The B2B & SaaS marketing agency
that builds
qualified pipeline.

Most B2B and SaaS companies see meaningful pipeline starting at $8,000–$15,000/month in combined ad spend across LinkedIn, Google, and retargeting — LinkedIn alone carries high CPCs, so budgets need room to gather data. Early-stage startups testing a single channel can start around $5,000/month, while venture-backed companies pushing aggressive pipeline targets often invest $30,000–$100,000/month or more. The right number depends on your average contract value (ACV), sales cycle length, and target CAC. Higher-ACV enterprise deals justify far more spend per lead than low-ticket self-serve products.

B2B SaaS cost per lead typically runs $30–$100 for a content download or MQL, $100–$300 for a demo request or SQL, and higher in competitive enterprise categories. What matters more is CAC relative to lifetime value — a healthy SaaS business generally targets an LTV:CAC ratio of at least 3:1 and a CAC payback period under 12 months. A "good" CPL is meaningless if those leads never become pipeline. We benchmark against your ACV and MQL→SQL conversion rate, not generic averages, and optimize toward cost per SQL and closed revenue.

Most strong B2B programs use all three for different jobs. LinkedIn is unmatched for reaching a specific ICP by job title, seniority, company, and industry — ideal for ABM and demand generation, though CPCs are high. Google Ads captures active, high-intent demand from buyers searching your category or competitors, and usually converts at the highest rate. Meta is dramatically cheaper for retargeting the audiences LinkedIn and Google surface, keeping the buying committee warm across a long cycle. The best strategy sequences them: LinkedIn and Google create demand and intent, Meta retargeting closes it affordably.

You will see leads and demo requests within the first 2–4 weeks, but pipeline follows the length of your sales cycle. B2B cycles often run 30–120 days, so marketing-sourced opportunities take that long to mature into qualified pipeline and closed-won revenue. The first 30–60 days are a learning phase for testing targeting, offers, and messaging. Real efficiency — a predictable, scalable pipeline engine — usually shows up by month 3–6, once closed-loop data has taught the platforms which leads actually convert.

Closed-loop attribution connects your ad platforms to your CRM so you can trace every deal back to the campaigns that influenced it. When a lead converts to an SQL or closed-won opportunity in your CRM, we push that offline conversion back to Google, Meta, and LinkedIn. This does two things: it lets the platforms optimize bidding toward leads that become revenue rather than cheap form fills, and it gives you a true CAC and ROI by channel. We layer in multi-touch attribution so you can see first-touch, last-touch, and everything in between across a long buying cycle.

Yes — and the funnels are built very differently. For product-led and self-serve models, we optimize for frictionless free-trial or freemium signups and activation, then use lifecycle automation to convert trials into paid seats and expand accounts. For sales-led and enterprise motions, we optimize for demo requests and SQLs, with ABM targeting and nurture that moves the buying committee toward a booked deal. Many modern SaaS companies run a hybrid — self-serve for smaller accounts and sales-led for enterprise — and we build a distinct path for each so budget goes to the right motion.

A good B2B or SaaS marketing agency optimizes for pipeline and revenue, not MQLs or vanity metrics, and can prove it with closed-loop CRM attribution. They should understand long multi-stakeholder sales cycles, know how to run ABM and control LinkedIn’s high CPCs, and be fluent in both product-led and sales-led motions. Look for a team that integrates with your CRM (HubSpot, Salesforce), reports on cost per SQL and CAC rather than cost per click, and has a track record with B2B or SaaS clients at your stage. The ability to tie spend to closed-won revenue is the single biggest differentiator.