€3.5M
Total company revenue
€2.1M
(60% of total)
Marketing-attributed revenue
80%
Share of inbound revenue from marketing
No marketing function. No positioning. No messaging. No ICP definition.
The company was running LinkedIn ads. Not targeted, not tracked, not tied to any defined buyer profile. Just spend going out, impressions coming back, and nobody able to connect either to revenue.
Sales carried everything. The assumption was that ads would generate awareness and sales would close it. In practice, sales closed what they found themselves, and marketing spend was a cost line with no attribution.
This is not unusual. Most B2B consulting firms at this stage operate this way. It works until it doesn't — and then the question becomes whether to hire more salespeople or fix the machine underneath.
Before building anything, the first job was understanding why the existing spend was producing nothing useful.
Three problems, in order of severity:
1. No ICP — targeting everyone, converting nobody
The company knew its service offering (Atlassian tools, cloud infrastructure) but had never defined who the ideal buyer actually was. Job title, company size, industry vertical, buying triggers, decision-making structure — none of it was documented or agreed.
Every SDR had their own version. Every ad campaign targeted a different assumption. No targeting is consistent. No message lands.
2. No positioning — indistinguishable from every other Atlassian partner
"We implement Atlassian tools" is a service description, not a position. It answers "what do you do?" but not "why you, why now, why not the other three partners the prospect is already talking to?"
Without a clear position tied to specific buyer problems, every touchpoint — ads, emails, calls — competed on volume. The only differentiator was persistence.
3. No closed loop — spend going out, no signal coming back
There was no mechanism to learn from what worked. Closed deals didn't feed back into targeting. Lost deals didn't update messaging. The same campaigns ran on the same assumptions quarter after quarter, with no way to know if they were working.
The rebuild happened in sequence over two years. Each layer depended on the one before it.
The first step was an ICP workshop with the people who had the most relevant data: the sales team, the senior consultants who ran delivery, and the founder. The goal was to extract patterns from the deals that had actually closed — not to build a target profile from assumptions.
Output: a documented ICP with firmographic criteria (company size, industry, tech stack), behavioral signals (buying triggers, timing indicators), and decision-maker profiles (who initiates, who approves, who blocks).
Positioning came next. Built directly from the ICP — what these specific buyers cared about, what language they used, what objections appeared on every call. Not a brand exercise. A sales infrastructure document that every touchpoint had to be consistent with.
With positioning defined, the website was rebuilt around the ICP's actual questions, not the company's service catalogue. Content addressed the problems the ICP had, in the language the ICP used.
LinkedIn was set up as an inbound channel — organic content and targeted ads built around the defined buyer profile. Not spray-and-pray. Campaigns targeted by job title, company size, industry, and geography. Creative and copy tested against the new messaging framework.
Once inbound was generating leads, the next problem was qualification. Not all leads were equal, and the team was spending time on conversations that predictably went nowhere.
An enrichment layer was built on top of the inbound flow. Every lead was automatically enriched before reaching a human:
AI-generated summaries compiled these signals into a pre-call brief. The SDR or sales manager arrived at the first conversation knowing why this lead was there — not guessing.
The final layer was a feedback mechanism that made the system learn.
Every sales call generated a transcript. An AI workflow extracted four data points from each: pains voiced, gains sought, jobs to be done, and specific objections raised. A validation agent compared these against the existing ICP document and flagged genuine new signals — not noise.
Proposed ICP updates were reviewed monthly. If "risk reduction" appeared in five consecutive calls but "efficiency" had stopped appearing, the ICP updated. Marketing adjusted copy. Sales updated scripts. Both teams operated from the same current reality.
Before the rebuild:
Marketing contributed nothing measurable to revenue. All pipeline came from sales outreach and referrals. Marketing spend was a cost line.
After the rebuild:
Marketing was the primary revenue driver, generating €2.1M of €3.5M — 60% of total company revenue attributable to marketing infrastructure.
The sales team did not shrink. The service did not change. The market did not get easier. The inputs changed — and the output followed.
How long does it take to see results from a marketing rebuild?
In this case study, significant revenue results materialized within 6 months of the infrastructure going live, though the full strategic rebuild took two years.
What is the best channel for Atlassian partner marketing?
LinkedIn is the primary channel for Atlassian partners when combined with a narrowed ICP. Success requires targeting by specific job titles and tech stack signals rather than broad interest-based ads.
Why is marketing attribution important for IT services?
Attribution allows firms to identify which campaigns drive actual revenue versus just impressions. This shifts marketing from a "cost center" to a measurable "revenue driver," allowing for confident scaling of ad spend.
Can AI replace SDRs in IT consulting?
No, but AI acts as a force multiplier. In this project, AI was used to enrich leads and summarize intent signals, allowing SDRs to have more informed and effective sales conversations.
If you are a Consulting Founder or Marketing Lead tired of creating more content, more ads, more of everything without getting any results.
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