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Looking Inside the Consumer Wallet Key Success Factors for Driving Loyalty in a Competitive Environment
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Looking Inside the Consumer Wallet Key Success Factors for Driving Loyalty in a Competitive Environment

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  • Enables predictive modeling and analysis of consumer spending behavior across industries and time for a variety of marketing and risk outcomesSeasonal and geographic (zip code level) spend data analysis including merchant industry and discretionary/non-discretionary codingAdvanced benchmarking capabilities
  • Transcript

    • 1.
    • 2. Information Services for Merchants
      Enabling decisions at the speed of consumer behavior
    • 3. Looking Inside the Consumer Wallet Key Success Factors for Driving Loyalty in a Competitive Environment
      Top Themes for Today:
      Big Data delivers key macro and micro business insights
      New world realities require new models
      Full-wallet, 360o view is the key to marketing success
      Bring it All Together – A Roadmap for Success
    • 4. Big Data Delivers Business Insights
    • 5. Effective Marketing Starts with Big Data
      Member
      Reported Data
      MasterCard Network Data
      Survey Based Data
      Third Party Data
      Real Consumer Behavior Based Data Sources
      Products & Tools Deliver Quick Insights
      Accelerant for growth
      • Early indicator of Sector and Channel trends
      • 6. Clear understanding of consumer behavior
      • 7. Competitive view at a location level
      • 8. Economic trends Competitive benchmarks Customer insights
    • Big Data in Action: Understanding Economic Drivers
      Example – 2011US Retail PerformanceOverall retail sales (ex auto) showing resilience through August of 2011
      The growth rates may decelerate into Q4 with a more difficult comparison environment and poor consumer confidence.
      Source: MasterCard SpendingPulse September 2011
    • 9. Big Data in Action: Understanding Economic DriversExample – US retail sales
      Economic conditions remain unfavorable for an aggressive expansion
      • Elevated total retail sales growth rates may moderate as we move through the rest of 2011
      • 10. Employment, housing and confidence have to improve for sustainable retail sales growth in 2012
      One of the largest domestic opportunities is to capture $ migrating from brick and mortar to the online sales channel
      • In Apparel alone over $8 billion will move from brick and mortar to online over the next three years
      • 11. Online Apparel sales are approaching 20% share of Apparel market
      Source: MasterCard SpendingPulse September 2011
    • 12. Big Data in Action: Understanding Economic Drivers
      Example – US Sector Performance
      Source: MasterCard SpendingPulse September 2011
    • 13. Big Data in Action: Understanding Key Channel Trends
      Example – Online Sales
      Online Apparel sales had year-over-year growth of almost 20% in August 2011.
      Online sales accounted for over 22% of Apparel sales on Tuesdays in August!
      Online sales only represent 7% of Apparel sales on Saturdays in August.
      Electronics was marginally above zero growth to halt a two month string of negative growth rates.
      Source: MasterCard SpendingPulse September 2011
    • 14. Big Data in Action: Monitoring Key Channel TrendsExample: US ecommerce sales shift online
      Source: MasterCard SpendingPulse September 2011
      Online retail sales growth has accelerated in August to almost 17% compared to August 2010.
    • 15. Big Data in Action: Monitoring Key Channel TrendsExample: US Apparel had 16.4% of sales online in 2010
      16.7% of Jewelry sales are now occurring online this is up from 12.8% in 2007.
      Source: MasterCard SpendingPulse September 2011
    • 16. Big Data in Action: Monitoring Key Channel TrendsExample - US Online Apparel sales as a share of Total Apparel sales has dramatic shifts throughout the week in August
      Source: MasterCard SpendingPulse September 2011
      • Tuesdays are consistently the busiest online day of the month.
      • 17. Weekends show the lowest penetration for online sales.
    • Big Data in Action: Daily ViewDaily online retail sales forecast for September 2011
      Source: MasterCard SpendingPulse September 2011
    • 18. Big Data in Action: Using Trends to Plan Ahead Example - US Daily Total Retail sales during holiday season
      Source: MasterCard SpendingPulse September 2011
    • 19. Big Data in Action: Using Trends to Plan Ahead Example - US Online daily total retail sales during holiday season
      Source: MasterCard SpendingPulse September 2011
    • 20. Insight into Action: Apparel Case Study
      Follow the Trends.
      The Challenge
      • A multi-channel apparel retailer seeking to better understand fluctuations in sales, including the trends impacting overall demand.
      • 21. In addition, the retailer needed a more strategic way to predict future shopping trends.
      The Solution
      • Provide insight into sales trends across sectors trended over time, by month and by year on a monthly basis.
      • 22. Forecasting reports predict key shopping days for total Apparel by channel.
      The Opportunity
      Insight into channel sales and trends pertaining to key shopping days used to inform future marketing initiatives and promotional calendars.
    • 23. New World Requires New Models
    • 24. Acknowledging the New World RealityDespite recent retail sales growth, we are in a more challenging time for acquisition and loyalty marketing
      Consulting
      Services
      Corporate
      Consumer
      • Fewer resources to drive higher returns
      • 25. Proliferation of noise at consumer level
      • 26. More choices and channels than ever
      • 27. More devices (research and buying)
      • 28. New global options
      • 29. More retail options
      • 30. More ways to access (social, etc.)
      Managed
      Services
      Information
      Services
    • 31. Current Models are Limited Use of demographic data and in-store spend is no longer enough given the marketplace changes
      An incomplete picture
      • In-store spend does not show category opportunity or competitive positioning
      • 32. Demographics only takes segmentation so far
      • 33. Survey data is inferred behavior but does not equate with real behavior
      Spend with you:
      $35.44
      Spend with you:
      3 times per year
    • 34. Success Requires A Whole Wallet ViewA whole wallet view and near real-time insight complete the picture
      Transaction data builds on your existing customer insight and provides a 360o, whole-wallet view to consumers
      Combined intelligence delivers:
      • Early Industry trends
      • 35. Near real-time channel trends
      • 36. 360 degree behavioral spend view
      • 37. Competitive performance at a local level
      Likely to spend in category in the next 3 months:
      4x your average customer
      Spend with you:
      $42.17
      Total Wallet:
      $16,273.81
      Spend with you:
      3 times per year
      Spend in industry:
      19 times per year
    • 38. Insight in Action: Retail Case Study Dive into the Channel.
      The Challenge
      A multi-channel retailer wanted to understand if customers shopping in-store exhibited different characteristics than those shopping online, and whether the two segments should be marketed to differently.
      .
      The Solution
      Analytics using transaction data to compare customer activity, by channel:
      • Identify unique purchase patterns filtered by seasonality to account for gift / holiday buying periods.
      • 39. Purchase Cluster scoring included to enhance insights at the channel level.
      The Opportunity
      Merchant’s marketing team can leverage full wallet view to better tailor their marketing messages against retention and acquisition strategies by channel.
    • 40. Whole Wallet, 360o View is the Future
    • 41. Looking Inside the Consumer Wallet
      Do You Know?
      US: 6 sectors drive 45% of card spending with Restaurant, Apparel, & Home Improvement ranking as top 3.
      New York, Chicago, Los Angeles and Philadelphia drove 20% of total retail spending in Q2 2011 (these top 4 drive 26% of Apparel spend).
      68% of restaurants guests visited 5 or more restaurant merchants in Q2 2011.
      Canada is the top cross border country for US based cardholders when purchasing Home Furnishings, Italy is #2.
      Source: MasterCard anonymized data warehouse 2011
    • 42. Looking Inside the Consumer Wallet
      Do You Know?
      Philly is the #4 DMA driving retail sales, ranking ahead of Dallas andSan Francisco
      Due to the proliferation of restaurants and frequency of visits, 84% of industry customers visited at least 5 different restaurant merchants from July ’10 – June ‘11
      International Competition and Opportunity - OutboundUS merchants are competing with the UK for Men’s and Women’s Apparel purchases, but with Canada and Italy for Children’s Apparel and Home Furnishing
      International Business Coming to the US – InboundSpend has declined marginally year-over-year, but Brazil, being less impacted by global economy, is growing +34% on average across industries
      Source: MasterCard anonymized data warehouse 2011
    • 43. Track Key Performance Indicators vs. CompetitionIdentify opportunities to focus on customer acquisition or basket size efforts
      Total Spend
      Customer Accounts
      Average Spend
      Average Transaction Size
      Average Purchase Frequency
      Increase Customer Acquisition and/or Average Spend to Drive Total Spend UP
      Increase Average Transaction Value and/or Average Purchase Frequency to Drive Average Spend UP
    • 44. Discretionary Income and Purchase Behavior
      Revisiting the thought around the correlation between income and spend
      Customer “A”
      % Total Spend Allocated to Discretionary Spend
      Traditional View
      Total Income = Traditional
      Behavioral view= New Approach
      Customer “B”
      % Total Spend Allocated to Discretionary Spend
      Discretionary Spend
      Traditionally: Discretionary Spend = F (Total Income) But: Higher income does not imply different spending preferences
      Now: Discretionary Spend = F (Actual Consumer Spending Behavior) Because: Discretionary spend in relation to total spend can reveal general spending preferences irrespective of other allocation of income (i.e. savings)
    • 45. Enhanced Targeting & SegmentationMapping the customer journey for more relevant offers, engagement and response
      Increased Engagement & Sales
      Purchase Sequence
      Recency & Frequency
      Spend Value
      Recent purchasing behavior and how often customer segment purchases
      Improve segmentation and lift overall customer engagement by adding another dimension to the equation
      Optimal time to reach customer segment with offer
      Customer segment average transaction size
      Purchase Sequence
      Purchase with
      You
      Where else are they engaged and how can this be leveraged?
      What up-sell/cross-sell opportunities exists?
    • 46. Whole Wallet: Achieving Sales LiftA whole-wallet customer view based on transaction data can dramatically improve marketing ROI
      • Append transaction data to existing customer profiles to identify high likelihood segments
      • 47. Use transaction data insight to identify strong shopping days for targeted offers
      • 48. Leverage transaction data insight to identify shopping behavior outside your store for high-value customers
      Lift from addition of near real-time transaction data
      CUSTOMER VALUE
      In-store, demographic and survey data
      CUSTOMER LIFE CYCLE
      ACQUISITION
      GROWTH & RETENTION
    • 49. Insight In Action: E-Commerce Case Study
      Sequencing analytics help determine customer promotions
      The Challenge
      • Lack of insight on the sequencing of actual customer online purchases across different industries
      • 50. Identify correlations that can drive insights to inform advertising strategy and planning
      The Solution
      A Customer Analytics purchase sequencing exercise to:
      • Illustrate the time elapsed distribution between purchases across two different industries in the online channel.
      • 51. Day and week- part analysis to help inform merchant’s internal marketing planning and external advertising strategies
    • Insight In Action: Retail Case Study
      Understanding customer migration helps refine site selection
      The Challenge
      • Lack of insight on the impact that new locations might be having on existing ones
      • 52. Insights were needed to drive and influence future site selection and format decisions
      The Solution
      A Customer Analytics solution that:
      • Analyzed customer migration from existing to new stores
      • 53. Impact on spending across merchant locations over time
      • 54. Correlation between new location cannibalization and proximity measures
      The Opportunity
      Creation of more stringent site selection guidelines around the proximity of new stores vs. existing ones.
    • 55. Insight in Action: Fuel Case Study
      Customer segmentation helps grow high-value customers
      The Challenge
      • Lack of insight into “best customer” definitions for marketing targets
      The Solution
      A Customer Analytics segmentation that included:
      • Engagement-level measures with the merchant
      • 56. Spending outside the merchant franchise that could fuel customer development strategies company-wide
      The Opportunity
      Provide a clearer understanding of purchasing dynamics across high value customer segments
    • 57. Holistic Customer View Drives New World SuccessAchieving effective acquisition and loyalty marketing
      Market
      • Industry trends
      • 58. Economic influences
      Customer
      • Whole wallet view
      • 59. Transaction-based purchasing trends
      Competition
      • Competitive share
      • 60. Performance benchmarks
      Opportunity
      • Underpenetrated segments, zip codes
      • 61. Share shift based on potential customer purchasing behavior
    • Realizing More Effective Marketing in the New World
      Big Data delivers business insight from macro economic to micro consumer purchasing trends
      Understand and acknowledge today’s new world realities and limitations of current data models
      Enhance current customer intelligence with transaction data for full-wallet, 360o understanding of the customer, market, competition, and opportunity
    • 62. Information Services Product SuiteTools, that range from macro to micro insights, to address your business challenges
      Insights
      My Industry:
      Understand sector trends
      and outlook
      My Markets:
      Measure competition and identify specific market challenges/opportunities
      My Segments:
      Reveal customer loyalty trends and spending behaviors
      My Customers:
      Improve customer acquisition and retention marketing
      Spending Pulse
      Benchmark Analytics
      Merchant Need
      Product
      Customer Analytics
      Customer File Enhancement;
      Acquisition Targeting
      Actions
    • 63. Andrew_Mantis@MasterCard.com