1. ? Examining alternative ways of predicting election results
Using online research ?
Jon Puleston
Alex Wheatley
Lightspeed Research

2. Spent 2 years experimenting 2015 UK election Brexit 2016 US election

3. In that time we have run about 40 online experiments

4. With mixed success

5. Conducted extensive review of the causes of errors in opinion polls
Mr Undecided
The waverer
The man fed up with
Answering the phone
The shy voter
The lady who says what she
thinks you want to hear
The guy who doesn’t go out if its raining
The news anchor man
The client with just a small PR budget
The tinkering pollster
The tactical voter

6. This much we have learnt

7. Some of the issues from our perspective
Its an emotional decision – not just about who to vote for but whether or not to vote
For some its not a case of either or, many people are in two minds
Some people don’t know
Some people are shy
Some people change their minds
We are asking people to speculate about their own behaviour - notoriously difficult
The people who participate in online are not necessarily representative of their whole caucus

8. Having to predict 2 things that are inter-connected: Who they will vote or and whether on not they will vote

9. Dealing with people in two minds about who to vote for

10. Switching from a binary voting choice to relative chances of voting for
More clarity, can track movements, delivers back the same headline voting intention figures

11. Measuring feelings

12. Implicit research methods
Intelligent
Honest
Fun
Caring

13. Implicit research methods
I.A.T. showed clear lead for Cameron across positive attributes

14. Investigating the emotions of Brexit
A much less clear message
*percentage thumbs up of undecided voters

15. Investigating the emotions of Brexit
A clearer picture?
percentage thumbs up

16. Simple speed evaluation of various visual stimulus…

17. The US Election
A very close call
percentage thumbs up

18. The emotional factors paint a picture
Scope to model?

19. The emotional factors paint a picture
Scope to model?

20. Measuring decisions and passions
Using opened data

21. Provides understanding of the reasoning behind their choices
A gap of passion & reasons
Talking about:
Making America great again
He is a smart business man
An opportunity for Change
Hillary is Corrupt/Criminal/ Liar
Talking about:
She is not Trump
Trump is Dangerous
She as experience
Average word count - 20
Average word count - 16
IBM Watson sentiment analysis -16%
IBM Watson sentiment analysis -59%

22. Understanding the repulsions
Who was the lesser of two evils?
“He seems like the lesser of two evils”
“I think she is the lesser of two evils”

23. Measuring how hard you have to pinch your nose
The ‘ Scandal’ and ‘Sexism Scandal’
Both Forgivable
Both Unforgivable
Trump Unforgivable
Hilary Unforgivable

24. Exploring attitudes towards issues as proxy voting indicators
The ‘ Scandal’ and ‘Sexism Scandal’

25. Exploring attitudes towards issues as proxy voting indicators
The ‘ Scandal’ and ‘Sexism Scandal’

26. An added factor:
Trump was an establishment underdog

27. Trying to find the experts
Using prediction research

28. Can we predict the result?
GOT IT RIGHT!
Can we predict the result?
Longitudinally measuring predictive abilities to find the experts

29. Emotions bias prediction
More work needed
Hard to separate predictions from preferences
Emotions bias prediction
2015 Conservative victory makes sample self selecting

30. Asking consumers
A non-expert prediction

31. Can we make consumer predictions more informed?
Breaking down the problem

32. Stubbornness of views Red flag for confirmation bias
Percentage predicting Remain Victory

33. Same again in the US
Consumer predicting her victory

34. More informed predictions
Breaking down the US election into 7 swing state puzzles

35. More informed predictions
Red flag for confirmation bias
Breaking down the problem measures the confidence gap
6% got all States correct – ‘expert predictors’ of the future?

36. Finding the unbiased:

37. Near field prediction methods
Predict who will win
Predict how your friends or family will vote

38. The issues with all alternative forms of election prediction….
A question of calibration
You need 20 directionally accurate data points to prove the effectiveness of any one technique – i.e. predict 20 elections correctly before you could take any seriously
You need upwards of 100 data points to start to effectively calibrate any one technique (and consumer researchers would argue you need a lot more e.g. Millward Brown Link test calibrated on 110,000 ads)

39. Not a lot wrong with classical polling techniques if they are executing effectively
Using carefully crafted sample sources
Using large enough regionally & demographically balanced sample
Using quota invite sampling techniques
Carefully calibrating intention to vote responses
Being mindful of shy voter issue, measuring and weighting accordingly
Using thoughtful weighting procedures calibrated against larger scale establishment surveys and historical voting data

40. Where next?
Recognise that nearly all our research represent anacdotal evidence
We are starting to testing out some of these alternative methods more widely