Follows and likes are the most honest form of intent data
Think about what a follow actually means. Someone saw an account, read the bio or scrolled the feed, and decided: "I want to see more of this." That is a micro-commitment. It is small, but it is real. Nobody follows an account by accident.
Now consider context. When a Head of Partnerships at a DTC brand follows three creator economy accounts in the same week, that is not random browsing. That is research. When an investor likes a startup's product launch post, that is not idle scrolling. That is interest.
The problem is not that these signals lack value. The problem is that nobody is watching for them systematically. Marketing teams track impressions. Sales teams track form fills. Nobody tracks who specifically followed a competitor and what that might mean.
Five real-world intent signal scenarios
The theory is nice. The examples are better.
Scenario 1: The VP researching vendors. A VP of Sales at a mid-market SaaS company follows three sales enablement tools on X in the same month. She is building a shortlist. If you sell sales enablement software, she is in-market right now.
Scenario 2: The investor circling. A partner at a venture fund likes a founder's post about their Series A metrics. He does not follow the founder. He does not comment. He just likes. That is a quiet signal - he is paying attention without committing publicly. If you are that founder, you want to know about that like.
Scenario 3: The brand manager scouting. A brand partnerships director at a consumer brand follows five creators in your niche over two weeks. She is building a shortlist for a campaign. If you are one of those creators, great. If you are not, you still want to know she is active.
Scenario 4: The competitor's customer migrating. A customer of your biggest competitor starts following you and liking your product posts. They are comparing. They might be unhappy. That is a warm lead you did not have to pay for.
Scenario 5: The talent signal. An engineer at a competitor follows your company account and your CTO. She might be exploring opportunities. Your recruiting team should know before she updates her LinkedIn.
Why traditional intent data misses these signals
Traditional intent data providers track content consumption across the web. They know that "Company X" is reading articles about project management software. That is useful, but it has three serious limitations.
First, it is account-level, not individual-level. You know the company is researching, but not who at the company is doing the research. You still need to figure out the right person to contact.
Second, it is delayed. Intent data is aggregated weekly or biweekly. The research might be over by the time you see the signal.
Third, it is inferred from browsing behaviour - cookies, IP matching, content consumption patterns. It is probabilistic, not explicit. Social media intent signals are the opposite: they are individual, real-time, and explicit. A person chose to follow or like. There is no probabilistic model. There is no aggregation delay. The signal is the action, and the action is public.
The 24-hour window on social signals
Social media intent signals have a short shelf life. A follow is most meaningful in the first 24-48 hours. After that, the person has moved on. They followed 20 other accounts. Your content is buried in their feed. The moment of active interest has passed.
This creates a speed imperative that most teams are not built for. If your process is: marketing sends a weekly report, sales reviews it on Monday, reps reach out by Wednesday - you have already missed the window. The signal was live on Thursday. You are acting six days later.
The teams that extract value from social intent signals are the ones that close the gap between signal and action to under 24 hours. That means automated monitoring, real-time classification, and daily surfacing of matches. Not weekly reports. Not monthly reviews.
How to systematically capture social intent signals
You cannot do this manually at scale. Checking a competitor's follower list every morning, scrolling through post likers, trying to remember who is new - that is not a system. That is hope.
The systematic approach has three components. First, define who you are looking for. Not "everyone" - specific criteria. Investors. Brand directors. Engineering leaders at companies over 500 employees. Decision-makers in your target vertical. The more specific, the less noise.
Second, define where to look. Your own account catches people interested in you directly. Competitor accounts catch people interested in your category. Industry thought leader accounts catch people interested in the broader space. Each source tells you something different.
Third, automate the monitoring and classification. Catch The Good Ones does this for X - you add the public accounts you want to monitor, define your criteria using customisable AI-powered filters in plain English, and the system classifies every new follower and liker against those filters daily. Matches surface on your dashboard. No manual checking. No missed signals.
Turning a like into a conversation
The worst thing you can do with a social intent signal is send a cold pitch. "Hey, I noticed you followed our competitor. Want a demo?" That is surveillance, not sales.
The right approach treats the signal as context, not as an opening line. You know this person is interested in your category. You do not tell them you know. Instead, you engage naturally.
Look at their recent posts. Find something you genuinely have a perspective on. Reply with value - an insight, a question, a relevant experience. Do this two or three times over a week. When you eventually send a DM or connection request, you are not a stranger. You are someone they have seen in their replies. Someone who adds to conversations instead of extracting from them.
The intent signal gave you timing. What you do with that timing determines whether it converts into a relationship or a muted notification.