Expert B2B PPC Services

OCI uploads CRM stages (MQL → SQL → Opportunity → Closed-Won) and deal values from Salesforce, HubSpot, or Microsoft Dynamics back into Google Ads, Microsoft Ads, and LinkedIn Campaign Manager. Smart Bidding then learns which click IDs (GCLIDs, MSCLKIDs, LinkedIn UUIDs) produced revenue - not which produced form fills. We typically upload weekly, weight conversions by stage (Opportunity = 5x MQL, Closed-Won = 25x), and give the algorithm 6–8 weeks of closed-loop data before letting it bid aggressively on Maximize Conversion Value. Without OCI, Smart Bidding optimizes toward whoever fills the most forms - which in B2B is rarely whoever signs the largest contract.

It starts with a named account list from your sales team - typically 200–2,000 target companies. That list ingests into LinkedIn as a Matched Audience (uploaded directly or via Demandbase / 6sense sync). On top, you layer job-function filters (Engineering, Operations, Finance, IT), seniority (Manager+ for influencers, Director+ for decision-makers, VP+ for economic buyers), and company-size or industry refinements. Then you split campaigns by persona: Conversation Ads to engineers, Document Ads (datasheet downloads) to procurement, Thought Leader Ads to VPs. Account-level reporting in Demandbase or 6sense shows which target accounts engaged, with which content, in which order - not just lead counts.

Every paid touchpoint stamps a UTM and GCLID/MSCLKID/LinkedIn UUID into the CRM. We instrument conversion events at four stages: MQL (form fill or content download), SQL (sales-accepted, BANT-qualified), Opportunity (entered pipeline with $ value), and Closed-Won. Stage-to-stage conversion rates surface where paid leads stall - a healthy B2B pipeline runs roughly 25–35% MQL→SQL, 25–40% SQL→Opportunity, 20–35% Opportunity→Closed-Won, varying by ACV and category. Channel breakdowns in the same view show whether LinkedIn ABM is producing higher SQL rates than Google Search non-brand, even at higher CPL.

Gated content (datasheets, ROI calculators, peer benchmarks, on-demand webinars, analyst reports) wins at top and mid-funnel where the buyer is researching and not ready for sales. Direct CTAs (demo request, free trial, RFQ) win at bottom-funnel where intent is already qualified. In our accounts the rule of thumb: top-of-funnel LinkedIn Sponsored Content runs 3–6x higher CTR with gated offers; bottom-funnel Google Search non-brand converts 2–4x higher with direct CTAs because the buyer typed in a buying-stage query. The economics shift further when ACV is $100K+ - gated content captures the buying committee earlier, even if the engineer who downloaded the datasheet is not the one who signs.

No fixed split - it follows the buyer journey for the category. A representative starting allocation for a $500K-ACV B2B SaaS: 40–50% LinkedIn (ABM + persona targeting), 25–35% Google Search (high-intent non-brand, RFQ-stage queries, competitor conquest), 10–15% intent platforms (G2 buyer-intent retargeting, Bombora surge-account targeting, 6sense in-market audiences), 5–10% Microsoft Ads (LinkedIn audience targeting + lower CPCs on the same buying-stage queries), 5% experimental (Reddit Ads B2B, podcast sponsorships, niche trade publications). Reallocate quarterly based on stage-weighted ROAS, not CPL.

First-touch and last-touch both lie in B2B - first-touch over-credits awareness, last-touch over-credits the demo request. The honest models are W-shaped (40% first touch, 40% Opportunity creation, 20% last touch, remaining 0% spread across middle interactions) or position-based with opportunity-stage kickers (custom credit weights at each pipeline stage). Data-driven attribution in GA4 works in theory but rarely has enough volume on B2B-sized datasets. We typically run two models in parallel - W-shaped for budget decisions, last-non-direct for channel-level optimization - and reconcile monthly.

Account-level reporting rolls every touchpoint (impression, click, content download, page visit, intent surge) up to the company, not the individual contact. Tools: Demandbase, 6sense, or Bombora aggregate this natively; HubSpot ABM and Salesforce Marketing Cloud Account Engagement (formerly Pardot) do it via account scoring. We report on account engagement scores trending up over a 60-day window, target-account coverage (what % of your named ABM list saw at least 3 ad impressions this month), and account-to-opportunity rate (target accounts that became Opportunities). A single MQL means little; an account where 4 different stakeholders engaged different content over 6 weeks is a real pipeline signal.

Yes, when ACV justifies it ($50K+). Engineers want spec sheets, integration docs, technical proof points, and a sandbox or trial. Procurement wants pricing transparency, contract terms, vendor stability signals (SOC 2, ISO, customer logos at scale), and an RFQ form. VPs want business outcomes - ROI math, peer case studies, analyst validation, and a calendar link to a strategy call. Same product, three different landing pages, three different CTAs. LinkedIn campaigns route each persona to their page via job-function and seniority filters. CRO testing happens within persona, not across - comparing an engineer page to a VP page is comparing apples to airplanes.

LinkedIn is firmographic and personagraphic - it knows job title, company size, industry, seniority, function, member skills, and group memberships. Use it for ICP definition: "Manufacturing companies, 500–5,000 employees, Director of Operations or Plant Manager, US/Canada." Google Search is intent-driven - it knows what someone typed. Use it to capture in-market behavior: "erp software for mid-market manufacturing," "netsuite vs sap business one." The bridge is Customer Match: take your named account list (or a synced 6sense / Demandbase in-market segment), upload as a Google Customer Match audience, and bid up on those accounts when they search. ICP firmographics live in LinkedIn; ICP-account-intent lives in Google.

Floor for a serious B2B PPC program (LinkedIn + Google Search + OCI + ABM tooling) is $10K–$15K/mo in media spend, plus management fee. Below that, LinkedIn impression volume is too low to learn and Google Search non-brand can't compete on the buying-stage queries. Mid-market accounts ($150K–$500K ACV) typically run $25K–$75K/mo in media; enterprise accounts run $100K+. First-90-day spend priority: (1) OCI setup and CRM-to-platform plumbing, (2) LinkedIn ABM on the top 200 named accounts, (3) Google Search bottom-funnel non-brand and competitor conquest, (4) gated content production for top-of-funnel. Intent platforms and persona landing pages come in months 4–6 once baseline conversion data exists.

MQLs and SQLs land in weeks 2–8 once campaigns are stable. Opportunities (real pipeline) land in months 3–6 because B2B sales cycles are long. Closed-Won revenue with statistical significance lands in months 6–12. Anyone promising B2B PPC ROI in 30 days is either (a) cherry-picking a single deal, (b) measuring on form fills instead of revenue, or (c) running a non-B2B motion (transactional SaaS, low-ACV, no buying committee). The trade-off for the long ramp: B2B paid compounds - the OCI-trained Smart Bidding model, the persona LP library, the account intent history, and the closed-loop attribution all get better with each closed deal.