Data Science: Towards a 360° audience view
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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.
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Data Science: Towards a 360° audience view
- 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
- 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?
- 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 ‘