I of F South West Spring Conference 2012

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Paul Jackson and Steve Thomas discuss how to make the most of your database.

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  • Start of our journey of discovery!
  • Based on a hunch that SU were not achieving the support and engagement that they could
  • I of F South West Spring Conference 2012

    1. 1. Making the most of yourdatabasePaul JacksonSteve Thomas25th April 2012
    2. 2. About Purple Vision• independent consultants established 2003• charities, associations, schools and universities• services include: – fundraising consultancy and research – data analytics – Appeal and campaign planning – business process improvement – project management and training• ASI, Blackbaud, Salesforce• fundraising | technology | change
    3. 3. About Purple Vision
    4. 4. What’s your Database?
    5. 5. “From here on in I want to really ‘get toknow’ my data. Who is on there? Why arethey on there? What do I know? What doI need to know? How can I target certain groups?”
    6. 6. 12 Top Tips1. What you don’t know 1. Social Integration2. Supporter journeys 2. Reporting3. Data cleansing 3. Business Processes4. Single supporter view 4. Data Mining5. Segmentation 5. Engagement6. Email integration 6. Profiling
    7. 7. You don’t know what you don’t know!• Contact details • Products purchased• Employ & other bio • Services accessed• Donations made • Areas of interest • Type • Date • Mailing preferences • Value • Pay method • Comms in & out • GAD • Membership• Event attendance • Demographics• Volunteer details • Online interaction• Prospect info What & Who should you ask to find out?
    8. 8. Supporter journeys Volunteered Became committed giver JoinedFirst Gift membership Legacy Pledge Volunteered Became committed giver
    9. 9. First engagement: purchase
    10. 10. Database Cleansing• When was this last done?• Why do it?• Check list of options:- • Deduplication of contacts • Suppressions • Address correction (nb Postcode Anywhere) • NCOA • Email checking• Is there ‘stuff’ that’s never used?• What about ‘Old’ information?
    11. 11. From silos to one view• All charities do it! An existing client... Originally Now 3 contact files 11 files
    12. 12. From silos to one view• Main reason for your own files?• How to control your contacts• Data ‘amnesty’...and the benefits• Consider a database champion (in Fundraising)
    13. 13. One size doesn’t fit all
    14. 14. What is Segmentation?Classification of thepopulation intosubgroups that are:•Distinguishable•Identifiable•Manageable•Fit for purpose
    15. 15. How segmentation works Recency Engagement Value Rate
    16. 16. Creating segments Recency Engagement Value Rate
    17. 17. Creating segments 4 3 1 2 Recency 9 8 6 7 Engagement Value Rate
    18. 18. So what?Targeting:• Make targeting more appropriate to audience• Avoid scattergun communications• Protect against unsubscribes and lapsing• Makes internal expectations realistic
    19. 19. Email integration• Easy to record emails in most systems• Aids ‘360o view’ for contacts• What about email campaigns? • Raisers Edge: “chimpegration” • Cloud systems tightly integrated• Benefits • Easily record campaign against contacts • Update preferences, unsubscribes & bounces • measure level of engagement
    20. 20. Social media integration• Add-in – eg. Outlook
    21. 21. Social media integration• Add-in – eg. Outlook• CRM integration “bridges”• Monitor online activity• Benefits • Track friends and followers • Major donors • Advocates and viral “buzz” • measure level of infleunce
    22. 22. Social media integration• Add-in – eg. Outlook• CRM integration “bridges”• Monitor online activity• Benefits • Track friends and followers • Major donors • Advocates and viral “buzz” • measure level of infleunce
    23. 23. Social media integration
    24. 24. Reporting• Reporting with today’s systems should be easy:-
    25. 25. Reporting “a report, of % of bookings in a year that are made by an organisation that also booked in the previous year AND the % of bookings by an organisation that have made another booking within a two year period. Ability to specify start and end dates and look at summary or details”.
    26. 26. Reporting• If it isn’t easy – Why?• Consider using a reporting tool...or a consultant!• What reports do you need – Segmenting/targetting/campaigns – Performance: financial, KPIs• What reports do you need? What 5 reports would you find most useful?
    27. 27. Business Process Improvement Enquirer Passive Interest eg Sent within x leaflet 1. Code all days response Info devices, Pack/ Active record all Leaflet interest, eg interactions Consumer web, request info Welcome Sent within x Pack weeks 2a. Record Gift Supporter 2b. Record Welcome Pack response & tailor & target comms accordingly Thank You 3. Record Sent within x gifts/response & weeks 2nd Repeat use to derive next Appeal Supporter prompt. If no gift in x months offer If Lottery have Lottery? delayed upgrade/ conversion plan Lottery
    28. 28. Data mining• “...the purpose of data mining is to discover hidden patterns in large amounts of data in order to use these for data analysis and forecasting”. • Beers and Nappies!• In our world.. • RFM • How long Data Probability that • Membership supporter will • Engagement Mining stop giving • Events . .• Can I use it?.........Excel
    29. 29. Engagement 7119 2790 super close 5311 7 on holiday 2525 on sabbatical Segment 7 7 Segment 6 2295 keen but stuck Segment 5 4183 activists Segment 4 6671 first biters Segment 39457 Potentials Segment 2 Zeros Segment 1 Segment 0
    30. 30. Engagement - understanding shiftingProbabilities of being present in each segment next monthdepending on presence this month 7 0.12 0.47 1.10 1.85 3.25 9.20 11.08 88.74 6 79.48 4.62 5 0.01 87.23 7.16 4.62 4 96.75 3.57 2.27 3 0.27 0.18 0.89 92.88 2 0.42 2.22 93.74 3.97 1 0.01 97.12 4.25 1.24 0 99.18 0.01 0.05 0 1 2 3 4 5 6 7
    31. 31. Engagement – moves and blocks 8845 3511 super close 3301 7 on holiday 3976 on sabbatical Segment 7 7 Segment 6 3111 keen but stuck Segment 5 2213 activists Segment 4 6649 first biters Segment 37250 Potentials Segment 2 Zeros Segment 1 Segment 0
    32. 32. Look alike logic Universe Non-profit supporters Your Database Your Sector
    33. 33. Example profile - Age Supporters Regional Base Penetration Index TotalSketch Attributes Counts % Counts % % 0 100 200Age – Example 1    Rank 91-100 (High) 933 16.8% 23092 11.2% 4.04 150  █████ Rank 81-90 1012 18.2% 19816 9.6% 5.11 190  █████████ Rank 71-80 852 15.3% 20846 10.1% 4.09 152  █████ Rank 61-70 697 12.5% 20417 9.9% 3.41 127  ███ Rank 51-60 643 11.6% 23081 11.2% 2.79 104   Rank 41-50 459 8.3% 22491 10.9% 2.04 76         ██ Rank 31-40 316 5.7% 22152 10.7% 1.43 53      █████ Rank 21-30 202 3.6% 17995 8.7% 1.12 42     ██████ Rank 11-20 201 3.6% 19192 9.3% 1.05 39     ██████ Rank 1-10 (Low) 245 4.4% 17650 8.5% 1.39 52      █████ TOTAL 5560 206732 2.69  Age – Example 2    Rank 91-100 (High) 601 14.5% 23382 11.1% 2.57 130  ███ Rank 81-90 662 15.9% 21810 10.4% 3.04 154  █████ Rank 71-80 465 11.2% 18343 8.7% 2.54 128  ███ Rank 61-70 557 13.4% 23014 10.9% 2.42 123  ██ Rank 51-60 493 11.9% 22896 10.9% 2.15 109  █ Rank 41-50 375 9.0% 20015 9.5% 1.87 95          █ Rank 31-40 387 9.3% 22721 10.8% 1.70 86          █ Rank 21-30 270 6.5% 22811 10.8% 1.18 60       ████ Rank 11-20 171 4.1% 17574 8.4% 0.97 49      █████ Rank 1-10 (Low) 174 4.2% 17887 8.5% 0.97 49      █████ TOTAL 4155 210453 1.97  Age – Example 3    Rank 91-100 (High) 20 2.3% 10642 8.7% 0.19 27    ███████ Rank 81-90 14 1.6% 11145 9.1% 0.13 18   ████████ Rank 71-80 37 4.3% 10021 8.2% 0.37 53      █████ Rank 61-70 133 15.6% 12234 10.0% 1.09 156  ██████ Rank 51-60 144 16.9% 12409 10.1% 1.16 167  ███████ Rank 41-50 124 14.5% 11515 9.4% 1.08 155  █████ Rank 31-40 139 16.3% 14290 11.6% 0.97 140  ████ Rank 21-30 94 11.0% 14826 12.1% 0.63 91          █ Rank 11-20 69 8.1% 12608 10.3% 0.55 79         ██ Rank 1-10 (Low) 80 9.4% 13232 10.8% 0.60 87          █ TOTAL 854 122922 Sample   
    34. 34. Profile variables• Income • Age• Housing Tenure • Children• Spending Power • Household Size• Education • Property Type• Occupation • Urbanicity• Social Grade • Retail Accessibility
    35. 35. Where are they? New areas may have a different socio-dem. profile to the existing donorbase Different motivations require different communication strategies Missing all the towns!
    36. 36. Summary - 12 Top Tips1. What you don’t know 1. Social Integration2. Supporter journeys 2. Reporting3. Data cleansing 3. Business Processes4. Single supporter view 4. Data Mining5. Segmentation 5. Engagement6. Email integration 6. Profiling
    37. 37. Any questions? 0845 458 0250 info@purple-vision.com www.purple-vision.com @purple_vision

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