Building the CRS Online Community
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February 25, 2005
“Beyond RFM”February 2005 DMFA Roundtable
Kevin Whorton, Direct Response Fundraising ConsultantCatholic Relief Services
Modeling: Theory and Reality
Theory: RFM Has WeaknessesLimited use of information: gift history onlyOmits demographics, psychographicsMostly provides decision support for marginal audiencesNo prioritization: R<F<M? … M>R=F? … M=R=F?Uses language of discrete, not continuous variables
Reality: RFM Works Well Enough Most TimesHouse file mailings—very strong, long histories House file telemarketing Could be improved but little incentive to do so:
» Can only be so efficient on mailings
» Beyond some point minimizing cost may minimize revenue
CRS:Current PracticesLimitationsFuture Applications
Applying Techniques at CRS
House File Model Use• Target Analysis Group: affinity/other gift behavior
Powerful to screen the 50% waste, including lapsed in acquisition now outperforms a dedicated lapsed campaign
• Genalytics: full-file scoring by half-decileFull house file, by future probability of giving
Acquisition Model• Selection criteria used during list selection
Zip models and “Catholic Finder”
• Full acquisition modelCreated household database from 45 million past contacts File scoring after merge purge: typical 20% suppression
Expanding Demographic Data
Distinguishing between donors: marketing vs. DM• Profiling new donors: 62 years avg vs. “youth movement”• Drawing linkage between awareness and donation• Understanding relationship: first gift ongoing behavior
We now use data to categorize donors• By appeal: emergency, region, program area• By vehicle: catalog, calendar, newsletter, TM, e-• By timing: seasonality• By preferences: limited mailing, no mail, no TM
Especially critical, post-Tsunami Data used to drive frequency
• Segmenting beyond RFM, going deeper into filesOften based on Interest Codes (next slide)
Example: Interest CodesUsed for Inclusions/Exclusions
Interest Code Description Interest Code count ID
Fiscal Year 2003 counts FY2003 734794
Delivery Point Validation DSF1 663157
Emergency Donors ED 363374
Newer Donors ND 324288
Renewal Donors RD 103340
Catalog Overlay CAO 87914
Premium Donors PRD 82993
Hispanic Indicator HISIND 67420
Wooden Bell Donors WBD 65556
Telemarketing Donors TD 65489
Score 0-4 0S4 56137
Score 95-99 95S99 55684
Calendar Donors CD 49677
Low Dollar Donors LD 13843
Entire file• Coded with a mix of
Donor Service & DM codes
• Simplify our house file selection
• Behavior captured to:- simplify ad hoc analysis- extend RFM- develop profiles- crosstab “donor types”
Other (Non-Modeling) Data
Simulations: gift arrays• Demographic overlays beyond DM: mid-level PG, MG
Age & wealth trump typical RFM giving behavior Mail sensitivity analysis
• Finding correlation between total mailings, gifts per donor Goal: maximize satisfaction without sacrificing revenue
Maintaining "interest codes" library of preferences Merge-purge with greater control
• Moved internally, staff analyst & FirstLogic software Conversion analysis
• List life-cycle: tables showing LTV (2-year) by acq. list• Target Analysis: benchmarking/comparisons
Other Data: Research
Donor research• Analyzing share of market/share of wallet• Knowing what else donors give to
Qualitative/focus groups• Package/teaser/copy testing • Underlying motivations/drivers/perceptions
Market research• Measuring aided/unaided recall, aficionados• Cluster models (segmentation studies) • Positioning studies (branding, relative message) • Competitive intelligence
Limitations: Analyzing Results
Most segmentation build to drive reporting• Pledgemaker report writer• Occasional use of Business Objects/SAS for ad hoc
Most segmentation is by discrete RFM buckets• Segmentation continues in the "normal way"
$25-$49, 0-12 months, F1+ $50-$99, 0-12 months, F1+$100-$249, 0-12 months, F1+
• Extending universe based on interest codes • Applying excludes
Record types (PG, Corp, Spanish-language, Religious Orders) Individual preferences (1, 2, 6, 12x preferred mail schedules) Mutual omits from overlapping camapigns
Best Intentions: Other Applications
Original goal in 2003: "family of models"• Telemarketing
• Early warnings of defection
• Lapsed donors
• Upgrade potential: mid-level programReasons for using:
• High cost per contact/good stewardship
• Sensitivity to complaintsPredict positive and negative outcomes Complaints seen as proxy for reduced lifetime value
Reasons not pursued• Not a $$ limitation, but rather management time
Goal/Vision
Want to be more "donor focused" • Finding constructive ways to avoid treating all donors the
same
• RFM often treats as identical: $500 donor, every year, 1 gift very end of year$500 cumulative donor, monthly frequency$500 first-time donor
• Goal: sufficiently flexible systems to tailor contact sequenceHard to implement CRM systems to reduce costs/maximize
efficiency & donor satisfaction
Sample: Donor-focused Grid
Use the gift they give to this appeal
Consider lifetime seasonal giving activity
Sample Analysis: Years on File
• Graphing non-linear relationships: finding “sweet spots”
Analysis: Lifetime Avg. Gift
• And knowing when the relationships really are linear/predictive.
Quick Guide to Models/Techniques
Guide to Models
• Three major families: Parametric Methods
• Linear regression, logistic regressions Recursive Partitioning methods (i.e. CHAID)
• Tree diagrams—easier to see interaction between variables. Most time consuming.
Non-parametric methods
• Neural networks, genetic/natural selection algorithms
• Artificial intelligence—"learning models" used at CRS
• Results are far more important Results: more a function of data quality than technique
Source: Target Analysis Group: Jason Robbins, statisticians
Sophisticated Techniques, Simple Answers
Cross-tabulations Shows simple relationships between variables, typically
percentages "Grids" allow easy audience selection, but complex to review Correlation: relationships between two variablesRegression: X=f(x,y,z) or Membership=function of dues level, presence of
competition, penetration, service mix R2 “explains” relationship between one variable and everything
driving it• Projections and forecast models• Logistic regressions: “yes/no” predictions • Logarithmic: coefficients=percentage contribution• Dummy variables: use to measure seasonality, time trends,
effects of one-time shifts
Introducing Linear Regression
Linear regression defined
• PR=aR+bF+cM+dO
• In English, “predicted revenue is a function of donor’s recency of giving, frequency, agg value, other stuff"
• Model for a renewal program: with avg response rate 4.25%, avg gift $36.25, revenue/name mailed of $1.54:
Equation
Months since
last gift
Total gifts, relevant
time period
Aggregate total value of
gifts
Indexed wealth of
donor
Avg Value 6.5 2.4 $156 85
Coefficient -0.068 0.215 0.00465 0.0087
Contribution to RNM -$0.44 $0.52 $0.73 $0.74
1.54=-0.068(6.5) + 0.215(2.4) + 0.00465(156) + 0.0087(85)
Confusing, but potential "Holy Grail" tool for your house file program
More Sense from Regressions
Confusing exposition: briefly assume you know what this means! Alternative functional forms tell you more For example: logarithmic transformations of each independent
variable (R, F, M, Wealth) put them on equal "dimensions" Average values will no longer make sense, but coefficients will! In last equation:
0.182 Months Since 0.215 Total Gifts0.300 Aggegate Gifts 0.305 Indexed WealthMeans each value represents percentage contribution to results!!
Note on last slide, many combinations of specific values would add to the average revenue per donor
» The formula "predicts" it, because it represents the "best fit" expressing relationship between the dependent and independent variables
This is an overly simple equation: it assumes only RFM plus wealth» Often there are other hidden values that also influence» Equation level metrics (R-squared) and variable-level (t ratios) tell you
the degree of prediction and statistical significance
What You Should Know as a User
When these techniques are used …• Generally statistical software runs these: SAS at CRS• Fast process: takes less time to run than to explain• Key: some staff need to understand what the results mean
Younger staff are better, esp. if exposed to it in college—"data kids"
Once a formula is derived, the real output is a scored file• "Plotting the residuals" means taking best fit, multiplying through• Output can be indexed/scored according to predicted Rev/M etc.
Predict. Month Tot gifts Tot value Wealth
Frequent donor, low gift, well-to-do $3.66 2.5 10 $240 65
Lapsed occasional donor, big wealthy giver $3.89 13 2 $750 98
Periodic giver, average gift, well-to-do $1.58 6 4 $120 65
First-time donor, modest means $0.47 2 1 $32 28
This typically falls on a curve, with an index ranging from 0-99th percentile of predicted revenue per name mailed
Acquisition Modeling at CRS
Before: List Effectiveness
• Targeting based on list effectiveness• Focused on “finding more lists like these”
Campaign 1
Campaign 2
New Approach
• New analytic system to drive programsBuild prospect universe of likely respondersOverlay with demographic and census dataCatalog interaction over time by personDevelop insights over time with modelingSelect/suppress based on predicted behavior
After: Prospect Behavior
• Targeting based on prospect behavior• Focus on “finding more people like this”
+
Marketing History
Census & Specialty
Demographics
+
List & Campaign Attributes
Preparation
• Develop infrastructure• Collect and organize data
• Response behavior retained• Other available information added
Prospect Universe
ExternalDemographics
Data
FocusedLists
Prospect Lists
Matchcode and Geography
Campaign Data
Applying Analytics to Discover Patterns
Prospect Universe
Suppression List
Model Ready Data
Proliferation of Models
ActionableResults
StructuredData
Equation
ƒ(x) = * +Equation
ƒ(x) = * +
The Final Solution
Mailing Universe
Suppress
To Mail
Production
Acquisition Promotions
ƒ(x) = * +
CatholicDemographics
Donations
Census Demographics
Data Mart
Suppressed Mailing
Universe
Sample Scoring Equation
Results/Benefits
• Focused models on top segments rather than entire universeSuppressed mailing to bottom of prospect universe Discovered significant numbers of new prospects similar
to existing donors
• Savings more than paid for entire analytics program by:Removing bottom portion of prospect universe that
provides negative ROI Providing greater understanding of and insight into
characteristics of prospects and donors
