Advanced data collection tools are allowing companies to do some pretty amazing things. When it comes to corporate travel, particularly, tools are emerging that collect data about travelers’ expenses and spending habits—and some of those technologies, including our own product, Claire, can then recommend travel options for business travelers that suit their preferences and that are also within the limitations of a company’s corporate travel policy. This new slew of tools can be the difference between knowing how many travelers went to Hong Kong in 2016 versus knowing how many travelers will go to Hong Kong in 2018, if they will stay at an Airbnb versus a brand hotel, and why.
One aspect of travel analytics that has yet to be fully explored, however, is how using analytics can affect corporate travel policies or guidelines. One reason for this gap is that, in the past, companies have not had the people or skills necessary to perform complex data analysis. However, an Eye for Travel global survey of 450+ travel professionals on the state of data and analytics in travel shows that most travel organizations (65%) now have a dedicated data, analysis or insight team. This could be an argument for connecting your company with a travel management company that can manage that data, or for using a tool that manages the data.
Before we can look at how travel analytics can impact corporate travel policies, though, it is important to understand the different types of analytics. According to the Eye for Travel Survey, there are four main types of analytics:
- Descriptive – The most basic type of reporting analytics. Summarizes basic information about what has or is currently happening, such as site visitors over time.
- Diagnostic – An attempt to explain why the information collected through descriptive analytics is the way it is (finding correlations).
- Predictive – An approach that employs modeling and forecasting to anticipate future trends based on past data patterns.
- Prescriptive – The most advanced form of analytics. Involves identifying the best outcome for a given scenario and presenting it to the user as a recommendation. Predictive analytics can “identify causation and also identify time frames,” according to the EyeForTravel report. Descriptive and predictive analytics are needed as a foundation for prescriptive findings.
Each type of analytics has its value. For example, the travel manager who wants to know how many travelers went to Hong Kong in 2016 easily finds that through descriptive analytics. However, that travel manager who wants to anticipate how many travelers will go to Hong Kong in 2018, where they will stay, and why…well, that travel manager has some real power—power to save the company money and make travelers happy. The travel manager begins to become a bit like an oracle able to predict the future!
It is important, then, that travel managers aim to collect as much data as they can. As 30SecondsToFly’s David Braun shares in his article on travel analytics and bridging the budget gap, a variety of tools are emerging that make collecting this data easier. For example, he describes Traxo, a tool that collects travel data (in-channel or independently booked) and consolidates it into one place. Folio is a tool that even analyzes emails and credit card data. Claire allows companies to track what travelers are spending in real time. Yet, despite all the data collection advances, the travel manager is still the storyteller and problem solver. As a travel manager (or as your company’s designated person dealing with travel expenses with fifty million other things on her plate), you need to capitalize on that data in order to make effective decisions moving forward.
As Braun notes, these travel analytics you collect can impact your budget. They can also impact your corporate travel policies.
Predictive Analytics Redefines Travel Policy Best Practices
There are a variety of ways, both big and small, that collecting and analyzing data—particularly if your company has predictive analytics capabilities (though some do not)—can influence your company’s guidelines for travel booking and reimbursement.
Expense categories and restrictions
It is common for corporate travel policies to delineate a vast array of restrictions, guidelines, and more, as they relate to travel booking and spending. Your policies may…
- specify an advance booking deadline of two weeks for all flights and hotels
- delineate what type and size of rental car is appropriate given the amount of business travelers going on the trip
- indicate that a traveler can book a first-class seat on international flights only
…and much more.
Insights from your travel analytics will inevitably begin to inform your decisions about policy. Given that we can anticipate future actions based on past trends with predictive analytics, companies have reached a new threshold in being able to control spending more carefully.
Tips for Reigning in Wayward Expenses
Often, client entertainment expenses are the heftiest and most difficult to control. If your company is heavily courting a client, in some situations, the expenses can become enormous—but the ROI is worth it in the long run. TIP #1: Consider tracking client entertainment expenses by employee and setting a tag that indicates if the most senior member of the company in presence is the one who actually incurred the expenses for reimbursement (which is a common corporate travel policy restriction).
Then, collect information about if the company being courted actually became a client and how much revenue the business brings your own company by quarter or perhaps yearly. Collecting these kinds of data points can help you track a few key items: (1) Are you getting your return on investment? In other words, were the front-row Lakers tickets worth it in the long run, given the money brought to your corporation through a new partnership with the clients your colleagues were entertaining? (2) Are employees following the expense guidelines, and if not, can the breaks in policy be justified based on your insights about ROI?
It will take a while to gain insights about these issues, but then you can begin to adjust your policies. For example, perhaps you learned that your guideline requiring the most senior person in the company to charge the expenses was being broken on occasion, but, in fact, the non-senior members were actually being more thrifty while still bringing on clients. Maybe it is time for a policy change.
Ultimately, the travel data your company collects can tell you where your employees are staying, what airlines they are booking on, etc. That much is easy. Depending on your tracking tool, the data can also tell you if travelers enjoyed their stay, if they sent out for dry cleaning, and if bumped up their seat to the $20 extra legroom option at the last minute—to name a few examples.
More advanced analytics may allow you to predict if travelers will send out for dry cleaning or change their seat on future trips, under what conditions, and how much those items may cost depending on a variety of locations or airlines, and so forth. Letting that data drive your policies can be helpful in terms of traveler satisfaction. The $20 extra legroom fee may not seem like a big deal if it is a one-time thing, but it is a habit that can easily add up; at the same time, if this is something that makes employees more satisfied with the trip, it could be worth it. This brings us to traveler attrition.
In his article on actionable versus vanity metrics, Patrick Torres describes traveler attrition as an actionable data collection point. If you collect information about which employees seem to be dropping off the radar and are less likely to be willing to go on business trips, you may be able to make decisions about your travel policies that will make employees happier and thus drive attrition numbers back down.
Yet, traveler attrition as a single data point does not give you insights into what exactly is making travelers unwilling to travel. Collecting some of the information described above can give you insights into travelers’ booking preferences as well as spending habits. You may need to collect information through a post-trip traveler satisfaction survey in order to employ diagnostic analytics as a way to look for correlations between traveler attrition and satisfaction. If you have some business travelers who fail to complete the survey, diagnostic analytics may still help you find the problem if you know where to look for correlations. Collecting as much actionable data as possible may give you insights into travelers’ preferences, and you can use those insights to adjust your policies.
A very simple way that travel analytics may affect your policies is related to preferred providers. As Braun notes, travel managers can determine exactly when to book hotels based on running a predictive model, which will forecast when a vendor’s prices will be lowest over the course of the year. Based on your ability to gather this information, it is possible that your company’s travel policies can become more active and dynamic. Perhaps you can open up the hotel brand options more because you are able to predict when prices will be lowest. At the same time, you may have to work towards a stricter advance booking policy in order to take advantage of those deals.
Advanced data analysis capabilities can help you understand travelers’ preferred vendors, too. As you collect this data about many employees and begin to find trends, you may find it makes sense to change your policies as such: Let’s say that your employees are all required to book at a certain major hotel chain or they will not be reimbursed. The data about where travelers are booking does not do you much good. However, if you are collecting satisfaction rankings post-trip, you may learn that travelers hate this chain. You may find that a few have found loopholes, somehow managing to book at their favorite chain or an Airbnb instead. If your data predicts that travelers will continue to book out-of-policy because they hate the chain so much, then it might just be time to update your preferred providers or loosen the restrictions.
You might be wondering, why do I need to collect post-trip satisfaction data through a survey? Why can’t I just ask Juan in the hallway how his hotel stay was? Well, you can. And depending on the company culture, this might be the easiest approach; however, if you work for a TMC, work at a large company, or even if you just want to turn Juan’s comment into data for future analysis, a quick survey may be a better bet.
Another common section of a corporate travel policy is the reimbursement process. This part of the policy spells out for travelers what they will and will not be reimbursed for, as well as how they must go about making purchases (Company card? Submit a receipt later?) and filling out forms for reimbursement. These guidelines are often dense and strict—for good reason. No one wants to purchase something on a trip only to find out the company will not reimburse for it.
Yet, this is another category in which travel analytics can swoop in and make lives easier. Quite a few tools are available to make reimbursement more manageable. For example, some mobile travel applications allow employees to take a photo of a receipt and directly submit it for reimbursement. That receipt then becomes a line item—a piece of expense data for your company to analyze. Again, your policies may become shorter and less stringent once you have a set procedure in place, or a digital tool, for collecting and analyzing this information.
Some tools will allow you to actually track your travelers’ percentage of compliant and non-compliant charges. If you find that travelers are relatively compliant, that may be a sign your policies are working. If the non-compliant rate is high, you can use some of the other data points we have discussed to re-analyze your guidelines.
On the subject of compliance, personalization is all the buzz in corporate travel management. Through advanced data collection and analytics, travel technologies can now personalize hotel choices or airline routes for travelers and make recommendations based on preferences. As we’ve noted, Claire even makes recommendations based on a company’s travel policies.
So, how does personalization, driven by data, influence travel policies? In one sense, advanced analytics may allow you to have looser travel policies. One of the reasons why some companies do not allow open booking (allowing travelers to book through whatever platform they choose or use any providers they wish) is because it is more difficult to track expenses when travelers book on their own versus booking through a TMC or a company-specified application or site. Yet, with advanced analytics programs such as Traxo, travelers can book off-platform, and companies still have access to that data.
In such a case, companies adopting these tools may be able to loosen the reigns a bit and let travelers have more control over the travel booking preferences because now, companies can trust that independent traveler decisions will be caught in the wider net these applications allow them to cast. You might find your company’s travel policies getting shorter, which, frankly, may mean more travelers will read the policies in the first place.
(We should note that companies also stray from open booking because of preferred vendor rates that they often receive if they have negotiated a deal with a specific hotel chain, for example. However, if you have the power of predictive analytics showing you when to find the lowest rates, that becomes less of an issue.)
Most travel policies do not mention privacy, but as analytics begin to be factored into travel programs, the corporate travel policy might be a good place to mitigate a traveler’s concerns. For example, if you are using a mobile travel bot that uses GPS to track employees or a program that mines employees’ emails for travel data, you will likely want to consider spelling out for employees what types of data you are collecting and how it will be used. Let’s just say some employees may not want their boss to know where they went after the 10 pm business dinner—and, frankly, if they paid on their own dime, they likely have a right to that level of privacy. Let your travel policy be one place where travelers can go to learn more about their privacy rights if you are adopting a data analytics tool.
When the Data Isn’t In Your Favor: Taking Action When Your Budget is too High
While we may have covered many of the positives associated with collecting and analyzing corporate travel data, there is always a possibility that your data reveals things you don’t like—such as that your company is spending too much on travel.
In this case, your travel policies may need a change in the opposite direction, getting stricter than what we have previously discussed. Opportunities for action include consolidating traveling on airlines where you have SMB loyalty programs, adjusting your set budget caps for trips, or asking travelers to fly premium economy versus business class for long-haul flights. If you make these changes to your policies, though, keep collecting that data and learning from it. The data may just reveal other opportunities for savings.
Also keep in mind that gauging spend and bookings volume can actually help you when you find you need to negotiate new deals with vendors.
Collecting the Right Kinds of Data
Wired Magazine’s Mark Rabe notes that the travel industry “has never been one to lack data.” Car rental companies, airlines, hotels—they all gather massive amounts of data; the issue has been companies lacking the knowledge or tools necessary to take advantage of that data. Rabe recommends triangulating—cross-referencing your company’s data with third-party information, such as airline bookings to a particular city.
He suggests this offers a fuller picture, and provides an apt example: a hotel may have data indicating that a traveler is a loyal customer because she booked with them a few times, but pulling third-party data may reveal that that the same traveler actually traveled to the city more often and booked with a competitor. Before you consider how travel analytics may impact your travel policies, you will first want to establish a plan for how you will collect data and what types are most useful for the company. Then, as you begin to analyze or run reports on the data, you can allow what you learn to help you shape your policies.
Each of the factors described—from expense reporting categories to personalization—all link back to an important concept: traveler satisfaction. Business travel can be exhausting and stressful, but with the right effort and planning from corporate managers, it can also be fun. If you are managing travelers’ bookings or even setting their policies for reimbursement, making their trip enjoyable is important. At the same time, your goal is to save the company money, too, right? Travel analytics allows you to have it both ways. Loosening up your policies and learning from travelers’ preferences may just make for happier travelers who might be more likely to read and follow your policies—and policy compliance typically correlates with saving money.
Jenna teaches college-level writing courses at the University of New Haven, and she regularly freelances for 30SecondsToFly. When she’s not writing or teaching, she can be found traveling, running after her toddler, and/or enjoying some mac & cheese.