Crossing the exhibition chasm

In 1991, Geoffrey A. Moore penned the bible for technology marketers in Crossing the Chasm, speaking to the human behaviors of innovation adoption. Silicon Valley have long used Moore’s work to plot advertising strategies for the latest apps and devices. Unsurprisingly, given the innovative pursuit involved in bringing an exhibition to market, we find a similar behavior curve evident in analysing exhibition results.

Just as in Moore’s perspective, exhibition adoption curves can be segmented into:

  • Innovators – those that like to be the first to see an exhibition
  • Visionaries – early adopters who will wait for the initial reviews to come in
  • Pragmatists – the early majority who look for building momentum
  • Majority – late adopters who take time to see social evidence of success
  • Laggards – those who naturally leave attendance to the last minute

Much like with technology marketing, these audience definitions can then be used to adjust the marketing pitch of the exhibition, timing the shift from ‘new’ to ‘review’ to ‘social’, then finally appealing to the ‘Fear Of Missing Out’ (FOMO) factor to achieve the final uptick in visitorship.

Given exhibitions are often of varying lengths, some trends may be more evident when normalized for exhibition duration, whereas others may be firmly attached to the timeline of days in. Others may be more pronounced when analyzed as a conversion rate of total visitation versus those than are clearer on their attendance alone, dependent on whether they rely on or drive general admission. Digging deeper, we discovered ways in which these curves tend to group together when split by various classifications of a given venue – such as art, history or science, blockbuster versus specialist, single artist versus collective – with each venue having a unique experience in which categories are significant to behavior curve shapes. We’ve found it useful to have a variety of flexible data visualizations on hand to help isolate the most useful views.

The core difference between the traditional technology adoption lifecycle and the exhibition behavior curve is the appearance and shape of its peaks, with most exhibitions more reminiscent of a wave or a camel than a traditional bell shape distribution as technology economic theory would have us believe.

In predicting exhibition performance and forecasting down to a daily granularity, the curve itself is a fairly macro feature. However, understanding its appearance and predicting the timing of each transition is an important addition to the exhibition optimization playbook, such as planning exhibition related shows, press activity or marketing messaging. This is particularly the case in planning for the timing for crossing the ‘chasm’ – as Moore puts it – the gap prior to the bulk of market adoption where advertising spend should be concentrated, or more specifically to exhibitions, the ‘waning’ factor – the long ramp downwards indicating an exhibition has worn out its appeal, which should be used to evaluate the decreasing returns of an extended showing.

Refer to the data visualisation library in Dexibit for exhibitions specific analysis and to forecasting to enrol in exhibition scenario simulation, or ask your Dexibit data concierge.