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Alerting + Data = ?

Alerting + Data = ?. Leveraging Partnerships to Improve Mass Notification Policies. Lauren Stienstra, M.Sc., CEM Senior Manager, Policy & Research- Arlington County OEM. Agenda. From Problem to Research to Results Research Process Results Solution Development

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Alerting + Data = ?

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  1. Alerting +Data = ? Leveraging Partnerships to Improve Mass Notification Policies Lauren Stienstra, M.Sc., CEM Senior Manager, Policy & Research- Arlington County OEM

  2. Agenda • From Problem to Research to Results • Research Process • Results • Solution Development • Implementation & Lessons Learned • What’s Next?

  3. Research Questions • HOW MANY ENROLLMENTS DO WE NEED? • Who and Where are our people? • How do alerts affect behavior? • How do keywords/topics of the alerts affect enrollment? • How many people need to be enrolled? • Quality vs. quantity • Who shares information?

  4. Where are our people? • Raw enrollments • Allotment by civic association

  5. Better Answer: • Heat map of counts within census tract (yellow borders) • Weighted by population density • Arlington National Cemetery and Reagan Airport tracts removed

  6. By Location Name • Location name: a write-in option to describe location receiving alerts • Work, office, ofc, trabajo, travail • Most common: 2100 Clarendon Blvd.

  7. Alerts: What are We Saying • Analyzed word frequency in all of the alerts sent out in the past two years • Most common words: • Warning • Watch • Severe • Issued

  8. Alerts: What do People React To • Analyzed word frequency of alerts where users replied “STOP”. • Most common words: • Traffic • Open/Reopened/Closed • Street • Lanes • Final

  9. How Should we Say it? • 3 & 30 Principle (Chandler, 2010) • Optimal messages relay key information in 3 short sentences with no more than 30 words • 6 & 60 Principle (Chandler, 2010) • 60 reading ease score & 6th grade comprehension level • Be honest, transparent, and direct • Include actions for people to take in response to the emergency

  10. Who Shares Information • Intrinsic and extrinsic motivations influence member’s intention to share (Yeon et al., 2016) • If there’s motivation, people will share • People who enjoy helping others are more likely to share • Sharing is less likely if the information is widely available (Park, 2014) • If it’s easy, people will share information (Park, 2014)

  11. WHAT ARE THEY LIKE? • Openness to experience: active imagination, aesthetic sensitivity, attentiveness to inner feelings, intellectual curiosity, and independence of judgment. • Agreeableness: good-natured, forgiving, courteous, helpful, generous, cheerful and cooperative. • Conscientiousness: dutiful, dependable, responsible, hardworking, and achievement-oriented. • Extroversion • Propensity to trust • Affective commitment: emotional attachment to, identification with, and involvement in the organization and its goals. • Affiliative tendency- preference for friends and attachments vs. independence and preference for group vs. individual activities. • Public individuation, but only when there is high level of interpersonal interaction and they are given personal credit for their contribution.

  12. Strong Interest Inventory Results • Teaching/Training (middle, high, elementary) • Counseling (religious, medical, career, etc.) • Restaurant/Arts/Entertainment Managers • Medical (Nursing, Allied Health, etc.) • Sales (especially insurance)

  13. What did we do with this Information? • Based on per capita account enrollment distribution, direct outreach programming to the civic associations adjacent to Columbia Pike, as well as Hall’s Hill and Nauck, for the next calendar year. • Based on the psychological profile of information sharing, target marketing towards the identified careers and community roles

  14. What did we do with this Information? • Based on the “STOP” message vocabulary assessment, evaluating how the number of traffic messages can be effectively reduced. • Based on the literature review of notification fatigue, review messaging policies and refrain from extraneous messaging (esp. around automatic weather alerts).

  15. Lesson Learned: • Based on the data collection itself, improve the way data is collected, organized, and structured within the system.

  16. Enrollment Campaigns Putting Results to the Test

  17. National Preparedness Month • Civic Association Contest • “Check Your Prep” audits at Sugar Shack • Emergency Kit Cook Off • ARLNow posts • Preparedness Displays at 3 Libraries

  18. Distribution of New Accounts • Improvement! • Fairlington • Dominion Hills • Ballston-Virginia Square • STILL NOT WHERE WE WANT TO BE

  19. “Clicks for a Cause” • Pro-social behaviors can be leveraged when it’s easy to do: • People like performing altruistic behaviors, especially when it’s easy/low cost • People engage in internet activity when it’s easy (Facebook likes, retweets, etc.) • Non-profit community has been able to leverage this for pro-social behaviors: • “Click this button to donate food to an animal shelter” on a page of ads • Page views/ad revenue fund the charitable work • Essentially, I got you to look at ads because you wanted to save the animals. • Can we use a similar system? • Translate pro-social impulses into Arlington Alert Enrollments?

  20. Simplicity • Enroll in Arlington Alert, and we’ll make a donation on your behalf (enrollee gets to choose!): • Help yourself, help your community. A Can of Beans (AFAC) A Pair of Socks (ASPAN) A Cup of Food (AWLA) A Box of Crayons (APS/PTA)

  21. Unexpectedness • Is this unique? • Not in the non-profit world… • Greater Good suite of Websites • Heifer International • OXFAM

  22. Concreteness • “Price Framing and Rewards” (Donor Psychology Term) • Allows us to use the following persuasive forces: • Visual Imagery (more motivating) • Stories • Value Attribution • Ownership Bias

  23. Results • Only 250 new enrollments!

  24. POSTmortem: Why did it fail? • Enrollment still too burdensome • Little message amplification from partners (AFAC, etc.) • Competing messages/causes during “season of giving” • Privacy Concerns • “Preaching to the Choir” -> the people we target are already enrolled • Manpower/Short timelines

  25. But… • Average Monthly Growth (previous 7 months): 165 • Average County Population Growth: 179 • Total population in Arlington 2016: 220,400 • Total enrollments: 23,661 • = 10.7% • Is this a ceiling?

  26. What’s next? • Continued Research with Virginia Tech • Collaboration in the Region • Develop new, more relevant strategic measures • Continue high impact, precisely targeted outreach

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