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November 8-10, 2004 - Hilton New York

Maximizing Online Conversions November 10, 2004 Hitendra Wadhwa. November 8-10, 2004 - Hilton New York. Focus of Presentation “Few SKU” businesses. Subscription/membership services Dieting Matchmaking Financial services Credit cards Online banking Home equity loans Lead generation

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November 8-10, 2004 - Hilton New York

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  1. Maximizing Online Conversions November 10, 2004 Hitendra Wadhwa November 8-10, 2004 - Hilton New York

  2. Focus of Presentation “Few SKU” businesses • Subscription/membership services • Dieting • Matchmaking • Financial services • Credit cards • Online banking • Home equity loans • Lead generation • Automotive • Business-to-business • Destination products • TiVo • Total Gym • Video Professor Examples You are trying to drive customers to perform a specific action (or one out of a small set of actions)

  3. Lost Lost Lost Lost Lost Lost Typical customer flow for a “few SKU” business Online Ads & Search Customers, Revenues Popup Email Landing page Signup/ Purchase page Cross/up-sell page OfflineMarketing

  4. Lost Lost Effect of improving conversion rate Online Ads & Search Customers, Revenues Popup Email Landing page Signup/ Purchase page Cross/up-sell page OfflineMarketing

  5. Lost Lost “All roads lead to Rome” • 1-3 pages drive all the value • Tiny conversion improvement at these pages can have substantial financial impact • $10 million media spend • Conversion 2% --- > 2.2% • $1 million media cost saving • Incremental revenue increase could be $2-5 million • Why? Because all media spending is directed to these pages Effect of improving conversion rate Online Ads & Search Customers, Revenues Popup Email Landing page Signup/ Purchase page Cross/up-sell page OfflineMarketing

  6. Three steps to Conversion Nirvana Conversion strategy Testing A/B -- Multi-cell -- Structured Financial objective Offer/ creative Flow Real-time optimization Targeting

  7. Conversion strategy • Select from alternative objectives • # conversions • $ revenue per visit, $ profit per visit • $ lifetime value • How to handle downstream (post-conversion) metrics • Analyze correlation with upstream metrics • Does this relationship change based on media source or offer? • Where appropriate, use correlated upstream metrics instead Financial objective

  8. Lifetime value considerations will influence your results Product value Lifetime Conversion Total value per (initial sale) (future) value rate 1,000 visitors Initial sale focus PRODUCT 1 $25 - 2% = $500 PRODUCT 2 $40 - 1.5% = $600 Lifetime value focus PRODUCT 1 $25 $50 2% = $1,500 PRODUCT 2 $40 $50 1.5% = $1,350 12

  9. Conversion strategy Look at all options for improvement - but prioritize based on likely impact Marketing elements Product, Price Offer/ creative Increasing Impact Promotions, Incentives Copy, Layout Graphics

  10. Conversion strategy • Track conversion performance at each stage of flow • Identify key leakage points to help prioritize conversion improvement efforts • Investigate alternative flow designs • Having a landing page vs. going directly to signup page • 1-page vs. multi-page signup process • Directed vs. branching flows • Use of pop-ups / pop-unders • Most effective site content may depend on front-end creative Flow

  11. Conversion strategy • Keep it simple initially, and then evolve the level of targeting sophistication • Stage 1: One-size-fits-all • Stage 2: 3-6 segments based on type of media source • Stage 3: Add other customer variables to the mix • Repeat vs. new visitor • Time of day, geography • Key sites, keywords • Other customer-specific information • Ultimately, you are likely to find 2-3 variables that give you most of the lift from targeting Targeting #1 #2 #3

  12. Testing A/B testing How it is done • Split visitors randomly into “Test” and “Control” groups (A vs. B) • Track conversions in each group • Rollout with the winner What to watch out for • Pick an adequate sample size • Sample size = p x (1-p) x 1.962 / Precision2 where p = average conversion rate expected • Example: • If p = 3.5%, and Precision = .2% (i.e., you want to be 95% confident that response rate is within 3.3% & 3.7%), then…Sample Size = 32, 437 for EACH of Test and Control groups • Extrapolate the results with care • Anticipate and proactively address customer reactions • Catalog co. • Online subscription service

  13. Testing Multi-cell testing How it works • Identify the different factors you’d like to test • Template • Offer • Price - $39.99 vs. 4 payments of $9.99 • Select a few designs (test cells) • Test these designs • Roll out with the winner Challenges you will face • What if some other combination of factors was superior? • You can’t test them all - that would be too many • 4 templates, 5 offers, 3 prices = 60 designs • What factors and levels are actually driving performance? • Is it the template? The offer? The way price is communicated?

  14. Testing Structured Testing How it works • Statistical “design for experiments” technique • Scientifically selects a small group of designs (test cells) to test • Once these cells are tested, the results are analyzed to compute performance estimates for each factor and level Benefits of structured testing • Get a performance estimate for ALL the designs - even though you test just a few • 10 designs instead of 60 designs • Identify which factors and levels are driving performance

  15. Structured Testing example Landing Page + Pop-up POP-UP LANDING PAGE • Site navigation tabs Logo Fixed Text • Promos • Scrollbar • Personalities • Events • Product graphic • About Us • 24/7 availability • Payment methods • Promotion Signup button

  16. Structured Testing example Factor levels

  17. Structured Testing example Test design

  18. Structured Testing example Performance • 2*2*3*4*6*4*2*3= 6,912 designs in total. This was reduced to 19 designs • Best-performing landing page estimated to perform 20-100% better than the average page

  19. Structured Testing example Relative performance

  20. Structured Testing: Strengths & limitations • Structured testing identifies the key design factors and levels that are driving campaign performance • However, which design factors are important - and which levels of these factors are winners - can vary. That is, performance is often unstable. • Across time • Changes in customer mix • Time-of-day/Day-of-week • Seasonality / shift in customer preferences • Burnout / overexposure • Competitive activity • Across traffic sources

  21. Real-time Optimization addresses the limitations of Structured Testing • To maximize campaign performance over time, it is critical to establish campaign design strategies that address these performance variations • Performance of factor-levels should be evaluated across time and traffic sources • Different campaign design strategies should be deployed based on the amount of performance variation identified across these dimensions DYNAMIC STRATEGIES Winning factor-levels vary across time • Eliminate consistent losers • Put other factor-levels into play & deploy real-time optimization • Use “champion-challenger” approach to continuously test and adjust mix of campaign design TARGETED STRATEGIES Winning factor-levels vary across traffic sources • Establish a traffic threshold for implementing targeted strategies • Two criteria - enough volume for statistical significance + enough volume to justify targeted effort • For each channel that meets the threshold, develop & implement targeted designs • For other channels, put multiple designs into play and use real-time optimization CORE STRATEGIES Valid across traffic sources and time • Re-test occasionally (e.g., every 3/6 mths)

  22. Example of unstable performance

  23. 1st half of campaign Example of unstable performance 2nd half of campaign

  24. Real-time Optimization • Continuously tests and learns - works automatically • Prunes poor performing designs • Pits “challengers” against “champions” • Swaps designs as soon as performance shift is detected Period 1 Period 2 Period 3 Challenger Champion 10

  25. Real-time Optimization - Example

  26. For a deeper dive… • Sample size, A/B testing • “The New Direct Marketing: How to Implement a Profit-driven Database Marketing Strategy” -- David Shepard & Associates, McGraw Hill, 1999 (from your favorite bookstore) • Structured testing • Article: “Boosting Your Mareting ROI with Experimental Design”, Almquist & Wyner, Harvard Business Review, October 2001 (from www.hbsp.harvard.edu) • Real-time optimization • Whitepaper: “Real-time Optimization: The Key to Unlocking Value from Interactive Marketing”, Delphinity (from me) • My contact information Hitendra Wadhwa Columbia Business School Marketing Division Uris Hall Room 512 3022 Broadway New York, NY 10027 (212) 854 8955 hitendra@columbia.edu -- or -- hitendra@delphinity.com

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