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Building the CRS Online Community. “Beyond RFM” February 2005 DMFA Roundtable Kevin Whorton, Direct Response Fundraising Consultant Catholic Relief Services [email protected] Test #1 Email Campaign. February 25, 2005. Modeling: Theory and Reality. Theory: RFM Has Weaknesses

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building the crs online community

Building the CRS Online Community

  • “Beyond RFM”
  • February 2005 DMFA Roundtable
        • Kevin Whorton, Direct Response Fundraising ConsultantCatholic Relief Services
        • [email protected]

Test #1 Email Campaign

February 25, 2005

modeling theory and reality
Modeling: Theory and Reality
  • Theory: RFM Has Weaknesses
      • Limited use of information: gift history only
      • Omits demographics, psychographics
      • Mostly provides decision support for marginal audiences
      • No prioritization: R<F<M? … M>R=F? … M=R=F?
      • Uses language of discrete, not continuous variables
  • Reality: RFM Works Well Enough Most Times
      • House 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
applying techniques at crs
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-decile
      • Full house file, by future probability of giving
  • Acquisition Model
    • Selection criteria used during list selection
      • Zip models and “Catholic Finder”
    • Full acquisition model
      • Created household database from 45 million past contacts
      • File scoring after merge purge: typical 20% suppression
expanding demographic data
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 files
      • Often based on Interest Codes (next slide)
example interest codes used for inclusions exclusions
Example: Interest CodesUsed for Inclusions/Exclusions

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
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
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
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
Best Intentions: Other Applications
  • Original goal in 2003: "family of models"
    • Telemarketing
    • Early warnings of defection
    • Lapsed donors
    • Upgrade potential: mid-level program
  • Reasons for using:
    • High cost per contact/good stewardship
    • Sensitivity to complaints
      • Predict positive and negative outcomes
      • Complaints seen as proxy for reduced lifetime value
  • Reasons not pursued
    • Not a $$ limitation, but rather management time
goal vision
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 sequence
      • Hard to implement CRM systems to reduce costs/maximize efficiency & donor satisfaction
sample donor focused grid
Sample: Donor-focused Grid

Use the gift they give to this appeal

Consider lifetime seasonal giving activity

sample analysis years on file
Sample Analysis: Years on File
  • Graphing non-linear relationships: finding “sweet spots”
analysis lifetime avg gift
Analysis: Lifetime Avg. Gift
  • And knowing when the relationships really are linear/predictive.
guide to models
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
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 variables

Regression:

  • 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
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:

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
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
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.

This typically falls on a curve, with an index ranging from 0-99th percentile of predicted revenue per name mailed

before list effectiveness
Before: List Effectiveness
  • Targeting based on list effectiveness
  • Focused on “finding more lists like these”

Campaign 1

Campaign 2

new approach
New Approach
  • New analytic system to drive programs
    • Build prospect universe of likely responders
    • Overlay with demographic and census data
    • Catalog interaction over time by person
    • Develop insights over time with modeling
    • Select/suppress based on predicted behavior
after prospect behavior
After: Prospect Behavior
  • Targeting based on prospect behavior
  • Focus on “finding more people like this”

Census & Specialty Demographics

List & Campaign Attributes

Marketing History

+

+

preparation

External

Demographics

Data

Campaign

Data

Prospect

Universe

Focused

Lists

Matchcode and

Geography

Prospect

Lists

Preparation
  • Develop infrastructure
  • Collect and organize data
  • Response behavior retained
  • Other available information added
applying analytics to discover patterns

Equation

Equation

ƒ(x)=

ƒ(x)=

+

+

*

*

Applying Analytics to Discover Patterns

Structured

Data

Model Ready Data

Proliferation of Models

Actionable

Results

Prospect

Universe

Suppression List

the final solution

ƒ(x)=

+

*

The Final Solution

Sample

Scoring Equation

Acquisition

Promotions

Donations

Data Mart

Census

Demographics

Suppress

Catholic

Demographics

To

Mail

Production

Mailing

Universe

Suppressed

Mailing

Universe

results benefits
Results/Benefits
  • Focused models on top segments rather than entire universe
    • Suppressed 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
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