Attribution Modeling | Marketing Automation

Marketing

Cross-Channel Attribution Modeling & CAC Optimization

Unified attribution model with Markov chain analysis reflecting real buyer journeys across Meta, Google, TikTok, LinkedIn, and email.

Marketing automation case study featuring ai implementation and reporting.

-21%

CAC Reduction

Dynamic

Budget Optimization

First Time

Accurate Exec Reporting

Overview

A company spending millions annually across Meta, Google, TikTok, LinkedIn, and email needed to unify data into a real attribution model that reflected the real buyer journey. CAC was reduced by 21% and the exec team gained accurate reporting for the first time.

Business Context

The marketing team was flying blind despite spending millions annually across five major advertising platforms. Each platform reported conversions differently, leading to double-counting and inflated ROAS calculations that made budget allocation decisions essentially guesswork. The executive team had lost confidence in marketing's ability to demonstrate real impact, and budget discussions had become contentious debates rather than data-driven conversations. The company needed a single source of truth that could track the complete buyer journey from first touch through conversion, accounting for the complex multi-channel interactions that characterize modern B2B purchasing behavior. Without unified attribution, the marketing team couldn't justify budget requests or demonstrate which investments were actually driving revenue growth.

How We Built It

We architected a comprehensive attribution data lake that ingests data from Meta Ads API, Google Ads API, TikTok Ads API, LinkedIn Campaign Manager, and the company's email marketing platform. The technical challenge centered on identity stitching — connecting anonymous website visitors across devices and sessions to eventual conversions using a combination of first-party cookies, UTM parameters, and CRM matching logic. We built time-series normalization to handle the different reporting windows and attribution models each platform uses natively, creating a standardized event stream. The attribution engine implements multiple models simultaneously — Markov chain probabilistic attribution, time-decay weighted models, position-based attribution, and custom weighted models the marketing team can configure. UTM parameters are correlated with CRM pipeline data and product usage signals to capture the full journey from awareness through activation. The executive dashboard surfaces CAC by channel, campaign, and creative with drill-down capabilities into assisted conversions and content influence on pipeline. An AI anomaly detection layer monitors for unusual spend patterns, performance degradation, and opportunities for budget reallocation. Custom webhook integrations capture offline conversion events and sync them back to advertising platforms for improved campaign optimization. The system processes millions of touchpoints daily while maintaining sub-second query performance for interactive dashboards.

Challenges

Fragmented ad platforms with different structures

No consistent KPI definitions

Dark-funnel behaviors invisible

No unified timeline for user interactions

Marketing & execs lacked a single truth source

What We Delivered

Attribution data lake with cross-channel identity stitching

Multi-touch attribution engine (Markov, time decay, position-based, custom weighted)

Executive dashboard with CAC by channel and journey

Assisted conversions and content → pipeline influence

Budget optimization simulator

AI anomaly detection and spend reallocation suggestions

Tech Stack

Custom ingestion from all ad APIs, Identity stitching, Time-series normalization, UTM + CRM + product usage correlation, Markov chains, Next.js dashboards

Results

-21%

CAC Reduction

Dynamic

Budget Optimization

First Time

Accurate Exec Reporting

Strategic Impact

The 21% reduction in CAC represented millions of dollars in annual savings that dropped directly to the bottom line. For the first time, the marketing team could demonstrate with confidence which channels were driving real pipeline, not just clicks and impressions. Budget allocation transformed from political negotiation to data-driven optimization — when the Markov model showed that LinkedIn touch points were significantly more influential than last-click data suggested, the team reallocated budget and saw pipeline quality improve within weeks. The executive team now receives accurate weekly reporting that builds rather than erodes confidence in marketing investment. Dark-funnel behaviors that were previously invisible — like prospects consuming ungated content before entering the funnel — are now tracked and valued appropriately. The budget optimization simulator allows the team to model scenarios before committing spend, reducing the risk of expensive channel experiments. Cross-channel attribution revealed that email nurture campaigns were dramatically under-credited by last-click models, preventing what would have been a costly decision to reduce email investment. The unified timeline of user interactions has improved sales handoffs, with reps now seeing the complete marketing journey before their first conversation. Marketing-sales alignment improved as both teams finally agreed on the same conversion numbers. The platform has become essential infrastructure that the marketing team relies on daily for every significant budget and strategy decision.