Come Back Soon! Measuring the Effectiveness of Tourism Campaigns with Geolocation Data

By Cuebiq Marketing Team / 8 minutes

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Tourism is a massive industry, with millions of dollars spent to promote local, national and international travel. Governments and tourism boards all over the United States (and the world) spend a large percentage of their marketing budgets to promote tourism in their territory. Some campaigns are focused on bringing in visitors from surrounding states or on a specific landmark of a region (a national park, a beach town, museums, etc.), while others promote an entire state or country to potential national and international travelers.

Yet despite its prominence, decision-makers in the industry have a quite limited set of tools to understand how effective their campaign dollars are. Governments and marketers have a number of tools to assess how their campaigns are doing—that is, whether indeed people exposed are visiting. For example, they can match online purchases of airfare to the destination, use data from hotels and other tourist destinations where the origin of guests is logged, official data from industry reports, etc.

While these methods are useful in their own way, they lack immediacy because they rely on external sources with multiple layers in between the exposure and the marketing insight—multiple data sources need to be cleaned, processed, and stitched together to provide some insight about the campaign effectiveness.

At Cuebiq, we have a powerful new solution that allows marketers and governments to answer this question in a much more immediate fashion with the use of geolocation data.

The Power of Geolocation

Imagine the following: a state’s tourism board runs a campaign to attract visitors from surrounding states and wants to understand if the people who are being exposed are visiting within a specific conversion window. Instead of relying on any of the aforementioned methodologies, wouldn’t it be much more powerful and immediate to have privacy-safe, real-time information about the devices exposed and their behavior after the exposure?

This is exactly the core logic driving our new solution for tourism campaigns. We take an impression log of a tourism campaign and are able to determine which exposed devices live in the target locations of the campaign (in this example, the surrounding states). We are then able to understand which of these exposed devices are later in the locality of interest post-exposure, and generate useful metrics based on their mobility—which brands they are visiting, which verticals are the most popular, and which specific areas of a city or state tourists are traveling to.

Beyond these already valuable insights about where exposed visitors are coming from—and where they go—we are also able to generate aggregate, privacy-safe metrics about the general income of the visitors by using highly granular census data paired with the approximate home location of a device. This allows you to parse where your visitors are coming from and understand their general income profile. You can then slice and dice the data as needed to understand if there are different patterns of visitation according to income, and if there are differences in brands or verticals different groups are visiting.

A New Jersey Tourism Campaign

Let’s look at an example of a campaign with the goal of promoting tourism to New Jersey at large. 

The video above shows an interactive heatmap of all the visits to brands in New Jersey from devices that live outside of the state and were exposed to the tourism campaign. You can see a visual representation of where in NJ the exposed visitors are visiting brands, and can change the geographic aggregation parameters if you need a more detailed view of where people are visiting. Since these are branded visits users can also filter visits by vertical (in this example we looked at banks) or even by specific brands (here we use Bank of America and Citibank).

Another layer of the map (shown above) shows a different set of insights: the geographic distribution of the campaign exposures and which states are actually being served impressions from this campaign. Of the surrounding states, most impressions are being served in New York, Pennsylvania, and Maryland, while fewer impressions are being served in areas further away from NJ like Vermont, North Carolina, and a small percentage in Florida. This campaign is mostly targeting tourism from the surrounding states and geographies bordering NJ.

The last layer of the interactive map above shows yet another set of insights: the county of origin of those converted visitors. While the previous map showed where devices were exposed, this map shows the geographic distribution of the home county of all the converted visitors (darker blue means higher proportion of devices that come from that county). We can see that most of the visitors come from counties that border NJ, which is expected since visitors likely drive into the state, so geographic closeness increases visit density. Interestingly though, not all of the counties with high density of origin directly border NJ. The shades of darker blue around Cleveland, Pittsburgh, Columbus, Albany, and even all the way to Miami, mean the campaign is generating conversions in non-obvious places marketers should be paying more attention to.

Beyond the geographic analysis explained above, our methodology allows us to quickly show insights about which verticals (or economic areas) the converted devices are visiting. The video above shows over 70 verticals and the relative proportion of visits to each one out of all registered visits. The top three verticals are retail banks, malls and quick-service restaurants. You can also see the geographic distribution broken down by the top 4 states of origin, which account for approximately 90% of all visits. This allows a direct breakdown by state of origin and vertical in a visually immersive way.

Finally, a very powerful feature of our tourism solution (shown above) is the breakdown of visitors by their income–specifically, we use the official Census Block Group (BG) data to associate a device’s approximate home location with the median household income of their BG. In the video above you can see on the horizontal axis the median income of the home BG, and on the vertical axis you can see the number of devices that fall within an income range. As a useful summary of the histogram, above it you can see the min/max range and median income for visitors by state of origin. We can see that visitors from Virginia come from BGs with the highest median income at about 108k, then visitors from Maryland at about 86k, New York at about 73k, and finally visitors from Pennsylvania have the lowest median income at about 69k.

The non-obvious insight here is that there are fewer visitors from higher income regions, and the bulk of visitors comes from relatively lower income areas. The goals of the campaign dictate which set of visitors are most important: for this campaign, if marketers or governments want to bring in tourists from higher income areas then they should focus their campaigns on Virginia and Maryland, and if not, on New York and Pennsylvania. This is an important result because if a campaign’s goal is to bring in higher income visitors, then simply looking at volume is not enough.

Just imagine how powerful these insights can be for your own campaigns. 

The amount of depth and detail that our tourism solution has will surely spark insights and discussions between marketers, agencies, and tourism boards that will help understand who the campaign is driving, and where to, with a level of detail that no one else in the market can offer. To learn more about how Cuebiq can measure tourism campaigns, book a demo.

About the Author

Cuebiq Marketing Team

The Cuebiq Marketing team is a group of data-driven marketers with a focus on strategy and a flair for the creative! Our team is broken down into Growth and Product Marketing departments, and we work on everything from running the Cuebiq website, to developing thought leadership content, to sales enablement and pipeline generation.