Additional data doesn't always equate to faster or more insightful answers though. Nor does it necessarily warrant hiring an army of data scientists to deliver those answers. According to Sabre, travel buyers spend an average of 40 hours per month reconciling travel expenses and payment data. Think about how much time and money that equates to over the course of a year.
Machine learning advancements have the potential to strip the laborious aspects of analysing data for travel programme managers, allowing them to spend more time taking action.
Predictive analytics means agile businesses
When data is unlocked, business leaders have access to the information they need to make strategic decisions that directly impact the bottom line and ensure investments made are in the right areas - both internally and with partners. Data is the backbone of digital transformation for organisations today.
Corporate travel management companies (TMCs) are heavily investing in the power of predictive data analytics to impact travel spend in various areas, including:
- Spend concentration: Looking at spend concentration in hotel or airline markets across the world helps leaders comprehend how much money is spent by region to inform and/or predict consolidation. Further optimisation can provide insights ahead of supplier negotiation meetings and influence which markets to target for additional outposts. While manual aspects of this process remain, the ability to use machine learning to make modifications based on user preferences is on the horizon.
- Pricing predictions: Optimising inventory recommendations is emerging as an effective tactic in negotiating deals and contracts with suppliers. As these systems evolve, the level of detail will become more granular and could further help with rate and policy structure negotiations.
- Expense: Overall advancements in technology, specifically machine learning, can significantly cut costs for organisations in the corporate travel space. In addition to reducing the time needed for consolidation, machine learning can provide insight into travel spend by analysing past trends, observing current trends and predicting decisions for total spend in the future.
- Traveller risk management: Advances have the potential to help businesses forecast travel disruptions such as weather patterns or airports and routes with the most delays; mitigating most customer concerns by communicating updates to travel managers.
Egencia says: "What will ultimately give companies a competitive edge now is more agile use of data. From traveller risk management to pricing predictions and travel spend optimisation, technological advances can turn these old school, and often manual processes into automated transactions that will ensure business operates smoothly and efficiently. In corporate travel, the way forward is to unlock the power of data and leverage this new currency. You can be sure that doing so will not only increase traveller satisfaction but will positively impact your T&E programme and your bottom line."