This is part four in our four-part series exploring the data destination marketers need to consider at each stage of the “Awareness-Interest-Desire-Action” funnel. 

With the first three steps complete, it’s time to look at how we might measure a post-coronavirus recovery in travel in your destination by looking at DMO metrics for understanding the action phase, i.e. the one where they actually book a vacation. We got right to the doorstep last time, assessing traveler desire by looking at referrals from the DMO site to the partner, but of course, we haven’t really finished the job until someone gets physically to the destination.

In truth, referrals are probably as close as we get to a good conversion measure from DMO website metrics. While many DMOs offer actual booking options on their site, through niche-specific vendors like Book › Direct, it’s been obvious for over a decade that those will never replace OTAs like Kayak and Travelocity as the primary way that travelers book hotels or experiences. The OTAs have too much money invested in doing this one thing really well, and insofar as booking a trip is largely a similar process no matter which destination you’re going to, it’s likely that users will always prefer to have one tool they can return to each time they travel.

So if users don’t complete the purchase that we’re trying to measure on DMO sites, it’s time to move away from DMO web metrics alone. Ideally, a DMO could work with its local partners, at least the big hotels and attractions, to put their own tracking code on partner sites. That would offer the best accounting of DMO site contributions to bookings. But doing that would almost be more of a political campaign than a marketing project, so instead let’s look at something your organization can do on its own.

The subfield of location analytics has been gaining steam for a while in many industries. It’s huge in brick-and-mortar retail, where companies use it to measure foot traffic and shopping sequences. This type of data comes from individual cell phone activity, with actions on various websites or in apps being recorded and aggregated by vendors such as UberMedia, who then sell the information and analyses of it to companies looking to enhance their marketing efforts. In practice, the data comes in as sporadic and suggestive sonar pings rather than a continuous stream of precise activity markers.


There are several ways a DMO can use this information to measure when users take action to become true travelers:


Origin markets 

With location data, we know not only when a traveler arrives, but where they came from. With enough volume, that opens the possibility of attributing lift to campaigns based on the source markets where they ran. This works better with large budgets and traveler flows (everything in this post does), but it offers a promising means of validating the effectiveness of geo-targeted outbound advertising campaigns.

Changes in traffic within the destination 

Some of the most exciting analyses of location data for DMOs involve examining where user activity is seen the most frequently, at what times of day, and how those patterns have changed over time. Particularly in the middle of a world-shaking event like the Coronavirus quarantine, this kind of data can be very revealing because the contrasts in behavior will be so different.

Location sequencing 

We know where people come from and where they go, but how about the order of their activities? Do visitors stop between the airport and their hotels to eat? After they leave the convention center, where do they go to spend the evening? Which nightlife districts do better in the late night vs the happy hour period? Answers to these questions can help justify the expanded economic impact of hotel marketing and event hosting.



Location data and the analyses built from it are relatively new, but some best practices are starting to come together for their use in the DMO industry. We look forward to seeing better cross-destination benchmarks and standardized tools developing as we go forward. Because this data speaks so directly to the outcome that DMOs are trying to affect (putting travelers in destinations), it’s one of the best ways a DMO can demonstrate its impact on visitors who take the action necessary to complete the purchase journey.