How Tiege Hanley Replaced a $300K In-House Data Stack with Polar to Power Proactive Analytics
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About Tiege Hanley
Tiege Hanley is a leading men's skincare brand on a mission to help men look and feel their best. Known for its simple, high-quality products and direct-to-consumer model, Tiege Hanley has built a loyal global customer base and a reputation for making skincare uncomplicated and accessible.
Facing Growing Data Complexity and High Costs
As Tiege Hanley expanded across Shopify Plus, Amazon, Google Analytics, Meta, and more, so did the challenge of making sense of fragmented data.
They considered building an in-house data stack with Snowflake, Fivetran, dbt, Tableau, and Looker, but quickly realized the significant costs and complexity.
In addition to the financial and technical barriers, Kevin K., Director of Ecommerce and CRM, identified broader operational gaps:
- Subscription health and retention trends were difficult to track in real time.
- Attribution modeling lacked precision across diverse acquisition channels.
- Offline data, including influencer marketing and affiliate spend, remained disconnected from core analytics.
- Non-technical teams struggled to self-serve data for marketing, finance, and operational decisions.
Tri Ngo, Senior Manager of Advanced Analytics at Tiege Hanley, emphasized,
“BI tells you what happened today. But the why and the how? You need to dig into the data for that. My role is to give leadership proactive insights, so they can fix things before they go wrong.”
Tiege Hanley needed more than dashboards. They needed flexible, clean, unified data that could power predictive analytics, without the burden of a full-scale in-house data stack.
Transforming Raw Commerce Data Without Technical Overhead
With Polar Analytics, Tiege Hanley bypassed the complexity of traditional data infrastructure and gained access to:
- A centralized Data Warehouse environment with direct SQL access and ready-to-use commerce data.
- A Semantic Layer consolidating Shopify, Amazon, Meta, Google Ads, Klaviyo, and other key sources.
- Automated pipelines connecting customer, subscription, retention, product, and acquisition data in one place.
- Integrated offline data sources, including influencer marketing and affiliate spend
- Built-in cohort analysis, lifetime value tracking, and attribution modeling tailored to their subscription business.
- Self-serve access for technical and non-technical teams, streamlining collaboration and reducing bottlenecks.
Tri said,
"Before Polar, we had to build everything manually through Fivetran and other platforms. It was costly, complicated, and time-consuming. Now, with Polar, I focus on the deeper analytics that actually move the business forward."
Saving $300k with Clean Data and Clarified Decisions
Since adopting Polar, Tiege Hanley has driven measurable improvements across operations and decision-making:
- Comprehensive, automated reporting for subscriptions, retention, acquisition, and channel performance.
- Real-time, reliable insights empowering faster, more informed decisions at every level.
- $300K+ annual savings by eliminating the need for fragmented, complex infrastructure.
Tri was one of the earliest adopters of Polar’s Data Warehouse Access. With direct SQL access to Polar’s environment and its built-in Semantic Layer, Tri and his team eliminated the manual work of sourcing, cleaning, and syncing data from multiple platforms. The Semantic Layer delivers ready-to-use, business-friendly data models, which allowed the team to focus entirely on transforming raw commerce data, building predictive models, and driving strategic insights, without the burden of technical maintenance.
With Polar in place, Tri was able to:
- Build tailored metrics that explain not just "what" is happening, but "why."
- Create predictive models to identify churn risks and revenue growth opportunities before they impact performance.
- Run advanced attribution analyses connecting marketing spend to long-term customer lifetime value with greater accuracy.
Tri mentioned,
“Without Polar, I’d have to build everything from scratch. With Polar, the heavy lifting is done. I just go in and model what I need.”
What’s Next for Tiege Hanley
Tiege Hanley continues to scale data-driven growth, focusing on expanding advanced retention and subscription health models to reduce churn. They plan to enhance attribution accuracy across paid, organic, and influencer channels, connecting long-tail impacts, such as a YouTube video from last year, to purchases happening today. The team remains committed to growing efficiently while keeping data operations simple, scalable, and focused on delivering strategic value to leadership.
“Polar Analytics transformed how we approach data. Their integration capabilities, intuitive platform, and personalized support have enabled us to make more informed decisions and significantly improve our marketing efficiency.”
- Kevin K., Director of Ecommerce and CRM | Tiege Hanley
What where your goals ?
What are your marketing challenges ?
Facing Growing Data Complexity and High Costs
As Tiege Hanley expanded across Shopify Plus, Amazon, Google Analytics, Meta, and more, so did the challenge of making sense of fragmented data.
They considered building an in-house data stack with Snowflake, Fivetran, dbt, Tableau, and Looker, but quickly realized the significant costs and complexity.
In addition to the financial and technical barriers, Kevin K., Director of Ecommerce and CRM, identified broader operational gaps:
- Subscription health and retention trends were difficult to track in real time.
- Attribution modeling lacked precision across diverse acquisition channels.
- Offline data, including influencer marketing and affiliate spend, remained disconnected from core analytics.
- Non-technical teams struggled to self-serve data for marketing, finance, and operational decisions.
Tri Ngo, Senior Manager of Advanced Analytics at Tiege Hanley, emphasized,
“BI tells you what happened today. But the why and the how? You need to dig into the data for that. My role is to give leadership proactive insights, so they can fix things before they go wrong.”
Tiege Hanley needed more than dashboards. They needed flexible, clean, unified data that could power predictive analytics, without the burden of a full-scale in-house data stack.
How did you monitor growth before Polar Analytics ?
Transforming Raw Commerce Data Without Technical Overhead
With Polar Analytics, Tiege Hanley bypassed the complexity of traditional data infrastructure and gained access to:
- A centralized Data Warehouse environment with direct SQL access and ready-to-use commerce data.
- A Semantic Layer consolidating Shopify, Amazon, Meta, Google Ads, Klaviyo, and other key sources.
- Automated pipelines connecting customer, subscription, retention, product, and acquisition data in one place.
- Integrated offline data sources, including influencer marketing and affiliate spend
- Built-in cohort analysis, lifetime value tracking, and attribution modeling tailored to their subscription business.
- Self-serve access for technical and non-technical teams, streamlining collaboration and reducing bottlenecks.
Tri said,
"Before Polar, we had to build everything manually through Fivetran and other platforms. It was costly, complicated, and time-consuming. Now, with Polar, I focus on the deeper analytics that actually move the business forward."
Saving $300k with Clean Data and Clarified Decisions
Since adopting Polar, Tiege Hanley has driven measurable improvements across operations and decision-making:
- Comprehensive, automated reporting for subscriptions, retention, acquisition, and channel performance.
- Real-time, reliable insights empowering faster, more informed decisions at every level.
- $300K+ annual savings by eliminating the need for fragmented, complex infrastructure.
Tri was one of the earliest adopters of Polar’s Data Warehouse Access. With direct SQL access to Polar’s environment and its built-in Semantic Layer, Tri and his team eliminated the manual work of sourcing, cleaning, and syncing data from multiple platforms. The Semantic Layer delivers ready-to-use, business-friendly data models, which allowed the team to focus entirely on transforming raw commerce data, building predictive models, and driving strategic insights, without the burden of technical maintenance.
With Polar in place, Tri was able to:
- Build tailored metrics that explain not just "what" is happening, but "why."
- Create predictive models to identify churn risks and revenue growth opportunities before they impact performance.
- Run advanced attribution analyses connecting marketing spend to long-term customer lifetime value with greater accuracy.
Tri mentioned,
“Without Polar, I’d have to build everything from scratch. With Polar, the heavy lifting is done. I just go in and model what I need.”
What were your needs ?
What’s Next for Tiege Hanley
Tiege Hanley continues to scale data-driven growth, focusing on expanding advanced retention and subscription health models to reduce churn. They plan to enhance attribution accuracy across paid, organic, and influencer channels, connecting long-tail impacts, such as a YouTube video from last year, to purchases happening today. The team remains committed to growing efficiently while keeping data operations simple, scalable, and focused on delivering strategic value to leadership.
“Polar Analytics transformed how we approach data. Their integration capabilities, intuitive platform, and personalized support have enabled us to make more informed decisions and significantly improve our marketing efficiency.”
- Kevin K., Director of Ecommerce and CRM | Tiege Hanley