Case Studies

The Work, Described Honestly.

Client engagements are confidential, so these stories are anonymized and outcomes are described qualitatively. We would rather be credible than impressive. References and detailed walkthroughs are available during a strategy call.

Retail

One Customer, One Number

Creating a unified customer and analytics foundation from fragmented commerce, marketing, operational, and customer data.

Anonymized client engagement

The situation

A multi-channel retail organization was running commerce, marketing, and store operations on disconnected platforms. Customer records existed in multiple systems with no reliable way to connect them. Executive reporting pulled from different sources, and marketing attribution was guesswork.

The approach

Assessed the current data environment across commerce, marketing, operations, and customer service
Designed a Customer 360 foundation with unified definitions and identity resolution
Established governed executive reporting and marketing attribution logic
Sequenced the work into a practical roadmap the internal team could execute

The outcomes

Customer 360: a single, governed view of the customer across channels
Executive reporting built on agreed definitions instead of competing extracts
Marketing attribution grounded in traceable data
Customer segmentation and revenue analytics the leadership team could act on

SaaS

From Metric Chaos to a Single Operating View

Modernizing a fragmented reporting environment and establishing consistent business metrics.

Anonymized client engagement

The situation

A growing SaaS company had critical data spread across CRM, product analytics, billing, marketing automation, customer success, and finance systems. Every department reported ARR, churn, and pipeline differently, and board reporting required days of manual reconciliation.

The approach

Mapped metric lineage from each source system through to executive reports
Unified KPI definitions for core SaaS metrics with named owners
Defined a target data architecture and consolidated overlapping reports
Built executive dashboards on a governed metric layer

The outcomes

Unified KPI definitions adopted across all departments
Executive dashboards the leadership team and board could trust
SaaS metric governance with clear ownership
Customer lifecycle analytics connecting product, revenue, and success data

EdTech

Analytics and AI for Personalized Learning

Using analytics and AI to create more personalized student experiences.

Anonymized client engagement

The situation

An education technology organization had rich student engagement and learning progress data, but it lived in separate systems. Leadership saw the potential for personalization and AI-supported learning but lacked the data foundation and a clear sense of where to start.

The approach

Assessed data readiness across student engagement, learning progress, and performance data
Identified and prioritized AI and analytics use cases by student impact and feasibility
Defined the data architecture needed to support recommendations and personalization
Established measurement criteria so pilots could prove value before scaling

The outcomes

A prioritized portfolio of personalization and AI tutoring opportunities
Performance analytics giving educators a clearer view of student progress
A data foundation designed for recommendations and adaptive experiences
Parent and stakeholder reporting built on consistent definitions

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