If you’re reading this, chances are you’re not the average travel industry dweller. Except perhaps IT gurus and bus drivers, everybody’s on it for the glamour, the allure, the prospect of being involved in something the rich and famous expect on a weekly basis. Come on, be honest: Isn’t that true? Casually greeting King Hussein of Jordan and his wife Noor in Lanzarote, having a beer and talking about football with Pierce Brosnan in a Jamaican beach, serving tea in bed to the most stunning Miss Italy ever in an undisclosed location… These are the kind of experiences you can collect in this environment if you stick long enough. All true, by the way, and I’ve got quite a few more, but you wouldn’t believe me.
If most people are on the industry for the fascination, the travelling eagerness, the international ethos… how can we expect them to behave like scientists when checking company’s performance and – more to the point- to make critical business decisions? The more I delve into travel businesses, the harder it is to find people with business acumen combined with methodological savvy (not to mention mathematical/statistical aptitudes). Hence, if the title of this article caught your attention, I am ready to bet you’re one of them. Congratulations! For that very reason, I would also bet you have been asking yourself for a while how to establish and consistently profit from a data-driven culture in your organization, or even attempted it already.
I’ll provide a few tips, but first let me point out the bigger roadblocks you’ll be facing.
Challenges
It takes time
Half a year, at the very least, for a small company with two or three departments. Adopting analytics tools is easier and cheaper than ever, but if you got a lot of reports or maybe a few dashboards floating around that nobody pays attention to, there is no data-driven culture. If insights are not used to take action (instead of trusting hunches) to advance and grow, there is no data-driven decision-making process, and the resources assigned to this avail, wasted forever.
Depending on how many persons must consume and act on the reports, your implementation timeline must allow for a few months of learning and adaptation, even if the data visualizations are simple. Sooner than later, users will start actually seeing the benefits reaped for themselves and the company, eventually becoming data-depending themselves. And first results will come soon enough, in the form of positive financial impact, granted.
You’ll make enemies
People is averse to changes, that’s a fact. “If it isn’t broken, don’t fix it”, they’ll say. Cushy comfort zone, many years doing the same old same, pseudo-strategies based on past seasons, the works. This is the highest barrier you’ll have to overcome in the transition to a data-driven culture.
People is also scared of what they don’t know, they might feel their jobs are threatened by this crawling analytics monster, or they’ll assume the workplace will become more a boffin’s lab than a fashionable gig. Whenever you start changing the status quo, dumping the well-loved procedures, people will oppose. People will even hate you. Major haters will of course be those whose mediocre performance is exposed by data analysis. Brace yourself for the hate wave, because I can guarantee it will happen, as much as I can guarantee that lousy performers will be uncovered somewhere, somehow. Hopefully, the worst performer is you, dear decision-maker: in that case, you based your management style too much on intuition, for too long. But hey, you’re smart enough to take a different road from now on, and that’s the first step to shake things up again.
Worst-case scenario, though, materializes if the poorest performer happens to be your boss, company owner, CEO, whatever their title is. What if data clearly shows his or her hunch-based decisions are damaging the company’s future? Well, my friend, you have a very sensible diplomacy task ahead…
Data collection and processing = not easy
The bigger your company, the harder it will be to properly organize, collect, store and process amounts of data that may become humongous… even for a tiny DMC with only a booking engine and an ERP application to get the data from. I could write a whole series of articles on this subject, based only on my superficial knowledge of it! Even at the most basic level, data processing requires expert intervention to set-up a scalable system (because you’re doing all this to grow, right?) and a monetary investment proportional to the project’s scope. Because people love to get answers from data, but generally they couldn’t care less about gather, clean and prepare it to be converted into valuable information.
Worry not: if properly set up, all that data prep nuisance must be done once and you can (almost) forget about it. At least for a while, until your growing pains force an update.
Here’s the good news
Timelines, rooted bad habits and technical matters aside, the benefits from adopting a data-driven culture company-wide are absolutely worth the wait and the struggle. I am not going to list such benefits here since I suspect you know them already very well, otherwise you wouldn’t be interested in this text. A hard fact: it’s scientifically proven (from financial as well as fiscal publicly available information) that companies operating under a data-driven culture boast lower overheads and diminished risks from decision making, thus consistently increasing profits. Google it if you don’t believe my statement!
The real good news here is that, although it takes some sort of scientific approach to embrace a data-driven culture, it can be attained with reasonable effort, even in the glamour-ridden travel industry.
Practical advice
Each company, each vertical would employ different methods and tools, there is no one-fits-all model. However, there are a few guidelines to create and embrace a data-driven culture within a travel (or any other) organization.
One step at a time! Either start with one area or department (the one you suspect may be critical to the company’s bad performance) or with a simple KPI metrics exchange between departments, possibly to find common ground and avoiding at all costs eventual fault-searching and finger pointing among stakeholders.
Ask business questions! You might establish that analytics implementation should bring a 5% revenue improvement by year’s end, and that’s fine (and realistic, I should add). However, instead of just pursuing objectives, try checking “what-if” scenarios or run simulations based on the predictions bought by data.
Measure! The point in collecting data is to measure parameters, which in turn will be used to experiment and predict, to answer business questions. Not everything must be measured, though, rather opt for “outcome-based” performance measures to begin with, the so called KPIs… In order to maximize analytics ROI, focusing on KPI metrics is paramount, although it is a good idea to collect ALL possible data: you never know what might be valuable to measure in the future! And I’m not referring only to quantifiable stuff, also qualitative measures should be accumulated. For instance, if you want to record certain predictions and have no numbers, use “low/medium/high probability”: better to have an approximation than not collecting data at all.
Experiment and Gamify! In order to confidently take data-driven decisions, you have to blindly trust the quality of your measures. If the prep was done correctly you shouldn’t have to worry about it, right? Not so: always check for outliers, question everything and test alternatives. In short: act as a scientist. Before adopting a data driven-culture, everyone must embrace an unbiased experimentation culture. Establish what would be considered a success beforehand, try something, measure its results, home in on the lessons learnt, rinse and repeat. This will work especially well to verify predictions’ accuracy! Interestingly, there is no such thing as an experiment failure: people will get valuable lessons and -incredibly but true- might find amusement in doing so, while establishing new correlations, discovering new opportunities or even competing to see which department gets higher scores or better performance in whatever is deemed useful or important for the whole enterprise. Staff ends up auto motivating themselves to be data-driven: not only their jobs will be easier, they’ll also get fun from it.
Fancify! Data and related activities are naturally boring for our flamboyant travel execs: ditch tables and spreadsheets, give them elegant flashy dashboards to play around, to easily and immediately visualize whatever data comparison, aggregation or cross-analysis they might need on their daily or monthly basis. Insights can be conveyed into images, or even whole stories, which can be presented with (or through) emotions. At the end of the day, people base their decisions on their feelings: if after having a go at the evidence brought by data they still choose to follow their instinct, it’s their prerogative. Beware of the HiPPO effect >>, though!
Share! Every organization tends to compartmentalize information in silos (in the travel industry, even more so), and that’s something to get rid of. A good idea for starters would be to run bi-weekly or monthly meetings in which all areas or departments exchange a general vision of their KPIs, their experiments results. The long-term objective will be to have a common repository of insights company-wide, so everyone can verify or cross-analyze their datasets against the big picture (company’s general performance). Perhaps the financials officer can discover a way to improve operational aspects… or vice versa!
Commit! There’s no turning back, burn your Excel ships. Make it abundantly clear to everybody that from now on, every decision, every course of action, must be backed up by data. Psychology of resistance is really hard to eradicate, more so in large companies, but if you are the decision-maker you’d better start educating by example and acting based on what data brought forward. If you’re just the main analyst, your superiors must accept that the truth is not whatever their judgement says: truth lies in experiments results (or very close to them).
A few final pointers
If you feel you’re sitting in a mountain of data and don’t know where to start, you’d better invest in REVVA: consultancy for small projects is included in the subscription. They can help you create your data infrastructure and long-term data strategy, define which information to focus on, and -more importantly- train you and/or your staff to interpret analytics results and act upon them.
Please be aware that it is extremely hard to find data experts willing to be indefinitely hired for this kind of job: as they’re all millennials they’ll get bored way too soon; besides you’ll probably never need a dedicated team for these tasks, lest you’re TUI or Hotelbeds.
Being a small to medium-sized company, your best bet would be to nurture this data-driven culture to your existing staff and any new recruits. Then, whenever you need to update the data processing system, you can call in specialized third-parties and pay just for the project… Unless you adopted REVVA, that is. In such case, you won’t need to worry about scaling the system, ever.
If staff or bosses are still unable see the benefits of accepting a data-driven culture, it shouldn’t be difficult for you to prove that leveraging information brings more money for the company, its investors, and even its clients (yes, everyone benefits). Why do you think a fundamental task of analytics is called “data mining”? They’re digging for GOLD, not for the whitened bones of the “this is the way we’ve always done it” extinct defenders. Allow me a cliché here: data is the new oil.
In fact, with no data-driven culture in place, it would be impossible to implement revenue management strategies >>, dynamic pricing >>, etc. Like it or not, it’s a question of survival in today’s harsh competitive environment.
Finally, always keep in mind that data drives no company: people does. Machines and artificial intelligence can feed on data to help see the big picture, check the company’s health and predict outcomes, but ultimately the decision lies on your lap (or your boss’).
How do I know all this? Because I tried and FAILED, again and again. I studied dozens of data-driven companies (travel related or not) and attempted to implement some sort of data-driven culture in several types and sizes of operators for years, failing miserably until I started to learn from my mistakes. Maybe I was ahead of my time, most probably I am a slow learner. But to my meagre satisfaction, thanks surely to buzzword spreading, I’m noticing more and more interest in the tour operating arena towards data monetization. About time!
Did you try all this in your organization? Are you planning to do it soon? Drop me a line, I’d love to share experiences
Thanks for reading!
Marcello Bresin