATA ANALYTICS INSIGHTS INFORMATION MANAGEMENT
    25. 25. Predictive Analytics “Encompasses a range of techniques for collecting, analyzing, and interpreting data in order to revealpatterns, anomalies, key variables, and relationships.” Customer Segmentation Predictive Profitability & Social Network Modeling Analytics LTV Data Data Data Metadata Quality Integration Model ERP CRM EDW Online Social Other Data Sources
    26. 26. BIG DATA
    27. 27. OURPERSPECTIVE THE ANALYTICS GAP Most organizations:  Can‟t generate the information they need.  Can‟t generate information fast enough to act on it.  Continue to incur huge costs due to uninformed decisions and misguided strategies. The opportunities afforded by analytics have never been greater!
    28. 28. The Predictive Analytics LifecycleBUSINESS BUSINESSMANAGER IDENTIFY / FORMULATE ANALYSTDomain Expert EVALUATE / PROBLEM Data ExplorationMakes Decisions MONITOR DATA Data VisualizationEvaluates Processes and ROI RESULTS PREPARATION Report Creation DEPLOY MODEL DATA EXPLORATION VALIDATE MODEL TRANSFORMIT SYSTEMS / & SELECTMANAGEMENT BUILD DATA MINER /Model Validation MODEL STATISTICIANModel Deployment Exploratory AnalysisModel Monitoring Descriptive SegmentationData Preparation Predictive Modeling
    29. 29. Lifecycle Challenge… 20% 80% = :*( IDENTIFY / FORMULATE EVALUATE / PROBLEM MONITOR DATA RESULTS PREPARATION DEPLOY MODEL DATA EXPLORATION VALIDATE “Data is the number one challenge in the MODEL TRANSFORM adoption or use of business analytics.” & SELECT BUILD Companies continue to struggle with data MODEL accuracy, consistency, and even access. Bloomberg BusinessWeek Survey 2011
    30. 30. Data Visualization & Exploration
    31. 31. Information Is Beautifulhttp://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization.htmlVideo (Time: 0:00-5:10)
    32. 32. Digital Channel Exploration
    33. 33. Geographic Exploration
    34. 34. Mr. Data: Talk To Me Visually!
    35. 35. Customer Case Study: Telco Handset vs. Network Compatibility • Which customers should be upgraded to 4G? • Which handsets should be pushed in which region? Dropped Calls Analysis • Do dropped calls contribute to churn? • Are there handsets that are more likely to drop calls? Handset Penetration Analysis • Which cities have the greatest handset penetration? • Which handsets have the greatest ROI in each market? iPhone Launch Analysis • Which markets are being hit the hardest by your competition‟s iPhone launch? • Which cities are the responding the best to your iPhone campaign?
    36. 36. Customer Case Study: Telco Inner circlerepresents %of calls each switch type Total number carried. of drops that occurred over each handsetOuter circle type represents% of dropseach switch type % of Drops is the drop rate carried. for each switch. Total calls and minutes Handset %s represent are displayed for each the distribution of individual switch by handset over each region switch
    37. 37. Vendor Independent Report: Forrester Wave Predictive Analytics And Data Mining SolutionsThe Forrester Wave™: Predictive Analytics And Data Mining Solutions, Forrester Research, Inc.,The Forrester Wave is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave are trademarks of Forrester Research, Inc. The Forrester Wave is a graphical representationof Forresters call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or servicedepicted in the Forrester Wave. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change. © 2011, Forrester Research, Inc. Reproduction Prohibited
    38. 38. Predictive Analytic Marketing Applications Acquisition Ad Targeting Personalization Retention Content Experience / Engagement © 2011, Forrester Research, Inc. Reproduction Prohibited
    39. 39. This Is What You Want Probability scores are the output of predictive models, and are an essentialingredient to making data driven decisions © 2011, Forrester Research, Inc. Reproduction Prohibited
    40. 40. Why Do You Want It? APPLICATION SCORING BEHAVIORAL SCORING COLLECTION SCORING DECISION ASSESSMENTANALYTICALLIFECYCLE © 2011, Forrester Research, Inc. Reproduction Prohibited
    41. 41. Is It Hard To Do? © 2011, Forrester Research, Inc. Reproduction Prohibited
    42. 42. Now What? © 2011, Forrester Research, Inc. Reproduction Prohibited
    43. 43. No Silly…We Bring It To Life! Scoring is nothingmore than applying a formula created by your model to your customer records © 2011, Forrester Research, Inc. Reproduction Prohibited
    44. 44. Let’s Think Bigger – What If I Could…. . . deliver personalized offers andservices to ALL customers basedon up to the minute profiles. . . gain first-mover advantage byintroducing new products andservices to micro market segmentsthat havent been identified byanyone. . . evaluate the impact ofmarketing campaigns hourly &make adjustments in real-time © 2011, Forrester Research, Inc. Reproduction Prohibited
    45. 45. BIG Data Architecture – Game Changing!ARCHITECTURE HIGH-PERFORMANCE ANALYTICS FOR BIG DATA UNSTRUCTURED DATA STRUCTURED & ANALYTICAL IN-DATABASE ANALYTICS INSIGHTS IN-MEMORY GRID OPERATIONAL DECISIONS DATABASE APPLIANCE © 2011, Forrester Research, Inc. Reproduction Prohibited
    46. 46. Customer Case Study: 11 DEVELOPMENT HRS EXPLORATION DEPLOYMENT MODEL MODEL DATA 15% improvements in Marketing campaigns 10 SECONDS  GRID enabled analytics process to improve marketing © 2011, Forrester Research, Inc. Reproduction Prohibited
    47. 47. Big Data, Analytics, & In-Database http://youtu.be/TUHspP8irzQ 48 Copyright © 2011, SAS Institute Inc. All rights reserved.
    48. 48. Segmentation“The practice of dividing a prospect/customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests, spending habits, etc..” Customer Predictive Social Network Segmentation Modeling Profitability & Analytics LTV Data Data Data Metadata Quality Integration Model ERP CRM EDW Online Social Other Data Sources © 2011, Forrester Research, Inc. Reproduction Prohibited
    49. 49. Classic Marketing Approach: RFM © 2011, Forrester Research, Inc. Reproduction Prohibited
    50. 50. Advanced Analytic SegmentationDecision Trees Clustering(Supervised Learning) (Unsupervised Learning) Business Use Case Business Use CaseAcquisition Marketing Marketing Strategy © 2011, Forrester Research, Inc. Reproduction Prohibited
    51. 51. Decision Trees• Decision trees are a form of multiple variable (or multiple effect) analyses• Allow marketers to explain, describe, or classify an outcome – Use Case 1. After analyzing Dec 2011 campaign results, we use Decision Trees to calculate the classification probability of a prospect responding to the acquisition campaign 2. Score “look-a-like” prospects for Dec 2012 campaign
    52. 52. Decision Tree
    53. 53. Data Driven Segmentation Rules Segment #1 #2 Recency Score: High Engagement Score: Medium Engagement Score: High Age: Young Adult (25-44) Affiliate: Organic Search Affiliate: Email Response Probability: Medium Response Probability: High
    54. 54. Benefits Of Decision Trees• The multiple variable analysis capability enables one to discover & describe outcomes in the context of multiple influences• The appeal of decision trees lies in their relative power, ease of use, robustness with a variety of data and levels of measurement, and interpretability Bootstrap Forests CHAID / C5 / RP Boosted Trees
    55. 55. Clustering• Marketing can use cluster analysis to partition prospects/customers into segments – without the bias of a historical consumer decision• Understand the organic synergies between different groups – Use Case 1. Marketing is planning a new campaign, and historical information is not available 2. Tag prospects with cluster results for our Dec 2012 campaign, and influence creative execution
    56. 56. ClusteringFinding groups of observations such that the observations in agroup will be similar (or related) to one another, and differentfrom (or unrelated) to the observations in other groups
    57. 57. Data Table Step 2 Step 1 Approach: K-Means Number of Clusters: 3
    58. 58. Cluster #1 Cluster #2 Cluster #3Weight Management Guilty Pleasures Health Management Diet Focused Taste Focused High Fiber
    59. 59. Benefits Of Clustering• Segmentations arise from varied business needs & demands – Marketing vs. Sales vs. Advertising• Integrating data streams allows greater capabilities – When combined, Marketing gains an increased understanding of customer behavior, demographics and psychographics Expectation- Centroid Hierarchical Maximization
    60. 60. Customer Profitability & LTV “Customer lifetime value (CLV) is a prediction of thenet profit attributed to the entire future relationship with a customer.” Customer Social & Predictive Segmentation Modeling Profitability Network Analytics & LTV Data Data Data Metadata Quality Integration Model ERP CRM EDW Online Social Other Data Sources
    61. 61. Customer Lifetime Value & Influence http://youtu.be/BRhPS0-rx6I?hd=1 62 Copyright © 2011, SAS Institute Inc. All rights reserved.
    62. 62. Value of Your Company = Value of Your Customers The only value your company will ever create is the value that comes from customers–the ones you have now and the ones you have in the future. To remain competitive, you must figure out how to keep your customers longer, grow them into bigger customers, make them more profitable and serve them more efficiently. By Don Peppers and Martha Rogers, Ph.D., Founding Partners, Peppers & Rogers Group 63
    63. 63. Perils Of Ignoring Customer Profitability • 20% of the customers represent 80% turnover • Some customers repeatedly contact the call-center • Sales channels are incented by revenue • Identification and retention of the profitable customers is a challenge Situation • Marketing campaigns segment customers without considering profitability • Profitable and loyal customers are not recognized/rewarded • It is not the profitable customers who are retained • It is not the most profitable products which are offered to the customers • Sales and call-center staff spend their time on the unprofitable customersConsequence • Sale of unprofitable products result in losses and wasted resources • Low return on sales and marketing activities 64
    64. 64. Competitive Advantage & Profitable GrowthFocus resources on gaining and retaining the most profitablecustomers with the most relevant offers at the opportune time.Positive & Negative Profit: Predict & Execute Proactively:• Many are profitable customers • Identify customers most at risk• Other customers reduce profits • Identify customer influence factors• The key is to understand which Customer • Execute proactive customer retentioncustomers fall into each category Profitability Revenue Customer Growth Retention Customer Relevant conversations: Centric • The way the customer prefers • At the time they prefer 65
    65. 65. Path to Optimized Profitable MarketingHarness customer insights that result in smarter more personalizedmarketing execution to improve customer profitability. Define Execute Define Consolidate and Analytically Optimized Customer Value Organize derived Marketing and Cost Customer data Customer Based on Metrics Segmentations Essential Insight 66
    66. 66. Define Customer ValueChallenges Expenses are allocated with broad strokes to Costs Revenue customer segments Lack of visibility into the true drivers of profitabilitySolution: An advanced profitability costing and allocation engine A full cost view of individual customer profitability to uncover profit drivers and detractors Understanding the root causes of adverse trends for margin, revenue, and cost for individual customers and segments. Profit Retention Potential Predicting future profitability including various scenarios for customers and segments Lifetime Understand role and influence of social network Value 67
    67. 67. Costs At The Customer LevelIn order to determine customer profitability in a reliable andrepeatable way, a comprehensive source of cost data atthe lowest possible level of granularity is required: The data should be available on product, service and customer level, where appropriate. Aggregated costs need solid decomposition algorithms, accepted by business and financial analysts Average costs might be misleading, as the same product sold to two different customers may have differing cost profiles Customer, product and service profitability are not universal and transferable across the entire database Other costs to serve should be calculated using a proven methodology, like Activity-Based Costing 68
    68. 68. Define Analytically Derived Customer Segmentations  Create individual segmentations for each of the profit levels  Uncover profit drivers or profit detractors for each profit level Segment Name Description Top 20% Most • Uncover Why they are Most Profitable Profitable • High influencer/ leader? Usage? highest churn rate? High • Uncover Why they are Profitable Value • Is it High usage? How high is the churn rate? Middle • Determine which customers have potential to move up in profit.Middle 70% • Learn why they have lower margins Low • What is the churn rate?Bottom 10% Negative • Determine why they are negative value? 69
    69. 69. Accumulated Profit CurveA smaller percentage of your customer base is driving themajority of the profit. Migrate / Spend Keep & Shift to to keep migrate lower cost May be some of your largest customers Source: Gartner 70
    70. 70. Customer Profitability – The Life Cycle Acquisition Development Retention Churn/ Win- backNet Margin 71
    71. 71. Customer Profitability – 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 72
    72. 72. Customer Profitability – 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 73
    73. 73. Customer Profitability – 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 74
    74. 74. Customer Profitability – 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 75
    75. 75. Examples of Elements Affecting Customer Lifetime Value (CLV) - +(1) – Start-up of customer case(2) + fee income(3) – Continuing “cost to serve”(4) + Sale of additional products, “cross-selling”(5) – Advice Opportunities(6) – Marketing Through Customer‟s “Lifetime”(7) – Initiatives for retention of customer(8) – Influence others to churn= Customer lifetime value = CLV 76
    76. 76. How Is Competitive Advantage Created? Retention of the profitable customers Profitability Realization of the per customer customers’ potential Profitability Pricing of per product products/services and service considering profitability Development of newInsight in profitable productsprofitabilitythrough the Profitability Restructuring ofentire per market organization accordingbusiness segment to the segment’smodel profitability Make processes more efficient 77
    77. 77. Broaden Use for Profitability Metrics Once Profitability Metrics are calculated, the information can be leveraged across departments.Sales/Marketing• Offer Strategies Finance • Improved information for business• Promotion strategies analysis• Product portfolio management • Interconnection rates• Customer segment management • Cost control• New product intro • Process improvement• Channel effectiveness • Proper capital investment• Marketing direction Operations • Network optimization strategy • High cost process that needs to be reengineered • Utilization review • Infrastructure decisions • Optimize contact center strategies • Prioritize service treatments 78
    78. 78. Case Study: Verizon• Business Issue: Needed to analyze and understand shared expenses and overhead costs such as sales, engineering, and product development and meaningfully allocate those costs to the products sold and the sales revenue generated. Lacked right information and ability to do this on a timely basis “The cost and profitability initiative at MCI, and subsequently Verizon• Results/Benefits Business, supported by SAS Activity-Based Management, • Created P&Ls used to hold business provided key information in the leaders accountable for financial results transition of the business through by sales-channel segment profitability. acquisition and continues to • Expanded model to calculate more provide value that only cost and detailed profitability information on a profitability insight can deliver.” monthly and annual basis in: • Channel profitability, Customer segment profitability, Product or service profitability, Cost of business processes and Cost of shared services (such as IT)
    79. 79. Social Network Analytics“Social network analysis views social relationships in terms of network theory, consisting of nodes (representing individual actors within the network) and ties (which represent relationships between individuals).” Customer Social Predictive Segmentation Modeling Profitability & Network LTV Analytics Data Data Data Metadata Quality Integration Model ERP CRM EDW Online Social Other Data Sources
    80. 80. T-Mobile & Social Network Analytics http://youtu.be/Orr5lzLul8c?hd=1 81 Copyright © 2011, SAS Institute Inc. All rights reserved.
    81. 81. What is Social Network Analysis (SNA)?Overview The practice of identifying and measuring the relationship structure that exists between individuals within a social network.. This is most commonly used in the telecommunications industry where it is used to understand the links formed through voice, text and picture messaging. Individuals can be differentiated by the number and nature of their connections to others. 82
    82. 82. Business Value of SNASocial Network Analysis provides both a deep andbroad understanding of customer behavior. Whencombined with proven advanced analytics this enablesthe development of many powerful business focusedsolutions which help build strong and measurablecustomer advocacy. 83
    83. 83. SNA Based Business SolutionsBelow are examples of business solutions that rely on SNA: Social Network Propensity Scores - eg. improve churn prediction, average $, or customer advocacy. Persistent Individual Identification - Enables multi-SIM use, prepaid SIM recycling, and improved churn reporting. Customer, Household, and Life-Stage Segmentation. Customer Value - Understood in terms of relations and influence upon purchase behaviour of others. Acquisition Of High ARPU Prospects - And competitor customers through referral and highly targeted viral campaigns. Agile Campaigns - Insights and data provided which indicates when specific customer actions occur (enables a shift from monthly routine of mass campaigns). 84
    84. 84. Better Customer Understanding Most mobile providers perform customer segmentation, usually based upon call usage behavior or profile. Also predictive analytics to identify churn risk customers. Social Network Analysis reveals relationships and measures the influence customers have upon others. Churn Churn 2 85
    85. 85. Agile Customer Management Social Network Analysis is used to develop event-based campaigns and customer management strategies. Churn is an example; - contact friends immediately after a customer churns. SNA enables a move from traditional monthly batch analytics. Churn High Risk High Risk Churn 2 High Risk High Risk High Risk High Risk 86
    86. 86. Community Detection In addition to better understanding of individual customers SNA can be used to create or enhance household segmentation by identifying communities. The purpose of Community Detection is to identify the strongest relationships within the customer base. 2 87
    87. 87. Communities Detection The allocation of communities need not be mutually exclusive. These can be hierarchical communities which may first represent immediate family and then extended friendships. Supporting hierarchical communities is essential when solving conflicting business goals such household segmentation (which requires close communities) or viral marketing (which requires larger communities for optimum results). 2 88
    88. 88. Household Segmentation Because Community Detection finds the natural social groupings of all customers it is a powerful mechanism for Household Segmentation. Using analytics to combine information about social links with, for example, customer age, gender or location it is possible to accurately infer household type and customer life-stage.  Male & Female Postpaid (age 40 yrs)  Single Prepaid (age 19 yrs)  Mature Family Segment 2  Different Surnames  Matching Address  Age Group 25-30 yrs  Young Couple Segment 89
    89. 89. Know True Customer Value Customer advocacy is critically important in today‟s marketplace. SNA is used to track adoption and spread of new services and identify key influencers. Community detection is used to attribute $$$ value that is not visible at an individual customer level. Households that span competitor networks indicate share-of-wallet. I‟m a high value 2 customer on a competitor network I just bought I influence my partner‟s an Android I‟m a highest purchasing decisions… It looks cool, value customer now I might buy an Android.. 90
    90. 90. Not All Links Are Created Equal Customer relationships can be distinguished and analyzed by  Their strength (e.g. number of calls)  Their interval class (e.g. days between calls) 2 We chat everyday We chat everyday I‟m a high value We discuss sports customer on a scores on the weekend competitor network 91
    91. 91. Identification Of Roles Customers are categorized by links and position within the entire social network (in some cases roles are relative to the community).  Leaders: Highest number of links and centrality measures.  Followers: Similar to Leaders, to a lesser extent. Usually directly connected to a Leader.  Marginals: Similar to Followers, but not often connected to a Leader.  Outliers: Few links and often low centrality measures.  Bridges: Connect Communities and isolated individuals 2 92
    92. 92. Improve Retention of “Leaders”Capability Marketing Action BenefitIdentify highly Target retention More efficient targeting ofconnected strategies to marketing spend.“Leaders” within “Leaders”. Reduced attrition / improvedcustomer base. retention. Communications rapidly spread throughout the customer base. 2 93
    93. 93. Improve Retention of “Followers”Capability Marketing Action BenefitIdentify Implement highly Minimise viral churn.“Followers”. reactive event-driven Efficient timing & targetingKnow when a retention strategies for of marketing $‟s.“Leader” churns. “Followers” at-risk Reduced attrition / improved retention. Churn Churn 2 High Risk High Risk High Risk High Risk 94
    94. 94. Use Viral Effect For Acquisition & GrowthCapability Marketing Action BenefitIdentify influential Target cross / up-sell Understand acquisition"Early Adopters" & strategies to "Early value of campaigns and“Bridges” to better Adopters". Leveraging indirect outboundunderstand viral viral power of “Bridges” communications. Improveadoption of new to competitor customer timing & relevance of newproducts. bases. offers. 2 95
    95. 95. Persistent Customer Identification By examining a customer‟s position within the social network it is possible to infer persistent identification even after churn, mobile service number, or address changes. This approach can, for example, also be used to identify Prepaid SIM recycling and multi-SIM use. Accurate reporting of monthly „Churn & Adds‟ numbers are critical to correct strategic decision making. 96
    96. 96. CLA In Banking / Financial Services Data is different and does not capture a true social network Pseudo-social network (PSN) where consumers are linked if they transfer money to the same entities Effectiveness of targeting network neighbors can be attributed to similarity rather than to social influence 97
    97. 97. SNA in banking / financial servicesAn analytic framework that enables marketing analysts to enhancecustomer insight by identifying and incorporating consumer purchasingsimilarities and their strength in profiling and segmentation. Use SNA derived variables to generate superior customer understanding and improve campaign effectiveness:  Target those individuals that are strongly connected to key individuals Enhance campaign management process by introducing new consumer variables and methodology (e.g. campaign selection and response attribution). Data can be exploited in a privacy-sensitive way, since it is not necessary to know the identities of the connected consumers or the institutions that connect them 98
    98. 98. Oi & Social Network Analyticshttp://youtu.be/1O75bcTpb_M?hd=1 99 Copyright © 2011, SAS Institute Inc. All rights reserved.
    99. 99. Saturday Afternoon Preview• Know how to gain efficiencies and boost ROI with marketing automation.• Recognize the keys to achieve real-time relevance in both inbound and outbound channels.• Understand how to plan, prioritize and execute to maximize profits.
    100. 100. Orchestration & Interaction Marketing Decisions Multi-Channel Campaign Management Real-Time Decisions Marketing Optimization Case Studies Information Management & AnalyticsERP CRM EDW Online Social Other Data Sources
    101. 101. Questions?

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