At A Glance
Brands can scale local marketing without losing brand consistency when they separate fixed brand elements from variable local decisions and automate only where the stakes are low. In this Ask the Expert conversation, Experian and Butler/Till discuss how brands can spot real local signals, decide what to automate, and keep people in the loop on decisions that carry brand risk.Key takeaways
- Manual oversight of local marketing tends to break down at around 100 store locations, according to Butler/Till, since decision latency makes it hard to keep pace with simultaneous market-level calls.
- Butler/Till recommends separating fixed brand elements, like voice and positioning, from variable ones, like media mix, audience targeting, and local partnerships, when scaling marketing across many store locations.
- When comparing a local campaign’s performance against a market baseline, a gap of 20% or more usually signals something real, while a gap of just a few percentage points is more often noise.
- Brands running local campaigns across thousands of store locations can automate budget allocation within clear guardrails, but should keep people in the loop on creative selection and other brand-risk decisions.
In our Ask the Expert series, we interview leaders from our partner organizations who are helping lead their brands to new heights in advertising. Today’s interview is with Ryan Lammela at Butler/Till.
When a national brand says it wants to market locally, what does that usually mean to them, and what does effective local marketing require? Where’s the biggest gap between the two?
Most national brands think “local” marketing means communicating the same message with different prices. They want consistency at scale, without surrendering national control. Effective local marketing requires something more demanding: adapting strategy, not just price, to each market’s competitive intensity, seasonal patterns, media preferences, and regulatory environment, while keeping brand integrity intact.
The biggest gap between national and local marketing is structural. Brands underestimate how different markets actually behave. A media mix that works in Seattle might fail in a small rural town in the Mid-Atlantic. Local audiences respond to different messaging angles. The friction comes from centralized decision-making trying to enforce one strategy across fundamentally different market dynamics.
What we’ve found is that successful brands need a framework that distinguishes fixed elements from variable ones. Brand voice and core positioning? Fixed. Media mix, audience targeting, local partnerships? Variable. Without that clarity, you get either chaotic fragmentation or missed local opportunity.
At what point does scale fundamentally change how you have to operate? What starts to break when you move from 50 or 100 locations to tens of thousands of campaigns?
In our experience, the breaking point tends to show up around 100 physical locations. Below that, marketers can still oversee most decisions. Beyond it, you hit the wall: you can’t have people make thousands of media decisions monthly and stay competitive on timing or cost.
Coordination breaks first. When you’re managing multiple channels across multiple markets, decision latency becomes critical; manual approval chains that worked at 50 locations can’t keep pace with thousands of simultaneous market-level decisions. Execution consistency degrades because there’s no visibility into what’s actually running everywhere.
Then measurement becomes a nightmare. You’re collecting data from dozens of sources: platform dashboards, local partners, point-of-sale systems. The noise drowns out signal. Without a consistent way to tie those sources back to the same person or household, you’re comparing apples to oranges across markets.
What’s a pattern that can come to light when you can compare results across thousands of campaigns, something you wouldn’t have trusted from a single market?
When you run 5,000 campaigns across 4,000 locations, patterns emerge that a single market would never reveal. A media mix that looked like a failure in one market? It works in similar demographic clusters. A creative asset that seemed like an outlier? It’s the winner among underserved audiences nationally.
You also spot seasonality patterns you’d miss regionally. When you compare July performance across 2,000 markets, you learn that summer media preferences shift in ways single-market data would dismiss as noise.
Local markets behave differently. How do you decide which local signals are worth acting on versus which are noise, and what data are you leaning on to tell the difference?
Telling a real local signal from noise starts with a baseline: establish expected performance benchmarks across similar market clusters, then look for deviations that are large relative to a market’s normal swing. A gap of 20% or more usually warrants investigation; something in the low single digits is more often noise.
Layer in market context: competitive intensity, demographics, media consumption patterns, seasonality. When a campaign spikes in one market, ask why. Is the market fundamentally different, or are we optimizing for the wrong thing?
Local signals worth acting on are consistent across similar market types, tied to something measurable (seasonal shift, competitive move, audience behavior change), and actionable without breaking brand consistency.
The data that matters most is comparative data, not absolute performance. When Phoenix outperforms Denver on a tactic, and both are similar sunbelt markets, that’s worth investigating.
With that volume of decisions, where do you let the system decide and where do you still want a person in the loop?
Automate based on decision complexity and consequence, not based on volume.
Budget allocation across channels within a market? The system could handle it. The variables are clear, the data is clean, and wrong decisions are recoverable. We set guardrails—don’t shift more than 15% week-to-week, don’t kill a performing channel below threshold—and let optimization work.
Creative selection or messaging? That stays with people. Those decisions carry cultural and brand risk that algorithms can’t weigh. Same with market-level strategic pivots or major partnerships.
Audience targeting is trickier. There’s a lot of data, patterns aren’t always obvious, and bad targeting ripples through the program. We build algorithmic recommendations, but a strategist validates them against market context. The algorithm says shift audience targeting toward women 35 to 44. The person asks why and either confirms the insight or catches something the algorithm missed.
We also keep humans in the loop for anything that breaks the established playbook. When a market starts behaving unexpectedly, we escalate.
The rule: automate routine, standardized decisions with clear success metrics. Keep people in the loop on decisions that shape strategy or carry brand risk.
What have you learned running local programs at this scale that a brand with 50 or 100 locations could apply on Monday?
Brands running campaigns for 50 or 100 locations should start by building their decision framework before they build their system. Know which decisions are local and which are national before you automate them. We’ve seen brands implement tools that actually slow them down because they skipped this step.
Establish market benchmarks now instead of waiting until you’re at scale. Group your locations by similarity and start comparing performance across similar markets. You’ll spot inefficiency fast and understand what good performance looks like in different market types.
Treat automation as a channel tool, not a thinking replacement. Ask yourself what decisions are slowing you down. What insights are you missing because you don’t have bandwidth? Start there, not with the fanciest tech.
Invest in measurement rigor. When you’re coordinating across multiple locations, measurement becomes competitive advantage. Know which metrics matter locally and which are noise. Build data infrastructure to compare performance across markets without creating reporting chaos.
And here’s what matters most: successful local marketing at scale gives local teams better information and faster decision velocity while maintaining brand guardrails. That balance—autonomy with consistency—is where real leverage lives.
Thanks for the interview. Do you have any recommendations for our readers if they want to learn more?
If your brand is managing multiple locations and feeling friction, the question isn’t “should we automate?” It’s “which decisions are slowing us down, and why?” Start there. Talk to teams running campaigns in the field. They’ll tell you where the friction actually is. Then build systems to solve that friction, not to solve a problem you think you have.
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About our expert
Ryan Lammela
VP of Channel Activation, Butler/Till
Ryan Lammela is VP of Channel Activation at Butler/Till, an independent media agency. He leads media execution strategy for brands managing distributed marketing across hundreds or thousands of locations, translating integrated strategy into measurable market impact at scale.
FAQs
Butler/Till defines effective local marketing as adapting strategy, not just pricing, to local market conditions like competitive intensity, seasonality, and media preferences, while keeping core brand voice and positioning fixed. Most brands think local marketing means the same message at different prices, which misses the structural differences between markets.
Manual oversight tends to break down around 100 locations, according to Butler/Till. Below that threshold, people can still review most decisions directly; beyond it, decision latency and inconsistent visibility start to undermine execution.
A performance gap of 20% or more against a market baseline usually signals something real, while a gap of just a few percentage points is more often noise. Butler/Till recommends grouping similar markets, setting expected performance benchmarks, and investigating deviations tied to a measurable cause like seasonality or a competitor’s move.
Budget allocation within clear guardrails is well-suited to automation, since the variables are clean and mistakes are recoverable. Creative selection, messaging, and market-level strategic decisions should stay with people, since they carry brand and cultural risk that algorithms aren’t built to weigh.
Experian’s identity resolution and measurement capabilities help brands tie data from disparate local sources, like platform dashboards, local partners and point-of-sale systems, back to the same person or household. That consistent view is what allows comparative analysis across markets to actually hold up, rather than comparing apples to oranges.
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Learn how brands can close the activation gap by carrying audience intelligence from identity to execution and outcomes across channels.
In our Ask the Expert series, we interview leaders from our partner organizations who are helping lead their brands to new heights in AdTech. Today’s interview is with Samantha Zhang, Senior Data Scientist, and Jim Meyer, General Manager of the DASH TV Universe Study at the Advertising Research Foundation (ARF). DASH is an annual tracking study conducted by the ARF to define and better understand TV audience behavior and household dynamics. What does DASH measure, and how does it help the industry understand TV consumption today? By capturing hundreds of individual- and household-level data points from each respondent in a rigorous and nationally projectable sample, DASH creates a comprehensive picture of U.S. consumer TV “infrastructure” – how America watches. Core elements in DASHElements that create context in DASHTV setsLocation | brand | smartness | service modes | sources DemographicsConnected devices Game consoles |video players | streaming devicesYesterday viewing Daypart | TV/device genre | Out-of-home viewingMobile devicesOwners | sharing usersShoppingOnline and in-store | Exposure to major RMNsInternet serviceModes | ISPs | connectivity by device Streaming audio Streaming TVSVOD/AVOD tiers and sharing | FAST Email accounts and apps Live TV Modes of access | including casting from devices Social media For example, DASH gathers: Data on every TV set, including brand, room location, age, “smartness,” and connection devices and modes Household connectivity and video service data, even in homes with no TV set Internet Service Providers (ISP) and TV service usage, including Multichannel Video Programming Distributors (MVPDs), virtual vMVPDs, streamers (ad-supported and premium), and Free Ad-Supported Television (FAST) channels Person-level ownership and usage of video-capable mobile devices, including smartphones, tablets, and laptops Measures of viewing and co-viewing across dayparts, devices, and services Additional modules covering shopping and retail media networks, streaming audio, social media, email, and apps Broad coverage and granularity make DASH a uniquely robust source of truth for practitioners across the industry, including measurement experts and ad programming strategists. DASH also reports regularly (and publicly) on key industry dynamics. DASH identified a growing segment of device-only viewers – now nearly 9 million households that watch TV, but do not own a TV set – and highlighted the implications of that trend for traditional ratings systems based only on households with TV sets. Households (HHs – million)2025 HHs (M) U.S. penetrationChange vs. 2024 (M)Total US134.8100%+2.7Connected TV (CTV)114.685%+2.1TV (Set)124.292.2%+1.1Device-only8.86.6%+1.6TV-Accessible133.198.7%+2.7 DASH called out the rise in app-based pay TV and proposed a new connection framework that better represents the modern TV world, in which linear and streaming overlap. DASH also defines the universes of households reachable with advertising. This graphic, for example, shows how all ad-supported linear and streaming properties in aggregate define the true scale of TV advertising. While 35 million households (and growing) are reachable only with streaming ads and 13 million (and falling) only with linear ads, most households are reachable with both, underscoring the importance of understanding the “overlap.” Who uses DASH data, and what decisions does it help inform? There are three primary users of DASH, each with its own use cases: Measurement providers, including Nielsen, use DASH to calibrate viewership data, turn household data into persons data (and vice versa) and estimate potential reached audiences–what the providers call media-related universe estimate (MRUEs)–for the calculation of ratings. Not surprisingly, measurement companies were the first to see the value that an independent TV universe study could provide. Media companies, including major broadcasters and streamers, use DASH to add context and color to their ad sales presentations – and to track the measurement providers, whose ratings play a major role in valuing ad inventory. AdTech companies, including Experian, use DASH to create high-value audience segments for activation. The recent accreditation of DASH by the Media Rating Council (MRC) and adoption by Nielsen as an input to its TV ratings have generated interest from a broad range of companies. We are actively pursuing new licensees and partners to make DASH more useful within, and even outside, the TV ecosystem. What does MRC accreditation signify, and why is it meaningful for DASH? MRC accreditation means DASH passed a rigorous audit conducted by Ernst & Young over many months, which validated our methodology, controls, and data quality. MRC accreditation establishes that DASH is an industry-standard dataset. While the service provider normally announces its own accreditation, the MRC took the unusual step of issuing its own release on DASH, announcing the accreditation of DASH for TV universe estimation and endorsing the study for broader, cross-media use. How does Experian use DASH data to build audiences? The segments combine specific TV usage habits and behaviors from DASH with Experian data on demographics, spending, and other contextual inputs to create a fuller view of consumer viewing behavior. They are designed to be valuable to advertisers in many categories and planning contexts – and to be customizable to fit advertisers’ media targets. The segments can be used to: Apply or suppress audiences to improve target coverage across a campaign Better align media and creative Reach elusive but high-value viewers, such as Ad Avoiders Drive valuable consumer behavior Achieve specific advertising objectives What are some practical use cases for DASH-based audiences? Here are some practical use cases for four different kinds of DASH segments in five different advertiser categories. Travel Co-WatchersA couples-only resort uses TV Co-Watching Households without Children to strengthen target reach and ad memory recallA big theme park destination uses TV Co-Watching Households with Children to reach families in moments of togetherness Home Entertainment TV Owners and Brand LoyalistsA premium TV manufacturer uses the overlap of Multi Brand TV Owners and Single Brand TV Loyalist Households to market its newest TV model to its most loyal consumers. Fast Food Screen Size ViewersA fast food chain with a high-impact new brand campaign uses Large Screen TV Viewers to better align the media and creativeThat same fast food chain uses Small-Screen TV Viewers to drive store traffic by increasing exposure of its retail campaign among on-the-go viewers Financial Services Cord Cutters A personal cost management app and a cash-back credit card target Streaming-First Cord Cutter Households to reach young, tech-savvy, cost-conscious consumers Thanks for the interview. Where can readers learn more about DASH? We started work on DASH seven years ago, and it’s been fun to watch it “grow up.” Our partnership with Experian is a big step toward putting DASH to work for advertisers and agencies. To learn more, visit our site at https://theARF.org/DASH or contact us at DASH@theARF.org. Contact us About our experts Samantha Zhang, Senior Data Scientist at ARF Samantha Zhang is a Senior Data Scientist at the Advertising Research Foundation working on the DASH TV Universe Study, with additional research spanning areas including attention measurement, digital privacy, and artificial intelligence. Jim Meyer, General Manager, DASH, at ARF Jim Meyer is general manager and co-founder of the ARF DASH TV Universe Study and managing partner of Golden Square, LLC, which advises media and research technology companies on growth strategy and development. Latest posts
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