Welcome Back to The Ecom Edge!

Hey there,

If you’re running a Shopify brand doing $3M-$75M+ in revenue, you already know that scaling eCommerce isn’t getting any easier. The playbook is constantly evolving, ad costs are unpredictable, and customer expectations are higher than ever. That’s why I created this newsletter—to cut through the noise and bring you the best strategies that actually work.

👋🏻 I’m Marc. I’ve spent nearly 15 years in sales and marketing, including time as an ecommerce Director at a $70M Shopify brand. I’ve been in the trenches—launching A/B tests, optimizing checkout flows, and building high-converting product pages that drive millions in revenue.

Now, I lead growth at Because, where we help Shopify brands personalize their store dynamically—without dev work, without the hassle. If this email landed in your inbox, you’ve probably joined one of our webinars, sat in on a demo, or grabbed one of our conversion guides.

What You’ll Get from The Ecom Edge

Every week, I’ll be sharing real-world CRO strategies, A/B testing insights, and personalization tactics designed to:

•  ✅ Increase conversion rates (without discounting your margins into oblivion).

• ✅ Maximize AOV and customer LTV through smarter site experiences.

• ✅ Keep you up to date on the latest trends, tools, and strategies in Shopify & DTC.

Let’s get into it. 🚀

🧪 The A/B Testing Mistakes I’ve Seen Too Many Times (And You Probably Have Too)

A/B testing is one of the most powerful tools for improving your Shopify store’s performance—but most brands get it completely wrong.

I’ve seen it happen over and over again (including at the brands I’ve worked with).

Here’s one of the biggest A/B testing “mistakes” I see all the time:

• 1️⃣ A brand creates an entirely new landing page experience—new colors, new images, new offer, new button placement, new everything.
  

• 2️⃣ They drive new external traffic specifically to this page, often from paid ads.
  

• 3️⃣ Conversions skyrocket, and everyone celebrates like they just unlocked eCommerce nirvana. 🎉\

But wait—what actually worked?

If everything changed—the layout, the colors, the messaging, the pricing, the discount, the product images—was it the landing page that worked?

…Or was it just better traffic?

You are only as strong as your weakest link.

If you’re driving low-quality traffic, even the best-designed page won’t fix that.

Similarly, if you’re driving high-intent, ready-to-buy traffic, they’ll push through almost anything—bad design, confusing copy, a checkout flow that looks like it was coded on a dial-up connection, and even one of those CAPTCHA tests that makes you click on ‘all the crosswalks’ until you start questioning if you even know what a crosswalk is. 🫨

So, let’s talk about the biggest A/B testing mistakes brands make—and how to fix them.👇

🚨 A/B Testing Mistake #1:
Changing Too Many Things at Once

We’ve all been there. You want big results, fast, so you launch an A/B test where you change everything—new images, new CTA, new offer, new layout, new button colors, and heck, why not update the entire checkout flow while you’re at it?

🚀 Then you see conversions go up and declare victory! 🚀

But hold up. What actually moved the needle?

• Was it the headline?

• Was it the offer?

• Was it the trust badges?

• Was it the fact that the new design looked way cleaner than the old one?

Nobody knows. Because you changed too many things at once.

👀 What to Do Instead:

✅ Test ONE major change at a time—otherwise, you’ll never know what actually impacted performance.

✅ Prioritize high-impact elements that influence conversion rates the most:

• Headline & messaging clarity – Does pain-driven copy work better than benefits-driven copy?
  

• Offer structure – Does free shipping convert better than 10% off?
  

• Social proof placement – Do reviews perform better at the top of the page or near the ‘Buy Now’ button?

🚨 A/B Testing Mistake #2:
Calling a Test Too Soon

Raise your hand if you’ve done this before 🙋‍♂️.

You launch a test, check the results after three days, see that Variant B is outperforming Variant A by 12%, and immediately declare it the winner.

📢 “This is the one! Let’s roll it out to the whole site!”

…Only to check back a week later and realize conversions dropped back to normal, or worse.

😬 That’s because early test results are often just noise.

➡️ Why You Shouldn’t Call a Test Too Soon:

• ✅ Your test needs time to collect enough data – Running a test for just a few days means you might be looking at a temporary spike (or dip) rather than a real trend.
  

• ✅ Buying cycles vary – Your audience doesn’t all shop on the same day. You need to capture weekday vs. weekend behavior, payday patterns, and repeat visitor interactions.
  

• ✅ Statistical significance matters – If your test hasn’t reached a large enough sample size, the results could be random chance instead of a true performance shift.

🔎 How to Know When Your A/B Test Is Ready to Call:

Instead of guessing, use this three-step approach to determine if your test is ready to act on:

1️⃣ Give It at Least 7-14 Days

Your test should run for at least one full buying cycle to account for different shopping patterns.

Why? Many consumers browse early in the week and buy later. Ending your test too soon might mean missing the natural decision-making process of your shoppers.

💡 Try This: If your business sees clear weekly sales trends (e.g., more sales on weekends), extend the test to two full weeks to account for those fluctuations.

2️⃣ Reach Statistical Significance

If you don’t have enough traffic, your results might not be reliable. A good benchmark: Aim for at least 1,000 conversions per variant before making a call.

Instead of guessing, use a free A/B test significance calculator like this one:

👉 AB Test Guide’s A/B Significance Calculator

💡 Try This: Run your numbers through the calculator before ending a test. If it says “Not Significant Yet,” keep going.

3️⃣ Segment Your Results by Traffic Source

If one version is performing better, dig deeper: Is it working better for ALL traffic, or just certain segments?

🚀 Example: A brand tested two different checkout layouts and saw a 10% conversion lift in the new version. But when they segmented the data:

• Mobile users converted 15% better.

• Desktop users actually converted worse.

If they had rolled out the test without checking traffic source segmentation, they would have hurt their desktop conversion rate.

💡 Try This: Always check performance across different traffic types—mobile vs. desktop, new vs. returning visitors, organic vs. paid ads.

✨ Key Takeaway: A/B Testing is a Marathon, Not a Sprint

• 🚨 Don’t pull the plug too early.

• 🚨 Don’t assume short-term wins mean long-term success.

• 🚨 Don’t call a test a win until it’s statistically significant.

✅ Your Next Steps:

• ✔ Run your test for at least 7-14 days to capture buying cycles.

• ✔ Use an A/B test calculator to confirm statistical significance.

• ✔ Segment results to make sure the change benefits all traffic types.

🚨 A/B Testing Mistake #3:
Ignoring Traffic Quality

A/B testing can’t fix bad traffic.

If you’re sending low-intent visitors to your site—people who were never likely to buy in the first place—your test results will be skewed.

It won’t matter if you make your checkout perfectly optimized—because bad traffic doesn’t convert.

Here’s what happens when you don’t segment your test results:

• You assume a new headline didn’t work, when really your TikTok traffic just wasn’t as high-intent as your email traffic.
  

• You see a lift in conversions, but later realize it was just because paid traffic converts better than organic.
  

• You roll out a change to everyone, only to find it’s hurting your returning customers even though it helped first-time buyers.

👀 What to Do Instead:

You assume a new headline didn’t work, when really your TikTok traffic just wasn’t as high-intent as your email traffic.

• ✅ Segment your test results by traffic source – TikTok shoppers behave differently than email subscribers.
  

• ✅ Compare new vs. returning customers – First-time buyers need more reassurance than repeat customers.
  

• ✅ Look at device breakdowns – Mobile vs. desktop can impact results dramatically.

💡 Example: A brand tested a new product page layout and saw a 20% lift in conversions—but when they looked deeper, they realized:

• Paid traffic converted better.

• Organic traffic actually converted worse.

Turns out, the paid audience was just more ready to buy. The page layout had nothing to do with it.

🔎 Key Takeaway: A/B testing results only matter if you’re testing against the right audience.

📈 What’s Trending: The Rise of AI-Powered Personalization in E-commerce

Look, I get it. AI is everywhere. Every tool, software, and company is slapping “AI” on their name like it’s some kind of magic spell.

• ✨ “Now with AI!”

• ✨ “AI-powered insights!”

• ✨ “AI-enhanced synergy optimization for maximum ROI.” (What does that even mean?)

It’s starting to feel like when brands started to put “gluten-free” on bottled water. Cool, but was it ever necessary?

But here’s the thing: AI isn’t just a trend—it’s a cheat code. If you’re not using AI in your daily workflow, you’re probably working harder than you need to.

And the best part? The most powerful AI tool (ChatGPT) is free.

So, instead of rolling your eyes at the next AI buzzword, let’s actually use it to make our work easier, faster, and maybe even impress the team.

🔥 5 ChatGPT Prompts to Save Time & Make You Look Like a Genius

📌 1. Stuck on writing product descriptions?

💬 “Write a high-converting product description for a [product name] that highlights its unique benefits and speaks to a [target audience]. Make it engaging, benefit-driven, and optimized for eCommerce.”

📌 2. Need a social media post that doesn’t sound boring?

💬 “Create a fun, engaging LinkedIn post about [topic]. The tone should be [witty/professional/educational], and it should include a call to action to drive engagement.”

📌 3. Want to test a new email subject line?

💬 “Generate 5 compelling subject lines for an email about [topic]. They should be curiosity-driven, short, and optimized for high open rates.”

📌 4. Need an A/B testing idea for your Shopify store?

💬 “Give me 3 A/B testing ideas to improve conversions on a Shopify product page. They should be high-impact, data-driven, and focused on user experience.”

📌 5. Trying to explain something to your boss in a way that actually makes sense?

💬 “Summarize [topic] in one paragraph in a way that makes it clear, simple, and compelling for a busy executive who doesn’t have time for fluff.”

📢 Closing Thoughts – Almost to the End of Q1

I don’t know about you, but it’s wild to think we’re already less than two weeks away from the end of Q1.

Maybe this quarter didn’t go exactly how you planned. Maybe traffic didn’t hit projections, conversion rates didn’t climb as high as you needed, or a test you were sure would crush it ended up being… well, a dud.

If that’s the case, take a breath. You’re not alone.

Ecommerce is unpredictable, and no brand—no matter how big—gets it 100% right all the time. But what does separate the brands that win from the ones that struggle? Their ability to reflect, adapt, and move forward.

So before we step into Q2, I encourage you to take a moment:

• 👉 What’s one thing you learned this quarter that will make you stronger moving forward?
  

• 👉 What’s one thing you’re grateful for—big or small—that happened in Q1?

Growth isn’t just about fixing what went wrong. It’s also about recognizing what’s working and doubling down on it.

So let’s step into the next quarter smarter, stronger, and more focused. You got this. 💪

See you next week with more insights to help you grow.

🚀 What’s one lesson you’re taking from Q1 into Q2? Reply and let me know—I’d love to hear it.

Until next time,

~ Marc