Best of LinkedIn: Strategic B2B Marketing · 2026-07-29 · 22 min
Key moments - from our scoring
Substance score
56 / 100
Five dimensions, 20 points each
LinkedIn's reach has collapsed 60% over two years, but the platform hasn't broken - it's evolved into a semantic AI system (internally called 360 Brew) that reads your entire professional biography rather than checking keywords. This architectural shift punishes old tactics: sharing company posts drops visibility 40-60%, external links reduce reach 50-70%, and templated AI hooks get suppressed. The winners are niche creators with consistent expertise, high dwell-time content, and authentic human voice. For B2B marketing teams facing this new reality, the operational imperative is decentralizing distribution away from corporate brand pages toward individual employees - engineers, support staff, and product managers - who carry credibility and battle scars buyers trust. When companies compensated employees $300-500 per post with creative control instead of forcing scripted advocacy, results improved dramatically: six team members generated 472,000 impressions and 1,000 job applications. The second half addresses closing revenue: 40% of B2B deals die in no decision because buying committees lack confidence, so warm social content must precede sales outreach. The final challenge is attribution - last-click models miss the silent majority of lurking prospects who consume content privately before requesting demos. Marketing must track influence pipeline across entire deal cycles and monitor digital intent signals (profile views, role changes, funding events) to trigger contextual, value-led outreach.
Active creator reach has dropped 60% because LinkedIn's algorithm shifted from keyword-matching to a 150-billion-parameter AI model (360 Brew) that evaluates semantic context and professional biography. The platform now filters content that doesn't align with your proven expertise and penalizes generic tactics like shared posts (40-60% reach loss) and external links (50-70% loss).
Focus on one operational topic you know deeply and write with plain, conversational language. Post three high-quality posts per week (not daily), prioritize strong hooks that challenge assumptions or highlight operational pain, and prioritize dwell time and saves over vanity engagement. AI-assisted content with human voice outperforms fully AI-written posts by 32%.
B2B buyers trust battle scars and authentic human experience, not polished corporate messaging. Since buying committees include 13+ stakeholders with different concerns, decentralized teams of engineers, support staff, and product managers can speak to each persona. When employees were compensated and given creative control, six team members generated 472,000 impressions versus corporate pages achieving minimal reach.
Monitor intent signals like profile views of your executives, role changes, and funding events in your target account list. Reach out with contextual, value-led messages timed to their operational need, but avoid revealing the surveillance mechanism or leading with a pitch - instead provide utility related to their new mandate or challenge.
Replace last-click attribution with self-reported attribution (adding a mandatory 'how did you hear about us' field) and track influence pipeline across the entire six-month deal cycle. This captures silent prospects who consume content privately, screenshot it in internal Slacks, then search your company three weeks later - a journey last-click models completely misattribute to organic search.
Our reviewer’s read on each dimension, with quotes from the episode.
The episode delivers moderately dense insights about LinkedIn's algorithm shift (60% reach decline, 150B parameter AI model, topic consistency rewards) and actionable tactical advice (3 posts/week outperforms daily, AI-assisted beats fully-AI content by 32%). However, it relies heavily on cited external sources rather than original analysis, and several claims lack substantive depth - e.g., the '95% out-of-network reach' claim is stated without context on what 'distribution' means, and the connection between AI search engines and LinkedIn strategy feels underdeveloped. The episode avoids obvious fluff but doesn't consistently dig into the mechanics behind each claim.
active creator reach has dropped by roughly 60%
The AI is incredibly adept at recognizing other AI generated patterns
The core framework - that LinkedIn's algorithm now rewards niche expertise, topic consistency, and human voice over spray-and-pray tactics - is sound but largely derivative of existing LinkedIn creator discourse. The 'librarian' metaphor is novel and memorable, and the connection between content strategy and B2B buying committee composition (13 stakeholders) adds structure. However, the episode recycles well-worn B2B truisms (people buy from people, authenticity matters, AI can't replace battle scars) and leans heavily on attributed insights from other voices rather than synthesizing a genuinely fresh perspective. The forward-looking point about AI search engines training on LinkedIn consistency is interesting but underdeveloped.
Instead became this highly observant librarian. Like this librarian reads the last 50 chapters of your professional autobiography
AI doesn't have battle scars. Humans do. And buyers trust battle scars
This is a critical weakness: the episode cites ~25+ names (Jay Clouse, Greg Dristus, Chris Timolean, Justin Hardy, Heather Adkins, Ike Singh Cajal, Alex Lieberman, Devin Reed, etc.) but provides no credential context, company affiliations, or evidence that these are practitioners who've executed at scale. Many appear to be career podcast guests or LinkedIn thought-leaders rather than operators with verifiable track records. The two hosts (Thomas Allgaier and speakers unnamed) are introduced only as representatives of Frenas, an event targeting firm - no indication they've built or scaled social selling programs. This is pure appeal to authority without substance; the audience has no way to judge whether these cited sources actually know what they're talking about.
Based on the most relevant LinkedIn posts about social selling in calendar weeks 29 and 30
Jay Clouse, um, analyzing over a million posts
The episode provides abundant specific metrics (60% reach drop, 150B parameter AI, 40-60% penalty for share button, 50-70% penalty for external links, 78% boost for niche content, 30-40% drop for fully-AI posts, 32% boost for AI-assisted, 472,000 impressions from 11 posts, $300-500 per-post compensation, 40% deals die in no-decision, 90% of leads are lurkers, 13 stakeholders in buying committee). However, most of these figures are attributed to other sources without original data or methodology disclosure. The '472,000 impressions from 6 people's 11 posts' claim is striking but lacks context - impressions vs. engagement vs. revenue? The episode rarely grounds claims in named companies or time periods, making it hard to validate or apply.
472,000 impressions and drove over 1,000 job applications
When they transition from an unpaid corporate scripted advocacy program to actually compensating employees and practitioners 300 to $500 per post
The hosts maintain good conversational momentum with natural back-and-forths, strategic pauses for emphasis, and occasional playful asides ('360 brew sounds like a coffee blend'). They do push back twice - questioning why B2B buyers care if a human vs. AI wrote a post, and asking how sales reps avoid seeming like 'digital stalkers.' However, these pushbacks are mild and don't generate real tension; the challenged speaker easily pivots with pre-formed answers. The hosts rarely drill deeper into contradictions - e.g., if 90% of valuable buyers are lurkers who never engage, how does a decentralized content network actually work at scale? They also fail to ask follow-up questions on specifics (e.g., which companies ran the $300-500 per-post compensation programs, and what were actual revenue outcomes?). The episode reads as two well-prepared voices executing a script rather than genuine exploration.
But I mean, let's be honest about the internal friction here
But wait, let me challenge the practicality of this
Computed from the transcript - who did the talking, and the words that came up most.
LinkedIn Strategy in 2026: AI Rankings, Thought Leadership, and Revenue-Driven Engagement We curate most relevant posts about Social Selling on LinkedIn and regularly share key takeaways. This edition examine the shifting landscape of LinkedIn strategy in 2026, highlighting a move away from generic automation toward authentic professional expertise. A major update to the platform’s AI-driven ranking system, often referred to as 360Brew, now prioritises niche authority and meaningful conversation over traditional engagement metrics like likes or hashtags. Experts recommend adopting employee-led content and direct messaging frameworks to bridge the gap between social visibility and quantifiable revenue. While generative AI remains a powerful tool for research, the consensus warns against "AI slop," as the algorithm increasingly rewards human storytelling and unique perspective. Successful B2B teams are now integrating intent signals with targeted outreach to build trust throughout long sales cycles. Ultimately, the reports suggest that consistency and native platform features are the most reliable methods for sustaining reach in a crowded digital environment.
Transcribed and scored by The B2B Podcast Index.
Speaker A: Provided by Thomas Allgaier and Frenas. Based on the most relevant LinkedIn posts about social selling in calendar weeks 29 and 30. Freniss supports clients with identifying target attendees for events, crafting outreach that cuts through the noise and driving qualified registrations through strategic LinkedIn engagement. You can find more info in the description.
Speaker B: Right, so we're really jumping right into the deep end today.
Speaker A: Yeah, exactly. And you know, over the last two years you've probably noticed your posts are getting like a fraction of the views they used to.
Speaker B: Oh, absolutely. Everyone's feeling it.
Speaker A: Right. And if you are a strategic B2B marketing professional managing a social selling strategy, you are not imagining it. I mean, data from Jay Clouse, um, analyzing over a million posts, it actually reveals that active creator reach has dropped by roughly 60%.
Speaker B: 60%. I mean that is just massive.
Speaker A: It's huge. But the algorithm isn't broken. It's uh, it has just evolved into something entirely different. So today we are breaking down the underlying mechanics of that shift.
Speaker B: Yeah, we are going to deconstruct the absolute top social selling trends across the platform right now. And just to set expectations for you, uh, we are skipping the basic fluff.
Speaker A: No fluff today.
Speaker B: Exactly. We are diving straight into the operational realities of, you know, how this platform evaluates content, how you activate your internal teams, and really how you actually track this activity down to closed 1 revenue.
Speaker A: Because that 60% drop in reach, it fundamentally changes the math for B2B pipeline generation.
Speaker B: It really does.
Speaker A: We used to rely on a certain volume of top of funnel awareness to just make the numbers work. And if that volume is cut by more than half, well, we have to understand the machine that controls the board. I mean, the platform clearly isn't just counting hashtags or chronological timestamps anymore.
Speaker B: Oh, far from it. The uh, the foundational architecture has been completely overhauled.
Speaker A: Right.
Speaker B: The old rules based algorithm, it's been replaced by a 150 billion parameter AI
Speaker A: model W. 150 billion?
Speaker B: Yeah. And Greg Dristus and Chris Timolean highlighted that this new system is often referred to internally as 360 brew.
Speaker A: 360 brew. Sounds like a coffee blend.
Speaker B: Right? But it actually sits in the same architectural family as large language models like ChatGPT. It doesn't look at a post as a, you know, a checklist, uh, of keywords. It evaluates semantic context.
Speaker A: So it's actually reading it.
Speaker B: Exactly. It essentially reads your profile, your entire posting history and your comment behavior and it weaves together this like, cohesive biography
Speaker A: of Your professional expertise, which changes the entire paradigm. I mean, we've been taught for a decade to cast the widest net possible.
Speaker B: Oh yeah, spray and pray, Right?
Speaker A: But if the platform is writing a biography about my specific expertise, it means it's actively filtering out anything that doesn't fit that narrative.
Speaker B: Yeah, and, um, it is actively penalizing tactics that used to be considered standard growth hacks.
Speaker A: Yeah.
Speaker B: Justin Hardy and Tim Keane pulled some fascinating data illustrating this.
Speaker A: What'd they find?
Speaker B: Well, if you rely on the share button to distribute company news, you are instantly hit with a 40 to 60% reduction in visibility.
Speaker A: Just for hitting share?
Speaker B: Just for hitting share. And if you drop external links directly into the body of your post, you're losing like 50 to 70% of your reach, man.
Speaker A: Which means the platform is punishing anything that tries to pull the user away. Or, you know, anything that feels lazily distributed.
Speaker B: Exactly. Keep them hm. On the platform.
Speaker A: And I assume that extends to how we write the hook hooks too. Like if you start a post with templated AI language phrases like stop X, start Y or here's what nobody tells you about B2B sales. The system flags that structural predictability, right?
Speaker B: Yes. It sees it as generic and suppresses it. The AI is incredibly adept at recognizing other AI generated patterns. But, uh, there's a flip side to this.
Speaker A: Okay, good. Give us the good news.
Speaker B: The algorithm heavily rewards deep topic consistency. Cindy Dodd noted that niche and even so called boring industries like highly specialized aerospace engineering or defense logistics, they're seeing up to 78% higher distribution.
Speaker A: 78% just for being niche?
Speaker B: Yeah, because there was less noise and fewer people claiming that specific expertise. So the system confidently distributes their content to a highly targeted audience.
Speaker A: That makes a lot of sense. And the metrics the system uses to measure success, Those have shifted away from vanity engagement, right?
Speaker B: Late, late.
Speaker A: Yeah. Heather Adkins pointed out that saves and dwell time are the new super metrics. So a superficial comment like great insights barely registers anymore.
Speaker B: Not at all.
Speaker A: The machine wants to see that a user stopped their scroll, spent two minutes reading a complex breakdown, and physically bookmarked it for later reference.
Speaker B: Right, and when you combine that deep topic consistency with high dwell time, the distribution model just flips in your favor. Christy K. Jones and Tony Restel observe this massive spike in out of network reach.
Speaker A: Out of network meaning people who don't follow you.
Speaker B: Exactly. In some cases, up to 95% of a post visibility is going to non followers.
Speaker A: Wow.
Speaker B: The platform is essentially matchmaking. It takes your verified expertise and pushes it directly into the feeds of strangers who have demonstrated a behavioral need for that exact subject matter.
Speaker A: So it's like LinkedIn stopped being a slot machine where you just pull a hashtag lever hoping for a jackpot of random views.
Speaker B: Right.
Speaker A: And instead became this highly observant librarian. Like this librarian reads the last 50 chapters of your professional autobiography, analyzes your actual depth of knowledge, and then walks across the library to hand your book to a stranger who just asked a hyper specific question.
Speaker B: I love that. That is a brilliant way to conceptualize it. The librarian knows exactly what the stranger needs to solve their business problem. And he knows exactly whether your historical footprint qualifies you to answer it. Yeah, which brings up a critical operational bottleneck for marketing teams. I mean, how do you consistently produce content for that AI librarian without falling into the trap of mass producing what the industry is calling AI slop.
Speaker A: Oh, the AI slop. Because if the machine is judging your unique value, you cannot just outsource your original thinking to a basic prompt.
Speaker B: No, you really can't.
Speaker A: The data from Leah Bliss paints a very stark picture of this. Fully AI written posts are experiencing a 30 to 40% drop in reach, accompanied by a massive reduction in actual human engagement.
Speaker B: People can smell it a mile away.
Speaker A: They really can. But here is the nuance. AI assisted posts where the technology is used for ideation or outlining, but a human voice drives the actual writing. They are actually seeing a 32% boost in engagement.
Speaker B: And that nuance right there reveals how the platform actually operates. Tim S. Dodd clarified this dynamic perfectly.
Speaker A: What did he say?
Speaker B: Well, the platform is not strictly penalizing the use of AI tools. I mean, they are actively integrating AI into their own user interface.
Speaker A: Right. They have their own AI tools.
Speaker B: Now. Exactly. What they're penalizing is the homogenized generic output that AiI typically produces when it's left unguided. And they are making that judgment based strictly on reader reaction.
Speaker A: Ah. Uh, so it's behavior driven.
Speaker B: Yes. If the audience recognizes the robotic cadence and just scrolls past it, the algorithm kills the reach.
Speaker A: And in a B2B context, Katie Reithall makes the argument that trust requires a visible, identifiable human.
Speaker B: Absolutely.
Speaker A: You might be able to use faceless AI content to sell like a $10 consumer gadget at scale. But no enterprise software buyer is signing a six figure contract based on a listicle generated by an avatar.
Speaker B: The stakes are Simply too high. B2B purchases carry real career risk for the buyer. But, uh, there is an even deeper systemic issue here for marketing professionals. Anna Lerner Nesbitt warned about this Psychological shift she calls cognitive surrender.
Speaker A: Oh, uh, I want to spend a minute on this because it feels incredibly relevant to how marketing teams are operating right now. It really is cognitive surrender. It basically happens when we stop using our slow, careful reasoning and just hand the analytical heavy lifting on over to an AI.
Speaker B: Yeah. And it degrades our capacity for genuine thought leadership.
Speaker A: Uh-huh.
Speaker B: When marketers rely on an LLM to summarize a complex industry white paper, instead of reading it and wrestling with the data themselves, they lose the ability to form an original, contrasting opinion.
Speaker A: They just get the average.
Speaker B: Exactly. They confidently accept the AI's flattened average consensus. They end up wearing the blazer of an expert, but there is absolutely no substantive battle tested experience underneath it.
Speaker A: They completely lose their edge. So for the practitioners staring at a blank screen trying to avoid cognitive surrender, the tactical advice from Zoe Hartsfield is to just stop over complicating the strategy.
Speaker B: Keep it simple.
Speaker A: You do not need 12 intricately mapped content pillars. You need to pick one operational topic, you know, cold, and write about it with the plain, unpolished language you would use when talking to a colleague over coffee.
Speaker B: Right. And pay meticulous attention to the entry point. Ryan Yocchi observed that it is rarely the algorithm that arbitrarily buries a post. It is almost always a weak opening line.
Speaker A: The hook is everything.
Speaker B: The first two sentences carry the entire weight of the content. You have to hook the reader immediately by challenging an industry assumption or, you know, highlighting a specific, painful operational reality.
Speaker A: And from a capacity standpoint, Tiffany Spinalnalli ran the numbers and found that a cadence of three high quality, deeply considered posts a week and easily outperforms daily forgettable fluff.
Speaker B: Less is more.
Speaker A: The sheer volume approach is totally dead. But, you know, let me push back on this entire premise for a second.
Speaker B: Yeah, go for it.
Speaker A: Are we just penalizing efficiency here? Like, if an AI writes a structurally perfect, highly informative post about supply chain logistics, why does the B2B buyer actually care if a human wrote it?
Speaker B: Well, because a structurally perfect post demonstrates formatting skills, not implementation experience. Uh, B2B buyers are looking for signals that you understand the messy, complicated reality of their specific business environment. I mean, an AI can list the five steps to optimize a supply chain.
Speaker A: Sure.
Speaker B: But only a human practitioner can tell a story about how step three completely falls apart when the procurement team uses legacy software, and then how to navigate that internal political friction.
Speaker A: That's a great point.
Speaker B: AI doesn't have battle scars. Humans do. And buyers trust battle scars.
Speaker A: The messy middle is where the trust is actually built.
Speaker B: Exactly.
Speaker A: By the way, if you are finding this breakdown of the mechanics helpful, make sure to hit subscribe on whatever app you are using to listen. We do these deep dives every two weeks, pulling out the actionable signals from the noise so you can adjust your strategies in real time.
Speaker B: Highly recommend subscribing.
Speaker A: Which brings us to a massive structural challenge for B2B brands. If authentic human voice and actual battle scars are the only currency that buys reach and trust, well, a faceless corporate logo is fundamentally disadvantaged. A brand page cannot have battle scars.
Speaker B: No, it can't. It is a profound bottleneck. Distribution has to be routed through individual people.
Speaker A: People buy from people.
Speaker B: Always. Dan Rosenthal broke down the arithmetic on this the average modern B2B buying decision involves roughly 13 internal stakeholders.
Speaker A: Thirteen? That's a huge committee.
Speaker B: It is. And a single corporate marketing account cannot possibly speak to the highly specific concerns of the cfo, the Chief Information Security Officer, and the end user all at once. But a decentralized team of employees, utilizing their combined networks and distinct professional lenses, they can absolutely cover that entire buying committee.
Speaker A: And the results of shifting that distribution model are pretty staggering. AJ Eckstein detailed a scenario where a company stopped muting their internal experts.
Speaker B: What'd they do?
Speaker A: Instead of forcing employees to copy paste those sanitized corporate press releases, they permitted them to post freely about the honest, unpolished chaos of their daily jobs. I love that six, uh, team members publishing just 11 posts generated 472,000 impressions and drove over 1,000 job applications just from being honest.
Speaker B: Yeah, that proves that the most effective corporate influencers are rarely the polished marketing executives. PJ Catalano emphasized that the real influence lies with your engineers, your customer support staff, and your product managers, the ones
Speaker A: doing the actual work.
Speaker B: These are the people operating in the trenches. When an engineer explains exactly how a piece of technology circumvents a common infrastructure failure, the technical buyer on the other end trusts them implicitly.
Speaker A: And Philip Werner and Kesha Fowler backed this up through their own pilot programs. They found that the content that drove the highest engagement wasn't coming from the employees with massive existing followings or like media training.
Speaker B: Really?
Speaker A: Yeah. It was raw, authentic reflections from practitioners simply sharing their daily operational hurdles. But I mean, let's be honest about the internal friction here. Getting a team of engineers or executives to consistently write content is incredibly difficult. Usually marketing just drops a plea into a company wide slack channel and it is met with, uh, total silence.
Speaker B: Crickets. Because nobody wants to risk their personal professional brand by acting As a mouthpiece for a corporate marketing message they didn't even write right. The incentive structure is entirely broken.
Speaker A: So Ike Singh Cajal proposed a radically different incentive structure. Just stop asking for favors and start paying them.
Speaker B: Money talks.
Speaker A: It does. When they transition from an unpaid corporate scripted advocacy program to actually compensating employees and practitioners 300 to $500 per post, and allowing them total control over their own voice and angle, the program outperformed the legacy model by a factor of.
Speaker B: That's incredible. And it makes sense. You are essentially taking a budget that would have been burned on low, converting sponsored ads, and reallocating it directly into the pockets of the most credible sources available. Alex Lieberman expanded on this dynamic, noting that internal executives are proving to be the Most cost effective B2B creators on
Speaker A: the market over influencers.
Speaker B: Oh yeah. By consistently sharing their strategic level expertise, they are driving pipeline generating pr and in some cases facilitating massive funding rounds.
Speaker A: I look at this like we are moving away from a single centralized corporate megaphone standing on a stage, and we are replacing it with a decentralized Mess network of 50 highly specialized walkie talkies.
Speaker B: That's a great image.
Speaker A: Each walkie talkie is tuned to a very specific frequency, speaking directly to a specific stakeholder in the market.
Speaker B: A mesh network that surrounds the entire buying committee. But, uh, having that network operational and capturing top of funnel attention is only the first phase.
Speaker A: Right? Attention doesn't equal revenue.
Speaker B: Exactly. The critical failure point for most marketing teams is how they transition that attention into closed won revenue.
Speaker A: Because organic impressions do not meet payroll. So what does the actual conversion architecture look like in this new algorithm?
Speaker B: Well, let's ground this in a reality check from Devin Reed. 40% of B2B deals currently die in no decision.
Speaker A: 40%?
Speaker B: Yeah, the buyer doesn't go to a competitor. The initiative just stalls out entirely. And this happens because the buying committee lacks the collective confidence to sign off on a risky change.
Speaker A: That makes sense.
Speaker B: So the primary function of this decentralized content network is to build that confidence in public months before a sales representative ever schedules a discovery call.
Speaker A: But the sales team cannot just sit back, stare at the CRM and wait for inbound content leads to magically request a demo.
Speaker B: Um, they have to work it.
Speaker A: Daniel Disney and Adia Tolle stressed that the highest conversion rates occur when you aggressively combine this organic social strategy with targeted cold calling.
Speaker B: It's a one, two punch.
Speaker A: Yeah, the content warms the prospect and establishes credibility. And the phone call forces the timeline and closes the deal. They are interdependent the friction arises in
Speaker B: how marketing departments attempt to measure that warmup period. Meilan um Kong detailed the systemic failure of relying on last click attribution software.
Speaker A: Oh, uh, last Click is the worst.
Speaker B: It is blind to the actual B2B buyer journey. I mean, it cannot track a chief operating officer who reads your engineer's post on their phone during a commute. Screenshots it shares it in a private executive Slack channel, and then directly Googles your company name three weeks later to request a demo.
Speaker A: In that scenario, the CRM assigns 100% of the pipeline credit to organic search.
Speaker B: Exactly.
Speaker A: And the Chief Financial officer looks at the marketing dashboard and concludes that the social selling program is a complete waste of budget.
Speaker B: Precisely. To solve this, marketing operations must implement self reported attribution.
Speaker A: Like asking them directly.
Speaker B: Yeah, adding a mandatory how did you hear about us? Free text field on the intake forms. Furthermore, they need to track influence pipeline logging every single digital interaction across the entire six month deal cycle, rather than just crowning the final click.
Speaker A: Because the behavioral reality of these buyers, as Adam Noir highlighted, is that the vast majority are lurkers.
Speaker B: Yes. Silent lurkers.
Speaker A: Up to 90% of the leads you eventually close will never publish their own content. And they will never publicly comment on yours.
Speaker B: Never.
Speaker A: They are a, uh, silent majority. Watching your mesh network of walkie talkies until the exact quarter they have budget to deploy.
Speaker B: Which means the actual selling relies entirely on monitoring intent signals behind the scenes.
Speaker A: Right.
Speaker B: Nancy d', Onofrio, Aiden Collins and Eduardo Schuch. Detailed operational frameworks for this. Your sales development reps need to monitor who is repeatedly viewing the profiles of your executive team.
Speaker A: Okay.
Speaker B: Who is engaging with a competitor's technical teardown? Who within your target account list just changed roles or secured a new round of funding.
Speaker A: You're looking for the digital footprint.
Speaker B: Exactly. You score those digital footprints against your ideal customer profile, and then you shift the conversation into the direct messages.
Speaker A: But wait, let me challenge the practicality of this.
Speaker B: Okay.
Speaker A: What's the concern if our most valuable buyers are intentionally lurking in the shadows and deliberately avoiding public engagement? How does a sales rep transition that into a direct message without coming across as, like, a digital stalker?
Speaker B: It's a very fair question. And it's a critical distinction to make. Monitoring intent signals is not stalking. It is active listening.
Speaker A: Active listening?
Speaker B: Yeah. If a potential buyer walks into a physical showroom and spends 15 minutes analyzing a specific piece of machinery, a, uh, competent sales professional approaches them to answer questions.
Speaker A: Yes, that's just good service.
Speaker B: Digital intent requires the exact same contextual Awareness.
Speaker A: You just have to be careful not to reveal the surveillance mechanism. Like you don't send a message saying, hey, I saw you looking at my CEO's profile.
Speaker B: Exactly. Do not do that. You reach out with contextual relevance. You message them saying, I notice your team is expanding its footprint in this specific sector based on the operational challenges we typically see at this stage. Here is a technical resource you might find useful.
Speaker A: So it's value led.
Speaker B: It is about providing utility at the precise moment of need.
Speaker A: But there are massive operational pitfalls here if you get the execution wrong. Connor Paulson and Mandy McEwen audited several failed social selling strategy and provided a clear list of what to avoid.
Speaker B: What's on the do not do list?
Speaker A: First, do not fully automate your outreach sequences. The AI algorithm can detect the lack of human variants and it will aggressively filter your messages right into the hidden other inbox.
Speaker B: Oh yeah, you'll never be seen.
Speaker A: Second, do not pitch slap a prospect in the very first message. You have to lead with their operational problem, not your product solution.
Speaker B: Nobody wants a pitch on the first interaction.
Speaker A: And finally, stop leaving generic congratulations on job update notifications just to check a box in your CRM.
Speaker B: It is so transparent. Yeah, because it completely blends into the noise. You look exactly like the 50 other automated bots sending the same congratulatory note.
Speaker A: Exactly.
Speaker B: The strategic play is to wait three weeks, research the new mandate they were hired to execute, and check in with a highly specific insight related to their new role. It requires intense patience.
Speaker A: It is a forever game, not a frantic end of quarter sprint.
Speaker B: It really is. And you know, before we wrap up, there is one final forward looking strategic shift that we really need to address.
Speaker A: What's that?
Speaker B: Chris Long and Terry Heath brought up a massive emerging factor. AI search engines like ChatGPT and Claude are actively summarizing the web to answer user queries. When they formulate those answers, they are scanning digital footprints for verified evidence of expertise.
Speaker A: So they're reading LinkedIn too?
Speaker B: Yes. The consistency of your headline, your newsletters, and your deeply technical posts. They are building a permanent digital knowledge graph.
Speaker A: Wow.
Speaker B: When a buyer asks an AI engine for the best vendor in your specific niche, that engine will cite the most consistent authoritative footprints it can find.
Speaker A: So it's not just about the feed anymore.
Speaker B: No. The ultimate question you need to be asking yourself right now isn't just how you reach a human buyer on a chronological feed today. It is, are you systematically writing to train the AI engines of tomorrow?
Speaker A: That is something to think about if you enjoyed this episode. New episodes drop every two ricks. Also check out our other editions on field Marketing, MarTech AI and B2B. Go to Market, ABM and Channel Marketing and Partner Ecosystem. Thank you for joining us for this deep dive into the underlying mechanics of social selling. Make sure you hit subscribe and we will see you next time.
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