Resonue Blog

How to spot a buying committee forming on LinkedIn, weeks before it reaches your CRM

Resonue — Fri, 18 Sep 2026 00:00:00 GMT

Take a 70-person company that sells scheduling software to freight forwarders. Between the first week of June and the middle of July, four people at the same mid-size forwarder reacted to the company's LinkedIn posts. An operations manager liked the CEO's post about detention fees. Two weeks later a dispatcher supervisor commented on a product marketer's post about driver no-shows. A few days after that, the head of IT liked the company page's integration announcement, and in July a regional director liked the CEO again.

Nobody at the software company noticed. Each of those reactions landed in a different person's notifications, on a different day, next to forty other names. The CEO saw "an ops manager somewhere liked my post." The product marketer saw one comment among nine. The company page admin doesn't read reaction lists at all.

In late August the forwarder filled in the demo form. The AE opened the CRM, found an empty account record, and started discovery from zero with a single contact, six weeks after four people across three departments had shown their hand.

This article is about not being that AE. The method is simple to describe: stop reading LinkedIn engagement person by person and start reading it company by company. It is tedious to run by hand, and we'll be plain about where it breaks, but you can start on Monday with a spreadsheet and nothing else.

The person is the wrong unit

Most advice about LinkedIn and sales is built around individuals. Someone liked your post, so send them a message. Someone viewed your profile, so connect. It treats each reaction as a small lead.

We think that's the wrong unit of analysis for anyone selling to companies, and the reason is arithmetic. A like costs the person who gives it about one second and no commitment. People like posts because the author is a former colleague, because they're job hunting, because they work for a competitor and are keeping tabs, because the post showed up while they were waiting for a coffee. Any one reaction has a dozen plausible explanations, and "this person's company is about to buy software like ours" is rarely the most likely one.

Meanwhile, the purchase you care about isn't made by a person. Forrester's 2024 survey of business buyers put the average at 13 people involved in a buying decision, with 89% of purchases involving two or more departments. 6sense's 2025 buyer research, run on more than 4,000 buyers, found that buying groups average ten or more members on deals around $250,000. Your deal size may be a tenth of that and your groups smaller, but the shape holds for almost any B2B sale above a credit card swipe: several people, more than one department.

The same 6sense research has the number that should bother a sales leader most. Buyers made first contact with a seller about 61% of the way through their journey, and 94% of buying groups had ranked their preferred vendors before that first contact, buying from the early favorite 77% of the time. By the time the form is filled in, the group has existed for a while, it has talked, and it has opinions about you.

So there's a stretch of weeks or months in which a group is forming and your CRM knows nothing. Some of that group's activity is invisible to you and always will be: internal Slack threads, analyst calls, a peer's recommendation over dinner. A slice of it is public, though. If your company posts on LinkedIn, some of those people will react to what you publish, under their real names, with their employer in their headline. The information is sitting in your reaction lists. It's just filed by post and by person, when the question you need answered is filed by company.

Why one person is weak evidence and four people on three posts is strong

Here's the reasoning, because the threshold matters less than understanding why any threshold works.

One person from an account reacting once tells you almost nothing, for the reasons above. One person reacting five times tells you something about that person. It's worth a look (scoring individuals is a separate subject and we cover it in another article), but it still says little about the company. Enthusiasts exist. Some people like everything.

Two people from the same company is where it gets interesting and also where people fool themselves. LinkedIn shows your activity to your connections, and colleagues are connected to each other. When the ops manager likes a post, the dispatcher supervisor may see it in her feed precisely because he liked it. Two reactions from one company on one post can be a single event with an echo. They aren't independent observations, and treating them as two data points overstates what you know.

That's why a good rule counts distinct posts as well as distinct people. Four people on one post might be a cascade through one office's feeds. Four people spread over three posts, published on different days by different authors, is much harder to explain away. Each post is a separate occasion on which someone at that company saw your content and chose to react. Coincidence gets expensive as an explanation. A shared interest inside that company gets cheap.

The rule we use, and the one built into Resonue, is four or more people from the same company, across three or more posts, within 90 days. The honest status of those numbers: they're a judgment call. Nobody ran a study showing that four is the inflection point. The reasoning for each goes like this.

Four people, because two can be a manager and a direct report, and three can still be one tight team. At four you usually start to see a second function or a second level of seniority, and cross-functional is what a buying group looks like.

Three posts, because it kills the single-cascade problem and the single-viral-post problem in one move.

Ninety days, because a quarter is roughly the rhythm at which companies plan and budget, and because longer windows fill up with people who have since changed jobs. If your sales cycle runs nine months, a 120-day window is defensible. Much shorter than 60 days and you'll miss slow-forming groups, and slow is probably the normal case: the 6sense report cited above puts the average buying cycle at about ten months.

Now the concession. The threshold should really scale with company size, and a flat rule doesn't. Four people out of a 60-person company is 7% of the staff paying attention to you, which is remarkable. Four people out of a 40,000-person bank may be four strangers in four countries who will never be in the same meeting. For large accounts, tighten the rule by hand: require that the people share a business unit or a country, or raise the bar to six. For very small companies, two founders engaging repeatedly may be the entire committee, and waiting for four means waiting forever.

Pick a number, write it down, and keep it fixed for a couple of quarters so you can learn whether it was right. A threshold you keep adjusting after the fact teaches you nothing.

Rolling engagement up by company, by hand

You need one spreadsheet with two tabs. Resist adding a third.

Tab one: the touch log

One row per person per post. This is the raw material and it's deliberately dumb.

Column	What goes in it
`date_logged`	The day you logged it. LinkedIn only shows relative times on reactions ("2w"), so your logging date is the best timestamp you'll get. This is one reason to log weekly and not monthly.
`post_id`	A short label you invent: `ceo-0603-detention`, `page-0617-integration`.
`post_author`	Who published it: company page, CEO, a named employee.
`person_name`	As shown on LinkedIn.
`profile_url`	The dedupe key. Names collide, URLs don't.
`headline_title`	Copy their headline as written. Don't tidy it yet.
`company_raw`	The employer exactly as it appears.
`company_key`	Your normalized name for the account. More on this below.
`employer_confidence`	`high`, `low` or `none`.
`touch_type`	`reaction`, `comment` or `repost`.
`notes`	Only if a comment said something worth keeping, in their words.

Tab two: the account roll-up

A pivot on company_key, filtered to employer_confidence = high, with these columns:

Column	How it's built
`people_90d`	Distinct `profile_url` in the last 90 days
`posts_90d`	Distinct `post_id` in the last 90 days
`touches_30d`	Row count, last 30 days
`touches_prev_30d`	Row count, days 31 to 60
`people_30d`	Distinct people, last 30 days
`first_touch_date`	Earliest `date_logged` for the account. You'll need this later for attribution, so never overwrite it.
`committee_flag`	`people_90d >= 4` AND `posts_90d >= 3`
`roles_seen`	Filled by hand, see the next section
`owner`	The AE or SDR who owns the account
`next_step`	One line, dated

The weekly ritual

Pick a fixed slot. Friday afternoon works because the week's posts have finished collecting reactions. Whoever runs it opens each post published by the company page and by the three to six employees whose posts reliably draw an audience, clicks into the reaction list, and logs every name that isn't a colleague, a relative or an obvious bot. Comments get logged from the comment thread. For a team publishing eight to ten posts a week at 30 to 60 reactions each, the first session takes about two hours. Once you know the regulars by sight, it drops to around 45 minutes.

Two practical warnings. First, a post keeps collecting reactions for a week or more, so revisit last week's posts before starting on this week's. Second, log customers too, and tag them as customers in notes or in a separate column if you like. A customer account where four new people start engaging is an expansion signal, and a churned account reappearing is worth a call on its own.

People whose employer is ambiguous

This is where the hours go, so set rules in advance and stop deliberating case by case.

Use the current primary position, not the headline. Headlines say things like "Helping logistics teams scale" and tell you nothing. If the person lists two current roles, take the one that looks like a full-time job and ignore the advisory board seat.

Freelancers, fractional executives and consultants get employer_confidence = low and stay out of the roll-up. One exception deserves a note: a consultant or agency person who engages repeatedly may be running a vendor evaluation on behalf of a client. You can't tell which client from a like. Log them and let the AE decide whether to ask.

"Stealth startup", "Self-employed", students and open-to-work profiles with no current position get none. Don't guess.

Subsidiaries and groups are the hard case. Roll up to the entity that would sign your contract. If you sell to individual country operations, DHL Freight Sweden and DHL Supply Chain UK are two accounts. If you sell group-wide, they're one. Decide per account type, write the decision at the top of the sheet, and apply it the same way every week.

Job changers need a judgment. If someone engaged in May at one company and moved to another in August, their May touches belong to the old employer. Leave history alone, and log new touches under the new company. (A champion who moves to a new company is a lead in its own right, but that's an individual-level play.)

Normalize names by website domain where you can. "Kuehne+Nagel", "Kuehne + Nagel" and "Kuehne and Nagel (AG & Co.) KG" all become one company_key. A lookup tab is tempting here. A find-and-replace on Fridays is enough.

Reading who is in the group, and who isn't

A flagged account tells you that several people are paying attention. It doesn't tell you what kind of attention. The next step is to look at the titles and sort them into the roles a purchase needs, which is a human job and takes about two minutes per account.

For most B2B software deals, the roles are roughly these: the people who would use the product day to day, the manager who owns the problem and would champion a fix, the person who holds the budget, the technical evaluator (IT, security, data, whoever has to connect and approve it), and the late-stage gatekeepers in finance, legal and procurement. Write in roles_seen which of those you can see, using titles only. Don't infer anything else.

Then read the gaps, because the gaps are where the instructions for the AE are.

What you see	What it probably means	What the AE does next
Users and a line manager, nobody senior	Real pain, no sponsor yet. The group can want you and still have no money.	Start with the manager. The goal of the first conversations is to learn who would have to approve spend and whether the problem is on that person's list.
Senior people only, no users	Strategic curiosity, or an executive who follows your CEO. Might be a mandate forming from the top, might be nothing.	Go executive to executive, lightly. Ask what prompted the interest. Don't send an SDR sequence to a VP who liked two posts.
Users, manager and IT	An evaluation is likely underway or close. IT doesn't wander into vendor content for fun.	Move now. Offer something technical and concrete: architecture notes, a security summary, a sandbox.
Several people, all one function, all one level	A team that finds your content useful. Often fans, not buyers.	Keep them warm, invite them to things, and check in each quarter to see whether anyone senior has appeared.
A strong group, then a new senior title appears	Someone escalated internally.	This is the best time to reach out, and the new person is often not the right first contact. Go to the earliest engager and ask what's changed.
Customer account, new names from a different department	Expansion interest, or a reorg.	Hand to the account manager with the names.

One absence is not a signal. Finance, legal and procurement almost never react to vendor posts, and their silence on LinkedIn means nothing. The 6sense and Forrester numbers above describe groups of ten to thirteen. If you can see four of them, you're seeing the four who happen to be active on LinkedIn, which skews toward commercial and operational roles and away from the people who will scrutinize the contract. So when the AE builds an account plan from a flagged group, the plan should already include a line for whoever signs off on security and whoever negotiates terms, even though neither will ever show up in the log.

Multi-threading without being weird about it

Knowing that four people at an account have engaged creates a temptation to contact all four on Tuesday morning. Don't. They sit near each other, or at least share a Slack channel, and "did you also get a message from that scheduling vendor?" is a conversation that ends with all four ignoring you.

The second temptation is to show your work. Compare these two openers.

The bad one: "Hi Marta, I noticed you and three of your colleagues at Nordfracht have been engaging with our content recently, so I wanted to reach out and see if scheduling is a priority for the team."

The better one: "Hi Marta, you commented on Jonas's post about driver no-shows, the bit about carriers confirming and then not turning up. We hear the same from most mid-size forwarders. Are you handling that with phone calls today, or is there tooling in place?"

The first message tells her she's being watched and tells her you've profiled her colleagues. The second refers to something she did in public, on purpose, and asks a question she can answer from her own desk. The account-level picture is why you're writing to her. It doesn't need to appear in the message. (How to write that first message well is the subject of another article in this series. Here we care about the order and spacing of the threads.)

A sequence that works without setting off alarms:

Start with one person. Pick the one whose role is closest to the problem and whose engagement is most recent, and prefer a commenter over a liker, because a comment is an open door you can reply to in public first.
Wait for a response, or a week, before opening a second thread.
Open the second thread from a different sender matched to the recipient. The AE talks to the manager. Your head of product or CTO talks to their IT lead. Your CEO talks to their director, briefly, peer to peer.
In the first real conversation, ask the question that turns your private map into shared knowledge: "Who else on your side cares about this?" Once Marta says "you should talk to Henrik in IT", you can contact Henrik openly, with her name, and the fact that Henrik liked your integration post three weeks ago stays what it should be: background that told you the question was worth asking.

If the first person doesn't reply, that isn't a verdict on the account. Go to the second person with a different angle suited to their role, not a copy of the first message.

What cold to warm looks like over 30 days

An account warming up has a recognizable pattern in the roll-up tab, and it helps to know it because several things imitate it.

Here's the freight forwarder again, week by week, as the spreadsheet would have shown it. Week one: one person, one touch. Week two: nothing. Week three: a second person, on a different post by a different author. Week four: the first person again, plus a third person. In the 30-day comparison, touches_prev_30d was 0 and touches_30d is 4, people_30d is 3, across three posts. One more person in the following fortnight and the committee flag trips.

Three properties separate that from noise. The number of people is rising along with the number of touches (one fan liking everything inflates touches and leaves the people count flat). The touches are spread across several posts and ideally several authors. And they're spread across weeks, with gaps, which is how busy people behave when something is slowly becoming a priority.

Now the imitations.

One viral post is the big one. Your CEO writes something that travels, it collects 900 reactions where a normal post gets 50, and the following Friday half your roll-up tab is up 300% against the previous 30 days. Almost none of it means anything. The test is quick: exclude that post_id and recompute. An account that is still gaining without the viral post is warming. An account whose entire gain comes from it has noticed you once, which is worth something, but it goes on a watch list and not to an AE. This is also why posts_90d sits in the committee rule. A viral post can deliver four people from one company in an afternoon, and it's still one post.

Hiring posts attract people who want a job with you. Funding announcements attract investors, vendors and recruiters. Event weeks produce a burst from everyone who visited the stand, which is real interest but already known to the field team. Competitors' employees show up steadily, and you should tag their company_key once and filter them out for good. Your own employees' former colleagues react out of friendship, and cluster suspiciously at whichever company your newest hire just left.

None of this needs sophisticated handling. It needs a person who looks at the top movers each week and asks "which posts did this come from?" before anything gets forwarded to sales.

Who does what: marketing keeps the log, sales reads the accounts

This work falls between two teams, and in our view that, more than the tedium, explains why it rarely gets done. Marketing sees the engagement and doesn't own accounts. Sales owns accounts and never opens a reaction list. A workable split follows.

Marketing owns the log and the roll-up. They publish or coordinate most of the posts and they're already in the analytics. They run the Friday session, apply the employer rules, run the viral-post check, and produce a short list for Monday: newly flagged accounts, top movers against the previous 30 days, and any customer or churned account that reappeared.

Sales owns the reading and the move. The AE or SDR who owns each account fills in roles_seen, decides who to approach first, and writes the next_step. Marketing shouldn't guess at committee composition for accounts it doesn't know, and should never relabel a flagged account as an MQL and push it into a sequence. An MQL implies someone raised a hand. Nobody here has. These are accounts worth a thoughtful, human first move, and an automated five-step cadence is the opposite of that.

RevOps, if you have it, owns the definitions: the threshold, the subsidiary rule, what "influenced" means in reporting. One person should own these in writing, because they will be challenged the first time a number shows up in a board deck.

The meeting is twenty minutes on Monday. Marketing brings the list, sales says what they already know ("we're in a late-stage deal with them, that's why"), and each new account leaves with an owner and a next step or an explicit "watch". Feedback has to flow back too. When an AE reports "I called, it's four interns", that's what tells you whether the threshold is set right.

Giving LinkedIn credit without lying to yourself

Eventually someone asks what all this posting is worth, and the roll-up can answer more honestly than most attribution can, provided you respect one rule about time.

An account counts as influenced only if its first logged engagement came before the deal stage moved. That's why first_touch_date is never overwritten. If the forwarder's first touch was June 3 and the opportunity was created August 27, LinkedIn was in the picture twelve weeks before sales was. If an account's first engagement comes two weeks after the AE's first meeting, it doesn't count, however warm the account looks today. That engagement is a consequence of the sales process: people look up the vendor they just met. Counting it credits the channel for your AE's work.

Report two numbers per quarter: influenced pipeline (open opportunity value where engagement preceded the stage change) and influenced revenue (the same for closed-won).

Then be clear about what these numbers are. They establish sequence, not cause. "They engaged first and bought later" is compatible with "the posts helped them choose us" and equally with "they were already going to evaluate us, and people who are evaluating you tend to read your posts". You can't separate those from a spreadsheet. A comparison makes the number more useful without pretending to settle it: of all opportunities created this quarter, what share had prior engagement, and do those deals differ from the rest in win rate or cycle length? If engaged-first deals close no better than the others, say so. If they do close better, you have an association worth investing in, and you should still call it influence, because that's what it is.

There's a further reason to log dates carefully. Research by John Dawes at the Ehrenberg-Bass Institute for the LinkedIn B2B Institute argues that up to 95% of business buyers aren't in the market for a given category at any one time, since companies switch providers of things like software roughly every five years. If that's even approximately right, most accounts engaging with you this quarter won't buy this quarter. Some of them will in eighteen months, and the only way you'll be able to show that the relationship started on LinkedIn is a first-touch date logged when it happened.

What this method can't see

It only sees accounts where somebody has engaged at least once. An account on your target list with zero rows in the log isn't a no. Most people who read posts never react to them, and whole companies have cultures of reading silently, particularly in regulated industries where employees are wary of public activity. Silence tells you nothing either way. Your outbound plan for those accounts shouldn't change because of an empty row.

It only sees what your team publishes. If you post twice a month and get fifteen reactions, there's nothing to roll up, and the right move is to fix the publishing before building the spreadsheet. As a rough floor, you want a few posts a week across several people before account-level patterns become readable.

It can't see intent on channels you don't own: review sites, communities, a competitor's webinar. It complements the intent data you may already buy and doesn't replace it.

It works with small numbers. Four people across three posts is a sensible flag and a poor statistic. Expect false positives, the four-interns kind, and expect accounts that buy without ever tripping the flag. Judge the method by whether the Monday list produces better first conversations than the AEs would have had otherwise.

Where doing it by hand breaks, and what Resonue automates

The spreadsheet works. We'd recommend a quarter of running it by hand to anyone, partly because it forces the definitions to get settled. It breaks in predictable places.

Volume goes first. At ten posts a week the Friday session is manageable. At thirty posts across fifteen employees, it's most of a day, and the person doing it starts skipping the low-reaction posts, which is exactly where the fourth person from a flagged account tends to show up. Timing degrades next, since weekly logging with relative timestamps gives you dates accurate to within a week. Employer resolution is the slowest step and the most error-prone. And the 30-day comparison needs a clean history that a hand-kept log rarely has after the third month.

Resonue is the product we build, and the account side of it is this method running continuously. You track your company page, your employees and any outside voices who post about you, and every person who engages with a tracked post is recorded and rolled up by company. The companies board shows, for each account, interactions and distinct people over the last 7 and 30 days, plus an account heat figure, which is the sum of the heat scores of the individual engagers at that company (heat weights recent and repeated engagement above old, one-off engagement). Buying committee detection applies the rule described above, four or more people across three or more posts within 90 days, and flags the account on the board and the dashboard. Opening an account lists the people behind the number with their headlines, their individual heat, and which of your voices reached them.

The 30-day momentum report does the cold-to-warm comparison: new engagers, new warm accounts, and a movers list of accounts gaining interactions against the previous 30 days. The Monday list arrives by itself too. A weekly brief email goes out on Monday morning to everyone in the workspace, flagging accounts where a committee is forming and customer or churned accounts that re-engaged in the past week.

The pipeline board handles the attribution rule. Every account that engages lands in an inbox, and you move it through Lead, Opportunity, Customer, Churned or Disqualified by hand. When you set a deal value on an opportunity or a customer, the account counts toward influenced pipeline or influenced revenue only if its first tracked engagement predates the stage change. Engagement that arrives after you moved the card earns nothing, by construction.

One habit from the hand method is worth keeping. The three-post condition protects the committee flag, but a movers list after a breakout post still deserves a skeptical look.

If you'd like to see this on your own accounts, talk to sales. We prepare the demo on your company's own recent posts before the call, so the conversation starts from the accounts that have actually been reacting to you, including, quite possibly, a group of four you hadn't noticed.

Employee advocacy on LinkedIn usually dies by week six. Here is the version that survives

Resonue — Fri, 18 Sep 2026 00:00:00 GMT

Take a 140-person company that sells procurement software. Its LinkedIn company page has 6,200 followers and the marketing team posts on it three times a week. Every month someone exports the page analytics into a slide: impressions up 8%, 41 new followers, engagement rate a little under 2%. The slide gets about ninety seconds in the marketing review and nobody in sales has ever asked to see it.

In the same month, one of the account executives wrote a post about a deal she lost because the buyer's finance team was never in the room. It got 40 reactions. Eleven of them came from people at companies on the sales team's target list, two of them finance directors. One of the solutions engineers posted a screenshot of an ugly approval chain he had untangled for a customer, and a procurement consultant with a large following reshared it with a comment of his own. A customer's head of operations mentioned the product by name in a post about her quarter, unprompted.

None of that is on the slide. The slide only knows about the company page, and the company page is the smallest part of what happened.

This article is about running LinkedIn as a revenue channel across everyone who talks about your company, and about why the usual way of organizing that (an employee advocacy program) tends to collapse within a couple of months. Most of what follows can be done with a spreadsheet and some discipline. Near the end we'll say where the spreadsheet stops being enough and what we built at Resonue to take over from it.

Four kinds of accounts talk about your company

Start with an inventory, because most teams have never made one.

The first kind is the company page. You own it, you have its analytics, and it is the only one of the four that LinkedIn hands you numbers for.

The second is your employees. Founders, AEs, product managers, the support lead who writes sharp posts about onboarding. LinkedIn's own advocacy guide claims that on average employees' networks are at least 10x larger than the company's follower base, and that only 3% of employees share content about their company while accounting for a 30% lift in the engagement the company sees. Treat those figures with some care: the guide is several years old and was written to sell an advocacy product LinkedIn has since retired. You don't need LinkedIn's average anyway. Add up the connection counts of your twenty most active colleagues and compare the total to your page followers. For the procurement company above, twenty people averaging 1,800 connections is 36,000, against 6,200 followers. There is overlap between those networks, so the real number is smaller, but it is still a multiple of what the page reaches.

The third kind is outside people who mention you without being asked: customers, consultants, analysts, a former employee who still likes the product, occasionally a critic. You control nothing here. It is often where the most credible posts come from, for the plain reason that the author has nothing to sell.

The fourth, which only some companies have, is contracted content partners: people with an audience in your market whom you pay to post. The wider market calls them influencers. It is a minority practice in B2B and we'll deal with it in one section further down.

(At Resonue we call each of these accounts a Voice, and that is the last you'll hear from us about the product for a while.)

Here is the inventory to build. One row per account, and it takes an afternoon:

Column	What goes in it
`name`	Person or page
`type`	company page, employee, external, partner
`role`	AE, founder, PM, customer, consultant
`profile_url`	Their public LinkedIn profile
`connections_or_followers`	Rough count, from the profile
`posts_last_90d`	Total posts in the last 90 days
`posts_mentioning_us_90d`	How many of those name the company or product
`opted_in`	For employees: did they agree to be part of this, yes or no

To find the externals, search LinkedIn posts for your company name and your product name, filter to the past month, and do it again next month. It is tedious and you will miss some.

The posts_mentioning_us_90d column tends to surprise people. At most companies, five or six employees produce nearly all the posts that mention the company, and marketing had never listed them. Those people are your program. Whatever you launch should be built around them and not around the 134 colleagues who have never posted and have no wish to start.

The page report tells you about the page

There's nothing wrong with company page analytics. They accurately describe one account. The trouble starts when that report stands in for "how LinkedIn is going", because then most of what LinkedIn is doing for you, or failing to do, sits outside the measurement.

There is also a structural reason the page matters less than its report suggests. In the 2024 Edelman and LinkedIn study of 3,484 business executives, 73% of decision-makers said an organization's thought leadership is a more trustworthy basis for judging its capabilities than its marketing materials and product sheets. A company page post reads as marketing material however good it is, because a logo posted it. The AE's post about the lost deal reads as a person who knows something. Same company, same subject, a different amount of belief on the other end.

The same study has a number that should bother anyone who runs this channel: only 29% of the companies producing thought leadership said they can link sales leads back to specific pieces, 42% still judge it by looking for traffic bumps, and 19% have no measurement process at all. So plenty of teams are in the position of the procurement company: they can describe the page in detail and have nothing to say about the rest.

One concession before going further. The page still earns its place. It is where a prospect lands when they check whether you are a real company, it is the account that can run ads, and it is the natural home for hiring posts and announcements nobody wants on their personal profile. Keep posting there. The argument is only that it shouldn't be the whole report.

Why advocacy programs die around week six

"Six weeks" is our shorthand for a pattern, not a measured statistic. Nobody has surveyed this, as far as we can find, so take it as a rough shape. The shape is consistent enough to be worth walking through.

Week one: the CMO announces the program at all-hands. There is a Slack channel, maybe a tool that queues up suggested posts. Thirty people join.

Weeks two and three: marketing drops a message in the channel whenever the company page publishes. "New post is live, please like and share!" A dozen people repost it. Their networks now see the same graphic twelve times with no commentary, or with the suggested caption pasted in unchanged, which is worse. An identical repost gives a reader no reason to stop: they already scrolled past the original. The people doing the reposting can see this in their own notifications, where each repost collects two likes, both from colleagues.

Week four: someone builds a leaderboard to revive things. The same three people win it. Everyone else learns they are in the bottom twenty-seven.

Week five: the head of sales asks what the program has produced. Marketing answers with total impressions. The head of sales nods politely and stops asking.

Week six: the Slack channel has one message in it, from marketing, with a single emoji reaction from an intern.

Look at what failed and none of it was laziness. The ask was wrong (amplify our post, when it should have been say something of your own). Nobody who participated got anything back: no sign that their effort reached a person who mattered, no sense of what worked for them. And the one executive whose support would have protected the program was given a vanity number. Each of those has a fix, and the fixes are the rest of this article.

Start smaller than feels respectable

A program that survives usually starts with five to eight people, all volunteers, drawn from the ones who already showed up in your inventory. Include at least one AE, because sales leadership will believe a colleague's results before it believes marketing's, and at least one person who builds or supports the product, because those posts contain details marketing can't write.

You cannot force employees to post. Their profiles belong to them, their networks were built before they met you and will leave with them, and a mandated post reads like one. Some companies put LinkedIn activity in AE performance plans. We'd advise against it: you get the minimum number of posts at the minimum level of effort, and the good posters start to resent a thing they used to do willingly. What you can do is make posting easier and more rewarding for the people inclined to do it, which comes down to two things. Tell them what is worth talking about this month, and show them what their posts did.

Give it a named owner with about two hours a week. Here is what that person's Tuesday looks like: twenty minutes checking which posts from last week touched the current brief, thirty minutes going through who reacted to the three or four most relevant posts, ten minutes sending two individual notes ("your post on approval chains pulled in four people from target accounts, two of them at Halden, I've told their AE"), and the rest on next month's brief. If nobody has those two hours, don't launch. A program with no owner doesn't even make it to week six.

Brief people like you're running a sprint

The share-this-post message fails partly because it has no shape. It arrives at random, it asks for a mechanical action, and it never ends. A brief is different in three ways: it has a subject, a priority, and dates.

Here is one for the procurement company, tied to a feature launch:

Brief: approval workflows launch. Priority: HIGH. Window: October 6 to October 24. What's happening: multi-step approvals ship on October 13. The problem it addresses is invoices stalling because one approver is on holiday and nobody else has authority. What we'd love covered: that problem, from wherever you sit. An AE has heard buyers complain about it. Support has seen the tickets. Product knows why it was hard to build. What's true and safe to say: it ships October 13, it's included in all plans, it supports up to five approval steps. What to leave out: customer names unless marketing confirms permission, anything about the Q1 roadmap. There is no suggested copy. Write it the way you'd say it to a peer.

A few things about this are deliberate.

The window is under three weeks. Open-ended asks get ignored because there's never a reason to act today. A window that brackets the launch gives people a reason to post this week and gives you a period over which to measure. Events work the same way: for a conference on March 12, open the window three weeks before, since the point is to have conversations warmed up before you arrive at the booth.

The priority exists so that people can ignore most briefs. If you run one HIGH brief a month and a couple of LOW ones ("we're hiring two engineers", "new case study is up"), your volunteers know where to spend their one good post. Marking everything HIGH is the same as having no priorities at all, and your volunteers will work that out within a month.

There is no copy, and that is where most programs lose their nerve. Marketing worries about message consistency, so it supplies a caption, and twelve identical captions later you are back in week three. Give people the facts and the guardrails and leave the writing to them. If someone asks for help getting started, sit with that one person and work from how they already write.

Then close the loop, which is the step nearly everyone skips. When the window ends, go through what your people posted during it and mark each post: did it cover the brief, yes or no. Add a tab to the spreadsheet:

Column	What goes in it
`brief`	approval workflows launch
`post_url`	Link
`author`	Who
`date`	When
`covered_brief`	yes / partly / no
`reactions`, `comments`	Raw counts
`right_audience_count`	Filled in using the method two sections down

Now you can answer questions you couldn't before. Six of eight volunteers posted about the launch. Those posts drew reactions from 31 people at accounts sales cares about. The March event brief got one post, which says something about the brief or the event. Reading posts and judging "covered or not" by hand is slow and somewhat subjective, and it's still worth an hour a month.

Give each person their own numbers

The leaderboard fails for a reason anyone who has managed salespeople will recognize: ranking motivates the top three and tells everyone else to stop. It's also unfair in a specific way on LinkedIn. A founder with 14,000 followers and a support engineer with 600 connections aren't playing the same game, and raw engagement will always put the founder on top, whatever either of them wrote.

Compare each person to their own past instead. For every volunteer, keep their last 30 days next to the 30 days before: how many posts, total reactions and comments, how many distinct people engaged. Then send it to them privately, with one observation a human wrote. "Your posts with a screenshot of a real workflow get about double your usual comments" is worth more than any rank, because it tells them what to do next and because it is about them.

A comparison to the team average is fine if it stays private and kind. "You're at about 1.3 times the team's average engagement per post" is encouraging. Publishing the same figure in a channel turns it back into a leaderboard.

Be open about what you're tracking. Everything here comes from public posts, but it is still colleagues looking at colleagues' activity, and the volunteers should have opted into that (hence the opted_in column). Don't run numbers on employees who never signed up and then present them with a report. Someone will call it creepy, with some justification, and you'll spend the next month on that conversation when you meant to spend it on the program.

Judge a post by who it reached

Here is the claim we'd defend hardest in this article: a post with 40 reactions from the right accounts beats a post with 900 from strangers, and most teams reward the second one.

The 900-reaction post is usually a personal story, a hiring opinion, a take on remote work. There's nothing wrong with those posts, and they do grow the author's reach over time. But open the list of reactors and sample a hundred names. For a company selling procurement software, you'll typically find recruiters, students, other people's salespeople, and a handful of relatives. Perhaps three people out of the hundred could ever buy.

Now the 40-reaction post about the lost deal. Go down the list, all 40, and put each person in one bucket:

Bucket	Count
Colleagues (exclude from everything)	9
Works at a target account, or has a title you sell to	13
Existing customer	4
Peer, vendor, consultant	8
No connection to your market	6

Thirteen people you sell to, plus four customers worth keeping warm, from one post. The 900-reaction post, extrapolating from its sample, reached perhaps 27 such people, at a rate of 3%. The small post ran at 13 of 31 non-colleagues, 42%. They are closer in absolute terms than the like counts suggest, and the smaller post came with context: those 13 reacted to a post about finance being absent from procurement deals, which tells the AE what to open a conversation with.

Track two things per post, then: right_audience_count (people in the second and third buckets) and the rate (that count divided by non-colleague reactions). The count tells you what the post was worth. The rate tells you whether the topic attracts buyers or passers-by.

Always strip out colleagues. At small companies, internal reactions can be a quarter of the total, and they flatter every number you look at.

Doing this by hand costs about fifteen minutes per post for 40 reactors, and it isn't practical for 900, which is why you sample. Do it only for posts that covered a brief and for anything that performed unusually. What to do with the 13 names (which to contact, how warm each one is, what to write to them) is a subject of its own, covered in our article on turning post engagement into sales conversations. Whether several of those people work at the same account, and what that signals, is covered in the one on buying committees.

Let your own history pick your topics

Every quarter a new list circulates: best times to post, ideal length, carousels are back, carousels are finished. These lists are averages across millions of accounts that mostly don't sell what you sell to the people you sell to. Your own posting history is a smaller dataset and a much more relevant one.

Tag each post from the last six months, across all your tracked voices, with one or two topics. Keep the vocabulary short, fifteen topics at most: "approval workflows", "finance and procurement alignment", "hiring", "company news", "customer stories". For each post, compute a simple engagement figure (reactions plus comments, divided by the author's follower count, so the founder doesn't dominate). Average that across every post, and you have your company baseline. Then average it per topic and compare.

You'll hit a trap almost at once. "Customer stories" has two posts and an average three times the baseline, because one of the two went unusually well. "Approval workflows" has fourteen posts and sits at 1.2 times baseline. A naive ranking crowns customer stories on the strength of a single good day.

The fix is to pull small samples toward the baseline:

adjusted = (n × topic_average + 3 × baseline) / (n + 3)

where n is the number of posts on the topic. With two posts at 3.0 times baseline, the adjusted figure is (2 × 3.0 + 3 × 1.0) / 5 = 1.8. With fourteen posts at 1.2, it's (14 × 1.2 + 3) / 17 = 1.16. Customer stories still ranks first, and it should, but now by a margin that reflects how little you know. The constant 3 is a judgment call: it means a topic needs a handful of posts before its own record outweighs the company average. Use 5 if your volume is high.

Then tier them. We'd use these cutoffs: at or above 1.5 times baseline with at least two posts is an S, at or above baseline is an A, down to 0.6 is a B, below that is a C. S and A topics go into next month's briefs. B topics need a better angle. C topics are where you stop asking volunteers to spend their posts. "Company news" is often one of them, and that's useful to know: the page can carry it and the volunteers can be spared.

Two cautions. Engagement rate is a proxy, and the better measure is the right-audience rate from the previous section, if you have enough classified posts to compute it per topic. Most teams won't for months. And a topic can score badly because the posts about it were dull. Reread the posts before you retire the topic.

Competitors: look monthly, then close the tab

It is worth knowing how much of the conversation in your category belongs to you. It isn't worth checking every morning.

Once a month, pick three or four competitors. For each, count posts in the last 30 days that mention them: their page, their visible employees, and outsiders. Sum the engagement on those posts. Your share of voice is your engagement divided by the total across you and them. Note the topics each competitor's posts cluster around, since a competitor owning "AI in procurement" while you own "approval workflows" is a positioning fact you should know about. And glance at tone: are outside mentions of them mostly warm, neutral, or complaints? A rough positive, neutral, negative tally across twenty posts is enough.

What to do with it: if a competitor's share jumped, find out whether it was one post, one person, or a sustained push. One viral post is noise. Six employees who started posting weekly is a program, and you'll be seeing more of it.

And what to refuse: copying their topics because those topics work for them. Their baseline isn't yours. The monthly look is there so you don't get surprised, and it shouldn't be where your editorial calendar comes from.

If you pay content partners

Some B2B companies pay people with an audience in their market to post about them on a recurring basis. Few talk about it publicly. If you don't do it, skip this section, and don't take its existence as advice to start.

If you do, the arrangement usually specifies a cadence (say, two posts a month mentioning the product) for a monthly fee. The most common failure is mundane: the posts quietly stop happening. The partner gets busy, month two has one post, month three has none, and nobody notices until renewal, because nobody's job includes counting.

So count. A tab per partner: agreed cadence, posts delivered this period, shortfall, days since the last post. Three states are enough. Delivered the agreed number: fine. Under target but posted recently: slipping, send a friendly note now. Silent for two full cycles: the arrangement is effectively off and needs a conversation about whether it continues.

Then look at what each partner delivers beyond the count, with the same lens you use for employees: engagement relative to your own average, and above all who reacted. A partner with a big audience of the wrong people is an expensive way to collect likes. You know what you pay each one. Put the delivered side next to that figure and make the call yourself. No formula makes it for you, because the value of a partner who reaches the right 50 people depends on your deal size.

The parts that stay hard

Legal and comms will have opinions, and you should ask for them before launch. If they find out in week four, they'll have more of them and less patience. What usually satisfies them is a one-page guide: no customer names without written permission, no financial figures that aren't already public, no roadmap dates, no commentary on a competitor's outage or layoffs, and a named person to ask when unsure. In regulated industries (financial services especially) some posts may need pre-approval, and that will slow everything down. Accept it and keep the volunteer group smaller. If your company is public, quiet periods apply to employees' posts too.

People leave, and their audience goes with them. The AE with the best posts in your program is building an asset she owns. That's the deal, and it's a fair one. It is also the reason a program with eight voices is sturdier than one built on a single star.

Attribution is the hardest part, and the place where overclaiming does the most damage. If someone at a target account reacted to three of your people's posts in September and their company entered the pipeline in November, the posts influenced that deal in some way you can't quantify. They didn't source it in any sense a CFO would accept. The buyer also saw an ad, got a referral, and met your AE at an event. LinkedIn's own B2B Institute argues that 95% of potential buyers aren't in the market on any given day, which means most of what a post does is make a future purchase slightly more likely, invisibly, months ahead.

So report it as what it is. "Fourteen accounts now in pipeline had people engaging with our posts before the opportunity opened" is a true sentence, and sales leadership can weigh it. "LinkedIn generated $400k" is a sentence that will get you taken apart in the first meeting where someone asks how you know. The only defensible rule we know of is about timing: count an account as influenced only if the engagement came before the stage change. Engagement after a deal opens is nice and shouldn't be counted, since people look up vendors they are already talking to.

Where the spreadsheet gives out

Everything above works by hand for about eight voices and one brief a month. Then the arithmetic catches up with you. Eight people posting weekly is around 35 posts a month. Reading each one for brief coverage, opening each reactor list, classifying perhaps 1,200 people, recomputing topic averages, redoing the competitor count: that is well past the two hours a week the owner has. What gets dropped first is the classification of who reacted, the part that was producing the evidence sales cared about.

This is the work Resonue does, and here is specifically what it covers.

It keeps the inventory as a roster of Voices in the four types described at the start: company page, employees, external voices and content partners, tracked from their public profiles. Each voice gets a verdict line with its posting cadence, its engagement compared to your workspace average, how many engagers its posts captured, how often its posts mention your brand, and the pipeline its engagers' accounts touch.

Briefs live on a calendar with a start date, an end date, and a HIGH, MEDIUM or LOW priority. When posts come in, AI matching marks which ones covered which brief, and each brief shows how many engagers its posts drew, so the "covered or not" tab and the per-brief count fill themselves in. Current briefs also show up when a person opens Compose to write.

In Compose, a draft gets a score from 0 to 100 before anyone posts it, broken into topic fit, hook strength, format fit and tone match. Topic fit is judged against your workspace's own pooled post history, all voices together. It isn't a generic playbook. For the person staring at a blank box, Rosa (the assistant inside the product) can generate a starting draft for that one voice, built from that person's own top posts and the topics they keep returning to, anchored to a brief if you choose one. It happens on demand, one voice at a time, and nothing is ever published on anyone's behalf: the person edits it, or throws it away, and posts from their own account.

Each employee with a seat has a personal view showing their own last 30 days against the 30 before (posts, engagement, people reached) along with the current briefs. There is no ranking on it.

Topics and hashtags are tiered S, A, B and C against your own baseline, with the small-sample correction described earlier built in. Share of voice, share of topic and share of sentiment are computed against the competitors you choose to track. For content partners, you enter the cadence and dates of the arrangement, and each partner shows as Honored, Slipping or Off contract, with the shortfall and days silent. It shows what each partner delivered, and the judgment about whether that was worth the fee stays with you.

The pipeline view applies the timing rule from the previous section: an account counts as influenced only when its first tracked engagement predates its stage change. Stages are moved by hand on a board.

If you'd like to see this on your own company and not a sample workspace, talk to sales. The demo is prepared beforehand on your company's recent posts, so the first thing on screen is your own voices and who has been reacting to them.

The people liking your LinkedIn posts are a lead list. Nobody is working it.

Resonue — Fri, 18 Sep 2026 00:00:00 GMT

Take a 70-person company that sells maintenance software to regional trucking fleets. On a Tuesday its company page publishes a breakdown of what a day of unplanned downtime costs per truck. By Friday the post has 84 reactions, 11 comments and 6 reposts. Marketing pastes the numbers into the weekly update with a green arrow next to them. Sales never hears about it.

Now open the reaction list and go through it name by name, which almost nobody does. In this example, 19 of the 84 are employees and former employees. Nine are agencies and vendors who sell to the company. Six are recruiters or people looking for a job. Four work for competitors. Fourteen are peers of whoever runs marketing: other marketers, founders, people who follow the CEO and have never seen a truck up close. Three are related to the founder. Eight are existing customers.

That leaves 21 people who could plausibly buy. Nine of them turn out to be a maintenance director, a VP of operations or a fleet manager at a trucking company of the right size in the right country. Two of those nine have now reacted to three different posts in a month.

Your proportions will be different, and you should count them once so you know what they are. But the shape of the problem doesn't depend on the proportions: the post "did well", the 84 is a number on a slide, and the nine names inside it are sitting in a list nobody on the revenue team has opened. Those nine people know who you are and have shown an interest in the exact problem you solve. An SDR would give a lot for that on a cold list, and here it's free, and it expires.

This article is the method for working that list. It runs in a spreadsheet, it takes about an hour a week once it's set up, and it needs no software. (We make software that automates it, and we'll get to that near the end, including where the manual version falls over. The method stands without us.)

What LinkedIn shows you, and what it keeps in aggregate

The reason this pipeline goes unworked is mostly mechanical. The information exists, but it's spread across places that don't add up.

Notifications are built for the author, not for a sales team. "Priya and 23 others reacted to your post" is one line, per post, in the feed of the one person who posted. If your CEO, two AEs and the company page all post in the same week, that's four separate notification feeds belonging to four logins. No one sees the union. The person who liked the CEO's post on Monday and commented on the page's post on Thursday looks like two unrelated events to two different people, and like nothing at all to the rep who owns that account.

Page analytics go the other way and lose the names. A Page admin can export visitor, content, follower and competitor reports as XLS files, and they're useful for what they are: impressions, clicks, reaction counts and engagement rate per post. Post analytics on a personal profile add viewer demographics, but as aggregates, and LinkedIn keeps the demographic breakdown for 180 days. "32% of viewers were in operations" is a fine thing for marketing to know, and there's nobody in it a rep can call.

To be fair to LinkedIn, the names aren't hidden. Click the reaction count on any post and you get the full list: name, headline, connection degree. There's also a Pages Data Portability API through which a Page's owners and their authorised developers can export data that may include members who interacted with the Page, subject to each member's own privacy setting. That's a developer project, though, not a button in the admin screen, and it covers the Page only, not your employees' posts.

So in practice, the list is a thing you click into, one post at a time, and it has two annoying properties. The headline is whatever the person wrote about themselves ("Helping fleets run leaner | Speaker | Dad of 3"), which often isn't a job title. And reactions carry no date. Comments show "2d" or "3w"; a like shows nothing. Hold on to that second point, because it shapes how you have to log things.

Build the raw list first: one row per touch

The most common mistake in a first attempt is making a list of people. Make a list of touches instead. One row for every reaction, comment or repost, even when it's the same person for the fifth time. The repetition is the signal you're after, and a people list throws it away on day one.

Create a sheet with two tabs. The first is called touches and has these columns:

Column	Header	What goes in it
A	`date_logged`	The day you captured it (see below)
B	`person`	Full name
C	`profile_url`	Their LinkedIn profile URL, which is your unique key
D	`type`	`like`, `comment` or `repost`
E	`post`	A short label you'll recognise: `downtime-cost`, `ceo-hiring-mechanics`
F	`posted_by`	`page`, or the name of the employee who posted
G	`weight`	Formula, below
H	`days_ago`	Formula, below
I	`points`	Formula, below

Key everything on the profile URL, not the name. You will meet two David Millers within a month.

On dates: since likes aren't timestamped, the honest date for a like is the day you logged it. If you log every Tuesday, a like is at most a week older than your sheet says. With the scoring below, a week of error shifts a touch's value by about 15%, which is tolerable. If you log once a month, the error swallows the signal, and this is the first reason the weekly rhythm matters more than the formula.

Treat every LinkedIn reaction type (Like, Celebrate, Insightful and the rest) as like. It's tempting to weight "Insightful" above "Like". Don't. The extra column costs you time and tells you nothing a comment wouldn't tell you better.

Which posts to log: every post from the company page, plus every post from an employee that touches your market or your product. Skip the CEO's marathon photo. It will have the most reactions of the quarter and none of them mean anything commercially.

Most of the list is not buyers, and that's fine

The second tab is people, one row per profile URL. Before any scoring, every new person gets sorted into a bucket in a column called who. It takes about ten seconds per person from the headline alone, and it's where the 84 names in the opening example became 21.

The buckets we'd use:

buyer: could plausibly buy, or influence a purchase, at a company that could be a customer
customer: already pays you
team: employees, former employees, investors, the founder's mum
peer: people in your own function at unrelated companies, who like the post because it's a good post
vendor: agencies, consultants and tools that want to sell to you
talent: recruiters and job seekers
competitor: works for one
unknown: you can't tell in ten seconds

Only buyer rows go on to grading. But don't delete the rest, because three of those buckets are worth something to somebody else. A customer who starts engaging heavily after months of silence is something the account manager wants to know before the renewal call. Competitor employees who react to everything you post are telling you which of your messages they're watching. And a peer with a large following in your market who keeps reposting you might be worth a conversation that has nothing to do with selling.

The peer bucket deserves a warning because it's the one that flatters you. A post about B2B marketing written by a marketer gets liked by marketers. If you sell to fleet managers, a thousand reactions from marketers is applause from the wrong room, and a post with 30 reactions of which 12 are fleet managers is the better post. Once this column exists you can work out the buyer share per post, and it will change what your team thinks a good post is. That's a marketing conversation we'll leave there, but the sheet gives you the number for free.

Be strict with unknown. Give each one a single profile visit, then either rebucket it or leave it. Don't spend four minutes working out what a "Growth Catalyst" does.

Grade against a written ICP, not a feeling

Ask three reps who the ideal customer is and you'll get three answers that differ in precisely the cases that matter. So write it down, as four lists:

Titles, as keywords you'd accept: fleet manager, maintenance director, VP operations, director of operations, head of fleet
Industries: trucking, logistics, freight, transportation
Company size, as headcount bands: 51-200, 201-500, 501-1000
Geography: United States, Canada

Then add four yes/no columns to the people tab, next to a title and a company column you fill in from the profile (title_ok, industry_ok, size_ok, geo_ok) and one grade column. Four out of four is an A, three is a B, two is a C, and one or none is off. If you mark the checks y or n in columns F to I, the grade is one formula: =CHOOSE(COUNTIF(F2:I2,"y")+1,"off","off","C","B","A").

We're deliberate about equal weights. The temptation is to say title matters more than geography and build a points table, and within a month nobody can explain why someone is a 71. With four equal checks, anyone can glance at a row and see why it's a B: right title, right industry, right country, company too small. A B with a named gap is more useful to a rep than a precise score, because the gap tells them what to check. Maybe the company is small and growing fast. That's a judgment a person should make, with the reason in front of them.

Two practical notes. First, grade on the current job title, not the headline. That means opening the profile and reading the top entry under Experience, and it's the slowest part of the whole method, around a minute or two per person. It's also why you bucket first: you only pay that cost for the buyer rows. Second, if you sell to more than one kind of buyer, write more than one ICP. The fleet company above probably has an operations buyer and a finance buyer (a CFO who cares about cost per mile). Name them, grade each person against both, and keep the better grade. Forcing two buyers into one blended ICP produces a profile that describes no actual human.

And when four people from the same company show up on this tab within a quarter, stop treating them as four leads. That's an account-level signal and it needs different handling, which we cover in a separate article on buying committees.

One like means almost nothing. Four touches mean something.

A like costs half a second and zero commitment. People like posts while waiting for a meeting to start. The standard social selling advice, which is to reach out quickly to everyone who engages, treats that half second as a raised hand. We think that advice is wrong for likes, and the reason is simple arithmetic about what each action costs the person doing it.

A comment costs a thought, and it puts their name and opinion under your post in front of their own colleagues. A repost puts your content in front of their whole network with their reputation attached. So weight them differently: like = 1, comment = 3, repost = 5. You can argue for 1, 2 and 4 instead. It won't change who ends up at the top of the list, so pick one and stop tuning.

Then make every touch fade. We use a 30-day half-life, which gives you a rule you can say out loud: a touch is worth full value today, half after a month, a quarter after two months, an eighth after three. Anything older than 180 days counts as zero.

In the touches tab, that's three formulas:

weight (G2): =IF(D2="repost",5,IF(D2="comment",3,1))
days_ago (H2): =TODAY()-A2
points (I2): =IF(H2>180,0,G2*0.5^(H2/30))

And in the people tab, per person:

heat: =SUMIF(touches!C:C,B2,touches!I:I)
touches: =COUNTIF(touches!C:C,B2)
posts: =COUNTA(UNIQUE(FILTER(touches!E:E,touches!C:C=B2)))
last_touch: =MAXIFS(touches!A:A,touches!C:C,B2)

(These work in Google Sheets and in current Excel. B2 is the person's profile URL.)

By this point the people tab has grown to seventeen columns, and the formulas later in this article assume this order:

A	B	C	D	E	F to I	J	K	L	M	N	O	P	Q
`person`	`profile_url`	`who`	`title`	`company`	`title_ok`, `industry_ok`, `size_ok`, `geo_ok`	`grade`	`heat`	`touches`	`last_touch`	`posts`	`band`	`owner`	`notes`

It's wide, but only C to I need your judgment. Everything from J to O is a formula.

Here is what that does to three people from the trucking example, all graded A.

The first liked one post 85 days ago. Her heat is 1 × 0.5^(85/30), which is 0.14. The second liked a post three days ago: 0.93. On a people list these two look identical (one like each), and the sheet says one is nearly seven times warmer than the other, though neither is warm.

The third commented two days ago (2.86), reposted six days ago (4.35), commented on a different post 13 days ago (2.22) and liked a third post 21 days ago (0.62). Four touches across three posts, heat of 10.05.

We treat 10 as the line for "warm". The reasoning: you can't get to 10 by accident. It takes something like a fresh repost plus a fresh comment plus a like, or four recent comments. Ten fresh likes would also do it, and a person who has liked ten of your posts in a few weeks is paying attention by any definition. Below about 4, a person has done one or two light things and the right move is nothing. Between 4 and 10 they go on a watch list.

Why 30 days and not 14 or 90? With a 14-day half-life the list is just last week's reactors and you're back to pouncing. With 90, someone who was active in the spring still looks warm in autumn, and reps learn to distrust the sheet after the third stale conversation. Thirty days fits a sales cycle measured in weeks to a few months. If yours is measured in days, shorten it.

The decay is the part people skip and it matters most, because it makes the list maintain itself. Nobody has to remember to remove stale leads. They cool off on their own, and the person who came back this week rises without anyone deciding anything.

Deciding who gets worked this week

Sort the people tab by grade, then by heat, and read it against this grid:

	Heat 10+	Heat 4 to 10	Heat under 4
A	Message this week	Watch; engage back in public	Leave alone
B	Message this week, check the gap first	Watch	Leave alone
C	A rep decides, case by case	Ignore	Ignore
off	Find out who they are; don't sell	Ignore	Ignore

"Engage back in public" means what it says: reply to their comment, react to something they've posted themselves. It costs a minute and means your name is familiar when a message eventually arrives.

Fill in the owner column before anyone sends anything. The failure this prevents is ugly: three reps each spot the same warm VP on three different posts and all message her in one week.

Expect the top-left cell to be small. For the 70-person company above, five to ten names in a normal week would be a healthy result. It feels like a poor return on 84 reactions until you compare it with what the same rep gets from the 50 cold emails they'd otherwise send with that hour.

The first message

One number should set your expectations here. LinkedIn's B2B Institute, drawing on work with the Ehrenberg-Bass Institute, argues that 95% of your potential buyers aren't ready to buy today. The figure is a rule of thumb inferred from how rarely companies switch suppliers, not a census, and you can quibble with it. The direction is hard to argue with. Most of the people on your warm list, including the A-grade ones at heat 12, are interested in the topic and not shopping for a vendor.

So the first message shouldn't try to book a demo. Its job is to start a conversation that a reasonable person would want to be in, so that you're the one they think of when the truck breaks down.

The opener to avoid

Hi Dana, I saw you liked our post about downtime costs! We help fleets like yours cut unplanned maintenance. Do you have 15 minutes this week for a quick demo?

Everything is wrong with it, and in a specific order. It opens by announcing that you watch who likes your posts, which everyone half knows and nobody enjoys being told. It treats a half-second gesture as a buying signal. It asks for fifteen minutes before offering anything. And apart from the first name it could have gone to all 84 people, which Dana can tell.

A better one

Say Dana is the four-touch person from the scoring example, and her most recent comment said that the repair bill is never the real cost of a roadside breakdown, the missed delivery is. First, reply to that comment in public, with substance. Then, a day or so later:

Dana, your point that the missed delivery costs more than the repair is the part most downtime math leaves out, including ours until recently. We built the per-truck worksheet behind that post and it now has a line for missed loads. Happy to send it if it's useful. Either way, I'd like to hear how you put a number on a missed load, because nobody we've asked does it the same way.

It references something she said out loud, which is different from something she clicked. A comment is a public statement made in the hope of a reply, and answering it is normal behaviour. It offers a thing of value with no meeting attached, and it asks a question she's qualified to answer and probably has opinions about.

For someone who has only ever liked (say, three likes on three posts in five weeks, grade A, never commented), don't mention the likes at all. Write about the subject they keep showing up for:

Tom, you run maintenance for a fleet spread across several depots, so you live the downtime problem more than we do. One question, because we're about to publish on it and I'd rather be corrected before than after: do you track the cost of a missed load next to the repair bill, or only the repair?

He knows perfectly well why you're writing, and you don't need to say it.

One constraint: if you're not connected, the message rides on a connection request, and on a free account LinkedIn caps that note at 200 characters and only lets you add one to a few requests a month (the help page currently says three). So the note is one sentence about their point and a plain "would like to connect". The longer message waits until they accept.

Timing changes the message

Add a band column driven by last_touch:

=IFS(TODAY()-M2<=7,"hot",TODAY()-M2<=30,"warming",TODAY()-M2<=90,"cooling",TRUE,"cold")

Hot means a last touch within 7 days. The engagement is fair game as a reference, if it was a comment or a repost. They remember it. Write now, because in ten days they won't.

Warming is 8 to 30 days. They remember the topic and probably not the post. Lead with the subject, keep the ask small, and don't be in a hurry.

Cooling is 31 to 90 days. Referencing the engagement now reads as a rep working through a stale list. Bring something new (a fresh piece of data, a change in their industry, a relevant hire at their company) and let the old engagement be the private reason you chose them.

Cold is anything over 90 days. Treat them as a cold lead who happens to have heard of you, because that's what they are. "I noticed you engaged with our content" about something from the spring is worse than no personalisation.

Heat and band answer different questions. Heat tells you how much someone has done, and the band tells you whether it's recent enough to mention.

What not to do

Some of this is covered above, so briefly.

Don't pounce on a single like. There is one exception worth making: a lone like from the exact person you've been trying to reach at a named target account is worth a row on the watch list and perhaps a connection request with no pitch in it. It still doesn't justify a sales message.

The counter-argument is speed to lead, and it deserves a straight answer. Speed matters enormously when someone fills in a demo form, because they've asked to be contacted. Nobody who likes a post has asked for anything, and a sales message inside the hour mostly tells them you were watching.

Don't automate the first message. A templated DM fired at every reactor is the quickest way to teach your market that engaging with your posts gets them spammed, and then they stop engaging and you've burned the channel that made the list.

Don't sell to the customer bucket from this sheet. Pass those names to whoever owns the account.

Don't grade from the headline, and don't let a rep quietly override a grade without writing why in the notes column. In three months you'll want to know whether your ICP was wrong or the rep was hopeful.

What a Tuesday looks like

Once the sheet exists, the weekly run is about an hour for one person, usually someone in RevOps or a sales manager who cares, at a company putting out two or three relevant posts a week.

Twenty-five minutes logging: open each post from the last seven days, click into reactions, comments and reposts, add the rows. Fifteen minutes on new names: bucket everyone, open profiles for the buyer rows, fill in the four ICP checks. Ten minutes sorting by grade and heat, reading the top 20 rows, and looking for who moved since last week. Ten minutes assigning owners and posting the five to ten names in the sales channel with a one-line reason for each: "Dana R., A, heat 10, four touches on three posts, last one a comment on Sunday."

The first week is not an hour. Backfilling a month of posts to give the scores some history takes closer to three hours. Do it anyway, because a sheet with one week of data can't tell repetition from coincidence.

Where the hand method breaks

We'd rather you try this by hand before buying anything, ours included, because a month of doing it teaches you what your engagement is made of. But it does break, and in predictable places.

Volume is the first. The example above was one post. Suppose five people post twice a week and each post draws around 60 reactions: that's 600 touches a week. At ten seconds a row, logging alone is 100 minutes, before anyone has opened a single profile to check a title.

The second is that the people posting aren't the people logging. Each employee sees reactions only on their own posts, in their own notifications. Whoever keeps the sheet has to visit every colleague's posts by hand, and will miss the one the VP of Sales put up on Saturday morning that turned out to be the best-performing post of the month.

The dates stay fuzzy, for the reason given earlier. A weekly capture keeps the error tolerable, but skip one week and everything from those 14 days lands on the same date.

And the real killer is week three. The first two weeks are interesting because you're discovering things. By the third, it's data entry, it's quarter-end, and the person doing it has a forecast due. A decay-weighted list that isn't updated doesn't hold its value. It drifts toward zero and takes the team's trust with it. Run by hand, this either becomes someone's actual job or it's dead within two months, and we'd bet on the second.

There are also things no version of this method can see, manual or not. Most of the people who read your posts never react, and they're invisible here. And neither heat nor grade can tell you whether someone is in the market. They tell you who is paying attention and whether they fit. Those are the two best reasons to start a conversation, and neither one makes that person a forecastable opportunity.

What Resonue does with the same method

Resonue is the product we build, and its revenue side is this method, run continuously.

You tell it which voices to track: the company page, the employees who post, and if you want, outside people who talk about you. Every person who engages with a tracked post lands in one engagers list, across all of those voices at once. The person who liked the CEO's post on Monday and commented on the page's post on Thursday is one row with two touches, which is the thing no individual notification feed will ever show you.

Each engager gets a heat score that will look familiar. Likes count 1, comments 3, reposts 5, each touch halves every 30 days, anything older than 180 days drops out, and 10 is where a lead starts reading as warm. We didn't simplify the product for this article; the spreadsheet above is the same arithmetic. The difference is that touches carry their actual dates, the score recomputes without anyone opening a sheet, and every score opens into a breakdown of which touch contributed how many points, so a rep can see why someone is a 12 and not take it on faith.

ICP grading works the way we described too. You define one or more named ICP profiles in the workspace (titles, industries, company sizes, geographies), and each engager is graded A, B, C or off against the best-matching profile, on their current job title as well as their headline. Any dimension you leave empty is skipped, not counted against people. The grade badge shows which dimensions matched and which didn't, so "B because the company is too small" is visible at a glance.

For the first message, a rep can ask Rosa, the assistant inside Resonue, for an outreach draft on any engager. It's generated on demand from that person's own activity: which themes they keep engaging with, which voices, what they actually wrote in a comment, and when. The timing bands above (hot within 7 days, warming to 30, cooling to 90, cold after) are built into how the draft is written, so a cold engager doesn't get an "I noticed you" opener. It comes with a suggested angle and the reasoning behind it, the draft stays under 700 characters, and it's a draft. Nothing is sent by Resonue. The rep edits it and sends it from their own account, and we'd still nudge a hot-band draft toward the topic and away from the action when the action was a bare like.

Sales reps get a home view built around this: a warm leads table with heat and ICP grade on each row, which leaves out existing customers, your own voices and anyone the workspace has blacklisted, and a filter for "Engaged with my posts" so a rep who posts can start with the people who responded to them personally. A rep can discard a lead they've judged not worth it, and it disappears from their view only.

If you'd like to see what your own list looks like, talk to sales. We prepare the demo on your company's own recent posts before the call, so the first thing you look at is the names already sitting under them.