---
title: "Demand-Side Spotlight: Simpli.fi"
description: Explore how Simpli.fi uses location intelligence to improve programmatic targeting, measurement, and campaign performance across channels.
---
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# Demand-Side Spotlight: Simpli.fi
September 29, 2026
Location is playing an increasingly important role in advertising as data and technology evolve, enabling campaigns to deliver the right message to consumers when they are most likely to act.
In this edition of Demand-Side Spotlight, we sit down with Amanda Shelton, VP of Product at [Simpli.fi](https://simpli.fi) to get the lowdown on location and how it can transform consumer intent into store visits and sales.
---
### 1. Hi Amanda, and thanks for joining us today. To get us started, can you tell us about Simpli.fi and what makes it different from other DSPs?
Simpli.fi is a media platform built around location intelligence. Essentially, it’s about connecting online activity to real-world behavior. To do that, we process billions of location signals a day, covering more than 130 million U.S. households, and combine them with behavioral, contextual, and keyword signals through our Signal-Level AI Architecture.
What sets us apart from a typical DSP is that we don’t treat location as a targeting checkbox within layers of a tech stack. We are an end-to-end stack, meaning the programmatic infrastructure, location intelligence, and attribution all live under one roof. That gives us one team and one roadmap, so improvements to targeting, optimization, and measurement happen faster and more consistently than they would across a fragmented vendor stack.
We’re also intentionally more than a DSP. Advertisers can run everything through Simpli.fi – including programmatic media, platform media such as Meta and Amazon, connected TV platforms, and creative production – via flexible engagement models ranging from self-service to fully managed. This enables us to be a single partner for brands where location matters, rather than a point solution for one channel or tactic.
### 2. Can you provide more detail on how that “location lens” affects the way measurement and performance are evaluated when buying media?
When location is central to a campaign, measurement needs to answer two separate questions: Who did we reach, and what were the real-world results of that reach? We build our platform around that distinction.
Geofences can define an audience to target, or they can act as conversion zones that measure whether someone who saw an ad actually visited a location afterward. That gives advertisers foot traffic attribution: a way to tie an impression directly to a verified store visit, not just a click or a view.
Matchback attribution, which ties campaign exposure to purchases and other CRM outcomes, adds a second layer of confirmation. Taken together, location-based measurement becomes less about vanity metrics like clicks and more about what is actually driving business outcomes, whether that’s store visits, appointments, or sales.
### 3. Is there a common misconception about location-based advertising that you think the industry still gets wrong?
The biggest one is that location-based advertising is synonymous with geofencing, and that geofencing is a single targeting tactic. In reality, location intelligence touches targeting, measurement, and optimization all at once. The same geofence that defines an audience to target can also become a conversion zone used to measure foot traffic, and the data it generates feeds back into how a campaign is optimized in real time.
That misconception tends to pigeonhole location-focused platforms as a niche or bolt-on capability rather than a foundation. We’d argue the opposite: Location-based advertising, done well, comes down to architecture rather than a single feature. It determines how signals are collected, how independent they remain from one another, and how much visibility an advertiser has into what is driving performance beyond where the budget was spent.
### 4. Diving into a bit more detail, how does Simpli.fi plan campaigns across multiple digital channels – display, mobile, CTV, audio, and others – so advertisers get more value from their programmatic investments?
We plan across channels by keeping every signal – location, keyword, contextual, and behavioral – independent rather than collapsing them into broad audience segments as many DSPs do. This is the Signal-Level AI Architecture referred to earlier. This independence allows our AI to evaluate what is actually driving performance at a granular level, such as a specific placement, geofence, or keyword, rather than only reporting on how a segment performed as a whole.
That same architecture allows us to plan across display, mobile, CTV, and audio as one connected strategy instead of a set of separate buys. Sequential messaging can carry a consumer from an upper-funnel CTV impression to a retargeting message on display or native, while the same location and attribution data measures the entire path, not just one channel in isolation. Combined with flexible engagement models – self-service, hybrid, or fully managed – advertisers get one platform and one team managing that plan instead of having to stitch together reporting from multiple vendors themselves.
### 5. Within that multichannel approach, how do emerging formats such as CTV and podcasting fit into the broader location intelligence story?
CTV and audio are, in a sense, the same location intelligence story on a different screen or speaker. The signals we use to understand where someone goes and what they’re interested in don’t change. What shifts is where we can reach them with that information.
The biggest risk in programmatic CTV is fragmented supply and inflated costs eating into a budget before it ever reaches the right person. To tackle this, our approach combines location and behavioral signals so we target people who are ready to act, not just people watching a particular show. This gives advertisers ZIP-code-level targeting down to the household level, access to premium streaming environments, and precision controls such as bid shading and supply tier management. To ensure a CTV impression can be tied to an actual store visit, as is the case for display and other programmatic impressions, we also extend foot traffic attribution to CTV.
Streaming audio and podcasts fit into the same architecture. We reach audiences across major streaming audio platforms using the same location, keyword, and contextual signals we deploy elsewhere. Audio therefore becomes another channel in a unified strategy rather than a separate buy with its own targeting logic. As dedicated podcast-only inventory continues to grow across the industry, we expect it to become an even larger part of the story.
### 6. To demonstrate what this looks like in real life, can you share some examples of how advertisers are leveraging location data to plan media more effectively?
We work with a wide range of multi-location and local businesses, including franchises, retail chains, healthcare groups, auto dealerships, and home services companies. A few patterns come up again and again.
Franchise and retail brands use geofencing around their own locations and those of their competitors. This allows them to build audiences and then measure the campaign’s impact on store visits. For example, one national electrical supply retailer ran an eight-month campaign that combined geofencing, address-based geofencing, and ZIP-code CTV targeting. It drove nearly 50,000 combined online and in-person actions at a cost per action well below the company’s goal.
Sports and entertainment venues use the same approach to drive ticket sales and attendance. An example of this is when a professional baseball team combined household targeting with geofencing around its home stadium and saw incremental visits increase by 131% over the course of the season.
Home services and healthcare businesses, however, often use location data a bit differently. It’s less about competitor conquesting and more about reaching households in a specific service area with the right message at the right time, then measuring whether that translated into appointments or leads.
### 7. When working with clients to achieve these objectives, what role does the quality of supply and inventory signals play in how Simpli.fi operates?
Supply quality shapes nearly every decision we make on the ad tech side, from which partners we work with to how a campaign is built. To understand the supply that we can access and bid on, we work closely with exchange partners, such as BidSwitch, and directly with publishers. At the same time, we look to ensure scale and give our advertising partners the ability to define and target what they consider premium supply.
We think about supply in more concrete terms: where a piece of supply actually comes from, the user base it provides access to, and what that user base does after clicking or viewing an ad. Those three things tell us far more about whether an impression is worth bidding on than a publisher’s name recognition alone.
To evaluate those characteristics at scale, we integrate directly with HUMAN, Integral Ad Science, and DoubleVerify for fraud detection, invalid traffic filtering, and brand safety and suitability signals. This enables us to avoid low-quality or fraudulent supply before a bid is ever placed instead of finding out after the fact. We combine that with our own inventory quality tiering, allowlist and blocklist capabilities, and a curated domain library to provide clients with the tools they need to access quality supply.
We also maintain a dedicated private marketplace team focused on securing access to premium environments, including Disney and Max. This gives advertisers consistent access to that inventory rather than one-off deals. Between the technology and the team behind it, our goal is to ensure quality is something we can support with data, not just claim in a pitch.
### 8. Are there specific inventory characteristics or signals that help Simpli.fi bid more effectively?
We rely heavily on the signals available in the bid request itself, beyond just the domain and user ID. This level of granularity allows for more advanced targeting and enables our algorithms to make more complex decisions.
We also take supply path optimization seriously. Our focus is reducing the number of resellers and hops between an advertiser’s dollar and the actual publisher. We use sellers.json – a public record showing whether inventory is sold directly by a publisher or resold through additional parties – along with partnerships with companies such as Jounce to identify and limit reseller traffic and reduce the number of hops in the distribution path. Fewer hops mean more of an advertiser’s budget reaches real inventory instead of being absorbed by intermediaries along the way.
### 9. As the industry operates in an increasingly privacy – focused world, what does a privacy-first approach to location-based advertising look like in practice?
A privacy-first approach starts with not depending on any single location signal. Precise GPS is valuable, but relying on it exclusively means every new regulation becomes a direct threat to the product. We built our architecture to combine multiple independent signals, including location, keyword and contextual intent, and first-party CRM data, so a change affecting one signal doesn’t undermine the entire strategy.
In practice, that means when a state passes new geolocation legislation, we don’t need to pull location out of the campaign entirely. Instead, we can shift to an approximate location approach in that state, using a more generalized location signal in place of precise GPS while maintaining targeting and measurement capabilities elsewhere. We also build compliance into the platform itself rather than handling it campaign by campaign, so advertisers get consistent guardrails regardless of which states their campaigns reach.
The bigger point is that privacy and performance aren’t in tension in the way they are sometimes portrayed. A platform built around signal diversity from the start can adapt as regulation evolves, rather than facing a hard trade-off between compliance and capability every time a new law passes.
### 10. Sustainability is another area of concern in digital advertising and one to which Simpli.fi is committed. How does the company reduce its greenhouse gas emissions?
We take a measured, methodical approach, completing a Greenhouse Gas Emissions Inventory Report each year. It follows the standardized Greenhouse Gas Protocol and covers everything from building energy use to business travel, employee commuting, and emissions from our third-party infrastructure and SaaS providers.
Establishing a solid baseline is the foundation of real improvement. Our 2025 report marks our third year of tracking, showing we’ve cut our total emissions by roughly 35% overall, including a 46% drop in our direct operational footprint. We share the full report externally and in RFPs so clients and prospects get complete transparency into the actual numbers.
### 11. Finally, looking ahead, how does Simpli.fi see the advertising landscape changing over the next five years, and how should brands prepare for the future?
A few forces are converging.
Privacy regulation will continue to expand, and platforms built around a single location signal will face harder trade-offs with each new law. We think the winners will be platforms built around signal diversity from the start, so a regulatory change to one signal doesn’t threaten the entire targeting strategy.
AI will also move from optimizing broad audience segments to optimizing at the level of individual signals, such as a specific keyword, geofence, or placement. That’s a meaningful shift because, in addition to giving advertisers better performance, it offers real visibility into why a campaign is working – something segment-level AI can’t really provide.
We also expect measurement to extend further into platforms that have historically been walled gardens. Treating platform media as a black box will become less acceptable. Advertisers will increasingly expect the same real-world measurement, such as foot traffic attribution, on their CTV and platform media spend as they’ve had on programmatic display.
Finally, new surfaces are emerging quickly, from ads inside AI chat assistants to ads inside mapping apps, and brands should expect the channel list to continue expanding. Our advice is to prioritize partners whose architecture can flex as new formats and signals appear, rather than locking into a single channel or vendor relationship. Brands that build that flexibility into their media strategy now will position themselves well as the advertising environment continues to evolve rapidly.
*Many thanks to Amanda Shelton and the Simpli.fi team for contributing to our Q&A series. To explore how BidSwitch can help your DSP access and optimize supply, data, and activation opportunities, contact your BidSwitch account manager.*
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