1 Attitudinal segmentation December 2008 © 2008 TNS UK Limited. All rights reserved TNS Job Number: Taking Part 2008

2 Contents Attitude statements – mean scores Distribution of population by segment Segment 1 – Time Poor Segment 2 – Prudent Participants Segment 3 – Restricted Segment 4 – Opting Out Segment 5 – Free & Easy Segment 6 – Experienced Seekers Summary – Engagement Summary – Interest v Ability Appendix – Segmentation method

3 Attitudes statements– mean scores 1= disagree strongly - 5 = agree strongly Base: 2110 At this ‘overall’ level, most respondents like to learn and experience new things in their leisure time and value for money is a priority.

4 Distribution within population Cluster analysis segments the population according to their responses to the attitude statements. Each segment has been given a name and profiled to identify demographic and behavioural traits. ‘Time Poor’ ‘Prudent Participants’ ‘Restricted’ ‘Opting Out’ ‘Free’ ‘Experienced Seekers’ 17%

5 Segment 1 – ‘Time Poor’ - 21% of population Difficult to find time to take part in or attend arts and cultural activities The needs of family come first when planning leisure time Would attend more arts and cultural events if closer to home, easier access Like to learn and experience new things in leisure time Value for money is important when deciding what to do in leisure time Slightly higher than average attendance & participation levels esp. cinema. Slightly higher TV and Radio viewing and listening esp. rock/pop music and films. High use of Internet including social networking sites. More likely to be, but not exclusively: Aged 25 – 44 No disabilities Full or part time employed Higher, HNC or HND qualifications C1C2 socio-economic groups See graph on next chart for details

6 Population index = 100 Segment 1 – ‘Time Poor’ - 21% of population Activity Age Disability Ethnicity Working status Highest Qualification Socio-economic group

7 On-line activities in last 12 months Segment 1 – ‘Time Poor’ - 21% of population Factors influencing choices events attended Will travel an average of 35 miles to attend arts event, slightly longer than population average (32 miles).

8 Segment 1 – ‘Time Poor’ - 21% of population ACORN groups Compared to adult population, more likely to be in ‘Flourishing Families’, ‘Secure Families’ and ‘Settled Suburbia’ groups. *Respondent postcodes have been profiled using ACORN (A Classification of Residential Neighbourhoods). For more details see

9 Segment 2 – ‘Prudent Participants’ -17% of population Would attend more arts and cultural events if closer to home, easier access Value for money is important when deciding what to do in leisure time Like to learn and experience new things in leisure time Have time to attend Higher than average attendance & participation levels esp. art galleries and museums. Average TV and Radio viewing and listening esp. plays, variety shows, traditional music. Fairly low use of Internet and on-line activity. More likely to be, but not exclusively: Female Aged 55+ Retired A range of levels of affluence and education levels See graph on next chart for details

10 Population index = 100 Activity Age Disability Ethnicity Working status Highest Qualification Socio-economic group Segment 2 – ‘Prudent Participants’ -17% of population

11 On-line activities in last 12 months Segment 2 – ‘Prudent Participants’ - 17% of population Factors influencing choices events attended Will travel an average of 32 miles to attend arts event, same as the population average (32 miles).

12 Segment 2 – ‘Prudent Participants’ - 17% of population ACORN groups Compared to adult population, more likely to be in less affluent groups. *Respondent postcodes have been profiled using ACORN (A Classification of Residential Neighbourhoods). For more details see

13 Segment 3 – ‘Restricted’ - 17% of population Difficulties attending due to age, a disability or long term illness Feel out of place in an art gallery or museum Have difficulty finding information about arts and cultural activities If more events and better access would go more often Lower than average attendance & participation levels. Lower TV and Radio viewing and listening Very low use of Internet and on-line activity. More likely to be, but not exclusively: Aged 75+ Disabled or long term illness Retired Low income No qualifications DE socio-economic groups See graph on next chart for details

14 Population index = 100 Activity Age Disability Ethnicity Working status Highest Qualification Socio-economic group Segment 3 – ‘Restricted’ - 17% of population

15 On-line activities in last 12 months Segment 3 – ‘Restricted’ - 17% of population Factors influencing choices events attended Will travel an average of 20 miles to attend arts event, less than the population average (32 miles).

16 Segment 3 – ‘Restricted’ - 17% of population ACORN groups Compared to adult population, more likely to be in ‘Struggling Families’, ‘Burdened Singles’ and ‘High Rise Hardship’ groups. *Respondent postcodes have been profiled using ACORN (A Classification of Residential Neighbourhoods). For more details see

17 Segment 4 – ‘Opting Out - 14% of population Spending my time attending or participating in the arts and cultural activities is of little interest Feel out of place in an art gallery, museum or theatre The needs of family members take priority Would not attend more performances if closer to home, easier access Lowest attendance & participation levels Low TV, Internet and Radio viewing and listening. Very low use of Internet and on-line activity. More likely to be, but not exclusively : Aged 65+ Retired No qualifications DE socio-economic groups See graph on next chart for details

18 Population index = 100 Activity Age Disability Ethnicity Working status Highest Qualification Socio-economic group Segment 4 – ‘Opting Out - 14% of population

19 On-line activities in last 12 months Segment 4 – ‘Opting Out’ - 14% of population Factors influencing choices events attended Will travel an average of 22 miles to attend arts event, less than the population average (32 miles).

20 Segment 4 – ‘Opting Out’ - 14% of population ACORN groups Compared to adult population, more likely to be in ‘Post Industrial Families’, ‘Blue Collar Roots’, ‘Struggling Families’ or ‘Burdened Singles’ groups. *Respondent postcodes have been profiled using ACORN (A Classification of Residential Neighbourhoods). For more details see

21 Segment 5 – ‘Free’ - 17% of population Value for money is not a high priority when deciding what to do in leisure time Needs of family are not a high priority when planning leisure time Fairly high attendance & participation levels esp. music events Slightly higher TV and Radio viewing and listening inc. opera and jazz music. High use of Internet and on- line activity including social networking sites, purchasing and downloading music and film. More likely to be, but not exclusively : Aged Full time employed or in full time education Have a degree or higher degree ABC1 socio-economic groups See graph on next chart for details

22 Population index = 100 Activity Age Disability Ethnicity Working status Highest Qualification Socio-economic group Segment 5 – ‘Free’ - 17% of population

23 On-line activities in last 12 months Segment 5 – ‘Free’ - 17% of population Factors influencing choices events attended Will travel an average of 38 miles to attend arts event, longer than the population average (32 miles).

24 Segment 5 – ‘Free’ - 17% of population ACORN groups Compared to adult population, more likely to be in ‘Affluent Greys’ and ‘Educated Urbanites’ groups. *Respondent postcodes have been profiled using ACORN (A Classification of Residential Neighbourhoods). For more details see

25 Segment 6 – ‘Experienced Seekers’ – 14% of population Attending and participating in arts and cultural activities helps to enrich the quality of my life I like to learn and experience new things in my leisure time High attendance & participation levels esp. theatre, museums, art galleries. Average TV, Internet and Radio viewing and listening. Listen to plays on radio & orchestral music recordings. High use of Internet esp. to purchase tickets for arts performances. More likely to be, but not exclusively : Aged No disabilities Employed or retired High income High levels of education AB socio-economic groups See graph on next chart for details

26 Population index = 100 Activity Age Disability Ethnicity Working status Highest Qualification Socio-economic group Segment 6 – ‘Experienced Seekers’ – 14% of population

27 Factors influencing choices events attended On-line activities in last 12 months Segment 6 – ‘Experienced Seekers’ - 14% of population Will travel an average of 38 miles to attend arts event, longer than the population average (32 miles).

28 Segment 6 – ‘Experienced Seekers’ - 14% of population ACORN groups Compared to adult population, more likely to be in ‘Prosperous Professionals’, ‘Educated Urbanites’ or ‘Secure Families’ groups. *Respondent postcodes have been profiled using ACORN (A Classification of Residential Neighbourhoods). For more details see

29 Summary – Varying engagement Disengaged Engaged Levels of engagement in arts, in terms of overall attendance and participation levels varies between segments Opting Out 49% attendance 58% participation Restricted 58% attendance 54% participation Free & Easy 84% attendance 74% participation Time Poor 86% attendance 73% participation Prudent Participants 83% attendance 78% participation Experienced Seekers 95% attendance 84% participation

30 Summary – Interest v Ability Ability Interest Opting Out 49% attendance 58% participation Prudent Participants 83% attendance 78% participation Restricted 58% attendance 54% participation Free & Easy 84% attendance 74% participation Experienced Seekers 95% attendance 84% participation Plotting the position of segments according to ability to attend and interest in the arts can facilitate the targeting of resources. Time Poor 86% attendance 73% participation

31 Appendix – Segmentation method (1 of 2) The Scottish adult population has been segmented on the basis of their responses to a series of 11 attitude statements (see slide 3). This segmentation was undertaken using a combination of factor and cluster analysis approaches as described below. 1) Factor Analysis Factor analysis is a mathematical technique that groups together statements (in this study the series of 11attitude statements) into factors on the basis that statements within a factor are highly correlated i.e. answered in a similar way. The factors enable us to understand the structure amongst a larger group of statements and to simplify further analysis and interpretation. The factors are such that at a respondent level we can calculate a score for each factor and these scores can, for example, be used to group respondents with similar attitudes using cluster analysis (see next slide). Mathematically we start by using Principal Component Analysis to simplify the data into a series of independent components which explain as much of the variation of the data as possible by linear combinations of the statements put in. We determine the number of “real” factors within the data by looking at the magnitudes of a mathematical parameter known as Eigenvalues which are associated with these successive principal components. At this stage, individual statements may be associated with more than one principal component (factor). In order to simplify the interpretation we apply a further mathematical technique known as factor rotation (Varimax usually) to these principal components. This rotation maintains the level of variance explained and the independence of the factors to create factors that are reasonably well correlated with the individual statements and don’t have one big ‘positivity’ factor that masks the more interesting aspects of the data. The factors then are, loosely, groupings of correlated statements.

32 Appendix – Segmentation method (2 of 2) 2) Cluster analysis In contrast to factor analysis which groups together attitudinal statements, cluster analysis is a technique that groups together survey respondents who have similar profiles of attitudinal statements. To carry out the cluster analysis we run it in three stages using K-means cluster analysis to produce well defined clusters that are not unhelpfully small or influenced by outliers. The first run identifies a number of potential “seed” points for the initial centres of the clusters by doing a cluster analysis with a large number of clusters and using the larger of the resultant clusters as seeds for the second stage. The second stage excludes outliers and clusters all remaining respondents into groups. The third stage assigns the outliers to the groups achieved in the second run. In this way the outliers will be in the cluster to which they have most in common but they will not have been allowed to influence or bias the creation of the cluster. We repeat this process for a number of different cluster “solutions” and, although we monitor statistics such as the r-squared value to ensure that the clusters explain a good proportion of the variation within the data, the final choice of number of clusters is determined by the researchers looking at the interpretation of the clusters created against the factors used in the analysis and often against other data from the survey (in the case of this study the cluster solutions were looked at against data regarding arts participation and demographic details).