Someone signed up 6 months ago. Are they still with you?Someone handed you their address six months ago. You've mailed them every week since. Are they still opening, or are you shouting into an empty harbor? An engagement decay curve answers that in one picture. Let's build one.
⬡ talking-stat, big "45% → 12%", caption fades in: "week 1 opens vs month 12 opens"A decay curve just shows how subscriber interest fades over time. Week one, people are keen. Month twelve, a lot of them have drifted. The curve tells you two things: by how much interest drops, and when. That "when" is the useful part.
⬡ timeline, a line that starts high at "week 1" and slopes down toward "month 12"Here's the build, five steps. One, pull your subscriber list with signup dates. Two, for each person, work out their open or click rate over a fixed window, say the last ninety days of sending. Three, sort everyone into buckets by how long they've been aboard: nought to thirty days, thirty-one to ninety, ninety-one to a hundred-eighty, up to a year, and over a year. Four, average the engagement inside each bucket. Five, plot those averages. That line is your curve.
⬡ checklist, five ordered steps: pull signup dates · calc per-person rate · bucket by tenure · average each bucket · plot the lineNow read it. If your fresh crew opens at forty-five percent and your over-a-year crowd sits at twelve, that's a steep drop, interest falls off a cliff. If it's forty-five easing down to thirty-five, your list holds attention unusually well. Same chart, two very different stories about your list's health.
⬡ split-compare, LEFT "steep: 45% → 12%" (line dives) vs RIGHT "gentle: 45% → 35%" (line drifts)If this is clicking, subscribe, we go deep on all of the analytics stuff.
⬡ title-card, on-screen: "Subscribe, the whole metrics series"The point of the curve isn't the chart, it's the timing. Where the line dives is where your win-back email should fire. Drop off sharply after ninety days? That's your trigger. Gradual slope? You've got more runway. It also sets an honest growth target: if most folks go quiet by month six, your new signups have to outpace that fade.
⬡ timeline, marker drops onto the curve at the steep point, labelled "win-back fires here"One honest warning. Apple's Mail Privacy Protection auto-fetches images for a lot of iPhone users, so their "opens" fire whether they read you or not. That inflates the early part of your curve and can flatten it artificially. If Apple Mail is a big chunk of your audience, use click rate as your main signal instead of opens. And compare like with like: people from a lead magnet and people from a giveaway often decay very differently, so don't blend them into one curve.
⬡ myth-fact, MYTH "opens tell the whole story" vs FACT "Apple MPP inflates opens, lean on clicks"So: bucket by tenure, average each bucket, plot the line, then fire your win-back where the line dives. One picture, and you finally know when your subscribers quietly slip overboard.
⬡ title-card, on-screen: "Bucket, average, plot, act."Next up: cohort analysis, the tool that turns this one curve into a fleet of them so you can see which signups age well. Subscribe and it's right there.
⬡ end-card, Subscribe + Next: "What is cohort analysis in email marketing?" (004.013.002)How to calculate an engagement decay curve for your email list, step by step. Group subscribers by how long they've been on your list (0-30 days, 31-90, and so on), average each group's open or click rate, then plot the curve. The slope tells you when interest drops and exactly where your re-engagement campaign should fire. Plus the Apple Mail Privacy Protection caveat that quietly inflates the early part of your curve, and why you should split acquisition sources instead of blending them.
Concepts in this video:
Next: What is cohort analysis in email marketing? → [link 004.013.002]
Full written guide → reviewmyemails.com/emailalmanac (How to calculate engagement decay curves)
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