Declarations and Ambience
The first type is declared data – the information the attendees themselves actively volunteer. Most obviously, this includes the information provided at registration (name, job title, role, budget responsibilities, etc.). Declared data would also encompass attendee responses to any in-session polls or post-event surveys and would include such data as the education sessions they have signed up for and the networking meetings they have set up. Marketers can acquire most of the declared data they need (and the lengthy questionnaires we are required to complete at registration suggests they are getting increasingly smart about asking for it) but this does not, in itself, provide an attendee portrait sufficient to support truly 1:1 marketing.

Ambient data is the second type: this is the unstructured information that we leave like a vapour trail as we make our way through an event. In a world where social media is increasingly at the heart of the event world, this would include speakers that attendees had ‘liked’ or content they may have tweeted, re-tweeted or ‘favourited’.  In addition, heat mapping software gives us attendees’ navigation paths and dwell times – where they went and how long they stayed there. We are increasingly overwhelmed by this type of data – a situation only likely to get worse as location-based capabilities, such as those enabled by iBeacons, become the norm.

Drinking from a Fire Hydrant
The response to this explosion of data by those companies that have large face-to-face meetings at the heart of their marketing strategy is to throw resource at the problem. Speaking to Convene in January, Mike Stiles, a senior corporate events manager for Adobe Systems, said: “We have massive teams to mine through that data and see where the opportunities are.” The effort and expense involved in this is justified by the value of the individual leads generated from the data that comes out of his company’s live events; but this approach is neither efficient nor cost-effective and – moving forward – is not the sustainable solution that marketers are looking for.

A smarter approach is to make use of inferred data: this is information not collected either passively or actively from the user but rather inferred from the behaviour of other people with similar profiles. In other words, we might not know what you want but we can make an informed guess about what people like you want.

This approach is not intuitive as much as deductive. For example, if someone knows that you are big fan of Friends, they might intuitively grasp that you like ensemble comedy dramas about groups of young people and recommend that you check out The O.C. The technology underlying Netflix or Amazon has no concept of ensemble performance but, by looking at your purchase or viewing history and comparing it to those with similar preferences, it would still be able to make the same recommendation. At any event, tapping into the ambient and declared data can give you a wealth of inferred data about your target audience.

The challenge for marketers is to make sense of the huge volumes of declared and ambient data generated at events. The solution is to follow the example of those large Internet companies that have learned to profile their user base and make smart inferences about their preferences based on an analysis of the behaviour of others within a similar demographic. While we are at the beginning of that journey, the technology now available around events shows that we are at least on the road to the ultimate destination of true 1:1 event marketing.

By Michael Douglas, Business Development Director, GenieConnect