Signal Over Noise

Your dashboard shows you thousands of data points. Views, likes, comments, shares, saves, clicks, impressions, reach, engagement rate, follower growth, story views, profile visits...

95% of it is noise.

Noise doesn't just waste your time—it actively misleads you. It makes you optimize for vanity while your business stagnates.

Signal is different. Signal tells you what's actually working and what moves the business forward.

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The Fundamental Question: Does improving this metric directly increase revenue, or is it just correlated with other things that might?

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Why Most Metrics Are Noise

Vanity Metrics: High Numbers, Zero Impact

These feel good but don't pay bills:

• Total Followers - Doesn't measure engagement or buying intent
• Post Impressions - Doesn't measure who actually saw or cared
• Likes - Easiest action, least commitment
• Total Reach - Meaningless without context on who was reached

You can have 100K followers and make $0/month. You can have 5K followers and make $50K/month.

The metric that matters isn't audience size—it's audience quality.

Lagging Indicators: True But Too Late

These metrics are accurate but don't help you make decisions:

• Monthly Revenue - Tells you what happened, not what to do
• Follower Count - Result of many factors, can't be directly improved
• Overall Engagement Rate - Averaged across all content, hides what works

Lagging indicators are scoreboard metrics. They tell you if you won or lost, but not how to win next time.

The Signal Hierarchy

Not all metrics are equal. Here's the hierarchy from noise to signal:

Tier 1: Vanity Noise

Likes, followers, total impressions, profile visits

Use Case: Trend awareness only. Never optimize directly for these.

Tier 2: Engagement Indicators

Comments, shares, saves, story replies, DMs

Use Case: Content quality feedback. Higher quality than likes, but still not revenue.

Tier 3: Intent Signals

Click-through rates, link clicks, bio link visits, CTA completion

Use Case: Audience interest in your offer. This is where monetization begins.

Tier 4: Conversion Signals

Email signups, lead magnet downloads, waitlist joins, discovery calls booked

Use Case: Pre-revenue behavior. These people are raising their hand.

Tier 5: Revenue Signals

Sales, MRR, LTV, CAC payback period, retention rate

Use Case: Business metrics. This is the actual scoreboard.

Rule: Never optimize a lower tier at the expense of a higher tier.

The Three Metrics That Matter

If you could only track three metrics, these should be them:

1. Qualified Reach

Not total reach. Qualified reach.

How many people in your target audience (who could actually buy) saw your content?

• 1M impressions to random people = noise
• 10K impressions to your ideal customer profile = signal

2. Conversion Rate (Audience → Email)

What percentage of people who discover you join your email list or community?

This metric tells you:

• Is your positioning clear?
• Is your offer compelling?
• Is your audience actually interested in what you sell?

Good benchmark: 2-5% of engaged audience should convert to email subscribers.

3. Revenue Per Follower

Total revenue divided by total engaged followers (not total followers).

This is your true business metric. It combines:

• Audience quality
• Positioning effectiveness
• Offer-market fit
• Conversion ability

Target: $10-50/follower/year depending on niche.

How to Find Your Signal

Use this framework to separate signal from noise:

The Signal Test

For every metric you track, ask:

1. Does this directly predict revenue?

   • Yes → Signal
   • No → Continue to #2

2. Can I take action on this metric this week?

   • Yes → Leading indicator (useful signal)
   • No → Lagging indicator (scoreboard, not actionable)

3. Will improving this metric increase business results?

   • Yes → Keep tracking
   • No → Stop tracking, it's noise

Example: Decoding Your Last 10 Posts

Look at your last 10 posts. For each, track:

1. Impressions to target audience (not total)
2. Saves + Shares (commitment actions)
3. Link clicks (intent to learn more)
4. Conversions (email signups, purchases)

Now calculate:

• Engagement Rate = (Saves + Shares) / Target Impressions
• Intent Rate = Link Clicks / Target Impressions
• Conversion Rate = Conversions / Link Clicks

The posts with the highest conversion rate? That's your signal. Make more content like that.

Platform-Specific Signal

Different platforms have different primary signals:

Instagram

• Primary Signal: Saves and Shares (algorithm reward)
• Secondary Signal: DM replies (intent)
• Noise: Likes, total followers

TikTok

• Primary Signal: Completion rate + rewatch
• Secondary Signal: Shares
• Noise: Views, likes

YouTube

• Primary Signal: Average view duration %
• Secondary Signal: Click-through rate
• Noise: Total views, subscribers

LinkedIn

• Primary Signal: Meaningful comments (>10 words)
• Secondary Signal: Shares with commentary
• Noise: Reactions, impressions

Twitter/X

• Primary Signal: Replies and quote tweets
• Secondary Signal: Link clicks
• Noise: Likes, retweets

The Cohort Analysis Advantage

Most creators look at aggregate data. Smart creators look at cohorts.

Cohort: A group of followers who joined in the same time period.

Track each cohort separately:

• Cohort engagement rate over time
• Cohort conversion rate
• Cohort revenue contribution

Why this matters: If November's cohort has 3X the engagement of October's cohort, you did something different in November. Find out what.

Action Items

This Week

1. Audit your current metrics

   • List everything you currently track
   • Run each through the Signal Test
   • Stop tracking anything that fails

2. Set up your Signal Dashboard

   • Add the Three Metrics That Matter
   • Add your platform-specific primary signal
   • Add your conversion funnel steps

3. Run a cohort analysis

   • Group your last 6 months of content into monthly cohorts
   • Compare performance
   • Identify what changed in high-performing months

This Month

• Calculate Revenue Per Follower
• Identify your top 10% performing content (by conversion, not engagement)
• Create a hypothesis for why it performed better
• Test that hypothesis with new content

Remember: You can't improve what you can't measure. But measuring the wrong things is worse than measuring nothing.

Find your signal. Ignore everything else.