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Leading a data-driven action program

Leading a data-driven action program. Katie Ellis New Organizing Institute. Introductions. NOI O n Demand Norms You. FOLLOWING THE LAW. Elections.neworganizing.com. FOLLOWING THE LAW. www.afj.org. Katie Ellis. Data Training Manager. New Organizing Institute.

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Leading a data-driven action program

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  1. Leading a data-driven action program Katie Ellis New Organizing Institute

  2. Introductions NOI On Demand Norms You

  3. FOLLOWING THE LAW Elections.neworganizing.com

  4. FOLLOWING THE LAW www.afj.org

  5. Katie Ellis Data Training Manager New Organizing Institute

  6. In this session, you’ll learn to enhance organizing campaigns through the use of data.

  7. Introductions • Context-Setting • 3 Uses for Data • The Data Cycle • Case Study 1: Issue Campaign • Case Study 2: Electoral Campaign • Wrap Up

  8. Context-setting • What do I mean by “data”? • What types of data do you collect? • Why do we need data?

  9. Why do we need data? • Strategic Use of Limited Resources • Setting Strategic Goals & Maintaining Accountability • Demonstrating Success & Power

  10. Why do we need data? • Strategic Use of Limited Resources • Setting Strategic Goals & Maintaining Accountability • Demonstrating Success & Power

  11. Money Time Limited resources People

  12. How do we best use limited resources?

  13. Why do we need data? • Strategic Use of Limited Resources • Setting Strategic Goals & Maintaining Accountability • Demonstrating Success & Power

  14. Program design and evaluation

  15. Audit the situation • STRATEGY is turning the resources you have into the POWER you need, to win the CHANGE you want • What change do we want? • Who has the resources to create that change? • What do they want? • What do we have? (that they want)

  16. Program design and evaluation

  17. Set goals and benchmarks • What specifically does success look like? • How do we get from there to here? • Define benchmarks so you can measure progress and make adjustments

  18. Program design and evaluation

  19. Metrics

  20. Program design and evaluation

  21. SOFT REPORTING HARD REPORTING Results attached to names (volunteers, supporters, donors, registrants) Long term strategic resource allocation Verify soft reporting, increased accountability Collect data • Organizers and volunteers self-report • Quick allocation of resources • Day-to-day accountability • Narrative feedback from the front lines

  22. Program design and evaluation

  23. evaluate • Are you on track to reach the benchmarks that you set? • If not, can you identify the reason? • Is one staffer or volunteer coming up short? Can you coach them? • Is one region producing different results? Can you adjust your methods in that area, or shift resources elsewhere? • Is one metric affecting others? What can you do to correct the imbalance?

  24. Program design and evaluation

  25. adjust • Now that you know what’s going on with your program… what will you do with this information? • Keep your eyes on the prize – how do you adjust your tactics and benchmarks, without losing track of the goal?

  26. Questions?

  27. Case study: tenure campaign

  28. Auditing the situation

  29. Auditing the situation • Leadership team: Me, Aliya, Professor Murphy, students on list-serv, Facebook group • Our constituencies: • Students • Alumnae • Faculty • Why has power? • Committee on Faculty Appointments • Dean Shennan • Other resources: • Professors who have appealed successfully

  30. Set goals and benchmarks

  31. Setting goals and benchmarks • What does a win look like? • CFA overturns tenure decision at May meeting • What will it take to get there? • Research • Testimonials (Letters)

  32. Setting goals and benchmarks • What does a win look like? • CFA overturns tenure decision at May meeting • What will it take to get there? • Research (Faculty) • Testimonials (Letters) – 100 • Students – 70 • Alumnae - 30

  33. Set benchmarks • How do we find people to write testimonials? • Online forum • Facebook • Ask people • Petition

  34. Set Benchmarks

  35. Set benchmarks • Students • Student organizations • Classes • Dorms • Beebe – 5 • Cazenove – 5 • Pomeroy – 5 • Bates – 3 • French House – 1 • Total - 74

  36. Interdependent leadership team • List-Serv • Facebook Group

  37. Interdependent leadership team Facebook List-Serv

  38. Campaign timeline January February March April May

  39. Choose metrics

  40. Choose Metrics • Successful Submissions • Commitments • Signatures • Universe Penetration • Volunteers

  41. Collect data

  42. SOFT REPORTING HARD REPORTING Names of commitments Physical petitions Volunteers Collect data • Overview Numbers • How many signatures? • How many commitments?

  43. Analyze

  44. analyze

  45. Program design and evaluation

  46. Adjust • Dorm storm working – ramp up! • X 3

  47. AND WE WON!

  48. Case study: Delaware democratic party

  49. The data cycle • We know: • How many voted in 2006/2008 • Who voted in 2006/2008 • Now we need: • 220,000 calls • 1629 volunteer shifts • We track: • Volunteers recruited – 535 • 45 calls/hour • Contact rate – 25% • We need: • 55,000 voters • 167,000 universe • 132,000 calls • 733 volunteer shifts • We track: • Volunteers recruited • Calls/hour • Contact rate • We track: • Volunteers recruited – 535 • 45 calls/hour • Contact rate – 25%

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