How TripActions makes business travel easy with Snowplow data
Managing travel expenses is a necessary evil, but it can certainly suck the joy out of a work trip. Fumbling through receipts to record and expense payments for flights, hotels and meals is rarely fun, particularly when the tools you use to upload expenses are clunky and unintuitive.
At Snowplow, we’ve discussed at length the merits of capturing rich data to better understand your product users, and the opportunities present in funneling said data into features and enhancements that improve the user experience. Sadly, many of the tools that exist for travel expenses have been designed to support businesses rather than users, resulting in suboptimal UX/UI.
But there is hope. TripActions, the fastest growing travel and spend management company, aims to marry a seamless product experience with the backend systems businesses need to facilitate painless travel expenses.
How TripActions uses Snowplow:
- Capturing granular activity to continuously improve their product UI;
- Delivering data to machine learning algorithms to predict and solve customer issues as they happen;
- Leveraging event-level data to understand and triage the customer support queries in real time.
Smoother journeys for business travellers
Unlike many travel expense tools out there, TripActions’ application is intuitive, encouraging travellers to use the app organically rather than being forced by a company mandate. TripActions was able to make their application user-friendly through robust product analytics — capturing data from their call logs, website, CRM and financial data and the application itself — and surfacing that data to multiple outputs, including machine learning applications, to streamline the customer journey.
To take one example, let’s consider a typical booking experience: Devon is travelling from Dallas to Tel Aviv, so she needs the most direct and affordable flight possible. She signs into the TripActions app, and enters “Dallas to Tel Aviv flights” in the search function. At this stage the back-end system works to select flights to recommend to Devon, but there are a lot of choices to be made. Should the system show Devon the most direct flights, or the cheapest? Are certain flight carriers or hotels preferred?
This is where TripActions’ data shines. By feeding event-level activity data into machine learning algorithms, over time TripActions has trained itself to predict (based on Devon’s history and context) the most relevant options for her travel itinerary. So when she searches for a flight, the recommendation system shows the airlines Devon most prefers, the hotel chains she likes to stay at and her favorite means of transportation. By the time she has used the app a few times, Devon can easily book her ideal business trip. All of this information is carefully tracked, with customer events captured by Snowplow to improve the customer experience for Devon (and users like her) on a continuous basis.
Responding to customer needs at scale
TripActions’ data team uses data in sophisticated ways to deliver a seamless booking experience. But inevitably in the travel booking and expensing process, things go wrong, and it’s down to the customer support team to provide a fix.
In this instance, TripActions’ support team is able to surface data to provide an efficient service. With the help of data models, TripActions can predict the volume of support calls through reliable forecasting, enabling support staff to manage their workload effectively. The ability to predict call volume ahead of time means that TripActions can skillfully manage supply and demand — so more resources can be deployed when it’s needed, and customers can get help quickly.
At the same time, TripActions’ customer success team uses event data to identify potential issues before they become genuine problems, so that they can deal with them in the most efficient way. Data science is ideally suited for spotting patterns and making predictions to prevent churn, rather than relying on human intuition. At TripActions, machine learning highlights potential issues to customer success managers at the most appropriate time, so they can respond rapidly. This is another instance of where data can drive internal optimization that results in a better outcome for the customer.
Smarter support with data
Event data answers key questions at TripActions that bring front-line employees closer to their customers. For their customer team, it’s not just a case of identifying when to provide support, but how.
To tackle this problem, TripActions deploys a live feed to customer support staff, so that when a support enquiry comes through, agents can see the context behind the customer search, what they were trying to do and how far they got in their experience. This feed enables the agent to immediately dive into solutions, decreasing average handle time (AHT) and generating satisfying outcomes for customers — who are spared the pain of explaining their challenge from scratch.
Because the feed is delivered in real time, support agents have what they need to deliver excellent service without delay.
Building better with data-informed testing
At every stage in the journey, TripActions is able to leverage data to provide a smooth ride for customers and invaluable intel for internal teams. But the team at TripActions is not content to rest on their successes, striving instead to continuously identify where they can enhance the customer experience.
The next step for Director of Data Rob Winters and his team is to build continuous testing and model optimization into their workflow, so new permutations of product features can be surfaced for different customer cohorts — with results tracked in real time. Armed with this information, TripActions will be able to experiment with countless variations of their platform, incrementally optimizing until the pain of business travel is all but eliminated.