QSR Omnichannel: 2.5x ROAS in 2026

Winning in the QSR space means getting your omnichannel advertising to actually drive sales at the local level, and that’s a lot harder than it sounds. You aren’t just trying to reach a big audience. You’re trying to convince someone in a specific neighborhood that your burger is the answer *right now*, pushing them to either walk in or tap ‘order’ on their phone. How do you actually connect a big national campaign with the kind of hyper-local ads that get people moving across all the different channels they use?

Campaign Teardown: Local-First Omnichannel for “BiteNow Burgers”

Here’s the breakdown of a recent campaign we ran for BiteNow Burgers, a regional QSR chain with 85 restaurants in the Southeast. Our goal was to increase both foot traffic and online orders during the crowded summer promo season. The main job was to grab local market share by deploying a sophisticated omnichannel advertising strategy that felt genuinely local. We ran the campaign for 12 weeks, from May to August 2026, with a total budget of $750,000 focused on our key markets in Georgia, Florida, and Alabama.

Strategy: Hyper-Local Targeting Meets Cross-Channel Teamwork

Our whole strategy was built on treating every single store as its own market instead of using broad DMA targeting, which gave us incredibly granular control. We worked with Viamedia to handle our TV placements, using their system to hit specific zip codes and neighborhoods with household-level precision on both connected TV (CTV) and linear cable. On the digital side, we tied together Google Ads, Meta Ads, and programmatic display, making sure our ads only appeared to people within a 3-mile radius of each BiteNow Burgers location.

We operated on the belief that people in a specific town respond best to offers for that specific town. This meant we were constantly tailoring promotions, store hours, and even the photos in the ads to reflect the local community. For example, a BiteNow Burgers in Atlanta’s Midtown got ads that mentioned its proximity to Georgia Tech, while a store near Jacksonville Beach ran creative about grabbing a quick meal on the way to the sand. It’s a lot of work (no question), but that kind of specific detail is what made our engagement metrics jump.

Creative Approach: Dynamic and Relevant

The creative had to walk a fine line: keep the BiteNow Burgers brand identity consistent everywhere, but let the actual content of the ad change based on location. Every ad used the brand’s colors and logo, but the message itself was always in flux. For our TV spots, we created a set of modular commercials. We could take a 15-second core ad about a new burger and attach a 5-second tag that showed the closest store address or a special offer only valid at that location. We used Adobe Dynamic Media to keep all these versions from turning into a complete mess.

Our digital ads were even more flexible. We went all-in on Dynamic Creative Optimization (DCO) for our display and social ads. This meant if you were in a certain zip code, the ad you saw would automatically show you a picture of your nearest BiteNow Burgers, tell you exactly how far away it was, and give you a coupon that only worked at that store. Someone browsing a local news site in Sandy Springs, Georgia, might get an ad saying “Fresh Burgers at BiteNow Sandy Springs! Just 1.2 miles away. Get $2 Off Your Order Today!” This isn’t just some fancy extra. For effective local marketing in 2026, this is what customers expect.

Targeting & Placement: Precision Over Volume

We were surgical with our targeting. For TV, Viamedia’s geo-targeting let us layer census block data with household income and interest segments (like “fast food enthusiasts” or “families with young children”). This made sure our CTV and linear ads hit the right kind of households within a very tight radius of each store. We also ran conquesting campaigns, specifically targeting households within a half-mile of our main competitors, which turned out to be one of our highest-converting tactics.

On the digital front, we mixed standard geo-fencing with interest and behavior targeting. We created custom audiences of people who’d visited competitor QSRs in the last 30 days and people who were heavy users of food delivery apps. In Google Ads, we turned on “store visit conversions” tracking, which let us directly connect ad views to people walking into a physical store. We then tweaked the conversion windows to match the short, impulse-driven purchase cycle of a typical QSR customer. Our Google Ads bid strategy was set to “Maximize Conversion Value” with a target ROAS, letting the algorithm hunt for high-value actions like app downloads and completed online orders.

One of the big lessons from this campaign was how much value we got from running display ads on local news sites and community forums. While Meta and Google delivered the sheer volume, those smaller, hyper-local placements often gave us much higher engagement from an audience that was already invested in their community. A small, dedicated budget for direct buys on sites like the Atlanta Journal-Constitution’s local pages was a great addition to our main programmatic buys.

Performance Analysis: What Worked and What Didn’t

The 12-week campaign produced solid results, proving that a properly integrated, local-first omnichannel strategy really works. Here’s how the numbers shook out:

Overall Campaign Performance

• Total Impressions: 125 million
• Total Conversions (Online Orders + Store Visits): 62,500
• Overall ROAS: 2.5x
• Average Cost Per Conversion (CPC): $12.00
• Average Click-Through Rate (CTR) – Digital: 1.8%

Channel-Specific Performance

Our budget split was a strategic bet: we put about 60% ($450,000) into TV (both CTV and linear cable) and 40% ($300,000) into digital channels like display, social, and search. We did this because while digital is incredibly precise, localized TV is still one of the best ways to get broad reach and build the brand’s reputation.

TV (CTV & Linear) Performance:

• Impressions: 75 million
• Estimated Store Visits (Attributed by Viamedia’s geo-lift analysis): 30,000
• Cost Per Store Visit: $15.00
• Brand Recall Lift (Post-campaign survey): +12% in targeted zip codes

The geo-lift analysis from Viamedia was a huge help here. By comparing foot traffic from households that saw our ads to a control group that didn’t, we could show a clear bump in visits to BiteNow Burgers in the areas where we concentrated our TV spend. No attribution model is perfect, but this gives a much clearer picture of TV’s real-world impact than just looking at reach and frequency numbers.

Digital Performance (Display, Social, Search):

• Impressions: 50 million
• Online Orders: 28,000
• Store Visits (Attributed by Google Ads & Meta): 4,500
• Cost Per Online Order: $10.71
• Cost Per Store Visit (Digital): $11.11
• Average CTR (Display & Social): 2.1%

The digital channels did the heavy lifting for driving online orders, especially Meta Ads, where the audience targeting is so powerful. That DCO strategy we ran for display ads led to a 25% higher CTR than what we saw in past campaigns that just used one-size-fits-all national creative. It just goes to show that making ads feel personal, even when you’re doing it at scale, really works.

What Worked Well

• Hyper-Local TV Targeting: Being able to send specific commercials to defined geographic areas, sometimes down to a single neighborhood, was a massive win. It let us push an offer in the Buckhead area of Atlanta without wasting money showing it to people in rural North Georgia. This kind of precision is a huge step forward for TV ads, and a lot of brands aren’t using it yet.
• Dynamic Creative Optimization: Automatically personalizing our digital ads with local store info, distance, and offers had a direct impact on how many people engaged with them. It gives the ad a sense of urgency and relevance that a static ad just can’t match.
• Integrated Measurement: We were able to stitch together Viamedia’s geo-lift data with Google’s store visit conversions and Meta’s offline conversion tracking. This gave us a pretty complete picture of how the campaign was affecting both online sales and in-store foot traffic which is exactly what you need to prove ROAS in this kind of complex campaign.
• Competitor Geo-fencing: Hitting people with ads when they were physically near a competitor’s store was a killer tactic for driving impulse buys. We saw clear spikes in our own store visits right after we turned on those geo-fences.

What Didn’t Work as Expected & Optimization Steps

• Initial Budget Split for Search: We definitely messed up at first by putting too much of our search budget on broad terms like “burgers near me.” We got a lot of impressions, but the conversion rate was terrible compared to more specific searches like “BiteNow Burgers Atlanta.” We caught this in the first two weeks and shifted that money over to branded keywords and local SEO.
• Creative Overload: Our original plan for TV had way too many creative variations. It turned into a logistical headache for trafficking and quality control. We ended up simplifying the modular approach, cutting back on the number of moving parts but still keeping the ads locally relevant. Sometimes simpler is just more effective when you’re trying to scale dynamic content.
• Attribution Complexity: Even with all our tools, tying a specific online order back to someone seeing a TV ad is still incredibly difficult. We had to rely on a mix of last-click models for digital and the geo-lift data for TV. Getting a single, unified view of the customer’s journey is the industry’s holy grail, not a current reality. We’re experimenting with a probabilistic attribution model in a data clean room to get closer, but it’s an ongoing project.

We made some key optimizations mid-campaign, like cranking up the bid multipliers for mobile devices inside our geo-fenced zones after realizing how many QSR decisions are made on the go. We also went back into our social media audiences and narrowed our interest groups based on the early data. For instance, we found that people interested in “local events” converted at a higher rate than people in a broad “food and drink” audience.

Future Implications and Recommendations

The big lesson from this campaign is pretty simple: local relevance makes people act. If you’re a brand with physical locations, you’re leaving money on the table by not having a real local-first omnichannel advertising strategy. The ability to talk to a customer about their immediate surroundings and needs, whether they’re on their couch watching TV or on their phone downtown, creates a connection that generic ads just can’t.

My advice for any similar brand is to start investing in the data infrastructure that actually lets you do hyper-local targeting and dynamic creative. That means getting your CRM in order, having good location data, and using platforms that can serve ads in real time based on context. And don’t forget to build real relationships with local media partners, not just the big national players. They can open doors to community engagement and authentic messaging that you can’t get anywhere else. The future of advertising isn’t just about finding an audience. It’s about becoming part of a community.