The B2B Pipeline That Last-Click Lied About
A 14-person B2B SaaS company selling a compliance workflow tool to mid-market manufacturers has just hired its first demand-gen manager. Annual contract value is $28,000, the sales cycle runs 90 to 120 days, and the founder has committed $45,000 per month to paid media for two quarters with one condition: pipeline must be visible by week six. The previous agency spent 80% of budget on LinkedIn lead-gen forms optimized for cost per lead, producing 600 leads per month at $22 each, but sales says fewer than 2% are qualified and most are students or job seekers. The CRM is clean but not connected to any ad platform; attribution is last-click in Google Analytics 4, which credits paid search with 71% of closed revenue. The founder now wants to move the entire budget into Google Search because that is what converts.
1. Which foundation gaps should be closed before any budget is reallocated, and in what order?
Read the worked answer
Prioritize measurement over channel shuffling, because the founder's belief about search is currently an artifact of the tracking setup, not evidence.
- Lead definition. With $28,000 ACV and a 90 to 120 day cycle, cost per lead is a vanity metric. Sales and marketing need written MQL/SQL criteria (company size, title, manufacturing vertical, active project) so the 600 leads can be re-scored. Expect the real cost per SQL to be 20 to 40 times the $22 CPL.
- Offline conversion feedback. The CRM is clean but disconnected, which is the root cause: LinkedIn's algorithm was rewarded for form fills, so it found students. Import SQL and closed-won stages back into each platform so bidding optimizes on revenue-bearing events. This alone typically changes which audiences and keywords platforms favor.
- Attribution. Last-click giving 71% to paid search on a 120-day cycle is a classic under-credit of upper funnel. Layer a data-driven model, a self-reported field on the demo form, and periodic geo or platform holdouts.
- Sales handoff. 2% qualification may partly be speed-to-lead. Audit follow-up time before blaming media.
Only after four to six weeks of clean SQL data can anyone credibly judge which channel deserves budget.
2. The founder wants 100% of budget in Google Search. How do you respond?
Read the worked answer
Test the premise with arithmetic before agreeing or refusing. Pull keyword volume and impression share for the category terms. If total addressable search demand is, say, 25,000 impressions per month, that inventory cannot absorb $45,000 a month at any sane CPC, so the plan fails on capacity alone. Search also captures existing demand, while a niche compliance tool for mid-market manufacturers likely needs to create demand among buyers who do not yet search by product name.
Propose a staged reallocation with a decision rule instead of an all-or-nothing move: shift 30 to 40% into search and retargeting in month one, keep LinkedIn but change its optimization event to SQL rather than lead, and hold the rest as a control. Define success as pipeline created and cost per SQL at 90 days, not return on ad spend at 30 days.
The judgment being tested is refusing a plausible-sounding but untested pivot while still giving the founder a fast, visible experiment. If search impression share is already above 80% on core terms, the honest answer is that search is nearly saturated and incremental dollars must go elsewhere.
3. Build the six-week checkpoint the founder asked for. What do you report and what are the thresholds?
Read the worked answer
Week six is too early for closed revenue, so report leading indicators that predict it and state that framing explicitly up front.
- Week 1 to 2: conversion tracking live, CRM stages mapped to ad platform events, lead scoring agreed with sales.
- Week 3 to 4: campaigns rebuilt around SQL optimization; baseline recorded for cost per SQL, lead-to-opportunity rate, and pipeline per $1,000 spent.
- Week 5 to 6: first read on directional movement, plus a documented forecast of expected pipeline at day 90.
Thresholds should be set before launch, for example: cost per SQL under $900, at least 15% of MQLs reaching opportunity, and pipeline per dollar improving week over week. Also track volume alongside efficiency, since a channel can hit cost-per-SQL targets while producing too few leads to matter.
Present a dashboard that separates diagnostic metrics (CTR, CPL) from business metrics (SQL, pipeline, cycle length). That structure prevents the next conversation from collapsing back into cost per lead.