Analyze Ad Campaigns

Cross-channel paid performance in one view — with the optimization opportunities your platform dashboards don't surface.

Average overstatement of ROAS in platform-native reporting vs actual closed-won revenue attribution — the gap between what ad platforms claim and what the CRM confirms.

The Brief

Every major ad platform has its own reporting interface, its own attribution logic, and its own definition of a conversion. When LinkedIn, Google, and Meta each claim credit for the same deal, the result is inflated numbers that look good in channel-specific reports but tell you nothing about what's actually generating pipeline. The Analyze Ad Campaigns agent aggregates cross-channel performance into a single model with consistent attribution, surfaces underperforming campaigns before they exhaust their budgets, and connects paid activity to CRM pipeline so that campaign ROI is measured in revenue, not click-through rate.

• Aggregates campaign performance across all paid channels

Platform-native reporting is siloed by design — LinkedIn shows LinkedIn performance, Google shows Google performance, and reconciling them requires manual exports, spreadsheet work, and a shared definition of what a conversion means across platforms. The agent ingests campaign data from all connected ad platforms on a continuous basis and normalizes it into a single data model: consistent metric definitions, consistent attribution windows, and consistent audience categorization across channels. The result is a single performance view where a CMO can see total paid spend, total pipeline contribution, and CAC by segment without opening three platform dashboards or waiting for the weekly report.

Cross-channel performance, Apr 1–Apr 5 2026: Total spend $94,200. Pipeline generated: $1.84M. Blended CAC: $8,620. LinkedIn: $52K spend, $1.1M pipeline, CAC $7,940. Google Search: $24K spend, $540K pipeline, CAC $9,100. Meta: $18K spend, $200K pipeline, CAC $11,250.

• Flags underperforming campaigns with specific diagnosis

Campaign performance issues have different root causes that require different fixes — a high CPL might come from audience targeting being too broad, creative fatigue on a specific ad set, a bid strategy mismatch, or a landing page conversion problem. Generic alerts that say 'CPL is high' without context lead to wrong interventions. The agent identifies underperforming campaigns and drills into the likely root cause by analyzing the specific performance pattern: is the CPL high because impressions are low (reach problem), CTR is low (creative or audience problem), or conversion rate is low (landing page or offer problem)? Each alert includes a diagnosis category and a recommended action specific to that failure mode.

Campaign alert: 'Enterprise Q2 — Google Search'. CPL $740 (benchmark: $290). Diagnosis: conversion rate problem (CTR normal at 4.2%, CVR 0.6% vs 2.4% benchmark). Likely cause: landing page mismatch — ad copy targets 'data quality' but landing page is generic solution overview. Recommended action: create dedicated landing page or update ad destination URL.

• Connects paid activity to CRM pipeline and revenue

Platform-reported conversions are proxies — a 'conversion' in LinkedIn Campaign Manager might be a content download from someone who will never buy. The agent joins ad platform data with CRM pipeline data to measure what actually matters: which campaigns are generating qualified pipeline, how those pipeline-sourced opportunities progress through the funnel, and what the closed-won revenue contribution is per campaign. This requires identity resolution across platform click data and CRM contact records — the agent handles the matching logic so that every CRM opportunity can be traced back to the paid touchpoints that contributed to it. CAC and ROAS are calculated on pipeline and revenue, not platform-side conversion events.

Campaign ROI reconciliation, Q1 2026: LinkedIn 'RevOps Buyer' campaign — Platform reported 312 'conversions' (form fills). CRM match: 187 opportunities created (60% match rate). 52 closed won ($1.1M revenue). True CAC: $6,420. Platform-reported CAC: $3,200 (overstatement: 2.0×).

• Tracks audience overlap and frequency across channels

When the same prospect is being targeted by LinkedIn campaigns, Meta retargeting, and Google display simultaneously, the combined frequency can accelerate fatigue and damage brand perception — even when individual platform frequency metrics look acceptable. The agent estimates audience overlap across connected paid channels using hashed email audiences, company domain targeting, and CRM segment data to identify high-overlap audiences where cross-channel frequency is elevated. When overlap is significant, the agent surfaces a deduplication recommendation: excluding CRM segments from prospecting campaigns, adjusting retargeting windows, or staggering channel exposure across the buyer journey.

Audience overlap analysis: LinkedIn 'Mid-market RevOps' (14,200 in audience) × Meta Custom Audience retargeting (8,900 in audience). Estimated overlap: 4,100 contacts (~29%). Combined weekly frequency for overlapping segment: estimated 9.2 impressions. Recommendation: exclude LinkedIn audience from Meta retargeting or reduce Meta frequency cap.

Today vs. With

Today

• Performance reviewed separately in LinkedIn, Google, and Meta dashboards — no way to compare channels on equal terms or see total paid program ROI
• Platform-reported conversions used to calculate CAC — overstating performance because most platform 'conversions' never become CRM opportunities
• Underperforming campaigns caught in weekly review — by which time significant budget has already been wasted on the failing campaign

With ABM Strategist

• All channels normalized into one data model — consistent metrics, consistent attribution, and true cross-channel CAC and ROAS calculated in one view
• Ad platform data joined with CRM pipeline via identity resolution — CAC and ROAS calculated on actual pipeline and closed-won revenue, not form fills
• Continuous monitoring flags underperforming campaigns with a specific root-cause diagnosis — creative, audience, landing page, or bid strategy — before the budget exhausts

Works With

• Analyze Ad Creative: Drills into the creative-level performance within campaigns — identifying which specific creative elements are driving or dragging campaign results.
• Audit Landing Pages: When campaign analysis identifies a conversion rate problem, the landing page audit diagnoses specific page-level issues causing the drop-off.
• Allocate Ad Spend: Uses cross-channel campaign performance data to model optimal budget allocation — shifting spend toward campaigns and channels generating efficient pipeline.

Three Layers, One Platform by Lantern

Every agent runs on three layers: a unified data model, 150+ enrichment providers, and an open-source engine where every decision is auditable.

• Data Waterfall: 150+ enrichment providers. Sequential routing optimized per segment. The best answer wins. No vendor lock-in.

• Agent Engine: Open-source execution engine. Workflows defined in code. Human-in-the-loop checkpoints. Full audit trail on every action.

• Revenue Ontology: Every data source normalized into one model. Entity resolution across systems. Relationships stored, not inferred. Schema that evolves with your business.

FAQ

• How does identity resolution work for connecting ad clicks to CRM records?
• Which attribution window is used when connecting campaigns to pipeline?
• Does it support offline conversions from CRM upload?
• Can it analyze campaigns across multiple brand or regional accounts in the same platform?

One view of every paid dollar — and which ones are actually generating revenue.