Editorial Team Key Takeaways
- We hit a 4.2x ROAS by getting super-granular with audience segmentation, splitting people up based on their pre-pandemic travel habits versus their current booking signals.
- Using dynamic creative optimization (DCO) to serve up things like gate-change alerts and personalized amenity offers on the fly gave us a 35% CTR bump on mobile ads.
- A/B testing early in 2025 showed that health and safety messaging beat luxury messaging 2-to-1, but by Q4, we had to switch gears completely to focus on convenience.
- Our initial CPL was a high $12.50, but we got it down to $7.80 in under three months by constantly tweaking bids and building out our negative keyword lists on search.
- Plugging real-time airport data feeds directly into our ad targeting let us do micro-moment stuff, like showing ads for nearby restaurants when a flight was delayed, which pushed ancillary service conversions up by 15%.
People coming back to airports after the pandemic are a completely different breed. They’re all about efficiency, personal health, and getting an experience tailored to them, the old perks don’t matter as much. For marketers, figuring out how to connect with this new mindset is everything. But how do you actually turn those observations into a digital campaign that performs?
Case Study: “Fly Smarter” Campaign for CityHub Airports
Back in early 2025, we started working with CityHub Airports, a group that runs three mid-sized regional hubs. They launched the “Fly Smarter” campaign with a straightforward goal: get travelers back in their terminals by focusing on modern convenience and safety, which would hopefully drive up passenger numbers and ancillary revenue. The problem was that they were up against bigger international hubs fighting for the same exact people. Our whole plan was data-first, using what we knew about the new traveler to build messages that would actually hit home across their digital footprint.
Campaign Overview and Objectives
The “Fly Smarter” campaign ran for six months, from January through June of 2025. We were chasing three main goals:
- Get unique passenger visits up 10% over the first half of the previous year.
- Increase ancillary revenue from things like parking, retail, and food pre-orders by 15%.
- Push brand sentiment scores (which we tracked with social listening) up by 20%.
The total media budget was $750,000. For CityHub, this was a huge investment and showed they were serious about clawing back market share. We knew we had to make every single dollar count.
Strategic Pillars: Data-Driven Personalization
We built the strategy on three pillars: hyper-segmentation, dynamic creative optimization (DCO), and real-time contextual messaging. A generic, one-size-fits-all message was guaranteed to fail with how fragmented the post-pandemic travel audience had become. We weren’t talking to one group. People’s needs changed dramatically based on why they were traveling (business or fun), how often they flew, and even their personal comfort level in a crowd. The first thing we did was dig into anonymized loyalty program data from before the pandemic (2018-2019) and mash it up with recent booking data from late 2024. That gave us what we needed to build some really specific audience segments. For instance, we were able to isolate a group of frequent business travelers who only cared about fast security lines and lounge access, and keep them totally separate from families who were more worried about kid-friendly spots and just getting around easily.
Creative Approach: Addressing Anxieties and Offering Solutions
Our creative was all about giving people reassurance and real solutions. In Q1 of 2025, the ads were heavy on health and safety. We used visuals of clean spaces, clear social distancing signs, and touchless check-in. The copy was direct: “Travel with Confidence” and “Your Well-being, Our Priority,” and it worked. As we moved into Q2 and people started to relax about traveling again, we began to pivot the creative. The messaging shifted from safety to convenience and personalization. Some examples of what we ran:
- Short video ads that showed a super smooth trip from the parking garage to the gate.
- Carousel ads on social that showcased all the different food options you could pre-order with the airport app.
- Display ads that promoted new stores and local artisan shops inside the terminals.
A huge piece of this was our use of dynamic creative optimization (DCO). This tech let us automatically switch out ad components like headlines, images, and CTAs based on user data and what was happening at the airport right then. So, if a user had been looking at parking on the site, our ad might show them a deal for pre-booked parking. Or if we knew from airport data that Terminal B’s security line was long, we could serve an ad to people in the area with directions to a shorter line or a promotion for expedited screening. That kind of personalization is what separates a modern campaign from the old spray-and-pray approach.
Targeting & Channel Mix
We used a mix of channels to hit travelers at different points in their planning process:
- Search Engine Marketing (SEM): We used Google Ads and Microsoft Advertising to grab people with high intent. Our keywords were things like “airport parking [City Name],” “flights from [City Name],” and “airport amenities [City Name].” We also built out massive negative keyword lists to keep from wasting money on irrelevant clicks.
- Social Media Advertising: On Meta platforms and LinkedIn, we focused on building audiences. We uploaded first-party CRM data to create custom audiences, built lookalikes from them, and layered on interest targeting for groups like frequent flyers.
- Programmatic Display & Video: Using DSPs like The Trade Desk, we could reach users on tons of websites and apps. Our targeting here included geo-fencing competitor airports, using third-party travel intent data from providers like Foursquare, and retargeting people who had recently searched for flights.
- Email Marketing: We sent personalized emails to existing loyalty members with offers and real-time updates for their upcoming trips.
We put a big chunk of the budget, around 40%, into mobile-first formats. That was a no-brainer. People live on their phones, especially when planning travel. A recent eMarketer report even said mobile bookings are on track to be almost 70% of all digital travel sales by 2026, so ignoring mobile would have been a fatal mistake.
Performance Metrics and Analysis
Here’s how the campaign KPIs shook out over the six months:
Budget Allocation & Spend
Total Budget: $750,000
- SEM: $250,000 (33%)
- Social Media: $200,000 (27%)
- Programmatic Display/Video: $250,000 (33%)
- Email Marketing (Platform Costs): $50,000 (7%)
Total Spent: $748,920
Initial Performance (January-February 2025)
- Impressions: 45 million
- Click-Through Rate (CTR): 0.85%
- Cost Per Click (CPC): $1.20
- Conversions (e.g., parking bookings, app downloads): 36,000
- Cost Per Conversion (CPL/CPA): $12.50
- Return on Ad Spend (ROAS): 2.1x
The starting ROAS was fine, but it showed we had a lot of room to grow. The CPL was way over our $10 target, mostly because our initial programmatic targeting was too broad.
Optimization Steps and Mid-Campaign Adjustments (March-April 2025)
Seeing the initial numbers, we made a few key changes fast:
- Refined Audience Segmentation: We immediately tightened our programmatic audiences to only target users showing direct intent, like people who had visited a competitor’s website or searched for flights out of our region. That cut down on wasted spend.
- Bid Adjustments: We got aggressive with bids, pushing them up on keywords and audiences that were working and cutting them on those that weren’t. For example, we increased mobile bids by 15% during evening and weekend booking spikes.
- Creative Refresh: Our A/B tests showed that by mid-March, ads talking about “time saved” were starting to crush the ones about “safety.” We pulled the old safety creative and pushed more solution-focused ads.
- Landing Page Optimization: We got the mobile load time for our key landing pages (like parking and dining) down by 20%, which cut our bounce rate by 10%. A small change with a big impact.
- Geo-fencing Enhancements: We expanded our geo-fences to include big local business districts and convention centers to hit business travelers more directly.
Final Performance (January-June 2025)
- Impressions: 110 million
- Click-Through Rate (CTR): 1.15% (35% increase from initial)
- Cost Per Click (CPC): $0.95 (20% decrease from initial)
- Conversions: 96,000
- Cost Per Conversion (CPL/CPA): $7.80 (38% decrease from initial)
- Return on Ad Spend (ROAS): 4.2x (100% increase from initial)
The campaign blew past its goals. We saw a 12.5% increase in unique passenger visits, beating the 10% target. Ancillary revenue grew 17%, just ahead of the 15% goal. And brand sentiment on social media went up 25%, showing a real change in how people felt about CityHub. A final 4.2x ROAS meant we were making $4.20 for every $1 we spent. Strong.
What Worked Well
The granular audience segmentation was the MVP of this campaign, no contest. By splitting travelers into different buckets based on their needs, we could send them messages that were actually relevant. Business travelers got ads about fast security, and families saw ads about play areas. That precision is what stops you from just burning money. The dynamic creative optimization was the other big win. The ability to automatically change the ad content based on real-time data like flight delays or security lines made the ads feel incredibly useful and timely. It’s about getting the right ad to the right person at the exact right moment. Finally, the iterative optimization process was key. We didn’t just launch the campaign and walk away. We were in there every day, looking at the data, running weekly reviews, and making quick changes to bids and creative. So many campaigns fail because they launch but never evolve.
Challenges and What Didn’t Work as Expected
We got burned early on with a high cost per conversion from some of our broad programmatic buys. We thought casting a wide net would bring in conversions fast, but it was just inefficient. We learned quickly that for this market, precision was much more valuable than volume. We had to pull way back on general interest targeting and focus almost entirely on intent signals. Another thing that caught us off guard was how quickly the “safety” messaging lost its punch. It was a killer message in January, but by late March, nobody cared. If we hadn’t been A/B testing constantly and been ready to pivot, we would’ve seen engagement fall off a cliff. It’s a good reminder to test your core themes, not just tiny copy changes. We also saw that display ads on general news sites did terribly compared to ads on travel blogs or booking apps. The context was everything. A person looking at flight prices is in a totally different headspace than someone reading about politics, and they’re far more open to an ad about airport services.
Key Learnings for Future Campaigns
The “Fly Smarter” campaign really hammered home a few things. First, the consumer journey is a chaotic mess, and airport marketing needs to reflect that. Your message has to adapt from the planning phase to the in-airport experience and even after the trip. Second, your first-party data is gold. Being able to segment audiences using CityHub’s anonymized loyalty data gave us a serious edge. Third, you have to be agile. Market sentiment can turn on a dime, traveler behavior changes, and airport operations have their own daily chaos. A campaign manager has to be ready to pivot, always. The era of the static six-month campaign plan is completely over. The success here shows that if you really understand what the post-pandemic traveler wants and use the right digital tools, you can get their attention and drive real growth. The future of airport marketing is personal, dynamic, and always being optimized.
How did the “Fly Smarter” campaign measure brand sentiment?
We used social listening tools to track every time CityHub Airports was mentioned on social media, in news articles, and on review sites. The software analyzed the tone of the conversation, positive, negative, or neutral, and we tracked keywords associated with the airports, like “safety,” “convenience,” and “easy.” We took a baseline measurement before the campaign started and then tracked the changes month by month.
What specific platforms were used for dynamic creative optimization (DCO)?
We ran the DCO through Google’s Display & Video 360 (DV360). It’s built for programmatic and integrates with ad servers that can handle dynamic creative. This setup let us feed in our audience segments, location data, and even real-time airport data (like flight statuses and security wait times) to change ad components on the fly.
How was the ROAS calculated for this campaign?
ROAS (Return on Ad Spend) is a simple formula: the total revenue generated from the campaign divided by the total ad spend. The hard part was tracking the revenue. We tracked sales from things like pre-booked parking, lounge passes, and food orders that came through campaign-specific landing pages. We also used unique discount codes in some ads to tie retail purchases back to our spend.
What kind of A/B testing was conducted for the creatives?
We were constantly running A/B tests on everything: headlines, ad copy, images, videos, and calls-to-action. For example, we ran one test comparing an ad that talked about “speed” through security against one that talked about “comfort.” In another test, we compared visuals of empty, clean spaces to photos of a busy but well-organized terminal. The winners were decided by pure performance, whichever had the better CTR and conversion rate got more budget.
What specific data sources were integrated for real-time contextual messaging?
We plugged several real-time data feeds from the airport’s own operational systems directly into our ad platform. This included the flight information display systems (FIDS) for real-time delay and gate change info, sensors from the parking garages showing occupancy, and an API that provided security checkpoint wait times. This data allowed the DCO platform to trigger hyper-relevant ads, like promoting a restaurant when a flight was delayed.
