United States · EdTech · Higher Education
Broken tracking, no MQL or SQL definitions, and spam-heavy lead flow. We rebuilt QuantHub's entire demand generation infrastructure and handed their sales team a pipeline machine, not a list of non-ICP leads.
QuantHub sells AI literacy and data fluency curriculum to universities, community colleges, and corporate training teams across the US. Their course catalog spans AI Foundations, Marketing in the Age of AI, AI in Accounting, and Applied Data Science, all built to drop into existing LMS environments in a single semester. The product was strong. The sales team was capable. But the demand generation infrastructure underneath it all was fractured.
The Google Ads and LinkedIn campaigns that were already running had no documented account structure, no conversion tracking, and no exclusion lists. The campaigns were live but flying blind. Form submissions were coming in, but a significant share were students and job seekers, not deans, directors, or program heads. Marketing had no MQL or SQL definition, no CRM lifecycle logic, and no way to tell leadership which spend was actually producing revenue.
Rebuild QuantHub's demand generation infrastructure from the ground up: clean attribution, qualified decision-maker targeting, and a multi-channel pipeline engine that connects ad spend directly to closed revenue.
Google Ads had no conversion goals configured. Without downstream signal, Smart Bidding had nothing to optimize against. UTM structures were inconsistent, making it impossible to tie pipeline back to specific campaigns, ad groups, or keywords.
Target audiences were not built around QuantHub's actual buyers: deans, associate professors, department directors, and VP-level decision-makers in higher ed and accounting. Broad targeting meant impressions were being served to the wrong people entirely.
Without negative keyword lists, audience exclusions, or form qualification logic, a significant portion of inbound submissions were students and job seekers. Sales was spending time chasing leads that would never convert, and the CRM data was too noisy to trust.
Paid traffic was landing on pages optimized for general visitors, not for deans or program directors evaluating a multi-seat institutional purchase. Messaging hierarchy, social proof selection, and CTA design all needed rebuilding around the buyer journey, not the product catalogue.
ads.expert entered as a full-stack performance partner, not a campaign manager. Before a single ad was optimized, we audited every layer of the funnel: tracking architecture, CRM data hygiene, audience logic, and conversion infrastructure. What came next was methodical, not reactive.
Rebuilt Google Ads conversion tracking from scratch: goal-level attribution, value-based bidding signals, and cross-channel UTM taxonomy. Inside HubSpot, we defined MQL thresholds, mapped lifecycle stages, and built lead routing workflows so every inbound lead was scored, staged, and routed before it hit a sales rep's inbox.
Defined detailed ICP profiles for three buyer segments: higher-ed decision-makers (deans, department chairs, program directors), accounting faculty, and enterprise L&D leads. Built layered audience exclusions including job title negatives, student cohort suppression lists, and HubSpot-synced suppression audiences, cutting spam submission rate from high double digits to <2%.
Launched and iterated a complete campaign structure: intent-based Search campaigns, PMAX for brand visibility and demand capture, and remarketing Display campaigns for site visitors and HubSpot lifecycle segments. Weekly performance reviews tied directly to pipeline output, not just CTR.
Built and launched multi-cohort Lemlist sequences targeting accounting professors, AACSB conference attendees, and higher-ed program directors. Prospect lists were enriched and qualified via Clay before entering sequences. Reply handling and follow-up branching were automated via Make, giving QuantHub a scalable outbound motion that required no additional headcount.
Built remarketing audiences on Google and LinkedIn using site visitor behavior and HubSpot lifecycle stage, then activated them with targeted ad sequences. Designed HubSpot email workflows for mid-funnel lead nurture: stage-triggered sequences for higher-ed MQLs, eBook download follow-ups, and playbook leads, keeping warm prospects engaged without manual sales intervention.
Generated enriched target persona lists and prioritized lead queues for the sales team, segmented by buyer type, engagement signal, and funnel stage. Built instructor and accounting professor databases to support outreach campaigns, and delivered weekly closed-won/closed-lost deal analysis to identify which campaigns and channels were producing revenue versus pipeline noise.
For every $1 invested in Google Ads, QuantHub's sales team received $38 in qualified, CRM-attributed opportunity pipeline, with a spam rate below 2%.
"Working with Nilay has been a consistently positive experience. He is incredibly helpful and proactive, often anticipating needs before they arise and bringing thoughtful solutions to the table. What really sets him apart is his ability to think outside the box, constantly testing new ideas and strategies that push our campaigns forward.
He's also highly dependable, delivering on time and maintaining clear communication throughout. Most importantly, his efforts have directly improved our ROI on ad spend, making a measurable impact on our overall performance. A strong partner for any growth-focused team."
We build demand generation systems for B2B and EdTech companies that turn marketing budgets into qualified, attributed pipeline, not vanity metrics.
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