How Arsalan Faysal designed separate target architectures for two completely different buyer personas — reducing average cost per lead by 47% and generating $1.3M in new pipeline inside 4 months.
An innovative AI SaaS platform had built two outstanding products. The first was an enterprise-grade AI ERP system designed for manufacturers (requiring high-touch, long-sales-cycle demos). The second was a plug-and-play AI Employee system for SMBs (self-serve, low-ticket trials).
They were running standard, generic ads across Meta and Google, targeting the same broad "small business" and "manufacturing" interest pools. Their pixel attribution was completely broken. They had no idea which campaign triggered which demo.
The result of this blended, unoptimized traffic spend? Their Cost-Per-Lead (CPL) skyrocketed. They were spending $215 per demo request, and trial-to-paid conversion stayed below 2.5%. They were throwing high-intent ad capital directly into a furnace.
"Build separate GTM paid acquisition engines end-to-end. We need absolute attribution clarity, localized ad structures for each buyer persona, and a massive decline in acquisition costs."
I split their GTM strategy into two distinct, structured pipelines. We mapped separate intent keyword triggers, designed persona-specific ad creatives, and linked click attribution directly to HubSpot deal stages.
Our pre-campaign audit revealed severe diagnostic leaks. Because the client was running duplicate ad pixel cookies across both the high-ticket ERP landing page and the self-serve AI Employee page, Google's smart bidding model was completely confused—routing enterprise CFOs to the trial checkout, and SMB owners to the high-ticket booking form.
Bidding against themselves on target keywords.
Extremely inflated CPA due to pixel confusion.
Slow load times on high-ticket forms.
No server-side conversion mapping implemented.
Flat structures cannot trace separate, multi-product funnels. I restructured their data architecture. Every conversion record is mapped to a standardized, custom company-object relationship model with zero-drift field mappings.
| Custom Property Key | Mapped HubSpot Object | Data Type | Automation / Segment Logic |
|---|---|---|---|
saas_product_vertical |
Contact (Ad Payload) | Dropdown Select | Enforces segmentation rules (AI ERP vs. AI Employee) |
ad_click_utm_source |
Contact (Attribution) | Single Line Text | Directly maps original ad networks (LinkedIn, Google, Meta) |
qualification_score |
Contact (Lead Score) | Number | Score ≥ 75 triggers automated n8n AE task alerts |
stripe_subscription_status |
Deal (Revenue) | Dropdown Select | Updates deal pipelines instantly on Stripe payment confirm |
To stop wasting capital, we designed separate, custom ad funnels. High-ticket manufacturing targets received enterprise LinkedIn and Google search campaigns, while SMBs were served self-serve, direct-to-trial Meta and YouTube ads.
Targeting COOs and CFOs. We ran high-intent search keywords (e.g. "manufacturing erp system," "industrial supply chain software") paired with LinkedIn direct-sponsored content comparing cost structures.
Direct-response trial acquisition. Targeted broad SMB interests on Meta using fast, visual video ad formats showcasing the AI executing real tasks in real-time, driving direct-to-trial conversion.
To maximize conversion values, we redesigned their landing pages. We cut form fields by 50% and implemented instant webhook routers using n8n to notify sales reps immediately on lead ingestion.
Forced forms simplification. Replaced long, multi-page questionnaires with a simple 4-field capture. Deployed trust badges, fast CDN layouts, and clear social proof elements, dropping bounce rates to 34%.
When an enterprise ERP demo is submitted, an n8n webhook extracts the data, verifies corporate parameters, and triggers an immediate Slack and SMS notification to active AEs.
Enter your monthly ad budget and estimated Average Contract Value (ACV) below to see how our 47% lower CPL and 3.2x ROAS benchmarks multiply your SaaS pipeline.
Based on our verified 47% average CPL reduction ($70 blended) and 35% demo-booking rate.
Audited broken tracking. Set up demo and trial conversion events. Launched initial Search and Meta tests.
Ran 12 creative tests. Found the "salary vs autonomous" SMB angle and "ERP mess" manufacturer angle.
Scaled winners to $15k/mo. Pipeline hit $310k in ERP ops and $220k in AI Employee contracts.
Final CPL: $113 (ERP) and $27 (SMB). Blended 3.2x ROAS. Delivered full campaign playbook.
“Arsalan built a structured machine for two different products. He knew exactly when to use LinkedIn vs Meta. Within 90 days, our CPL was cut in half and we had a repeatable system. Best ad hire we've made.”
— Nadia (Founder, AI ERP & Employee Platform)
Toggle system modes below and select nodes on the pipeline map to run forensic diagnostics on GTM leakage points.
Agencies purchase outdated, stale target lists and execute bulk outreach. Messages land directly in spam folders, resulting in low delivery and burned domain reputations.
Low engagement metrics, damaged domain reputation, and high acquisition costs with zero high-intent opportunities created.
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