Avoiding the Top 5 Mistakes in A/B Testing: A Guide for Shopify Merchants

Imagine your Shopify store is a restaurant kitchen. A/B testing is your recipe development process - a powerful way to perfect your digital menu and keep customers coming back for more. But just like cooking, if you don't follow the right process, you might end up with a mess rather than a masterpiece. Let's explore the five most common ways merchants accidentally spoil their A/B testing soup, and how to keep your testing kitchen running smoothly.

TL;DR:

Avoid these 5 critical A/B testing mistakes on your Shopify store:

1. Testing multiple variables at once > test one change at a time
2. Running tests with too few visitors > wait for 10K views & 200 orders minimum
3. Ending tests too early > run for at least 2 weeks/business cycles
4. Ignoring statistical significance > aim for 95% confidence
5. Testing during unusual periods (like Black Friday) > stick to regular business periods

1. The "Everything But The Kitchen Sink" Syndrome: Testing Too Many Variables at Once

Picture throwing every spice in your pantry into a single dish. That's what happens when you test multiple variables at once. One merchant tried to simultaneously test their product description, photos, pricing display, and "Add to Cart" button, then wondered why they couldn't tell which change made their conversion rate jump.

The Recipe for Success: Test one ingredient at a time. If you're curious about your product page's performance, start by testing just the headline. Maybe "Handcrafted Leather Wallet" vs. "Luxury Everyday Carry Essential." Once you know which headline works better, move on to your next ingredient. Want to test multiple elements? That's like creating a tasting menu, in which it is possible, but requires more traffic and planning.

2. The "Half-Baked" Results: Insufficient Sample Size

Running an A/B test with too few visitors is like judging a recipe after serving it to just two people. One loves it, one hates it, now what? For example, a brand once declared their new product page design a winner after just 50 visitors, only to see their conversion rate drop when they rolled it out to everyone.

The Recipe for Success: Use an A/B test calculator to determine your minimum sample size - think of it as your recipe's serving size. ABConvert suggests accumulating at least 10,000 total views and 200 total orders before interpreting the test results.

3. The "Timer Anxiety" Trap: Stopping Tests Prematurely

Pulling your cake out of the oven too early because it "smells done" is just as dangerous as stopping your A/B test because early results look promising. One merchant ended a button color test after three days because the green button was "clearly winning", only to later discover that their weekend shoppers had completely different preferences. This might also be referred to as “p-hacking”, which means the practice of manipulating data analysis to find statistically significant results that may not actually represent true effects.

The Recipe for Success: Let your test fully bake. Run it for at least 1-2 business cycles, like a full recipe needs all its specified cooking time. For most stores, this means at least 2 weeks of testing.

Bonus tip! Check out this video from Stanford University explaining the problem of stopping a test prematurely and how to correctly use real-time results: Peeking at A/B Tests - Why It Matters and What to Do About It

4. The "Taste Test Fallacy": Ignoring Statistical Significance

Just because your friend says your soup tastes better doesn't make it statistically better. Similarly, seeing a 2% lift in conversions doesn't automatically mean your variation is a winner. A merchant once redesigned their entire checkout process based on a 5% improvement that wasn't statistically significant, essentially meaning that they changed the whole recipe based on a few positive reviews.

The Recipe for Success: Use statistical significance as your measuring cup. Most A/B testing tools such as ABConvert includes built-in calculators. Aim for 95% confidence before declaring a winner, like waiting for your thermometer to hit the right temperature before calling your roast done.

5. The "Holiday Season Special": Overlooking External Factors

Testing a new menu during restaurant week won't tell you how it'll perform during regular service. Similarly, running A/B tests during Black Friday or while running a major promotion can give you misleading results. A beauty brand once tested new product photography during their semi-annual sale, not realizing their discount-hunting customers behaved very differently from their usual customer base.

The Recipe for Success: Time your tests during "regular service" periods. If you intend to test seasonal promotions, compare results only to similar periods from previous years.

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The Master Chef's Mindset: Continuous Testing and Improvement

Think of A/B testing like running a successful restaurant - it's not about finding one perfect dish and never changing the menu again. It's about constantly refining your recipes, understanding your customers' tastes, and creating better experiences over time.

The most successful Shopify merchants treat their stores like a well-run kitchen: they test methodically, measure accurately, and make improvements based on solid data, not guesses. By avoiding these common mistakes, you'll be on your way to serving up a shopping experience that keeps customers coming back for seconds. Stay tuned! We will be sharing about how to build and maintain a well-operated A/B testing “kitchen” in future posts.

Thanks for reading!

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