Google Ads strategy for B2B tech: A specialist guide for engineering marketers
In the specialized world of B2B technology, PPC specialists know all too well that Google’s automated bidding strategies can often lead to wasted budget and a flood of low-quality leads. At Napier, we’ve seen this challenge firsthand across engineering and deep-tech campaigns. This guide is designed for those in the engineering sector who want to move past the one-size-fits-all ‘maximize conversions’ approach and master advanced techniques tailored for niche audiences. The focus here is on elevating your Google Ads strategy for B2B tech, ensuring you reach the right professional buyers.
Here, you’ll discover how to combine audience layering with value-based bidding, ensuring your campaigns attract genuine buyers, not just hobbyists. With expert advice addressing complex queries, this resource aims to deepen trust and improve your brand’s discoverability across AI platforms. Whether you’re refining your lead generation or seeking actionable tips to boost campaign performance, this blog is your go-to for elevating your Google Ads strategy in the B2B tech landscape.
Why ‘maximize conversions’ attracts the wrong leads
At its core, the ‘Maximize Conversions’ algorithm is built to chase the highest number of conversions, regardless of their actual value to your business. For niche B2B tech marketers, this creates a fundamental mismatch: while you’re seeking high-value buyers with specific roles, Google’s bidding strategy is simply optimizing for volume. In our experience managing B2B engineering campaigns, this results in a disproportionate number of students, hobbyists, and other non-qualified leads, diverting precious budget away from genuine prospects.
What is ‘maximize conversions’ actually optimizing for?
The algorithm’s sole focus is on conversion quantity, not quality. It will always seek the easiest path to a conversion, which often means targeting audiences who are most likely to take action—even if they’re not your ideal buyer. In the B2B engineering space, this frequently results in non-buyer personas, such as students downloading whitepapers or hobbyists signing up for demos, dominating your lead pool.
The ‘hobbyist vs. buyer’ scenario in engineering software
Consider a typical search for “FEA software”: it could be initiated by a Director of Engineering with a substantial budget, or by an undergraduate student working on a coursework project. Because ‘Maximize Conversions’ cannot distinguish between these user intents, it will often favor audiences that deliver higher conversion volumes, skewing results towards student traffic rather than genuine buyers. This demonstrates how the algorithm’s priorities diverge from B2B marketing objectives and the specific needs of engineering software vendors.
You’re training the algorithm with junk data
By allowing the algorithm to optimize for low-quality leads, you inadvertently create a feedback loop. Each conversion from a non-qualified lead reinforces the algorithm’s behavior, further degrading the quality of your marketing-qualified leads (MQLs) over time. The first step in breaking this cycle is to acknowledge that a different, more targeted approach is essential for success in niche B2B tech campaigns.
Layering audiences to pinpoint buyers in your Google Ads strategy for B2B tech
To tackle the challenge of attracting only genuinely qualified leads, this section offers a practical framework for Google Ads audience layering. By strategically combining targeting signals, you can exclude irrelevant users and focus your spend on those most likely to be professional buyers—such as senior engineers. The approach hinges on blending intent, demographics, and exclusions to create a refined audience profile, ensuring your ads reach the right people.
How can you target specific job roles like engineers on Google Ads?
While you can’t directly target job titles on Google Ads, you can construct an effective proxy by layering targeting signals. This is the central principle of the framework, enabling you to isolate likely buyers based on their online behavior and characteristics, such as search history and engagement with industry content.
The audience layering framework
- Layer 1 – High-Intent Keywords: Start by selecting precise, long-tail keywords that signal commercial intent. For example, targeting phrases like “enterprise CFD software pricing” ensures you reach users actively researching business solutions, rather than generic searchers asking, “What is CFD?”
- Layer 2 – Custom Intent Audiences: Build audiences from users who frequently visit competitor websites, read industry publications, or engage with regulatory bodies. This indicates genuine professional interest and narrows the audience to those involved in your field.
- Layer 3 – Demographic & Company Attributes: Incorporate company size and industry data where possible. Use demographic filters—such as age and household income—to exclude typical student profiles, thereby focusing on likely professionals. Negative targeting further refines your audience, reducing wasted spend on non-buyers.
By layering these signals, you build a composite audience that mirrors your ideal customer profile. This strategic approach answers the core question: you can’t target engineers by job title directly, but with smart audience layering, you can zero in on the people most likely to be professional buyers, maximizing the quality of your leads and the effectiveness of your Google Ads campaigns.
Build your first high-value audience layer
While Google Ads does not enable you to directly target individuals who have visited a competitor’s website, its audience tools allow you to reach users with similar online behaviors and interests. This approach is not about pinpointing specific site visitors, but rather about identifying broader traits common among your target professional audience, such as engagement with technical forums or industry news.
To enhance your targeting and exclude less relevant users, a practical strategy is to set age filters, such as excluding those aged 18–24 and users with unknown ages, as these segments are often associated with students who are unlikely to be qualified buyers. Additionally, refining your audience by excluding interest categories like ‘education’ can help filter out students and academics, while including categories linked to specific professional activities, which increases the likelihood of reaching individuals who are active in the workforce and more likely to be more senior decision-makers. By layering these exclusions and inclusions, you create a more focused and valuable audience for your campaigns.
How to use value-based bidding for B2B tech
Once you have established a high-value audience through careful targeting, the next step is to let Google know that these users are more valuable to your business. This is achieved by implementing value-based bidding (VBB), a strategy that allows you to assign different monetary values to various conversion actions or audience segments—even in cases where there is no direct e-commerce transaction. By using value rules and target ROAS (tROAS), you can effectively communicate to Google’s algorithm which leads are most important, ensuring that your bidding prioritizes those conversions that have the greatest potential impact on your business. This approach bridges the gap between your targeting strategy and your bidding strategy, maximizing efficiency and lead quality for B2B tech campaigns.
How does VBB work for lead generation?
VBB empowers advertisers to assign unique values to different types of conversions or audiences. This means you can inform Google’s algorithm which leads are worth more to you, even if you don’t have a checkout process. By doing so, you guide the system to focus its efforts and budget on the conversions that matter most to your business objectives.
Introducing conversion value rules
Conversion value rules are the practical tool that enable this strategy. They enable you to apply a multiplier to your base conversion value for specific audiences. For instance, you can increase the value of conversions from your target audience, ensuring Google’s bidding algorithm recognizes their higher importance and thus allocates your spend accordingly.
An example of assigning value
If your standard ‘whitepaper download’ carries a value of £50, you can set a rule whereby conversions from your ‘target buyer’ audience are multiplied by three, raising their value to £150. This enhanced value provides Google’s algorithm with a clear quality signal, encouraging it to focus on the prospects most likely to have value to your business.
Calculate your base lead value
To get started, use this simple formula: (Average Contract Value × Sales Close Rate) = High-Quality Lead Value. This calculation helps you assign an appropriate base value to your leads, ensuring your bidding strategy is grounded in real business outcomes.
Common Pitfalls and Future Outlook
Managing advanced value-based bidding strategies requires careful attention to detail and a forward-thinking approach. There are several common mistakes that advertisers make, and understanding these pitfalls is crucial for maximizing success. For example, applying value rules without sufficient conversion data can lead to unpredictable results, as smart bidding algorithms rely on a steady stream of data to operate effectively.
Mistake #1: Applying value rules with low conversion volume
Smart bidding needs data. If your campaign gets fewer than 30 conversions per month, applying tROAS with value rules can be volatile.
Start with enhanced CPC, gather data, then graduate to target CPA, and finally implement tROAS with value rules once you have a stable conversion history.
Mistake #2: Ignoring performance max campaigns
These principles are even more critical for PMax. Use your high-value audience lists as ‘Audience Signals’ and implement Offline Conversion Imports with value to give the PMax algorithm the data it needs to find the right engineers.
First-party data is your competitive advantage
As cookies deprecate, your ability to feed Google’s AI with high-quality, offline data from your CRM will be the single biggest factor in successful B2B advertising. The framework outlined in this guide is the foundation for that future.
Key Takeaways
Audience Layering: Build composite audiences using intent, demographics, and exclusions to target professional buyers.
Value-Based Bidding: Assign higher values to conversions from desirable leads to guide Google’s algorithm.
Avoid Low-Volume Pitfalls: Ensure sufficient conversion data before applying advanced bidding strategies.
Leverage First-Party Data: Use CRM and offline data to improve targeting and campaign performance.
Continuous Optimization: Monitor and refine your Google Ads strategy for B2B tech to maintain lead quality.
Conclusion
To achieve higher-quality leads and maximize ROI, B2B technology marketers should implement a Google Ads strategy for B2B tech that combines audience layering with value-based bidding and first-party data. This approach ensures campaigns are future-proofed and focused on meaningful business outcomes.
FAQ
What is the most effective way to optimize Google Ads for B2B tech lead quality?
The most effective method is to combine audience layering with value-based bidding, ensuring your campaigns target professional buyers and assign higher value to desirable leads using conversion value rules.
Where can I find tools and resources to implement Google Ads strategy for B2B tech?
Resources such as Google Ads Help Center, Napier’s B2B marketing guides, and industry forums provide frameworks and step-by-step instructions for setting up advanced audience targeting and value-based bidding.
How can I start implementing value-based bidding for my B2B tech campaigns?
Begin by calculating your high-quality lead value, then use Google’s value rules and tROAS features to assign higher values to conversions from your ideal audience. Monitor results and adjust as your data grows.
How does Google Ads strategy for B2B tech compare to traditional B2C strategies?
B2B tech strategies focus on layered audience targeting, lead quality, and value-based bidding, while B2C often prioritizes conversion volume and broader targeting. This makes B2B approaches more specialized and data-driven for niche audiences.
