Looking to run a successful, long-term paid search lead generation campaign that delivers steady, qualified leads? Continuous A/B testing is your secret weapon.
By pinpointing high-performing variations through testing, you can roll out those improvements across all campaigns for optimal results. But where do you start?
This article will be your guide, walking you through the core elements of A/B testing for lead generation search ads and how to implement them for continued campaign improvement.
How A/B Split Tests Work
The general concept involves running two or more simultaneous campaigns with slight variations. Begin with a primary campaign as your control, and then create another variation of the campaign with minor differences to compare their performance.
For instance, you might run five campaigns with an identical ad, but direct users to five unique landing pages, each featuring a distinct heading. Alternatively, your landing pages could have varying layouts, colors, or fonts. Any element, regardless of how minor, can be tested, and sometimes the small details make the greatest difference.
After a set period of time, you’ll determine which change yielded the best results. From there, you can identify top-performing elements, implement them across all your campaigns, and then begin testing other aspects to further enhance performance.
What Should You A/B Test?
Lead generation ad success relies on structured A/B testing. While there are endless elements you can test, the following are essential to ongoing growth:
- Ad headlines and descriptions
- Keyword match types
- Bid strategies
- Demographics/target market
- Landing page headlines, copy, layout, CTAs and more
Once you identify the top-performing elements you will want to analyze the shared traits of those winners, apply them to your campaign, and then test further elements for even better results.
Structuring a Successful A/B Test
While running A/B tests is straightforward, setting them up for actionable insights can be tricky. Here are key tips for success:
Test one variable at a time
The key takeaway is to avoid trying to test too many variables simultaneously. Testing multiple elements at once makes it challenging to determine which change is causing a rise or fall in conversions. Focus on testing a single variable until you’re pleased with the results, and only then begin testing the next aspect.
Allow sufficient time for tests
When conducting a test, it’s essential to allow sufficient time to accumulate enough traffic, clicks, and sales to understand what’s performing well between each version being tested. You won’t achieve conclusive results overnight or even within a week. Your A/B tests must run for a duration that establishes statistical significance in the results.
The reason is that the success of A/B testing depends more on the quality and volume of data collected than the test duration itself.
For all businesses, it’s crucial to accumulate sufficient data (whether from a large budget or a smaller budget spread over more time) to yield statistically significant results with a clear winner. Without this, applying your findings on a larger scale still involves some degree of uncertainty.
Act fast to avoid wasted ad spend
Imagine having the utmost confidence in your A/B test results, but realizing you’ve overspent on long-term testing. Knowing when you have a clear test winner and acting swiftly is key to efficient budget use during testing.
If you’ve invested substantially in a test and one variant is outshining the other, it’s vital to pause the underperformer and reallocate budget as soon as those insights emerge.
Define clear goals
Conducting A/B tests without clear objectives won’t yield meaningful results. It’s essential to define precisely what you aim to achieve through testing. While more leads are always desirable, it’s important to consider what that means beyond the obvious. Would 500 new but unqualified leads be considered a success, or do you only count it a win if you attract leads with future purchasing potential?
Targeted lead generation is just one potential goal. Perhaps you aim to capture leads who will join your email list, engage with a video, or follow your Instagram account. For sustained success, aligning your A/B testing with overarching objectives is crucial. Otherwise, you risk dedicating significant time to testing irrelevant elements – those that don’t directly drive your desired outcomes.
Clear success metrics
For your tests to yield actionable insights, look beyond just the lead count. Pinpoint the metrics that truly measure success or failure, such as:
- Conversion rate
- Clickthrough rate
- Average cost-per-click
- Return on ad spend (ROAS)
Determining your tests’ success demands robust data analysis. Tracking key metrics reveals how changes impact user behavior, and visual behavioral analytics facilitate this. Analytics enable simultaneous hypothesis testing and streamline experiment evaluation.
Key Takeaways
To maximize your Google Ads PPC campaign, A/B testing is essential. Begin by clearly defining your paid search goals, then design tests that modify only one element at a time.
Resist the urge to act on incomplete data. Wait for statistically significant results before implementing changes. Your control version should remain consistent, updated only when a new ad or landing page demonstrates improved conversions. Testing is an ongoing process – even after seeing success, potential for refinement remains. Expect to adapt your goals and hypotheses accordingly.
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