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Prospect research: building a bridge to fundraising success

Prospect research: building a bridge to fundraising success . Presenter: . Lawrence Henze, J.D., Principal Consultant. Founder of Blackbaud Analytics, which became Target Analytics 34 years as development officer and consultant

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Prospect research: building a bridge to fundraising success

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  1. Prospect research: building a bridge to fundraising success

  2. Presenter: Lawrence Henze, J.D., Principal Consultant Founder of Blackbaud Analytics, which became Target Analytics 34 years as development officer and consultant Author and Frequent Presenter on Planned Giving, Major Gifts, Prospect Research and Industry Topics Law degree, University of Wisconsin-Madison Lives in Lafayette, California

  3. agenda Prospect research philosophy Entire database Research Metrics Descriptive analytics Predictive analytics Individual research Worth knowing? Discoverable? Where is the information found? Putting it together – Cohesive Development Summary and Questions How much information do I need to know?

  4. Identifying Prospects: Best Practices

  5. Prospect research philosophy

  6. Prospect research philosophy

  7. Prospect research philosophy

  8. Start with true understanding of your fundraising ‘status quo’ Traditional Pyramid ‘unlikely’

  9. A more likely scenario

  10. prospect identification success… • Is most likely when: • You possess accurate information on and awareness of your current status • The process is fluid, ongoing, and not reactionary • The results are integrated into engagement, cultivation and solicitation strategies • Those strategies are supported by a relationship management system • You have a commitment to evaluate and monitor your progress, and revise strategies, programs to fit the data • There is accountability

  11. What are your prospect identification needs? ? ? ? ?

  12. What are your prospect identification needs? Create a gift chart to reveal answers

  13. What are your prospect identification needs? Create a gift chart to reveal answers

  14. Bottom-Up or top-down?

  15. prospect identification through analytics • What analytics has taught us: • Analytics may validate tradition, and also… • Indicate change • Certain fundraising axioms are not supported by data • Predictive analytics more accurately identifies giving prospects at all levels • Planned giving: • Loyalty and fiscal conservatism more important than age • Major giving: • Most major giving prospects are discoverable through annual giving behavior

  16. Segmentation for Actionable Results Combining Likelihood and Capacity • Highest scores and high assets • Further qualification and research • May need individual cultivation • High likelihood scores and low or mid-level target giving ranges • Targeted upgrade, mid-level gift strategies • Increase annual giving Upgrade or Acquire High Touch • Lower likelihood scores, but high target giving ranges and assets • Need to be sold on your mission • Longer term cultivation • Low likelihood scores and low target giving ranges • Minimize investment • Consider reduced resource application Long Term Prospects Low Potential giving capacity giving likelihood

  17. Lessons from 21 years of analytics Predictive modeling is far more effective at identifying major/principal donors Only 31% of the revenue came from new donors with publicly identifiable assets Only 13% of the revenue from the test group had publicly identifiable wealth. Modeling is very effective identifying mid-level and planned gift prospects Wealth data alone very ineffective at identifying annual and planned gift prospects

  18. The changing landscape of prospect research • More research professionals are comfortable or proficient in analytics • More emerging experts • Demand for ongoing metrics and analytics is increasing • Wealth is becoming harder to quantify, let alone obtain • Growing interest in determining affinity, engagement, as more professionals accept the dominant role of affinity in the giving equation • Better, more accurate behavioral, financial, and demographic data available for analytics • Terms such as BI (business intelligence), BIG DATA, and analytics are found everywhere • The question is: Are we doing the right analytics?

  19. Analytics/Prospect Screening • Using data mining, predictive modeling, and wealth/hard asset appends to augment your knowledge of donors and prospects • To provide a rating or ranking system that allows you to differentiate or segment your prospects • Increase effectiveness and efficiency • A process that begs for an implementation plan • A process that begs for implementation

  20. What drives screening projects? • Identifying the Needs of the Organization • Planning for the Research • Develop Consensus and Commitment • Application of the Results • Owning the Results • Distributing the Results • Initial Action Items • Relationship Management • Evaluation

  21. Fair warning: theRe will be math in this presentation

  22. Standard deviations and outliers

  23. Identifying the Screening Needs of Your Organization - Warnings • “There is great pleasure to be gained from useless knowledge” Bertrand Russell (1872-1970) • The screening info you need is the data you use…everything else is fluff…don’t buy fluff • Prioritize the needs of your organization • If you are screening with a vendor, make sure they care about and respond to your needs • Beware of packages • See ‘fluff’ above

  24. Screening Needs - Timing • “Should we screen?” has been replaced by “When should we screen?” • “When should we screen?” may be as painstakingly simple as “we will screen when we have the budget” • Avoid budget limitations by undertaking internal data mining whenever possible • Simple strategies presented later in PowerPoint • Increase the likelihood that you will have future budget funds by implementing your current (next) screening effort fully

  25. Identifying the Need for Screening • “Wisdom outweighs any wealth” Sophocles, (496 B.C. – 406 B.C.) Antigone • Don’t be seduced by wealth data alone, even if your needs are primarily in major giving and principal giving • Propensity combined with capacity provides the best comprehensive measure of a prospect • This has been tested and proven

  26. Identifying the Need for Screening • Annual fund screening – the best place to start? • Remember the pyramid and ultimate giving • Annual fund questions? • Who makes annual gifts? Is a member? • What does a loyal donor look like? • Who can give more? • Who is unlikely to give again? • Which lapsed donors are likely to be recaptures • Who gives at the same time every year? • Who gives via direct mail? Email? Telemarketing? Website? • Who is most likely to make an unrestricted gift? Restricted gift?

  27. Identifying the Need for Screening • Major giving or capital campaigns? • Identifying emerging major gift prospects • Pre-campaign screening • Mid-campaign screening • Post campaign screening • Planned giving • Wealth Identification • Membership modeling/cluster analysis • Likelihood to be a member, renew, etc. • Membership groupings and characteristics • Which other questions would you like answered?

  28. Internal data mining – RFM Analysis • RFM Analysis • Recency (most recent gift this year, last year, or more distant) • Frequency (gave in each of the last three years, one of the last three years, none of the last three years) • Monetary value (largest gift in three years > $100, $50-100, < $50

  29. Planning for the Research • “A goal without a plan is just a wish” Antoine de Saint-Exupery (1900-1944) author The Little Prince • Whether DIY or hired research, your goal should be to have the outline of a plan in place before receipt of the results. • What are the key elements of the plan?

  30. Obstacles to prospect development: the silo approach Membership Shadow database Prospects Prospect Research Major giving Annual giving Special events Donor relations Planned giving CRM

  31. Key Elements of the Plan • Integration into your database/CRM software (priority) • Plan in advance to have technical resources scheduled for incorporation of results into database • Speak with vendor references to identify potential issues for software upload • Seek deadline from technical resource staff • Utilization of delivered software • May be best place to house wealth data detail and conduct additional searches • If upload into CRM is delayed, this software becomes more critical

  32. Key Elements of the Plan • Creation of an implementation team • Include representative of each functional area to receive results or to be affected by implementation • Opportunity to eliminate silo barriers • Team should explore cross-functional implementation ideas • Team leader should be “owner” of the data with authority granted by advancement leadership. • Use deadlines rather than meetings to move action items forward • For example, discussion items for meetings to be shared in advance along with specific implementation proposals

  33. Key Elements of the Plan • Creation of an implementation team • Include representative of each functional area to receive results or to be affected by implementation • Opportunity to eliminate silo barriers • Team should explore cross-functional implementation ideas • Team leader should be “owner” of the data with authority granted by advancement leadership. • Use deadlines rather than meetings to move action items forward • For example, discussion items for meetings to be shared in advance along with specific implementation proposals

  34. Key Elements of the Plan • Distribution of the data to users • Research and development services staff probably best owners of the data • Create business rules for data use • Access rules • Update rules for wealth data • Warning • Avoid data manipulation, replace with results implementation! • Vendor delivered tools can be very addictive • Data addicts often fail to implement

  35. Key Elements of the Plan • Implementing wealth results • Will wealth data supplement modeling scores or serve as a stand-alone rating system? • Will you share wealth results with development officers without verification? • How will you use this data to identify new prospects? • What criteria will be used to eliminate or remove prospects from existing portfolios? • How do you capture verification data from development officer call reports?

  36. analytics on your Own • Simple age analysis by size or type of gift • Gift size and frequency of solicitation • Years of giving before first major gift • Creating an engagement score • Quantity and quality of internal information

  37. Internal data mining – RFM Analysis • RFM Analysis • Recency (most recent gift this year, last year, or more distant) • Frequency (gave in each of the last three years, one of the last three years, none of the last three years) • Monetary value (largest gift in three years > $100, $50-100, < $50

  38. Cluster Analysis • Grouping individuals of similar characteristics into respective categories • Way of taking a lot of data and grouping people into subsets in a meaningful way • Mosaic, PRIZM, PersonicX, Niches are all pre-made cluster data overlays you can purchase

  39. Cluster Analysis • Mosaic Clusters • http://www.experian.com/marketing-services/consumer-segmentation.html • Example:

  40. Clusters and bequest intentions Applications of cluster data Segment by known bequest intentions For example, 76% of bequest donors are described by 7 of the 26 clusters 24% of the non-bequest donors were also described by the same 7 clusters Concentrate on these prospects who are included in 1 of the 7 clusters for bequest cultivation and solicitation

  41. MODELING 101: 3 STEPS What are you looking for? Common Denominators: The Model Scoring the Database Annual donors?

  42. Annual giving likelihood Membership likelihood Transitional giving likelihood Major giving likelihood Principal giving likelihood Planned giving likelihood Bequest likelihood IRA contribution? Target Gift Range Cause/Mission/Fund Restricted or Unrestricted giving Event attendance to giving Engagement modeling CRT likelihood Annuity likelihood Gifts of real estate? Questions modeling may address

  43. Analytics through Modeling

  44. sample segmentation

  45. Sample Results – Database SEGmentation Database 10,000 records Annual Giving Tier 1 Principal Giving Tier 2 Major Giving P P P Tier 3 Mid-Level Giving P P P P P P Tier 4 P P P P P P P P

  46. Annual Fund Implementation • Upgrade potential • Acquisition of new donors from current prospect pool • Acquisition of new donors from new prospect pool • Elimination of poor prospects from solicitation process • Saving solicitation money • Reallocating to more fruitful fundraising activities

  47. Major Giving Implementation • Proactive prospect identification • Highlighting new prospects • Eliminating comfort prospects • Cleansing portfolios • Placing emerging prospects into an upgraded annual fund solicitation

  48. Planned Giving Segmentation

  49. Planned Giving Implementation • Avoid concentration on older, wealthy individuals • Integration of communication strategies with annual giving and donor relations • Train major gift officers on the basics of planned giving • Create awareness of split gift opportunities • Recognition of loyalty • Put planned giving prospects into a relationship management system

  50. Research Implementation • PROACTIVE RESEARCH! • Become the architect of a prospect identification strategy • Expand the borders of prospect research • Career success and personal rewards • Think beyond traditional practices • Think globally, include prospects of all types • Have a plan for everyone • include those who will not be solicited • Use analytics to determine: • Those who should be solicited • Those who should be personally solicited • Those who should be solicited less • Those who should not be solicited at all

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