There's an easy trap that even businesses with years of experience running their social media fall into: confusing content that gets liked with content that actually works. They're related, but not the same thing, and the difference between measuring one or the other can lead to completely wrong content decisions, month after month, without anyone realising why sales aren't keeping pace with social media activity.
Likes, comments and shares are real signals, not to be dismissed entirely, but they mainly measure one thing: whether the content entertained or moved whoever saw it at that moment. They don't measure whether that person remembers your brand a week later, whether they trust you more than before, or whether they're any closer to buying something from you. For that you need other metrics, less flashy but far more honest.
The first question: what is that specific piece of content actually for?
Before deciding which metric to look at, you need to be clear about the content piece's function, because not all content pursues the same thing. Discovery content (meant to reach new people who don't know you) is measured mainly by reach and by how many new people interacted with the brand for the first time. Consideration content (meant to make people who already know you trust you more) is better measured by watch time, saves and comments with real intent (questions, doubts, not just emojis). Conversion content (meant to sell directly) is measured by link clicks, website visits and, ultimately, sales attributable to that piece.
The underrated metric: saves
On platforms like Instagram, the number of people who save a post (to come back to it later) tends to be a more reliable signal of real value than the number of likes, because it implies a different intent: "this is useful to me, I want to find it again" versus "I liked this for a second while scrolling". Content with few likes but many saves usually indicates useful content (a guide, a checklist, a tutorial) that the platform, moreover, tends to reward with more organic reach precisely because of that lasting-value signal.
The metric almost nobody looks at: time to conversion
Almost no content converts instantly, especially in mid-to-high-ticket businesses. Someone watches your video on a Tuesday, does nothing at that moment, and buys three weeks later after seeing several more pieces. If you only measure sales that happen the same day someone interacted with a piece of content, you're ignoring most of your content strategy's real effect. Tools like Google Analytics 4 let you see "conversion paths" that include earlier interactions, not just the last one, and reviewing that report gives a much fuller picture of which content is actually influencing purchase decisions, even when it isn't the last touchpoint before buying.
How to connect content with sales without a complex system
You don't need a sophisticated attribution system to start seeing this relationship. Three simple steps help a lot: using different tracking parameters (UTMs) for each relevant piece of content, so you can distinguish in Analytics where each visit came from; asking in the sales process or contact form "how did you hear about us?" with specific content options ("I saw a video on Instagram", "I read a blog post"); and reviewing monthly which specific content generated the most of those visits or mentions, instead of only looking at the account's overall performance.
An example of how the conclusion changes when you look properly
A natural cosmetics brand published humorous content about its sector, which generated huge reach and fun comments, alongside educational content about ingredients, which barely generated visible interaction. Looking only at likes and comments, the obvious conclusion would have been "the humour content works, the educational one doesn't". Reviewing the actual conversion paths, we found that the educational content, while generating less public engagement, appeared in 60% of new customers' purchase paths, while the humour content generated reach but practically no subsequent conversion. The correct decision was the opposite of the intuitive one.
When "vanity metrics" genuinely do matter
This doesn't mean likes, reach and impressions are useless. For pure brand-awareness goals (getting more people to know you, even if they don't buy right away), those metrics are relevant and shouldn't be dismissed. The problem arises when they're used as the only yardstick for all content, including content with a clear commercial goal, where they should carry much less weight than real conversion metrics.
A case where the video with fewer likes sold the most
An online skincare store published two videos the same week: one showing a customer's aesthetic "before and after" following a treatment (lots of motion, good music, 4,200 likes), and another explaining in detail why a certain active ingredient shouldn't be combined with two other specific products in the catalogue (no music, no flashy editing, 180 likes). Comparing engagement alone, the obvious conclusion would have been to invest in more of the first type.
Reviewing the UTM-tracked links for both videos over the following three weeks, the educational video generated more than double the qualified traffic to the relevant product page, and a conversion rate 90% higher than the before-and-after video's. The likely explanation is that whoever arrived looking to resolve a specific doubt about ingredient compatibility was already much closer to buying than whoever simply enjoyed a nice video with no active purchase intent behind it.
How to build a simple tracking panel so you don't miss this kind of signal
You don't need a complex system to start cross-referencing engagement with real results. A spreadsheet with one row per relevant piece of content, and columns for likes, saves, link clicks (with UTM) and sales attributed over the following two weeks, already lets you spot over time which type of content generates flashy engagement but little business, and which type does exactly the opposite, even if it looks less successful at first glance.
How to present these metrics to someone who only understands likes
A common challenge within teams themselves is explaining to a partner, boss or client used to looking only at likes why a piece with less visible engagement can be more valuable. The most effective approach isn't a theoretical speech, it's a direct side-by-side comparison: showing the two pieces of content with their visible engagement, and next to each, the qualified traffic or attributed sales figure it generated. Seeing the contradiction with your own data convinces far faster than any abstract explanation of why vanity metrics aren't the whole story.
Why qualitative metrics matter too, even if they're harder to measure
Not everything that matters can be reduced to a number in a spreadsheet. The tone of comments, the type of questions a piece generates, or whether a piece of content starts getting mentioned spontaneously by customers in sales conversations are qualitative signals that complement hard data, and that sometimes anticipate a shift in brand perception before it shows up in traffic or conversion figures. Ignoring the qualitative entirely, just because it isn't as easy to quantify as a click, leaves out a real part of the available information.
Common mistakes when trying to measure content beyond likes
The first mistake is trying to build a perfect measurement system before starting, which usually ends up stalling any attempt for seeming too complicated. It's better to start with the basics (UTMs on links, a "how did you hear about us?" field) and refine the system over time, than to wait until everything is sorted before measuring anything. The second mistake is measuring only during the peak-enthusiasm period right after publishing a piece (the first two or three days) and discarding the rest of the effect that happens weeks later, especially in businesses with a long decision cycle. The third mistake is not sharing these findings with whoever creates the content: little use comes from discovering that a certain type of piece converts better if that information never reaches whoever decides what to publish next week.
Frequently asked questions
How long should I wait to know if a piece of content "worked" in terms of sales?
It depends on your business's sales cycle. For quick-purchase products, about two weeks is usually enough. For longer-decision services (training, consulting, high-ticket purchases), it's worth waiting between one and three months before concluding a piece of content had no effect on sales.
Should I stop publishing purely entertaining content if it doesn't sell directly?
Not necessarily. Entertainment content serves a reach and awareness function that lays the groundwork for conversion content. The key is not relying on it alone, and balancing it with content that has a clearer role along the path to a sale.
What free tools help measure this without spending on expensive software?
Google Analytics 4 (free) for conversion paths and traffic source, a UTM link generator (also free) to tag each piece of content, and a simple "how did you hear about us?" field on any contact form or checkout. With those three elements, you already cover most of the essentials.
Do comments count as a signal that content is working?
It depends on the type of comment. Comments with real questions about the product or service are a strong signal of genuine interest; comments that are just emojis or "how pretty" are a much weaker signal, even though they add to the total interaction count.
How do I measure the effect of content with no link at all, like an educational carousel?
With indirect metrics: saves, watch time, and above all the trend in brand searches (people searching your name directly on Google) in the weeks following that kind of post, which is usually a reasonable indicator that the content is generating interest that later turns into active search.
Is it a mistake to focus on a single post's performance, or should I look at longer trends?
It's a common mistake. A single piece's performance can depend on one-off factors (time of posting, that day's algorithm, the news cycle). Reliable conclusions come from looking at sustained patterns across several pieces of the same type over several weeks or months, not from an isolated post.