Illustration by Marion Renoux & Mia Brown

For the past few weeks we’ve been asking if it is possible to use social media to predict how people will behave, and how to go about doing it. Our testing ground has been fashion at Glastonbury Festival 2011 – but while knowing what colour wellies to bring could be fun, knowing how to predict behaviour can help businesses grow.

Now that Glastonbury Festival has come and gone, it’s time to see how our predictions did? All in all, we think we got it pretty spot on…

From this exercise, we’ve learned a thing or two about how to make predictions. Here’s a few things that we learned.

1. Know your limits – detail

Predictions are more useful when they’re specific – more detail provides more information for retailers and brands to act upon. However, in any predictive method the level of detail you can achieve is restricted by the data you have, and people don’t always share everything you might want about their activities and thoughts.

Through social media monitoring for a wide range of brands, we have found different communities online have different norms around talking about products. Some are very specific – e.g. tech fans often talk about equipment model numbers. But others are less so, as turned out to be the case for Glastonbury fashion. For every detailed description of clothing (“my new shiny black Hunter Carnaby wellies), several people mentioned clothing categories (wellies, shorts) without any details of brand, colour or style. Consequently extrapolation is required from the detailed mentions to the overall population, requiring a careful balance between predicting specifically enough to be useful, without going too far and risking accuracy.

2. Look for steady not spiking

When we looked at our most accurate predictions we noticed a common pattern. All had maintained a steady volume, rather than being characterized by spikes or increases in volume.

To take our Hunter wellies example, volumes stayed relatively steady until people switched to discussing their packing rather than their plans. What we can begin to take from this is that the bigger trends tend to stay pretty static over time, so look for solid underlying volume rather than dynamic spikes of conversation to identify them

3. Who’s watching?

Social media is about more than people talking: it’s also about people listening. One of the things we have to understand in social media is whether a piece of content is likely to be read or seen by other people – a measure we refer to as visibility. By weighting the data to take account of this we can see whether one person’s opinion or expression will be read and seen by a wider audience.

At first you might question why this is relevant to prediction, as this is about individuals’ intentions. But choices are often social, through choosing what others choose or just experiencing anxiety around what others think of choices. By weighting data we can take account of not just what people say, but their likely impact on the social environment.

4. Take it beyond social media data

It’s easy when working in social media to forget that a piece of content is surrounded by a whole other world. When looking at the predictions we made that were less accurate, there is a clear impact from external factors; coats caught up with ponchos because of weather conditions, and the two items which needed the most effort (full fancy dress items and customized t shirts) were disregarded in favour of easier options.

This reinforces the need to incorporate data outside of social media into the equation. This could include other data sources such as weather data, or it could simply mean conducting a short piece of qualitative research to establish which clothing options were actually perceived as worth the effort.

So can you use social media data for prediction?

The short answer is yes, certainly most of our predictions did ring true and were accurate. But there is always room for improvement and we’re already looking at how we can draw social media together with other methodologies to form what we call Augmented Research. There’s an exciting future ahead for social media – part of which may well be social media helping us to predict that future.

The Glasto Goes Social series was written by Facers Riki Neill, Jessica Owens & Kate Davids. Click on their names to say hello on Twitter.