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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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 Georgia Campbell, Head of Strategic Partnerships at Kontext. What types of audiences does Kontext provide, and what are some top use cases for these insights in marketing strategies? Kontext leverages its 1st-party, deterministic shopping data to generate real-time online audiences. What sets Kontext apart is our ability to see the entire consumer journey, from shopping interest to intent and purchases, at a SKU-level. This comprehensive visibility allows us to create purchase-based audiences across various consumer verticals, such as frequent online shoppers, consumers shopping for beauty, segments using Mastercard, or Black Friday enthusiasts. Our data engine, built on a foundation of approximately 100 million consumer profiles and over 10 billion full-funnel, real-time shopping events, enables the creation of precise audience segments. This real-time 1st-party shopper data is invaluable for partners aiming to understand and engage with consumers more effectively. Whether a brand wants to activate past shoppers in a specific category or reach new audiences with a propensity to buy, Kontext provides the insights needed to make informed decisions. Some examples of audience types include these (and hundreds more): In-Market Shoppers: Consumers showing high intent to purchase specific categories, like skincare or electronics, based on recent online behavior. Past Purchasers: Shoppers who have made verified purchases within specific time frames, such as beauty products in the last 18 months. Frequent Shoppers: High-frequency buyers identified through repeated purchasing behaviors. Seasonal Shoppers: Consumers active during key shopping seasons, like Black Friday, Mother’s Day, Valentine’s Day, etc Premium Buyers: Shoppers who used a premium CC (eg. Amex) and a higher AOV (average order value) Beauty Buyers: an audience that has indicated intent to purchase beauty products (deterministic past purchasers also avail) By using Kontext data, brands can identify the right audiences across multiple verticals, such as retail, CPG, health & wellness, auto, business, energy & utility, financial, and travel. Additionally, our collaboration with Experian allows further refinement of these audiences through layered data from specialty categories like demographics, lifestyle & interests, mobile location, and TV viewing habits. How is Kontext’s data sourced, and what differentiates it from other data providers? Kontext’s data is unique because it is deterministic, 1st-party, and collected as transactions occur. We capture the entire path-to-purchase, down to the SKU-level product detail, across 100 million consumer profiles and more than 10 billion real-time shopping events. Our proprietary technology, embedded in widgets across our 5 million premium online destinations, tracks the full consumer journey—from reading an article of interest to clicking on our dynamic commerce modules, adding items to cart, and completing purchases. This real-time data collection ensures there is no lag between digital events and their connection to consumer profiles. Unlike other providers, we do not aggregate data from multiple platforms; instead, we focus on building our models and insights based on authentic online consumer behavior. Our data stands out due to its: Deterministic Nature: We capture 1st-party data as transactions occur (all in real time) Full-Funnel Coverage: We capture consumer journeys from awareness to purchase, providing a complete view of consumer behavior. Real-Time Insights: Our data engine processes events in real-time, enabling timely and relevant marketing actions. How does Kontext ensure the accuracy and reliability of its audience data? Kontext ensures accuracy and reliability through our unique technology and direct data sourcing. By not aggregating data from other platforms, we maintain control over the quality and integrity of our insights. Our continuous investment in refining our models around online consumer behavior further enhances the precision of our audience data. What types of brands or verticals might resonate the most with Kontext audiences for activation? Any brand looking to understand and activate online shopping behavior – informed by 1st-party transaction data – will resonate with Kontext audiences. Essentially, any vertical that benefits from understanding real-time shopping behaviors, such as retail, health & wellness, auto, and financial services, will find our data invaluable. We have particularly strong insights in beauty, hair care, health & wellness, and values-based online shopping habits, as well as the food & beverage space. Retail & Consumer Goods: Leveraging shopping behavior data for targeted campaigns. Health & Wellness: Identifying consumers with specific health and wellness interests. Automotive: Targeting potential buyers of electric vehicles or eco-friendly products. Financial Services: Engaging high-value shoppers with premium credit card usage. And many more How does Kontext’s data help advertisers navigate the challenges posed by the deprecation of third-party cookies? As third-party cookies become less reliable, Kontext’s 1st-party data becomes invaluable. Our deterministic data engine, which does not rely on cookies, offers: Direct Consumer Insights: Accurate and consented data directly from consumer interactions. Privacy Compliance: Our data collection methods are fully compliant with privacy regulations, ensuring secure usage. Cross-Device Coverage: We use verified digital identifiers, allowing seamless unification and targeting across multiple devices. What measures does Kontext take to maintain data privacy and compliance, and how does this benefit advertisers? Data privacy and compliance are fundamental to Kontext. We meet or exceed all privacy compliance and security standards, ensuring that our data sourcing and usage are transparent and comply with regulations (CCPA, CPRA, VCDPA, etc). Kontext prioritizes data privacy and compliance through: Consented Data Collection: All data is collected with explicit consumer consent. Robust Security Protocols: Data is encrypted and secured with industry-leading practices. Compliance with Regulations: We adhere to global privacy laws, including GDPR and CCPA. User Control: Consumers have the ability to opt-out and manage their data preferences. Can you share success stories / use-cases where advertisers significantly improved their campaigns using Kontext’s data? To give you a sense of how Kontext data can be applied, here are two use-cases: Beauty Brand Campaign: An agency hoping to activate an audience of beauty purchasers for a Major Beauty Brand could utilize Kontext’s custom audience of high-value beauty product purchasers. By targeting those consumers who had bought similar products in the last 12 months and had an average cart size of over $50, the campaign would significantly increase performance and ROAS. Electric Vehicle Launch: For a major auto manufacturer’s EV launch, Kontext could be used to identify eco-friendly consumers who had not yet purchased an EV but had shown interest in sustainable products. This precise targeting could lead to higher engagement and conversion rates for the campaign. Thanks for the interview. Any recommendations for our readers if they want to learn more? For those interested in learning more about Kontext, reach out for a personalized consultation. Contact us About our expert Georgia Campbell, Head of Strategic Partnerships, Kontext In her current role as Head of Strategic Partnerships at Kontext, Georgia plays a pivotal role in shaping the company’s strategic direction within the data space. With a deep-seated expertise in leveraging data to drive impact for companies, Georgia has been forging key partnerships that enhance the effectiveness and reach of Kontext’s offerings. Georgia comes from a background in emerging technology, where she has been focused on cultivating partnerships and employing data-driven approaches to spearhead market expansion efforts. She started her career in finance, managing investments across equity, debt, and alternative assets at Brown Advisory. In this Q&A, Georgia shares her insights on Kontext’s Onboarding partnership with Experian, offering perspective on how Kontext’s unique insights can unlock new opportunities for advertisers and brands alike. Latest posts
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