The team at consultancy JWC share their thoughts about doing business in an industry buzzing with information.
Let’s begin by stating something quite obvious: having lots of data doesn’t really solve anything. Now this is not because all that great data at our finger tips isn’t good (accurate or relevant), in fact many trade fair organises and venue operators are getting better and better at accumulating data.
But what we observe quite frequently is that organisers and venue operators don’t know how to handle all this data. It is not easy to structure work flows, systems, and business decision-making processes in such a way that something actually gets done with all this good data we have. It seems the gap between the value that data can bring to the running of trade fairs and what we seem to actually be doing with it is growing.
In the next few paragraphs we will focus on data-driven management, clarifying what we mean by some key concepts. This will focus our attention on questions that are very much top of mind for exhibition organisers. For example: why is it essential trade fair organisers get better at making decisions with data? What are some of the business functions that data-driven decision making is most relevant to? How can the organisation be made to start moving in that direction? In the next instalment of Learning Curve, we will expand the discussion to include some ideas about data that you may not have at your fingertips; data you are not accumulating as part of ‘doing business’. But first, let us reflect on a few simple steps to make better use of the data being accumulated by, for example, organising a show.
A recent EIU study revealed that 69 per cent of companies surveyed think usage of data is extremely valuable to their competitive advantage. This is confirmed by another study that showed around 75 per cent of executives from top performing companies view data as ‘very important/essential’ for creating competitive advantages. Executives at companies that claim to be using collected data effectively emphasise their competitive advantage is achieved in a multitude of performance indicators. Judging by recent financial performance, leading companies have attained a major breakthrough (attributable to data-driven initiatives) in at least one of the following areas: (a) improved customer service, (b) increased market share, (c) reduced costs (d) faster speed-to-market, and (e) improved brand image.
When interviewed about this topic, trade fair executives are acutely aware of the importance data usage has on the quality of interactions their organisations have with their exhibitors and visitors. They also know how impactful it is for good decision making at the operational level. On the other hand, many immediately qualify this by saying the exhibition businesses they run have done very well for long periods of time with minimal use of data. They are quick to add that their teams’ imagination and creating a buzz around an event has been key to success.
And they make a very good point; think about the business of creating and running a good exhibition versus, for example, the business of distribution and retail of the likes of Wal-Mart or Tesco. It is after all a very different business to be in. One should not make the mistake of assuming the potential competitive advantages gained by more and better data in one business environment will also hold true in another. On the face of it, there is probably little in common between, say, the volume and cost leadership play that a mass-market retailing giant is in, and the very unique (and different from show to show) exhibition business. Here, success is driven by anticipating and organising future markets. The opportunity to test ideas (change and innovate) typically comes in cycles of one year or more, and revenue models are mostly based on getting everything right in the course of three to six days by having created momentum during prior months that stimulated all the ‘buzz’. Even the words that define our business are a bit ‘touchy-feely’.
Take a goal-oriented approach
But this is not to say that organisers are advised to ignore specific and attainable goals of being better at capturing, and later making use of, the data they had at some point or another at their fingertips, as they were going about their day-to-day business. On the contrary, in our work covering various activities undertaken by organisers, we see tangible bottom line effects when they take a goal-oriented approach towards data-driven decision making. Using a high level conceptual model (Picture 3) might be useful. A few practical examples will help illustrate some points about this model.
Consider for a moment all the data you have on square metres booked, invoiced and collected during the cycle of the show. Now, more specifically how much of that very useful data is available to the decision makers at the point in time when decisions are taken about budgets or investment plans relating to the show.
Turning our attention to another decision level, let’s look at the transactional data we can access when we need it. Wouldn’t it be nice to have the booking patterns over recent years with regards to key accounts, or all accounts for that matter. Those that are growing or shrinking, those that are booking and paying later and later in the cycle, those that have stopped booking and so on. If this feels basic to you then let’s consider two possibilities to exploit this value. For organisers of multiple exhibitions, it might prove quite challenging to process this data (for the same exhibitor/visitor, for example) consistently across multiple shows or several geographies.
Returning to focus on one exhibition, consider additional important data points that can provide a lot of intelligence about spending patterns in your show and what is driving value for your exhibitors and visitors. Are the customer satisfaction surveys designed in such a way that insights are easy to retrieve? Are they done in such a way that you can tell (easily and over time) how your exhibitors and visitors are drawing value from the show? Can the data obtained easily translate into decisions and actions that will improve the yield (read: profit) and customer satisfaction at the same time? Benchmark workflows will enable this and more. They will for example incorporate additional services and offerings taken up on the trade show floor, travel in and out of town, and spending patterns relating to overnight stay.
These just serve as ideas. There are many more. But if there is one area where we see trade fair organisers gain the most when implementing improved data-driven workflows, it is in the area of sales execution. Resources spent on making most relevant data available to the sales teams, prior to making the next customer call, is where some of the biggest and quickest payoffs can be recovered.
Which leads us to the final point; experience shows that to bring about change in data-driven decision making can be quite tricky. The risk that a lot of resources are sent on a mission to collect and process as much data as possible, but then nobody knows what to do with all of this stuff, is very real. But there are some things that can be done to make this transition a little less bumpy.
Going back to the model in Picture 3, the foundation has to be in place first. Many times that means before anything else (the implementation of fancy IT systems and tools, for example) people have to believe this whole idea makes sense, that their non data-related creative juices and decisions will be needed more than ever.
Being comfortable working with lots of data rests on two very distinct aspects and they must both be there for it to work. The decision makers (read: everyone in the organisation) need to have trust in the data and in their capabilities to work with the data.
All these facets of getting individuals and teams to be comfortable with more data is not really an option anymore. The days of lots of data are behind us. It looks like the storm of ‘big data’ is coming, and the days of ‘mega data’ will surely be here before long. It is therefore a good time to stop thinking about how much data is right for us. As data quantities grow and grow and threaten to overwhelm, we will do well to think through four elements in our relationship with data around us:
(1) in what decisions do we want to be supported by data; (2) what kind of data is it that we consider important for us to have (and at which point). Providing this data in (3) an actionable format and (4) across the organisation will lead to improvements throughout our industry’s operations, innovation and bottom line.
By Anna Holzner.
This was first published in Issue 3/2013 of EW. Any comments Email exhibitionworld@mashmedia.net
