Miami, Philadelphia, San Francisco, New York, Boston. Those are the five strongest US feeder cities into Lisbon by direct-flight volume, and the list takes ninety seconds to pull from Skyscanner. A Lisbon walking tour operator with zero customer history and an afternoon of focused research can build a defensible targeting plan from that list and four other public data trails. None of it requires a first booking.
That is the premise Chris Torres, founder of The Marketing Coach and a thirty-four-year veteran of tourism brand and marketing work, makes whenever an early-stage operator tells him paid acquisition has to wait until they have their own audience data. The data exists, he argues, just not in their CRM. Travelers leave a public trail on the way to every destination. Searches, flight routes, lodging patterns, cruise itineraries, competitor ad spend. Each trail is enough to build a working first profile of where customers come from and how they behave once they arrive.
What follows is the six-tool stack he uses to assemble that profile from scratch, in roughly an afternoon, for under thirty dollars a month total.
Google Trends Classic Explorer surfaces what the new interface hides
Google Trends has a useful report hidden one click away from its default view. The default view shows search volume as a curve over time. That curve is not what helps an operator decide where to point a first ad campaign. The by-country search-origin report is, and it now lives in the Classic Explorer view tucked into the top right corner.
Open trends.google.com, type the phrase a traveler would use (“things to do in Barcelona”), set the geography to Worldwide and the timeframe to the last three months. The default view will show a curve. That curve is not the point. Click into the top right and switch to Classic Explorer. The Classic view exposes a by-country search-origin chart that the new view has quietly removed.
For “things to do in Barcelona” over a recent ninety-day window, the top non-Spain origin markets ranked as United Kingdom, Ireland, Canada, United States, and Singapore. For a Barcelona operator with no customer database, that is the starting list. It is not the final list, but it is the question of “where do we even point our first ad campaign” answered in under a minute, for free.
Use this while you still can. Google is steadily collapsing the older Trends views into the newer interface, and there is no guarantee the Classic Explorer will be available a year from now.
Skyscanner reveals your real feeder cities
Search-origin data tells you which countries are interested. Direct-flight data tells you which cities can actually get to you without a layover, which is a far better proxy for who is likely to book.
In Skyscanner, set the origin to a target country (or use Explore Everywhere), set the destination to your city, toggle direct flights only, and pick a wide date range. The map that loads shows feeder markets ranked by route availability. A Lisbon search filtered to the United States surfaces Miami, Philadelphia, San Francisco, New York, and Boston as the strongest US feeders. Those five cities are not a hypothesis. They are where the airline capacity is, and airline capacity is a leading indicator of where bookings will come from.
For a North American destination, run the same search in the other direction. London, Glasgow, and a handful of European hubs almost always sit near the top. For 2026, that matters more than usual: the World Cup is bringing inbound travel from cities that have not been priority markets in years, and Skyscanner shows you which ones connect directly to your stage.
The output of this exercise is a short list of source cities. That is the list you point your first Meta and Google campaigns at.
AirDNA tells you who the typical visitor actually is
Flight data tells you where they come from. AirDNA tells you what they do when they arrive.
For around twenty dollars a month, cancellable, AirDNA aggregates Airbnb and Vrbo listing data into market dashboards. Pulling up Lisbon as an example yields a useful set of inferences in five minutes:
- Most common rental size is one bedroom. The default visitor is solo or a couple, not a family of four.
- Seventy-seven percent of rentals are entire homes, not shared private rooms. The visitor wants privacy.
- Average length of stay is four nights. The default window is Friday to Monday, a long weekend.
- Fifty-one percent of stays approach a full year, which signals a substantial remote-worker segment that travels and books differently than weekend tourists.
Those four data points alone reshape product and ad copy. Your three-day itinerary is the right product, not your seven-day. Your messaging speaks to couples, not families. Your ad creative shows two people, not a tour group. And there is a parallel audience of long-stay remote workers who deserve their own funnel.
AirDNA does not tell you where the visitors came from. It does not need to. Flight data already answered that.
Listing clusters double as ad-pin targets
AirDNA’s map view is the part of the tool that pays the subscription back on its own. When the map shows a tight cluster of short-term rentals in one neighborhood, that cluster is a Meta ad target.
Drop a pin directly on the cluster with a one-mile radius. The audience is, by definition, short-stay visitors in your destination. You are not targeting an entire city. You are targeting the dozen blocks where the people who matter to you are physically standing.
The same logic applies to hotel clusters near corporate event venues. Chris ran successful campaigns in San Diego by pinning ads on hotels around a convention center, scheduled to run a few days before the event, throughout it, and a few days after. For food tour operators, group experience operators, and anyone running corporate offerings, identifying the event venue and pinning the surrounding hotels is one of the cheapest high-conversion plays available.
The fact that Facebook and Google have stripped the older “people interested in travel to X” targeting parameter is not a barrier here. Geographic precision does the work that interest-based targeting used to do.
CruiseMapper turns ports into upstream targeting opportunities
For operators in cruise destinations, the most expensive race is the day-of-arrival shore excursion fight. Every other operator is in that race, and most of the booking is happening through the cruise line’s own platform before guests step off the boat anyway.
The upstream play is more interesting. CruiseMapper publishes every ship’s itinerary, including past and upcoming port stops with dates. That means you know, three or four days in advance, exactly which ships will arrive in your port, where those ships currently are, and what their passengers are doing.
Run a Meta ad campaign geo-targeted to the prior port. The creative is straightforward: “You arrive in [destination] in three days, book your tour now.” Passengers see the ad while they are still in Cozumel, or Naples, or wherever the ship docked yesterday. They book before they hit your harbor. You capture them upstream of the day-of scramble.
Operators in Alaska-feeder ports and Mediterranean stops have used this approach to lock in pre-arrival bookings instead of competing for last-minute attention. It is a small play, but it is one of very few cruise-tourism tactics that does not require a deal with the cruise line.
Meta Ad Library and Google Ads Transparency Center expose what competitors are doing
You can also work backwards from your competitors. Both Meta and Google publish public ad libraries that show every active ad any business is running, free, with no login.
Open the Meta Ad Library, search a competitor by name, and the full set of currently-running ads loads with creative, copy, and run dates. For EU-based advertisers, the ad detail page goes further and exposes the targeting parameters: age ranges, locations, gender, interests. A travel brand pulled up in this example was targeting thirty to fifty-five-year-olds across a handful of specific destinations. That is your free competitive intelligence, the kind agencies used to charge real money to assemble.
Google’s equivalent, the Ads Transparency Center, does the same for search and display ads. USA Guided Tours, pulled as a demo, was running fifty active Google ads with three creative variations of each. The total count alone is a useful signal about ad maturity and budget posture. The keyword and creative variations tell you what your competitors think is working.
You are not copying their ads. You are reading the file they have left open and using it to inform your own campaign before you spend a dollar.
ChatGPT’s agent mode runs the whole research stack for you
The final tool is not a tool in the same sense. It is a way to package everything above into a single research workflow.
Chris built a prompt for ChatGPT’s agent mode that takes a few inputs (website, destinations served, day or multi-day format, any known customer assumptions) and runs roughly twenty minutes of structured web research before returning a regional source-market report. For Spoon and Compass, a Slovenia-based hiking and culinary operator, the agent returned New York, New Jersey, Boston, Washington, and San Francisco as primary US targets, justified by income, cultural travel patterns, and Europe flight connectivity. Then secondary markets like Sydney, Melbourne, Seattle, and Portland with reasoning attached.
The output is markdown, which the agent will convert to a Google Doc on request. The total cost is the price of a ChatGPT subscription and twenty minutes of compute time that the operator can spend doing other work.
The agent does not replace the manual research above. It runs the same plays at lower fidelity, faster. For an operator deciding which markets even deserve a real ad test, that is the right tradeoff.
What it takes to make any of this work
The catch on every Meta and Google campaign in 2026 is patience. Both platforms have stripped most interest-based targeting and now rely on their own machine learning to find the audience inside whatever geographic and demographic frame you set. That learning phase needs time. Chris recommends a minimum two-to-three-month run for any new Meta campaign before drawing conclusions, and cutting the test short is the most common reason operators conclude paid ads do not work for them.
The corollary: do not start running paid ads until your targeting is informed. The platform AI is now doing the work of finding converters inside the frame you give it. If the frame is wrong, no learning phase will save the campaign.
The whole point of the stack above is to set that frame correctly on day one. Google Trends gives you origin countries. Skyscanner narrows to feeder cities. AirDNA shapes the visitor profile and surfaces high-density ad targets. CruiseMapper adds a pre-arrival channel if you are in a port destination. Meta Ad Library and Google Ads Transparency Center calibrate against competitors. ChatGPT’s agent assembles a draft of the whole picture.
Used together, an operator with zero first-party data can produce a targeting plan as defensible as one built from two years of customer history. The difference is that the operator without history can start the campaign on a Monday morning instead of waiting.
About Chris Torres
Chris Torres is the founder of The Marketing Coach and the author of the best-selling Lookers Into Bookers, which breaks the customer journey into repeatable steps any tour operator can apply. Over thirty-four years in tourism brand and marketing, he has worked with operators on every continent, from early-stage businesses to industry leaders. He speaks at global tourism events and co-runs Tourpreneur, the global community of over twenty thousand tour operators.
To connect with Chris or learn more about his work with operators, visit themarketing.coach.
For more insights and strategies on building a better tour business, join the community at community.tourpreneur.com.