How do you approach A/B testing in Bing Ads?

Approaching A/B testing in Bing Ads (now Microsoft Advertising) is all about structure, precision, and staying laser-focused on meaningful results. While Bing doesn't have a full-blown built-in A/B testing suite like Google Experiments, you can still run highly effective tests using a combination of campaign duplication, ad variation, and performance monitoring.

Here's the full breakdown of how I approach it:

🧱 1. Define a Clear Hypothesis
Before launching anything:

What are you testing? (e.g., ad copy, bidding strategy, landing page)

What's your primary KPI? (CTR, conversion rate, CPA, ROAS)

What's your success threshold? (e.g., "Improve CVR by 15% with equal CPA")

🔍 Example Hypothesis:
"If we use urgency-focused CTAs instead of benefit-driven CTAs, then CTR will increase by 20% due to increased motivation."

🧪 2. Structure the A/B Test in Bing Ads
Since Bing Ads doesn't have native experiments like Google Ads, here's how I do it:

Option A: Within the Same Ad Group (for Ad Copy/Extensions)
Create two ad variants (A and B) under the same ad group.

Make sure ad rotation is set to 'Rotate ads evenly' (under campaign settings).

Let them run until they hit statistical significance.

💡 Best for testing:

Ad headlines/descriptions

CTAs

Display URLs

Ad extensions

Option B: Split Campaigns or Ad Groups (for Bid Strategies, Landing Pages, Targeting, etc.)
Clone the campaign or ad group.

Change only the one variable you're testing.

Keep everything else identical (budget, targeting, keywords, schedule, etc.).

Monitor performance separately.

🧪 Use this method when testing:

Manual vs. automated bidding

Different landing pages

Device or geo bid modifiers

Audience targeting

📊 3. Monitor Key Metrics with a Testing Dashboard
Use Microsoft Ads reporting or export data to Excel/Google Sheets to track:


Test Area   Primary Metric
Ad Copy   CTR, Quality Score, CPC
Landing Page   Conversion Rate (CVR), Bounce Rate
Bid Strategy   CPA, ROAS
Targeting   Impression Share, Engagement Rate
👀 Use tools like Looker Studio or Power BI to visualize trends if you want to scale up insights across clients/teams.

⏳ 4. Let the Test Run Long Enough
Test duration depends on traffic. General rules:

Minimum 7–14 days (to account for day-of-week cycles)

Wait for statistical significance (95% confidence level preferred)

Use an A/B significance calculator to validate

📌 Avoid adjusting bids, budgets, or targeting mid-test unless absolutely necessary.

📁 5. Analyze, Document, and Act
Once the test ends:

Review primary + secondary KPIs

Check for consistency across devices/audiences

Look for anomalies (e.g., one location spiked results)

📘 Log:

What was tested

Final performance metrics

Outcome (win/loss/inconclusive)

Next steps: Scale, retest, or pivot

🧠 Bonus Pro Tips:
Use UTM parameters when testing landing pages to keep tracking clean.

Run only one major test at a time per campaign to avoid data contamination.

Use Microsoft's Experiments (beta) if available in your account—this feature is slowly rolling out and functions similarly to Google Ads experiments.

🔁 Test, Learn, Scale
Testing is not a one-time project—it's a continuous loop:

Generate insight from the current test

Build your next hypothesis from what you learned

Test again (new copy, new audience, new bid strategy, etc.)