How Dopple Saved 10+ Hours Weekly and Uncovered $175K in Revenue Insights with Gitforce
Gitforce was amazing to work with! I was pleasantly surprised with how quickly they integrated with our team. They automated a lot of manual reports saving my team 10 hours. They also built solutions that gave us insight into our inventory performance. Would love to work with them again!
WHAT YOU’LL LEARN
- How to design a unified data architecture that supports self-serve insights across business units.
- How to pinpoint root causes of revenue leakage using well-modeled data pipelines.
- How to turn data warehouse improvements into operational gains across product, finance, and merchandising.
THE PROBLEM
Dopple, a fast-growing New York-based eCommerce brand focused on kids’ fashion, faced mounting operational inefficiencies. Teams across Merchandising, Finance, Product, and Engineering were working in silos, relying heavily on manual reporting and facing discrepancies in performance and revenue metrics. Leadership struggled with visibility, while frontline teams lacked clarity on which actions would drive growth. The absence of a centralized, reliable data system was leading to lost time, missed revenue opportunities, and low confidence in decision-making.
THE HYPOTHESIS
Dopple believed that building a unified, automated data infrastructure, supported by expert partners, would empower each team to gain clear, real-time insights, eliminate redundant effort, and surface opportunities for revenue improvement. With the right data architecture and analytics layer, teams could work faster, make sharper decisions, and confidently align with strategic business goals.
THE SOLUTION
Gitforce approached the engagement with Dopple by focusing on long-term outcomes and tight collaboration with each business function. The solution was executed across six integrated steps:
1Map Data Needs
Gitforce began by conducting structured stakeholder interviews across the Merchandising, Product, Finance, and Engineering teams. They mapped the specific questions each team was trying to answer, audited current reporting pipelines, and documented key data gaps. From these inputs, Gitforce created a unified data blueprint that aligned business logic across units and defined the architecture changes required to support reporting at scale.
2Build Merch Dashboards
To improve merchandising decisions, Gitforce built a set of parameterized Tableau dashboards backed by materialized views in Snowflake. These views calculated sell-through rates, days-on-hand, and markdown impact at the SKU and brand levels. The dashboards were designed for self-service, allowing teams to dynamically filter and export insights without relying on analysts for weekly refreshes.
3Trace Revenue Leakage
Gitforce ingested and transformed return-level data using dbt, enabling detailed classification by style profile, frequency of return, and customer cohort. They implemented a reverse logistics model to trace high-return items back to poor style matches and underperforming personalization logic. This model became the basis for a new internal KPI: style accuracy rate.
4Clarify Revenue Gaps
The Finance team faced unexplained discrepancies between booked revenue and cash collected. Gitforce joined transactional and ledger-level data across Dopple's systems and wrote modular dbt models to classify unpaid balances into distinct categories: payment gateway issues, expired trial conversions, and invoicing mismatches. They packaged these into a monthly debt aging dashboard.
5Fix Funnel Drop-offs
Gitforce collaborated with Dopple’s Product and Growth teams to tag new events using Segment and Amplitude. By analyzing drop-off points in the funnel, they identified UX friction in the outfit selection and payment steps. Gitforce provided recommendations for A/B tests, such as progressive disclosure of form fields and simplified onboarding copy, with instrumentation to measure impact.
6Stabilize Data Models
In coordination with Engineering, Gitforce refactored legacy dbt models that had accumulated technical debt. They introduced standardized naming conventions, modularized transformations into staging and mart layers, and fixed broken joins that caused double-counting of orders. They also implemented unit tests and documented lineage to improve maintainability and reduce future reporting errors.
THE IMPACT
- Saved Merchandising team more than 10 hours weekly, enabling better supplier negotiations.
- Recovered potential revenue of $175,000 by addressing styling inadequacies and debt sources.
- Improved signup retention rates by 20%, enhancing customer acquisition.
- Increased accuracy of revenue reporting metrics by 10%, significantly boosting trust and reliability in reporting.
- Streamlined data reporting into one automated source, significantly simplifying investor and executive insights.