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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


  • Crs current practices limitations future applications

    CRS:Current PracticesLimitationsFuture Applications


    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.


    Quick guide to models techniques

    Quick Guide to Models/Techniques


    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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