Adcieo recently co-hosted a data management webinar with Third Sector Labs. The topic - "Why don't you have a data management plan?" - focusing on why you need, and how to create, a nonprofit data management plan.

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Present
&
Why Don’t You Have a Data Management Plan?
(it’s not that hard)

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Welcome and Agenda
Please participate in our online poll
while we get organized
Today’s agenda
1. Why we don’t have data management plans
2. Why we need them
3. How to create one
4. What to do with it

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Our Experts
Gary Carr
President & CEO
Third Sector Labs is a data services
company helping nonprofits to re-think
their data management practices and
solve data problems.
ThirdSectorLabs.com
gcarr@thirdsectorlabs.com
www.linkedin.com/in/gpfcarr
Leading digital marketing firm serving the
nonprofit community through strategic
planning, implementation and support for
multi-channel fundraising solutions.
adcieo.com
debbie.snyder@adcieo.com
linkedin.com/pub/debbie-
snyder/0/448/89b
Debbie Snyder
VP, Sales & Marketing

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LEVEL 1:
ASSESSMENTS AND
CLEANING
LEVEL 2:
DATA MANAGEMENT,
ENRICHMENT, MIGRATION
LEVEL 3:
WAREHOUSING, MINING,
VISUALIZATION
Also Exclusively Serving Nonprofits
---- Data Services ----
Founded in 2013 by professionals with 20+ years of technology and data experience with
Fortune 500 companies, the federal government, and nonprofits
Offices in Washington, DC and Seattle, WA metro areas
www.thirdsectorlabs.com

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FUNDRAISING STRATEGIES
• Strategy consulting
• Multi-channel campaign
development
• Change management
• End user support
DIGITAL MARKETING
• Website design
• Custom development
• Implementation
• Mobile apps
DATA MANAGEMENT
• Database migration
• Data integration
• Data hygiene
Exclusively Serving Nonprofits
Digital marketing consultants helping nonprofits create, launch and manage multi-channel
fundraising strategies.
www.adcieo.com

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Let’s get started
If you are a typical nonprofit …
• You have a strategic plan
• You have a fundraising plan
• You do NOT have a data management plan
• But your fundraising success depends on data
Hmmmm ….

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Why don’t we have data
management plans?

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Why don’t we have data management plans?
1. We have lots of other plans!
2. Data is intimidating
3. We have a dba, what else do we need?
4. We cleaned our data last year – is that what you
are talking about?
5. Plans are time consuming to create
6. We plan events … we react to data

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When we think of data management plans …
Examples of data
management plans

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It may feel like this …
But it’s not

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Why do we need data
management plans?

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6 reasons why we need them
1. Data degrades
2. More data than ever to deal with
3. More (newer) technology
4. Fundraising plans change
5. Competition for donors‟ attention
6. Not having a plan is wasting time and money
Let’s talk about these a bit more …

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1. Data degrades
“If your data isn‟t getting better, it‟s getting worse.”
-- TSL data scientist
“Why?”
-- audience

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Data degradation
What does that mean?
Data degradation – [DAY-tuh deg-ruh-DAY-shun], noun
Refers to the worsening of data quality over time. With assets like a donor database, degradation is inevitable. Why? Because
of the many, sometimes unavoidable, negative influences acting on your data quality. These include: consumer data naturally
changes as people change jobs, relocate, have families, and go through the normal cycles of life; data inputs are often flawed
and/or manual, and the manual labor can be poorly trained; related data is changed, purged or updated; data migration to new
systems such as CRM software.

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Data degrades
Cause #1: your organization
– Lack of data entry standards
– Unskilled data entry workers
– Common mistakes
– Record fragmentation
Cause #2: the technology
– Multiple, disparate systems
– System upgrades
– Integration, processing errors
– Sheer volume of data
Cause #3: those darned donors … life!
– Change in address … every 5 to 7 years
– Change in jobs … 9 to 11 jobs in a lifetime
– Family / life event … divorce rate, birth of children, death … what else?

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Your CRM view of your data
Manage those
contact data
changes.

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Our view of your data (once we export and analyze)
Sal
utat
ion
Last
Name
First
Name
M.
I.
Address 1 City State Zip Phone Email DOB Gender
MR Setters m
MS SIMMS Laurie 1313 Danger Ln Appleton CA 73111 310.555.5555 Laurie@mail 04/29/81 F
Mr. singletary Mike T 310.555.1234 mts@mail.com
Singletary Michael 310.555.1234 mike@mail.com M
Solvington Allen 5201 Marshall
Lane
Cupertino CA 91001 323.555.5990 also@mail.com 05/30/75
Mr. soprano Cindy P. 222 Main St. Cupertino CA 91002 cindy@mail.com f
Dr. Standish Bradford 1141 Duke Ave Los
Angeles
CA 91010
Stevens Allison 8726 Elm Ave Appleton CA 90009 310.555.5551 01/01/01 f
STEVENS ROBERT 2 2101 Data Ave Los
Angeles
CA 91010 rs2@mail.com 12/14/60 m
Sr Tahoma Juan 20B Eldora Mexico
City
+52-55-5222-
2222
jtahoma@mail.com 01/14/59 M
Incomplete
record
Salut./gender
mismatch
Incomplete email
CA ≠ 73111
Multiple
normalization
issues
M.I. = 2 ?
Moved last year
(NCOA)
Foreign address ?
DOB is bad
Potential dupe (ph #)

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2. More data than ever
Donation$
Special events
volunteering
Emails
and
emails
Broker lists
Socialmedia
Job change
Address change
Financial transactions
newsletters
Directmail
Alma mater
Family updates
polls
Robo calling
spreadsheets
Donations to
other charities
Onlinesurveys
We leave
footprints
everywhere

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3. More technology Takes us from
this …

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More technology
Aha!
Here she is!
To this …

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Recent TSL webinar poll question
How many different technologies doe you
depend upon to manage a fundraising
appeal/campaign?
• 53% of attendees used 3 – 5 technologies
• 47% used 6 or more
• 0% used 1 - 2
But …

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4. Fundraising plans change
Raise
more
money
1. From
new / more
donors
2. Who we
reach over
more
mediums
3. With
specific
messaging
4. Relying
on outcome
reporting
5.
Targeting
donor
segments
6.
Identified
by data
analysis

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5. Competition
1. Nonprofits encounter donors / prospects /
volunteers / advocates in more „places‟
2. There is more “noise”
3. Data cuts through the noise to
– Anchor outcomes
– Communicate results
– Establish and maintain relationships

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6. Wasting time and money
Some examples are very visible
• Direct mail production and delivery costs
• Spam, ISP blocking
• Staff time

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Wasting time and money
Some are hidden
• Mail to a deceased donor
• Make a large ask to a donor with small giving potential
• Make a small ask to a donor with a large giving potential
• Hospital references wrong healthcare issue when trying to
build up a new relationship
• University sends rejection letter to a student and donation
request to his parent … on the same day!
Lost donors = ??

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Remember
Data is your organization’s knowledge
and memory
What you know
What your organization
knows
Vs.

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How do we create a data
management plan?

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How to create a data management plan
• Multiple types of data
– Donor
– Event
– Newsletter
– Social
– Service outcomes
– Financial
• Plan for each
• We will focus on constituent / donor data for the
rest of this presentation
Constituents

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How to create a data management plan
Remember …
• We aren‟t trying to get to the moon
• Start simple and …
• Be practical.
• Think of a data management plan as a
commitment to proactively manage
your data!

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Data management plan: 3 questions
1. Where do you want to
go?
2. Where are you now?
3. How will you get from
here to there?
You need a map … and a
plan
Map first!!
Assess problems
Revise db
Web capture
THERE
HER
E
Assess
problems
Clean
data

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The map
1. Determine the data you
need to support your
fundraising strategy
– Markets, segments, messaging
2. Get a data quality
assessment
3. Determine your data gaps
– What do you need but are not
collecting?
Assess
problems
Clean
data
Web
capture
THERE
HERE

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The plan – phase 1
1. Document your
fundraising/events/communications schedule
2. Set that aside!!!
We are going to separate data management from
events management Fundraising / events
schedule
Data management
schedule

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The plan – phase 2
1. Prioritize the data quality
problems from the data
assessment / gap analysis
that you intend to address
2. Create one or more tasks
to close each gap
Assess
problems
Revise db
Web
capture
THERE
HERE
Assess
problems
Clean
data

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The plan – phase 2
3. Create a new schedule for data
management … monthly,
quarterly, per data type needed
4. Establish data quality standards
for data governance

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Sounds harder than it looks
… an example

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Remember: start simple
The map
1. What data do you
need
2. Data assessment
3. Gaps
The plan
4. Prioritize gaps
5. Action item for each
6. Schedule
7. Governance

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Step 1: (The map) What data do you need?
Start with your fundraising plan
Fundraising strategies Tasks
1 Target lapsed donors 2 communications
2 Increase prospects 10% Outreach thru web, events
3 Convert 20% of Target conversion plan for
constituents to donors newsletter subscribers, event
attendees, volunteers
4 Develop donor Data analysis to produce
segmentations 3 – 4 segments for future
communications
… helps #3

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Step 1 (cont.)
Continue to the data requirements
Fundraising strategies Tasks Data requirements
1 Target lapsed donors 2 communications Lapsed report, contact info
2 Increase prospects
10%
Web, event outreach Upgrades to website features,
Event forms for data capture
3 Convert 20% of
constituents to donors
Target conversion plan
for newsletter
subscribers, event
attendees, volunteers
Run a constituent report,
identify donor data fields
needed, can you create
donor profiles?
4 Develop donor
segmentations
Data analysis to produce
3 – 4 segments for
future communications
Same as #3

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Step 2: (Map) Data assessment says
1. 20% of records are duplicates
2. 30% lapsed donors
3. 15% incomplete addresses
4. Not tracking gender, DOB,
alma mater, family status and
other data needed to segment
messaging
5. 35% of all constituents are
also donors

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Step 3: (Map) Gaps to close
1. Bad data to be cleaned
2. Duplicate records to be removed
3. Address fields to be completed
4. 10 new data fields to be captured
– DOB, gender, alma mater, family, employer, other
charitable interests, contacting preference, social media
usage, etc.
5. Donor profiles to be completed (from #3 and #4)

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Step 4: (The plan) Priorities
1. Clean bad data and remove duplicate records
2. Enable capture of more data – easy tasks
– ID needed fields
– Add fields to db
– Add website registration option for Facebook login
– Create Facebook page
3. Address clean up
– NCOA check
4. Enable capture of more data – harder tasks
– Marketing service to capture website visitors
– Add survey and poll questions to website, newsletter
– Develop communications piece to invite more constituent
“conversations” and sharing of data
5. Determine new data segmentations to support fundraising
in the future

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Step 5: (Plan) Actions
Fundraising
strategies
Tasks to support Data requirements Actions to improve
data quality
1 Target lapsed
donors
2
communications
Lapsed report,
contact info
A – clean data
B – update addresses
2 Increase
prospects 10%
Web, event
outreach
Upgrades to website,
other forms of data
capture
A – marketing service
to capture web
visitors
B – event capture tool
C – social media / Fb
3 Convert 20% of
constituents to
donors
Target conversion
plan for
newsletter
subscribers,
event attendees,
volunteers
Constituent report,
expanded donor data,
donor profiles
A – add new db fields
B – add website data
capture features
C – data broker
D – test donor profile
capacity and analysis
4 Develop donor
segmentations
Data analysis to
produce 3 – 4
segments for
future
communications
Same as #3 Similar to #3 … but
focus on segments
you want to market to
for future fundraising

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Step 6: (Plan) Schedule
First 3 months
Clean bad data
Modify dstabase(s)
Improve website registration
Create Facebook page
NCOA clean up
Second 3 months
Data broker service for one-time
augmentation
Implement website survey, polls
Lapsed donor data cleanup (after
lapsed donor campaign has
completed)
Third 3 months
Create new donor segmentations
Test against target message
marketing program
Fourth 3 months
Measure results of segmentation
Revise data management plan
Every quarter
Data cleaning
Re-run assessment
Measure new data
collection methods

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Step 7: Data governance
What is that?
Data governance– [DAY-tuh GUHV-er-nuhns], noun
A set of rules or policies that encompass the people, processes and technologies required to create and maintain higher quality
data assets for an organization. Data governance goals resulting from higher data quality include: better compliance with third
party standards, decreased risk of regulatory violations, improved decision making, improved data and organizational security,
and greater profitability.

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In other words, standards
1. Schedule for monthly or quarterly data
management … cleaning, enrichment, etc.
– Be proactive, not reactive
2. What defines a “complete” record?
– Focus on better data, not more
3. How old is too old?
– Depends on the type of record?
4. How many versions do you retain?
– How many old addresses?
– Event attendance records?

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Also …
1. Control data inputs
– Via people, systems, imports
2. Review data capture tools against strategic data
needs for fundraising regularly
3. Do you enable donors/consumers (or a subset) to
manage their own information via online
accounts?
4. Do you have self-select removal processes from
(e)mailing lists?

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When it comes to data …
garbage in, garbage out

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What do we do with our
data management plan?

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Do this!
1. Assign responsibilities
2. Budget
3. Work it for 6 months
4. Measure results
– Quality of data up?
– Event or appeal results?
– Data governance standards being followed?
– No more delays for communications due to reactive data
cleaning?
– Cost savings
5. Review with leadership
6. Revise and continue

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Let’s wrap it up
Takeaway
Old thinking: we plan our events, we react to data
New thinking: we plan our events AND we plan our
data management

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We’d like to hear from you!
Please submit your questions…
Q & A

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Thank You!
Gary Carr
President & CEO
ThirdSectorLabs.com
gcarr@thirdsectorlabs.com
linkedin.com/in/gpfcarr
adcieo.com
debbie.snyder@adcieo.com
linkedin.com/pub/debbie-snyder/0/448/89b
Debbie Snyder
VP, Sales & Marketing