The old workhorse of email marketing still delivers: it has one of the highest ROIs of any marketing channel, on average, $42 for every dollar spent. But it can always be better. Artificial intelligence (AI) has been a significant driving force in optimizing and automating email marketing, including tasks such as scheduling, personalization, A/B testing, segmentation, and more.

In my experience, one of the most effective and engaging uses of AI for email marketing is sentiment analysis. Sentiment analysis is popular for analyzing customer emails to identify aspects such as tone of voice, emotion, topic, and other recurring themes. However, you can also apply these principles and techniques to your emails to discover what language drives the conversions and KPIs you need.

In this article, I’ll examine how combining AI-driven sentiment analysis with historical campaign data can uncover the emotional triggers that drive clicks and conversions. This process can guide every aspect of your email copy, including subject lines, sign-offs, CTAs, and more. It will allow you to make more informed decisions and conduct more effective testing of the copy of one of your most important marketing channels.

What is AI-Driven Sentiment Analysis?

Sentiment analysis, sometimes called ‘opinion mining’, is a form of text analytics that categorizes the emotional tone of written content using AI, machine learning, and natural language processing (NLP) algorithms. In the past, AI could struggle with basic sentiments and was limited to sorting between ‘positive’, ‘negative’, and ‘neutral’. Modern AI sentiment-analysis tools can dissect every word, phrase, and syntactic cue in your emails to identify more distinct emotions, such as joy, anger, or sadness. 

When conducted on a large scale, sentiment analysis enables the quantification of qualitative data by identifying recurring patterns. This data can then be analyzed against other sets and metrics, which is particularly important for marketers.

The Traditional AI Sentiment Analysis Use Case for Email Marketing: Analyzing Customer Emails

Typically, when AI-driven sentiment analysis is discussed in marketing, it’s often in the context of customer communications, such as inbound emails, support tickets, and survey responses. This analysis of large sets of feedback and customer messages, enriched with demographic and behavioral data, can be used to examine aspects such as brand perception, common pain points, and ways to optimize customer service. 

It’s an incredibly effective way of understanding your audience and provides valuable insights for product roadmaps and customer experience strategies. It can even inform your email marketing in terms of content and copy. And that’s where you can start refining things even further, by turning sentiment analysis inwards.

Flipping the Script: Sentiment Analysis On Your Emails

While leveraging sentiment analysis on customer replies is valuable, the real power lies in applying the same techniques to your past email sends. AI can ingest historical campaign data and use sentiment analysis to see what might have moved the needle in terms of your desired metrics. 

You’ll have a better idea of the copy that performed the best and was most effective in terms of driving complete open rates, click-through rates (CTR), and conversion rates, allowing you to make informed decisions and conduct more refined tests on the copy of your marketing emails. You can predict future performance and, crucially, understand why specific messages succeeded or failed.

Here are just a few use cases for refining email copy with AI-driven sentiment analysis.

Analyzing Subject Lines 

AI tools trained on your campaign archive can surface which keywords, subject line length, or emotional cues drove the highest open rates. We’re often told that subject lines with urgent, benefit-driven language outperform generic headers. However, with machine learning analysis, you can identify the specific language that best conveys urgency and which benefits are most relevant to your audience, allowing you to craft more effective subject lines.

Analyzing Body Text

Diving into the body copy, sentiment models can highlight passages that evoke strong positive emotions, such as curiosity or excitement, or the types of information that most engage readers and push them towards conversion.

This information can be used to create templates and guidelines for your email copy, whether it’s machine-generated or written by copywriters that evolve over time.

Analyzing Calls-to-Action (CTAs)

You could say CTAs are where sentiment meets action, which makes them prime for analysis and refinement. You can find out what emotive triggers and tone of voice had the highest uplift for click-through rates.

This information can create a bank of CTAs for specific purposes and audiences, based on data-backed trigger words you can refine with further A/B testing.

The Tangible Benefits: How AI Sentiment Analysis Boosts Results

When deployed thoughtfully, AI-driven sentiment analysis delivers measurable uplifts across key metrics. Personalized subject lines informed by emotional insights can boost open rates by up to 10%. In comparison, optimized CTAs increase CTRs by as much as 13%, and companies leveraging these AI recommendations have reported a 41% increase in overall email-driven revenue. 

Beyond raw performance, AI turns raw data into actionable intelligence, enabling marketers to prioritize high-impact edits over guesswork. Finally, the automation and scalability offered by AI free teams from manual analysis, allowing them to focus on strategy and creativity rather than spreadsheet wrangling.

Best Practices for Improving Email Copy with AI Insights

To put these insights into action, follow a structured approach:

Data Preparation

Start by exporting your historic email campaign data and copy, making sure each email is formatted with the data metrics you’re looking to improve, like open, click, and conversion rates. Ensure all your historic data is formatted uniformly, as this will make it easier for the AI to analyze and draw correlations between performance and copy.

To make this process effective, sort your historic emails into specific categories so that in the end, you get a better picture of what language triggers specific conversions. For example, the language that gets people to click through to your Black Friday deals is likely to be quite different from how you might promote your newest whitepaper.

Tool Selection

Choose an AI sentiment-analysis solution that fits your scale and budget. There are dedicated sentiment analysis tools if you want a deep dive, but there are affordable alternatives. Something as simple as a paraphrasing tool can give you a good summary of recurring themes and language, but I’ve found great success with standard LLMs like ChatGPT.

Develop Effective Prompts for Deep Analysis

Suppose you’re using generative AI (e.g., ChatGPT or Anyword). In that case, you need to craft prompts that specify the dataset context, performance metrics to consider, and what you’re trying to find out about your email copy. https://blog.hubspot.com/service/sentiment-analysis-tools

Here’s an example prompt: “Conduct a sentiment analysis of the sales-focused email subject lines with an open rate of 40% or higher. Identify recurring patterns of sentiment, trigger words, tone of voice, and length in terms of characters. Use these recurring patterns to create a framework for optimized sales-focused email subject lines.”

Analyze Both High- and Low-Performing Emails 

Analyzing high-performing emails can be effective for telling you what you should do with your email copy, which is excellent, of course! But by analyzing your low performers, you can see how you’ve missed the mark and know what to avoid in the future.

Use the Analysis for Automated Copy Generation 

The insights derived from your sentiment analysis can be fed into copy generation tools to act as guidelines for new subject lines, body snippets, and CTAs that align with proven trigger words. 

Keep Testing

Using the sentiment analysis insights can elevate your A/B testing by allowing you to use variants based on more informed choices. The results of the A/B testing can then be fed back into your sentiment analysis model to refine its output even further. 

Mining your historical emails through AI-driven sentiment analysis can be the ultimate tool for understanding how subscribers truly feel about each piece of email copy. Armed with these insights, you can make data-driven choices to craft subject lines that grab attention, body text that captivates, and CTAs that compel action.