Editorial Team Key Takeaways
- We hit a 3.2x return on ad spend (ROAS) with a Lumo Digital OOH campaign by segmenting Auckland audiences based on what they drove and their travel routes.
- Using dynamic creative that changed with real-time traffic and weather gave us a 15% boost in average attention rates over the static versions.
- An A/B test running for two weeks showed QR code scans had a 22% higher conversion rate than just telling people to visit the brand’s website.
- We used anonymized mobile location data in our post-campaign analysis, which confirmed we drove a 12% lift in foot traffic to retail stores inside a 5km radius of our digital billboards.
- The whole campaign cost $180,000, and we ended up with an effective cost per lead (CPL) of just $12.50, which tells us our media buying and targeting was on the money.
Out-of-Home (OOH) advertising has always been about getting noticed, but now we can actually measure OOH attention with some real precision. Back in 2025, a big NZ electronics retailer wanted to make a splash with a new line of smart home gear. They needed high visibility and a way to get customers to act. So, they teamed up with Lumo Digital to run a campaign on Auckland’s busiest roads. Here’s a breakdown of the strategy, how it was executed, and what we learned from this roadside OOH initiative, proving that measuring attention isn’t just theory, it’s what makes a campaign successful.
Campaign Strategy and Objectives
The main goal was to drive awareness and, in the end, sales for the new smart home device line. Specifically, the retailer was targeting a 3x return on ad spend (ROAS) and wanted a 20% bump in website traffic from Aucklanders during the campaign. Our strategy was to hit a tech-friendly audience, mostly commuters, when they were most likely to be paying attention during peak travel times. The basic assumption was simple: people stuck in traffic are a captive audience for good digital billboard content. We set a total budget of $180,000 for the two-month run (Oct 1 – Nov 30, 2025), which had to cover all media placement, the creative work for the dynamic ads, and the tech needed for attention measurement. Our target profile was people aged 25-54 who were into tech and home improvement and lived in wealthier Auckland suburbs like Remuera, Ponsonby, and St Heliers.
Creative Approach and Execution
The creative strategy was all about dynamic content and clear calls-to-action. Lumo Digital’s network is great because it lets you change ad creative in real-time based on outside data. For example, when it rained, the ads automatically switched to visuals of indoor comfort using smart lighting. When it was sunny, we focused on smart security for when you’re out of the house. This approach, which we ran through a proprietary CMS, kept the ads relevant. The ads themselves were clean and simple: a high-res product shot, a short value proposition, and a big, obvious CTA. We A/B tested two main CTAs:
- A QR code that took people straight to a specific product page.
- A short, memorable URL (like “SmartHomeNZ.co.nz”) to encourage direct website visits.
We picked billboard locations strategically along high-traffic corridors, including the Southern Motorway (State Highway 1) near Greenlane and the Northwestern Motorway (State Highway 16) heading into the CBD. Key spots were the massive screens at the Khyber Pass intersection and the boards you can see from the Spaghetti Junction interchange.
Targeting and Audience Segmentation
OOH gives you broad reach, but digital OOH lets you get much more specific with targeting. We tapped into anonymized mobile data from telcos, running it through platforms like Nielsen’s OOH measurement tools to figure out traffic patterns and who was driving past our billboards at what time. This let us get smart with scheduling. For instance, on weekday mornings from 7-9 AM, the creative was all about convenience for busy professionals on their way to work. In the evening commute (4-6 PM), the messaging shifted to relaxation and home security. We even got a bit experimental and tried segmenting by vehicle type. Working with Lumo, we used data from traffic flow sensors that could make a decent guess at whether a vehicle was a sedan, an SUV, or a commercial van. It wasn’t perfect, but it let us tweak the creative slightly. For example, an ad shown to a larger family vehicle might have a message about child safety features. This kind of targeting is still pretty new for OOH, but it shows where things are headed.
What Worked: Data-Driven Successes
The campaign hit its marks and then some, beating several of our main KPIs. Across the Lumo Digital network, we clocked 14.4 million total impressions over the two months. More importantly, the OOH attention metrics were solid. These were based on benchmarks from third-party eye-tracking studies (similar to those cited in eMarketer’s OOH insights) and our own analysis of dwell time, showing an average attention rate of 65% for our dynamic ads. The static benchmark is around 50%. That 15% lift is a big deal and it proves that making the ads contextually relevant really works. The A/B test was a blowout: the QR code CTA beat the direct URL by 22% on direct engagement. We counted 28,800 QR code scans that sent people straight to a product page, while the direct URL only drove about 23,600 unique visits we could attribute to the campaign. It seems the easy, zero-friction path of a QR code was way more appealing to people in their cars. The overall click-through rate (CTR), which we defined as scans and visits divided by impressions, came out to 0.36%. For OOH, that’s a very strong engagement number. The geo-fencing analysis we did after the campaign, using anonymized mobile location data from a local analytics firm, showed a 12% increase in foot traffic to the retailer’s physical stores within 5km of our billboards. This was the metric that confirmed our OOH ads were successfully driving people to brick-and-mortar locations. Total leads (QR scans + URL visits + attributable in-store visits) hit 54,000. On a $180,000 budget, that put our cost per lead (CPL) at $3.33, which is incredibly efficient for a campaign of this size. The campaign generated $576,000 in direct sales for the new smart home line, which gave us a final 3.2x ROAS. That smashed our initial 3x goal. Our attribution model was a mix, factoring in online conversions from the QR and URL traffic, plus in-store purchases from people whose devices were spotted near the billboards.
Campaign Performance Snapshot
| Metric | Value | Target/Benchmark |
|---|---|---|
| Total Impressions | 14.4 million | N/A |
| Average Attention Rate (Dynamic) | 65% | 50% (Static Benchmark) |
| QR Scans | 28,800 | N/A |
| Direct URL Visits | 23,600 | N/A |
| Overall CTR | 0.36% | 0.2% (Industry Average OOH) |
| Foot Traffic Increase (within 5km) | 12% | N/A |
| Cost Per Lead (CPL) | $3.33 | $5.00 (Target) |
| Total Sales Attributable | $576,000 | $540,000 (Target) |
| Return on Ad Spend (ROAS) | 3.2x | 3.0x (Target) |
What Didn’t Work: Challenges and Learnings
Of course, not everything went perfectly. The dynamic creatives worked well, but setting up and testing the real-time weather integration was a bigger headache than we planned. We also ran into some delays with the retailer’s brand team on creative approvals because of all the dynamic variations, which pushed our pre-campaign setup by an extra week. Another real challenge was attributing the direct URL visits. Even with unique tracking parameters, telling the difference between organic traffic and people who actually typed in the URL from a billboard was tough. Online attribution is still a challenge in OOH, and while the geo-fencing helped on the retail side, there’s definitely room for improvement online. Our assumption that people would remember a URL was just wrong. The data’s clear: reducing friction is everything. We also noticed something interesting: attention rates actually dipped a little during really bad traffic jams, especially on Friday afternoons. We thought more dwell time would mean more attention, but it seems that extreme driver frustration might lead people to tune out their surroundings. Maybe during total gridlock, people are just doomscrolling on their phones instead of looking at billboards.
Optimization Steps Taken
We made changes based on what we learned. After reviewing the first month’s data, we quickly re-weighted the creative to heavily favor the QR code CTA and made the URL less prominent. That one change led to a 7% increase in daily QR scans within a week. We also got smarter with our dynamic content scheduling. Instead of just using weather data, we integrated real-time traffic data more deeply. When congestion got really bad, we stopped hitting people with a hard sell and switched the ads to softer brand-building content, or even a simple “Drive safely, arrive home smarter” message. Our hypothesis was that a less demanding message would build goodwill when people were stressed. We don’t have hard numbers on the sales impact of that particular tweak, but the retailer told us they got positive feedback. For the next campaign, we’re looking at better attribution models that can tie our OOH exposure data to their first-party CRM data. This could mean showing unique discount codes on the billboards that are trackable online, or working with mobile ad platforms to match anonymized device IDs. The goal is to close that attribution loop as tightly as we can. This campaign showed that while digital OOH has incredible reach, its real value comes from tough campaign measurement and a willingness to change tactics based on the data you’re seeing. Being able to track OOH attention and link it to actual business results is essential for anyone trying to get real efficiency and impact from their media budget. OOH’s future is data-driven, plain and simple. Marketers who get good at advanced measurement, dynamic creative, and smarter attribution are the ones who are going to win with this channel.
What is OOH attention measurement?
It’s about quantifying how many people actually see and engage with an out-of-home ad. We use methods like eye-tracking studies, analyzing anonymized mobile data for dwell time, and running surveys to check ad recall. It’s the difference between counting potential viewers (impressions) and measuring actual engagement.
How does dynamic creative optimization (DCO) work in OOH?
DCO in OOH uses live data feeds, like weather, traffic, time, or local events, to automatically change what’s shown on a digital billboard. So, the ad creative is always tailored to what’s happening in that specific moment for the people seeing it, which grabs more attention.
What role did Lumo Digital play in this campaign?
Lumo Digital provided the actual network of digital screens across Auckland and the tech backend for delivering the dynamic content. They were our execution partner, helping us place the media based on traffic patterns and collect the data we needed.
How was the 3.2x ROAS calculated for this campaign?
Simple math: we took the total revenue we could directly attribute to the campaign ($576,000) and divided it by the total campaign cost ($180,000). The attributable revenue was a mix of online sales from QR scans and URL visits, plus in-store sales from geo-fenced shoppers who’d been exposed to the ads.
Why was the QR code more effective than the direct URL?
Because it was frictionless. A driver or passenger could just point their phone and instantly be on the product page. Expecting someone to remember a URL and type it in later, especially when they’re in transit, just adds a layer of work that most people won’t do.
