Cross-Platform Analytics

What metrics to compare across platforms and what not to. The metrics that translate vs the ones that don't. The portfolio scorecard.

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Lesson 1: Metrics That Translate Across Platforms

Some metrics are roughly comparable across platforms. Use these for cross-platform decisions.

Engagement rate per post

Likes + comments + shares + saves divided by impressions. A normalized measure.

Why it translates: every platform has these signals; the ratio is comparable.

Caveats: "saves" only exist on some platforms. "Boosts" on Mastodon are different from "shares" on LinkedIn. Don't compare nominal counts; compare ratios.

Follower growth rate

Net follows per week or month, as a percentage of starting followers.

Why it translates: every platform has followers; growth rate is platform-agnostic.

Caveats: lagging indicator. Tells you what your content was doing 2-4 weeks ago, not now.

Reach

How many unique people saw your content.

Why it translates: every platform reports reach (or impressions divided by repeat-views).

Caveats: Mastodon's reach is harder to measure (no centralized analytics).

Conversion-adjacent metrics

Profile visits, link clicks, DM volume — anything that signals intent to engage further.

Why it translates: these correlate to business outcomes regardless of platform.

Caveats: different platforms surface different conversion-adjacent signals. Compare what each gives you.

Lesson 2: Metrics That Don't Translate

Some metrics look comparable but aren't. Avoid the trap.

Raw follower count

A 10K X account isn't equivalent to a 10K LinkedIn account. Audiences are differently composed, engagement is different, conversion power is different.

The trap: declaring one platform "better" because the follower count is bigger.

The fix: measure value-per-follower, not raw count.

Likes / favorites

Likes mean different things on different platforms:

  • X like: mostly bookmark-style, doesn't drive distribution.
  • Bluesky like: similar.
  • Mastodon favorite: private, doesn't distribute at all.
  • LinkedIn like: modest distribution signal.
  • Instagram like: modest signal.

Comparing raw likes across platforms is meaningless.

Reply / comment counts

A reply on X often means engagement; a reply on Mastodon often means substantive conversation. Same word, different stakes.

The trap: "I get more replies on X than LinkedIn, so X is better."

The fix: look at quality, not just count.

Watch time

Reels watch time on Instagram vs Reels watch time on Facebook vs native video on X — the platforms measure differently.

The trap: comparing seconds-watched as if they were equivalent.

The fix: watch-through rate (percentage who watched past 3s, 25%, 75%) is more comparable than total watch time.

Hashtag reach

Hashtags on Mastodon route differently than hashtags on Instagram than hashtags on LinkedIn. Direct comparison doesn't work.

Lesson 3: The Portfolio Scorecard

The right way to evaluate cross-platform performance: a portfolio scorecard, not a leaderboard.

The scorecard structure

For each platform, track:

  1. Cadence consistency. Are you hitting your committed cadence?
  2. Engagement rate. Trend over 30 days.
  3. Follower growth. Net follows per week.
  4. Conversion proxy. Profile visits, link clicks, DM volume.
  5. Effort hours per week. How much time you spent.
  6. Output count. How many posts.

What you're looking for

Three things:

  1. Per-platform health. Each platform compared to itself over time. Trending up, flat, or down?
  2. Effort-to-output ratio. How much time per platform vs how much it's producing?
  3. Strategic alignment. Is the platform driving the outcomes that matter to your business?

The portfolio decision

Each quarter, evaluate:

  • Which platforms are earning their effort?
  • Which platforms are auto-pilot maintenance with no upside?
  • Which platforms have surprised you (good or bad)?
  • Where should you reinvest the next quarter's effort?

This is portfolio management. Most creators don't do it; most underperforming portfolios don't get fixed.

Bolta's role

Analyst (the Bolta agent) generates per-platform summaries automatically. You don't need to compile metrics manually:

  1. Weekly summary across all platforms.
  2. Per-platform health signals.
  3. Anomalies flagged (e.g., engagement dropped on Bluesky this week).
  4. Suggestions for adjustment.

Use Analyst's reports as the input to your quarterly portfolio review.

Common myths

  1. "More platforms = more reach." Only if each is well-tended. Otherwise it's just dilution.
  2. "I can't compare platforms." You can — using ratios and trends, not raw counts.
  3. "Analytics are noise." They're noise if you watch daily. They're signal if you watch quarterly.

Action Steps

  1. Build a portfolio scorecard for the platforms you're on. List each platform; fill in metrics from the last 30 days.
  2. Identify which platforms are earning their effort. Honestly.
  3. Decide one shift for the next quarter — drop a platform to maintenance, double down on a winner, or test a new one.

Cross-platform analytics are about portfolio decisions, not platform comparison. Use them deliberately. Last lesson: building a 1-hour-a-week workflow.