Social Signals Are Data. Most Marketers Treat Them as Decoration

Social signals are the behavioural and engagement cues that audiences generate around your brand across social platforms: shares, saves, comments, mentions, and the patterns underneath them. When read correctly, they tell you what your market actually thinks, not what you hoped they would think.

The problem is that most marketing teams collect social signals as proof of activity rather than as intelligence. They report on them. They screenshot them for presentations. They rarely use them to make better decisions.

What Are Social Signals and Why Do They Matter Commercially?

A social signal is any measurable action a person takes in response to content or brand presence on a social platform. Likes, comments, shares, saves, reposts, story replies, profile visits after a post, and direct messages all qualify. So do the absence of those things, which is often more instructive than the presence.

The commercial relevance of social signals is frequently undersold. Teams focus on follower counts or reach figures because they are easy to report. But the more useful question is: what is the signal beneath the metric? A post that generates 200 comments asking “where can I buy this?” is worth more than a post that generates 2,000 passive likes. One signals latent demand. The other signals mild entertainment.

I spent a long time earlier in my career overvaluing lower-funnel performance data and undervaluing the softer signals that sat upstream of it. The performance numbers looked clean. The attribution models were tidy. But they were mostly capturing intent that already existed rather than creating new demand. Social signals, read properly, are one of the few places where you can see demand forming before it converts. That distinction matters enormously when you are trying to grow a business rather than just harvest it.

If you are thinking about how social signals fit into a broader go-to-market approach, the Go-To-Market and Growth Strategy hub covers the wider framework these signals should feed into.

Which Social Signals Actually Mean Something?

Not all signals carry equal weight. Treating every engagement type the same is one of the most common analytical mistakes I see in marketing teams, including experienced ones.

Here is a practical hierarchy worth working from:

Saves and bookmarks sit at the top. When someone saves a post, they are signalling intent to return. That is a considered action, not a reflexive one. On Instagram and TikTok in particular, save rates on product or educational content correlate meaningfully with downstream purchase consideration. If your content is generating high save rates, something in the message is landing with people who are in an active decision-making mode.

Shares and reposts come next. Sharing is advocacy. The person sharing is putting their own credibility behind your content. It extends reach organically, but more importantly it tells you which specific ideas or messages your audience finds worth attaching their name to. That is positioning data. The posts that get shared the most often reveal what your brand actually stands for in the market, as opposed to what you intended it to stand for.

Comments with substance are gold. Not “great post!” but the comments that ask questions, share experiences, push back, or add nuance. These are conversations. They tell you what your audience is thinking, what they need clarification on, and where your messaging has gaps. I have seen a single comment thread surface a product objection that a brand had been quietly ignoring for two years. The signal was there. Nobody was reading it.

Likes and reactions are the weakest signal. They require almost no commitment and reflect a wide range of emotional responses, many of which have nothing to do with purchase intent. Likes are useful for understanding broad content appeal, but they should not be driving strategic decisions on their own.

Mentions and tags, particularly unsolicited ones, are among the most commercially valuable signals of all. When someone tags your brand in a post you did not prompt, that is organic word-of-mouth. When they tag you in a complaint, that is a customer experience failure visible to their entire network. Both deserve attention.

How Do Social Signals Inform Go-To-Market Decisions?

The brands that use social signals well do not treat them as a reporting exercise. They treat them as a continuous market intelligence feed.

There are four specific ways social signals should be feeding go-to-market decisions:

Positioning validation. Your positioning is a hypothesis. Social signals help you test it in real time. If the messages you are leading with are generating engagement from the wrong audience, or generating no engagement at all from the right one, that is a signal your positioning needs work. I have seen brands spend months in positioning workshops and then launch campaigns that the market ignores. The feedback loop that social signals provide would have caught those misalignments weeks earlier.

Audience intelligence. The people engaging with your content are telling you who your real audience is, which is often different from the audience you assumed you had. Demographic and psychographic patterns in your comment sections and follower base can surface segments you had not prioritised. This is particularly useful when entering new markets or launching new products, where your assumptions about who cares are most likely to be wrong. Vidyard’s analysis of why go-to-market feels harder than it used to touches on exactly this challenge: the audience has fragmented, and the signals are less predictable than they were.

Content strategy refinement. Social signals tell you which content formats, topics, and tones are working, and which are being politely ignored. Over time, patterns emerge. A financial services brand I worked with was producing long-form educational content that generated almost no engagement. When we looked at what their audience was actually sharing and saving, it was short, specific, actionable posts on single topics. The long-form content was not wrong in principle. It was wrong for that platform and that audience at that moment. The signals had been saying so for months.

Competitive intelligence. Social signals are not just about your own brand. Monitoring the engagement patterns on competitor content tells you what their audiences value, where they are falling short, and where there might be unmet needs you can address. If a competitor’s posts about a particular product feature consistently generate frustrated comments, that is a positioning opportunity. BCG’s work on commercial transformation makes the point that growth-oriented organisations are systematically better at reading market signals than their peers. Social listening is one of the most accessible ways to build that capability.

The Difference Between Monitoring and Reading

Most brands monitor social signals. Very few actually read them.

Monitoring means collecting data. Reading means interpreting it in context and connecting it to a decision. The gap between those two things is where most of the value gets lost.

I remember sitting in an agency new business meeting years ago, looking at a prospective client’s social analytics dashboard. It was beautifully built. Reach, impressions, engagement rate, follower growth, all tracked weekly, all colour-coded. Nobody in the room could tell me what any of it had changed in the last six months. The data was being collected. It was not being used.

Reading social signals properly requires three things. First, a question you are trying to answer. Signals without a question attached are just noise. Second, a baseline to compare against. A 3% engagement rate means nothing unless you know what your category average is, what your historical average is, and what you would expect given the content type. Third, a feedback loop that connects the signal to a decision. If the signal does not change anything, you are not reading it. You are decorating a dashboard.

Tools like SEMrush’s overview of growth intelligence tools include social listening capabilities that can help systematise this process. But the tool is not the point. The discipline of asking “what does this tell us and what should we do differently?” is the point.

Social Signals and Creator Partnerships

One area where social signals are particularly underused is in evaluating creator and influencer partnerships.

Most brands still evaluate creators primarily on reach and follower count. These are the weakest possible inputs for predicting whether a partnership will drive commercial outcomes. The signals that matter are engagement quality, comment sentiment, save rates on similar content, and the alignment between the creator’s audience behaviour and your target market’s behaviour.

A creator with 80,000 followers whose audience saves 4% of their product posts is more commercially valuable than a creator with 800,000 followers whose audience passively scrolls. The signal tells you which one has built genuine influence over purchase decisions and which one has built an audience that watches but does not act.

Later’s work on go-to-market with creators explores how to build creator strategies that convert rather than just generate impressions. The underlying logic is the same: signals over vanity metrics.

There is a broader point here about reach and new audiences. I spent years watching performance budgets grow while brand budgets shrank, and the argument was always that performance was more measurable. It is. But measurability is not the same as effectiveness. Performance marketing is largely capturing demand that already exists. Social signals, particularly around content and creator activity, are one of the few places where you can see new demand being created in real time. That is where growth actually comes from.

Building a Social Signal Feedback Loop

A feedback loop is a process where outputs from one stage become inputs to the next. In the context of social signals, it means taking what you observe on platform and using it to improve what you do next, systematically and repeatedly.

Most teams operate in a linear way. They plan content, publish it, report on it, and then plan the next round. The signals from one cycle rarely inform the next in any structured way. A feedback loop changes that.

Here is what a basic version looks like in practice:

Step one: Define your signal questions. Before you publish anything, decide what you are trying to learn. Are you testing a new message? Validating audience interest in a product feature? Seeing whether a particular tone resonates? The question shapes what you look for.

Step two: Publish with intent. Each piece of content should have a hypothesis attached. Not just “this should perform well” but “we expect this framing to generate saves from our target segment because it addresses a specific concern they have expressed.”

Step three: Read the signals against the hypothesis. Did the expected signals materialise? If not, why not? What did the actual signals tell you that you did not expect? This is where most of the learning happens.

Step four: Connect to a decision. What changes as a result? This could be a messaging adjustment, a format change, a targeting refinement, or a product insight passed to another team. If nothing changes, the loop is broken.

Hotjar’s thinking on growth loops applies a similar logic to product and user experience. The principle transfers cleanly to social. The goal is compounding improvement, not one-off optimisation.

Forrester’s intelligent growth model makes a related point about how high-performing organisations build systematic learning into their commercial processes rather than treating it as an occasional exercise. Social signal feedback loops are a practical implementation of that principle at the channel level.

Common Mistakes in Reading Social Signals

A few patterns come up repeatedly when I look at how teams handle social intelligence, and most of them are avoidable.

Averaging across content types. Comparing a product post’s engagement rate to an entertainment post’s engagement rate tells you almost nothing. Different content serves different purposes and attracts different responses. Benchmark within content categories, not across them.

Ignoring negative signals. Low engagement is a signal. High reach with low engagement is a signal. Sudden drops in comment quality are a signal. Teams that only celebrate the positive numbers are reading half the data.

Treating platform signals as equivalent. A save on Instagram and a repost on LinkedIn are not the same thing. The platforms have different user behaviours, different content norms, and different commercial contexts. What counts as strong engagement on TikTok would look very different on LinkedIn. Read signals in their platform context.

Conflating brand sentiment with content performance. A post can perform well because the content is entertaining, not because it builds brand affinity. Viral content that has no connection to your positioning is not an asset. It is a distraction. The question is always whether the signals connect to something commercially meaningful.

Waiting for enough data. Social signals are most useful when they are acted on quickly. Waiting for statistical significance before making any content decision is the wrong framework for a channel that moves fast. Make provisional decisions, test them, and refine. The feedback loop only works if you are willing to act on incomplete information.

Where Social Signals Fit in a Broader Growth Strategy

Social signals are not a standalone discipline. They are one input into a broader intelligence picture that should include customer research, sales data, search behaviour, and competitive analysis.

The brands that use them most effectively are the ones that have built connective tissue between their social teams and their commercial functions. When a social team surfaces a recurring question in comments about a product feature, that should reach the product team. When it surfaces a consistent objection to a price point, that should reach the commercial team. When it reveals that a particular audience segment is engaging unexpectedly, that should reach the strategy team.

I have worked in agencies where the social team and the strategy team operated in almost complete isolation from each other. The social team produced content and reported on metrics. The strategy team produced plans and reported on frameworks. The signals that the social team was sitting on never made it into the strategic conversation. That is a structural failure, not a talent failure.

The broader go-to-market context matters here. BCG’s analysis of go-to-market strategy in complex markets highlights how commercial decisions made without adequate market intelligence consistently underperform. Social signals are accessible, real-time market intelligence. Not using them in commercial decisions is a choice, and not a good one.

There is more on how to connect channel-level intelligence to commercial planning across the Go-To-Market and Growth Strategy hub, which covers the broader framework these decisions sit inside.

About the Author

Keith Lacy is a marketing strategist and former agency CEO with 20+ years of experience across agency leadership, performance marketing, and commercial strategy. He writes The Marketing Juice to cut through the noise and share what works.

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