Dana Hines, CFRE, President & CEO, Membership Consultants, Inc.

Steve Jacobson, CEO, JCA

Rosie Siemer, Founder & CEO, FIVESEED

Analytics to Action:

Translating Data to Strategy

@MemberConsult

fb.me/MembershipConsultants

linkedin.com/company/mem

bership-consultants

Dana Hines, CFRE dana@membership-consultants.com

314-771-4664 ext 105

membership-consultants.com

@JCA_inc

fb.me/JacobsonConsultingApplic

ations

linkedin.com/company/jca

Steve Jacobson

steve.jacobson@jcainc.com

212-981-8405

jcainc.com

@RosieSiemer

fb.me/RosieSiemer

linkedin.com/in/rosiesiemer

Rosie Siemer

rosie@fiveseed.com

303-880-7105

fiveseed.com

What We’ll Cover Today

Best practices in:

• Using data to uncover trends

• Analytics/scoring framework

• Identifying high risk members

• Strategies to retain first-year members

• Ways to reach new audiences Analytics to Action

Membership is Changing

• Big Data

• Data-driven

• Audiences expect more

• Technology is accelerating

_____

“In God we trust, all others must bring data.”

_____

W. Edwards Deming

American Engineer, Statistician, Author

METHODOLOGY:

Scoring to Find At-Risk Members

Analytics Method

• Make it a project

• Involve stakeholders

• Create milestone dates

• Begin preliminary data review (make friends with IT)

Prepare

• Define your “analytics” goal

o What is the behavior of interest?

o What are we trying to affect?

o What have we done to affect it in the past?

• Understand the current process/strategies related to the goal

• What are the current metrics?

• How will you measure success?

Understand

• How do we grow our member base?

Analytics Question • How do we make more money?

• How do we increase at-risk renewal rates?

• We’ve seen our rates for first year members falling and don’t

know why. We want to understand them so we can improve.

• We have tried a lot of things to increase renewal rates, we

think we’ve exhausted that. Instead, let’s focus on getting

more people to join (grow the denominator).

• We want to cut costs while keeping our rates the same.

• We are not sure where we should focus. Our goal is to

increase rates across the board. Where should and shouldn’t

we spend our money?

Example Business Questions

• Establish the number you will use to measure success

• What are your renewal rates?

• Are they stable?

• Are you including the grace period?

Set the Baseline

Renewal Rates Over Time

New Members Over Time

Are Your Rates Stable?

What is the Right Grace Period?

• Modeling begins with brainstorming

• What are the things that might predict renewal likelihood?

• Where does that data live?

• How do we bring it all together?

Model

Model Demographics

Address

Distance to museum

Geographic Region

Requested address change

Age

Contact Preference

Butterfly

Do Not Contact

Do Not Email

No Acknowledgments

Send Newsletter

Gender

Interest

Ancient

Asia

Contemporary

Photo

Film

Textiles

Engagement

Art Class

Attended an Art Class for free

Day of week of Art Class

Discount used on Art Class

transaction

Purchased Art Class

Engagement

Audio Tour Purchase

Made a purchase in the shop

Film

Attended a film festival

Attended a Film for free

Genre of Film Transaction

Lecture

Workshop

Visitation

Exhibits

Exhibit Name

Exhibit Ticket Purchases

Number in Party

Average number in party

Maximum number in party

Median number in party

Visitation

Count of visits during

membership

Museum Visit

Recency of Visit

Visited museum on a free day

Giving

1st Transaction

Appeal

Discount used on first trx

First Gift Pay Method

Gift Amount

Giving

% change in size of second gift

Days to 2nd gift

Gave a Tribute gift

Number mail gifts

Number of event gifts

Pay Method

Cash

Check

Credit Card

• Evaluate each factor and determine if it is statistically

significant

• Examples from brainstorming

Are the Factors Significant?

Prior giving (Y/N) Gift of membership

Gift amount List

Time since first gift Member level

Channel Acknowledgment turnaround

Expiration month Special exhibit

Distance to venue

Learning What Matters

This is a Model

This is a Model • All models are wrong. Some are more wrong than others.

• What is the sweet spot?

Deploy the Model • Plan the deployment

• How will you measure the effectiveness?

• Experiment

• Identify the specific change you will test

• Verify how you will segment the groups to adjust for known

effects

• Use results to improve model

What Was Learned: Atlanta Botanic Garden We’ve seen our rates for first year members falling and don’t know why.

We want to understand them so we can improve.

• Learned the change to renewal strategy had a negative impact, undo

that.

• Get rid of the “guilt” message on the renewal notice.

We have tried a lot of things to increase renewal rates, we think we’ve

exhausted that. Instead, let’s focus on getting more people to join (grow

the denominator).

• New members coming from lists are more likely to join (and renew),

grow that effort.

• Those living within 50 miles are much more likely to join, focus there.

What Was Learned: Appalachian Mountain Club We want to cut costs while keeping our rates the same.

• Reduce investment in the very low and very high scores.

• Design an experiment to reduce mailings to selected groups to see

how far they could cut back without affecting return.

We are not sure where we should focus. Our goal is to increase rates

across the board. Where should and shouldn’t we spend our money?

• Reduced the investment in sure things and reallocated it “saveable”

members.

• Noticed bi-modal peaks, members between ages 30 and 50 less likely

to renew, determined is was due to having kids. Reallocate the

renewal investments to marketing to 30 year old, marrieds.

Case Study: Museum of Fine Arts, Boston

Member demographics Membership Details Member Experience

Giving Prior Relationship

300 Variables

11 Significant Variables

Geography

TRUE OR FALSE?

Renewal rates were highest for members living in the local

community.

FALSE – The only geographic significance was that they lived

in New England.

Visitation

TRUE OR FALSE?

Renewal rates were higher for those members who visited the

most often.

KINDA TRUE – Members who visited 3 or more times were

more likely to renew. However, if they visited more often, the

renewal rate was not any higher.

Age

TRUE OR FALSE?

As a first time member, the older you are, the more likely you

are to renew.

TRUE – The older you are, the more likely you are to renew –

until you hit 74+, then you might not renew due to other

factors, like death…

MULTICHANNEL MARKETING:

Insights to Action

Botanical Garden

Following a blockbuster exhibition,

a botanical garden improves first-

year renewal rates.

• The challenge – retain more

members after the blockbuster

• Attracted 10,000 new members

during a repeat of an amazing

blockbuster

• After previous blockbuster,

retained 20% of new, first year

members

• Strategy: Use the phone to pre-

emptively renew those

vulnerable members

• Results:

o Retained 32% of first year

members after the full

renewal process was

complete

o Phone results achieved a

14% renewal rate prior to the

regular renewal process

being deployed

Botanical Garden

FY FY 09-

10

FY 10-

11

FY 11-

12

FY 12-

13

FY 13-

14

FY 14-

15

First Year

Retention 31.19% 22.00% 30.45% 23.79% 28.33% 32.34%

Renewal Rates

• Tracking renewal rates by month, recognizing seasonality

• Know the effect of each renewal touch

• Identify vulnerable months, seasons, target offers for

those times

Tracking Renewal Rates By Month Membership Number of First Number of Second Number of Third Number of Fourth Current

Expiration Dates First Renewal Second Renewal Third Renewal Fourth Renewal Number of

Renewals Rate Renewals Rate* Renewals Rate* Renewals Rate Non-renewals

January 1,035 0.29% 1,032 19.49% 822 29.08% 734 51.88% 498

February 900 5.00% 855 17.22% 745 29.11% 638 47.22% 475

March 1,021 4.41% 976 13.22% 886 24.19% 774 40.45% 608

April 1,328 9.11% 1,207 27.56% 962 35.47% 857 46.99% 704

May 1,543 14.19% 1,324 29.81% 1,083 36.94% 973 47.96% 803

June 1,254 8.93% 1,142 20.33% 999 32.78% 843 44.90% 691

July 1,368 7.75% 1,262 21.86% 1,069 32.82% 919 39.04% 834

August 1,259 8.02% 1,158 16.04% 1,057 31.77% 859 41.22% 740

September 816 6.37% 764 14.34% 699 32.11% 554 38.11% 505

October 645 7.44% 597 21.24% 508 32.25% 437 37.83% 401

November 761 8.02% 700 23.26% 584 29.96% 533 36.01% 487

December 901 6.33% 844 16.87% 749 30.08% 630 36.18% 575

Total 12,831 7.56% 11,861 20.79% 10,163 31.80% 8,751 42.94% 7,321

Marketing Automation, Mobile & Retargeting

• Trigger events

• Welcome series

• Automated renewal sequence

• Personalize by behavior

• Dynamic content

• Text messaging

• CRM retargeting

Insights to Action

DATA MODELING:

Identifying Your Best Prospects

Acquisition

• Modeling lapsed members

• Identify best prospects from ticket

buyers: Conversion Tags

• Modeled acquisition lists from

Blackbaud for prospect lists

• Response modeling/List optimization

Target Tags

# Prospects # Response Income Ave. Gift Resp. Rate Cost Net Income CPDR PPM Cost ROI

Tag A 7,265 347 $45,318 $131 4.78% $6,329 $38,989 $0.14 $0.37 $7.16

Tag B 7,503 285 $37,905 $133 3.80% $5,949 $31,956 $0.16 $0.37 $6.37

Tag C 7,813 200 $27,062 $135 2.56% $5,948 $21,114 $0.22 $0.37 $4.55

Tag D 7,731 195 $25,469 $131 2.52% $5,379 $20,089 $0.21 $0.37 $4.73

Tag E 7,967 135 $17,762 $132 1.69% $5,444 $12,318 $0.31 $0.37 $3.26

Tag F 7,958 130 $17,252 $133 1.63% $5,369 $11,883 $0.31 $0.37 $3.21

Tag G 7,852 120 $16,219 $135 1.53% $4,963 $11,256 $0.31 $0.37 $3.27

Total 54,089 1,412 $186,987 $132 2.61% $29,048 $157,938 $0.16 $0.27 $6.44

Science Center Lapsed Tag Performance

ACQUISITION:

Advanced Techniques for Targeting

The Marketing Funnel

Create a path to membership and

giving. Awareness

Interest

Desire

Action

Who?

• Behavior

• Lookalike audiences

• Niche and aspirational audiences

Expanded Email

• Reach new audiences

• Take the first step

Target Profile

Expanded Email

Retargeting is a behavioral

based targeting technique.

A cookie-based technology that

uses simple a Javascript code to

“follow” your audience around the

Web.

Kinds of Retargeting

Behavioral based targeting. • Website visitors

• Email openers and/or clickers

• Abandon cart

• Search and browsing

• Related or competitor websites

• CRM (data onboarding)

• Facebook ad interaction

Primary Cult Fan

Facebook

• Lookalike

• CRM Retargeting

• Website Retargeting

• Birds of a Feather

Critical Mass + Data

LOYALTY:

Data in Action

Integration

• Internal Systems

o POS, Databases, Website,

Email

• External

o Social Media, Partners

• Exhibits

o Beacons, NFC

• Data capture

• 360° view of your members

• Top of mind awareness

• Nurturing

• Repeat visitation

Why Loyalty?

Reward

Good

Behavior

Buy a gift

membership

Attend an

event

Click on an

email link

Watch a

video

Donate

Partner

Activity

Volunteer

Purchase

tickets

Visit

Interact

with an

exhibit

Conservation

messaging

Visit a

webpage

Like the

Facebook

page

Take a

survey Buy at

the gift

shop

Check in

on

Facebook Tweet a

hashtag

Join

Loyalty

Research shows that

cultural organizations

are underserving this

critical audience by

19%; and 80% of

Millennials are more

likely to choose an

organization that offers

a loyalty program over

one that doesn’t.

Q & A

1. Retaining high risk members

2. Converting highly qualified prospects

3. Encouraging repeat visitation

4. Increasing joining, renewal, upgrade, and giving

5. Growing membership revenues

6. Rolling out a loyalty program