Businesses all over the world are beginning to engage the power of their employees and customers to come up with the next big idea. For example, late last year McDonalds asked its customers to help design a new range of burgers. The rise in popularity is due in part to companies realising the amount of power that lies in the crowd, as well as the increasing connectivity between brands and customers through social media.
It's not just customers either. More and more business leaders are beginning to use their employees to develop innovative ideas through the use of crowdsourcing platforms such as Mindjet's SpigitEngage. The emergence of crowdsourcing is due to the realisation that getting ideas from thousands of diverse minds is much more effective than leaving it to eight people sat around a boardroom table.
However, with such a large amount of people giving their ideas, how do you keep track of all the data? This is where crowd science comes in.
The impact of crowd science
In the digital age, crowd science happens everywhere, from shaping better customer experiences for online stores to helping governments develop policies. For example, Amazon uses crowd science techniques to recommend items to users based on their purchase history and search behaviours, by comparing individual actions to those of their entire user base, or "crowd."
Twitter, too, is a prime example of a social network which facilitates idea flow – it's utterly commonplace to hear about a topic "trending on Twitter," whether in conversation or the national media. Twitter employed hashtags as a form of metadata, making it easier to query the data and see the topics that are currently on peoples' minds and spreading across the world. Crowd scientists have studied how this spread of information facilitated powerful events such as the Arab Spring, and how we can use that information to influence and shape future networks and events.
Facebook's newsfeed is a constant flow of information and opinions, which demonstrably facilitates interactions and the exchange of knowledge. You may recall that last year, Facebook was involved in a controversy in which their scientists manipulated user data feeds to understand how this would affect the tone of those users' posts.
In the end, it backfired – users who had been unaware of the experiment felt that Facebook was using them as lab rats, and unethically altering their mental well-being. This is the darker side of crowd science – a field in which it's typically necessary to use real people as subjects in order to advance.
Experimenting with the user experience is not a new concept, however – techniques such as A/B testing have been employed for years to measure and optimise desired outcomes, such as conversion rates on an ecommerce site. The reality is that without the use of human subjects for testing and analysis, research would have to fill in the blanks with imprecise speculation. Still, these experiments have social implications that must be considered, particularly when the pool of data at hand is so unequivocally huge.
Crowd science and innovation
By applying traditional data science techniques to the information gathered from your company's social networking and innovation platforms, you can discover patterns, heat maps and recurring themes that appear in people's ideas. This is useful, because it allows you to see exactly where the most innovative ideas come from. You're also able to gauge the mood and quickly discover what people want or don't want, information which is invaluable to keeping your customers and employees happy.
This level of information is possible because when dealing with innovation, you aren't dealing with quantitative responses. Instead, you are asking people to divulge their personal thoughts, feelings and ideas, something which is much harder to measure than traditional number-based data, and requires a much deeper analysis – hence the need for crowd science.
It's this shift from top-down power to communal idea creation that makes crowd science so valuable for companies dependent on meeting changing market needs. As the repository of data – social, behavioural, demographic, and otherwise – continues to inflate, it's the humanisation of what we learn that will bring concrete meaning to the why, what, and how of where we're trying to go – and give us the motivation to get there.
- Anna Gordon is a data scientist at Mindjet
