How to Split Test Email Sequences

Most people test email copy the wrong way. They change everything at once then guess what worked or worse, blame the ESP 😅. Here’s what actually happens behind the scenes when an email hits spam: It’s rarely the entire email. It’s usually one specific part triggering filters. • A subject line pattern • A From-name change • An image-to-text ratio • A footer link • One tracking URL • Even a single paragraph But when you test emails the usual way, all of these variables are mixed together. So you never really know what caused the drop. This is where Multi-Variant Testing (MVT) in Mailora becomes interesting. Instead of testing “Email A vs Email B”, Mailora breaks one email into multiple controlled variations: 1. Original version 2. Subject & From-name changed 3. Footer removed 4. Links & images removed 5. Content simplified Each variation is tested across specific inbox providers of your choice. 1. Same infrastructure. 2. Same IP. 3. Same domain. 4. Only one variable changed at a time. The result? You can clearly see which exact element contributes to spam placement and which one doesn’t. No guesswork. No rewriting everything blindly. No “ESP dashboard says it’s fine” confusion. But here’s the part many people miss: Even the best content won’t save you, if your sender reputation is already weak. And even perfect infrastructure won’t help, if the content isn’t relevant to what users actually want. Inbox placement happens first. Engagement happens next. If you don’t understand the first part, you’ll keep optimizing the second part forever. That’s what Mailora focuses on. Visibility before optimization. Mailora, LLC #email #emailmarketing

Day 5 - CRO series Strategy development ➡A/B Testing (Part 1) What is A/B Testing? A/B testing, also known as split testing, is a method used to compare two versions of a marketing asset, such as a webpage, email, or advertisement, to determine which one performs better in achieving a specific goal. Most marketing decisions are based on assumptions. A/B testing replaces assumptions with data. Here’s how to do it effectively: 1. Formulate a Hypothesis Every test starts with a hypothesis. ◾ Will changing a call-to-action (CTA) button from green to red increase clicks? ◾ Will a new subject line improve email open rates? A clear hypothesis guides the entire process. 2. Create Variations Test one element at a time. ◾ Control (Version A): The original version ◾ Variation (Version B): The version with a change (e.g., a different CTA color) Testing multiple elements at once leads to unclear results. 3. Randomly Assign Users Split your audience randomly: ◾ 50% see Version A ◾ 50% see Version B Randomization removes bias and ensures accurate comparisons. 4. Collect Data Define success metrics based on your goal: ◾ Click-through rates ◾ Conversion rates ◾ Bounce rates The right data tells you which version is actually better. 5. Analyze the Results Numbers don’t lie. ◾ Is the difference in performance statistically significant? ◾ Or is it just random fluctuation? Use analytics tools to confirm your findings. 6. Implement the Winning Version If Version B performs better, make it the new standard. If no major difference? Test something else. 7. Iterate and Optimize A/B testing isn’t a one-time task—it’s a process. ◾ Keep testing different headlines, images, layouts, and CTAs ◾ Every test improves your conversion rates and engagement Why A/B Testing Matters ✔ Removes guesswork – Decisions are based on data, not intuition ✔ Boosts conversions – Small tweaks can lead to significant growth ✔ Optimizes user experience – Find what resonates best with your audience ✔ Reduces risk – Test before making big, irreversible changes Part 2 tomorrow

"We tried Cold Email, but didn't see results." Has to be one of the most common challenges I hear. Let me explain. Over the course of 2024, I’ve spoken with many B2B SaaS Founders, Marketing Directors, Sales Directors, and GTM Leaders. They all share one problem in common: They’ve tried Cold Outreach, but they don’t get any results. So naturally, I start asking questions and offer to have a look at what they’re doing. When I review their campaigns, one thing becomes crystal clear: They understand how to build prospect lists, but there's little to no split testing happening. Here’s the reality: If you’re only sending 100-200 emails without testing different angles, you’re gambling on the success of your campaign, and in most cases, that gamble doesn’t pay off. Let’s break this down. There are two types of companies: 1️⃣ The 1% that doesn’t need to split test (they already know their ICP and what works for them). 2️⃣ The 99% that absolutely MUST split test to find what works best. If you’re part of the 99% (and most of us are), here’s how to do it effectively: Step 1: Test Pain Points Start by identifying the key problems your target audience is facing. Let’s say you’re an agency targeting e-commerce brands. You could test angles like: → High customer acquisition costs → Low lifetime value → Low return on ad spend Each email script stays consistent, only the pain point changes. 💡 Example: If you’re targeting a Sales Director, one angle might focus on the challenge of getting unqualified leads filling up their pipeline, while another might highlight how their team spends too much time on lead nurturing rather than closing. Allocate a set number of leads to each angle (e.g., 1,000 leads per angle) and track results. Step 2: Analyze & Scale Winners Once you’ve sent out the emails, review your data. Ask yourself: → Which angle is getting the most positive replies? → Are certain pain points resonating more than others? If one angle shows promise, double down. If another flops, drop it. Step 3: Test Offers After narrowing down the best angles, shift your focus to your offer. Split test variations of your offer to see which drives the most engagement and demo bookings. Forget vanity metrics like open rates (for now). Instead, track the ratio of PRRs. Many B2B companies: ❌ Send a small volume of Cold Emails (100-200) and expect big results. ❌ Focus too much on minor variables like subject lines before testing major factors like pain points or offers. ❌ Don’t analyze campaign performance enough to refine their approach. 💡 Pro tip in the PDF below👇 💬 Drop a comment below, or DM me for a free campaign audit.

Cold email doesn't work cause you don't know how to test. Save this post, here's the framework that 3X'd our response rates and got us meetings with CTOs at Microsoft, Oracle, and Salesforce: Most companies screw this up completely. They create 50+ variations. They tweak signature formats. They A/B test subject lines endlessly. But they're optimizing the wrong things. Here's the framework we use to crack outbound for our clients in under 30 days: 1. TESTING vs. OPTIMIZING Testing = the big stuff that actually matters - Value proposition - Pain points addressed - Target persona - Core offer Optimizing = minor tweaks that only matter AFTER you have a winning message - Subject line variations - CTA placement - Signature style - Send time 2. THE TESTING SEQUENCE THAT WORKS: Step 1: Start with ONE core message - Focus on a single, clear value proposition - Target ONE specific pain point - Keep it under 150 words Step 2: Test different OFFERS with the same message - Not getting responses? Don't rewrite the entire email - Change what you're offering at the end - Demo → Case study → Quick call → Coffee chat Step 3: Once an offer converts, create 3-5 VARIATIONS - Same core value prop and offer - Different opening hooks - Different proof points - Test with a small sample (100-200 sends each) Step 4: Scale the winner & start optimizing - ONLY now should you tweak subject lines - ONLY now should you test send times - ONLY now should you add personalization This approach cut our testing cycle from 3 months to 3 weeks. Most teams waste months on microscopic changes when they haven't even validated their core message. The best part? Tools like Smartlead make this systematic testing simple. Once you've got your winner, then you can go wild with personalization using Clay. But not before. Cymate 🛠️♠️