Cimeon Ellerton, Head of Programme at The Audience Agency, and Eva Kabzinska, Data Scientist at The Audience Agency, joined Claire Round, Director of Marketing and Brand at the ENO, delivered the keynote 'Data Science: towards a 360° degree view of audiences' at the AMA Digital Marketing Day on 8 December 2015. The keynote shared the success and outcomes of Arts Data Impact, a Big Data initiative supported by the Digital R&D Fund for the Arts, which embedded data scientists into three of the UK's largest arts organisations - the National Theatre, English National Opera (ENO) and the Barbican.

1. Data Science
Towards a 360°audience view
Cimeon Ellerton & Eva Kabzinska,
The Audience Agency
Claire Round, English National Opera

2. What is a Data Scientist?

3. The Holy Grail?
Knowing our audiences so well,
we can predict what they’ll do next

4. The “problem” with new audiences

5. Example: ENO’s La Bohème

6. Maria’s night at ENO – what the data tells us
• Her first booking at the London Coliseum
• Out for an evening with her guest at
La Bohème
• It’s likely to be a special night:
• She planned it 6 months in advance
• She chose the best seats in the house
• She treated them to champagne in the
interval
• Lives in Islington, North London
How do you solve a problem like Maria?

7. Metrocultural Maria attendance elsewhere
8 Concerts
14 Plays
Attended 6 other venues in London over the last 3 years
6 Operas

8. Metrocultural Maria attendance elsewhere
Books 156 days in
advance
Pays £63 per ticket
Books 139 days in
advance
Pays £43 per ticket
Books 76 days in
advance
Pays £17 per ticket
(All figures are averages)

9. Metrocultural Maria attendance elsewhere
CROSSOVER: ENO BOOKERSWHO ARE METROCULTURALS

10. What else do we now know about Maria?
• she’s a regular opera-goer in London
• She also enjoys plays and classical concerts
• She’s a frequent attender
• Her booking lead time varies between about 3 and 6
months
• She likes good seats and will pay for them
• She’s quite like a substantial number of other
bookers in the ENO database – which means there
are more like her to engage
Maria - what the combined data tells us

11. Turning data into action
Carmen seduces first time bookers

12. Understand the bigger picture

13. Improved campaign targeting for FTAs/light touch audiences
People per
segment, ‘000

14. Target
Carmen 2012 Early Bookers Geography
Selecting Previous Early Booker Data

15. Aim
Carmen 2012 Early Bookers

16. Deliver
0
200
400
600
800
1,000
1,200
1,400
UK Potential (000)
ENO Previous

17. Outcome
• 25% overall sales uplift on previous production; 32% in areas where TV campaign was active
• 50% new bookers (11% above season average)
• Box office target achieved, reversing earlier sales trend

18. What next?

19. From description to
prediction

20. What we can do already
• Describe audiences in terms of demographics
and lifestyle
• Segment audiences by culture participation,
socio-economic background, lifestyle
• Analyse repertoire journeys, audience flows and
retention and preferences
• Find crossover between audiences of different
artforms and organisations

21. What we are working towards
• Predicting audience types for performances
• Predicting success and failure
• Predicting effects of different pricing strategies
• Predicting audience flows, audience retention and
acquisition
• Knowing the predictive power of audience segmentation
systems
• Knowing the precision of prediction
• Using the predictions to inform planning and campaigns

22. What has to happen before
• Data sharing
• Comparability of information
• Understanding of the possibilities and limitations
• Data used to assist decision making
• Feedback on the practical value of insights

23. Our Tool: Audience Planner
• Organisation Report
• Performance / Production Report
• Predictor

24. Audience Predictor – next steps
• The richer the aggregated dataset, the more
accurate the predictions
• Testing and evaluation of what has the
predictive power
• Tweaking the prediction model
• Extending the model

25. That’s all very well, but how
does this help me?

26. Our commitment to you
Work with the sector to:
• Share learning and access to any tools as
widely and freely as possible
• Open up the aggregated data for all to
benefit and experiment
• Widen the datasets to include:
• Exhibitions and survey data
• Social media, websites and digital content

27. Final thought
‘We need to debunk the myth that data is
uncreative, impersonal, or a threat to artistic
decision making. Data is only useful where it
enhances existing ideas or current
understanding.’
Professor Paul Moore
Head of the School of Creative
Arts & Technologies, Ulster University
‘

28. Thank you!
cimeon.ellerton@theaudienceagency.org
eva.kabzinska@theaudienceagency.org
cround@eno.org