Ask ten social media managers which metrics they report and you will get ten different answers, most of which are chart exports rather than measurements. Impressions go up, so impressions are good. A post got 4,000 likes, so it was a success. Followers grew by 300, so the month went well. Each of those numbers is real. None of them tells you whether the work is doing anything for the business.

This is not an argument for abandoning everything soft in favour of revenue attribution, which is its own kind of fantasy on social media. It is an argument for matching each number to a question someone actually asked. Impressions answer "did anyone see it". Saves answer "was it useful". Profile visits answer "did it make them curious about us". Link clicks answer "did it move them one step closer". Revenue answers "did any of this pay for itself". Reporting a number that answers no question is decoration.

In this post we work through the whole measurement stack. First, how to start from a business question instead of a dashboard. Then the small set of metrics worth reporting weekly, broken down by the job each post is doing. Then the platform-by-platform picture of what "good" looks like in 2026, because a 2 percent engagement rate is excellent on one network and mediocre on another. After that: the metrics that do not matter and the few situations where they briefly do, how to write a report a human will read, a decision tree for diagnosing a sudden drop, honest attribution without inventing credit, and how to wire the whole loop into PostHub so the analysis changes what you publish next week.

A metric earns its place on the report only by answering a question someone is going to act on.

Start with the question, not the dashboard

Every reporting failure starts the same way: someone opens the analytics screen and writes down what is visible. The fix is to reverse the order. Write the question first, then find the smallest number that answers it.

The five questions worth asking

In practice, social teams are asked one of five things. Find yours before you choose anything.

• Are we reaching new people? Answered by reach, non-follower share of reach, and follower growth rate — not by impressions.
• Is our content worth remembering? Answered by saves and shares per post, and by return-visitor behaviour if your platform reports it.
• Do people trust us enough to talk? Answered by comment rate, comment sentiment, DM volume, and reply times.
• Does attention turn into action? Answered by profile visits, link clicks, click-through rate, and assisted conversions.
• Is the effort worth the cost? Answered by output per week against hours spent, or cost per result for paid.

A single weekly report can answer two of these well. It cannot answer five. Most reports fail because they attempt all five with one screenshot and end up answering none.

Assign a primary metric per post before you publish

The cheapest upgrade to any analytics practice: decide what each post is for at creation time. Not after. If you know the Tuesday carousel is an education post, you read its saves and shares. If the Thursday reel is awareness, you read its reach and three-second view rate. If the Friday link post is conversion, you read clicks.

This changes behaviour as much as it changes reporting. Writers who know a post is measured on saves write differently — they make something worth returning to — than writers who know it is measured on likes, who learn to write something easy to like.

Write your targets as ranges from your own history

Benchmarks published by other people are orientation, not goals. Your goal should come from your own last ninety days. Take the median, then set a target band around it:

• Weak: below your own 40th percentile
• Normal: your 40th to 70th percentile
• Strong: above your 70th percentile

You are now comparing your posts to your own best work, which is the only comparison that produces useful decisions. External benchmarks tell you whether you are average in the world. Internal percentiles tell you whether Tuesday's post was better than last Tuesday's.

Question-first checklist

• The business question written in one sentence before any data is pulled
• One primary metric chosen for that question, plus at most two supporting ones
• Each post labelled with its job at creation time, not in retrospect
• Targets expressed as bands derived from your own 90-day history
• Report length capped — if it takes more than five minutes to read, it is too long
• Every number on the page has a decision attached to it

The six metrics worth reporting every week

If you report nothing else, report these six. Together they cover reach, quality, curiosity and action without a single vanity number.

1. Non-follower reach

Total reach tells you how many people saw you. Non-follower reach tells you how many of them were new. For accounts trying to grow, the non-follower share is the more honest number — a post seen only by people who already follow you may be excellent for retention and useless for growth. Watch the ratio: if 95 percent of your reach is followers, the algorithm is treating you as a community account rather than a discovery account, which is a strategic signal, not a failure.

2. Saves and shares per post

These are the two strongest content-quality signals available to ordinary accounts. A share says "I want a friend to see this". A save says "I want to see this again". Both require intent; a like requires almost none.

Report them as a rate rather than a raw count, because raw counts punish your smaller posts and hide the shape of your work:

save rate  = saves / reach * 100
share rate = shares / reach * 100

Example: a post reached 8,400 accounts, was saved 168 times
         save rate = 168 / 8,400 * 100 = 2.0%

Interpretation: roughly 1 in 50 people who saw it kept it.

A save rate around 1 percent is respectable for most feed content; 2 percent and above usually means the content taught something or offered a reference worth keeping. Compare like with like — a how-to carousel and a brand announcement are not competing on the same axis.

3. Engagement rate, defined precisely

Never report "engagement rate" without stating the denominator. Three common versions produce three different numbers from the same post.

• By reach: engagements divided by reach. Best for judging content quality, because it measures the people who actually saw it.
• By impressions: engagements divided by impressions. Useful only when you are comparing placements where repeat exposure matters.
• By followers: engagements divided by follower count. The oldest formula, still used for sponsor rate cards, and the least informative — it punishes you for having an audience that does not see every post.

engagement rate by reach =
  (likes + comments + shares + saves) / reach * 100

Report reach-based engagement rate as your default and state the formula on the report so nobody has to guess. Ranges you will typically see on feed posts in 2026: under 0.5 percent is weak, 0.5 to 1.5 percent is ordinary, 1.5 to 3 percent is strong, and above 3 percent is either genuinely excellent or the product of a very small denominator.

4. Profile visits and the visit-to-follow conversion

Profile visits are the "curiosity" metric, and they are quietly one of the most useful numbers you will ever track. A post that drives 600 profile visits from people who do not follow you is doing more for growth than a post that gets 3,000 likes from your existing audience.

Pair it with conversion:

follow conversion = new followers / profile visits * 100

Example: 600 profile visits, 54 new followers
         follow conversion = 54 / 600 * 100 = 9%

A conversion below about 4 percent says your bio, pinned post or grid does not deliver on what your content promises. A conversion above 10 percent says the opposite — and usually means you should publish more of whatever drove the visits, even if that content looks modest in the likes column.

5. Link clicks and click-through rate

For any account with a business model that happens off-platform, this is the number closest to money. Track clicks on the post and, where you can, what happened after the click — because the gap between "clicked" and "did something" is where most landing pages quietly fail.

• Click-through rate = clicks / reach for link posts
• Landing conversion = desired action / clicks
• Cost per result = spend / desired action, for paid posts

If click-through is healthy and landing conversion is poor, stop optimising the caption. You have a website problem wearing a social media costume.

6. Follower growth rate, monthly

Raw follower count is a snapshot and means almost nothing. Growth rate is a trend and means plenty.

monthly growth rate =
  (followers end - followers start) / followers start * 100

Example: 10,000 at the start, 10,300 at the end
         growth rate = 300 / 10,000 * 100 = 3.0% per month

For accounts in an active growth phase, 2 to 5 percent per month is a healthy band. Under 1 percent with steady output suggests reach is trapped inside the existing audience. A sudden spike without a corresponding spike in non-follower reach usually means something other than content worked — a paid boost, a mention from a large account, or a follow-for-follow pattern you should not repeat.

Weekly six checklist

• Non-follower reach, and the share of total reach it represents
• Save rate and share rate, both calculated against reach
• Engagement rate by reach, with the formula printed on the report
• Profile visits plus visit-to-follow conversion
• Link clicks plus click-through rate where links are in play
• Monthly follower growth rate, never raw follower count alone

Metrics by post job

A single account publishes for several reasons at once, and averaging them together destroys the signal. Split your report by job.

Awareness posts

These exist to be seen by people who do not know you. Read: reach, non-follower share, three-second view rate on video, and follower growth on the day of publishing. Ignore likes — a like from an existing follower costs you nothing and teaches you nothing about discovery.

Education posts

These exist to be useful enough to keep. Read: saves, shares, and average watch depth on video. The secondary signal is comment quality: people asking follow-up questions are a stronger indicator than people saying "great post". If your education posts get high reach but low saves, you are entertaining rather than teaching.

Community posts

These exist to make people feel part of something. Read: comment count, comment sentiment, repeat commenters (the same names returning week after week), and DM volume in the following 24 hours. Reach is close to irrelevant here; a community post seen by 900 of your most loyal followers and answered by 60 of them is a better result than one seen by 90,000 and ignored.

Conversion posts

These exist to move someone one step closer to buying, signing up or booking. Read: clicks, click-through rate, landing conversion, and assisted conversions over a seven-day window. Judge these over weeks, not per post — a single link post tells you almost nothing, while twelve of them across a month reveal a clear trend.

Retention posts

These exist to keep current customers engaged. Read: repeat engagement from existing followers, comment sentiment, and — if you can capture it — support deflection, meaning whether posts about common questions reduce the volume of those questions arriving in your inbox.

What "good" looked like in 2026, platform by platform

Numbers without context are noise, and context changes constantly. The ranges below are orientation for planning, drawn from what accounts we work with typically observe on organic feed content. Treat them as starting hypotheses and replace them with your own percentiles within a quarter.

• Instagram. Feed posts commonly land between 0.4 and 1.5 percent engagement by reach; carousels consistently outperform single images on saves, often by a factor of two or more. Reels reach far beyond followers, so judge them on reach and three-second view rate rather than engagement. Profile visits per reach are the number that predicts growth.
• TikTok. Reach distributions are extremely wide and unstable, so medians over ten posts are more meaningful than any single result. Watch completion rate and the average watch percentage; a video that holds 60 percent of viewers to the end is working even if its likes look modest. Comment rate is high relative to other platforms because commenting is low-effort there.
• LinkedIn. Text-first posts with short paragraphs typically outperform link posts by a wide margin. Comment rate matters more than like rate — the algorithm reads comments as distribution fuel. Dwell time on a document post is worth watching if you publish carousels or PDFs.
• YouTube. Shorts live or die on the first three seconds and on average percentage viewed; subscriptions driven per video are the metric that compounds. Long-form shifts to click-through rate on the thumbnail combined with average view duration, and both must be read together — a high click-through with a terrible retention rate means you are misleading people.
• Facebook. Reach inside existing audiences is the norm; judge page posts by engagement by reach and by clicks if links are central. Group posts should be measured on comments and repeat participation, not reach, because groups are conversational surfaces.
• X and Threads. Reply count and profile visits dominate as signals; impressions are the easiest number to inflate and the least predictive of anything. Shares and bookmarks (where available) are your quality filters.

The comparability warning

Never put an Instagram engagement rate and a TikTok engagement rate in the same row of a comparison and declare a winner. Different formats, different feed mechanics, different norms of what a "like" means. Compare each account against itself, month over month, and only compare across platforms when you are deciding where to invest next — and then use the job-specific metrics from the previous section, not a single blended rate.

The metrics that do not matter (and when they briefly do)

Being blunt about this section is the point of the post.

• Raw follower count. It is a scoreboard for a game nobody is playing. It includes dormant accounts, bots and people who followed you in 2021 for a giveaway. Read it only as the denominator for growth rate.
• Impressions on their own. Impressions count deliveries, including the same person four times. Without reach, or without a click or engagement attached, an impressions number is a claim about nothing.
• Total likes per month. Heavily volume-dependent and easy to buy. If your posting frequency doubled and likes doubled, you learned only that frequency scales likes.
• Reach without a rate. "We reached 1.2 million people" is meaningless until you know how many accounts you reached, how many were new, and what they did.
• Vague sentiment scores. A single number from -100 to +100 that nobody can trace back to specific comments is a number nobody can act on. Read the actual comments.
• Follower growth spikes with no source. Unless you can name the cause, a spike is an event, not a trend.
• Any metric with no decision attached. The real test: if the number moved 20 percent in either direction, would you do something differently tomorrow? If not, delete it.

When vanity metrics briefly earn their place

Two exceptions, both narrow.

• Follower count for partnerships. Brands and creators still negotiate from audience size, so keep an eye on it if you sponsor or are sponsored — but pair it with a real engagement rate, because anyone experienced will ask for the rate immediately after.
• Impressions during a launch window. In the first 72 hours of a campaign, raw visibility has genuine value because you are trying to get the message in front of as many people as possible before the price change or the sale ends. Even then, pair it with clicks.

The pattern in both cases is the same: a soft number becomes briefly legitimate when it is tied to a specific decision with a deadline. Outside that, it is comfort.

Ask one question of every number on your report: what would I do differently if this moved by a fifth? Nothing you would change is not a metric, it is a mood.

Building a report people actually read

The best analysis in the world is worthless if the document gets skimmed once and never opened again. Reports die from length, format and vagueness — all three fixable.

The one-page weekly structure

Five blocks, in this order, capped at one page:

1. 1Headline. One sentence on what happened and why. "Non-follower reach up 18 percent, driven by three how-to reels; link clicks flat." A reader who stops here has the news.
2. 2The six. Your weekly six numbers, each with a direction arrow and the previous week's value for comparison. Numbers without a comparison are trivia.
3. 3Top three posts with the job label and the primary metric for that job, one line of explanation each. Include one failure — readers trust reports that contain a loss.
4. 4One diagnosis. Something you noticed that needs a decision, phrased as a question if the decision belongs to someone else.
5. 5Three actions for next week. Specific enough to be checkable: "Cut link posts from three per week to one and move the clicks target onto the newsletter instead."

Write findings, not descriptions

The difference between a useful report and a data dump is a single editing pass. Take each line and ask whether it describes a number or states a finding.

• Description: "Reels generated 42,000 impressions."
• Finding: "Reels generated 42,000 impressions from 86 percent non-followers, so they are our only real discovery format — we should triple the volume."
• Description: "Engagement was 1.4 percent."
• Finding: "Engagement fell to 1.4 percent because our two announcement posts dragged the median; education posts were unchanged at 2.6 percent."

Same data. Only the second version can change someone's afternoon.

Report cadence that matches the decision

• Weekly: the six numbers, top posts, next week's actions. Read in five minutes.
• Monthly: trends, percentiles, what to stop doing, and a review of your worst five posts. Read in fifteen minutes.
• Quarterly: channel-level investment decisions, content mix changes, and whether the audience you are reaching is the audience you want. This is where platform comparisons finally belong.

Anything more frequent than weekly invites reactive changes based on noise. Anything less frequent than monthly means you discover problems two months late.

Report checklist

• One page, five blocks, five-minute read
• Every number shown with a prior-period comparison
• Posts grouped by job, not lumped into one blended average
• At least one failure included, with an explanation
• Findings written as sentences with consequences, not as chart labels
• Exactly three actions for the next period, each checkable
• Vanity metrics excluded unless tied to a dated decision

Reporting to clients and stakeholders

If you report for clients, or upward to a founder who does not live in the tools, the report carries a second job: it has to explain a bad month without either hiding it or catastrophising it. Three habits make that possible.

Agree the yardstick before the first report

At onboarding, write down three things: the primary metric for each post job, the reporting period, and what a bad month looks like in advance. A client who has agreed that education posts are judged on saves will not ask why a save-light week underperformed on likes. Expectations set in week one prevent about eighty percent of the difficult conversations in month six.

Show the trend, then the cause, then the correction

Every uncomfortable number follows the same three-beat structure. The trend: "Reach fell 22 percent month over month." The cause: "We published six times instead of eleven, and our two announcement posts reached only existing followers." The correction: "Volume returns to eleven next week and the announcement format moves to a single carousel instead of three single-image posts." Never present a problem without a named correction, and never present a correction without the number it is meant to move.

Curate ruthlessly in both directions

Leave out anything that flatters without informing. A spike caused by one viral post belongs in the report only if you can explain what to repeat. Equally, leave out metrics the client cannot act on — impressions in isolation, sentiment scores, platform-by-platform engagement league tables — because each of them invites a conversation that leads nowhere. If a stakeholder asks for a number you believe is misleading, provide it once with a one-line explanation of what it does not prove. Refusing outright creates suspicion; supplying it silently creates a bad decision.

Client report checklist

• Yardstick, period and definition of a bad month agreed at onboarding
• Trend, cause and correction stated for every unfavourable number
• At least one failure included with a named experiment attached
• Flattering-but-uninformative spikes excluded or explicitly explained
• Every requested number answered, misleading ones annotated
• Next period's three actions written as commitments, not aspirations

Diagnosing a drop: a decision tree

Numbers will fall. When they do, run the sequence below in order rather than guessing. Most "algorithm changes" turn out to be one of the first four causes.

1. 1Did our output change? Compare posts per week, format mix, and posting times against the prior period. A drop that follows a drop in volume is not a platform problem.
2. 2Did the audience mix change? If follower growth spiked, your new followers may be low-intent, which drags engagement-by-reach down even while reach rises. Check follower quality before touching content.
3. 3Is one post type carrying the blame? Recalculate the median excluding your two worst posts. If the number recovers, you have an outlier problem, not a trend.
4. 4Did reach fall or did engagement fall? This branch decides everything. Falling reach with stable engagement means distribution changed — test new formats and topics. Stable reach with falling engagement means the content stopped landing — test hooks, opening lines and formats, not posting times.
5. 5Did the platform change something? Only after the four above. Check the platform's own newsroom and announcement accounts, then look for corroboration across peer accounts. One account's drop is an anecdote; twelve accounts dropping on the same day is a change.
6. 6Is it seasonal? Compare against the same period last year if you have the data. Ramadan, summer holidays, election periods and end-of-quarter all move numbers in predictable directions for specific audiences.

Three weeks of tests, one variable at a time

Once you have located the branch, resist the urge to change five things. Pick one variable, run it for three weeks or twelve posts, and record the result. The highest-yield tests in order of typical payoff:

• Opening line of the caption — the single biggest lever on save and completion rates
• Format rotation, such as switching from single images to carousels
• Topic angle within the same format, tested against your caption bank
• Posting time, which matters far less than most teams believe
• Hashtag and keyword sets, which matter mostly for discovery surfaces

Keep a running test log — a simple dated list of what you changed and what happened. After six months it becomes the most valuable document your team owns, because it encodes what your specific audience responds to in a way no benchmark can.

Attribution without lying

Social media sits late in most buying journeys and early in most reports, and the mismatch produces a lot of invented credit. There are three honest ways to connect social to outcomes, in ascending order of difficulty.

Last-click attribution, used honestly

The simplest version: count the clicks and the conversions that follow within a short window. It under-credits social for people who saw you, came back later, and converted through search. Use it, state the limitation, and never present it as the full picture.

UTM discipline

Tag every link you publish with consistent parameters so analytics can see social as a source. Keep the naming flat and boring, because inconsistent tagging is worse than no tagging.

utm_source   = instagram
utm_medium   = social
utm_campaign = launch-sept-26
utm_content  = carousel-hooks

Review the tags monthly and delete any campaign name nobody uses. A report that says "38 percent of trials came from social" is defensible only if every social link was tagged the same way for the entire period.

Assisted conversions and the seven-day window

Look at how often social appears anywhere in the path, not just at the end. Most analytics tools can show assisted conversions; the pattern you are hoping to see is social appearing early and often. If social shows lots of assists and few last-click conversions, that is not a failure — it means social creates demand that search collects, which is a completely normal and valuable role.

The questions attribution cannot answer

Be explicit about the limits in your report. Attribution will not tell you whether a comment section softened a sceptic's opinion, whether a founder's post recruited an employee, or whether being consistently present made you the default choice when someone finally had budget. Those effects are real and unmeasurable. Say so once a quarter so nobody mistakes the measured for the whole.

Wiring the measurement loop into PostHub

Measurement only changes anything when it closes the loop back to what you publish. Here is how to run that loop inside one workspace.

1. 1Connect every account you publish to, so reach, engagement and click data land in one place instead of six exports.
2. 2Label each post with its job as you schedule it — awareness, education, community, conversion — so the report can group by intent automatically rather than by gut feel.
3. 3Set the primary metric per job once, and let the dashboard compare each post against the right yardstick.
4. 4Review weekly using the five-block structure, pulling top posts straight from the ranked list rather than copying numbers by hand.
5. 5Carry one finding into the next week's calendar. If saves told you carousels win, next week's plan has one more carousel in it. If clicks told you link posts are dead weight, they come out.
6. 6Re-derive your percentile bands every quarter, because your own best work moves as you improve.

The value of doing this in one tool is not the dashboard. It is the absence of export. Every time a report requires a spreadsheet, the report eventually stops being written, and the team goes back to reporting likes. PostHub keeps publishing, inbox and analytics in the same workspace, which you can explore on the PostHub overview, with the plan limits showing how much historical data each tier retains — history length matters more than most teams expect when you are trying to spot a seasonal pattern.

The quarterly reset

Every ninety days, run a hard reset on your measurement:

• Delete any metric that has not changed a single decision in three months
• Re-derive percentile bands from the most recent 90 days
• Re-check that each post type still has a primary metric that suits it
• Re-read last quarter's failures and note which diagnosis would have caught them earlier
• Update the report template if it has grown past one page

Metrics accumulate the way clutter does. A deliberate reset four times a year keeps the report honest and short.

Key takeaways

• Choose metrics by the question you are asking; a number that answers no question is decoration, not data.
• Label every post with its job before publishing, then read the metric that matches that job instead of one blended average.
• Report a weekly six: non-follower reach, save rate, share rate, engagement by reach, profile visits with follow conversion, and monthly follower growth rate.
• Always state the denominator for engagement rate; reach-based engagement is the default that best reflects content quality.
• Use your own 90-day percentiles as targets — external benchmarks orient you, only internal history can set a goal.
• Drop raw followers, standalone impressions, monthly like totals and untraceable sentiment scores unless a dated decision depends on them.
• Diagnose drops in order: output, audience mix, outliers, reach versus engagement, platform change, seasonality — one variable per test.
• Attribute honestly with tagged links and assisted conversions, and say out loud which effects social has that no dashboard can show.

FAQ

Which single metric should I report if leadership will only accept one?

Profile visits. They sit in the middle of the funnel, respond to content quality within days, and predict growth better than likes or impressions do. Pair it with follower growth rate if you are allowed a second number, because visits show curiosity and growth shows whether you converted it. If the business is purely transactional, use link clicks instead — the best single metric is always the one closest to the decision the business is trying to make.

Is engagement rate by followers ever useful?

Rarely, and mostly for external comparisons. Sponsorship rate cards and some agency benchmarks are still calculated against follower count, so you may need to report it when someone asks a question in that language. Internally, prefer engagement by reach: follower-based rates punish you for the portion of your audience the platform did not show your post to, which is a distribution decision rather than a content decision.

How many posts do I need before a comparison means anything?

Roughly ten posts per variant for a content comparison and about twenty before you should trust a trend. With fewer than that, a single unusually good or bad post moves your median enough to fake a result. For low-volume accounts, compare monthly against the previous month rather than post against post, and always look at the median rather than the mean so one outlier cannot carry the conclusion.

Should I report follower growth when it is negative?

Yes, especially then. A shrinking audience with strong saves and reach is a healthy account pruning an uninterested list, which is survivable. A growing audience with falling profile-to-follow conversion means you are attracting people who will never buy, which is expensive. The report should always show growth rate alongside conversion so the direction can be judged properly rather than reacted to emotionally.

Do comments still matter if the algorithm has de-emphasised them?

They matter more for community and conversion than for reach. Comments are the clearest signal you have that someone is willing to be seen engaging with you, and for professional services, local businesses and considered purchases, a comment section full of real questions is a sales asset rather than a ranking factor. Measure comment quality and repeat commenters, not comment count, and stop treating them as an awareness metric.

How long should I wait before judging a new content format?

Give a new format at least twelve posts or three weeks, whichever is longer, and judge it on the metric that matches its job. A new carousel series should be judged on saves, not on how many followers it brought in during week one. If after twelve posts the format has not beaten your own median on its primary metric, retire it or change one variable — usually the opening slide — and run another twelve before abandoning it entirely.