It’s time for the Moneyball of destination marketing

Destination marketers are rising up. Or as some of them put it: geeking up.

They’re daring to bypass their ad agencies and parse big data to figure how to convert their relatively small marketing budgets into higher-spending visitors and fill off-season vacancy gaps. And they are pointing to proof that it’s paying off.

NB: This is a guest analysis by Doug Lansky, travel writer, author and speaker.

One of the leaders of this new DIY data-driven marketing movement, Matt Clement, marketing and partnership manager at the Convention and Visitor Bureau of Fort Worth, Texas. He summed it up the motivation for transition this way:

“My theory it is that I’d rather spend 20% of my budget on data and feel good about targeting the other 80%, than spend 100% blindly, not knowing if my media buys are doing anything – that would keep me up night.”

Clement calls it the Moneyball of Destination Marketing, giving a nod to Moneyball, the book and movie based on the Oakland A’s 2002-2003 managerial strategy, which used rigorous statistical analysis to acquire overlooked bargain players and beat teams with a player budget three times larger.


Similarly, Phil Klassen, the marketing director at Travel Alberta, is another believer.

“We used to just chuck money over the fence,” he said, referring to the budget they’d give to their ad agency to buy media. “Now we’re doing a good portion of the targeting and buying ourselves.”

It’s easy to glaze over when big data is involved. So I’m going to start by dumbing it down – way down – with the help of a fishing analogy.

Here it goes: Let’s say you’re trying to feed a big family with fish on a very limited budget.

Your research shows that the fish are biting really well in a few different locations at certain times of the year. So you concentrate on those locations and dates and try a few different kinds of bait to see which brings in the fish most easily.

It works, but you need to do some bait testing yourself. You learn how effective the bait is by trial-and-error in each location, counting how many fish you catch with each type of bait.

Then you fillet and weigh it to see how much meat you get and assess your results based on total meat weight.

Maybe one location requires more expensive bait or you hook fewer fish, but you get A LOT more meat per catch, so the bait or wait is worthwhile. At the end of it day, you’re trying to get the most meat for your fishing effort, not the most fish or most nibbles.

That’s the basic strategy. If you understand this much you’re well ahead of the pack. Now the details.
This new breed of marketer is not all that interested in clicks, click-thru rates or impressions.

That’s only a small part of the story. They want to see visitor booking rates on various campaigns, then look at how much those types of visitors are spending per day, how many people they’re bringing along and how long they’re staying.

And then they want to learn more about those visitors and their booking habits, so they can fine tune the messaging and make sure they get it delivered at the right time and figure out how to crack other markets.

The tricky bit is that no single data company can provide a DMO/CVB with all the pieces of information needed to optimize a fully dialed-in campaign.

There’s a good deal of data overlap among the companies, some provide more accurate data for certain pieces, and for some things, there’s simply no great data. So a data cocktail is required. (Yes, I started with baseball, moved on to fishing, and now it’s a cocktail!)

Before I get into the ingredients of this data cocktail, let me pause for a moment to point out what’s missing.

Measuring the conversion of social media is still tricky because major social media platforms don’t allow cookies (yet). And there’s not great travel data on visitors staying with friends or relatives or using all peer-to-peer rentals like Airbnb (a considerable part of the market in some areas), but it’s likely just a matter of time.

Also – just for the record – using research and digital targeting isn’t necessarily the most effective way to achieve the biggest marketing impact with the least amount of money.

You could make a few calls and get Hollywood to set a TV series in your city; ask Beyoncé to sing about it; or encourage private stakeholders to create an amazing new world-class attraction that’s brand-aligned with your destination.

But, from a marketer’s perspective, influencing Hollywood filming locations or hit songs and jumpstarting successful destination developments are far more difficult to achieve. Or take credit for.

Destination marketers need to justify their existence and their budget each year, and the most sure-fire way to demonstrate return on investment are the metrics connected to digital marketing.
The trend with this marketing is to avoid the trends.

That is, they want to see real data about their visitors and the market, not surveyed data showing national trends. (As survey expert, Matt Champagne, Ph.D., says: Don’t attempt to survey the facts.)


Adara and Sojern are the biggest players, not just in terms of the data they provide, but also helping destinations place ads (banner, native, social and pre-roll) at key points along the decision path (using proprietary algorithms), and then showing conversion figures that can be presented up the budgetary command chain.

Understandably, they’re often the first data purchase for many destinations. The difference between them, beyond the algorithm, is the data set.

Adara plays up their use of “first-party data” (from specific hotels and airlines and other travel companies), while Sojern says they use a mix of data from Online Travel Agencies and first-party partners.

Both will tell you that their data sets are superior.

Adara believes it’s better to get their data directly from the source (and offers paid data analytics services separately), while Sojern will tell you that they are better at seeing a snapshot of travelers’ search habits with access to OTA data.  

A side note: This article is not about choosing sides, just trying to explain the basic concept and what sort of data the various companies offer. Since much of the strategy with data is testing, one route might be to try campaigns with both companies and compare the results.

Sample Adara Data:

Adara data DMO destination marketing

Sample Sojern Data:



Nsight provides a unique forecast view of the market using online travel agency data from hotels.  

They can, among other services, show you where your current hotel bookings and searches are coming from, so you can keep an eye on active interest. They can show top feeder markets per capita, so you can see where there’s a high concentration of engaged potential visitors.

In other words, you can get a forward look at the markets where it will conceivably be easiest to move people from to your destination. And get the timing right.

If you are, for example, going after families, then you need to know when they’re booking, not just where they’re looking. They can also overlay hotel searches for bookings in the future compared to your competition and show how it is affected by your hotel prices.

If you’re getting a good search-to-booking rate, hotels don’t need to drop rates to bring in more visitors. They’ve also developed a user-friendly visitor profile. The downside is that this demographic data is based on IP address, which may only place users within 10km, so it may not be terribly accurate for some demographics.

They use OTA data (like Sojern), so that limits the size of the data set. How much it limits it depends on who you believe. Forbes says that in the U.S., OTAs only account for 15% of total hotel sales. puts the number closer to 50%.

Sample Nsight Data:

nSight destination marketing

Newcomer, Arrivalist, takes a more holistic approach, measuring the efficacy of a destination’s complete digital footprint. It allows you to show that people who visited your DMO website, for example, came from further away and stayed longer in your destination (if that’s actually the case).

If a potential visitor has looked at your website, TripAdvisor page, Lonely Planet page, or banner ad, they’d get a customized Arrivalist cookie on that device that tells you when and where that visitor shows up in your destination during the following two years. (It’s done while protecting the person’s identity, but it’s still a bit creepy.)

This also allows you to see, for example, that for those visitors exposed to your various media who do show up in your destination, they typically do so 60 days after viewing your TripAdvisor page, 15 days after viewing an ad on Expedia, or 100 days after viewing your Lonely Planet page, so you can tailor your message accordingly (By the way: those specific day figures are completely made up, but real ones are available).

It’s important to note that just because someone visited your website or saw an ad, then showed up in your city, doesn’t necessarily mean the website or ad were the things that convinced them to go, but by monitoring upticks during a few campaigns, you can show correlations. (Monitoring upticks may not sound entirely scientific… because it isn’t.

Another option, done by Palm Beach, Florida, was to conduct a survey, asking visitors to explain what got them to visit. Gustav Weibull, director of research at The Palm Beaches, found that not only are such surveys expensive, they take so much time to implement and analyze that his organization can’t made marketing adjustments until two years later.)

One of the big advantages with Arrivalist data is that, because it’s identifying arrivals by device, it will even include visitors who drive to your destination and stay with friends and relatives – provided they have a digital cookie on one of the devices they brought along.

Arrivalist’s tracking pixels (aka cookies) are able to go where Adara’s and Sojern’s can’t: on each other’s ads. Adara and Sojern are competitors and won’t go out of their way to help the other gather vital data, but they’re both willing to allow Arrivalist pixels. So, in this new data-gathering landscape, Arrivalist is Switzerland.

“We ran a summer campaign last year,” said Weibull. “It had a healthy click-thru rate, but Arrivalist data showed us that they weren’t turning up in Palm Beach. So, for this year’s campaign, we’re going to reallocate those resources.”

But… even among those who were exposed to a destination’s digital footprint, there are some noteworthy asterisks with the data: a small percentage of people turn off cookies on their devices, which will cause them to not register.

And a small percentage of device’s GPSs may be inaccurate. Still, Arrivalist is shining some light on this previously gaping dark data hole.

Sample Arrivalist Data:


While many of the data companies offer “travel profiles,” the new destination marketing geeks find them to be a little on the light side. That is, they want to drill deeper and get to know the DNA of their actual visitors so they can attract more like them.

To do this, they’re employing a company like Buxton.

It’s not cheap, but Buxton analyzes a sampling of credit card purchases in your destination and break down your customers by spending and lifestyle profile into 19 industry-standard groups that are further broken down into 71 subgroups with titles like: “Golf Carts and Gourmets”, “Kids and Cabernet” and “Couples with Clout.”

You can see, with almost freighting insight, exactly who your destination currently attracts, where your biggest spending visitors are coming from, what they’re doing while they’re in your destination and the sort of things that appeal to this group so you can better target more of them. (Visa View and Nielson also provide credit card data, but not served up with destination analysis.)

The reports are extremely detailed. 

And that may also be the downside… at least to some of the information. Unless you’re good at interpreting the data, it can be hard to slice and dice down to that level where you can create an actionable marketing plan.

What’s the difference between how you market to someone who drinks $200 champagne or $400 champagne?

Sample Buxton Data:

Buxton destination marketing


Another newcomer, AirSage, doesn’t require any pixels, hotel data or OTA data to provide destinations some interesting information.

They simply track everyone who shows up in your destination with a mobile phone… well, a healthy sample of them. (Their sample includes roughly 30% of the market, and they can access data for any destination back to March 2012, which makes instant growth comparisons handy.)

So what can a destination do with this data?

It could show (to use a hypothetical example), that 80% of out-of-state visitors who turn up in Fort Worth, Texas spend their first day in the stockyards, then visit museums on their second day, then head to Six Flags on the third day, and that 25% spend their evenings downtown (if this happens to be what they actually do).

It could show that visitors from New York spend more time in the stockyards than visitors from Oregon (if that’s the case). It could show the total annual visitors to the stockyards and which days have the highest number of visitors and at what time of day it gets most crowded.

It could show where people go in town when they leave the stockyards. And it could show if there was a bump in visitors following a media campaign (particularly useful for tracking traditional-media campaigns).

To use a real-life example, they were able to show St. Petersburg, Florida, an interesting snapshot of who was on their beaches: where they were from, which hotel clusters they were staying in, and how many of them were getting to the business district.

AirSage’s drawbacks: It doesn’t track GSM phones, so no special insights for international visitors. The accuracy of the location will not pinpoint someone on a sidewalk or specific shop… better at saying which hotel cluster a visitor is in than which exact hotel. And it’s not live data: they can serve up reports with a 30-day delay.  

At the moment it’s just offered in the US (they bought Decell, an Israeli company with similar technology with the hope of bringing the service to international destinations.)

So that’s the cocktail. Few, if any, destinations use all the ingredients.  Picking the right ones depends on the market and goals.  

If destinations decide to go this route, it likely means creating a new position for a data geek because, beyond just ordering, gathering and interpreting this data, there’s a good deal of trial and error involved to dial in any campaign.

For this article, I’ve also spoken to, and want to thank, Ted Sullivan at Adara, Sylvia Weiler at Sojern, Katrina Pruitt-Andrews at Nsight, Cree Lawson at Arrivalist, Katie Russell at Buxton, and Ryan Kinskey at AirSage.
Below, I’m including charts showing what the various companies can provide, and what their strengths are.

  • Dark green for “Extra-Strength Data”
  • Light green for “Useful Data”
  • Blank for “Data Not Provided”

doug lansky data cocktail destination marketers

doug lansky destination marketing cocktail data analytics

doug lansky destination marketing cocktail data analytics

doug lansky destination marketing cocktail data analytics

doug lansky destination marketing cocktail data analytics

NB: This is a guest analysis by Doug Lansky, travel writer, author and speaker. Images courtesy of Sony Pictures (Moneyball); image ofBen Taylor of the San Jose Giants using Gameday software by Intel Free Press/Flickr/Creative Commons)

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About the Writer :: Viewpoints

A founding principle of tnooz was a diversity of viewpoints from across the spectrum. Viewpoints are articles by guest contributors from around the travel and hospitality industries. The views expressed are those of the author. and do not necessarily reflect those of the author's employer, or tnooz and its partners.



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  1. Eric Rozenberg

    It’s refreshing to see that DMO’s are taking new initiatives in line with their markets and their budget. Enough of the same old thing!

  2. Bruce Rosard

    Doug – this is a great article with very useful information not just for DMOs, but for all travel marketers. I like the comparison to Moneyball. To continue the analogy, DMOs from NYC, FL, Vegas etc., are like the big market major league baseball teams: the Yankees, Red Sox and LA Dodgers, for example. They have the advantage of lots of cash to throw at superstar players. Where “DMO Moneyball” fits in is for smaller market DMOs who need to ensure they use their money wisely to attract as many guests as possible, similar to the Oakland A’s winning more than losing, getting to the playoffs, but never quite winning a championship because they just never spend enough money. But, that doesn’t preclude the big markets from being data focused as well, it’s been a while since the Yankees have won the World Series (2009).


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