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Donor Profiles and Data Analytics 101

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This presentation, delivered by InfoCision Chief Marketing Officer Ken Dawson during InfoCision's 2010 Fundraising Summit, explores how demographic and psycho-graphic data and business intelligence …

This presentation, delivered by InfoCision Chief Marketing Officer Ken Dawson during InfoCision's 2010 Fundraising Summit, explores how demographic and psycho-graphic data and business intelligence helps organizations better understand donors to receive the highest return on investment.

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  • As a second step, we studied various economic indicators to determine if they had an impact on giving.
    We found that Household Income showed the strongest correlation to giving.
    We used this information to derive variable gift asks.
    Other indicators we looked at were net worth, home values, education, and zip level income percent.
    It is my belief that income is the best indicator due to the nature of our business. We are asking for a monetary response in a matter of 5 seconds. People will quickly think about how much money they have readily available to give. It is also my belief that net worth is a better indicator for direct mail as people do not have to make a split second decision and as such think about what they can afford in a broader scope.
  • In analyzing results, we found these 5 buckets worked well for our variable gift asks.
  • Transcript

    • 1. DONOR PROFILES & DATA ANALYTICS 101 The Who, What, When, Where & Why of YOUR Donors
    • 2. 2 Business Intelligence Group • Dedicated marketing and consulting group. • Currently purpose driven to deliver the right message at the right time the right way. • Using higher levels of personalization in direct marketing. • Dedicated IT group. • Creates internal as well as external products and services. • Manages and oversees InfoCision Consumer Database.
    • 3. 3 InfoCision Consumer Database •A nationally compiled list of marketing and demographic data. •Data is updated every two months. •Data Base details: •205 million records •110 million households •80 million phone numbers •56 million distinct phone numbers
    • 4. 4 InfoCision Consumer Database Information in the database Demographic Who am I? Transactional What have I done? Psychographic What do I do?
    • 5. InfoCision Consumer Database Fields Sample • Age • Income • Length of residence • Level of education • Family position • Net worth • Voter party • Mail order buyers/responders • Financial services information • Internet shoppers • Religion • Ethnicity • Language preference • Country of origin
    • 6. 6 Business Intelligence Services • We are able to turn our Business Intelligence into actionable results by tying the information from our Consumer Database into: • Segmentation Strategies • Data Overlays • Predictive Models • Profiles • Inscription • P.U.R.L. • Lifetime Value Studies • Market Mapping • Campaign Enhancement • Marketing Tests • List Development • Analytic Studies/Research • Data Warehousing • Targeted Messaging • One-to-One Direct Mail • Inbound Routing Strategy
    • 7. 7 Profiles and Modeling Modeling and analytics enhances the efficiency and effectiveness of marketing endeavors , particularly: Current donor Retention New donor Acquisition Up-SellUp-Selling and Cross Selling
    • 8. 8 Profiles and Modeling Define the current donors with profiling Segment the current donors Model the current donors to target for cross selling and acquisition Step 1 Step 2 Step 3
    • 9. 9 Define the current donors with profiling Profiles and Modeling • Matching your current donors against the 165 attributes in the database allows you to know who your donors are.
    • 10. 10 Application of Profiles and Models to Donors • How do we leverage what we now know about our donors and customers? • Predict lapsing donors • Target donors for sustainer efforts • Target donors for major gifts/bequests • Target donors for special event/volunteerism • Build predictive models for acquisition of new donors
    • 11. 11 Profiles and Modeling • Using data to segment files to develop marketing strategies and variable offers allows for improved response rates. Segment the current donors
    • 12. 12 • Problem • Donations and response rates were flat. • Solution • Profile current donors. • Use income and home value indicators to drive segmentation for variable offers. • Lower gift asks to lower incomes and home values to drive up response rates. • Higher gift asks to higher incomes and home values to drive up average sale. • Bottom Line • Call results showed positive response rate growth in the low group (+18%) and positive average gift (+$12) in the high group. Profiling - Case Study #1
    • 13. 13 Profiles and Modeling • Next step is a predictive model to rank a prospect list on their propensity to donate. Model the current donors to target for cross selling and acquisition Decile Total Record Universe % of Total Records Cumulative % Records Total Customers % of Total Customers Cumulative % Customers Decile Response Rate Cumulative Rate Marginal Lift Cumulative Lift Most Likely 1 5,000 10.0% 10.0% 1,512 13.4% 13.4% 30.24% 30.24% 134 134 2 5,000 10.0% 20.0% 1,447 12.9% 26.3% 28.94% 29.59% 129 132 3 5,000 10.0% 30.0% 1,369 12.2% 38.5% 27.38% 28.85% 122 128 4 5,000 10.0% 40.0% 1,304 11.6% 50.1% 26.08% 28.16% 116 125 5 5,000 10.0% 50.0% 1,259 11.2% 61.3% 25.18% 27.56% 112 123 6 5,000 10.0% 60.0% 1,190 10.6% 71.9% 23.80% 26.94% 106 120 7 5,000 10.0% 70.0% 1,049 9.3% 81.2% 20.98% 26.09% 93 116 8 5,000 10.0% 80.0% 877 7.8% 89.0% 17.54% 25.02% 78 111 9 5,000 10.0% 90.0% 681 6.1% 95.1% 13.62% 23.75% 61 106 Least Likely 10 5,000 10.0% 100.0% 555 4.9% 100.0% 11.10% 22.49% 49 100 50,000 100.0% 11,243 100.0% 22.49% XYZ Corporation Gains Table Based on Development Sample Overall Results
    • 14. 14 •Problem • Client wanted to acquire new donors. •Solution • Build a predictive model scoring current donors. • Based on scoring, rank order prospects on their likelihood to respond. Modeling - Case Study #2 Decile Records Customers RR% Average Score BP Variance 1 5,000 1,512 30.24% 25.19 775.40 2 5,000 1,447 28.94% 24.56 645.40 3 5,000 1,369 27.38% 24.22 489.40 4 5,000 1,304 26.08% 24.00 359.40 5 5,000 1,259 25.18% 23.75 269.40 6 5,000 1,190 23.80% 23.54 131.40 7 5,000 1,049 20.98% 23.31 -150.60 8 5,000 877 17.54% 22.92 -494.60 9 5,000 681 13.62% 22.39 -886.60 10 5,000 555 11.10% 21.67 -1,138.60 Total 50,000 11,243 22.49% 188.44 0.00 Top Test Cume 15,000 4,328 28.85% 24.66 636.73 Bottom Test Cume 15,000 2,113 14.09% 22.33 -839.93 XYZ Corporation Model Scoring Performance
    • 15. 15 •Bottom Line • Call results showed the model worked well as a predictor of donating behavior with four deciles performing above average. Modeling – Case Study #2 MODEL DECILES Total Universe Completed calls Sales 1 56,624 5,630 1,707 2 56,623 4,897 1,290 3 56,623 4,408 1,092 4 56,622 4,442 1,070 5 56,622 4,332 990 6 56,622 4,254 959 7 56,622 3,769 842 8 56,622 3,460 705 9 56,622 1,732 303 10 56,622 1,891 268 Grand Total 566,224 38,815 9,226 23.77% InfoCision Management Corporation Start Date: 4/1/09 End Date: 4/30/09 14.17% 17.49% 20.38% 22.34% 22.54% 22.85% 24.09% 24.77% 26.34% 30.32% RR% Campaigns: XYZ0001
    • 16. 16 What tools do we have to unlock the value? • Inscription (Script on Screen) • Customizable screen prompts • Variable and branch scripting • Script changes can be made in real-time • Variable objection handling • Quantitative results tracking beyond final disposition • Dynamic gift asks • Custom built CRM solutions • Variable print campaigns. • Intelligent call routing on inbound.
    • 17. •Now that the audience is scored and segmented • How do we now impact the offer? •Analyze various affluence indicators and their relationship to gift amounts •Apply this information to develop a dynamic gift ask utilizing variable scripting technology Modeling – Case Study #3
    • 18. •Findings: • Household income displayed the highest correlation to gift amounts • Household incomes were then broken into five income bands ranging from low to high • Each income band was given a specific gift ask • The key metrics we were looking to influence were: • Response rate • Average gift • Dollars per call • Efficiency Modeling – Case Study #3
    • 19. • The second step was to index these incomes by the Cost of Living Index to normalize data Estimated Household Income $1 - $39,999 $40,000 - $74,999 $75,000 - $124,999 $125,000 - $249,999 $250,000 + Modeling – Case Study #3
    • 20. Control GRC $/CC CC RR% Avg. Gift $3.08 3,073 8.14% $37.47 Dynamic GRC $/CC CC RR% Avg. Gift $3.92 2,549 9.42% $41.66 Modeling – Case Study #3
    • 21. • Dynamic GRC results against control: • Revenue per call increased by 27% • Response rate increased by 16% • Average gift increased by 11% • Also showing an increase were credit card rates at 12% • Not only were gross conversions impacted but fulfillment rate and ROI dramatically improved Modeling – Case Study #3
    • 22. 22 Market Channel Coordination •Understanding the audience allows for true integrated marketing approaches • TM, DM, Interactive, Social, etc. • Timing, message, offers can all be coordinated to truly “reach” donors in a way that they choose to respond. •Many industry studies have shown that, if coordinated appropriately, these channels do not “cannibalize” each other.
    • 23. 23 De-Bunking the Myth! •Myth • Telemarketing has a negative impact on Direct Mail. •Approach • Study Direct Mail results as they relate to TM utilizing large nonprofit partner and 3rd party analysis •Reality • Multiple studies across various client data have shown a positive impact on mail results with coordinated TM.
    • 24. 24 Market Channel Coordination 1.105 1.081 1.070 1.090 1.081 1.116 1.081 1.084 1.106 1.093 1.000 1.010 1.020 1.030 1.040 1.050 1.060 1.070 1.080 1.090 1.100 1.110 1.120 1.130 1.140 1.150 CompleteCall"YES"Disposition- Fulfilled CompleteCall"YES"Disposition- Unfulfilled CompleteCall"NO"Disposition Non-Completed Call CombinedResults Mail Results for XY1/XY2 - XY3/XY4 AverageNumber of Gifts by Donor 90Day Window Average Number of Mail Gifts Pre-Call Average Number of Mail Gifts Post-Call 0.011 1.00% 0.000 0.01% 0.014 1.26% 0.016 1.43% 0.012 1.14% 3,999 3,316 4,127 6,898 25,142 26,778 18,257 20,581 51,525 57,573
    • 25. 25 Market Channel Coordination $26.82 $32.74 $29.26 $35.95 $25.00 $25.50 $26.00 $26.50 $27.00 $27.50 $28.00 $28.50 $29.00 $29.50 $30.00 $30.50 $31.00 $31.50 $32.00 $32.50 $33.00 $33.50 $34.00 $34.50 $35.00 $35.50 $36.00 $36.50 $37.00 $37.50 $38.00 Average Mail Gift by Donation Average Mail Gift by Donor Mail Results for XY3/XY4 Control vs. Non-Control Groups Average Gift 90 Day Window ControlGroup (NON-TM Contact) Non-Control Group (TM Contact) $2.44 8.34% $3.21 8.93%
    • 26. 26 Market Channel Coordination $28.31 $29.30 $30.69 $26.59 $26.60 $26.80 $54.90 $55.90 $57.49 $20.00 $25.00 $30.00 $35.00 $40.00 $45.00 $50.00 $55.00 $60.00 $65.00 30 Days 60 Days 90 Days TM/DM Results for XY1/XY2 - XY3/XY4 Positive "Yes Fulfilled" Disposition Group Average Gift by Donor DM Results TM Results Combined Results 1,353 2,369 3,9991,353 2,369 3,999
    • 27. 27 Market Channel Coordination $69,116.09 $130,257.79 $224,520.69 $64,895.91 $118,266.33 $196,039.25 $0.00 $25,000.00 $50,000.00 $75,000.00 $100,000.00 $125,000.00 $150,000.00 $175,000.00 $200,000.00 $225,000.00 $250,000.00 $275,000.00 $300,000.00 $325,000.00 $350,000.00 $375,000.00 $400,000.00 $425,000.00 $450,000.00 $475,000.00 30 Days 60 Days 90 Days TM/DM Results for XY1/XY2 - XY3/XY4 Positive "Yes Fulfilled" Disposition Group Total Gifts DM Results TM Results 1,353 2,369 3,999 93.89% 90.79% 87.31%
    • 28. 28 Market Mapping • CBSA and DMA data is available to do regional analysis and targeting. COLUMBUS
    • 29. DONOR PROFILES & DATA ANALYTICS 101 The Who, What, When, Where & Why of YOUR Donors