Optimize Substack Engagement Metrics: My 60-Day Experiment

You publish a post, refresh Substack stats, and get numbers that don't tell you what to do next. The open rate looks decent. A few people liked it. Maybe one person subscribed. Maybe nobody did. You still have the same problem tomorrow: what should you publish again, what should you change, and which signals actually predict growth instead of feeding your ego?

That was my situation for months. I wasn't short on effort. I was short on usable feedback. So I ran a 60-day experiment to figure out which Substack engagement metrics mattered, which ones wasted my attention, and how to turn the right signals into a repeatable distribution system instead of a post-by-post guessing game.

The Agony of Substack Stats You Can't Use

The worst part of weak analytics isn't low performance. It's ambiguity.

You can live with a bad post if the numbers tell you why it failed. What wears you down is seeing a mix of mildly encouraging and mildly disappointing signals with no clear diagnosis. A post gets opened but not shared. Another gets comments but doesn't bring in subscribers. A third feels strong, yet disappears after publication like it never existed.

For a long time, I treated Substack like a scorecard. I checked opens, likes, and subscriber movement, then tried to infer a story from whatever happened. That habit made me reactive. I wasn't building a publication. I was chasing isolated outcomes.

Where the confusion starts

A lot of writers get trapped by three questions:

• Was this post good? The stats don't make that obvious.
• Did it help growth? One post can spark conversation without creating momentum.
• What should I repeat? Without a system, each new issue feels like a fresh gamble.

The moment I realized I needed a different approach was when I stopped asking, "Did readers like this?" and started asking, "What did readers do after they opened it?"

That shift changed everything.

Substack stats become useful only when each metric points to a decision. If the number doesn't change your next move, it's probably vanity.

I also realized I was relying too heavily on the default top-line view. If you're feeling that same frustration, it's worth comparing Substack's native reporting with a more action-oriented lens like this breakdown of Substack analytics beyond native reporting.

What I wanted instead

I didn't need more charts. I needed a workflow.

Specifically, I wanted to know which posts earned enough real engagement to deserve a second life on Notes, LinkedIn, and X. That became the core idea behind the experiment. Stop treating engagement as a retrospective report. Start treating it as a distribution filter.

Once I framed it that way, Substack engagement metrics stopped being abstract. They became operational.

Finding the Real Signals in Your Substack Dashboard

Most writers underuse the dashboard because they only look at email performance and subscriber count. That's too narrow.

Substack's own metrics guide says the Stats page now includes up to ten categories: Network, Audience, Retention, Sharing, Notes, Email, Surveys, Traffic, Unsubscribes, and additional post-level metrics, and it also notes a new growth chart with markers that connect audience spikes to specific posts in a more actionable way, according to Substack's guide to publication metrics.

The dashboard areas that actually matter

I found it useful to group Substack engagement metrics into three buckets.

Bucket	What to inspect	Why it matters
Engagement	opens, clicks, likes, comments, shares	shows whether the post connected after delivery
Growth	subscriber movement, traffic sources, network contribution	shows whether content created momentum
Reader behavior	retention, unsubscribes, repeat patterns across posts	shows whether you're building a healthier publication over time

That structure made the dashboard less overwhelming. I wasn't staring at a pile of tabs anymore. I was looking at a funnel.

The tabs I checked every publishing week

I settled on this review rhythm:

• Email first: I checked opens and click behavior to see whether the packaging worked.
• Posts next: I compared post-level reactions, especially comments and shares, because they revealed which ideas had enough force to travel.
• Traffic after that: I wanted to know where readers originated, not just whether they arrived.
• Retention last: This kept me honest. Some posts attract attention but don't improve publication health.

If you want a cleaner process for monitoring these signals over time, this guide on tracking Substack metrics more systematically is a useful companion.

One practical mistake to avoid

Don't treat every platform benchmark as your benchmark.

I like checking adjacent creator ecosystems to get context. A curated resource like the SaaS product leaderboard can help you see how product-led teams compare growth signals across channels. But your real job on Substack is not to imitate broad internet averages. It's to identify which combinations of engagement and subscriber movement keep repeating inside your own publication.

The right metric isn't the one with the biggest number. It's the one that helps you predict which post deserves more distribution.

That was the operating principle for the rest of the experiment.

My 60-Day Experiment to Decode Substack Engagement

I ran the experiment because I was tired of making editorial decisions from vibes.

For 60 days, I published on a fixed cadence and reviewed every post the same way. I didn't want one lucky hit to distort the picture. I wanted patterns. The focus wasn't just which posts got attention, but which ones produced signals I could reuse.

What I tracked

I stopped judging posts by a single outcome.

Instead, I logged:

• Email response: whether the post got opened and whether readers clicked further
• Post-level interaction: likes, comments, and share behavior
• Growth relationship: whether the post coincided with useful subscriber movement
• Distribution potential: whether the idea felt strong enough to repackage elsewhere

One reason this mattered is that Substack already starts from a high-attention baseline. A 2024 roundup reported average email open rates above 45%, click-through rates around 20%, more than 1 million paid newsletter subscribers, and over $500 million in monthly subscription revenue, according to this Substack usage and revenue statistics roundup. That told me not to over-celebrate opens. If many Substack audiences are already attentive, the harder question is what happens after attention.

The post formats I tested

I rotated through a few recurring formats because I wanted enough contrast to spot what worked.

• Short opinion posts: fast to write, easy to publish, often punchy
• Long tactical essays: heavier lift, more structure, more depth
• Narrative posts: personal stories with a point
• Curated analysis posts: synthesis of an idea, trend, or operator lesson

Some failed in predictable ways. The short opinion posts often got a quick reaction but didn't create much downstream value. Narrative pieces sometimes drew warm comments yet didn't travel. Curated analysis did better when the insight was tight, but flopped when it drifted into summary instead of argument.

The pattern that emerged early

By the halfway point, one thing was obvious. I couldn't trust surface appreciation.

A post with a few likes might feel good and still be a dead end. A post with fewer visible reactions might have stronger depth signals and more repurposing potential. That's when I started separating "people noticed this" from "people acted on this."

Practical rule: I only marked a post as a winner if the engagement suggested readers wanted to continue the conversation, share the idea, or spend more time with it.

That distinction became the backbone of everything that followed. I also found that tooling helped me compare those signals more consistently. A dedicated Substack analytics tool overview is useful if you're trying to move from casual checking to weekly operational review.

How to Interpret Your Metrics Like a Growth Strategist

Once I had enough posts to compare, raw numbers became less interesting than combinations.

A single metric rarely tells the truth. The useful interpretation comes from how signals stack together. That's the difference between reading a dashboard and diagnosing a content system.

The metric I started trusting most

The most useful signal for me was engagement rate, because it combines multiple forms of reader action.

Substack's engagement rate is defined as the share of subscribers who perform one of seven actions after opening: liking, clicking, sharing, restacking, commenting, reading to the bottom, or watching or listening to media for at least 30 seconds, as outlined in this explanation of Substack post engagement rate. That definition matters because it rewards depth, not just curiosity.

If a post gets opened but doesn't lead to any of those actions, it may have had a strong subject line and weak substance. If readers reach the bottom or spend time with embedded media, the content likely held attention.

My interpretation framework

I used a simple diagnostic model.

• High opens, weak downstream action
  The promise worked. The experience didn't. Fix the intro, structure, or clarity.

• Likes without shares or restacks
  Readers appreciated it privately, but didn't feel compelled to attach their name to it.

• Comments without subscriber movement
  The post may be resonating with existing readers while failing to expand reach.

• Strong depth, modest visible reaction
  Don't dismiss it. Some of the best distribution candidates aren't the loudest posts.

Here's the trade-off I learned the hard way: content that feels polished to the writer often performs as "pleasant." Pleasant content doesn't travel. Posts that take a sharper position, offer a usable framework, or compress a messy problem into a clear angle create more share intent.

What changed in my writing

I started writing for one of those seven actions.

Sometimes that meant tightening formatting so readers could get to the bottom. Sometimes it meant embedding media when the format fit. Sometimes it meant giving the reader a sentence worth restacking.

I also borrowed thinking from other service businesses that rely on diagnosing signals instead of admiring them. This piece on scaling a marketing agency sustainably is useful because it shows the same principle in a different context. Stable growth comes from repeatable interpretation, not isolated wins.

If a metric can't tell you what to change in the next post, it hasn't become strategy yet.

That mindset made me more selective. Not every "good" post deserved more effort. The best candidates were the posts whose engagement suggested they could survive outside the inbox.

My Optimization Playbook From Data to Dominance

After enough comparison, I built a playbook around one idea: optimize for the first reaction wave, then build for depth.

That mattered because the app creates a short evaluation window. Substack says the iOS app shows a post's stats banner for the first 48 hours, and reporting around the feature notes that restacks are weighted more heavily than likes during that period, according to this write-up on post stats in the Substack app. I didn't need to know every detail of the ranking logic to use the implication. Early share-worthy response matters.

What worked before publishing

I stopped treating publication as the starting line.

My pre-publish checklist became:

• Sharpen the subject line and first paragraph: opens mean nothing if the opening doesn't cash the promise. If you need practical inspiration, this guide on improving email opens with simple tactics is worth studying.
• Write for one clear reader action: comment, restack, click, or full read. Not all four at once.
• Format for completion: subheads, spacing, and momentum matter more than elegance.

What worked in the first 48 hours

This was the biggest shift.

I started treating the first two days as an activation phase:

1. Seed the idea in Notes early. Not a summary. A strong fragment or angle.
2. Reply fast to comments. Comments create movement, and movement invites more movement.
3. Promote the most shareable line, not the full article pitch. People restack ideas, not descriptions.

That made my posts easier to circulate. It also gave me clearer evidence about what readers found portable.

What worked after the post proved itself

Once a post showed real traction, I moved it into distribution mode.

For Substack-native follow-up, I found it useful to look at tracking Substack Notes performance because Notes often surface whether a concept can survive in shorter form. If the core idea fell apart there, it usually wasn't strong enough for wider cross-platform repurposing either.

The key trade-off is simple. If you optimize only for opens, you get curiosity. If you optimize only for comments, you get conversation. If you optimize for restacks and depth, you get content with a chance to keep spreading after the publish day window closes.

Building a Distribution Engine from Your Best Posts

The biggest lesson from the experiment wasn't about email. It was about waste.

When a post proves it can hold attention, trigger discussion, or earn shares, leaving it trapped inside one newsletter issue is a bad operating decision. Your strongest Substack engagement metrics should decide what gets repurposed next.

The shift from analysis to system

Before the experiment, I published and moved on. After the experiment, I built a rule.

When a post showed strong depth and clear share signals, I turned it into:

• Substack Notes for fast idea reinforcement
• LinkedIn posts for professional framing
• X threads for sharper, more modular arguments

That gave me a content pipeline based on proven resonance instead of guesswork. I wasn't asking, "What should I post on LinkedIn today?" I was asking, "Which Substack idea already earned enough response to deserve a second format?"

What good repurposing actually looks like

Most writers make repurposing too literal. They summarize the article and call it distribution.

That rarely works.

A better approach is to extract the strongest unit from the post:

• the sharpest claim
• the most practical framework
• the line readers kept responding to
• the tension the piece resolved

Then rewrite for the platform's native behavior.

Later, when I wanted a smoother workflow for scheduling and cross-posting, I used one tool selectively in that process. Narrareach's guide to automatic Substack distribution is relevant here because it reflects the exact operational problem. Taking a validated Substack idea, turning it into Notes and social posts, then scheduling that output without doing manual copy-paste every time. That's the part that helps users grow faster and publish Substack posts and Notes more efficiently once the winning content is already identified.

One reason I like this model is that it respects the source material. You aren't manufacturing content volume. You're extending content that has already earned evidence.

A quick walkthrough helps make that concrete:

The practical filter I use now

I don't repurpose every post. I only repurpose posts that pass three tests:

Test	Question
Attention	Did readers move beyond the subject line?
Depth	Did the post hold attention or trigger meaningful action?
Portability	Is there a clear idea strong enough to stand alone elsewhere?

If a post passes those, it becomes an asset. If not, I learn from it and move on.

That's the part many writers miss. Substack engagement metrics aren't just for review. They're for routing. They tell you which ideas deserve another round of distribution and which ones should stay archived as useful drafts in your learning loop.

Stop Guessing and Start Growing

The 60-day experiment changed how I think about publishing.

I no longer look at Substack engagement metrics as proof that a post succeeded or failed. I use them as routing signals. They tell me whether the packaging worked, whether the content held attention, whether readers cared enough to share it, and whether the idea deserves more distribution.

That shift removed a lot of friction. I stopped obsessing over vanity metrics and started building a repeatable system. Good posts no longer disappear after send day. They become Notes, social posts, and subscriber acquisition assets. Weak posts still matter, but only as feedback. They don't get promoted just because I spent time writing them.

If you're serious about growth, that's the operating model to adopt. Publish. Measure the right signals. Identify what resonated. Then distribute the winners harder and more efficiently.

Keep publishing if you want. But don't keep guessing.

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Ready to turn proven posts into a real content distribution system? Try Narrareach to schedule, repurpose, and publish Substack content, Notes, LinkedIn posts, and X content from one workflow. If you're not ready for a tool yet, stay connected through my weekly newsletter and keep following these experiment-driven playbooks on what builds an audience.