The travel management profession is probably well aware of the need for data to drive decisions. Whether it is supplier management or policy promotion, data underlies everything most travel managers do.
Even though the travel industry acknowledges the importance of data, what we struggle with most is understanding exactly what data is needed and why we need it. In a world where we have more access to data than ever before, we need to develop an understanding of what type of data will give us a complete enough picture to make the right decision. Today, data sources range from the standard spend data captured in the TMC, credit cards and expense system through to data captured in meetings systems, Finance data, HR data, security data and even data captured by a CRM. The opportunities are truly endless. The upside is that most data sources and platforms make use of API integrations and software development kits which cut down on months of development work.
The snag in this opportunity, however, is the disparate nature of Travel & Expense (T&E) data. Anybody who has embarked on a data management project will know that T&E data is full of complexity, discrepancies, duplicates and inaccuracies. It needs specialised knowledge to fuse it all together. Not to mention the fact that new fields and even data formats like NDC are constantly being introduced to the market – keeping even the most specialised on their toes. Data quality has therefore become a common pain point most travel managers experience.
We cannot ignore the fact that introducing each new data source takes time. Jumping on and integrating every data source imaginable is just not feasible for already stretched travel teams and budgets. This is why we need to be picky about which data we use and whether it can truly add value.
What do we want to know from our data?
We basically want to be able to view, in a single source, exactly what happened on each trip our travellers embarked on in a summary and a detailed level. In other words, we want the picture described in our data to be a “blueprint” of the reality.
Any quality blueprint of a building does not describe the measurements of the building in only one dimension. They use at least three dimensions with adequate measurements for each. If we only knew the length and width of each wall and not the depth, how can we build a house? The same can be said for travel data. So much programme activity, whether it is policy compliance or supplier management, is based off our data or programme “blueprint”. We need to get a picture that is as accurate as possible. In order to achieve this we need a three dimensional data analysis.
What does a three dimensional data analysis look like?
A three dimensional data analysis looks at what travellers are spending, where they are spending it and why they are spending it.
The first dimension
What are we spending?
Our data needs to cover the basics first – it needs to know exactly how much is leaving the corporate bank account as a result of travel. How can we look at anything around travel if we don’t have a complete picture of what the travel is in the first place?
Examining the “what” seems simple enough yet this is slightly challenging. Spend happens in a variety of ways – mainly through the big three sources: TMC bookings, credit card payments and expense management systems. Many companies still only look at TMC data. This does not take into account any travel booked off-channel and therefore is essentially not analysing a complete picture of spend. According to our study of $8.5 billion in travel spend, the TMC misses an average of 41% of it. Often referred to as a “total trip cost” these three sources are our bread and butter when it comes to programme management. Reconciling this data with General Ledger (G/L) data from finance is also a handy way to assess if this figure is truly accurate. When your financials are right, you automatically hold credibility with internal stakeholders.
The second dimension
Where is the spend occurring?
Now we have a trusted view on what is leaving the bank account, we need to know the details around these transactions. When is the trip? Where is the trip? How long is the trip for? Does it take place in a risky area? Is there a tax impact? Who is spending it? This information becomes crucial when we assess risk, are looking at duty of care or are assessing traveller well-being. We cannot answer any of these questions if we are purely analysing data from a financial point of view.
Most TMCs and quality third-party data companies are easily able to provide this situational data feed from booking tools. What adds an extra dimension to this data is an overlay from the HR data feed and corporate hierarchies. This tells you who is travelling. The who becomes important if we examine traveller well-being, for example. When we know how many trips a certain traveller is taking and for how long these trips are, we have an understanding of their level of traveller happiness and we can relate this to many other aspects of the business, including employee retention. Are they in a department with a low retention rate? Are they in a senior position and may expect more TLC? Knowing this layer allows travel managers to make improved policy decisions based on what is actually happening in the company.
The third dimension
Why is the spend happening?
When we look at a two dimensional diagramme, we can more or less understand what it is representing in reality. In travel, two dimensions of data will allow you to adequately understand your traveller activity so you can run your programme. You will have an accurate picture of supplier demand, be aware of policy violations and be able to measure, and therefore, manage, spend accordingly.
When it comes to creating a programme that delivers a significant drop in spend, satisfaction to the travellers and even growth for the business, however, we need to start looking beyond these two dimensions. We need to start looking at the third dimension or the “why” for each trip. Once we understand the “why” we can understand the travel, the travellers and even the supply chain on a whole new level.
An easy example is matching booking data to shopping data to understand what the market is offering. This is instrumental in understanding why travellers are booking certain flights or rooms. Are they booking away from a preferred supplier due to unavailability? Is the rate more favourable via consumer sites? This data not only is telling you why certain suppliers are being booked or not being booked but can become an excellent bargaining chip in any supplier negotiation.
Another example is linking travel data to CRM data. What is the purpose of the trip? Is it driving growth for the company? Should more care be given to certain trips with a higher ROI attached? This data can also be used to understand whether travel is necessary or not. We can drive spend down in this way while boosting spend for trips that are linked to company growth.
How do we integrate all this data?
If we look at the what, where and why in spend we can pick out the data sources that are not only necessary but will guarantee growth. The next step is using the latest technology to make sure integrating and analysing the data is as efficient as possible. Luckily, AI technology is around and can allow us to automate the matching process as quickly as possible so that you can get the analytics you need.
In today’s data abundant environment there are infinite possibilities for what data we can analyse. As long as we know what questions to ask our data and how we can relate these questions to programme improvement we can take a giant first step in understanding our spend, our travellers and our supply chain as a whole.