Corporate travel management is a data-intensive practice that has at times resembled an arms race between suppliers and buyers. Many travel managers have closed the data disparity that opened between them and suppliers several years ago by appointing analysts to their teams. Now, a
new gap is opening as suppliers leverage their larger data sets and greater budgets to apply machine intelligence to analytics. It is about to become an imperative that travel managers do the same.
Machine learning and predictive analytics are complementary but separate technologies. Machine learning is better suited to transforming or extending data sets to make them more useful, while predictive analysis allows businesses to improve the way they use these transformed data sets to deliver
value.
Suppliers are already using these advanced techniques daily, primarily to adjust the prices of their products each day or even each hour based on predicted and actual demand. Suppliers are also able to use the wealth of data from sources like loyalty accounts to shape traveler behavior.
However, these same tools also can help the travel manager cut through the complexity inherent in today's complex programs. Areas where these techniques can help manage travel are:
- Building a total cost of trip view by traveler across multiple data sources and refining what constitutes a trip over time
- Predicting future activity based on trends or external market factors and telling business users when they could be impacted, thereby enabling them to prepare or even prevent the change, such as predicting flight delays through weather and past airline performance
- Identifying when suppliers' dynamic pricing models will result in advance-purchase opportunities, prices below negotiated or sellouts
- Predicting travel budgets based on performance, industry forecasts and future staffing levels
- Matching individual transactions across agency, corporate card and expense data to eliminate duplication and clearly understand total spend
- Predicting the impact that events like the Olympics, the Super Bowl and corporate conferences will have on pricing and availability
- Cleaning up data sets in which the data has been too 'dirty' to carry out meaningful analysis
Leveraging data using advanced techniques will help mature programs find incremental value through lower cost, consistent quality and reduced risk. Driving these advanced analytics requires data. Companies can get started by scoping what's possible:
- Analyze available sources to find how to build links between them
- Determine the data quality. Think about completeness of data rather than the presence of unstructured text. Machine learning often can work with the latter
Look at what you might want to predict. Start with a long list and perhaps work with a business intelligence partner to refine what is feasible.