SEO Case Study: B2B cloud consulting achieved pipeline +140% in 6 months
On January 6, 2025, a Chicago based B2B cloud consulting partner partnered with Goforaeo to rebuild how their demand engine generated qualified opportunities, not just leads. In the first 2 weeks, we aligned leadership, marketing, and sales on what “qualified” actually meant for their services, then built a system that could be measured, improved, and repeated.
Client context and starting point
They are a cloud consulting partner headquartered in Chicago, Illinois, selling cloud migration, modernization, security hardening, and managed services to mid market and lower enterprise companies across the Midwest. Their delivery reputation was strong, but their pipeline creation relied heavily on referrals and inconsistent campaign bursts.
The biggest issue was not effort, it was focus. They were generating activity, but the activity was not reliably turning into sales qualified opportunities.
Who their buyers were in early 2025
Their highest value deals were driven by a buying committee, typically a Director of IT, Head of Infrastructure, Security lead, and a finance stakeholder. The fastest moving opportunities were triggered by a measurable event like an upcoming renewal, data center exit, security audit findings, or performance bottlenecks tied to legacy systems.
Because the team sold complex work, the “first meeting” needed to feel like a diagnostic, not a sales pitch. That insight shaped everything we built.
The baseline and the proof
We anchored measurement on Sales Accepted Leads (SALs) and Sales Qualified Leads (SQLs), then tied those to qualified pipeline value and opportunity win rate. This kept the program honest, because vanity metrics alone can look good while revenue stays flat.
Before metrics (January 2025 baseline, Chicago)
These were the averages from January 1 to January 31, 2025 after cleaning duplicate records and standardizing lifecycle definitions:
- Qualified pipeline created per month: $410,000
- New SQLs per month: 18
- MQL to SQL conversion rate: 11%
- Website conversion rate to lead: 0.9%
- Cost per lead from paid channels: $240
- Sales cycle for typical projects: 72 to 95 days
- Percentage of pipeline from referrals: 58%
The team was capable, but they were over dependent on referrals, and their inbound leads were often misaligned with the services that actually drove margin.
After metrics (June 2025, end of the 6 month window)
By June 1 to June 30, 2025, with the new system operating steadily:
- Qualified pipeline created per month: $984,000
- New SQLs per month: 41
- MQL to SQL conversion rate: 19%
- Website conversion rate to lead: 1.8%
- Cost per lead from paid channels: $165
- Sales cycle trend: 64 to 86 days for the majority segment
This is the core proof point: qualified pipeline moved from $410,000 to $984,000, which is a 140% increase over the 6 month period.
What was holding growth back
The client did not have a single bottleneck, they had a chain of small leaks that multiplied together. Fixing any one issue helped, but fixing the chain is what created a step change.
Key blockers we found in January 2025:
- ICP was broad, so targeting and messaging felt generic to buyers
- Paid campaigns pushed to service pages built for awareness, not conversion
- Forms captured minimal context, making lead qualification slow and inconsistent
- Sales follow up varied by rep, which created uneven conversion rates
- Reporting tracked leads, not buying intent and sales acceptance
Once these were visible, the strategy became straightforward: tighten fit, speed up qualification, and remove friction from first contact to first meeting.
Strategy overview
Instead of running disconnected tactics, we built a simple operating model: define who we want, say something specific to them, offer a clear diagnostic, route leads fast, then iterate weekly based on SQL quality.
That strategy broke into six practical workstreams, executed in sequence with overlap.
Step 1: Define the ICP and buying triggers
We narrowed the Ideal Customer Profile to segments where the client had proven delivery wins, faster close velocity, and strong margins. We also identified the most common “why now” triggers that started serious conversations.
- Priority industries: healthcare services, manufacturing, logistics, B2B SaaS
- Priority company size: 200 to 2,000 employees
- Priority triggers: audit remediation, migration deadline, uptime and latency issues, cloud spend overruns
This gave every channel a clearer target and reduced wasted spend on accounts unlikely to buy.
Step 2: Rebuild messaging around outcomes, not services
Buyers rarely wake up wanting “cloud consulting.” They wake up wanting less risk, fewer outages, a passed audit, or predictable costs. We re framed messaging to match that reality and created 3 outcome based entry points.
- “Security and compliance remediation sprint”
- “Cloud cost visibility and optimization workshop”
- “Modernization plan for legacy infrastructure”
Each offer was designed to convert into a first meeting that felt like a working session, not a generic intro call.
Step 3: Fix the conversion path from click to calendar
We rebuilt landing pages and forms so visitors could self identify fit, while the team captured enough detail to qualify quickly. We reduced friction for the right people and increased friction for the wrong ones.
Key changes:
- One primary CTA per page, aligned to an outcome based offer
- Short form plus progressive profiling, not a long form up front
- Calendar routing based on company size and service interest
- Trust elements placed near the CTA, not buried in the footer
Step 4: Build a multi channel demand engine that matched how buyers research
Instead of betting on a single channel, we ran synchronized motion across paid, organic, partner co marketing, and outbound to warmed accounts. Each channel pointed to the same 3 offers, so performance data compared cleanly.
Channels activated:
- LinkedIn Ads targeting job titles, industries, and intent like “cloud security” and “migration”
- Google Search campaigns focused on high intent queries tied to triggers
- Retargeting to keep the brand present during longer consideration cycles
- Account based outbound to a curated list of high fit Chicago and Midwest accounts
Step 5: Improve lead scoring and sales handoff
The highest ROI change was tightening response time and making lead context usable. We built a simple scoring system that combined firmographics and behavior, then routed leads automatically.
- Score boosts for page depth, offer engagement, and return visits
- Score boosts for target industries and company size band
- Sales alerts when high intent accounts revisited key pages
- A standard “first 48 hours” follow up sequence for every SQL
Step 6: Weekly reporting and iteration
We built a cadence where marketing and sales reviewed the same numbers every week. The focus was not volume, it was SQL quality and pipeline created.
Weekly review covered:
- Which campaigns created SQLs, not just leads
- Which landing pages converted at the highest rate by segment
- Which objections appeared in calls and how to address them in copy
- Which accounts showed intent but did not convert, then how to re engage
Execution timeline with dates
Phase 1: Discovery and data cleanup (January 6 to January 24, 2025)
We started in Chicago with stakeholder interviews and pipeline forensics. The goal here was clarity, because tactics without clean definitions create fake success.
Actions completed:
- Standardized lifecycle stages: lead, MQL, SAL, SQL, opportunity
- Cleaned CRM fields for industry, employee band, service line
- Identified top 20 won deals from 2024 and extracted patterns
- Built a reporting baseline that sales agreed matched reality
Phase 2: Messaging, assets, and tracking (January 27 to February 21, 2025)
We translated the ICP into offers, landing pages, and tracking that could measure intent properly. We also created a content spine that supported the offers.
Deliverables:
- Three conversion focused landing pages and offer one pagers
- GA4 events for form submits, scroll depth, and calendar clicks
- Updated LinkedIn and Google Ads tracking, including UTMs
- Sales enablement: call structure, discovery checklist, follow up templates
Phase 3: Launch and learn (February 24 to March 31, 2025)
We launched initial campaigns with controlled budgets and tight targeting. The first month was about learning what the market responded to, then refining fast.
Optimizations made during this phase:
- Paused broad audiences and doubled down on top 2 industries
- Re wrote ads to highlight a single urgent trigger per campaign
- Improved form questions to capture timeline and project scope
- Added retargeting to visitors who reached pricing and SEO Case Study sections
Phase 4: Scale and optimize (April 1 to May 31, 2025)
Once conversion rates stabilized, we scaled spend gradually while protecting CPL and SQL rate. This phase also expanded outbound to accounts showing intent signals.
Scale actions:
- Increased budgets on the highest SQL producing campaigns first
- Expanded Google Search keyword sets tied to audits and migrations
- Introduced lookalike audiences based on SQL lists
- Added partner co marketing email drops to relevant regional lists
Phase 5: Close loop and stabilize (June 1 to June 30, 2025)
The final month focused on making the machine durable. We improved documentation, trained internal owners, and ensured reporting could be maintained without external support.
Stabilization tasks:
- Documented campaign playbooks and launch checklists
- Finalized dashboard views for exec, marketing, and sales
- Confirmed attribution logic and pipeline source rules in CRM
- Created a quarterly testing roadmap for new offers and segments
Monthly performance snapshot (January to June 2025, no tables)
Below is the month by month progression showing how the pipeline grew and what drove the change. Numbers reflect qualified pipeline created in that month, plus key leading indicators.
- January 2025: $410,000 qualified pipeline, 18 SQLs, website lead conversion 0.9%
Primary change was measurement cleanup and aligning definitions with sales. - February 2025: $520,000 qualified pipeline, 23 SQLs, lead conversion 1.1%
First campaigns launched, biggest lift came from clearer offers and better routing. - March 2025: $665,000 qualified pipeline, 29 SQLs, MQL to SQL 15%
Ad messaging sharpened by trigger, retargeting started, first strong outbound to warmed accounts. - April 2025: $790,000 qualified pipeline, 34 SQLs, paid CPL down to $190
Scaling began, more budget moved to high intent search and best converting LinkedIn segments. - May 2025: $915,000 qualified pipeline, 38 SQLs, website conversion 1.6%
Landing page improvements and sales follow up consistency boosted conversion across channels. - June 2025: $984,000 qualified pipeline, 41 SQLs, MQL to SQL 19%, paid CPL $165
Machine stabilized, reporting became predictable, and sales acceptance rose as fit improved.
Tools used
We intentionally kept the stack practical, with tools the team could own long term. The exact tools may vary by client environment, but this is what was used in the 2025 engagement.
Core systems:
- CRM: Salesforce or HubSpot CRM for lifecycle stages, routing, and pipeline reporting
- Analytics: GA4 for event tracking and conversion attribution
- Dashboards: Looker Studio for weekly reporting and executive visibility
- Ads: LinkedIn Campaign Manager and Google Ads for acquisition and retargeting
- Sales intelligence: Apollo or ZoomInfo for account lists, enrichment, and outbound support
- Automation: Zapier for routing, alerts, and workflow glue
- Scheduling: Calendly for fast meeting booking and reduced back and forth
Optimization and research:
- Ahrefs or Semrush for search demand and competitor positioning
- Hotjar for observing landing page behavior and friction points
- Google Tag Manager for consistent event deployment and rapid updates
What made the difference
Specificity created trust faster
The client stopped trying to sound relevant to everyone and started sounding deeply relevant to a few high value segments. That increased conversion rates because buyers saw their exact problem reflected back to them quickly.
This also reduced sales wasted time, because fewer low fit leads entered the funnel.
Speed and consistency won the handoff
Once routing and follow up became standardized, conversion improved without needing heroic effort from individual reps. In complex B2B sales, a fast response with context often matters more than an extra 10% of lead volume.
The combination of scoring, alerts, and a shared playbook made the pipeline growth repeatable.
Takeaways you can apply to your own B2B services pipeline
- Start by agreeing on what “qualified” means, then measure only what sales accepts
- Build offers around buyer triggers, not around your internal service catalog
- Route leads based on fit, not on whoever is available
- Use weekly reviews to cut what does not create SQLs, even if it creates leads
- Scale spend only after conversion paths are clean and sales follow up is consistent
Closing note
This six month Chicago engagement shows what happens when strategy and execution are connected end to end, from targeting to tracking to sales handoff. The 140% qualified pipeline increase was not a single tactic, it was the compound effect of tighter focus, better conversion paths, and disciplined iteration from January through June 2025.