Better Decision Making Through Analytics

Better Decision-Making Through
Analytics
JCC Association Executive Seminar 2013

Brian Hayden

Collaborative Strategies, Inc.

© 2012 | www.getcollaborative.com

1

Agenda!

!   Current Context!

!   Why Analytics Matter!

!   Applications!

!   Getting to the Next Level!

!   Summary!


2

output from the machine, or even actively prevented it. In a number of cases, the processes—the machine
itself—were not strategically designed and thus no amount of data input could help to create better
Relevant Factors!
decisions.

The Data Machine

Inputs
Financial and Expertise Prioritization Money Technology
Operations Data of Time
Internal
Marketing, Factors
Communications
and Fundraising
Data

Programs and
Outcomes
Data

External
Data

Data-
Driven
xternal
External Funder Funder
Decision
Factors Requirements Support
Making

A well-functioning nonprofit data decision-making 3
process provides numerous possibilities for nonprofits to

Deﬁnition! Source: Competing on Analytics!

What’s the best that !
Optimization!
can happen?!

Predictive modeling! What will happen next?!

Forecasting/
What if these trends continue?!
Competitive advantage!

extrapolation!

Statistical analysis! Why is this happening?!

Alerts! What actions are needed?!

Query/drill down! Where exactly is the problem?!

Ad hoc reports! How many, how often, where?!

Standard reports! What happened?!

Degree of intelligence!


4

It Doesn’t Have to be Calculus!

!   You ARE using data to make decisions if you:!
!   Have and review a budget!
!   Use a dashboard to communicate results to your board!
!   Track click-through rates to web and email campaigns!
!   Track early childhood or camp utilization rates!


5

But the Potential is Far Greater!

!   JCCs have a wealth of data… but it’s all over the place. !
!   Dedicated systems (membership, fundraising, ﬁnancials)!
!   Excel spreadsheets!
!   …!
!   Like most organizations, JCCs collect a lot of data that doesn’t
get analyzed.!
!   Like most not for proﬁts, JCCs tend to under-invest in
technology.!
!   The right skills aren’t on the team. !
!   Analytics is no one’s job. !
!   No one has time. !


6

Analytics - Real Time!!

https://www.surveymonkey.com/s/jcca-es!
!
!
(Answer ﬁrst 3 questions, click next, then stop.)!


7

Biggest Challenges to Utilizing Data!

he following section explores these challenges in greater detail.
30%
25% 27%

20% 24% 23% 22%

15%
10%
5% 6%
0%
Data Expertise Technology Prioritization and Money
Collection/Quality Time

ata Collection Source: Nonproﬁt Technology Network!
he ability to collect and work with data is a barrier reported by many nonprofits. In the course of the
urvey, © 2013 | www.getcollaborative.com

8
we asked specific questions about organizations’ abilities to collect data on programs and on

What We’ve Seen in Benchmarking!

!   System A doesn’t talk to system B. !
!   Example: Closing a membership sale from a tour.!

!   There are lots of holes, sometimes in surprising places. !
!


9

Tracking Programs and Outcomes
Tracking program and outcome-related data should be the bread-and-butter for nonprofits because it’s one
of the best ways to articulate what they are delivering and the extent to which they are delivering on their
mission. However, fewer Not-for-Proﬁt Community! were measuring information about
The Larger than two-thirds of survey respondents said they
programs in which their clients or constituents take part, and just half reported tracking information about
client or constituent outcomes.
90% Tracking
Finding it Useful for Spending or
80% 89% Budgeting Decisions
85%
Finding it Useful for Programming
70% Decisions
73%
60%
50% 59% 59%
49% 50% 49%
40%
41%
30%
20%
10%
0%
Your financial actuals vs budget Information about what programs Information about outcomes
specific clients/constituents take part in of clients/constituents

The ones who are tracking this information find it useful for making decisions about programs, and most of
Source: Nonproﬁt Technology Network!
them find it useful for budgeting purposes as well.
10
As the focus groups showed, the range of programmatic data that nonprofits track runs the gamut of

so—44 percent of those respondents report that they do not have the technology to do so, while 41 percent
report they lack the time and/or money.

Marketing, Communications and Fundraising Data
While many nonprofits are tracking various type of marketing, communications and fundraising data—what
we refer to as “outreach” data—our survey indicated a surprisingly low number are actually using that data
The Larger Not-for-Proﬁt Community!
to make decisions.

80% Tracking this Metric
Finding it Useful for Spending or
70% Budgeting Decisions
71%
69% Finding it Useful for Programming
60% Decisions

50% 56%
48%
40% 44%

30%
20% 23% 39%
17% 26% 17%
10% 12% 11%
7% 8% 9%
0%
Number of people on Number of new Number of visitors to Number of comments Number of people
your mailing list donors in the past your website you receive on who open emails that
year Facebook you send out
N= 396

Source: Nonproﬁt Technology Network!
Among this outreach data, metrics related to fundraising performance—such as the number of new people
added 2013anwww.getcollaborative.com

© to | organization’s mailing list or the number of new donors—are tracked by more than two-thirds of
11

Agenda!



!   Applications!


!   Summary!


12

Why Measure?!

To Rationalize:! To Inspire:!
! !
•  Transcend politics! •  To tell a story!
•  Prove value! •  To reassure ourselves!
•  Improve efﬁciency! !
•  Improve ROI!
•  What gets measured gets
managed!

Source: Performance Measures and the Rationalization of Organizations!


13

Funder Perspective!

!   Jim Collins: You want people to give you money because you are
good – not because you need it. !
!   Donors want a (social) return on their investment. Can you
demonstrate outcomes?!


14

What’s the Private Sector Doing?!

!   Capital One runs 300 experiments a day on new product
initiatives.!
!   Progressive Insurance offers lower rates to drivers willing to
install real-time monitors in their cars. !
!   Retailers of all varieties are pushing loyalty cards on customers
to monitor their habits. !
!   Amazon uses past purchases to show you advertisements for
other products you might like. !
!   Location-based advertising is one of the fastest growing
segments of marketing.!
!   And…!


15

Why?!

!   Beyond intuition!
!   Creating transparency!
!   Enabling experimentation to discover needs, expose variability,
and improve performance!
!   Segmenting populations to customize actions !
!   Supporting (or replacing) human decision making with
automated algorithms !
!   Innovating new business models, products, and services !

Source: McKinsey!


16

Agenda!



!   Applications!


!   Summary!


17

•  Greater revenue!
Objectives! •  Lower cost!
•  Higher satisfaction!

Participation Analytics!

Beginner! Advanced!
•  Cross-selling! •  Identiﬁcation of members
most at risk of cancelling!
•  Exchanging lists with
other community •  Targeted marketing!
organizations! •  Discounting/promotional
optimization!
•  New member acclimation!
!
!
!


18

•  Greater revenue!
Objectives:!
•  Greater retention!

Philanthropy Analytics!

Beginner! Advanced!
•  Campaign analysis! •  Cross-referencing
members and donors!
•  New/lost trends!
•  Lapsed donors!
!
•  Moves management!
•  Third party research tools!
!
!
!
!


19

•  Resource optimization!
•  Strategic direction!
Objectives!
•  Compelling
communications!

Outcomes Analytics!

Beginner! Advanced!
•  User surveys! •  EC: Kindergarten
readiness tests!
•  The JCC Benchmarking
Project! •  Senior Adults: mental and
physical wellness tests!
!
•  Longer-term Jewish
impact?!

!
!
!

20

Case Study: The “Value Matrix”!

Financial Results! Non-Financial!
Direct! After Participation! Outcomes! Unmet
Allocations! Need! Grade!
Program 1!
Program 2!
Program 3!
Program 4!


21

•  Identify top performers!
Objectives! •  Succession planning!
•  Optimize training!

Talent Analytics!

Beginner! Advanced!
•  HR database ! •  9-box talent planning!
•  Performance Reviews! •  Extend HR database to
include skills and training!
•  360 Reviews!
!
!
!
!


22

Case Study: 9-Box Talent Planning!
5
Expectations
Exceeds

High Exceptional Top Talent
Performer Performer
4
Performance!

Expectations

3
Achieved

Solid Key Rising Talent
Performer Performers
2
Development
Needs

Lower Inconsistent Under
Rating 1

Performers Performer Developed
.
Level 1 Level 2 Level 3

Potential!

23

•  Right skills!
Objectives!
•  High engagement!

Board Analytics!

Beginner! Advanced!
•  Skills/background matrix! •  Periodic assessments!
•  Fundraising involvement! !
! !
!


24

Agenda!



!   Applications!


!   Summary!


25

Two Paths!

Business Strategy!

Data Strategy!


26

Business!

Data!

Culture!

!   Analytics is a decision-making culture. Culture starts at the
top… with you. !
!   What expectations are you setting regarding decision criteria? !
!   How is this trickling down the organization? !
! Doesn’t mean YOU have to be a PhD in advanced mathematics. !
!   Don’t make analytics “one more thing”. Integrate it into existing
decision-making processes.!


27

Business!

Data!

Planning Your Strategy – Where to Start!
•  Understand your
performance drivers.!

2
1
!Yes!

•  Take it in stages. !
Data easy to get?!

•  Quick wins are
!

important.!

•  Better to capture less
!

data and actually DO

2
No !

something with it. !
ignore
•  Team: Leaders from
program, IT and
ﬁnance; use lay talent
No ! ! ! !Yes! when available!
High Impact?!


28

Business!

Data!

Expertise!

!   Every manager doesn’t need to be an Excel wizard. But every
manager should appreciate the use of data to inform decisions. !
!   Do you have sufﬁcient quantities of analysts?!
!   Make the necessary training available. !
!   Consider analytical abilities in future hires.!
!   This isn’t just about your staff. Who on your board can help with
strategy? Include analytics on your board skills matrix.!


29

Business!

Data!

The Role of the Data Champion!

!   Someone has to champion “good data” throughout the JCC. !
!   This doesn’t have to be a dedicated IT person.!
!   The days of managing IT as a utility are gone. !
!   Data (and IT) can be strategic differentiators and should be
managed accordingly. !


30

Business!

Data!

Your Data Strategy!
You Know You’ve Made it
When:!
!   Figure out what you have. Clean it up. ! •  Staff spend more time
ANALYZING data than
!   Based on your strategy, what don’t COLLECTING it. !
you have that you need?!
•  Staff never argue over
!   Standardize your deﬁnitions (e.g. whose data are more
accurate. !
when do you consider a membership
“cancelled”)! •  Staff that need data access
have it (directly).!
! •  Hypotheses can be quickly
tested without an inordinate
amount of manual
manipulation. !
•  Reports include data from
multiple sources.!
!


31

Low Hanging Fruit: The Benchmarking Project!

!   Cultural reinforcement!
!   Creates good habits!
!   Low cost!


32

Questions?


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