Best of LinkedIn: Account-based Marketing CW 14/ 15

00:00:00: provided by Thomas Allgaier and Frennus, based on the most relevant posts on LinkedIn about account-based marketing in CW-IV and XV.

00:00:08: Frennis is a B to V market research company working with enterprises to optimize their campaigns with research grade account profiling and insights.

00:00:16: You can find more info in the description.

00:00:18: Welcome

00:00:18: to The Deep Dive.

00:00:19: We are so glad you're joining us today.

00:00:21: Yeah

00:00:22: we've got a really great one for ya.

00:00:23: So imagine this scenario For a second...you pull A list of say five hundred perfect target accounts.

00:00:30: You run your absolute best playbook, you spend the budget ,you execute all of It

00:00:53: really is.

00:00:54: And honestly, that's exactly what we are tackling today!

00:00:57: We've spent time analyzing the absolute top insights—the trends and tactical shifts that surfaced across LinkedIn during calendar weeks fourteen or fifteen of twenty-twenty six.

00:01:08: Our mission here to cut through all those high level fluff was to distill what actually works in the trenches right now for you.

00:01:15: Absolutely No outdated theory today.

00:01:18: And to start, we really have to look at the foundation because before we even touch on targeting matrices or all these shiny AI tools there is this fundamental misunderstanding of what ABM actually Exactly.

00:01:32: Manscarlaut, Harlan and Tom Barnes they both hammered this point home recently They basically argue that ABM is not a campaign like you cannot just treat it Like some marketing add-on

00:01:41: right?

00:01:41: And This Is where so many companies Just stumble Right out of the gate.

00:01:44: they uh they decide they want to do abm.

00:01:46: So marketing builds A target account list they launch a few targeted LinkedIn ads Maybe they spin up a specific Outbound email sequence and he

00:01:53: dust off their hands.

00:01:54: great!

00:01:55: They say look at us We're doing ABM But as Tom Barnes pointed out, that's just targeted demand generation.

00:02:01: That's not ABM.

00:02:02: real ABM is a complete go to market engine.

00:02:05: it dictates how your entire business so sales marketing customer success everybody organizes around specific accounts

00:02:13: okay?

00:02:13: So what does that actually look like in practice?

00:02:16: because if the company treats It Just As A Marketing Initiative Like what specifically goes wrong?

00:02:22: Well, Amprakash Karupanan actually called this out as one of the six silent killers of ABM.

00:02:27: Oh wow!

00:02:27: Yeah.

00:02:28: so when ABM is owned solely by marketing it invariably fails because the sales team just ends up treating like a PR side project.

00:02:36: Oh I see.

00:02:36: Marketing over in their corner celebrating ad click through rates while sales basically ignoring target list.

00:02:42: They're still being measured on sheer volume of cold calls.

00:02:45: Right,

00:02:45: right.

00:02:45: if you don't have shared governance Shared KPIs and a shared revenue motion.

00:02:50: it just creates a lot of noise.

00:02:52: I mean that disconnect between sales and marketing is a tale as old as time?

00:02:56: Yeah

00:02:57: But why is fixing it so critical right at this specific moment in twenty-twenty six

00:03:02: because Of how the buyer has evolved And this is huge.

00:03:05: nary Charino highlighted A recent forester finding That should honestly make every revenue leader pause.

00:03:11: Forty-nine percent of BtoB purchases now require full multi department consensus.

00:03:16: We'd hold on, literally half of all purchases require a whole committee to agree?

00:03:20: Yes...half!

00:03:22: The days of you know winning over single enthusiastic champion and getting the deal signed with those days are ending.

00:03:27: You need the CFO, IT lead, end user or procurement officer.

00:03:32: Derek Shuttle noted that the average B-to-B buying committee now includes six to ten decision makers.

00:03:37: Six to ten?

00:03:38: Yeah, because of that we're seeing this necessary shift from ABM account based marketing to ABX account based experience Because marketing to one champion is functionally useless if The other eight people in the room have never even heard of your brand.

00:03:52: Okay

00:03:52: Let's unpack this and ground it with a quick analogy.

00:03:54: So If traditional demand gen Is kind like casting really wide net into the ocean

00:03:58: Right

00:03:59: Then ABM is spearfishing.

00:04:01: But are we saying that now have to spearfish the entire school of fish at this exact same time?

00:04:07: I love it.

00:04:07: Yes, essentially!

00:04:09: Let's use a different one though.

00:04:10: Think of old ABM as sending VIP limo to pick up CEO.

00:04:15: We just focused on that one key person.

00:04:18: But with these six to ten-person committees, sending one limo isn't enough anymore.

00:04:22: You have to coordinate an entire fleet of cars To pick up the CFO The IT lead and end user simultaneously.

00:04:30: Otherwise nobody comes to party.

00:04:32: Okay!

00:04:32: That makes total sense.

00:04:33: That's a perfect way to visualize it And coordinating the entire fleet requires whole company.

00:04:38: If marketing gets champion in their limo but sales doesn't have vehicle for the procurement officer, but whole deal dies.

00:04:44: Exactly!

00:04:45: Okay so we know that we need full company alignment to engage this entire committee.

00:04:50: But look if you're listening right now You are probably looking at your current SDR headcount in your marketing budget and thinking I literally do not have resources to send a fleet of cars to everyone.

00:05:00: Yeah nobody does

00:05:01: Right.

00:05:02: So We Have To Be Incredibly Selective About Which Accounts We Actually Target.

00:05:06: How Do We Pick The Right Ones?

00:05:08: Because just filtering by basic thermographics you know, like industry and company size that doesn't seem to cut it anymore.

00:05:15: It absolutely does not work.

00:05:17: I mean the old playbook was entirely firmographic.

00:05:19: You would just say hey give me a list of SaaS companies in North America with fifty-to five hundred employees.

00:05:25: Write The Classic Zoom Info Filter

00:05:27: Exactly!

00:05:28: But our pet PuroHit broke down exactly why that fails today.

00:05:32: He pointed out That a Firmagraphic Only Ideal Customer Profile or ICP might get you A List Of Five Hundred Target Accounts.

00:05:39: but going back to your opening scenario, it will only yield something like twelve replies and maybe one single demo.

00:05:45: But why is the conversion rate so abysmal if they technically fit that profile perfectly?

00:05:50: Because Firmagraphics alone cannot tell you whether a company actually needs your product.

00:05:54: right now A company can fit their exact criteria.

00:05:57: They have the right industry The right size The Right Revenue And still has absolutely zero reason for buying from today.

00:06:03: So what's missing layer then?

00:06:05: How do we find actual reasons?

00:06:07: According to ARPIT, you have layer in two critical elements.

00:06:10: Use case and product category.

00:06:13: So first, you have to define the specific workflow a company is actively struggling with.

00:06:18: You don't target them just because quote they are in logistics right?

00:06:22: You target them because They're manually doing route planning across multi-leg operations.

00:06:27: that Specific use case is the trigger.

00:06:30: That makes no sense.

00:06:30: and then second you need the product category Because buyers shop for categories like we need a contract management tool.

00:06:38: If they aren't actively in the market for your category, no amount of brilliant outreach is going to magically create that need.

00:06:44: Okay logically That makes perfect sense.

00:06:46: But how do you actually discover that use case from the outside?

00:06:50: Yeah You can just guess what software a company's struggling with internally.

00:06:53: Well you look for digital footprints and Chanda NS shared A really highly specific example Of this in action.

00:07:00: that shows exactly How To build This layer.

00:07:02: Oh I'd love to hear it.

00:07:03: So he was given a target to find accounts that were using this specific CRM competitor.

00:07:08: The goal wasn't just to find companies in the sauce space generally, but companies actively running a tool they might actually want to rip and replace.

00:07:17: Wait hold on standard sales platforms like LinkedIn Sales Navigator?

00:07:21: They don't just have a handy checkbox.

00:07:23: it says uses competitors CRM.

00:07:25: no they Don't.

00:07:26: so how did Chandan Actually pull That off?

00:07:27: without spending Like three weeks manually checking five hundred different company websites.

00:07:33: He had to engineer a custom workflow.

00:07:36: he started outside of traditional sales tools entirely, he used built with which is the tool that scans website source code to export a raw list of companies actively running that target CRM.

00:07:46: okay

00:07:47: then he layered an enrichment data to find recent tech stack changes.

00:07:51: But here is the really brilliant part.

00:07:53: He used a minimum two signal scoring system, Two

00:07:56: signals?

00:07:57: Yeah he didn't just stop at.

00:07:58: they use the competitor.

00:07:59: He cross-referenced that tech stack data with recent hiring activity.

00:08:03: specifically he was looking for open rev ops or sales ops roles.

00:08:07: Oh wow!

00:08:08: That's incredibly smart because new revops hires are going to want come in and make their mark.

00:08:12: Exactly

00:08:13: So

00:08:13: targeting it.

00:08:15: They use the competitor's tool, AND.

00:08:17: they are actively hiring The exact person who would be responsible for managing a transition to A new tool.

00:08:22: you got it?

00:08:24: The Hiring signal indicates a potential willingness To change and that workflow left him with a highly refined list of one hundred and sixty seven verified accounts And his reply rate on that batch was a massive sixteen point three percent.

00:08:39: That's

00:08:40: huge for cold outreach

00:08:41: it is.

00:08:41: and as he pointed out the targeting wasn't better because The email copy with some literary masterpiece.

00:08:47: It was better Because every single contact On that list had A concrete specific and timely reason to be there.

00:08:53: That level of precision is just incredible.

00:08:55: And what's wild, it that the tools to achieve this are evolving so quickly like Anthony Blatner recently posted about how he was using clawed AI to build target list Using criteria The major data platforms don't even offer yet.

00:09:06: Oh

00:09:06: really?

00:09:07: How?

00:09:07: So

00:09:08: yeah He's prompting the AI.

00:09:08: define things Like US mid-market BDB sauce Platforms do not have an AI feature.

00:09:14: Yeah Right You cannot filter for that in standard databases.

00:09:18: you need an AI To actually read the websites Analyze the product pages and make that determination.

00:09:23: That is wild, And it really forces a hard question for anyone running ABM right now.

00:09:28: Is your current ICP just a saved zoom info filter?

00:09:32: Or does it actually identify active buying triggers in specific immediate use cases exactly?

00:09:38: because if It's just a thermographic filter you are playing a volume game In what has basically become a precision sport.

00:09:43: okay So let's follow that logic.

00:09:45: Let's say we have done the work.

00:09:47: We built that hyper-specific list of a hundred sixty seven accounts using the competitor CRM and hiring a revops lead.

00:09:54: but If we just blast all one hundred and sixty seven of them with an email sequence on a Tuesday morning, We're still kind of guessing right?

00:10:01: Yeah.

00:10:02: So how do you know which are those one hundred sixty-seven actually care today?

00:10:05: that is exactly where he had to shift.

00:10:06: from looking at static profiles two observing real time intent in behavioral signals Nick Bennett showed really powerful story about the danger of static lists.

00:10:14: what happened so?

00:10:15: He was running a demand generation campaign for a sauce client.

00:10:19: They hit a really solid ICP Really good copy but the results were completely flat.

00:10:24: And the underlying problem was that they were targeting based on who the accounts looked like, not what they're doing.

00:10:30: Meaning?

00:10:31: They are working off a static list from a trade show six months prior and just assuming everyone is still in buying window.

00:10:38: Precisely!

00:10:39: It's old data.

00:10:40: so he completely overhauled strategy.

00:10:43: using better buyer intelligence He started tracking which account had actually visited pricing page last thirty days.

00:10:49: That's strong signal.

00:10:51: He looked at who was researching competitors on third-party review sites, he analyzed which contacts had gone completely dark in the CRM but were suddenly reengaging with product content.

00:11:01: And by targeting solely based on recent web activity and CRM history... ...he actually cut his target list by forty percent.

00:11:08: Wait!

00:11:08: He cut a target list almost in half?

00:11:10: I mean most sales leaders would have an absolute heart attack.

00:11:12: You're shrinking the pipeline.

00:11:14: It feels terrifying But look at the mechanics of what happens next.

00:11:18: By focusing only on accounts showing active buying intelligence, his team stopped wasting time on dead ends.

00:11:24: The BDR research time dropped by three hours a week and the pipeline conversion went up thirty-eight percent.

00:11:29: That's

00:11:30: incredible.

00:11:30: As Nick said AI didn't fix the campaign.

00:11:33: Better buyer intelligence did.

00:11:35: If you just apply AI to bad static data You just fail faster.

00:11:40: That makes total sense.

00:11:42: We really have to stop treating every contact the same, and actually Suraj Siddharman had a great insight related to this.

00:11:48: He argued that we need to stop labeling everyone a lead in our CRMs.

00:11:52: he called it The Laziest Label We Have.

00:11:56: Right because someone who downloaded a white paper three months ago And never came back is fundamentally not the Same as a CFO Who hit your pricing page Three times This week and opened Every single email.

00:12:06: But in most CRMs, they both just sit there with the exact same lead status.

00:12:10: Which means that automated systems and very often sales reps create them exactly the same.

00:12:14: They get dropped into a generic drip sequence.

00:12:18: We have to track account progression dynamically.

00:12:20: How does he suggest doing this?

00:12:21: Siraj breaks it down into five stages Identified Aware Interested Considering And Selecting.

00:12:28: If twelve accounts move from aware to interested overnight because say multiple stakeholders are suddenly reading your case studies, those are the ones that Rep needs to call today.

00:12:39: Okay but what do we actually do when we spot that movement?

00:12:42: Because knowing they're active is really only half of battle.

00:12:45: What does actual execution?

00:12:47: Yeah, Execution Is Everything.

00:12:48: Kevin Payne laid out a fantastic signal-to-pipeline standard operating procedure for exactly this.

00:12:54: He maps specific behavioral signals to entirely different messaging sequences.

00:12:59: Can you give me an example?

00:13:00: Well, most teams just send the exact same Just Checking In email to everyone.

00:13:04: but Kevin says if a buyer hits your pricing page that is direct website intent.

00:13:09: That gets high urgency.

00:13:10: twenty four hour sequence You strike immediately.

00:13:13: But what If they aren't on Your site?

00:13:15: What if The Signal Is?

00:13:16: They are reading reviews of your competitor On G too.

00:13:20: Ah, that is active evaluation intent.

00:13:23: And that triggers a completely different sequence.

00:13:25: you do not send them pricing.

00:13:27: You send them a sequence focused entirely on differentiation and feature comparison?

00:13:32: You have to map the specific message To this specific moment.

00:13:35: they're in

00:13:36: okay.

00:13:37: here Is where I have to pause because it gets into slightly tricky territory.

00:13:40: Well...is there danger of crossing the line Into being?

00:13:45: Well, creepy.

00:13:46: Like if we have person-level intent data... We know a specific individual was looking at our pricing page.

00:13:52: How do we use that intelligence without sounding like we've hacked their webcam?

00:13:57: It is very fine line to walk I agree but Camille Rexton articulated the difference perfectly.

00:14:02: Using Person Level Intent isn't about proving you are watching them.

00:14:05: it's about providing relevance

00:14:07: Relevance over surveillance.

00:14:08: Exactly!

00:14:09: If send an email saying hey noticed your company on site That is an incredibly weak opener.

00:14:15: It feels generic and vaguely surveillance-like, but if you say hey Sarah saw you checked out our integration docs that actually starts a real contextual conversation.

00:14:25: it says I see what you're trying to achieve And i have specific resources To help with that exact

00:14:31: problem looking through your window, and a helpful concierge holding the door open for you.

00:14:39: That's a great way to put it!

00:14:40: Okay so let's look at the operational reality of this.

00:14:42: we know who to target We Know what behavioral signals To Look For And we know that we need to engage The entire buying committee with hyper relevant concierges level messaging map to their specific actions.

00:14:53: But Let's Be Real Executing This Level Of Personalization Manually That Is Going Burnout Any SDR Team On The Planet In About Three Days?

00:15:02: Oh, absolutely.

00:15:03: And that operational bottleneck brings us to the final major shift we saw in these weeks—practical AI and ABM.

00:15:09: This is where AI steps-in not as a replacement for strategy but has a massive force multiplier for execution.

00:15:16: In fact Kyle McCarthy made very bold claims about this.

00:15:19: He stated that ABM tiering...is entirely dead.

00:15:22: Wait…dead.

00:15:24: But we have tiered accounts forever.

00:15:26: You know Tier One gets white glove treatment Tier Two get standard outreach Tier Three gives automated emails.

00:15:31: Yes but we have to ask why we tiered them in the first place.

00:15:35: Kyle argues that tiering was never actually a strategy, it's just...a capacity problem dressed up as a strategy.

00:15:41: Oh

00:15:42: interesting!

00:15:42: Think about it.

00:15:44: A human rep could only deeply research maybe fifteen accounts per week.

00:15:48: They had read The Ten K reports, Mac the Personas find their recent news.

00:15:53: So he gave those fifteen VIP Tier One treatment and everyone else got a mail-merge template.

00:15:58: because the humans literally just ran out of hours in map the buying committee and identify the pain points instantly.

00:16:15: Because of that, Kyle argues every single account on your list can now get tier one treatment.

00:16:19: That sounds amazing in theory but I really want to understand actual mechanics like how does a complex enterprise actually string this together?

00:16:29: It's definitely not!

00:16:31: Davis Potter shared a real-world application of this from a thirteen hundred person company called Bonterra.

00:16:37: They are running automated ABM orchestration on autopilot at an enterprise scale, and the data flow is just fascinating!

00:16:43: Okay walk me through it...

00:16:45: they start with six cents to identify the anonymous accounts showing those buying signals we talked about earlier.

00:16:50: okay so six cents spots traffic.

00:16:53: then what happens?

00:16:54: That intent data automatically flows into Clay then scrapes LinkedIn and other databases to identify and enrich the specific contacts who make up The Buying Committee at that specific company.

00:17:06: It finds the CFO, the IT lead ,the end user.

00:17:09: Wow okay next clay pushes those enriched context directly into in flu too.

00:17:14: Wait, clarify Influtu for me.

00:17:16: Are they just targeting the company's general IP address with ads?

00:17:20: No

00:17:20: that is a cool part!

00:17:21: Influtu serves highly personalized contact level ads.

00:17:24: The ads are hitting those exact individuals and simultaneously an AI tool called Tofu is triggered.

00:17:30: Tofu what does it do?

00:17:31: Tofu automatically builds custom landing pages tailored to that specific account and drafts the first pass of personalized sales emails.

00:17:39: So when that CFO clicks the ad, The Sales team gets a slack alert And the rep has a pre-drafted highly contextual email ready to send within a twenty four hour window!

00:17:51: The entire workflow is totally seamless.

00:17:53: That's absolutely mind blowing.

00:17:55: It compresses what used take months across functional alignment meetings into matter hours And Ivan Falco actually proved this speed recently.

00:18:02: Oh, what did he do?

00:18:03: He built a full ABM campaign across LinkedIn and Meta in just one hour using Claude Code literally just him at a terminal!

00:18:10: He had AI agents build the ICP matrix find contacts enrich data generate ad concepts write copy push campaigns live via API.

00:18:19: One

00:18:19: HOUR!

00:18:20: Yeah thirty minutes to build list.

00:18:22: thirty minute still launch ads.

00:18:23: That's

00:18:23: incredible.

00:18:24: but you know there is crucial point.

00:18:26: understand here.

00:18:27: The AI did not invent the strategy.

00:18:29: It didn't fix bad product or poor sales alignment, it took a sound meticulously crafted strategy and just executed the manual time consuming labor at lightning speed .It basically removes the operational friction so human marketers can focus on things that do NOT scale like building actual trust navigating complex nuanced enterprise relationships.

00:18:50: We have covered a massive amount of ground today.

00:18:52: I mean, we completely deconstructed ABM moving from treating it as a marketing campaign to a foundational go-to market engine.

00:19:16: As

00:19:17: we spend all this time optimizing our systems to reach human buyers, We have to recognize that the buying journey itself is evolving right under our feet.

00:19:36: It

00:19:37: means that AI agents are increasingly doing the initial research.

00:19:42: They're the ones comparing vendors, reading the documentation and influencing outcomes before a human buyer even enters the conversation.

00:19:49: Oh wow!

00:19:50: Yeah so The new battleground is no longer just how we influence Human Buyers with our ABM campaigns.

00:19:56: it Is How We Influence AI Decision Systems?

00:19:59: Let's pause on there for second because That completely flips how we think about content.

00:20:03: yeah...we

00:20:03: spent all this time making PDFs look pretty For Human Eyes.

00:20:07: But if Shashank is right, we need to structure our data so an AI agent scraping our site actually understands the context of our value proposition.

00:20:14: Precisely!

00:20:16: If your website is full marketing fluff a human might skim it and sort-of get the vibe.

00:20:20: but if An AI Agent is evaluating you technical specs against a competitor To build shortlist for CFO It will just score your vague copy as zero And move

00:20:29: on.

00:20:29: That's brutal

00:20:30: It Is...If The AI Doesn't Understand Your Brand You Won't Even Make Short List.

00:20:34: Your content has to be structured, contextual and AI readable.

00:20:38: That is a wild slightly terrifying thought.

00:20:41: to end on like are you optimizing your campaigns for the human?

00:20:47: or for the algorithm that The Buyer's AI is using.

00:20:50: That definitely something to seriously mull over as you build your next strategy.

00:20:54: Absolutely If you enjoyed this episode, new episodes drop every two weeks.

00:20:58: Also check out our other editions on Field Marketing Channel and Partner Marketing AI in B-to-B Martech Go To Market And Social Selling.

00:21:05: Thank You so much For Joining Us On This Deep Dive.

00:21:07: Don't Forget To Subscribe.

00:21:09: We'll Catch You Next Time.

00:21:10: Remember if Your ABM Strategy Is Just Sending One Limo You Are Going To Miss The Party Entirely.