Measuring Alumni Engagement

Presented by: Reggie Bustinza

Director of Alumni RelationsLewis University

Metrics system by Reggie Bustinza and Joe Volin

Romeoville, IL

Private Catholic institution

6,700 students (4,700 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 can handle it

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

Challenges◦ Not all board members are equally engaged.

◦ How stratified should we make giving levels?

◦ Free events vs. Paid events

◦ How long is an activity valuable?

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

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

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 550 top engaged alumni with high wealth scores that had not been

previously assigned through traditional prospect research Resulting in 170 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.

Category Giving Events Comm. Vol. Other TOTAL

All Alumni 1.36 0.19 9.52 0.06 0.22 11.08

Young Alumni 0.99 0.24 10.56 0.07 0.09 11.95

Athletes 3.68 .64 10.24 .10 .35 14.4

Volunteers 9.42 4.83 16.31 4.82 1.15 36.54

Aviation 0.84 0.1 8.72 0.07 0.19 9.92

Law & Justice .90 .12 8.82 .04 .25 10.13

Top X % Point Cutoff

1% 48

5% 26

10% 19

25% 13

50% 8

75% 6

Top X % Point Cutoff

1% 26

5% 19

10% 17

25% 11

50% 8

75% 6

Top X % Point CutoffGiving

ParticipationGiving

Participation

1% 48+ 402 / 402 100%

5% 26-47 1530 / 1621 94.4%

10% 19-25 1218 / 2540 48.0%

25% 13-18 735 / 5242 14.0%

50% 8-12 395 / 17701 2.2%

75% 6-7 17 / 6334 0.3%

100% 0-5 3 / 4736 0.06%

Top X % Point CutoffGiving

ParticipationGiving

Participation

1% 26 301 / 353 85.27%

5% 19-25 508 / 1113 45.64%

10% 17-18 652 / 2703 24.12%

25% 11-16 964 / 5944 16.22%

50% 8-10 623 / 6116 10.19%

75% 6-7 474 / 8202 5.78%

100% 0-5 284 / 11245 2.53%

2013 2015 Change

Engagement 9.81 11.08 +1.27

Giving-Independent Engagement

8.425 9.72 +1.295

Giving Participation

6.7% 7.0% +0.3%

Total Population: 35536

2013 2015 Change

Engagement 8.92 10.13 +1.21


8.05 9.23 +1.18


4.1% 4.7% +0.6%

GeneralPopulation

6.7% 7.0% +0.3%


2013 2015 Change

Engagement 12.83 14.56 +1.73


10.04 11.33 +1.29


14.4% 16.4% +2.0%

GeneralPopulation

6.7% 7.0% +0.3%


2013 2015 Change

Engagement 8.56 9.92 +1.36


7.72 9.08 +1.36


3.9% 4.3% +0.4%

GeneralPopulation

6.7% 7.0% +0.3%


2013 2015 Change

Engagement 29.1 36.54 +7.44


21.33 27.11 +5.78


36.7% 44.0% +7.3%

GeneralPopulation

6.7% 7.0% +0.3%


There IS a correlation between Engagement and Giving

Its too early to tell if a general engagement push can lead to increased giving

Volunteerism is the individual engagement component that can lead most directly to increased giving

The surest way to get more gifts is to ask for them… but metrics can show you who to ask

TBD: Strength of correlations within engagement (volunteerism vs. communications vs. events)

TBD: Different engagement techniques within variable groups

Contact:

Reggie BustinzaDirector of Alumni Relations(815) [email protected]

Joe VolinAssistant Director of Alumni Relations(815) [email protected]

mailto:[email protected]

mailto:[email protected]

Measuring Alumni Engagement

Data & Analytics

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