How I’d Run Airbnb’s SEM to Maximize Scale & ROI

Introduction

Marketplaces, particularly those in the travel sector, present one of the most complicated challenges for Google SEM campaigns. Brands with straightforward product offerings can utilize Google’s latest best practices around Smart Bidding + Broad Match to simplify account structure. However, AirBnB operates in a highly variable ecosystem where search terms span a vast array of location and product keyword combinations. Managing this complexity requires a strategic approach to keyword targeting, campaign structure, and value-based optimization. In this article, I’ll walk through three key areas Airbnb should focus on to maximize scale and ROI in its SEM campaigns: Match Types, Conversion Events, and Account Structure.

1. Match Type Strategy

Google recommends limiting campaigns to a small set of Broad Match keywords, relying on its algorithm to match relevant search terms. While this approach offers clear benefits in terms of optimization and efficiency by consolidating learning data, it presents significant challenges for the travel industry.

Take the Broad Match keyword “paris vacation rental” as an example. Without proper controls, Google’s algorithm might target irrelevant queries such as:

• paris, texas vacation rental

• paris hilton’s top vacation spots

• europe vacation packages

In simpler industries, brands can manage this by adding enough negative keywords over time. However, for Airbnb, the sheer scale of travel-related queries makes this impractical. Travel search queries are inherently long-tail, with endless combinations of [location] + [product] keywords.

Once you factor in all possible variations, managing these queries can quickly escalate into tens of thousands of keywords—too many to handle effectively with Broad Match alone.

At the same time, relying solely on Exact Match isn’t ideal either. The decentralized nature of travel keywords limits available search volume, and excessive segmentation can dilute data, slowing down campaign optimization by spreading learnings across too many low-volume terms.

To balance scale and control, I’d recommend adopting a Phrase Match strategy. Phrase Match captures a broader range of relevant search term variations while maintaining enough precision to ensure relevancy, leading to faster optimization and more efficient learnings compared to Exact Match.

2. Conversion Event

Not all conversions are equal in a travel marketplace like Airbnb. A single-night stay at a budget rental generates significantly less revenue than a week-long luxury booking. When fixed costs are factored in, the profit disparity becomes even more pronounced, further influencing campaign ROAS.

A simple Purchase Event signal—without accounting for value—can cause Google to overbid on low-value bookings and underbid on high-value ones. Additionally, seasonality and regional demand fluctuations significantly impact the travel industry. For instance, summer bookings often involve longer, more lucrative international vacations, whereas winter bookings tend to be shorter, less profitable domestic stays.

To address this, I recommend using ROAS or Value Optimization as the primary conversion signal. This approach ensures Google’s bidding algorithm prioritizes higher-revenue transactions. By leveraging value-based optimization, campaigns can dynamically adjust bids based on the relative value of bookings, enhancing both short-term efficiency and long-term profitability.

3. Account Structure

Within each campaign, ad groups should be tightly aligned around specific themes. Ensuring that ads, keywords, and landing pages are closely connected enhances relevance, which in turn boosts conversion rates, ad strength, and keyword Quality Scores.

Since Responsive Search Ads (RSAs) limit your text assets to 15 headlines and 4 descriptions, it’s important to keep keyword variations minimal to ensure they are effectively incorporated into your ads. On a quick note, using dynamic keyword insertion (DKI) is an excellent way to enhance keyword-to-ad relevance, which can further improve Quality Scores.

Although granular segmentation can improve performance, it should only be applied when there’s sufficient keyword and conversion volume to justify the added complexity. Otherwise, breaking out too many ad groups will just dilute learnings and increase manual campaign workload without much added benefit.

Campaign structure itself is more subjective and dependent on preference. Personally, I’d recommend splitting AirBnB campaigns at the top level by Keyword Category and Location Type. Specifically, I’d create two primary campaign groups:

1. Generic Terms Campaigns – Targeting broad terms like “vacation rentals” or “short-term stays” without specific locations.

2. Location-Specific Campaigns – Targeting searches that include specific locations, such as “new york apartment rentals” or “san diego villa stays.”

While granular segmentation can boost performance by improving relevancy, it should only be used when there’s enough keyword and conversion volume to justify the added complexity. Over-segmenting ad groups without sufficient data risks diluting learnings and increasing manual workload without yielding significant benefits.

Campaign structure, to some extent, is subjective and depends on individual preference. Personally, I’d recommend structuring Airbnb’s campaigns at the top level by Keyword Category and Location Type. Specifically, I’d create two main campaign groups:

1. Generic Terms Campaigns – Targeting broad terms like “vacation rentals” or “short-term stays” without specific location modifiers.

2. Location-Specific Campaigns – Targeting searches that include specific locations, such as “new york apartment rentals” or “san diego villa stays.”

In this setup, I assume that Value Optimization is the primary conversion event. Because of this, I don’t recommend splitting campaigns by platform (Desktop vs. Mobile). While desktop users typically drive higher Average Order Values (AOVs) and can sustain higher Customer Acquisition Costs (CACs) than mobile users, Value Optimization inherently accounts for these differences by dynamically adjusting bids based on expected revenue. By grouping platforms together, we can consolidate data, accelerate optimization, and reduce the time Google’s algorithm needs to learn—ultimately improving campaign performance.

Conclusion

Running SEM campaigns for a marketplace as complex as Airbnb requires a nuanced strategy that balances scale, ROI, and operational efficiency. By using Phrase Match, value-based optimization signals, and the optimal account structure to maximize learnings and relevancy, Airbnb can scale its campaigns while maintaining strong ROAS.

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