A/B Testing Email Content to avoid spam folders (10 test examples included)

Most teams still A/B test emails for clicks or opens, not realizing these surface metrics can quietly worsen inbox placement and domain reputation. Over time, that means lower visibility, fewer replies, and weaker pipeline performance - even when “open rates” look fine.

This guide introduces a deliverability-first A/B testing framework built for B2B outbound email cadences. You’ll learn 10 concrete content tests and clear guardrails to prevent reputation damage, using Allegrow’s observed benchmarks and testing methodology to help your team optimize for what truly matters: landing in the primary inbox and earning real replies.

TL;DR: Most revenue teams A/B test emails for vanity metrics like open rates, unaware that surface-level engagement data often hides spam placement issues. Because Gmail and Microsoft filters evaluate emails based on content patterns (HTML weight, link density, tone) alongside sender reputation, a winning subject line might actually be triggering the Promotions tab or Spam folder. Consequently, B2B senders must adopt a deliverability-first testing framework—prioritizing primary inbox placement and reply rates over clicks—and systematically test variables like plain-text footers, link budgets, and CTA phrasing to ensure their outreach is not just opened, but genuinely trusted by the receiving server.

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Why should you A/B test email content before sending?

Have you ever wondered, while looking at your email engagement stats, why certain subject lines and email templates have significantly higher open rates than others? The difference isn’t just about recipients opening emails more often. Inbox providers like Google and Microsoft use AI to evaluate your messages against patterns from past content, including emails that received low engagement or were flagged as spam, and automatically decide folder placement.

Based on your content, emails could end up in:

• Spam – flagged for risky links, attachments, or promotional language
• Updates – newsletter-style formatting or repetitive headers
• Promotions – heavy HTML, banners, buttons, or multiple links
• Unfocused (for Microsoft users) – inconsistent subject-body pairing or low engagement history

Even small edits like adjusting the subject line, reducing links, or simplifying formatting can shift placement. Filters evaluate subject lines, body length, link usage, sender reputation, attachments, formatting, and CTA phrasing in full context.

For cold B2B outreach, style matters. Marketing-style visuals (HTML-heavy templates, banners, buttons, large images) often trigger Promotions or Spam filters. On the other hand, plain, 1:1-style messages (simple formatting, concise paragraphs, minimal links) mimic a personal note and are more likely to reach the primary inbox.

When testing your content, prioritize metrics in this order:

1. Primary inbox placement – ensure your email reaches the recipient’s main inbox first.
2. Replies – track engagement and genuine interest from recipients.
3. Downstream meetings/opportunities – measure the ultimate business impact of your email sequence.

By using a deliverability-first A/B testing approach, you can optimize emails for inbox placement without sacrificing engagement, ultimately generating more replies and business opportunities.

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What email content factors impact inbox placement (and why)?

The key aspects of an email that contribute towards inbox placement can be summarised across the following points:

Subject line and body pairing:

Your subject line and body each contribute roughly 50% to inbox placement. Filters check for consistency between the two. Mismatched tone or keywords can appear misleading. Simple, context-aligned subjects often perform best, reducing complaints and improving deliverability.

Links and images:

Every link adds scrutiny from spam filters. Keep total links to two or fewer, including the footer. Avoid image banners, tracking buttons, or redirects that resemble marketing blasts. Use plain text links to secure domains only.

Signature and footer:

Your footer repeats on every send, so keep it clean and lightweight. Opt for plain text only, and include name, title, company, and one website link if needed. Skip logos or social icons in cold emails to stay out of Promotions folders.

Attachments:

Attachments trigger filtering in corporate inboxes. Avoid them when possible; if necessary, keep them under 2MB and clearly relevant. Linking to hosted content is almost always safer for inbox placement.

Tone and claims:

Overly promotional or pushy phrasing hurts both trust and placement. Use clear, calm, and factual language - think conversational, not salesy. Filters favour messages that read like genuine 1:1 communication.

Note: For more information on sending limits, throttling, and safe deliverability practices, check out Allegrow’s guides.

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Two filters to pass: literal vs mental spam filters

When it comes to getting your emails into the primary inbox, you’re not just up against one type of filter - you’re up against two. Understanding both helps explain why some emails that look fine still end up in Promotions or Spam.

• Literal filters are the automated, machine-level checks that inbox providers run on every message. They evaluate things like HTML weight, link count, phrasing, and domain reputation to decide whether your email looks legitimate or risky. Too many links, flashy formatting, or phrases that resemble promotional spam (“free trial”, “limited time”, “act now”) can trigger filtering even if your intent is good.
  
  
• Mental filters, on the other hand, are the human equivalent of literal filters - the split-second judgment your recipient makes when they see your email. Does it look like a real 1:1 note from another person, or does it feel like a marketing blast? The visual and tonal cues (layout, subject line, and opening sentence) can make or break engagement at that moment.

The overlap between the two is where success happens. Emails that earn genuine engagement, that is, opens, replies, and positive interactions, also improve your sender reputation over time, helping you pass both filters more consistently. In other words, the more your emails feel authentic and relevant to real people, the more likely they are to clear the algorithms, too.

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A Deliverability-first content A/B testing workflow plan

To understand how different versions of your email content perform, you need data from real inboxes, not assumptions. Allegrow’s deliverability-first A/B testing workflow is designed specifically for B2B senders who want to see exactly where their emails land and how small content changes affect inbox placement.

At a high level, the process involves sending two variations of your email to a live mix of B2B inboxes across Google and Microsoft environments. Each variation is tracked to measure what percentage of messages land in the primary Inbox vs. Promotions or Spam, along with any reply rates that follow.

For accuracy, only one variable should be tested at a time, such as subject line, link count, or footer style, and each test should run for 3–7 days, or until results show a clear directional trend. This keeps outcomes reliable without overcomplicating analysis.

To protect sender reputation during tests, we include built-in stop-loss rules: if complaint or bounce rates move above safe thresholds, testing stops immediately and reverts to the previous version. This ensures that you’re learning safely without risking domain health.

Behind the scenes, Allegrow is able to automatically filters risky contacts, removing spam traps, complainers, and catch-all addresses from your cadences, and continuously monitors SPF, DKIM, and DMARC records on an hourly basis throughout the testing window. That means you’re not only testing your content, but doing so on a clean foundation.

Sample size realities for cold B2B

When running A/B tests for cold B2B outreach, it’s important to recognise that you don’t need massive volumes to gather meaningful insights. Most cold campaigns operate on smaller, more targeted lists, and that’s perfectly fine. The goal isn’t statistical perfection; it’s directional learning you can replicate.

Rather than relying on large marketing-style benchmarks (like the “1,000-recipient minimum” often cited by big ESPs), focus on 3–7 day test windows with enough sends to see consistent patterns across providers such as Google and Microsoft. 

To strengthen your confidence in the results, look for trendlines across multiple test cycles instead of single bursts. When a change repeatedly improves inbox placement or reply rates over a few rounds, you’ve found a meaningful improvement you can scale safely.

In short, cold B2B testing rewards replication over raw volume, making it possible to run lean, data-driven experiments that still move the needle on deliverability and engagement.

Why do “spam keyword” lists often fail to improve results?

You’ll immediately want to know what keywords inside your emails are detracting from your performance and eliminate them. This draws most marketers to search for lists of the most ‘spammy’ keywords over emails (i.e Hubspot’s list of 394 email spamming keywords). 

Unfortunately, as HubSpot states in their article; spam filters have become far more sophisticated in recent years and look at the general context around your keyword selection rather than just the density of specific keywords. We’ve also found most marketers in our customer base are already using little to none of the keywords from lists like the above. 

Therefore, although lists of trigger keywords can help to add context, you’ll find yourself in the position of not knowing or not having data on the impact of the following content decisions:

• The impact of alternative keywords: Seeing as there is no definitive list of trigger keywords and all the lists across the internet combined will not even be close to competing with Google and Microsoft's content filtering algorithms, blindly swapping out one keyword for another leaves you at risk of going from bad to worse, or needlessly using a less explicit keyword, which will create less engaging content for your customers and prospects.
• There is no reference point to score content: Static keyword lists provide no consistency to score your content against prior templates or your domains general sender reputation, this is a vital baseline to establish so you can make decisions around the prioritisation of different templates and how far you can push the envelope on keyword choice, to provide the best overall marketing results.
• Analysis of the entire email is not provided: The context around specific keywords is just as important as the keywords you select due to the complexity of filtering algorithms nowadays. Therefore, it’s an overly simplified viewpoint to consider some keywords as ‘bad’ and others as ‘good’, instead you’ll need to analyse the entirety of your content in a testing environment to make data-driven decisions.

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What makes a good A/B test for email content?

When you’re creating a content test, you’ll want to ensure the data you get back is actionable and provides real value to your go-to-market strategy. Below is our key guidance on what defines a great email content test for both performance and deliverability:

• Impacts revenue or pipeline: Prioritise testing emails most closely tied to pipeline or revenue, such as the first email in your prospecting sequence, the initial message sent to new list sign-ups, or the template responsible for the highest conversion volume. These are where even small improvements in inbox placement can drive meaningful results.
• Pick high-impact deliverability variables: Focus on testing elements that meaningfully influence inbox placement, such as subject line phrasing, link count, footer format, and HTML weight. Avoid overtesting superficial design changes, and instead target the variables that affect whether your email actually reaches the inbox.
• A/B test variations of the same email: Always test two versions that serve the same purpose. One acts as your control (the current live email), and the other introduces a single, measurable change. This keeps results clear and tied to a specific variable rather than multiple overlapping edits.
• Test evergreen content: For long-term insight, focus on templates that new contacts consistently receive, such as prospecting sequences or lifecycle emails. Testing one-off sends, or static lists, won’t build a reliable data set over time.
• Keep variants human-acceptable: Even if one version is designed to optimise inbox placement, make sure both versions would be appropriate to send to real prospects. Avoid gimmicks that might feel robotic or reduce reader engagement. The goal is to balance human tone with deliverability discipline.
• Ensure adequate sample size and learning window: In cold B2B campaigns, smaller send volumes are normal, so aim for directional learning over perfect statistics. Run tests for 3–7 days or until you see clear trends, then replicate results across multiple runs to confirm consistency.
• Pre-register your hypothesis and rollback condition: Before testing, define a simple hypothesis (e.g., “Reducing links from two to one will improve primary inbox placement by 10%”). Also, establish a rollback condition - if complaints, bounces, or engagement drop outside safe thresholds, revert immediately to your control version.

By following these principles, you’ll create tests that not only improve inbox placement but also protect sender reputation and lead to measurable business impact.

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Examples of 10 content changes to A/B test

If you’d like inspiration for aspects of your email templates that you could iterate for testing the B version, here’s a list of 10 examples of changes you could make across content tests to get you started. Each includes what to change, why it helps inbox placement, and what success signal to look for.

Subject line length and tone

Iteration on the subject line is very low-hanging fruit when it comes to improving inbox placement and engagement. Test shorter subjects (≤3 words) against longer ones, remove punctuation or overly emotive words, and make sure the subject matches your body keywords.

Word count and message length

Less is usually more when it comes to email. Experiment with being more concise and how this impacts your inbox placement. We usually advise emails in a prospecting sequence to be between 1-4 lines of text. Any more than that, and you’re probably doing too much (over) selling. Try cutting your content down to 50% of the words and see if you get a lift in engagement and inbox placement.

Keyword selection and usage

Your choice of keywords and phrases used in the body of text can of course impact your inbox placement and email sentiment considerably. You can look to test specific ‘buzzwords’ to your value proposition with alternatives or rephrase content altogether. We advise your focus when testing this area should be to find a balance between language that reads well and is optimized for inbox placement.

Link budget: one link vs two vs none

Seeing as the overuse of links can be associated with spammy content for an outbound email - we advise a maximum of two links in your opening sequence email (including links in the footer). With content testing, you can quantify the impact of different types and quantities of links in your content on spam rate. (i.e. The impact of a Calendly link with a link to blog content).

Call to Action clarity

The primary call to action that you use in your go-to-market emails can be one of the main similarities between your content and spammy content, if you don’t test and iterate the emails ‘ask’ correctly. As Gong's research shows, you should look to confirm a prospect's interest with the CTA, rather than asking for time in cold outreach. As this is 2X as effective compared to average outreach emails.

Footer: HTML block vs plain text

Given that your footer/signature structure is added to every single automated email, it’s worth optimizing and paying attention to. To optimize, convert image-heavy or HTML-rich signatures into clean, plain text. Include only essential details: full name, title, company, and one website link. Plain-text footers tend to improve trust and deliverability.

Formatting: bold text and font choices

Many businesses are experimenting with using bold words or different font types in prospecting emails to stand out. You can test content to establish the implications these changes may have on the natural inbox placement of your emails.

Email structure and layout

You’ll typically want to separate content into single lines to ensure your emails do not look like ‘too much hard work’ to read when a prospect initially opens them. This inevitably means creating your content in a way that may seem to contain an unusual amount of paragraphs, while editing this structure you can test the impact of different changes on how often each version of your content will reach the primary inbox.

Sender name and profile tweaks

Both the job title and the name of the sender being used for cold emails can influence prospect engagement. Some businesses choose to repurpose the profile of the company's leadership across all their sending accounts for this reason. Whereas other businesses prefer the continuity of each SDR having their own name used for outreach to contacts that are assigned to them. Seeing as this is a relatively new area of demand generation set-up to experiment with, you may want to test your content going out from different mailbox names to see what aspect of the difference is due to contact sentiment vs natural inbox placement.

Use of personalization tags

Having the prospect's first name and company included in the email isn’t exactly pushing the boat out when it comes to personalization these days. However, the placement of personalisation tags and the quantity of them you use can influence inbox placement. Generally, it’s advised that personalisation is most effective at the beginning and end of a prospecting email - but don’t shy away from testing these general assumptions for yourself.

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How to choose the next email content variable to test?

Once you’ve run a few A/B tests, it’s easy to wonder which variable to test next. The fastest way to decide is by matching your performance signal (what changed) to the content area most likely causing it. Use the simple guide below to prioritise your next move:

• If open rates fall → test your subject line or first 8–12 words.
  Shorter, cleaner subjects and stronger preview text usually restore engagement.
• If reply rates fall → test your CTA phrasing or problem clarity.
  Shift from time requests to interest-based questions, or clarify the pain point earlier.
• If inbox placement worsens → test your links, footer, or HTML formatting.
  Reduce total links, simplify your signature, and remove unnecessary visual elements.
• If complaints rise → review tone, claims, and targeting accuracy.
  Tone down assertive language, remove hype terms, and double-check audience fit.

This quick matrix helps you identify where to focus your next test based on live performance trends, keeping your optimization process both structured and lightweight.

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Measurement that protects deliverability (What to watch out for)

To improve deliverability safely, it’s crucial to track the right performance indicators - not just the most obvious ones. Your north-star metrics should always be primary inbox placement and reply rates. These show whether your emails are being both seen and valued by real recipients.

Supporting metrics like soft and hard bounces, spam complaints, and per-provider foldering patterns (for example, Gmail vs. Outlook) provide context to understand when filtering issues are starting to emerge.

When interpreting your data, focus on trendlines rather than one-day spikes. Meaningful patterns tend to appear over time. Replicate successful tests with fresh samples to confirm performance before scaling to larger sends.

By monitoring these signals closely, you’ll ensure every A/B test builds long-term sending strength, not just short-term engagement.

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Key takeaways for A/B testing

We can conclude that the content and structure of your email directly determine which folder your message lands in, and that placement has a major influence on engagement. If you’re still optimizing emails purely for how the recipient reads them, that’s the equivalent of writing a blog post and ignoring technical SEO. Deliverability comes first.

A/B testing allows you to measure and iterate safely - not just for opens and clicks, but for inbox placement and replies, which are the real signals that drive meetings and pipeline. By using structured tests and the decision matrix outlined above, you can identify which content elements help you land in the primary inbox and connect more consistently with real prospects.

When running these tests, always do so in an authentic environment of real B2B inboxes. This ensures you can accurately see where each version of your email lands and make data-backed decisions that protect deliverability.

Allegrow helps teams take this a step further by filtering out risky contacts (spam traps, complainers, catch-alls) before testing and by providing a proprietary content spam placement feature.

If you’re ready to start testing smarter, try Allegrow’s advanced list verification and inbox placement tools with a 14-day free trial. Verify up to 1,000 contacts, uncover what legacy tools miss, and build a cleaner, safer foundation for every A/B test.

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FAQs

What part of the email content should I optimize first if placement drops suddenly?

If your inbox placement drops, start with your link budget, footer format, and subject/body keyword consistency. Reduce total links to two or fewer, convert your email footer from HTML to plain text, and ensure your subject matches your email’s body language. Then, check SPF, DKIM, and DMARC settings for any authentication drift.

How long should I run a content test for cold B2B?

For A/B testing email content in cold B2B email cadences, run each test for 3–7 days or until results are directionally clear. Prioritize trendlines across providers (Google, Microsoft, etc.) instead of single-day spikes. Replicate promising results with a fresh sample before scaling to protect deliverability accuracy and maintain consistent email reply rate optimization.

Can I test multiple variables at once?

When doing outbound email A/B testing, avoid testing multiple variables simultaneously - it makes it difficult to isolate what’s working. Focus on one variable per test, such as subject line, CTA phrasing, or email link limit, to maintain clear insight. Use staged rollouts for additional changes so you can build stable, compounding improvements in inbox placement testing.

Do images and buttons always harm deliverability?

Not always, but it is extremely common, especially in cold outbound. Heavily designed sales engagement email templates with banners or tracking buttons often trigger the literal spam filter, while simple, plain-text emails tend to pass both the mental vs literal spam filter more easily. Start plain and only reintroduce visuals once inbox placement and engagement remain stable.

Should I include attachments in my cold emails?

As a rule, avoid attachments when cold emailing. Large files can raise red flags with spam filters and slow delivery. Instead, link to a lightweight, SSL-secured page to keep your content accessible without increasing filter scrutiny.