Segmenting has always been important for hotels. Typically they fell into broad groups, the largest being corporate, leisure, groups and airlines. Some sub-groups subsequently emerged from these.
As the traditional segments were broad, it was hard to assign specific descriptors to them which in turn meant that it was very difficult to direct targeted marketing initiatives.
However, with the increasing computing power now available, it is possible to create specific micro-segments and create more targeted campaigns. As we have identified, it is also necessary to differentiate and create a customer-driven marketing strategy.
For example, there is the dynamic free-flowing data comes that comes from ongoing activity
- Website tracking information like cookies, prior history, call centre logs, response to promotions etc
- Social network profiles and activity like profile on LinkedIn and group participation, facebook activity), social influence (eg: comments on TripAdvisor etc)
- GPS and Map services like Google Maps
- Travel Apps like Tripit which offers itinerary planning and management
- Online travel agency and airline data
This dynamic data needs to be mashed with traditional demographic and psychographic profiles to create a more accurate and relevant profile of the customer. Once this exercise is complete, you are in a good position to identify the micro-segments. These micro-segments enable finer and sharper targeting of offers and promotions to allow greater returns.
For the hotel industry, a more useful approach might be to identify the unique cluster groups and to then conduct a separate value segmentation exercise for each cluster. For example, for a given hotel we identify 4 basic clusters or distinct customer groups such as tennis groups, ski group, pampered group (e.g. use spa and valet type services) and the nighthawk group ( fine dining and theatre goers). The segmentation approach might look as follows: initial learning from this type of segmentation could be used in developing a marketing strategy that is data-driven. Read more…
And if you want to challenge yourself and segment even finer, follow the Netflix or Amazon examples referred to as the “segments of one”.