engagement metrics: uses and discoveries

Reggie Bustinza

Director of Alumni Relations – Lewis University

Joe Volin

Asst. Dir. of Alumni Relations – Lewis University

Romeoville, IL

Private Catholic institution

6,500 students (4,500 undergrad, 2,000 grad)

40,000 alumni – primarily in Chicago area

Alumni Relations staff of 3

Database: Raiser’s Edge (“R/E”)

Why try to measure engagement?

The Lewis System◦ Process

The Value in Metrics

Results

Metrics can guide decision making◦ Spot trends

◦ Identify opportunities

◦ Abandon dead ends

◦ Quantify program success

More efficiency & efficacy

Justify our existence

Established in 2012

Created in-house

Created by Alumni Staff (Joe Volin and Reggie Bustinza)

Requirements◦ Work with existing data◦ Comprehensive◦ Searchable (integer data)◦ Valid as aggregate and/or individual data◦ Easy to understand

Not required, but nice to have…◦ Inexpensive to implement◦ Ability to run ourselves, as frequently as we want

OR dynamic◦ Option to exclude data to look for correlations◦ Simplicity

Process1. Make sure database is capable of outputting what

we desire, and we can import results

2. Identify what relevant data we track – “What information do we have that shows some kind of engagement?”

3. Assign relative values

4. Test

5. Repeat until values are no longer questioned

Step 1: Can Database Handle It?

Step 2: What do we Track?

Is it indicative of engagement?

Is the data consistent and accurate?

Will we keep tracking it?

• Event Attendance• Giving – how much

and how often• Valid email• Open emails• Social media• Valid address

• Valid business info• Board member• Award winner• Legacy parent• Campus visits• Interested volunteer• Active volunteer

Can we categorize?

EventsEvent Attendance

GivingGiving – how much and how often

CommunicationsValid emailOpen emailsSocial mediaValid addressValid business info

VolunteerismActive volunteerBoard memberInterested volunteer

OtherAward winnerLegacy parentCampus visitsEmployeeAffinity Partners

Strengths Weaknesses

Can run in house

Values recent activity over old activity

Results are easy to understand

Some data can be suspect (eg: acquired mailing lists)

Have to export, use two programs, then import for scores

As data points are added, historical scores are distorted

Challenges◦ Not all board members are equally engaged.

◦ How stratified should we make giving levels?

◦ Free events vs. Paid events

6 scores are actually produced◦ 1 for each category

◦ Overall Engagement Score (sum of each category)

Share values with colleagues for feedback

Run the numbers, see what results are

Spot check results

Pull top 10, top 20, top 50, top 100 alumni◦ Does it add up?

◦ If not, why?

Tweak values, repeat test

Run Quarterly (past 12 months)

Exported each category to Excel where values are assigned and coded

SPSS is used to merge data

Import integers back into Raiser’s Edge

Metrics are half of the battle. The real question is: How will you use this tool?

◦ Whittle mailing lists We have re-allocated more than $15,000 in printing costs in the 3 years

since we have had metrics.

◦ Identify prospects that were under-the-radar Identified 552 top engaged alumni with high wealth scores that had not been

identified through traditional prospect research Resulting in 186 portfolio assignments; and 45 initial visits during Fiscal Year

2015

◦ More efficient Annual Fund calling lists 118 new donors in the categories that utilized engagement metrics for

further segmentation (FY14 vs FY15)

◦ Identify potential affinity groups

◦ Evaluate programming

Advancement programming can create higher levels of engagement.

Higher levels of engagement lead to higher giving participation.

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Engagement Comparison

GivingScore Communications Events

Category Giving Events Comm. Vol. Other TOTAL Give %

FY15

All Alumni 1.329 0.184 9.531 0.095 0.235 11.375

Young Alumni

1.250 0.256 10.840 0.180 0.084 12.670

Athletes 2.942 0.684 10.572 0.138 0.528 15.279

Volunteers 6.84 2.94 15.08 5.33 0.96 31.26

Aviation 0.91 0.11 9.05 0.16 0.19 10.46

Nursing 0.96 0.06 8.83 0.06 0.24 10.45

Law & Justice

0.86 0.10 9.24 0.06 0.24 10.50

Top X % Point Cutoff

1% 48

5% 26

10% 19

25% 14

50% 9

75% 6

Top X % Point CutoffGiving

ParticipationGiving

Participation

1% 48+ 424/426 99.5%

5% 26-47 1491 / 1680 88.8%

10% 19-25 944 / 2811 33.6%

25% 14-18 468 / 5097 9.2%

50% 9-13 229 / 9940 2.3%

75% 6-8 1 / 14883 <0.001%

100% 0-5 0 / 4810 0.00%

Top X % Point CutoffGiving

ParticipationGiving

Participation

1% 28+ 397/397 100%

5% 12-27 1783/1783 100%

10% 2-11 1377 / 2218 62.1%

25%

50%

75%

100% 0-1 0 / 35249 0.00%

Top X % Point CutoffGiving

ParticipationGiving

Participation

1% 21+ 340 / 922 36.9%

5% 19-20 370 / 1335 27.7%

10% 17-18 500 / 2147 23.3%

25% 12-16 842 / 6657 12.6%

50% 8-11 1195 / 16882 7.1%

75% 4-7 305 / 10319 2.9%

100% 0-3 5 / 1385 0.36%

Top X % Point CutoffGiving

ParticipationGiving

Participation

1% 5+ 330 / 417 79.1%

5%

10%

25%

50%

75%

100% 0-4 3227 / 39230 8.2%

2013 2015

Engagement 9.81 11.374

Giving-Independent Engagement

8.425 10.045

Giving Participation

8.20% 8.42%

2013 2015

Engagement 11.43 12.33

Giving-Independent Engagement

10.266 11.375

Giving Participation

5.58% 5.25%

GeneralPopulation

8.20% 8.42%

2013 2015

Engagement 12.844 15.135

Giving-Independent Engagement

10.031 12.282

Giving Participation

14.46% 13.79%

GeneralPopulation

8.20% 8.42%

2013 2015

Engagement 29.16 36.81

Giving-Independent Engagement

21.41 27.94

Giving Participation

32.7% 29.4%

GeneralPopulation

8.20% 8.42%

2013 2015

Engagement 8.62 10.04

Giving-Independent Engagement

7.77 9.24

Giving Participation

3.86% 4.33%

GeneralPopulation

8.20% 8.42%

2013 2015

Engagement 8.89 10.22

Giving-Independent Engagement

7.96 9.26

Giving Participation

4.34% 5.07%

GeneralPopulation

8.20% 8.42%

2013 2015

Engagement 8.91 8.97

Giving-Independent Engagement

8.04 8.16

Giving Participation

4.17% 4.31%

GeneralPopulation

8.20% 8.42%

Next Steps

What would be do different?