Composite company profile
A Series B developer tools company, roughly 90 employees, $14M ARR, selling an infrastructure observability platform priced at $36K-$180K ACV. Buyer mix: platform engineers as champions, VPs of Engineering and CTOs as economic buyers, mid-market to lower enterprise (500-5,000 employees). Post-Series B mandate: pipeline coverage of 3.5x within two quarters, without diluting the technical brand the founders had built through open source contributions and DevRel content.
The problem
Pipeline had gone flat for three consecutive quarters. Inbound demo requests were declining even as content output stayed constant. The internal growth lead had scaled LinkedIn Ads spend from $18K/month to $62K/month over six months chasing MQL volume. CPL dropped on paper, but SQL rate collapsed from 22% to 6%. Sales was ignoring the leads. The founding team, both engineers, were skeptical of paid social to begin with, and the Series B board deck depended on those numbers.
Prior efforts failed for a predictable reason. The growth lead had optimized every campaign toward “Lead” objective with lead gen forms, targeting a broad “software engineers at 500+ employee companies” saved audience. The result: a flood of individual contributor developers who downloaded ebooks and never opened a sales email. Meanwhile the actual buying committee, platform engineering managers and staff engineers with budget authority, were nowhere in the audience.
What our team diagnosed
Three root causes, none of which showed up in the client’s LinkedIn Campaign Manager dashboard:
- Wrong objective for the funnel stage. Lead Gen Form ads optimize for the cheapest fillable form, not for a qualified fit. On a technical audience, the cost-per-form winners are almost always ICs downloading gated content for career development.
- No account layer. The campaigns had no matched account list. LinkedIn was serving impressions across 40,000+ companies. Actual TAM for the ACV band was closer to 3,800 accounts.
- No message-to-role mapping. One creative variant ran to platform engineers, VPs of Engineering, and CTOs alike. The champion needs a technical proof story. The economic buyer needs a business case. Both were getting neither.
The client was also losing attribution. LinkedIn’s Insight Tag was firing, but click-through conversions were being credited to Direct in HubSpot because the UTM structure collapsed on redirect through their landing page CMS.
Strategy MV3 shipped
We engaged under the Growth AI retainer with an ABM overlay. Vance oversaw the account; our paid team ran build and optimization, our analytics team rebuilt attribution, and our SEO/content team produced the creative and landing assets.
Core moves:
- Rebuilt the target account list from scratch. We combined Ahrefs technographic data, BuiltWith stack fingerprints for competing observability tools, and hiring signal from LinkedIn job posts mentioning platform engineering roles. Result: 3,847 accounts, ranked into three tiers by fit score.
- Split the LinkedIn program into three campaign objectives: Website Visits for tier-1 accounts (warming), Video Views for tier-2 (top of funnel), and Conversation Ads for tier-3 champions we could name.
- Killed all Lead Gen Form ads in the primary program. Retained one Lead Gen Form campaign, but only for gated technical deep-dive content aimed at the champion role.
- Built three creative tracks by persona: platform engineer (technical benchmark story), VP Engineering (team velocity story), CTO (build vs buy story). Each track had five ad variants tested in a 3-week structured rotation.
- Rebuilt UTM structure and fixed the Insight Tag placement on the landing page CMS. Added HubSpot workflow to reassign source when a self-reported field on the demo form flagged LinkedIn.
Implementation
Kickoff to first campaign relaunch: 18 days. Deliverables produced in that window: 15 ad creatives, 3 persona landing pages, 1 gated technical deep-dive (7,200-word benchmark study written with the client’s engineering team as source input), 1 conversation ad flow with 4 branches, revised HubSpot lifecycle stage rules, and a weekly performance readout cadence.
Tooling: LinkedIn Campaign Manager, LinkedIn Matched Audiences, HubSpot, Clearbit for enrichment on demo form submits, Ahrefs for technographic pulls, and a Supabase table our team maintained to sync fit scoring back into HubSpot on a nightly cron.
Reporting cadence: weekly 30-minute call with the client’s growth lead and VP Marketing, monthly written report to the CEO.
Outcomes
Measured across the 5 months following campaign relaunch, compared to the trailing 5 months:
- SQL rate lifted from 6% to 31%. The single largest movement. Reflects the shift from IC ebook downloaders to champion and buyer demo requests.
- Pipeline sourced from LinkedIn Ads grew 4.7x, from $412K to $1.94M in new opportunity value over the measurement window.
- CAC on paid social dropped 38%, driven by higher win rate on qualified pipeline rather than lower CPL.
- Average deal size on LinkedIn-sourced opportunities rose 22%, because the new targeting reached larger accounts inside the ICP.
- Two closed-won deals in the top ACV band ($140K and $168K) came directly from the tier-3 Conversation Ads track within four months of launch.
The board deck for the Series B milestone review showed 3.9x pipeline coverage, above the 3.5x mandate.
Timeline
Total elapsed from kickoff to measured outcome window closing: 7 months. Campaign relaunch shipped in week 3. First closed-won LinkedIn-attributed deal in month 4. Board reporting milestone in month 7.
NDA framing
Client identity is protected under mutual NDA. Category, funding stage, ACV band, and outcome metrics are accurate. Specific deal names, competitive product references, and internal team member names are omitted. Full case detail, including the creative library and the fit-scoring model, is available under mutual sign-off in a discovery call.
What a similar engagement looks like
Series B developer tools companies with $10M-$25M ARR and a technical buyer motion typically fit the Growth AI tier with an ABM overlay. Engagement runs 6 months minimum, with pipeline lift visible by month 3 and CAC improvement by month 5. If you’re scaling LinkedIn Ads spend but SQL rate is falling, that’s the signal we’re looking for on the discovery call.
Book a discovery call to walk through how this applies to your account list, or read more about our ABM program.
Composite testimonial: “Within three weeks of the relaunch our sales team stopped complaining about lead quality and started asking for more meetings. That was the moment I knew we had the model right.” — Priya, VP Marketing
Case study documented by the MV3 Marketing team. Vance Moore MBA oversees all engagements; our paid media, analytics, and content teams execute delivery.
