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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