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Magnifying Glasses and Crystal Balls
1. Magnifying Glasses
and Crystal Balls
Use your data to raise more today and
predict the future
1/2/2014
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2. YOUR PANEL
Bob Dillane, Director, Enterprise Information Systems
Lebanon Valley College
Cari Maslow, Senior Director, Donor Relations and Membership
Carnegie Museums of Pittsburgh
Teri Morrow, Membership Director
Appalachian Mountain Club
Stephanie Reyes, Manager, Business Intelligence Group
JCA
Steve Beshuk, Director, Business Intelligence Group
JCA
1/2/2014
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3. SESSION AGENDA AND GOALS
• What is Business Intelligence?
• Case Studies
- Lebanon Valley College: The Power of a Data Warehouse
- Appalachian Mountain Club: Tracking Members Source Using BI
- Carnegie Museums of Pittsburgh: The Power of Cubes
• Predictive Analytics
• Q&A
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4. WHAT IS BUSINESS INTELLIGENCE?
1/2/2014
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5. BI IS…
Business intelligence is a set of theories,
methodologies, processes, architectures, and
technologies that transform raw data into
meaningful and useful information for
business purposes.
1/2/2014
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6. BI IS…
Business intelligence is a set of
theories, methodologies, processes, architect
ures, and technologies that transform raw
data into meaningful and useful information
for business purposes.
1/2/2014
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12. BI AND NON-PROFITS
• The Challenge
- The data landscape has changed – there’s a lot more of it now
- Constant struggle to grow revenue and keep costs in check
- Today’s donors are demand results
- Unfortunately, many nonprofits are behind the curve
• The Opportunity
- Today’s BI gives you the tools to leverage your untapped ―data asset‖
- Sophisticated BI does not have to mean expensive
- BI can pay for itself – results!
1/2/2014
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13. CASE STUDY: THE POWER OF A DATA WAREHOUSE
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14. LEBANON VALLEY COLLEGE
Lebanon Valley College in Annville, Pa.,
FAST FACTS
welcomes 1,600 full-time undergraduates
• Guaranteed degree completion in 4 years
studying more than 30 majors, as well as self-
designed majors. Founded in 1866, LVC has
graduate programs in physical therapy,
business, music education, and science
education. Annville is 15 minutes east of
Hershey and 35 minutes east of Harrisburg;
Philadelphia, Washington, D.C., and
• 3 out of 4 LVC students choose to live in
excellent, safe, guaranteed campus housing
• Low student-to-faculty ratio of 13 students to
each professor allows for personal interactions
and a customizable education
• High-achieving students, 1/2 of whom were
in the top 20% of their high school class,
continually teach each other while learning
together
Baltimore are within two hours.
• 98 % of students receive some form of
financial assistance with an average aid
package of more than $24,500
1/2/2014
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15. TRANSACTION DATABASE VS. DATA
WAREHOUSE
• A transaction database is designed for efficient data entry
• A data warehouse is designed for ease and speed of reporting
• These are two very different design goals
1/2/2014
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16. DO YOU NEED A DATA WAREHOUSE?
• Technical Concerns
- Unless you have a large database, technology is not a big concern
• Practical Concerns
- Can I get answers with a data warehouse that I can’t (efficiently) get without
one? (the answer is virtually always ―yes‖)
- Will those answers improve our ability to perform our mission?
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17. COMPLEXITY OF THE RAISER’S EDGE
• SQL code to query event participant attributes directly from the
Raiser’s Edge database:
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18. SIMPLICITY OF A DATA WAREHOUSE
• Same query against the warehouse:
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19. GENERAL USES FOR OUR WAREHOUSE
• Reports we can’t do, or can’t do easily in RE
• Reports that need data from outside RE
• Perform calculations and import data back to RE
• Create custom gift analysis warehouse generated from JCA Answers
warehouse
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20. FUND REPORTING
• This is on fund categories, not funds. Our goals are by category
instead of individual funds.
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22. USING GIFT ANALYSIS WAREHOUSE
• The Gift Analysis table has information about gifts, not donors.
• ConstitGifts table has links donors and gifts, with a flag for the type of
linkage (hard credit, soft credit, match credit, etc.)
• Using these tables, we can quickly answer questions such as:
- ―How much did each donor give to the Mund Buidling Fund, the Annual Fund
and any endowment funds (3 separate totals) between July 17th and
September 12th?‖
• For frequently asked amounts (annual fund, etc.), we generate totals
nightly and import them to RE as constituent attributes. This extends
the capability of query and export in RE.
1/2/2014
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23. OTHER WAREHOUSE USE IN SUPPORT OF
RE DATA
• We also use the warehouse to calculate the alumni/parent year code
that is used with constituent names.
• We analyze:
- What degrees and graduation years the individual has
- What undergraduate degrees and graduation years the person’s children
have
- We calculate an alumni/parent code and import it back to RE as an Add/Sal
1/2/2014
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24. REPORTING TOOLS USED AT LEBANON
VALLEY COLLEGE
• SQL Server Reporting Services is our formatted report writer
• Entrinsik Informer as our primary add hoc query tool
• Crystal Reports for some formatted reports, but we are phasing this
out
• Excel with Microsoft Query to access the warehouse
• JCA Answers as our primary data warehouse
-
1/2/2014
Warehouse rebuild runs at 8:00 p.m.
Various calculations are run and exported from the warehouse
Calculations are imported into The Raisers Edge
Warehouse is rebuilt again at 7:00 a.m.
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25. CASE STUDY FROM APPALACHIAN MOUNTAIN CLUB:
TRACKING NEW MEMBERS BY SOURCE USING
BUSINESS INTELLIGENCE
1/2/2014
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26. ABOUT APPALACHIAN MOUNTAIN CLUB
• Helps people protect, enjoy, and understand the mountains,
forests, waters, and trails from Maine to Washington, DC.
•
•
•
•
•
1/2/2014
86,000 members; 16,000 volunteers; 20,000 advocates
12 chapters
Backcountry huts, camps & campsites, and front country lodges
Maintain over 1,800 miles of trail; 350+ miles of the AT
8,000 volunteer- and staff-led activities
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27. BUSINESS INTELLIGENCE AT AMC
• Implemented in 2010
• Reporting
-
Appeals
Member number
Member retention
New members
To Finance
Leaving Madison Spring Hut
Photo by Chris Lawrie, AMC New Hampshire Chapter
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28. THE “OLDEN DAYS”
• Standard 2010 new member report by source
Revenue
Avg. Dues
Payment
Total Cost
to Acquire
Cost/
Member
Net/
Member
351
$10,118
$28.83
$15,515
$44.20
($15.38)
Direct Mail
7,742
$250,936
$32.41
$619,509
$80.02
($47.61)
Email/Web
4,641
$242,555
$52.26
$21,693
$4.67
$47.59
Gift
Memberships
659
$35,048
$53.18
$5,374
$8.15
$45.03
Miscellaneous
988
$31146
$31.52
$27,897
$28.24
$3.29
Reservations
852
$52,277
$61.36
$10,651
$12.50
$48.86
1,374
$65,841
$47.92
$66,906
$48.69
($0.78)
16,607
$687,921
$41.42
$767,544
$46.22
($4.79)
Source
New
M’brshps
Collective
Buying
Telemarketing
to Formers
Total
1/2/2014
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29. THE UNKNOWNS
•
•
•
•
•
1/2/2014
What is new member retention by source?
What is retention rate, added giving, cost to renew?
Incorporated BI data into long-term reporting
Appeals coded with type and channel (AQDM, RNWB)
Type and channel split in BI—track at the channel level
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30. 2011 RAW BI DATA
1st Year
Retention Pool
1st Year
Retained
1st Year
Retained $
Avg. Dues
Payment
277
74
27%
$2,150
$29.05
7,742
3,367
47%
$113,566
$31.23
Email
174
49
28%
$2,331
$47.57
Miscellaneous
646
236
37%
$9,851
$41.74
Reservations
979
324
33%
$19,166
$59.15
TM to Formers
1,900
602
32%
$28,865
$47.95
Web
4,459
1,828
41%
$87,939
$48.11
Total
16,177
6,750
42%
$263,866
$39.09
Source
Collective Buying
Direct Mail
1/2/2014
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30
%
Retained
31. ADDING CONTEXT WITH BI
2011 #
Retained
M’brshp +
Annual Fund $
2012 #
Retained
M’brshp +
Annual Fund $
Avg. Dues
Payment
74
$2,328
33
$1,542
$37.24
Direct Mail
3,367
$135,693
2,443
$124,894
$39.82
Email/Web
1,877
$106,114
1,120
$79,875
$53.49
Gift Memberships
100
$5,254
85
$5,291
$50.51
Miscellaneous
236
$13,684
141
$11,541
$46.27
Reservations
324
$22,766
207
$15,981
$62.14
TM to Formers
602
$41,651
269
$22,496
$53.93
6,850
$327,489
4,298
$261,620
$45.74
Source
Collective Buying
Total
1/2/2014
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32. FILLING IN THE DETAILS
Year 3
Retention
Source
M’brshp
Revenue
Total
Cost
Total Ann.
Fund
Giving
Net/New
Member
Year 3
M’brshp
ROI
% Ann
Fund
Giving
Collective
Buying
9%
$17,825
$13,497
$491
($10.93)
$0.78
4%
Direct Mail
32%
$657,151
$461,779
$49,744
($18.81)
$0.78
11%
Email/Web
24%
$46,681
$392,731
$35,813
$82.28
$9.18
9%
Gift
Memberships
13%
$8,225
$43,670
$1,923
$56.70
$5.54
4%
Miscellaneous
14%
$33,212
$47,521
$8,850
$23.44
$1.70
19%
Reservations
24%
$15,039
$84,305
$6,718
$89.18
$6.05
8%
Telemarketing
• Re-calculated 20% $75,938of
retention as
to Formers
2012 and re-calculated$39.34
ROI.
$109,212
$20,776
$1.71
19%
Total
$1,152,714
$1.50
11%
1/2/2014
26%
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$854,071
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$124,315
$25.47
33. IN THE MEANTIME…
• 2013
- Dropped Groupon/Living Social
- Reduced cost of direct mail acquisition
- Hired staff to boost non-direct mail/non-telemarketing efforts
• Growth in web/email sales slow
• New members from guest reservations up nearly 300%
• Memberships purchased by visitors at AMC huts, camps, and lodges
fewer than 100 in 2010 now up to nearly 900 in 2013.
• Tracking 2011 and 2012 new members
• Guest stay data not available
• No volunteer data in database
1/2/2014
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33
34. PLAN FOR 2014
• Cut direct mail acquisition by 20%
• Use Google Grant (awarded 8/2013)
• Develop plan to boost retention and Annual Fund support from gift
memberships
• Assess non-Membership department sales for 2014 growth potential.
Crisp Colors
Photograph by Nicholas Gagnon, AMC New Hampshire Chapter
1/2/2014
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35. CASE STUDY: THE POWER OF CUBES
1/2/2014
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36. CARNEGIE MUSEUMS OF PITTSBURGH
• Founded in 1895
• 4 Distinct Museums
–
–
–
–
Carnegie Museum of Art
Carnegie Museum of Natural History
Carnegie Science Center
The Andy Warhol Museum
• Serve 1.3 million people annually
1/2/2014
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37. THE NEED FOR BUSINESS INTELLIGENCE
• Pressure to increase revenue with shrinking resources
• Mountains of data in Raiser’s Edge but no easy way to get answers to
questions
• Answers we had weren’t actionable and instead just led to more
questions
• No easy way to measure strategy performance which led to a
culture of adding initiatives but not discontinuing any already
implemented
1/2/2014
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38. THE QUESTION OF RETENTION RATE
• Projecting revenue was the impetus for taking what seemed to be a
simple question further
• With an on-going, multi-hit renewal series, we needed to estimate not
only what percentage would renew but when they would renew
• Knew the makeup of the population would impact the results but had
no way of knowing which characteristics were most impactful
1/2/2014
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39. WHY DOES IT MATTER?
The retention rate is 63%
SO:
# of members x average gift x 63% =
Overall Renewal Revenue Projection
BUT:
• Makeup of population impact both
the monthly and the year-to-year
results
• Needed to predict when in the
solicitation cycle renewals would
happen
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39
40. BEFORE JCA ANSWERS: THE EVOLVING
RETENTION SPREADSHEET
Multi vs. First Year Members
• Use different renewal strategies
• Run monthly query off an attribute set
for segmentation of the first direct
mail hit
• Didn’t answer the ―when‖ question
• Confirmed theory that the makeup of
the monthly populations mattered
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41. BEFORE JCA ANSWERS: THE EVOLVING
RETENTION SPREADSHEET
Timing
Next we started running multiple queries and plugging the counts into
this spreadsheet data table
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42. BEFORE JCA ANSWERS: THE EVOLVING
RETENTION SPREADSHEET
The retention rate at each time point then calculated into summary
tables
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43. SO…
•
•
•
•
•
1/2/2014
It worked but…
Labor Intensive
Risk of human error high
Not an easily transferable task
Technical: had to make friends in IT
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44. BUT…
• No clarity around the month to
month variations
- Large Variances
- Not Consistent Year over Year
• Multi vs. First was not enough
population segmentation
• Still left us without answers when
asked ―Why…‖
• AND then we changed our
upgrade solicitation method which
further skewed the model
1/2/2014
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44
45. MORE QUESTIONS
•
•
•
•
•
How many times do I have to get a member in the door?
Does renewal rate change if I have an active email address?
Does it matter whether they visit one, two or all three museum sites?
Are the increasing number of member events making a difference?
Retention gains in one year don’t appear to hold, why not?
Everything we did to try to impact retention was still just an educated
stab in the dark.
1/2/2014
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46. WITH JCA ANSWERS WE COULD DATA MINE
Is there a
difference
between joins
& rejoins?
What’s going
on with the
Individuals?
True of all
multi years
or does
upgrading
impact?
Does it matter
how many
years?
Do the rates by
category stay
consistent
month to
month?
Does the
purchase
method impact
the rate?
1/2/2014
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What about
timing?
47. DELVING INTO THE MONTH OF EXPIRE
By month:
SENR
5%
Membership category:
January
INDL
3%
PREM
19%
DUAL
23%
FMLY
50%
SENR
4%
INDL
2%
DUAL
18%
March
PREM
22%
FMLY
54%
1/2/2014
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48. DELVING INTO THE MONTH OF EXPIRE
• Greater Detail on Family Level
• Renewed vs. Upgraded:
1/2/2014
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• Number of Gifts:
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49. IDENTIFICATION OF SIGNIFICANT FACTOR
January
• Purchase Method:
DM - Direct
Mail
52%
March
DM - Direct
Mail
38%
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50. WITH JCA ANSWERS
• Ability to identify which factors determine the larger variances in
retention
• Role out testing that focuses on chosen factors and monitor results
- Years of Membership
- Join vs. Rejoin
- Direct Mail Retention by Zip Code
• Ability to target specific groups of people with different offers &
communication
- Non-visiting members
1/2/2014
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51. IS THERE A MAGIC NUMBER OF YEARS?
• Nugget: Getting
them into their 3rd
year is key
Why is the pattern
changing in 2012?
• New Strategy:
Extend 1st year
discounting to 2nd
year members
2012
2010
1/2/2014
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52. JOIN VS. REJOIN
• Nugget: Depends on the
level of membership
• Strategy Idea: Switch
telemarketing acquisition of
lapsed members from Family
& Premium to Dual, Individual
and Senior levels
1/2/2014
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53. ACQUISITION LIST PURCHASE
• Nugget: Zip codes with strong purchase
data don’t always have strong retention
rates
• New Strategy: Work with direct mail
firm to use data from Answers when
making list buys
1/2/2014
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55. AQUARIUM
• Implemented JCA Answers for The Raiser’s Edge and Gateway
Galaxy about 4 years ago
• Have an integrated BI environment
• Incorporated budget information into data warehouse
• Have been able to better understand deferred revenue reporting
• One department alone saw savings of 500 hours in staff time a year
due to efficiencies made possible by JCA Answers.
1/2/2014
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56. ART MUSEUM
• Converted to The Raiser’s Edge about 4 years ago
• Came to JCA initially for a data warehouse
• Now have an integrated BI infrastructure that combines their Raiser’s
Edge and Ticketmaster VISTA ticketing data
• Starting to do more work in Predictive Analytics – initial savings:
$20,000 annually
1/2/2014
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58. LOOKING INTO THE CRYSTAL BALL
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59. PREDICTIVE ANALYTICS IS HERE
• Rise in Google Searches of ―Predictive Analytics‖
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59
60. PREDICTIVE ANALYTICS IS HERE
• Rise in Google Searches of ―Predictive Analytics‖ versus ―Business
Intelligence‖
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61. WHAT IS PREDICTIVE ANALYTICS?
Predictive analytics is business intelligence
technology that uses predictive models built from your
data to make predictions about the future.
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61
62. WHERE DO WE BEGIN?
• Start with a business question
How do I raise more money?
versus
How can I increase the number of membership renewals?
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63. PREPARATION IS KEY
• Do we have all the data we need?
• Is our data usable?
1/2/2014
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64. LET’S EXPLORE
• Explore your data
• Keep your business question in mind
1/2/2014
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65. PREDICTIVE MODELS
• Models use patterns found in your data to identify risks and
opportunities.
• Models apply scores to your constituents, which can help guide your
strategy for improving outcomes
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65
66. PREDICTIVE MODELS
• Naïve Baise
- Used for classification
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66
67. PREDICTIVE MODELS
• Decision Trees
- Used for classification, regression and association
1/2/2014
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69. IMPROVE YOUR MODEL
• Models are a work in progress.
• Test before deploying your model.
1/2/2014
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70. THE THREE R’S OF PREDICTIVE ANALYSIS
• Reliable – Your predictive model must be accurate.
• Repeatable – You need to be able to use your model more than once.
• Relatable – You need to understand the results.
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72. FOR A COPY OF THIS PRESENTATION
• Leave us your card
• Send us an email at bi@jcainc.com
• Stop by our booth: 107/109
• Tweet us @BI_JCA
• Go to our website: www.jca-answers.com
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Editor's Notes
Magnifying Glasses: looking at yesterday to see what you can learnCrystal Balls: peering into the future - these are accomplished using BI…we are going to talk about what it is and how it can help youThe tools and technology that enable us to do that are called Business Intelligence. We spend a little time talking about the parts of BI (try not to be too boring, but its important to understand what “lies underneath”)Then we will move into the case studies, hear how about BI in the real-world, spend most of our time herePredictive analytics – crystal ball part. starting to get into the cutting-edge stuff here…goal is to introduce you to it so you can take the next stepLast, we will leave some time for questions, but really okay to answer any as we go along.
I don’t want to spend a lot of on the definition of BI, but I think its important that we are all thinking of the same thing.Here is the definition from Wikipedia – that about covers it…but there is a lot going on in that sentenceTheories – like what?Methodologies – which ones exactly?Processes – like entering a gift? Running a report?Technologies – oh yeah…enough to make your head spinDoes that include collecting data? Coding it? Queries? Reports? Visualizations? Analytics? Systems integrations? Yes. It is so general that it can mean a lot or not much, depending on your view point. There is one word that I think is important and we will focus on today: useful.BI must help you make more and spend using the less data in your database.We are going to hear about different ways that organizations are using BI to get results.That being said, there are a few things that we will need to understand before we jump into that. Let’s discuss a few key terms that we should all understand. These terms go hand-in-hand when we talk about BI and they will be things that you’ll hear much more about from our presenters today. This is the stuff that underlies BI.
These are the bones, the basics., that will form the core of BI. There is a lot to know about each step, but what we want to communicate here is that it is about optimizing the data to make it useable, to make it BI-ready. Taking it from data to knowledge.It starts with RE (or any OLTP system – where you enter the data).As we move left to right, the tools optimize the data for usability.I will talk a little more about each part, but wanted you get understand that BI, from a technology standpoint, isn’t that complicated in terms of what we need to understand.Put your data warehouse, cube it, and then make it graphic. Simple as that! (now doing it, well, it gets more complex)You need all parts of this. You can go out an buy really cool dashboard software, but its not worth a lot without the stuff in the middle. It’s like when my daughters brought home a goldfish from the fair…that was great, it was easy and it was visible. But I needed a lot more to make them viable.Let’s look a little closer at each part.
The data warehouse is a foundational pieces of a BI infrastructure. Start with RE – transactions, data entry. 700 tables, not very usable in that state.The magic of ETL happens. Take it out, transform it/denormalize it (not a term you need to know, it means make it a lot more usable, optimized for reporting and analytics) and then load into the warehouse.It goes from 700 tables to 100, or 150.The image on the screen is a very simplified view of what happensOn the left we have a snapshot of what tables might look like in a production database, such as Raiser’s Edge.On the right is the view of what that data would be transformed into in a data warehouse. We go from a huge jumbled-looking mess to a single, consolidated table that’s going to make it easier for us to pull data for reporting and analysis.You have achieved real BI power at this point, if you do nothing else. A warehouse is a beautiful thing.We don’t show it here, but the warehouse can and should be a central repository that brings together multiple sources. This puts your warehouse on steroids.I won’t talk too much more about warehouses as this is something that Bob will be talking about in his section.
The Data Cube. Now we are getting into the cool stuff. You will also here these called OLAP or OLAP cubes. We will call them cubes.Cubes are often pre-summarized across multiple dimensions to drastically improve query time. It’s hard to conceptualize what “multi-dimensional” means and you don’t need to, any more than you need to conceptualize an internal combustion engine to drive a carThe image on the screen shows you how you have multiple dimensions: constituent, gifts and events. The cube creates tremendous amount of data by intersecting those three perspective.What it means to us is speed and depth at the same time. You can report on millions of records at a time and then decide to change it in the it takes you to think about it.Pulling this sort of information directly from a system like Raiser’s Edge can be exponentially more time consuming.
At the beginning I talked about how BI is all about being useful, getting results. When we talk about results we often get into conversations about KPIs, key performance indicators. The usually leads to someone asking for a dashboard.Data visualization is the visible, flashy aspect of BI. Its extremely valuable. Communicating information is as important as the information itself.Some people can look at a large table and get what they need. Others, prefer basic charts, while some need dashboards that look like they belong on a spaceship.Don’t start with the tool, start with what you need to measure, what is the business question, what data do you need to see, and what is the simplest way to share it (without being too simple).The list of data visualization tools is as long as your arm and they range from really-not-free. Start simple.If you have a warehouse and cubes, that may be all you need.You may need only Excel (we are considering this at JCA).Or, you may need a powerful visualization tool.Let the need drive the choice, don’t choose the tool first (do push-ups at home before you buy the gym membership).But dashboards don’t have to be complicated nor expensive. You can get started with basic dashboards with Excel. And you can create some pretty sophisticated dashboards with Excel with a little effort.
Here are some examples of visualizations you can use. There are a sea of other options out there, but most are variations on these themes.
CHALLENGEMore data: we have access to more data now than ever before, and that is only going to continue in that directionThis is on a spectrum, whether it’s getting to the data you have, in one system, bringing in data from other systems (finance, programs, ticketing, the web), or adding data from third parties; or moving into the world of “big data” and seeing what your constituents are doing on facebook, twitter, linkedinIt’s not enough to just collect it allThe struggle to earn more, spend less. Most nonprofits are under tremendous pressure to take steps to make more, but aren’t always able or willing to invest.The unfortunate upshot form that is that nonprofits seem to lag behind because BI and technology can be expensive or intimidating or resource-intensive. OPPORTUNITYLeveraging BI, when done smartly, is the ultimate win/win. You are able to solve the riddle of “how do I unleash the power of my data” that will result in creating anew asset at your organization.“It’s too expensive” doesn’t have to apply to BI. It can, but it doesn’t have to.You can do all of the things that we have talked about today, and more, with tools that are reasonably priced.It can be the ultimate win/win. More power, better tools, and it pays for itselfWe are in the early stages of some very cool BI offerings at JCA. We are coming it at it with mindset: if they don’t pay for themselves, you shouldn’t have to pay for it.Enough with the preamble…One of the goals of this session is to talk about taking advantage of concepts that are leading edge and make them applicable and realistic for non-profits. Bob, Teri and Cari are going to share examples of what they’re doing at their organizations. They come with different experience and work in different areas and are at different points in their “BI journey”. Through each of their case studies we’re going hear about how they’re using BI to get results. Let’s start with Bob Dillane from Lebanon Valley College.
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* Where NPO BI is headed* How to build them* What they tell you
Now we’re getting into the “Crystal Ball” portion of our presentation. There are a lot of organizations out there that make business decisions based on assumption or gut feeling. BI is the direct opposite of that and we’ve heard about how some organizations are using BI to make fact-based decisions. The final area we’ll talk about today is also throwing that gut-based decision making to the side. We’re going to talk about “Predictive Analytics.”
Predictive analytics isn’t a new concept but it’s definitely on the rise right now. For-profits have been doing predictive analytics for years and some larger non-profits are already doing it. Some of you may already be looking at this. If we look at Google Trends we’d find that there has been a 300 percent spike in searches for predictive analytics over the last few years. The image here on the screen shows that spike. This image starts out in 2005 when not too many of us were thinking about predictive analytics. It ends where we are today and you can see there’s been a substantial increase.
I thought this image offered an interesting, slightly different perspective. This plots the number of searches for predictive analytics, in blue, against the number of searches for business intelligence, in red. As you can see, there are a lot more searches for business intelligence but what’s interesting is where the two are trending. We see that searches for Predictive Analytics are on the rise but business intelligence is starting to go down.So this is great, lots of people are interested in predictive analytics but what the heck is it? Before we go any further, let’s talk about what predictive analytics means.
Well, as you see from the definition on the screen here, predictive analytics is all about using data to make informed decisions about the future. No more relying on your gut alone!I thought this image did a nice job of summing up how BI leads us to this place where we’re talking about predictive analytics. Up to now everything we’ve been talking about is really focused on what’s happened in your business already and what’s happening now. This next level is anticipating what is about to happen. You can monitor and track real-time data and trends over time. That’s great but, again, the entire point of BI is about results. It’s about making smarter decisions going forward before its too late. Looking into that crystal ball!For example, if your analysis shows you that your direct mail solicitation did not go well because you included too large a segment of constituents who don’t have a history of responding to direct mail appeals, there isn’t much that you can do at that point if you’ve already paid for printing and postage. Predictive analytics helps you see what kind of challenges and opportunities you will face before you face them so that you have a chance to act on it proactively. Business intelligence just doesn't get more actionable than that.As Steve stated when we first started out, we wanted you to leave today’s session much more informed about predictive analytics. If you’re looking for results and you want to turn to predictive analytics to help you get there, I want to make sure you’ve heard more about what goes into it. As we saw when we talked about BI, there is a ton of technology out there to help you start down the path of predictive analytics: IBM, SAP, Oracle, Rapid Insight, Microsoft. Regardless of the tool you use or if you’re doing it yourself or hiring someone to do it for you, you need to be an informed consumer and understand more about what goes into predictive analytics. Let’s walk through this a little bit more.
The one place that absolutely every predictive model needs to start at is a business question. What is the business question you’re trying to answer? We have to define this question before we can begin. In order for any of this to be meaningful for your organization, your question must be specific to your business. It must take into account the specifics of your organization otherwise its not going to hold much value for you. So let’s use a hypothetical situation to guide us through this. Let’s say I have a client who comes up to me and says, my business question is: “How do I raise more money?”Well, good question but this is a big question. We need to break this down even further to get to something that we can work with. So I might tell my client, “Okay, how do you make money?”Let’s say my client is a zoo and they say: “I sell memberships.”So maybe we’d talk about selling memberships and determine that membership renewals is a huge revenue area. So one way to make more money would be to renew more members. Now we’re getting to a slightly different question. Now we’re asking, “How can I increase the number of members who renew?” which, as a result, is going to help me make more money.
So we have our question and we know that we’re going to be looking more closely at member renewals. If you think back a few slides when we had our definition of predictive analytics up on the screen, you remember that predictive analytics uses models built on your data to make predictions about the future. So before we can get too far, we’ve got to make sure that 1) we have the data we need and 2) that it’s usable. One great analogy I’ve heard is that you have to think about this step as the Zamboni machine that goes over the ice before a hockey game. It’s got to make the ice nice and smooth before they can play. This data preparation step is doing just that.If you’re data is a mess or you’ve got it spread across multiple, disconnected databases, it’s going to be hard to make sense of it. Going back to our membership renewals example, if you want to figure out how visitation impacts member renewals but you have no way of knowing when a member visits, it’s going to be impossible to include this factor in your model.Another thing to consider in this stage is getting the data to a place where it’s ready to be explored. Often we can’t do this directly from a source database. For example, it’d be much harder to prepare your data going directly from Raiser’s Edge. So this is when a lot of people turn to a data warehouse. Sound familiar? We already heard about data warehouses and what they can do for an organization. They absolutely come into play here as well.
Okay, so we’ve got our question, our data is in a good place. Now what?Next we want to explore our data. For many of us, we’ve spent years collecting a tremendous amount of collecting tons of valuable information about our constituents but we may not have spent a lot of time digging into the data and trying to understand what’s there. In this step, we want to get our hands a little dirty and see what’s in our database. What’s one of the ways you can explore your data? Well, you can use the data cubes you’ve also heard about.One of the objectives of this step is to make sure that you’re exploring your data all the while keeping your business question in mind. If we’re wanting to increase member renewals, we need to think about this as we’re exploring our data to make sure that our data is going to allow us to answer this question.
Now we’re ready to build a model. As I said earlier, predictive analytics is built on models. These models use patterns found in historical and transactional data to identify risks and opportunities. The model will apply predictive scores to your constituents. Each constituent's predictive score should inform the strategic actions that you’ll take so you can improve outcomes, in other words, get results.It would be so nice if there was just one model out there and it automatically did what we wanted it to do. Unfortunately that’s not the case. There are several different types of models so before we can even begin to build one, we have to choose which one we want to use.
JCA has a Business Intelligence Group where we’re doing a lot of work these days with predictive modeling so we’ve spent time thinking about these different types of models. I’ll share a couple today that are options as part of Microsoft‘s Analysis Services predictive models. I’m not going to cover all of them but I wanted to share a few so that you’d be familiar if you ever heard these come up again.The first is Naïve Bayes. This model is based on theorems that I’m not even going to attempt to go into.This model is primarily used for classification.So for instance, let’s say that we’re going to send out a direct mail solicitation. We want to reduce costs so we only want to send it to constituents who are likely to respond. Well, if we look in Raiser’s Edge, we’ve got a ton of demographic data and information about what a constituent has responded to in the past. We want to use this data to see how demographics such as age and location can help predict response to a mailing, by comparing potential donors to donors who have similar characteristics and who have given to us in the past. Specifically, we want to see the differences between those constituents who gave and those who haven’t. By using the Naive Bayes model, we can quickly predict an outcome for a particular donor profile, and can therefore determine which constituents are most likely to respond.
Another type of model that some of you might have heard of is called Decision Trees. This model is primarily used for classification, regression and association.Let’s say we’re throwing a gala. Our Events team is trying to identify the characteristics of previous event ticket buyers that might indicate whether those constituents are likely to buy gala tickets in the future. Again, we have our database where we store a wealth of demographic information that describes previous gala ticket buyers. By using the Decision Trees model to analyze this information, our events team can build a model that predicts whether a particular constituent will purchase gala tickets, based on what we know about the constituent, such as demographics or past buying patterns. As the model works through each characteristic of a constituent, we get closer and closer to seeing exactly the type of person who will buy a ticket.
The last model type I’ll bring up today is called Clustering. This model is primarily used for segmentation and grouping, as the name implies.This model will group constituents who share similar demographic information and who share similar profiles, such as donors who consistently give by mail versus those that don’t. This group of people represents a cluster of data. Several such clusters may exist in a database. By observing the characteristics that make up a cluster, you can more clearly see how records in a dataset are related to one another.
Predictive models are living things. They are approximations that are going to start out okay but get better and better as you continue to work on it. You keep tweaking the model until you can’t get it any better. You’ll often hear people refer to training the model. You need to keep testing your model so that if the type of model you chose or the data you have isn’t giving you good results, you can tweak a previous step in the process or consider using a different type of model.
Predictive models can be great to help you predict the most likely outcome, but what's the best that could happen. We couldn’t get into the nitty gritty details of predictive analytics in our one session alone but hopefully this brief introduction is helpful. Throughout your predictive analytics journey there are a few guiding principles you want to keep in mind. I thought these three R’s were a nice way to remember it all.Reliable – Your predictive model must be accurate. This means that you’ve got go through that data preparation step and make sure you’re building your model off good data.Repeatable – You shouldn’t be going through this modeling exercise one time and then walking away from it. You need to be able to replicate results across multiple time periods. You should have a framework that allows you to apply the model time and time again so you go beyond a one-time project.Relatable – This applies in several different ways. You need to make sure that you’re starting with a business question that means something for your organization. Once you get to seeing the results of your model, you need to understand the results. Seeing a bunch of impressive looking statistical terms or fancy looking models does you no good if you don’t know what they mean.