Why You Need Both Google and Bing Data for Comprehensive SEO Analysis

Most SEO professionals focus exclusively on Google. After all, Google dominates with ~90% global market share. But here’s what they’re missing: Bing controls significant market segments that could be your goldmine.

The Hidden Bing Opportunity

Bing’s Real Market Share

While global numbers show ~3-4%, the reality is more nuanced:

Translation: If you’re ignoring Bing, you’re ignoring millions of high-value users.

5 Reasons to Use Dual-Engine Data

1. Different Algorithms, Different Insights

Google and Bing rank pages differently:

Same Query: "best project management software"

Google Top 3:
1. monday.com
2. asana.com
3. clickup.com

Bing Top 3:
1. microsoft.com/project
2. monday.com
3. smartsheet.com

Insight: Bing favors Microsoft ecosystem and enterprise solutions. If you’re B2B, Bing data is crucial.

2. Validate Your SEO Strategy

Using only Google data creates blind spots:

# With SERPpost - Compare rankings easily
from serppost import SERPpost

api = SERPpost(api_key="your_key")

# Check your ranking on both engines
google_rank = api.search(s="your keyword", t="google")
bing_rank = api.search(s="your keyword", t="bing")

# If rankings differ significantly, investigate why
if abs(google_rank - bing_rank) > 5:
    print("⚠️ Ranking discrepancy detected!")

3. Enterprise Market Access

The Windows ecosystem is massive:
– 1.4 billion Windows devices worldwide
– Microsoft 365 has 345 million paid users
– Edge browser defaults to Bing
– Cortana uses Bing

For B2B companies: Your enterprise clients are likely using Bing more than you think.

4. Advertising Arbitrage

Bing Ads often have:
– �?Lower CPC (cost per click)
– �?Less competition
– �?Higher conversion rates (older, wealthier demographic)

With dual-engine SERP data, you can:
– Identify keywords where Bing competition is low
– Find arbitrage opportunities
– Optimize ad spend across platforms

5. AI Training Data Diversity

Building AI agents or LLM applications? You need diverse data:

// Bad: Single source bias
const results = await searchGoogle(query);

// Good: Multi-source validation
const googleResults = await api.search({ s: query, t: "google" });
const bingResults = await api.search({ s: query, t: "bing" });

// Combine for richer, more balanced data
const combinedResults = mergeAndDedupe(googleResults, bingResults);

Real-World Use Cases

Case 1: E-commerce SEO

An online retailer discovered:
– Ranked #3 on Google for “ergonomic office chair”
– Ranked #12 on Bing for the same keyword

After optimizing for Bing (more product schema, Microsoft-friendly meta tags):
– Bing ranking improved to #4
– 23% increase in enterprise orders

Case 2: SaaS Company

A B2B SaaS found that:
– 18% of their trial signups came from Bing
– These users had 40% higher conversion to paid
– Average contract value was 2.3x higher

Lesson: Bing users in B2B often have more purchasing authority.

Case 3: Content Publisher

A tech blog analyzed traffic sources:
– Google: 85% of traffic, 2.1% CTR on ads
– Bing: 15% of traffic, 4.8% CTR on ads

Result: Bing traffic was 2x more valuable per visitor.

How to Get Started with Dual-Engine Analysis

The Traditional (Expensive) Way

Option A: Multiple APIs
- Google SERP API: $75/month
- Bing SERP API: $50/month
- Total: $125/month
- Complexity: 2 different integrations

The Smart Way: SERPpost

// One API, both engines
const api = new SERPpost({ apiKey: 'your_key' });

// Google search
const google = await api.search({ 
  s: "seo tools", 
  t: "google" 
});

// Bing search - just change one parameter!
const bing = await api.search({ 
  s: "seo tools", 
  t: "bing"  // �?That's it!
});

Benefits:
– �?One API key for both engines
– �?Same code, same interface
– �?10x cheaper than competitors
– �?Bing included free (no extra cost)

Building a Dual-Engine SEO Tool

Here’s a practical example:

import requests

class DualEngineSEO:
    def __init__(self, api_key):
        self.api_key = api_key
        self.base_url = "https://serppost.com/api"

    def analyze_keyword(self, keyword):
        """Get comprehensive keyword analysis"""
        google = self._search(keyword, "google")
        bing = self._search(keyword, "bing")

        return {
            "keyword": keyword,
            "google_results": google,
            "bing_results": bing,
            "opportunity_score": self._calculate_opportunity(google, bing)
        }

    def _search(self, query, engine):
        response = requests.get(
            f"{self.base_url}/search",
            params={"s": query, "t": engine, "p": 1},
            headers={"Authorization": f"Bearer {self.api_key}"}
        )
        return response.json()

    def _calculate_opportunity(self, google, bing):
        """Find opportunities where Bing competition is lower"""
        # Your scoring logic here
        pass

# Usage
seo = DualEngineSEO("your_api_key")
analysis = seo.analyze_keyword("project management software")

Key Takeaways

1. Don’t ignore Bing – It’s 6-20% of valuable markets
2. Different algorithms = Different insights
3. Enterprise users heavily use Bing (Windows default)
4. Bing users often have higher purchase intent
5. Dual-engine data validates your SEO strategy

Start Your Dual-Engine Analysis Today

Ready to unlock the full picture of search engine rankings?

1. Sign up free – Get 100 credits to test
2. Try the playground – Test Google & Bing live
3. Read the docs – Integrate in 5 minutes

No credit card required. Both search engines included.

Related Articles

• How to Build an AI Agent with Google & Bing SERP API
• SERPpost vs SerpAPI: Complete Comparison
• What is SERP API and Why You Need It
• SERP API + LLM: Building Intelligent Search

Last updated: December 2025
Reading time: 6 minutes