Customer Equity-Driven Marketing Mix Decisions at Wachovia Bank MSI Conference on Marketing Metrics and Financial Performance December 6 8, 2006 Dan Thorpe SVP, Director of Statistics & Modeling at Wachovia Carl Finkbeiner EVP, Product Development at TNS North America
Outline 1. Introduction 2. Marketing Mix Models 3. Response/Value Definition 4. Marketing Mix framing at Wachovia 5. Data/Model Development and Cycles of Models 6. Main Insights/Impact 7. Next Steps 2
Who is Wachovia? Who we are: 554 billion in assets 4 th largest U.S. bank Largest Bank in South East 3 rd largest U.S. full-service brokerage firm 5 th largest Wealth Management Company 97,000 total employees 3
Strong National Franchises Key Business Lines General Bank Retail, small business & commercial banking Scope 11 Million Retail & Small Business Households Capital Management Retail brokerage & asset management Wealth Management Private banking, trust & commercial insurance 237 Billion Assets Under Management 39,000 Clients Corporate & Investment Banking Middle market, cash management 3,800 Corporate Client Relationships 4
Marketing Journey What is the health of our Brand? Is advertising working? Are we optimizing our marketing spend? Commitment to Invest Ad Spend $ Centralized Marketing Spend Marketing Spend Governance LOB Partnerships Marketing Mix Modeling Ad Copy Testing Brand Equity Tracker 2005 How do we establish our new brand? How much do we spend on advertising? Decentralized Marketing Spend LOB s 2000 1997 5
Project Objectives and Scope The Retail Marketing ROI project has three primary objectives: 1. What is the relative return on each major component of the Retail Marketing spend? 2. What is the overall return on Wachovia s Retail Marketing spend? 3. Is there a prospective mix of retail marketing spend that would have a higher return than the current mix? Create a Marketing Mix Model The model development was based on 112 weeks of data related to Retail Marketing spend from February 2003 through March 2005 A Cross-Sectional Time Series was formed by breaking out each week by the 40 DMA s within Wachovia s footprint (Small Business and non-controllable accounts were excluded from this analysis) 6
Marketing Mix Modeling In Banking CPG Years of extensive Marketing Mix Modeling (e.g., Coca Cola, Procter & Gamble, Kraft) Single product category Simple purchase transaction Sources of detailed competitor data Financial Services Relatively recent and very different from CPG Entire line of business Complex spend transactions on related products & services Detailed, identified customer data none on non-customers Ad Spend Trade Promotion Consumer Promotion Price Stores & Facings Seasonality CPG Model Sales Volumes (unit, $) Ad Spend News Coverage Targeted Direct Mail Yield Rates Branches & ATMs General Economy Banking Model Acquisition Rates Retention Rates Revenue Sources 7
Outcomes Being Modeled Multiple Outcomes of Interest to Wachovia Customer Counts (customer = household with multiple accounts) Acquisition New households Retention/Attrition Lost households Revenue By 3 general product categories Customer Equity Combines Them CE = long-term profitability of current and future customers Modeling Forecasting Ad Spend News Coverage Targeted Direct Mail Yield Rates Branches & ATMs General Economy Acquisition Attrition Revenue by 3 products Customer Base Growth NPV Profit Customer Equity 8
Marketing Mix Model Variables Potential Marketing Drivers Direct Control Drivers Advertising TV, print, and radio Internet Advertising Direct Mail Volumes Prospect and customer Sales employees per branch Branches per capita Rate Ratios Wachovia s APY rates relative to competitors for CDs, Money Market, and Interest Checking Sponsorships High Indirect Control Drivers News Coverage Net Effect Wachovia and combined competitors Customer Satisfaction with financial centers Brand Equity Corporate Marketing Control Economic Drivers Economics including: - CPI - Unemployment - Housing starts - Household income - 10 year T-Bill rates Low 9
New Relationships Lost Relationships Deposit Revenue Credit Revenue Investment Revenue Driver Impact on Expanded Retail Marketing Mix Marketing Drivers Retail Marketing Mix Models (20) Affluent Direct Control Indirect Control Economic Upper Mass/ Lower Affluent Mass Market Unknown 10
Modeling Approach Synergies among Drivers Diminishing Returns of Marketing Reduce Outlier Effects Lagged Effects of Past Marketing Log-Log Cross- Sectional Time Series Model DMA Differences in a Common Model Adjusts for Multicollinearit y Expected Directions of Effects 11
Modeling Process Regular Data Updates with Additional Months Three major updates since start of project 12 separate data sources with separate owners Constant data checking, cleaning and tweaking New Questions = Model Structure Changes Example: Ad Spend for TV, Print, Radio, Online Original Current Network TV Cable TV Print Radio Online Network, Cable TV, Print, Online split by: National Local TV, Print, Radio split by: Retail Bank ads Corporate ads 12
Drivers Relative Impact Index DRIVERS (Sample) Advertising News Coverage Direct Mail Rates Branches per Capita Sales Staff per Branch Customer Satisfaction 100 80 60 40 20 0 Index: Most powerful driver = 100 13
10 Year Customer Equity 10 Year Customer Equity Different Budget and/or Mix? $X Budget Increase Budget Allocation Reduce Increase Current Reallocation $X Budget Decrease Current Allocation Reallocation MIX OPTIONS: TV, Radio, Print, Online, Mail, other Current Reallocation 14
3 Yr New HH s Annual Budget 3 Yr CE Marketing Mix Optimization Example -- What is the optimal media mix given a specific marketing budget and what is the impact of that mix on Customer Equity and New HH s? Customer Equity and New HH Media Mix Optimization Recommendations CE Impact + 19% Baseline New HH Optimization CE Optimization Baseline Optimal CE New HH Impact + 30% A B C D E F G H Marketing Drivers I J K L M N O Baseline Optimal New HH's 15
10 Year Customer Equity ($B) Is The Current Budget Level Optimal? Marketing Spend Effects Current Spend Current Budget 6 0 Annual Marketing Spend ($M) 16
Reinvest Savings ROI Validation Test Three month test in Florida Measures: impact of advertising on new checking account households Test Market/ Mix Change Control Market No Change West Palm SPOT TV - X % Ft. Myers No Change Orlando Tampa Jacksonville SPOT CABLE TV SPOT RADIO - X % No Change 17
Application: Checking Advertising ROI TIER 3 Low Deposit Market Share High Deposit Market Share B Low Potential Low Branch Share x Population Growth Circle size: market size in Deposit dollars High Branch Share x Population Growth Color: local advertising impact brown = high-medium; orange = low; yellow = none; gray = no information Intersecting Axes: mean values ( TIER 2 A Medium Potential High Potential TIER 1 18
Next Steps 2007 Models DMA specific Branding Sponsorships Other Divisions More sophisticated CLV estimates 19
Collaboration of the bank, consulting, and academia produced creative solutions for Wachovia Marketing Mix Modeling in Financial Services is unique Not category specific, mergers & acquisitions, economic turns, lack of variation/collinearity different controllable drivers and dependent variables, detailed customer data Customer Equity can be forecast from a suitably structured Marketing Mix Model Model provides valuable recommendations and directional insight for: Marketing budget resource allocation recommendations to maximize either acquisition or long-term profits Total marketing budget, based on maximizing CLV 20