Are you struggling with high Google Ads spending and low-quality inquiries? Imagine if your Google campaigns could not only generate more leads but precisely target your ideal customers while significantly reducing acquisition costs. This is the dream scenario for every B2B marketer.
Value-Based Bidding: The Key to Higher Quality Leads
For international businesses, Google Search remains a vital channel for global expansion, with Google Ads serving as the cornerstone of overseas marketing. However, B2B companies face unique challenges in their sales cycles—long conversion paths, multiple touchpoints, and difficulty tracking actual revenue—which complicate traditional bidding strategies.
The solution? Value-based bidding.
This approach shifts focus from mere clicks to the actual value of each conversion. By optimizing bidding strategies, businesses can attract higher-quality prospects, improve lead conversion rates, and ultimately drive sales growth.
Yet B2B marketers often encounter these implementation challenges:
- Extended conversion cycles: B2B sales processes typically involve longer decision-making periods.
- Multi-step conversions: Prospects require multiple interactions before converting.
- Spam inquiries: Low-quality leads distort data analysis and bidding strategies.
- Variable conversion values: Significant differences in customer value complicate standardization.
- Limited CRM infrastructure: Many SMBs lack sophisticated customer relationship management systems.
To determine if value-based bidding suits your account, consider these two diagnostic approaches:
Method 1: Differential Value Assignment
When using multiple conversion actions with varying lead quality, assign different values to each. For example, set lower values for "contact form submissions" and higher values for "meeting bookings."
Method 2: Value Rule Configuration
For single conversion actions with geographic or device-based value variations, establish value rules. For instance, assign higher values to inquiries from North America versus developing markets.
Optimizing Account Structure: The Foundation for Lead Growth
A well-structured Google Ads account forms the bedrock of sustainable lead generation. Google's ad ranking algorithm considers three primary factors: bid amount, ad quality, and user experience.
When building account architecture, focus on these critical elements:
- Keyword grouping: Organize keywords by product/service categories to ensure strong intent alignment.
- Ad copywriting: Craft compelling messaging that highlights unique value propositions.
- Landing page optimization: Maintain content consistency between ads and destination pages while streamlining conversion paths.
Common pitfalls to avoid include:
- Geographic targeting errors: Poorly configured country-specific campaigns generating low-quality traffic.
- Inefficient budget allocation: Failing to prioritize high-performing keywords and ad groups.
Smart Bidding + Broad Match: Unleashing New Growth Potential
Google's revamped broad match functionality now operates as an intent-based, performance-driven matching system—far removed from its earlier keyword expansion approach. This evolution enables advertisers to reach broader audiences while maintaining relevance.
When encountering "limited by search volume" notifications, consider implementing broad match to unlock additional traffic—particularly when core keywords already achieve high impression share.
Frequently asked questions about broad match implementation:
Q1: Which keywords suit broad match?
Prioritize terms with strong existing performance (e.g., Top 10 impression share) and high precision.
Q2: Does broad match increase costs?
While CPC rates may decrease versus phrase/exact match, total spend typically rises due to expanded traffic. The key lies in pairing broad match with smart bidding to maximize conversions within target ranges rather than minimizing CPC.
Q3: How to prevent irrelevant queries?
Focus on maximizing conversion value. Smart bidding with broad match functions as a performance-oriented solution—optimize for overall results rather than individual query precision.