Wedge

The initial beachhead targets B2B SaaS companies spending over $100k monthly on LinkedIn and Google Ads. This niche experiences acute pain mapping top-of-funnel ad clicks to downstream Salesforce closed-won deals. Expansion moves from this Ad-to-CRM pipeline to incorporating web analytics, email marketing events, and finally offline event tracking.

Timing

Widespread adoption of cloud data warehouses shifts the center of gravity away from packaged SaaS tools toward warehouse-native data architectures. Simultaneously, privacy regulations break third-party tracking, forcing companies to own their first-party attribution data pipelines.

Why This ICP

Mid-market B2B SaaS companies have long, complex sales cycles spanning multiple stakeholders, making multi-touch attribution critical for ad spend allocation. They possess the budget to pay for pipeline infrastructure but lack the dedicated data engineering armies of enterprise companies.

Size Of Prize

Approximately 50,000 mid-market B2B SaaS and high-volume e-commerce companies globally spend an average of $30,000 annually on marketing data engineering labor or attribution software infrastructure. This yields a total addressable market of $1.5B.

Gap Narrative

Growth and data teams lack a reliable pipeline to map fragmented marketing touchpoints to revenue outcomes without writing custom ingestion scripts. Current solutions force them to either trust biased ad network reporting or build brittle custom SQL to stitch identities together. The product extracts raw event data from ad networks and CRMs, resolves identities across sessions, and writes clean multi-touch attribution tables directly to the customer's data warehouse.

Defensibility

Workflow lock-in compounds rapidly once installed. When marketing budget allocation processes and core BI dashboards read directly from the pipeline's output tables, replacing the tool requires rebuilding fundamental reporting infrastructure. Furthermore, maintaining hundreds of brittle API connectors creates an economy of scale that deters in-house replacement.

Why This Thesis

Software infrastructure is the right approach because attribution is fundamentally a data engineering problem requiring deterministic identity resolution rules and reliable daily syncs. Delivering this as a managed pipeline layer directly integrates with the modern data stack they already maintain.

Hardest Part

Maintaining deterministic identity resolution and event matching across fragmented, constantly deprecating ad network APIs and privacy sandboxes without dropping conversion signals.

Min Viable Scope

Focus strictly on server-side multi-touch attribution for Shopify-based D2C brands advertising exclusively on Meta and Google. Leave out predictive media mix modeling, B2B CRM integrations, and long-tail ad networks.

Cold Start Problem

Probabilistic matching models require high-volume baseline data to distinguish signal from noise. Break this by onboarding a single high-volume D2C design partner to ingest 12 months of historical server-side event logs.

Time To First Value

48 hours to connect Meta and Google APIs and surface the first ROAS discrepancy report

Data Moat Available

true

Technical Difficulty

High

SAM

~$500M-800M targeting North American and European mid-market direct-to-consumer brands

SOM

~$10M-25M realistic 3-year capture at current execution capacity

TAM

~150k scaling e-commerce retailers globally × ~$20k/yr for marketing data infrastructure ≈ ~$3B

Growth Rate

~15-20%/yr, driven by third-party cookie deprecation forcing brands to build first-party attribution infrastructure

Paid Comparable Spend

~$30k-60k/yr in outsourced agency reporting, fractional data engineer time, and generic ETL software

Kill Thresholds

• Time-to-first-sync exceeds 120 minutes for >50% of new signups
• Data synchronization failure rate >5% requiring manual engineering intervention
• CAC > $8k after 90 days of outbound sales
• D60 workspace retention < 60%

Leading Metrics

• Time-to-first successful warehouse synchronization in minutes
• Percentage of daily syncs completed before 8:00 AM local time
• Active ad platform connector count per workspace
• Manual engineering interventions per 1,000 sync jobs
• Daily query volume against the generated destination schema

What Proves Right

The product successfully ingests ad spend and conversion data from ad networks and storefronts without dropping records or requiring manual schema updates. Mid-market direct-to-consumer brands migrate from fractional data engineers or expensive agency retainers to a $20,000 annual contract. Retained cohorts query the synchronized data warehouse daily to drive their marketing budget allocations.

What Proves Wrong

Marketing teams abandon the setup process because configuring the warehouse destination requires a dedicated data engineer. API rate limits and unexpected schema changes cause data synchronization to lag beyond a 24-hour reporting cycle, destroying trust in the attribution models. Brands churn back to Supermetrics or manual CSVs because the warehouse integration adds overhead without uncovering new profitable marketing channels.