Using%20Data%20to%20Drive%20Your%20Organization

1
Using Data to Drive Your Organizations Success

• The Finance and Administration Roundtable
• Sacha Litman
• Managing Director
• Measuring Success, LLC

2
Agenda

• 45 minutes Presentation
• 15 minutes QA

3
Goals for Today

• See the potential in our CFO/CPA roles to go
  beyond budget and audit data
• Leverage our data competency into other data
  organization has to help achieve mission
• Help your organization identify and track new
  data to measure impact

4
Today 3 Examplesfrom data you have to data you
can obtain

1. Activity-based accounting
2. Fundraising data
3. Measurement systems

5
Client chapter-based community centers

• Experiencing some with member drops, financial
  insolvency.
• Challenge to business model brand consistency.

Individual Members / Users (N1000 to 10,000 per
affiliate)
6
Managerial Accounting using Activity Based
Costing

• Organization runs 13 different program areas
  (early childhood, after school, tutoring, sports,
  fitness, adult education, etc)
• Assumed were running surpluses in high-fee
  programs like early childhood and fitness based
  on direct budget analysis (fees direct costs)
• Indirect costs revenues allocated using
• Administrative staff time (time sheets)
• Square feet in facility
• Foot traffic (participant hours)
• Member priorities (survey)

7
1. On average, 73 of expenses were in black
box!
8
2. Organization did not understand how resource
intensive some program areas were
Early Childhood Elem School Middle
School Teen Adult Ed Counseling Religious
Services Community Events Weekend Events Social
Action
9
3. Early Childhood example

• 14 of administrative staff time
• 46 of indoor square footage
• 35 of foot traffic
• Yet only 2 of membership allocation

10
4. Early childhood was, once incorporated
indirect items, running a massive deficit!
Early Childhood Elem School Middle
School Teen Adult Ed Counseling Religious
Services Community Events Weekend Events Social
Action
11
5. Changes as a result of analysis

• New goal for early childhood cost neutral.
  Achieved in 2 years by raising fees and diverting
  some overhead resources to other programs.
• Changed budgeting process to activity-based
  accounting.
• Cost-neutrality of all programs eliminated
  cross-subsidization of programs unless board
  could justify it strategically
• Senior management team and board does better job
  prioritizing now understand the financial costs
  associated with their time (time is money)

12
B. Fundraising

• Client heavily dependent on annual fundraising
• In good years, fundraising flat or marginally up.
  In bad years, suffered 10-15 drops in
  fundraising
• Wanted to reverse the trend
• Fundraising staff overwhelmed 10,000 priorities
  on desk, did not know where to focus Events?
  Relationship building? Education?
• Sitting on treasure trove of fundraising data
  that was untapped

13
1. Seek to understand why some donors increased
gift by 100, while others barely increased,
others stayed flat or dropped?
Donor

• Leveraged database and donor survey
• Database held giving, events, solicitor, etc
• Hypothesized drivers tested 50 actionable
  activities
• Tested across 26 affiliates in various cities

14
2. Focused on what statistical analysis revealed
as top issues

• Organization educates me about charitable giving
  values

22
15
3. Dismissed other long-held assumptions

• Missions same effect on gift increase as
  sending person to a conference (which is much
  cheaper)
• Placement on board no effect on giving
• Criticism of operating efficiency drives down
  giving no evidence, in fact ignorance is the
  problem.

16
4. Used Dashboards to Motivate Action
Question Rank (of 15) Str Agr Priority Strategy
Aggregate charitable values 7 25 Low A
Improved alignment over 3 years 2 35 Low B
Education on charitable values 14 16 High C
16
17
5. Results

• One local affiliate
• Restructured staffing in its development
  department to focus on donor education,
  involvement, and personal relationships
• Eliminated missions program
• More transparent about educating donors on
  operating costs and why

18
C. New Data Measurement System

• Experiencing some with member drops, financial
  insolvency.
• Challenge to business model brand consistency.

Individual Members / Users (N1000 to 10,000 per
affiliate)
19
1. Association sought early warning system
understanding of what led to successful outcomes
20
2. Build measurement and dashboard system as
pilot experiment

• Engaged 6 willing chapters
• Built
• Customer survey
• Employee survey
• Financial analysis tool
• Member participation tool

21
3. Focused on rankings and metrics that were
statistically valid

• Regression analysis ties activities to outcomes
• Benchmarks and Comparisons
• Against peer chapters in other geographies
• Against local competition from other
  organizations
• Against own prior measures (longitudinal)
• Within demographic segments of member base
• (see following slides)

22
4a. Identify activities associated with outcomes
(multiple regression analysis)

• Budget Management versus Value for Membership
  Dollar
• Correlation

Value for Membership Dollar Average Score 1-5
Scale
Perceived Budget Management Transparency (1
strongly disagree, 5 strongly agree)
23
4b. Scores compared to peers
24
4c. Scores by demographic segments
Age
Years of Membership
Frequency of Participation
Income
25
5. Like doctor, ran diagnostics each year
Select Measures from Customer Survey Rank (of 15) Score Str Agree Priority Goals Strategy
Member Value for the Dollar 7 25 Medium Focus on quality, budget perceptions
Professionals welcoming 2 35 Low
Budget Perceived as well managed 14 16 High Double scores in 2 years.
26
6. Turnaround focus lead to improvement from low
to average in 2 years now aiming for top
Personal Conversations with Customers Monthly or
more often
27
27
7. Chapter ruled by anecdotes 80 of assumptions
were not supported by data

• Assumed they were strongest with eldest
  wealthiest portions of membership
• Shock, challenged the data, acceptance
• Management team focused energies on improvement
  with eldest and wealthiest. 2 years later,
  scores significantly higher there.

Likelihood to recommend to a friend Strongly
Agree
28
8. Top performer set out to redefine limits of
excellence marketed success
Surplus Margin for Early Childhood
Program Surplus as of Expenses, after
allocating all overhead
X
X
29
9. New association policy value, not price

• Assumed that key driver of member retention was
  price
• Analysis shows not price, but perceived quality
  and value-for-dollar
• Result association stopped encouraging price
  subsidization, encouraged perceived quality
  improvement
• Perceived value and value for dollar are tracked
  carefully and promoted system wide

Demand Function
30
10. Outcome improvement membership

• Chapters that embraced this approach outperformed
  others significantly in member enrollment and
  financial sustainability, despite the recession
• Member Retention Rate
• 91 to 96
• New Member Rate
• 5 to 10
• Net Membership
• 96 to 106
• Financial sustainability increased coverage
  ratio ( expenses from fees membership) grew
  from 74 to 80

31
QA
32
Data encourages prioritization80 of board
mgmt team hypotheses about what we anecdotally
believe is a problem or issue is not supported
by the data!
33
From Anecdotal to Data-Driven Decision Making

1. Identify issue
2. State hypothesis I believe
3. Perceived mechanism/ cause
4. Design experiment
5. Examine data
6. Confirm or reject hypothesis

34
Data Creates Alignment Intentionality
35
Alignment
36
Who is Measuring Success?

• Firm that combines advanced analytics,
  quantitative tools, and consulting to help
  management and boards improve their mission and
  financial performance
• Dedicated to shifting the culture of nonprofits
  and associations from anecdotal to data-driven
  decision making
• Designed custom shared measurement systems for
  more than 10 associations in the past 5 years,
  implemented across hundreds of affiliates and
  tens of thousands of members.
• Use survey tools, financial analysis tools,
  tracking tools, and benchmarking to design its
  custom dashboards
• Use quantitative prediction models to help
  organizations identify which activities have the
  greatest impact on desired outcomes.
• Offices in Washington, DC and Cambridge, MA
• www.measuring-success.com

37
Contact Information

• Sacha Litman
• Managing Director
• Measuring Success, LLC
• Office 202-684-7024
• Cell 917-370-5836
• E-mail sacha_at_measuring-success.com
• Websites www.measuring-success.com