How Kajabi Scaled Marketing Intelligence With Rockerbox Data Warehousing

The following was extracted from a webinar with Jethro, former Director of Business Insights and Strategy at Kajabi. Watch the recording here.

Kajabi, a leading knowledge-commerce platform empowering creators to build, market, and scale their online businesses, wanted to deepen its understanding of customer journeys, unify marketing data, and unlock advanced analytics across acquisition, lifecycle, and revenue.

As the company matured, its analytics function needed more than siloed channel reports or fragmented third-party tools. Kajabi required a centralized source of truth that could integrate path-to-conversion data, platform performance, and onsite behavior into its existing Snowflake environment.

To solve these challenges, Kajabi partnered with Rockerbox and adopted the Rockerbox Data Warehousing Integration, giving their analytics and marketing teams seamless access to granular, accurate, interoperable marketing data directly in their warehouse.

About Kajabi’s Analytics Lead

Jethro Perez, former Director of Business Insights and Strategy, is a seasoned analytics leader with 14+ years of experience, and was central to Kajabi’s data strategy. As a long-time Rockerbox customer and advisory board member, he helped shape how Kajabi leverages warehouse-ready marketing data to improve acquisition efficiency, LTV modeling, and predictive insights.

The Challenge: Too Much Data, Not Enough Alignment

As Kajabi scaled, the team faced several common but significant obstacles:

1. Fragmented Marketing Data Across Platforms

Google, Meta, TikTok, Pinterest, and other channels were each delivering data with different taxonomies, methodologies, and formats. Unifying this into a single, trusted framework was a major engineering lift.

2. Organizational Misalignment

Marketing, analytics, and product teams often measured performance differently. Without consistent KPIs and shared definitions, insights were difficult to operationalize.

3. “Too Much Data” Problem

Kajabi wasn’t suffering from data scarcity — but from data overload.

“Data is easier than ever to extract and store,” Jethro said. “The challenge is knowing what actually matters and building around that.”

4. Need for More Advanced Marketing Intelligence

Kajabi wanted to evolve beyond channel ROAS and lift reports to deeper layers such as:

LTV:CAC modeling
Contribution margin by channel
Predictive free-trial-to-paid forecasting
Lifecycle personalization

Achieving this required clean, connected, raw data delivered continuously.

The Solution: Rockerbox Data Warehousing Integration

Rockerbox shipped all marketing and onsite data directly into Kajabi’s Snowflake environment in an always-on sync, including:

User-level, path-to-conversion data

Every marketing touchpoint aligned to eventual conversion behavior.

Standardized, cross-channel performance data

Normalized spend + conversions from Google, Meta, TikTok, Pinterest, and more.

Rockerbox Clickstream dataset

A log-level view of onsite user behavior (landing pages, engaged sessions, bounces, etc.).

Order and revenue data structured for attribution modeling

Enabling connection to Kajabi’s internal CDP and customer-level attributes.

With this foundation, Kajabi’s analytics team finally had the “keys to the castle” — raw, clean, analytics-ready data they could shape however they needed.

Results: How Kajabi Uses Rockerbox Data in Its Warehouse

1. LTV:CAC Modeling to Prioritize High-Value Customers

Kajabi merges Rockerbox’s attributed conversions with their CDP customer attributes to compute LTV:CAC ratios by:

segment
channel
campaign
creative
lifecycle cohort

This helps Kajabi optimize spend toward its highest-value, highest-retention audiences.

“We use Rockerbox data to align marketing with the customers who drive the greatest long-term value,” Jethro said.

2. Contribution Margin by Channel

By joining Rockerbox’s order-level data with Kajabi’s internal cost structures, the analytics team builds a bottoms-up view of contribution margin — a more comprehensive profitability metric than ROAS.

This empowers:

More accurate budget allocation
Improved forecasting
Alignment between marketing and finance

3. Free-Trial → Paid Conversion Prediction

For Kajabi’s subscription model, understanding early conversion signals is critical.

Using Rockerbox + Clickstream data, Kajabi built predictive models to identify:

which marketing paths lead to higher trial-to-paid conversion
which onsite behaviors are precursors to retention
which early indicators correlate with long-term customer value

This allows for smarter retargeting, lifecycle messaging, and sales prioritization.

4. Personalized Lifecycle and Retention Marketing

By combining Rockerbox Clickstream data with product usage and CDP segmentation, Kajabi is able to create targeted lifecycle journeys based on:

features viewed
pages visited
engagement level
marketing channel of acquisition

Instead of generic nurture journeys, Kajabi can personalize experiences based on real user intent.

5. Meta View-Through vs. Click-Through Attribution Analysis

Kajabi uses Rockerbox log-level data to independently evaluate:

the incremental value of view-through impressions
differences in click-versus-view driven users
how to down-weight view-through credit based on their internal lift tests

Some brands treat views as highly incremental. Kajabi applies nuance and validates their weighting through ongoing testing.

Why It Worked

Early alignment across teams
Analytics and marketing aligned early on shared KPIs, methodologies, and success definitions.
A simple start that expanded over time
Kajabi focused on foundational use cases first, and then layered on predictive and lifecycle insights.
A flexible, raw data model
Rockerbox delivered ready-to-query data without engineering overhead, allowing Kajabi to innovate quickly.
A culture of testing and validation
Every model, weighting, or attribution adjustment is validated through lift tests and iterative improvement.