Overview

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Email performance shouldn’t rely on guesswork. In this course, you’ll learn how to use A/B testing to make smarter, data-driven decisions that improve your email campaigns. You’ll start by learning how to design structured A/B tests, including defining hypotheses, isolating variables, and setting success metrics. Then, you’ll gain hands-on experience setting up tests in tools like Mailchimp and interpreting results in platforms like Klaviyo. You’ll also learn how to determine whether your results are statistically significant and when they’re not. As you progress, you’ll explore the three variables that most impact email performance: subject line, content, and send timing. You’ll evaluate testing strategies and build plans to optimize lifecycle campaigns based on real-world scenarios. Throughout the course, you’ll work in an e-commerce marketing context, applying your skills to realistic campaign challenges. By the end, you’ll be able to confidently design tests, analyze outcomes, and use insights to improve campaign performance. This course is ideal for aspiring email marketers, CRM specialists, and digital marketers who want to move from instinct-based decisions to evidence-based optimization.

Syllabus

Testing: Methodology and Setup

Effective email optimization begins with structured testing rather than assumptions about what audiences might prefer. This module introduces the practical foundations of A/B testing, including variable selection, test structure, audience control, and setup workflows used in lifecycle marketing campaigns. You will explore how marketers design reliable tests for audience groups, subject lines, content, and timing decisions, then apply those concepts directly in Mailchimp through a hands-on testing workflow. The module emphasizes disciplined experimentation and operational decision-making over isolated campaign tactics. By the end of this module, you will be able to configure and launch structured email A/B tests aligned to specific campaign objectives.

Testing: Significance and Strategy

Running an A/B test is only valuable if marketers can accurately interpret the results and determine whether observed differences are meaningful. This module helps marketers move beyond surface-level metrics by focusing on statistical significance, result interpretation, and practical analysis techniques used to evaluate email performance tests. You will practice identifying statistically meaningful differences, spotting performance patterns, and deciding whether a result supports optimization, further testing, or no change. By the end of this module, you will be able to assess email testing outcomes for statistical significance and identify actionable insights that support campaign improvement.

Testing: Subject Line, Content and Timing Variables

High-performing lifecycle campaigns rely on systematic testing strategies that prioritize the variables most likely to influence engagement and conversion outcomes. This module focuses on evaluating testing plans across subject lines, email content, and send timing while balancing operational constraints and marketing objectives. You will explore how different variables affect subscriber behavior and practice designing optimization strategies within Klaviyo and Mailchimp environments. The module emphasizes structured experimentation planning and practical workflow integration across lifecycle marketing operations. By the end of this module, you will be able to evaluate and recommend testing approaches that improve lifecycle campaign performance through data-informed optimization decisions.