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
Information Services for Merchants Enabling decisions at the speed of consumer behavior
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
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
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
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
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
Online Apparel sales are approaching 20% share of Apparel market
Source: MasterCard SpendingPulse September 2011
Big Data in Action: Understanding Economic Drivers Example – US Sector Performance Source: MasterCard SpendingPulse September 2011
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
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.
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
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.
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
Big Data in Action: Using Trends to Plan Ahead Example - US Daily Total Retail sales during holiday season Source: MasterCard SpendingPulse September 2011
Big Data in Action: Using Trends to Plan Ahead Example - US Online daily total retail sales during holiday season Source: MasterCard SpendingPulse September 2011
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.
In addition, the retailer needed a more strategic way to predict future shopping trends.
Provide insight into sales trends across sectors trended over time, by month and by year on a monthly basis.
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.
Survey data is inferred behavior but does not equate with real behavior
Spend with you: $35.44 Spend with you: 3 times per year
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:
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
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.
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.
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
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
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
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)
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?
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
Use transaction data insight to identify strong shopping days for targeted offers
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
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
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.
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
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
Impact on spending across merchant locations over time
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.
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
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
Holistic Customer View Drives New World SuccessAchieving effective acquisition and loyalty marketing Market
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
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