The Problem
Sending an email campaign is the easy part. The real challenge is understanding its impact and figuring out how to improve future sends. Without analyzing the data, marketing efforts are based on guesswork. I was tasked with digging into the numbers to find actionable insights.
How I Used It
My role was to own the post-campaign analysis process. Rather than designing the emails, I focused on their performance.
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Data Monitoring: After a campaign was sent, I would dive into Mailchimp's analytics to track key metrics like open rates, click-through rates, and audience engagement.
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Reporting: I established a weekly reporting cadence. I would analyze the data and report the key statistical changes and performance insights directly to the Head of Marketing.
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Strategic Proposals: My reports weren't just numbers; I provided concrete proposals on how to improve. This included making recommendations to get underperforming campaigns "back on track" and proactively researching topics like the most effective send times for our B2B audience.
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What This Involved
This analytical role required me to:
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Interpret Marketing Data: Analyze email KPIs to understand campaign effectiveness and audience behavior.
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Communicate Insights: Clearly report on performance and statistical trends to marketing leadership.
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Develop Strategy: Formulate actionable recommendations based on data to improve future results.
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Conduct Audience Research: Proactively investigate factors like send-time optimization to increase engagement.
The Result
My work introduced a data-driven feedback loop into our email marketing process. Instead of just sending and hoping for the best, my analysis provided the insights needed for continuous improvement. This helped the team make smarter, data-backed decisions and turned Mailchimp from a simple sending tool into a source of valuable strategic intelligence.