This document discusses how advanced analytics and predictive modeling can help associations achieve strategic goals. It contrasts business intelligence with data science and explains how predictive modeling fits within an association's analytics framework. The document also provides examples of predictive models that associations could use, such as models to predict meeting attendance, purchasing likelihood, or long-term revenue from new members. Finally, it discusses how AFP has used predictive analytics to improve initiatives like customer journeys, onboarding programs, and community platforms.

1. The foresight of advanced
analytics
Date: December 14, 2016
Time: 1:45 pm
Galina Kozachenko, Director Strategic Data Analytics, AFP
Kelly Baker, Chief Analytics Officer, Association Analytics

2. What & Why Data Analytics
I never guess. It is a
capital mistake to
theorize before one has
data. Insensibly one
begins to twist facts to
suit theories, instead of
theories to suit facts.
Sir Arthur Conan Doyle,
Author of Sherlock Holmes
stories

3. BI or Data Science?
Business Intelligence Data Science
Deductive Reasoning Inductive & Deductive
Reasoning
Backward Looking Forward-Looking
Slice and Dice Data Interact with Data
Warehoused & Silo-ed Data Distributed, Real Time Data
Analyze the Past, Guess
the Future
Predict & Advise
Creates Reports Creates Data Products
Analytic Output Answer & Create
Questions. Provides
Actionable Answers.

4. Where Predictive Modeling fits into an
association's analytics

5. AFP Analytics Maturity
Always
2005
2012
2016
The value of
an idea lies in
the using of it.
Thomas A.
Edison,
American
Inventor

6. Data Analytics must align with Strategy
Is what you are trying to
explain going to help you
achieve your strategic goals?
Is insight being delivered
to the right people at the
right time?
How do you embed data
analytics into your
organization strategy?

7. AFP – Technology
Web
Data
CRM
Data
Demographic data
Computed attributes
Purchasing data
Collaborate
Emails
Google Analytics

8. AFP – Discover and Embed
Data
Discovery/
Visualization
Recommendations
Key insights
Data
Collection
Domain
Knowledge
Strategic
Roadmap
Business
Stakeholders

9. AFP – Discover and Embed

10. ACTAnalyzeCleanCollectScope
Predictive Modeling in the Association
Analytics Framework

11. A Typical Predictive Modeling Project:
Tablea
u
R

12. - Segmentation
- Marketing plan
- Strategy
alignment
- Execution
- A/B testing
- Collect
data
- Feed back
into the
model
From deployment to evaluation

13. Predictive Model - insights

14. Segmentation & Marketing Plan
Identified 4 segments (key business drivers channels)
Marketing plan – engage 90 days before renewal and 60 days after
Conference Channel 1, 2, 3, 4
Certified Email 1, 2, 3, 4
Certification Channel 1, 2, 3, 4
Membership Channel 1, 2, 3, 4
Email 5 (All four groups get this same email)

15. Performance – Aug-Oct 2016
E1 E1 Open E1 Click E2 E2 Open E2 Click E3 E3 Open E3 Click E4 E4 Open E4 Click E5 E5 Open E5 Click
Certification 188 46% 2% 166 30% 8% 130 33% 7% 115 31% 1%
Certified 210 50% 2% 219 43% 6% 100 31% 1.5% 100 32% 3%
Conference 83 11% 0% 116 28% 6% 87 21% 3% 88 19% 1%
Membership 199 28% 1% 211 29% 2% 130 25% 2% 125 26% 1%
Total 680 36% 1% 712 34% 6% 447 28% 3% 428 27% 2% 298 20% 2%

16. Things we are behind on

17. Predictive analytics is like painting a Golden
Gate bridge

18. Snow ball effect
Customer Journey
initiative
On-
boarding
program
Community
platform

19. Snowball effect – community platform
Selection + Implementation (with SSO) - 60 days

20. Snow ball effect - onboarding
DAY
1
DAY
3
DAY
7
DAY
10
DAY
17
DAY
24
DAY
31
Netforum
Welcome
MM
Welcome
Collaborate
Welcome
MM
Community
MM
Get Involved
MM
Content
Collaborate
Update Bio
Collaborate
Intro Yourself
Collaborate
Login & Browse
Collaborate
Upload Pic
DAY
38
DAY
45
DAY
52
DAY
59
MM
Career Development
DAY
90
MM
New Member Survey
DAY
275
MM
Renewal

21. Snowball effect – tell a story with Marketing
Automation
• Closer partnership Marketing+Data Analytics
• Re-designed automation
• Feed results from one into another

22. Customer Journey Initiative

23. New vs Existing customers
- Increased only marginally
- Conversion rate decreased
Decreased
New Customers
Existing customers

24. Customer Journey Initiative
Customer
New
Content
Marketing
Audience
Nurture
Existing
Predictive

25. Content Marketing and Audience Nurture
Awareness
Interest
Decision
Action
Easy
Money
Engaged
Interested
Expansion

26. Customer Journey
Identify your
best
customer
Collect
information
about them
Target
similar
customers
Ensure you
are meeting
their needs
Retain
them

27. Associations and predictive analytics
• Meeting Attendance Model to predict which members will attend an
upcoming meeting
• Decision Tree to visually show how different paths affect conversion
times.
• Interactive Tool created from a regression model that helps
individuals see how different decisions will impact outcomes.
• 20-Year Revenue Model that calculates net present value of a new
member and thus total membership revenue -- including what-if
scenarios from simulation results for each variable.
• Purchasing likelihood Model to predict whether/when an individual
or company will purchase an item.

28. “If you can’t explain it simply, you
don’t understand it well enough.”
- Albert Einstein, Physicist

29. Contact Us
Galina Kozachenko
Association for Financial Professionals
gkoza@afponline.org
Kelly Baker
Association Analytics
kbaker@associationanalytics.com