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Harder-Working Models: Scoring Consumers To Achieve Multiple Business Objectives
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Harder-Working Models: Scoring Consumers To Achieve Multiple Business Objectives

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  • Welcome!I am Mary-Jo Checco, VP of Consumer Marketing at Alliant.I will be the moderator for today’s session on “Harder Working Models”More than anything else, I hope that everyone who attends this session leaves here having learned three things:1. Modeling makes money for marketers. Marketing is expensive. More than ever before, it make sense for every marketer to have an arsenal of tools that optimize every marketing program.2. Be ready for unexpected results. I suppose this is another way of saying Test, Test, Test. Sometimes one insight in a modeling project can lead you in directions you hadn’t anticipated going. Both of our presenters today experienced early results in their modeling programs that made them re-think what they were doing 3. As all of you know, whether you are on the bleeding edge of digital marketer or you’re a died-in-the-wool direct mail maven, your are constantly generating new data and new insights about your customers, your offers and your products. Working with a statistician, or a team of statisticians, you become immersed in your data. And that helps you develop the insight you need to create more powerful, more successful marketing programs. In acquisition, in retention, and in reactivationLifecycle Modeling & Scoring to Achieve Multiple Business ObjectivesWith the right model, business objectives can be metModeling can benefit every lifestage of a consumer This session will show you some business objectives that have been met by implementing hard working models
  • So I am going to leave you with one last thought.With data as your rocket fuel, and a solid modeling strategy as your booster engine, You can apply the same insight and strategy across marketing channels.This is a key take-away, because I have seen many companies become intimidated by new channels, and fail to move with their customer base.With a solid strategy, and hard working models, you can enter any channel with the confidence that you are maximizing response and managing risk.
  • Please mute or turn off cell phones Social: Please feel free to post your impressions on twitter and linked inAlthough we will reserve time at the end of the session for Q+A, we want to keep this as interactive as possible. Feel free to ask questions during the presentation. Just raise your hand and I will ensure that you are addressed.If I see that we are running into a time problem. I’ll cut-off discussion and refer the question to the time at the end.
  • Different approachesPCH – payment score plus own resourcesMeredith uses internal resources plus third parties that can fill in where they are weak
  • Bio to come…
  • Bio to come…
  • Over the past couple of years, our strategies have focused on these three goals
  • The first step was to incorporate online order option within direct mail package. Tested various options within the package and envelope to get winner that didn’t depress response
  • The benefits to Meredith included cross sell opportunities both on the order and confirmation pages, 100% paid with credit card, no billing costs, and future cost savings with less paper transactions
  • With changes to the direct mail package we saw a steady increase in online order rate over a 12 month period. Online responders were not surprisingly younger with higher incomesInitially we didn’t see a big impact in response but as we made bigger, bolder pushes online there was a decrease.
  • What can be done on the modeling front to improve the online order rate while maintaining response?Our typical modeling exercise is to determine which lists/customers to promote (usually segmented into market segments of a regression model)
  • We spent about 3 months doing analysis before deciding on a testing strategy. Initially our efforts focused on segmenting customers at the list/model level.
  • Within a regression and comparing responders to non-responders, online response impact is too small to noticeably improve online order rate.
  • In order to get enough online responders to build a regression, I decided to use an entire direct mail campaign’s worth of data.
  • Took all the responders from the September 2011 direct mail campaign and compared the online responders to the offline responders via a regression (online orders/total orders). Online order = 1; offline order = 0.
  • In reviewing the model with the marketing team, a suggestion was made to use the model to determine online messaging. Rather than apply at a list level to decide who should be mailed, apply across the entire campaign to decide what package a customer should receive.
  • This slide shows how the model validated
  • The key marketing takeaway here is that by using minimal messaging to the lowest group, we got a 7% bump in response over the control.93 index did not outweigh the additional cost of the flyer or additional online orders generated
  • The 133 index can be compared to the 114 index shown on a previous slide when comparing 15-20 results to 1-20 results. The 114 is when mailed $2 off package; 133 is when mailed for fastest service package.

Harder-Working Models: Scoring Consumers To Achieve Multiple Business Objectives Harder-Working Models: Scoring Consumers To Achieve Multiple Business Objectives Presentation Transcript

  • Harder-WorkingModelsScoring to optimizeperformance inacquisition, reactivation andretention
  • Models Make Money For Marketers• Models make money for marketers• Data is the rocket fuel of successful marketing programs• Be prepared for unexpected results• Test more than once: it’s worth it!
  • Good segmentation tools driveprofitability across the business acquisition direct mail alternate mediaretention online store email reactivation upsell/cross-sell
  • Houskeeping• Cell phones off or silencer• Social media – Alliant twitter handle: @alliantinsight – DMA hashtag: #dma12• Ask questions at any time
  • Harder-Working Models Today’s SpeakersMary-Jo Jennifer KeithChecco Krob Bergendorff
  • Improving Segmentation in the Mail and Online Keith BergendorffAssistant Vice President of Analytical Services It’s all about winning.TM
  • PCH: Business Challenges• Convert prospects into long-term customers• Order response and payment rates for prospect mailings in decline• Attaining profit goals requires shifting consumers to high-margin merchandise offers• Extend PCH business across media channels
  • Mail ProspectOrder Screening
  • Challenges • All PCH mail offers are Bill-Me so managing payment risk is essential • PCH needs included: –Ability to qualify responders for Bill-Me offers –Improvement in pay rates on “sub-standard” lists • Tests of scoring lists prior to mailing ineffectiveMail Prospect Order Screening
  • Solution • Required a tool to improve order fulfillment decisions • Tested and rolled out with custom Alliant profitability model applied at order stage • Combined profitability score with re-developed PCH internal payment model to create a “Behavioral Profitability Score”Mail Prospect Order Screening
  • Order ScreeningAnd Offer PassSegmentation YES ? Incoming PCH Internal Internal Prospect Orders Score Score>A NO Alliant On-Demand Scoring ? Combined Fail Score Algorithm Pass NO >X YES
  • Results: “Mail Prospect” Behavioral Profitability Score • Substantial increase in prospect mail volume –Back-end score allows expansion into mail lists and segments not previously viable due to low pay • Substantial increase in new paid buyer generation • No significant deterioration in pay-up rate! • Combined score generates significantly higher margin dollarsMail Prospect Order Screening
  • “One-Timer”Segmentation
  • Challenge • Speed is a key factor in successfully re-promoting new mail buyers – Response declines rapidly with time elapsed before first customer mailing • Was not viable to re-mail new buyers until first payment received (6-12 weeks after order) • Internal payment model anemic due to lack of predictive data for new buyers“One-Timer” Segmentation
  • Solution • Reapply Alliant profitability scores already appended to all responders • New internal model uses Alliant scores to create a new One-Timer Behavioral Profitability Score • One-Timer score allows for effective payment segmentation before receiving payment • Huge improvement vs. previous internal model using only transactional data and demographics“One-Timer” Segmentation
  • Results: “One-Timer” Behavioral Profitability Score • Enables mailing the highest scoring half of new One-Timers without waiting for payment –Reduced interval between first order and first customer package from 6-12 weeks to 3 weeks • Significant increase in overall order response and conversion to Repeat Buyers • Lift in order response and future value more than compensates for decrease in overall payment rate“One-Timer” Segmentation
  • Online Prospect Scoring
  • Challenges • Payment rates for online orders abysmal • Publishers unhappy with paid subscription rates • Can’t offer merchandise and make a profit • Insufficient internal data to identify who is appropriate for Bill-Me offers • Order screening improved pay rates, but it’s not good marketing to solicit orders and then reject them!Online Prospect Scoring
  • Solution • Apply scores from same Alliant profitability model in Real Time at sweeps registration • Combine profitability scores with internal Real Time model to create “Ensemble Model” • Use Ensemble Model score to segment offers –“Prime” names get merchandise offers –“Restricted” names get magazine offers –“Lows” receive partner offers onlyOnline Prospect Scoring
  • Results “Ensemble” Behavioral Profitability Score • Targeted offers in online path and email deliver: – Much improved profitability for merchandise sales – Much improved pay rates for subscription sales – Minimal rejection of orders on back end – “Lows” routed immediately to partner email programs, improving partner revenue • Quality of new acquisition sources can quickly be evaluated via average profitability scoreOnline Prospect Scoring
  • Alliant Profitability Scores + House Data = Increased ROI • Mail Prospect Order Screening • “One-Timer” Segmentation • Online Prospect ScoringOnline prospect scoring
  • Multi-ChannelResponse Optimization Jennifer KrobSenior Manager – Database Marketing
  • Business Challenges Direct Mail Promotions• Acquisition of subscribers to maintain rate base
  • Business Challenges Direct Mail Promotions• Migrate consumers to transact online• Develop new sources of magazine subscribers• Maintain/improve response
  • Direct Mail Promotions Online Incentives Order Form Outer Envelope
  • Direct Mail Promotions Online Order Benefits• Cross sell• 100% paid with credit card• No billing costs
  • Direct Mail Promotions Package Change Results• 50% increase over 4 campaigns• New audience mix• Not a large impact on response
  • Direct Mail Analytics• Can analytics and data mining have an impact?• Determine which customers/lists to promote
  • Direct Mail Analytics List Level• Models focused on offline response compared to total mailed• Test on one model universe• Changes to target online response:  Model with profit as target  Add online cross sell revenue  Add online profit factor
  • Direct Mail Analytics List Level• Online response impact too small compared to offline response in regression
  • Direct Mail Analytics• To increase the online responder impact, use entire campaign’s worth of data• Model Development: – Online responders compared to offline responders – Online order rate – Similar to pay-up
  • Direct Mail Analytics• Better Homes & Gardens September ’11 Acquisition Campaign Order Form Copy
  • Direct Mail Analytics• Factors increasing online order rate: – Younger age – Online order activity from multiple sources – Presence of email address – Less magazine transaction history
  • Direct Mail Analytics Gains Chart Results Quartiles Response Index Online Order Index1-25% (Top) 86 24626-50% 90 12551-75% 102 5776-100% (Bottom) 123 14
  • Direct Mail Analytics• Difficult to maximize both targets and still be profitable.
  • Direct Mail Testing• Use model to determine online messaging• Test in March 2012 campaign• Scored all house database names• Divided into Rank Groups 1-5, 6-9, 10- 14, and 15-20
  • Direct Mail Testing Ranks 1-14 $2 off w/ flyer Test Ranks 15-20 No onlineRank Group incentive Control $2 off
  • Direct Mail Testing – Control Offer Online Messaging:
  • Direct Mail Results – Control Total v. Rank Groups Actual Actual Online Model Response Order Model Online Index to Index to Response Order Ranks Total Total Index Index 1-5 87 259 86 246 6-9 93 107 90 125 10-14 105 45 102 57 15-20 114 24 123 14 Total: 1-20 100 100 100 100
  • Direct Mail Testing – Ranks 1-14
  • Direct Mail Testing – Ranks15-20
  • Direct Mail Results Test Package v. Control Response Online Order Index to Index to Online Messaging Ranks Control Control $2 off flyer/order form 1-14 93 426 For fastest service 15-20 107 12• Lowest ranks more responsive if no online incentive
  • Direct Mail Analytics• Key takeaway: – Optimized response for a group of customers who were least likely to order online
  • Direct Mail Next Steps Put the Model to Work• Optimize response: – Continue minimal online incentive on bottom segments. – Similar testing on other magazines.
  • Direct Mail Next Steps Put the Model to Work• Optimize response/online order rate – Testing model in conjunction with pre and post email blasts. – Testing on top segments to further optimize response and online order rate.
  • Direct Mail Next Steps Put the Model to Work• Focus on ways to increase response for those more likely to order online. Data Analytics Offer Offer Optimization
  • Meredith and Alliant• Mining lower segments of house models to maximize response. – Particularly successful on cancels• Provide new sources of names. – Alliant’s data helps maximize use of database• Cut-back lower segments of Alliant model to increase profitability.
  • Harder-Working ModelsQuestions & AnswersMary-Jo Jennifer KeithChecco Krob Bergendorff
  • Harder-Working Models Thank You!Mary-Jo Jennifer KeithChecco Krob Bergendorff