1 © WolfBrown 2009 All Rights Reserved. 2 Knowing Me, Knowing You Emerging Practices in Arts...


Knowing Me, Knowing YouEmerging Practices in Arts Consumer Segmentation

Alan Brown, Principal, WolfBrown

San Francisco, USA


Agenda

• The backdrop: increasing diversity• Introduction to customer segmentation• General market models

- Arts typologies

• New customer models in the arts- Prospect models- Behavioral customer models - Attitudinal customer models

• Lessons learned thus far• Next steps – a vision for the future


http://eq.canada.travel/?sc_cid=eq1


The Backdrop: Increasing diversity within the audience (and community)

• Fragmentation and diversification of cultural tastes, especially music

• New frontiers of digital consumption• The critical role of social context in driving

attendance• Expectation that all types of leisure

experiences can be customized• Demand for shorter, more intense, more

convenient experiences• More value attached to setting and format• Demand for more interpretive assistance


What is customer segmentation?


Definition

•Customer Segmentation is the subdivision of a market into discrete customer groups that share similar characteristics.


In the commercial world, businesses use customer segmentation to…

• Prioritize new product development efforts• Develop customized marketing programs

(targeting)• Choose specific product features (packaging)• Establish appropriate service options• Design an optimal distribution strategy (sales

channel)• Determine appropriate product pricing

Source: Bain & Co. website


Types of Segmentation Models

• Demographic models (1950s - now)• Geodemographic models (Prizm, Mosaic)• Product-specific market models

- New ACE model of all U.K. adults based on their level and nature of arts engagement

• Prospect models for a specific product or category- Classical music prospect model (Knight Foundation, 2002)

• Institution-specific customer models- Major University Presenters- Philadelphia Orchestra- Steppenwolf Theatre Company- Welsh National Opera


Many arts groups do some customer modeling on a behavioral basis…

• Usually for direct mail or telemarketing purposes

• Predictive models are based on past purchase behavior- Donor-subscribers- Subscribers/series buyers- Dance buyers, classical music buyers, family buyers etc.- Single-ticket buyers- More sophisticated “response models”


… but attitudes, beliefs, self-perceptions and values drive purchase behaviors• Coming to Concurrence: Addressable

Attitudes and the New Model for Marketing Productivity - By J. Walker Smith, Ann Clurman and Craig Wood


General Population Model for Arts Attendance and Participation (Arts Council England)


New ACE Model


Traditional Culture Vultures (4%)


Prospect Models: Classical Music Consumer Segmentation Study (Audience Insight, 2002)


Prospect Model based on Relationship with the Art Form: Input Variables• # of types of concerts attended over the past

year (pops, classical, chamber, etc.)• Lifetime history attending different types of

classical music concerts• Frequency of consumption via radio• Frequency of consumption via recordings• Level of knowledge about classical music (self-

reported)• Desire to learn more about classical music• Critical vs. casual listener (self-defined)• Social context of attendance


Prospect Model based on Relationship with the Art Form

TEN CLASSICAL MUSIC PROSPECT SEGMENTSSOURCE: PHILADELPHIA PUBLIC TELEPHONE SURVEY (N=338)

Blue Moon 8%

Out of Reach 15%

Classical Lite10%

Casual Listeners9%

Aspiring Classical Enthusiasts

5%

Educated Classical Audience

7%

Uninterested 11%

Disinclined 11%

Family Occasion 9%

Classical Ghosts15%


Prospect Model based on Relationship with the Local Orchestra: Input Variables• Ever attended a concert by the orchestra• Ever subscribed• Ever personally bought tickets• Recency of last concert attendance• Frequency of current attendance• If friends or family members attend• Attitude about future attendance


Prospect Model based on Relationship with the Local Orchestra


Integrated Prospect Model

CONNECTION WITH THE LOCAL ORCHESTRA

REL

ATIO

NSH

IP W

ITH

TH

E ART F

ORM

Sophisticated Active Audience(1.7%)

Casually InvolvedActive Audience(2.0%)

Sophisticated Active Audience(1.7%)

Casually InvolvedActive Audience(2.0%)

Sophisticated Low-Frequency Alumni(1.7%)

Interested STB & Ghosts(4.4%)

Low- Interest Dabblers (4.8%)

Special Occasion Only (4.6%)

Uninitiated Prospects with Social Context (1.7%)

Uninitiated Suspects(3.6%)

Uninitiated Prospects without Social Context (2.4%)








Ticket Buyer Model: Major University Presenters (2007)


Online Survey Methodology

• Protocol builds on qualitative data from 195 interviews

• 51,541 invitations sent to 14 email lists• 7,645 responses received (~15% response)• Lengthy survey (about 15 minutes to

complete)• Aggressive use of incentives• Survey data matched to purchase data

through email address• Acknowledge bias from self-selection and

bias from online administration


Ticket Buyer Model: Input Variables

• Core values (13 inner-directed, 9 outer-directed)- e.g. “Rejecting authority and making your own rules”

• Cultural attitudes (e.g, interest in specific cultures)

• Preference levels for 27 types of performances• Appetite for educational content• Price sensitivity• Social context of attendance• Political and religious beliefs• Innate intelligences (Howard Gardner’s model)


Analysis Approach: Cluster Analysis

• Different combinations of variables were tried

• Segments are designed to be as different as possible

• Data on Gardner’s intelligences are intuitive and useful

• Very multi-dimensional model• Driven by core values, cultural attitudes,

preferences and tastes- NOT driven by demographics or purchase behaviors


Purchase behaviors paint an incomplete picture of preferences

UFPA UMD UMS % of all respondents who bought modern dance 9% 13% 16% % of respondents who reported high interest 35% 25% 27% in modern dance (6 or 7 on scale of 1-7), but who did not purchase modern dance in the past 2 years % who reported moderate to low interest in 56% 62% 57% modern dance 100% 100% 100%


Overview of the Ticket Buyer Model: Ordered by Risk Tolerance


1. Mavericks

• Fearless, values-driven consumers• Core value is challenging authority• Thought leaders with existential intelligence• Primary attraction is to linguistic art forms

- Fantasy-seeking theatre-goers

• About six in ten are students, many are artists • Quintessentially adventurous

- Risk for risk’s sake

• Very price sensitive• Most attend with friends, but also not afraid to

attend alone


3. Remixers

• Urban arts omnivores- Love that art can be digitized, remixed and sampled

• Culturally-directed- Strong sense of their own cultural roots- High interest in specific cultures

• Preference for contemporary art forms, not classical- Healthy appetite for new work by living artists- Multiple intelligences

• Socially-driven, most likely to be Initiators• Embrace technology• Younger, but not students


4. Diversity Seekers

• Most outer-directed of all segments- Driven by a need to understand the world and their place in it- Sense of duty to mankind, commitment to social justice

• Most emotionally reflective of all segments- Need and ability to empathize with others

• High preference for world/folk music and dance

• Naturalistic intelligence• Not into urban culture• 80% female, strong nurturing instinct• Likely to attend with children


10. Serenity Seekers

• Take comfort in the familiar, do not want to be challenged- 90% prefer ‘a sure choice’

• Desire a peaceful, calming experience- Not looking for emotional intensity

• High preference for symphonic music, chamber music- Little appetite for new works

• Attracted to authenticity and historical accuracy• Tend to be males, retired, age 65+• Attend with spouse• Conservative political views


Donor Model: Major University Presenters (2007)


Five Segment Donor Model, Based on Motivations for Giving

PERFORMING ARTS DONOR SEGMENTATION

MODEL (N=1,738)

Intrinsics 22%

Networkers23%

Co-Creators11%

Marquee Donors23%

Youth-Focused21%


Institution-Specific Attitudinal Customer Models: The Philadelphia Orchestra


Philadelphia Orchestra Ticket Buyer Model: Input Variables

• Musical tastes: eclectic vs. classical-focused• Knowledge level about classical music• Appetite for new works by living composers• Preferences for different concert formats• Motivations for attending• Influence of purchase decision factors

• Demographics and purchase behaviors were used only as descriptive variables, not segmentation variables


Four-Segment Customer Model

PHILADELPHIA ORCHESTRA CUSTOMER

SEGMENTATION MODEL (N=1,075)

Old School Connoisseurs

24%

Warhorses23%

Casual Followers28%

Adventurous Intellectuals

25%


Three out of four segments prefer a format with brief introductions from the stage.

PREFERRED CONCERT FORMAT, BY SEGEMNT

5%

66%

10% 5%

21%

74%

34%

70%67%

62%

20% 19%28%

17%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

War

hors

es

Old

Sch

ool

Con

noisse

urs

Adv

entu

rous

Inte

llect

uals

Cas

ual

Follo

wer

s

TO

TA

LA

UD

IEN

CE

Interpretation-richeducational format

Format with briefintroductions

Traditional format(no talking)


Three out of four segments prefer a format with brief introductions from the stage.

DESIRED FREQUENCY OF HEARING NEW PIECES ON POA PROGRAMS, BY SEGEMNT

5%13% 10% 7%5%

15%

43%

24%22%

44%

44%

61%

51%

53%61%

18%19%5%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%W

arho

rses

Old

Sch

ool

Con

noisse

urs

Adv

entu

rous

Inte

llect

uals

Cas

ual

Follo

wer

s

TO

TA

LA

UD

IEN

CE

Never or Almost Never

Every 3rd or 4th Program

Every Other Program

Every Program


Institution-Specific Attitudinal Customer Models: Steppenwolf


Steppenwolf Customer Model

• Motivated by a desire to more deeply engage single-ticket buyers- Research supported by Wallace Foundation

• Survey probes knowledge and background in theatre, attitudes about risk, etc.

• A major focus on how people engage with the art form- Before shows, after shows


Five Segment Customer Model

STEPPENWOLF CUSTOMER MODEL (Does not reflect frequency of attendance, but rather the overall

proportion)

High Impact

Loyalists

Ensemble Followers

22%

Progressive Theatre Geeks

19%

Topic-Driven

Socializers

Selective Story-Seekers15%


Progressive Theatre Geeks (19%)

•Highest self-described knowledge level of theatre•Most likely to be influenced by a critic’s review•Most likely of all segments to be Multiple STBs• In regards to the five engagement typologies,

they are most likely of all segments to be “Actors” and “Bloggers”

•Shortest planning horizon of all segments (55% reporting they plan a week or less in advance)

•Strong skew towards males•Many are found within the audience for visiting

companies (27%)


Engagement Questions

•How often do you do any of the following preparatory activities?- Read the play in advance of attending- Read a review of a production you are going to

see- Seek out information about the play online- Attend pre-performance talks/lectures- Read Steppenwolf’s Backstage magazine- Read program notes before curtain- Discuss an upcoming play with friends who’ve

already seen it


Engagement Questions

•How often do you do any of the following follow-up activities after a performance?- Talk about the play on the way home or over

drinks or dinner- Stay afterwards for post-performance discussions- Discuss the play with others over the ensuing

days and weeks- Read a review of the play- React to the play in an online blog or forum- Find out more about the cast, director, or

production team


Five engagement modalities overlap

Readers (94%)

Talkers (86%)

Bloggers(26%)

Listeners(18%)

Actors (12%)


Tactical Implementation: “Scaling Up the Model”


Next Steps: Commitment to Tactical Implementation – The Vision

• Revise the segmentation protocol• Add other, actionable data elements• Design a new marketing data warehouse

- TRG eMerge web-based marketing database- New module of TStats/Tessitura

• Survey the entire database• Survey new buyers continuously• Use the data on a daily basis for targeting

offers and information


Lessons Learned (So Far)


Negative Indicators

• No up front discussion about why we need a model, how we’ll use it- Research-phobia among artistic staff

• Belief that there’s no point in segmenting customers because there aren’t enough marketing resources to do targeting

• Surly ad agency• Disconnect between marketing and

programming- No continuity of product for different segments- No possibility of aligning product/packaging for different segments


Positive Indicators

• Entire leadership team is involved in defining the model

• Desire to align audience and programs• Embracing “taste diversity” in the audience• Valuing the full range of intrinsic impacts,

from social to intellectual• Willingness to adopt a new, common

language to describe audiences- Allow results to infuse long-term thinking about marketing,

development, programming, education

1 © WolfBrown 2009 All Rights Reserved. 2 Knowing Me, Knowing You Emerging Practices in Arts...

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