Lifecycle Modeling to Increase Response Payment and Retention

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  • A single modelcan improve marketing results at every customer touchpointImprove acquisition ROI -- offline and onlineIncrease promotable universeIdentify more targeted price points for offers/premiumsIncrease retention/renewal ratesImprove overall profitability
  • So let’s get started. I’d like to introduce our first presenter: of Martha Stewart Living Omnimedia
  • Business Situation:Gain optimal performance across all acquisition and retention marketing efforts Solution Overview:MSLO developed a “Lifecycle Mega-Model” built on an independent source of consumer transactional data Enables access to predictive consumer behaviors across a range of marketing applications
  • Business Situation:Gain optimal performance across all acquisition and retention marketing efforts Solution Overview:MSLO developed a “Lifecycle Mega-Model” built on an independent source of consumer transactional data Enables access to predictive consumer behaviors across a range of marketing applications
  • So let’s get started. I’d like to introduce our first presenter: of PCH
  • Business Situation:Gain optimal performance across all acquisition and retention marketing efforts Solution Overview:MSLO developed a “Lifecycle Mega-Model” built on an independent source of consumer transactional data Enables access to predictive consumer behaviors across a range of marketing applications
  • Business Situation:Gain optimal performance across all acquisition and retention marketing efforts Solution Overview:MSLO developed a “Lifecycle Mega-Model” built on an independent source of consumer transactional data Enables access to predictive consumer behaviors across a range of marketing applications
  • Lifecycle Modeling to Increase Response Payment and Retention

    1. 1.
    2. 2. Lifecycle<br />Modeling <br />To Increase Response, Payment and Retention<br /> Jennifer SchultiesMartha Stewart Living<br />Keith BergendorffPublishers Clearing House<br />Mary-Jo Checco Alliant<br />
    3. 3. 1<br />A single predictivemodel<br />can improve marketing results at every customer touchpoint<br />
    4. 4. acquisition<br />direct mail<br />alternate media<br />retention<br />reactivation<br />online store<br />email <br />upsell/cross-sell<br />The right segmentation tool identifies the behaviors that drive profitability across the business<br />
    5. 5. Martha Stewart Living Omnimedia<br />Jennifer Schulties<br />Senior Marketing Manager<br />
    6. 6. Martha Stewart Living Omnimedia<br />Business Challenges<br />Poor payment on sweeps<br />Deteriorating bottom-tier response on direct mail<br />Shifting renewal behavior<br />Unfavorable insert card/onsert metrics<br />
    7. 7. Martha Stewart Living Omnimedia<br />Daily Sweepstakes Payment<br />
    8. 8. Martha Stewart Living Omnimedia<br />Daily Sweepstakes Payment<br />Cons<br />Less targeted<br />Low payment on sub offers<br />Pros<br />Partner-driven<br />Exposes brand to new audiences<br />Traffic-driver for site<br />Promotes trial issue of magazine/ generates gross subs<br />
    9. 9. Martha Stewart Living Omnimedia<br />Daily Sweepstakes Payment<br />Solution:<br />Use “Mega-Model” to find “good” payers<br />What does a person who pays for a magazine look like?<br />
    10. 10. Martha Stewart Living Omnimedia<br />Daily Sweepstakes Payment<br />Mega-Model Insight:<br />Have they paid for other products?<br />How recently?<br />What kinds of products?<br />
    11. 11. Martha Stewart Living Omnimedia<br />Daily Sweepstakes Payment<br />Mega-Model Insight:<br />45% of sweeps responders were deemed “good payers” from the model<br />“Good payers” pay twice as well as the “bad payers” overall<br />Payment Rates per Billing Effort<br />
    12. 12. Martha Stewart Living Omnimedia<br />Direct Mail Response<br />
    13. 13. Martha Stewart Living Omnimedia<br />Direct Mail Response<br />Pros<br />32-pages of content is compelling<br />Free trial offer yields high gross orders<br />Premium and combo offer on payment pushes high payment<br />Cons<br />Payment is delayed<br />Lowest tier not performing well enough, but mail volume needed<br />
    14. 14. Martha Stewart Living Omnimedia<br />Direct Mail Response<br />Solution<br />Use Mega-Model to find the worst responders before they respond…<br />…and make them an offer they can’t refuse.<br />
    15. 15. Martha Stewart Living Omnimedia<br />Direct Mail Response<br />We’re talking about magazine subscriptions, of course…<br />Use Mega-Model score groups to strategically increase response by:<br />Lowering prices<br />Adding premiums<br />Shortening terms<br />And if that doesn’t work…<br />
    16. 16. Martha Stewart Living Omnimedia<br />Direct Mail Response<br />Use score groups to selectively cut costs<br />No premium<br />Shorter bill series<br />Less expensive direct mail package<br />
    17. 17. Martha Stewart Living Omnimedia<br />Direct Mail Response<br />Removed premium (cost cutting) and took $1 off subscription price (incentive) on modeled names<br />Test of bottom tier saw impressive gross response<br />Payment suffered with lack of premium<br />
    18. 18. Martha Stewart Living Omnimedia<br />Direct Mail Response<br />Additional Applications for Mega-Model<br />Cut bottom tier from mail altogether<br />Mine marginal compiled lists or other large universes for best names<br />
    19. 19. Martha Stewart Living Omnimedia<br />Renewal Behavior<br />
    20. 20. Martha Stewart Living Omnimedia<br />Renewal Behavior<br />Challenges<br />Everyday Food offered as a “combo” subscription with Martha Stewart Living<br />Everyday Food shares in the revenue, but is perceived as FREE<br />Even though acquisition offer is a combo, the titles are renewed individually<br />Renewals of EF are good, but at a discounted price<br />
    21. 21. Martha Stewart Living Omnimedia<br />Renewal Behavior<br />Solution<br />Use Mega-Model to:<br />Segment out best prospective renewers<br />Offer a slightly higher price to help bridge the gap in revenue<br />
    22. 22. Martha Stewart Living Omnimedia<br />Renewal Behavior<br />Mega-Model Test Results<br />Tested a 60% increase in price to best prospects (from $5 to $8)<br />Higher price increased revenue by 11% vs. control, but reduced response by 30%<br />Testing continues as we determine what our end-goals are (revenue vs. orders)<br />
    23. 23. Martha Stewart Living Omnimedia<br />Insert Efficiency<br />Challenges<br />Declining response in subscription cards<br />Rising paper costs<br />Still a large enough source of subscriptions, so don’t want to cut completely<br />
    24. 24. Martha Stewart Living Omnimedia<br />Insert Efficiency<br />Mega Model Application<br />Take inserts OUT of the issue copies going to LEAST responsive names<br />
    25. 25. Martha Stewart Living Omnimedia<br />One Model, Many Solutions,<br />Higher ROI<br />Daily Sweepstakes Payment<br />Direct Mail Response<br />Renewal Behavior<br />Insert Efficiency<br />
    26. 26. Publishers Clearing House<br />Keith Bergendorff<br />Assistant Vice President of Analytical Services<br />
    27. 27. Publishers Clearing House<br />Business Challenges<br />Convert prospects into long-term customers<br />Order response and payment rates for prospect mailings in decline<br />Attaining profit goals requires shifting consumers to high-margin merchandise offers<br />Extend PCH business across media channels<br />
    28. 28. Publishers Clearing House<br />Mail Prospect<br />Order Screening<br />
    29. 29. Publishers Clearing House<br />Mail Prospect Order Screening<br />Challenges<br />All PCH mail offers are Bill-Me so managing payment risk is essential <br />PCH needs included:<br />Ability to qualify responders for Bill-Me offers<br />Improvement in pay rates on “sub-standard” lists<br />Tests of scoring lists prior to mailing ineffective <br />
    30. 30. Publishers Clearing House<br />Mail Prospect Order Screening<br />Solution<br />Required a tool to improve fulfillment decisioning<br />Tested and rolled out with custom Alliant profitability model applied at order stage<br />Combined profitability score with re-developed PCH internal payment model to create a “Behavioral Profitability Score” <br />
    31. 31. Publishers Clearing House<br />Mail Prospect Order Screening<br />
    32. 32. Publishers Clearing House<br />Mail Prospect Order Screening<br />RESULTS:“Mail Prospect” Behavioral Profitability Score<br />Substantial increase in prospect mail volume<br />Back-end score allows expansion into mail lists and segments not previously viable due to low pay<br />Substantial increase in new paid buyer generation<br />No significant deterioration in pay-up rate!<br />Combined score generates significantly higher margin dollars than single internal scoring solution<br />
    33. 33. Publishers Clearing House<br />“One-Timer”<br />Segmentation<br />
    34. 34. Publishers Clearing House<br />“One-Timer” Segmentation<br />Challenge<br />Speed is a key factor in successfully re-promoting new mail buyers<br />Response declines rapidly with time elapsed before first customer mailing<br />Was not viable to re-mail new buyers until first payment received (6-12 weeks after order) <br />Internal payment model anemic due to lack of predictive data for new buyers<br />
    35. 35. Publishers Clearing House<br />“One-Timer” Segmentation<br />Solution<br />Reapply Alliant profitability scores already appended to all responders <br />New internal model uses Alliant scores to create a new One-Timer Behavioral Profitability Score<br />One-Timer score allows for payment segmentation<br />Huge improvement vs. previous internal model using only transactional data and demographics<br />
    36. 36. Publishers Clearing House<br />“One-Timer” Segmentation<br />RESULTS:“One-Timer” Behavioral Profitability Score<br />Enables mailing the highest scoring half of new One-Timers without waiting for payment <br />Reduced interval between first order and first customer package from 6-12 weeks to 3 weeks<br />Significant increase in overall order response and conversion to Repeat Buyers <br />Lift in order response and future value more than compensates for decrease in overall payment rate<br />
    37. 37. Publishers Clearing House<br />Online Prospect<br />Scoring<br />
    38. 38. Publishers Clearing House<br />Online Prospect Scoring<br />Challenges<br />Payment rates for online orders abysmal<br />Publishers unhappy with paid subscription rates<br />Can’t offer merchandise and make a profit<br />Insufficient internal data to identify who is appropriate for Bill-Me offers<br />Order screening improved pay rates, but it’s not good marketing to solicit orders and then reject them!<br />
    39. 39. Publishers Clearing House<br />Online Prospect Scoring<br />Solution<br />Apply scores from same Alliant profitability model in Real Time at sweeps registration<br />Combine profitability scores with internal Real Time model to create “Ensemble Model” <br />Use Ensemble Model score to segment offers<br />“Prime” names get merchandise offers<br />“Restricted” names get magazine offers<br />“Lows” receive partner offers only <br />
    40. 40. Publishers Clearing House<br />Online Prospect Scoring<br />RESULTS:“Ensemble” Behavioral Profitability Score<br />Targeted offers in online path and email deliver:<br />Much improved profitability for merchandise sales<br />Much improved pay rates for subscription sales<br />Minimal rejection of orders on back end<br />“Lows” routed immediately to partner email programs, improving partner revenue<br />Quality of new acquisition sources can quickly be evaluated via average profitability score<br />
    41. 41. Publishers Clearing House<br />Profitability Scores +<br />House Data =Increased ROI<br />Mail Prospect Order Screening<br />‘One-Timer’ Segmentation<br />Online Prospect Scoring<br />
    42. 42. Lifecycle Modeling to Increase Response, Payment and Retention<br />Questions & Answers<br />Mary-Jo Checco<br />Jennifer Schulties<br />Keith Bergendorff<br />
    43. 43. Lifecycle Modeling to Increase Response, Payment and Retention<br />Thank You!<br />Mary-Jo Checco<br />Jennifer Schulties<br />Keith Bergendorff<br />

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