Best of LinkedIn: Account-based Marketing CW 20/ 21

00:00:00: provided by Thomas Allgaier and Frenis, based on the most relevant posts on LinkedIn about account-based marketing in CW Twenty and Twenty One.

00:00:08: Frenes is a B to B market research company working with enterprises to optimize their campaigns.

00:00:19: You know, if you told a marketing team today that their big strategy for the next six weeks was to just run absolutely zero campaigns They would probably panic.

00:00:28: Oh they definitely think we're getting fired.

00:00:29: I mean in standard high-volume marketing That's just death sentence

00:00:33: right exactly.

00:00:34: But if you're in The B to be space and your selling these really complex high ticket solutions Stepping away from that campaign dashboard for a month and a half is well.

00:00:42: It's actually how you build a million dollar pipeline.

00:00:45: yeah And that structural shift is exactly what were getting into today.

00:00:48: So welcome to the deep dive.

00:00:50: We are unpacking The top account-based marketing or ABM trends that we've seen across LinkedIn over calendar weeks, twenty and twenty one

00:00:58: Yeah And were looking way past the flush passed the surface level tactics To figure out how winning BtoB teams Are actually executing right now in twenty twenty

00:01:07: six.

00:01:08: I think a big overarching theme from all these insights is That people need to stop treating ABM as just another marketing campaign.

00:01:16: It's an accountant investment model.

00:01:18: It really is.

00:01:18: A lot of organizations, they just treat ABM like standard demand gen with a much higher price tag and some fancier software

00:01:27: Which inevitably leads to some incredibly expensive failures.

00:01:30: Yeah, like Matt Royal laid out Some very stark parameters about this.

00:01:34: he basically argues that ABM is an operating philosophy.

00:01:37: It's not a tactic.

00:01:38: no Not all

00:01:39: and if your business model doesn't fit A very specific mathematical profile you honestly shouldn't be doing it at All

00:01:43: right?

00:01:44: He draws a super hard line in the sand regarding deal size.

00:01:48: Like according To his metrics abm Is only mathematically viable for deals with An annual contract value or acv above fifteen thousand pounds.

00:01:56: well yeah That's a hard line

00:01:57: and your sales cycles need to be longer than forty five days And you must be selling to a buying committee of like three or more stakeholders.

00:02:04: Let's actually break down the mechanics.

00:02:05: Of why those minimums exist, right?

00:02:07: Because if I have a product that costs say Five hundred dollars a year my allowable customer acquisition cost is tiny.

00:02:14: Yeah

00:02:14: You have zero margin

00:02:15: exactly.

00:02:16: i can't afford To Have A team Spend Hours Researching As Single Company Just To Sell Five Hundred Dollar License.

00:02:22: The unit economics completely collapse and I just need standard demand gen for that.

00:02:27: Yeah, and beyond the basic economics.

00:02:29: This psychology of the target is kind of shifting to like.

00:02:32: let's have.

00:02:33: Saraswati made this rather provocative statement recently.

00:02:36: He just straight up said B-to-B is dead

00:02:38: which mean?

00:02:39: That sounds like Standard LinkedIn high Kerbali

00:02:41: totally but his underlying point is actually intensely practical.

00:02:45: His argument is that ABM trained teams to target abstract logos, right?

00:02:50: Like you're targeting a company but companies don't reply to emails.

00:02:54: You are ultimately selling to people

00:02:56: Right.

00:02:57: so the operational shift there Is moving from...you know..is this company in our ideal customer profile To....Is There A Specific Human Inside This Organization Experiencing The Exact Problem We Solve Right Now?

00:03:10: Yes Think About What That Looks like In Practice.

00:03:14: Instead of an outbound team trying to just blanket reach a Series B sauce company.

00:03:20: They are looking for, say the VP of marketing at that company who has spent the last thirty days posting on LinkedIn about pipeline attribution problems.

00:03:30: It's the exact same target account

00:03:32: but you've

00:03:32: fundamentally changed the entry point.

00:03:35: You're stepping into their specific context

00:03:37: Exactly.

00:03:38: But to step into that context effectively...you need flawless structural preparation Which kind of brings us back to that six-week dormant period you mentioned at the start.

00:03:47: Yeah, and this is where I look at how teams actually operate today.

00:03:50: And there's just a massive disconnect.

00:03:53: right now companies buy this really expensive ABM software They write a few email templates and they just immediately want to turn the machine on.

00:03:59: yeah?

00:04:00: They want the leads yesterday.

00:04:01: Right

00:04:01: it feels like everyone wants to build a penthouse before laying The foundation.

00:04:04: It's like trying to furnish a house before even pour the concrete.

00:04:07: That is a perfect analogy.

00:04:09: And Trisha Charabra highlighted some fascinating data regarding that exact rush to launch.

00:04:15: She noted that ABM satisfaction across organizations actually drops the longer program runs.

00:04:20: Wait,

00:04:21: really?

00:04:21: You would naturally assume opposite.

00:04:23: right.

00:04:23: The engine gains momentum and efficiency over time.

00:04:26: you'd

00:04:27: think so But it only gains momentum if foundation is solid.

00:04:31: Charba points out that the first six weeks of a successful program shouldn't be campaigns at all.

00:04:36: They should involve deep research, mapping out the internal hierarchy of buying committee and tiering the ICP.

00:04:43: Yeah in.

00:04:44: Vladimir Blagojevich echoed this perfectly too.

00:04:46: he observed most marketing plans mistakenly start by picking channels like oh we're doing ads or events.

00:04:52: then they try to backfill content match some assumed audience

00:04:55: which is exactly why They are running these hyper-targeted ads to a list of accounts that just haven't been properly vetted for actual readiness.

00:05:05: Precisely!

00:05:06: The issue is rarely the creative or channel, it's almost always a shaky foundation…or misaligned sales and marketing teams...or just a complete lack of full funnel orchestration.

00:05:19: But let us look at reality from the marketing leader presenting this plan.

00:05:23: If I walk into my CFOs office I need a massive budget and my team is going to sit in the room doing nothing but research for the next month-and-a-half.

00:05:32: Yeah, i'm probably not surviving that meeting.

00:05:35: no they would laugh you out of their

00:05:36: right.

00:05:37: so how do you defend That heavy upfront investment?

00:05:40: will you defend it with commercial discipline?

00:05:42: You have to prove The math before you launch an.

00:05:44: Ryan Carlin built this brilliant framework for this after he audited over two hundred ABM programs.

00:05:50: Two hundred?

00:05:51: Wow!

00:05:51: Yeah, huge sample size.

00:05:53: and he found that the primary predictor of success isn't messaging or targeting.

00:05:57: it's entirely financial.

00:05:58: you have to build a rigorous comprehensive cost model.

00:06:02: And a true-cost model isn't just in your software license.

00:06:05: No not at all.

00:06:06: It covers three specific buckets media spend The tech stack, and most importantly the people costs.

00:06:12: You have to calculate the fully burdened salaries of the SDRs doing cold outreach... ...the marketing team designing assets…and the ops team maintaining data flow.

00:06:22: Okay so once you establish that baseline cost what's next step?

00:06:26: Because Carlin suggests mapping out the expected pipeline.

00:06:29: Right

00:06:29: exactly!

00:06:30: You multiply your target accounts by historical engagement rate your meeting conversion rate, opportunity conversion and average deal size.

00:06:38: Allowing you to basically calculate the expected revenue ROI before a single email is drafted.

00:06:44: Yep!

00:06:44: You have the whole picture beforehand.

00:06:46: But come on predicting close rates in pipeline Before you send a single message.

00:06:50: I feel like any CFO worth their salt Is going see right through made-up pipeline numbers.

00:06:55: They know marketing just guessing To get budget approved

00:06:58: And that's exactly why Carlin utilizes sensitivity table.

00:07:02: This is really what separates the operators from the amateurs.

00:07:05: You do not just present one optimistic projection, you walk into that meeting with a spreadsheet mapping out conservative base and aggressive scenarios.

00:07:16: Okay so actively show them the worst-case scenario.

00:07:19: You showed in a mathematical reality of what happens if, say your engagement rate drops to twenty percent instead of the projected thirty five percent.

00:07:27: when the CFO inevitably asks What is this completely flops?

00:07:30: you already have math ready.

00:07:31: and just point to column c. The financial structure program has sound.

00:07:37: That's super smart.

00:07:38: And Kiril Kirov provided a really great comparison to back up this financial logic, kind of contrasting ABM with traditional demand generation.

00:07:45: He looked at the mechanics of dropping one hundred k on Google ads versus spending that same amount in highly targeted one-hundred account ABM program.

00:07:53: The waste and traditional paid search for enterprise deals is just staggering.

00:07:58: It really is like if you are targeting enterprise accounts.

00:08:01: Dropping a hundred grand on paid search means you are bidding on broad industry terms.

00:08:06: You end up paying like twenty dollars a click for small businesses or students doing research and just low relevance traffic.

00:08:12: Yeah, ninety percent of your budget completely misses the target Exactly!

00:08:15: And your actual named accounts never even see.

00:08:18: your message ABM fixes that precise financial leak.

00:08:22: So your pitch to the CFO is never you know, ABM as an innovative strategy.

00:08:26: The pitches ABM delivers enterprise pipeline at a drastically lower cost per opportunity than our current ad spend.

00:08:32: perfect Okay?

00:08:33: so if the math has approved the cfo's happy and those six weeks of research are complete We know exactly who was on the buying committee.

00:08:40: Now we hit the operational wall, which is how does a lean marketing team actually execute deeply personalized outreach to an entire enterprise committee without spending all day manually typing out emails and building web pages?

00:08:54: This is where we bring in AI.

00:08:56: AI acting as the ABM execution layer.

00:08:59: And the crucial shift here is that we have finally moved past using AI.

00:09:03: just you know, slightly less robotic cold emails.

00:09:07: Yeah the technical execution stories coming from those sources are incredible.

00:09:11: like Jayla Rizai shared an example of AI first campaign run by Kevin Jong at Genesis Computing.

00:09:17: and get this they had a two-person team no dedicated design resource No developer, no marketing ops manager.

00:09:24: And

00:09:24: what they did was wild!

00:09:25: They utilized Claude alongside a dynamic routing platform called Mutiny to target twenty massive enterprise accounts and sent physical custom Formula One remote control cars.

00:09:37: but the digital follow through is where mechanics are just fascinating.

00:09:40: Usually building twenty completely unique personalized landing pages for Enterprise targets would take a team weeks?

00:09:47: Weeks of drafting, designing and coding.

00:09:49: Yeah

00:09:49: forever.

00:09:50: but instead they fed Claude their overarching brand guidelines in the specific researched pain points for those twenty individual accounts.

00:09:57: Claude generated a highly personalized copy in parallel.

00:10:00: then Mutiny acted as The Routing Layer automatically mapping that generated copy into pre-designed blocks on our website based upon IP address of visitor.

00:10:10: They spun up twenty custom one to one pages

00:10:15: for twenty custom pages.

00:10:17: Yep,

00:10:18: one day!

00:10:18: That's

00:10:19: insane.

00:10:19: and it wasn't just a technical party trick either.

00:10:22: Ten percent of those cold enterprise accounts converted into meetings.

00:10:25: I mean, that is triple the standard industry benchmark.

00:10:28: it Is and we are seeing that exact type of execution happening at even larger scales now.

00:10:33: Nicola Ian an Andy Van Oosterham reported using the optimizely opal agent to automatically generate between eight hundred And two thousand hyper personalized one-to-one landing pages.

00:10:43: Okay Let's actually look at the workflow how that Asian operates.

00:10:45: because its wild a marketer essentially just hits go and walks away To grab a cup of coffee.

00:10:50: yeah in background, the Opal agent connects directly to their CRM.

00:10:54: To pull live prospect data it fetches third party web signals competitor intelligence.

00:10:59: It even navigates to the prospects actual website takes a live screenshot extracts their brand's hex codes and recolors The landing page so visually mirrors the prospects own branding all the while they're drinking coffee.

00:11:11: It's

00:11:11: unbelievable, it entirely removes the content creation bottleneck that has historically choked ABM programs.

00:11:18: and Mojavezhi shared a really similar operational win.

00:11:22: her team used clay-and-clawed code to build seven distinct AI powered ABM plays.

00:11:28: They generated seven hundred seventy thousand dollars in pipeline In just six months completely avoiding any new headcount.

00:11:34: But okay, let me push back a little here using Orla Murphy's perspective.

00:11:38: If AI can scale one-to-one pages instantly and fetch brand colors while we sleep aren't we risking what she calls scaling mediocrity?

00:11:46: Because it's like giving a massive megaphone to someone who has absolutely nothing interesting to say you just get louder noise.

00:11:51: how do we keep it relevant?

00:11:52: that is the inherent danger of cheap personalization because its fast everyone gonna do which means buyers will become completely blind.

00:12:00: So Murphy applies a very strict filter to any automated AVM content, she asks.

00:12:06: Does it genuinely reflect the unique landscape of this specific account?

00:12:10: does It use their actual internal terminology or is it just industry buzzwords?

00:12:15: Yeah She suggests this ultimate stress test which Is Just Asking What A Sales Rep Actually Send This Manually To Strengthen A Relationship If The Human Wouldn't Stake Their Reputation On The Insight automated garbage.

00:12:27: Exactly,

00:12:27: which points directly to an insight from Umprakash Kharupanan regarding where AI actually provides a competitive advantage.

00:12:35: everyone is currently pointing AI at the top of the funnel to blast out personalized cold outreach but that space is already completely saturated.

00:12:42: so Where Is The Actual Leverage Then?

00:12:44: If Not At The Top

00:12:45: It's in the middle Of The Funnel Where Active Deals Quietly Stall Out.

00:12:49: Think About it.

00:12:50: Enterprise Buying Committees Usually Involve Six To Ten People But the reality is most sales reps are single-threaded.

00:12:57: They're only really talking to one champion.

00:12:59: Karupinan suggests AI shouldn't just be broadcasting, it should be listening

00:13:04: Listening.

00:13:04: what specifically?

00:13:05: Like social media?

00:13:07: No!

00:13:07: Call transcripts.

00:13:09: You plug AI into ninety days of gong or chorus transcripts across all your active deals.

00:13:14: It can aggregate every objection raised, but more importantly it can catch the exact moment a champion goes quiet Or spot new stakeholder whose name was briefly mentioned in meeting But who never officially entered CRM.

00:13:27: Oh, wow.

00:13:28: So AI flags those buying committee gaps and alerts the team that a stalled account is ripe for reactivation?

00:13:34: Exactly!

00:13:35: You're using AI to un-stall existing pipeline...

00:13:38: ...so you are using AI as highly sophisticated listening device in mid funnel rather than just a spanned candidate at top.

00:13:44: But if AI's listening That implies we need know what sounds.

00:13:47: to listen right We need the right signals

00:13:50: Yeah, and we are seeing a massive shift away from generic intent data to highly specific custom signals that actually indicate our real buying moment.

00:14:00: Jack Porter from Bird Dog had an amazing example of this.

00:14:04: so standard intent platforms might tell you someone visited your pricing page or they searched for a generic category on G-II.

00:14:12: but Porter had a client who needed to track companies actively moving off spreadsheets.

00:14:18: And

00:14:18: you can't just check a box for Moving Off Spreadsheets in a standard data tool, that doesn't exist?

00:14:22: You can!

00:14:23: So instead of relying on generic intent they wrote custom scripts to scrape job boards.

00:14:27: They set up alerts to trigger whenever a target account posted A new role for a data migration specialist while simultaneously scanning press releases for mentions Of digital transformation initiatives.

00:14:38: see that is a tangible Custom signal.

00:14:40: That represents an actual buying moment not just a

00:14:43: page view exactly.

00:14:45: and David Turvitz highlighted how relying on these signals Fundamentally changes the architecture of your marketing engine.

00:14:51: he points out that while outbound in AP ABM share about seventy percent of the same tools, The internal wiring is completely different.

00:14:57: How so?

00:14:57: Well in standard outbound signals are just used to enrich a list you already built but In a mature A-B-M program the engine Is entirely signal triggered!

00:15:06: The signals don't Enriched the list...the signals decide which accounts Actually get activated and when.

00:15:11: Right it's a reactive architecture But having this signal isn't the finish line.

00:15:16: Declan Mulkeen made an observation About operational reality.

00:15:19: inside most teams Marketers aren't actually short of signals.

00:15:23: They are drowning in email opens, generic intent spikes and webinar attendance

00:15:28: records.".

00:15:28: Yeah they don't have a data problem...they have what Mulkein calls A DECISION PROBLEM!

00:15:33: ...They lack a strategic layer that dictates exactly what to do next with those signals.

00:15:38: which account needs attention today?

00:15:40: What specific message did we send based on the job board

00:15:43: posting?".

00:15:43: And Yulia Lenakova-Antonias Si has brought up an important point about how we act upon these signals once this decision is made.

00:15:50: Their perspective on this is fantastic because it's completely counterintuitive.

00:15:54: Say, our custom scraping tool flags that a target company just secured massive new round of funding?

00:16:01: The immediate marketer instinct is to send an email saying hey I saw you raised thirty million congrats for the funding and want to buy your software!

00:16:09: And Yulia Olenikova argues doing that ruins magic entirely...

00:16:13: She uses this great metaphor, she says it's like a magician pulling a rabbit out of hat and then immediately stopping the show to explain how the trapdoor mechanism works.

00:16:23: By leading with trigger you expose automation

00:16:26: Exactly.

00:16:27: And Anthony Nuttoli expanded on this too, noting that a funding round or new executive hire is not inherently reason to reach out.

00:16:33: you need the context behind signal like how exactly it's planning to deploy capital?

00:16:39: What specific objectives and key results are newly hired VP of revenue operations responsible for hitting in their first ninety days?

00:16:47: Because if you lack that context your just reacting blindly and to.

00:16:52: Neosize pointed out that third-party intent data is now a total commodity.

00:16:56: Everyone has access to the exact same funding alerts when forty seven other vendors see that same signal, and send the exact Same Congrats on The Funding email.

00:17:06: you aren't doing account based marketing.

00:17:08: You're just participating in a synchronized spam event.

00:17:10: Exactly!

00:17:11: The tactical hack here Is To Use The Signal Strictly To Time Your Outreach.

00:17:17: You want the outreach to feel like incredibly serendipitous, well-researched timing.

00:17:21: You deliver specific value based on operational challenges they are facing due their new funding but you never expose that automated trigger that alerted

00:17:35: it."

00:17:37: not proving to the prospect that you bought a fancy tracking software.

00:17:48: Which perfectly encapsulates the evolution we've discussed today, really!

00:17:51: We move from doing nothing but deep non-campaign research for six weeks to lay the foundation... ...to managing CFO's expectations with rigorous sensitivity tables to utilizing AI to dynamically map out mid-funnel blind spots, and finally deploying custom signals to time our entrance without ruining the magic of

00:18:22: And

00:18:24: just to leave you with one final thought, I want to look at Bev Burgess' recent research.

00:18:29: As AI makes it incredibly easy to automate hyper-personalized landing pages and scrape the web for niche buying signals that technology will simply become table stakes.

00:18:39: everyone will have

00:18:39: it.

00:18:40: The real differentiator in twenty twenty six and beyond won't be your tech stack.

00:18:44: It'll be radical honesty.

00:18:47: Enterprise buyers are looking for partners who are willing to admit to previous failures and genuinely understand the messy internal corporate politics of their organization, things that an AI agent cannot replicate or navigate.

00:18:58: As you build out your complex ABM architecture ask yourself Are investing as much effort in human empathy?

00:19:06: Because at the end no matter how seamlessly that opal agent recolors a landing page You're still just trying.

00:19:16: Thanks for joining us and we'll catch you on the next deep dive.