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Project Duration
Ongoing
Client Industry
E-commerce & Retail
Target Markets
North America

PinWheel Analytics

Centralized Analytics Pipeline for Subscription E-Commerce Growth with Real-Time KPI Dashboards

Technology Stack

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Client Overview

Challenges in Modeling Subscription and Business Metrics

Pinwheel Analytics is a consumer tech company that offers eCommerce products through platforms like Amazon, Stripe, and custom online stores. Focused on subscription-based revenue, Pinwheel needed better visibility into customer behavior, product performance, and churn trends to support growth and retention strategies across regions.

Solutions Delivered

End-to-end Data Engineering

ETL and Data Modeling

Business Intelligence Implementation

Subscription Commerce and Marketing Analytics

Team Composition

Data Engineering Specialists

BI Developers

Marketing Analytics Consultants

ETL & dbt Modelers

Engagement Type

Offshore Hiring

Key Challenges

Foundational Data Gaps Hindering Business Insights

Siloed Data Infrastructure

Payment, sales, and engagement data existed in unconnected, inconsistent systems.

Manual, Delayed Reporting

KPIs were compiled ad hoc, limiting speed and accuracy of business decisions.

Lack of Funnel Visibility

No ability to measure churn, LTV, or user behavior across cohorts.

Strategic Roadmap

The team architected a scalable analytics pipeline with Amazon Redshift at its core. Fivetran was deployed to ingest raw data from Stripe, Amazon, and e-commerce platforms. dbt was used to transform these disparate datasets into clean, analysis-ready tables. A semantic layer was built on top to standardize KPIs and empower self-serve reporting via Lightdash.

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Using modular dbt models, the team stitched together data from all core platforms into a unified data model. This model powered a semantic layer that ensured metric consistency across departments, reducing reporting conflicts and unlocking faster iteration cycles.

Execution Approach

Unified Data Modeling and Dashboarding for Scalable, Consistent Insights Delivery

Using modular dbt models, the team stitched together data from all core platforms into a unified data model. This model powered a semantic layer that ensured metric consistency across departments, reducing reporting conflicts and unlocking faster iteration cycles.
Interactive dashboards were launched via Lightdash, tailored to specific teams: churn and cohort views for retention teams, attach rate and sales performance for product teams, and acquisition metrics for marketing. Stakeholders no longer had to wait for ad hoc reports—insights were available on-demand, at their fingertips.
The result was a powerful data ecosystem built for scale, accuracy, and decision velocity.

BUSINESS IMPACT

Real-Time Decision Support
Smarter Lifecycle Management
Accelerated Experimentation
Reduced Reporting Overhead

70%

faster dashboard build time

98%

team-wide reporting access

2.6x

improvement in retention