Prediction markets are games where you trade shares in future events. The Hollywood Stock Exchange is a famous example, where you 'bet' on the audience that films will achieve at the box office. The idea is that people (on average, in large numbers) are quite good at guessing what other people will do and the outcome of future events. Running a survey and asking people if they plan to see an upcoming film at the cinema is - runs the theory - less accurate than asking those same people whether they think lots of other people will watch it.
In one respect, it's easy to see that the theory works. In horseracing, horses become favourites because people bet that they're going to win and very often the favourite does win. Odds on betfair are effectively the punters' averaged view of what they think is going to happen in future. Websites like Political Betting take those market odds and use them as a prediction tool for election outcomes or how long the current Prime Minister will last.
If you fancy reading a bit more and playing with some toys, then Inkling is a good place to start.
The advertising applications of prediction markets are exciting. Instead of a focus group asking people if they like a new product, you could ask a sample of respondents if they think other people will buy it. Want to know which mobile phone platform will dominate in five years? Get people to bet on it. Don't ask people if they like your creative, ask whether they think it will be popular.
In terms of their output, prediction markets have some similarities to another research technique that I'm excited about; agent-based modelling. It's a bottom-up approach to modelling where you create an artificial simulated market that contains individuals, give them some rules and then see how they behave. You might set up a simulation for a new product launch and then model how shoppers trial and adopt the product as they are exposed to advertising messages. The crucial difference to top-down modelling where you analyse past sales is that the simulated individuals in an agent-based model have an element of randomness in their decision making - they don't necessarily do the same thing every time you run the simulation.
These two new techniques are similar in that their output tries to account for randomness. You don't get a single answer and in that, they're much more like reality than a lot of the techniques we use right now. What you get are predicted likelihoods that rank possibilities of things that might happen.
Think about what that means for a minute. An analyst can predict the best strategy for launching a brand and that 70% of the time in simulations, sales exceeded the target. It's the best strategy, but even in the simulation it often doesn't work. We can tune the strategy to improve our chances, but in the end, randomness in the model means we might fail even though our strategy was a good one.
Weather forecasters often give us predictions this way - they'll say that there's only a 20-30% chance of rain, so you get annoyed when you turn up for your meeting without an umbrella and soaking wet. The forecaster didn't say it wouldn't rain though, so it's your fault really - he said it probably wouldn't rain and you chose to risk it.
I'm incredibly excited about these emerging techniques, but they need some new thinking on the analyst's and on the decision maker's side. We analysts need to work out how to apply new predictive techniques to marketing. Marketers need to recognise that they're going to get some extra information on which to base a decision and not the perfect answer.
That's actually the way that analytics should always have worked, but both sides too often like to pretend otherwise.
If you ask for randomness to be included in marketing analysis, then you're going to get answers that far more often include the word 'probably'.
No comments:
Post a Comment