Building the crs online community
1 / 28

Building the CRS Online Community - PowerPoint PPT Presentation

  • Uploaded on

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

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'Building the CRS Online Community' - thuong

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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

    • 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


    • Shows simple relationships between variables, typically percentages

    • "Grids" allow easy audience selection, but complex to review

      Correlation: relationships between two variables


    • 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













    Matchcode and





    • Develop infrastructure

    • Collect and organize data

    • Response behavior retained

    • Other available information added

    Applying analytics to discover patterns









    Applying Analytics to Discover Patterns



    Model Ready Data

    Proliferation of Models





    Suppression List

    The final solution




    The Final Solution


    Scoring Equation




    Data Mart














    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