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An Insider's View of Direct Mail Marketing for the Credit Card Industry. David McPhillips, MH2 Direct Michael Walsh, JP Morgan Chase. Credit Card Industry. Mail Monitor ® Report US Credit Card Solicitations – 2003-2004 Mailing Trends January 2004.
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An Insider's View of Direct Mail Marketing for the Credit Card Industry David McPhillips, MH2 Direct Michael Walsh, JP Morgan Chase
Credit Card Industry Mail Monitor® Report US Credit Card Solicitations – 2003-2004 Mailing Trends January 2004 • Credit card solicitations reach an average of 65% of US households each month • Average credit card monthly solicitation volume = 350 million % Household Penetration Monthly Mail Volume (in Millions)
Credit Card Industry • Mature Market • Intro APR 0% for up to 18 Months • Risk Based Pricing/Lead with Platinum Offer • Rewards/Affinities/CoBrands • Industry Consolidation, 7-9 Major Issuers • B of A/Fleet • Chase/Bank One Mergers in Progress
New Account Acquisitions • Email and Telemarketing • Alternative Media - FSI, Inserts, Take Ones, DRTV • Portfolio Acquisitions • Prescreen and ITA Direct Mail-The Lifeblood of Acquisitions
Prescreen • Issuer sends Credit Criteria to 1 or more of the 3 Major Credit Bureaus, “Extracts” names to mail. Most major issuers have a productionalized monthly feed. • Offer must be Pre-Approved • Takes several months from time criteria is sent to Bureaus to time mailed • All Issuers have access to the same names
Prescreen (cont’d) • Batch Process with monthly Credit Score updates • Segmentation/Custom Models utilized to select best prospects • Critical but limited Component of New Account Stream • Credit Bureau Sourced Names allow Marketers to assess credit but not necessarily responsiveness
ITA • Invitation to Apply aka Non Pre-Screen • Issuers utilize ITA in conjunction with Prescreen to Maximize Account Goals • Source of Names is Marketing Lists • Legally restrictive in that Credit Information is reviewed only on Responders. However Credit Risk can be managed closely through Backend Criteria. • Reach additional/most responsive HH member
ITA (cont’d) • Names on Lists have proven to be more responsive-Purchasers, Subscribers • Lists can be Processed and therefore mailed more quickly • Take advantage of Bureau Fragmentation and Credit Score Fluctuations • More than 50% of credit card offers from top issuers are ITA
Client PS/ITA Comments • Direct mail remains the lifeblood of credit card acquisitions • A mix of Pre-approved and ITA works best – just look at the top issuers (January 2004 volumes, Source: Mail Monitor): • The top ten issuers contributed 85% of January credit card mail volume • The biggest issuers rely on the PS/ITA mix to achieve their volumes
Client Comments Contd. Pre-screen = Credit Driven • No idea if person is “marketing responsive” i.e. recently moved, recently made large purchases, etc. • Difficult to tailor a marketing message to a credit driven list • PS is a low responding, high approving channel ITA = Marketing Driven • Lists are “fresh” and needs and behaviors can be inferred • Marketing messages can be tailored – i.e., “Just moved? Use our card for new household purchases” PS = Low response x high approval = NRR x cost = $CPA ITA = High response x lower approval = NRR x cost = $CPA
Client Comments Contd. Speaking of cost: • PS is a straightforward cost structure channel – Credit Bureau Extract expense is consistent across time. • Utilize 1-3 credit bureaus • Typically large (10 million plus) volumes, driving strong economies of scale • ITA cost structure is more volatile • Upwards of 250 different lists, each priced slightly differently. (Base, reuse, net name arrangement, etc. • Test vs. rollout pricing may be different as well • Thus to be considered, any new list should not price itself out of consideration: • We’ll probably get most of your names from other sources • Little tolerance for price volatility in light of CPA constraints
Lists • Must be GLB and FCRA Compliant • Partner Files-Varies in volume by issuer • Types of Lists desired varies by Issuer • Professional/Credit Worthiness • Strong Responder Lists • Vertical/Single Source Lists of Purchasers and Subscribers • Multi-Sourced Compiled Lists – have evolved in the last 10 years to more closely resemble and perform like vertical lists
Lists Contd. • Always Interested in New Lists that have Transaction Data, Credit Proxy, New Entrants, Lifestyle, Life Changes • List does not have to be huge, 100K plus monthly is fine • Performance should be predictable and stable
Pricing • Test pricing must be low to entice Mailers and to allow for the inclusion of more list tests in each campaign • Continuation List cost per name is managed tightly, Base and Run Charges must be in line with these goals or a particular list is less likely to be ordered. • Net Name Arrangements must be low, issuers have detailed cleansing processes and may apply marketing models, no tolerance for situations where net obligations are not met
File Delivery • FTP is desired and may be required, costs need to be addressed • File Layouts are unpredictable and issuers would like to specify desired layouts – compliance from Service Bureau would be perceived as a value add. • Timing is critical: • Consistency of monthly update schedule • Mailers should choose cutoff dates based on optimal updates for most lists. • File Quantities must be Predictable and delivered as ordered – however some fluctuation is inherent and unavoidable • List Rental Agreements are a growing industry concern
Database Processing • Credit Bureau Extract and Lists are Converted, List Codes assigned and Date Received Stamped • Address is Standardized and Cleansed • NCOA is performed-Demographics Applied • Merge/Purge is performed, applying suppression files and building households • Household select – Household member(s) to mail are selected • Marketing selects-Times Mailed, Demographic models • Mailbase Finalized
Results • Lists are evaluated primarily based on Net Response and Cost Per Account • Gross Response must be strong, many of the top performing lists have average approval rates and many of the worst lists have very high approval but low response • Most issuers have counter offers and can make highly responsive lists work
List Cost/Analysis Example • Names Input 200,000 • Names Mailed 100,000 • Base Cost $60, Run Charge $5 • List Cost $6,500, CPN: 6.5cents (assumes no invalid names) • Gross Response 1.5%-1,500 • Approval Rate 30%- 450, NRR: .45% • Production/Postage .25cents-$25,000 • Back End $6.00-$9,000 • Total Costs: $40,500, CPA: $90.00
List Cost/Analysis Example 2 • Names Input 200,000 • Names Mailed 100,000 • Base Cost $47, Run Charge $3 • List Cost $5,000, CPN 5cents (assumes no invalid names) • Gross Response 1.5%-1,500 • Approval Rate 30%-450, NRR: .45% • Production/Postage: .25cents-$25,000 • Back End $6.00: $9,000 • Total Costs $39,000, CPA: $86.67 • In a 10,000,000 mailing: Savings of $150,000
Summary of Roles • List Broker- Must be expert in list properties, know what’s working for the Issuers, Understand GLB and FCRA, Provide best price possible, analyze results, modify list selections, have deep knowledge of how the lists are processed. Understand the Offer.
Summary of Roles • List Manager- The best list managers have a detailed understanding of the composition of names on their lists and work with Brokers to identify segments that work best for the issuers. The Managers should ensure that the List Datacards are explicit and comprehensive.
Summary of Roles • Card Issuer- Share as much information as possible with Brokers. Provide at least Indexed Results. Sample files of actual results are very useful. Should also be flexible to carry flags through process so that results can be analyzed by segments. • Other Critical Players-Service Bureau, DataProcessor, Creative Agency, Production
Conclusion • Credit Card Industry continues to rely on Direct Mail to sustain their Acquisition business • By utilizing both ITA and Prescreen mailings, they can maximize their new account acquisition goals • They will continue to rely on quality lists that are reasonably priced as a major source of prospect offers