So much emphasis is now placed on getting results that it’s crucial potential travel buyers are targeted with products they want. TripVision managing director David Jones explains why segmentation analysis is important in order to understand consumers’ travel behaviour and attitudes
Managers in the travel business in these difficult times must be frequently challenged over their results. Most good managers will be able to quantify what is happening and provide the percentage of performance variance versus plan.
More challenging is to figure out why the results are that way so activities can be initiated to correct the shortfall.
The first thing to recognise is that any total performance result is an aggregate of many different segments making up a business, all of which could be a series of pluses and minuses. Again, most managers will be able to provide sales channel and geographic data to try to provide a clue. But to perform the high-powered ‘segmentation’ analysis needed to fully understand the results, we need to look at the fundamental driver of all travel business – consumer demand. (There is seldom a problem with supply in this industry).
Consumer demand and its drivers have been very hard for the industry to predict, let alone segment. After all, we are talking about 60 million people in the UK, 33% of whom are making more than 63 million trips abroad every year resulting in a huge tangled conglomeration of data.
Fortunately, technology comes to the rescue. Firstly, using modern sampling techniques, TripVision is able to contact a statistically significant sample of consumers every month – and then collect information on their travel behaviour and plans, their socio demographics and, importantly, their attitudes.
This data is what researchers call ‘single point data’ as it all comes from the same consumer – you can get insight into why people behave the way they do.
To make sense of all that data, TripVision has created a consumer segmentation matrix using what we call cluster analysis. Cluster analysis is a method of making sense of complicated data by grouping together information that is similar and separating that which is different. People have been doing this type of analysis for millennia but on the basis of observable data – differences you can see.
However in travel, the key characteristics are life-stage, budget and most critically, attitudes to travel. Attitudes are more complicated because you can’t see them, so some maths is needed. For example, if there are seven ages of man, five socio-economic categories and 10 key attitudes that are important when choosing a holiday that gives us up to 350 different possible traveller types. Plot them in a three-dimensional graph and you can envisage them as fixed points inside a transparent cube. But these points are not all equally spaced out; some of them cluster together in groups while others lie in the spaces in between.
Cluster analysis finds these groups, or clusters, and uses a decision rule to allocate each traveller type to one cluster or another. At TripVision we use Euclidian (Alexandria) distance, or if you like, the cluster that is closest to each traveller type. As a general purpose solution for all travellers we find that there are five broad clusters that are the most useful when categorising travellers. We call them homebodies, golden years, conformists, explorers and spenders.
In chart two you can see there is not a lot of variation. However, when you look in chart three at the same people segmented by attitude cluster, a totally different picture emerges. The conservative homebodies are at one end of the scale, not using the Internet to book travel as much as the materialistic spenders who are at the other end and using it a lot.
Clearly if you are selling travel over the web you need to target spenders and explorers.
Chart three is helpful to pull out some of the ‘behaviour’ data when looking at those two groups as it’s different. Spenders go to different places than explorers – spenders like Greece, Portugal and Dubai and are less disposed to go to Spain, France and Central Europe. When they go to their preferred destinations, spenders like to do different things. The beach and swimming are of interest, followed by clubbing. Explorers, as their name suggests, like independent touring.
In terms of indicated action for the travel executive, the key is to target these consumer groups with destinations and activities you know will turn them on and use media that will reach them. All of the information you need to do that – newspaper readership, home postcode etc – is collected and can be put into Acorn/Mosaic mapping programs so you can see how these people live.
Penetration of these consumer clusters, both before and after, can be tracked and the performance measured over time. This provides a valuable new dimension to understanding business trends and staying in that destination we all crave to be in ‘on plan’.