Outbound (Dec ’24 → Oct ’25) · LinkedIn Ads (Jan ’25 → Oct ’25)

When AirOps first came to us, they weren’t struggling.

They were plateauing.

They’d built an incredible AI content platform trusted by brands like Webflow, Ramp, Carta, and LegalZoom. Their product helped companies dominate AI search results and refresh content that actually ranked. But their growth engine had hit a ceiling.

For almost a year, their monthly pipeline hovered around $536K. Solid, but not explosive. The marketing team had already tested outbound, traditional ads, and email. Nothing broke through.

That’s where we came in.

The real job wasn’t “run ads” or “send outbound”

It was to take a big market… and turn it into a monitored set of accounts we could repeatedly engage until they became pipeline.

So we didn’t build “a LinkedIn campaign” or “an outbound sequence.”

We built a full ABM motion:

TAM → TAL → Signal stack → Coordinated touches → MQL/SQL → Qualified deals

What we built (the system)

1) Start with TAM, then build a TAL using fit + intent — then tier it

First: define the Total Addressable Market (TAM).

Then: build a Target Account List (TAL) using fit + intent signals (SEO relevance, demand indicators, prioritization signals).

Then: tier the TAL from Tier 1–4.

Not as a label.
As a control system for intensity, sequencing, and next actions.

2) Map the TAL in HubSpot so the goal became clear

Once the TAL was mapped into HubSpot, the goal became clear:

Monitor engagement inside target accounts — and measure how that engagement turns into MQLs, SQLs, and qualified pipeline.

That matters because a lot of the best ABM touches don’t look like “book a demo”:

• thought leadership
  
• webinars
  
• positive replies
  
• product moments
  
• retargeting nurture
  

But they still move accounts forward.

So we used HubSpot visibility to answer, every week:

• Which target accounts are engaging?
  
• Which contacts inside those accounts are becoming MQLs / SQLs?
  
• Which accounts are moving toward pipeline?
  
• What stage are they in (awareness vs consideration vs conversion)?
  
• What play should happen next?
  

That’s what dictated strategy.

How we created demand across the TAL

LinkedIn Ads: Thought Leader Ads first, then bottom-of-funnel campaigns

We used a TLA-heavy approach because the audience was expensive:

• top US tech accounts
  
• senior titles
  
• high CPMs
  

So we led with Thought Leader Ads (≈80% of spend) to build familiarity and trust with C-level and VP-level buyers.

Then we saw a clear pattern:

Customer posts talking about the product were the strongest demo drivers.

So we used those posts more aggressively in consideration and bottom-of-funnel campaigns, alongside more direct-response campaign types (e.g., single image) once accounts showed intent.

Outbound: value-first plays to activate the same accounts

Outbound was designed to engage accounts, start conversations, and pull signals:

• webinar invitations
  
• “input request” plays
  
• role-based sequences across the buying group
  
• timing plays (job changes)
  
• iteration based on reply quality
  

Top positive engagement driver:

• Webinar “input request” play → 112 positive replies
  

The compounding layer: signals that controlled the next move

We didn’t “set and forget” any of this.

We stacked signals so we could change plays by stage.

• Trigify + Clay tracked LinkedIn engagement around keywords, creators, and topics
  → used as intent signals for outbound prioritization and ad audience logic
  
• Fibbler + HubSpot captured ad engagement signals (companies + people engaging with ads)
  → synced into CRM so we could trigger plays and prioritize accounts
  

And the plays changed depending on what the account did:

• If an account was cold → keep them in awareness (education, TLAs, TOF outreach)
  
• If an account showed repeated engagement (site revisits, webinar engagement, multiple touches) → move them into consideration (customer proof, use cases)
  
• If an account was clearly warm → run bottom-of-funnel sequences (direct-response campaigns, conversion-focused retargeting, outbound asks aligned to timing)
  

That’s how we created an ABM “flow” where every touch reinforces the next one.

Results (Dec ’24 → Oct ’25)

Core ABM outcome (qualified deals)

Because outbound and LinkedIn ads targeted the same account list and worked together, the clean business outcome is reported as one ABM result:

• $7,831,160 qualified pipeline
  
• $1,518,758 closed-won revenue
  
• 164 deals created
  
• 30 deals closed-won
  

Qualified pipeline = SQLs (not just meetings booked).

Outbound (Dec ’24 → Oct ’25)

• 7,057 outbound emails sent
  
• 1,058 replies across meaningful outcomes (interested / workable / meeting request / info requests)
  
• Best reply month: Oct ’25 (184 replies)
  

LinkedIn Ads impact (two definitions — different lenses)

1) HockeyStack modeled attribution (linear/weighted) | Jan ’25 → Oct ’25

Modeled attribution that assigns LinkedIn a share of pipeline and revenue across touches:

• $233.8K ad spend
  
• $3.6M attributed qualified pipeline
  
• $828.9K attributed closed-won
  
• 15.21x pipeline ROI | 3.55x revenue ROI
  

2) LinkedIn Revenue Attribution (HubSpot matched: engaged → became a deal)

Influenced attribution based on CRM matching:
Contacts that engaged with LinkedIn ads and later became associated with a deal:

• $5,263,401 pipeline influenced
  
• $1,841,880 revenue influenced
  
• Spend shown in this view: $187,269.88 (9.835x ROAS)
  

These are not additive. They’re two ways of measuring LinkedIn’s impact inside a multi-touch ABM motion.

Why it worked

• We treated accounts as the unit of work, not channels
  
• We built a monitored TAL in HubSpot and used engagement to control next actions
  
• TLAs earned attention from expensive audiences, then proof content drove consideration
  
• Outbound led with value (webinars/input) and activated the same accounts Ads warmed
  
• Signals (Trigify + Clay + Fibbler) made prioritization real, not guesswork
  
• Retargeting nurtured buying groups before and during pipeline
  

Summary (Dec ’24 → Oct ’25)

• Qualified pipeline: $7,831,160
  
• Closed-won: $1,518,758
  
• Deals created: 164
  
• Closed-won deals: 30
  

LinkedIn Ads (Jan ’25 → Oct ’25):

• HockeyStack modeled: $3.6M pipeline / $828.9K won on $233.8K spend
  
• LinkedIn influenced: $5.26M pipeline / $1.84M won on $187.3K spend