Regional linear television advertising involves buying scheduled TV commercials that air within specific geographic markets rather than nationally. These campaigns run on local broadcast affiliates, regional cable networks, or targeted cable systems within designated market areas (DMAs). Unlike national campaigns that blanket the entire country, regional linear TV lets brands focus their television spend on particular cities, states, or market clusters where their products are sold or where they want to build awareness.
You see regional linear TV everywhere in daily life. Local car dealerships advertise during morning news programs in specific cities. Restaurant chains promote regional menu items or store openings in select markets. Political candidates buy spots that only air in their districts or states. Consumer brands test new products in a few markets before rolling out nationally. These ads appear during the same programming breaks as national commercials, but they only reach viewers in the targeted geographic areas.
Brands use regional linear TV for several key purposes. They want to drive sales in physical retail locations within specific markets. They need to support product launches or promotions tied to regional distribution. They want to build awareness among potential customers who live near their stores or service areas. They also use it to test creative messages or media strategies before committing to expensive national campaigns.
Regional linear TV differs from digital advertising in important ways. It provides broad reach within a defined geography rather than narrow targeting of specific individuals. It builds awareness through repeated exposure rather than driving immediate clicks or conversions. It works through established broadcast and cable infrastructure rather than internet-based delivery systems. The measurement approaches also differ significantly from digital channels that can track individual user actions.
Measuring regional linear TV presents unique challenges that make it more complex than digital advertising. The fundamental problem is that television creates exposures at the household or individual level, but linking those exposures to specific purchase behaviors requires sophisticated methods. Unlike digital ads where you can track a user from click to conversion, TV viewers don't take immediate, trackable actions that clearly connect their ad exposure to their later buying decisions.
Marketers rely on several types of data to understand regional linear TV performance. They use panel data and set-top box information to measure how many people saw their ads and how often. They analyze sales data at the market level to see if revenue increased in areas where ads ran compared to areas without advertising. They conduct surveys to measure whether people remember seeing the ads and whether their brand perceptions changed. They examine foot traffic data to see if store visits increased in targeted markets.
The core challenge in attribution comes from proving causation rather than just correlation. Sales might increase in markets where you ran TV ads, but other factors could explain that growth. A competitor might have reduced their advertising, weather could have been better, or economic conditions might have improved. Regional linear TV also creates delayed effects where people see ads but don't purchase until days or weeks later. These timing gaps make it difficult to connect specific ad exposures to eventual purchases, especially when people see multiple touchpoints across different channels.
Geo experiments split geographic markets into treatment and control groups to measure the incremental impact of regional linear TV campaigns. In a randomized geo test, you randomly assign some markets to receive your TV advertising while holding other similar markets as controls. You then compare sales, store visits, or other outcomes between the two groups to calculate the lift caused by your advertising.
This method measures incremental impact well because it establishes true causation through controlled testing. It works with any outcome you can measure at the market level, including offline sales, retail foot traffic, or brand awareness surveys. Geo experiments also remain effective as privacy regulations limit user-level tracking in digital channels.
The main limitations involve time, cost, and statistical requirements. Geo experiments need sufficient markets to achieve statistical significance, which means larger brands with broad geographic reach get better results. You must hold out some markets from advertising, which creates opportunity costs. The tests take weeks or months to run properly, including pre-test baseline periods and post-campaign observation windows. You also need sales or outcome data that updates regularly at the market level.
Panel and set-top box measurement combines traditional TV panel data with return-path data from cable and satellite boxes to track viewership. Companies like Nielsen and Comscore use panels of households who agree to have their viewing tracked, then supplement this with anonymous data from millions of set-top boxes. This produces ratings that show how many people saw your ads, when they watched, and how often they were exposed.
This method excels at measuring reach, frequency, and basic campaign delivery. It provides the industry standard currency that media buyers and sellers use to price and evaluate TV advertising. The data offers detailed breakdowns by demographics, dayparts, and programs. You can see exactly which spots aired and verify that your campaign delivered the audience you intended to reach.
However, viewership data only shows exposures, not whether those exposures drove any business outcomes. High ratings don't necessarily mean high sales impact. Panel-based measurement also relies on statistical sampling and modeling that may not perfectly represent all viewers. The data tells you who saw your ads but cannot directly connect those viewers to their later purchase behaviors or website visits.
Marketing mix modeling uses statistical analysis of historical data to estimate how different marketing channels contribute to sales over time. The models analyze relationships between your TV spending, other marketing activities, external factors like seasonality or economic conditions, and your sales outcomes. They produce estimates of return on investment for each channel and help predict the impact of different spending scenarios.
MMM provides a holistic view that includes regional linear TV alongside all your other marketing channels. It captures long-term effects that immediate measurement might miss, such as brand building that drives sales months later. The models work well for strategic planning and budget allocation across channels. They also include offline channels that are difficult to measure through direct attribution.
The approach requires substantial historical data covering multiple years and various spending levels to produce reliable estimates. The models work better for brands with consistent data collection and relatively stable business conditions. MMM provides directional guidance rather than precise measurement, since it relies on correlation patterns in historical data rather than controlled experiments. The analysis typically happens quarterly or annually, making it less useful for tactical campaign optimization.
Brand lift surveys measure changes in awareness, ad recall, purchase consideration, and brand perception by comparing people who were exposed to your regional TV campaign against those who weren't. Survey companies identify exposed and unexposed audiences through panel data or geographic targeting, then ask both groups questions about your brand and advertising.
These surveys capture upper-funnel effects that sales data might miss, such as increased brand awareness or improved brand perception. They work quickly, often providing results within days of campaign completion. Brand lift measurement helps justify TV advertising for companies whose sales happen through long consideration cycles or retail partners where direct attribution proves difficult.
Survey methodology introduces potential bias through self-reported responses and panel recruitment. People who agree to join survey panels may not represent your actual target audience. Sample sizes for regional campaigns are often smaller than national studies, reducing statistical confidence. Most importantly, improvements in brand metrics don't always translate to proportional increases in actual sales or business outcomes.
Regional linear television drives significant business outcomes, but without proper measurement, marketers make budget allocation decisions based on incomplete information. When Jameson measured their Las Vegas Sphere activation through regional linear TV support, they discovered a 4.71% lift in depletions—a concrete number that justified the investment and informed future regional campaigns.
Measurement connects directly to budget decisions because regional linear TV competes with digital channels for marketing dollars. Without causal measurement, marketers often underestimate television's impact. Traditional metrics like reach and GRPs show exposure but not incremental sales. When Magic Spoon measured their YouTube TV campaigns, they found substantial retail and Amazon lift that platform attribution completely missed. This discovery shifted their entire media mix and budget allocation strategy.
Measurement insights change future executions in three key ways. First, they reveal which creative elements work in specific markets, allowing marketers to optimize messaging for local preferences. Second, they identify the most effective dayparts and programs for reaching target customers in each geography. Third, they demonstrate cross-channel effects—many regional linear TV campaigns drive significant online search, social media engagement, and retail foot traffic that would otherwise go unmeasured.
Regional linear television works best for specific marketing situations. Consider this activation when launching products in physical retail markets, driving warehouse depletions, opening local stores, or building awareness in geographically bounded areas. Political campaigns, CPG brands with retail distribution, and businesses with physical locations see the strongest results.
Regional linear television may not fit your needs in several situations. If you require precise person-level targeting or immediate click-to-conversion tracking, digital channels offer better solutions. Campaigns needing hyper-granular audience segments or real-time optimization work better through addressable or programmatic channels. Additionally, if your budget is limited and you need immediate performance data, the time and cost requirements for proper measurement may not align with your goals.
Getting started requires planning for both the media execution and measurement approach. Begin by defining your primary outcome metric—sales by market, retail depletions, store visits, or brand awareness. Secure the necessary data sources for measurement, whether that's point-of-sale data, distributor information, or retailer partnerships. Plan your creative production timeline, allowing 6-12 weeks for standard spot creation and additional time for network clearance reviews.
Common pitfalls include underestimating operational complexity and measurement requirements. Many marketers fail to plan adequate lead times for creative delivery—networks typically require assets five business days before air dates. Another frequent mistake is attempting to measure regional linear TV using digital attribution methods, which miss the majority of television's impact. Finally, avoid starting campaigns during high-inventory competition periods like political seasons without adjusting budget expectations accordingly.
Start by identifying your measurement approach before planning your media buy. If you need to prove incremental impact, plan for geo-experimentation using tools like Haus's GeoLift platform, which offers both randomized geographic tests and Fixed Geo Tests for situations where treatment markets are pre-selected. This measurement planning determines your campaign structure and timeline.
Define your success metrics and ensure you have access to the necessary data at the geographic level. Regional linear TV measurement works best with sales data, retail depletion information, or store visit data that can be tracked by market. Partner with your analytics team or measurement vendor to establish data ingestion and analysis procedures before launching campaigns.
Create your media and creative timeline working backward from your desired launch date. Book your media inventory early, especially during competitive periods. Develop creative assets that include strong hooks in the first seven seconds and clear brand messaging. Ensure all spots meet technical specifications including loudness standards and include proper Ad-ID tagging for trafficking.
Haus offers specific capabilities for measuring regional linear television through our GeoLift platform, which launched Fixed Geo Tests specifically for regional and out-of-home activations. Their approach uses synthetic control methods to measure incrementality even when you cannot randomize market selection, making it particularly valuable for regional campaigns where media buying constraints limit experimental design options.
Execute your test with proper baseline measurement, run the campaign according to your planned timeline, and collect both media delivery confirmation and outcome data throughout the flight period. Plan for a post-campaign measurement window to capture delayed effects, as regional linear TV often drives sustained lift beyond the immediate flight period.
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Whether you’re new to incrementality or a testing veteran, The Laws of Incrementality apply no matter your measurement stack, industry, or job family.
Incrementality = experiments
Not all incrementality experiments are created equal
Incrementality is a continuous practice
Incrementality is unique to your business
Acting on incrementality improves your business
