How Loop leveraged SELECT's observability to optimize Snowflake across thousands of embedded analytics users
Loop Returns helps e-commerce brands manage returns and exchanges with a focus on retaining revenue, automating workflows, and improving the customer experience.
E-Commerce Returns
300 employees
Columbus, Ohio, USA
2017
Ian FaheySenior Analytics Engineer at Loop
Challenges
Loop operates a Snowflake environment serving thousands of external users through embedded analytics and 100s of internal users across analytics engineering, machine learning, and business operations. After a "no holds barred" period focused on extracting maximum value from data insights, the team needed systematic cost visibility and management processes. Their previous approach provided limited awareness of spending patterns and waste identification opportunities.
Solution
SELECT provided comprehensive cost visibility with an intuitive diagnostic flow. The platform's automated insights enabled confident warehouse rightsizing decisions and early identification of expensive workloads. Regular monitoring through SELECT became integrated into Loop's operational routine, providing the quantitative foundation needed for cross-team cost conversations and architectural decision-making.
Results
- Enabled confident warehouse optimization including shifting CI jobs to larger warehouses while reducing production refresh warehouses
- Identified and resolved $20K sudden cost increase traced to expensive window function pulled into hourly reporting
- Established daily cost monitoring routine integrated into operational workflows
- Transformed tech debt prioritization by quantifying potential savings opportunities
- Enhanced cross-team collaboration through data-driven cost conversations with business and engineering teams
Snowflake powers both internal analytics and thousands of external embedded users
Loop operates a sophisticated data infrastructure platform with Snowflake serving as the central warehouse for both internal operations and customer-facing analytics. The platform supports thousands of external users through embedded analytics in their returns management application, while internally serving 100s of internal users across multiple tools.
The internal user base spans five analytics engineers developing in dbt, three analysts setting up workbench reports, and machine learning engineers building Streamlit applications for foundational merchant comparison models and recommendation systems.
Beyond traditional analytics, Snowflake powers operational use cases including automated merchant alerts that trigger when merchants leverage key features, enabling customer success teams to proactively reach out and understand issues.
Previous "no holds barred" approach provided limited cost visibility
Before SELECT, Loop operated under a "no holds barred" philosophy during their initial Snowflake adoption, prioritizing maximum value extraction from data insights over cost management.
This approach enabled rapid innovation and data platform development but provided limited visibility into spending patterns and optimization opportunities. As Loop began preparing for Snowflake contract renewal conversations, they recognized the need for systematic cost monitoring and right-sizing.
“After a period of rapid growth we needed to take a step back and invest in a proper cost management practice to ensure we weren't spending Snowflake credits unnecessarily, and that's where SELECT came in to fully automate that process from observability to remediation.”
Ian Fahey
Senior Analytics Engineer at Loop
SELECT's Automated Savings and Insights featured enabled instant ROI
Through SELECT's Automated Savings feature, Loop was able to immediately lower their spend by 20% with the click of a button.
After this initial cost reduction, the Loop data team was able to tackle more strategic cost optimization opportunities surfaced by SELECT's insights feature.
SELECT's insights provided the confidence needed to make strategic warehouse sizing decisions that balanced performance and cost. Loop was able to optimize their CI/CD infrastructure to prevent developer bottlenecks while reducing costs on production workloads.
“We were able to downsize our production warehouse because SELECT surfaced that the jobs were running in a reasonable amount of time on the smaller warehouse size, but bumped the developer warehouse up to increase developer productivity by allowing CI jobs run faster and avoid resource contention.”
Ian Fahey
Senior Analytics Engineer at Loop
This optimization enabled faster development cycles while maintaining cost efficiency, demonstrating how SELECT's visibility supports both performance and financial objectives.
New daily monitoring routines created a proactive cost management culture
SELECT transformed Loop's cost management approach by becoming integrated into daily operational workflows. The data team established a routine of regularly checking SELECT as part of their other data platform monitoring rituals.
“I love clicking through SELECT to understand how our environment and workloads are evolving. I probably check it every day. It's coffee and SELECT for me every morning.”
Ian Fahey
Senior Analytics Engineer at Loop
This daily monitoring enables early identification of trends, new workloads, and potential issues before they become significant cost problems.
“SELECT works the way my brain works. I love clicking through the query patterns and different workloads. It's a very intuitive diagnostic flow - I can easily identify how costs are evolving, spot which workload or dbt model caused that, then click into that job and immediately identify what changed and what action could be taken to remediate.”
Ian Fahey
Senior Analytics Engineer at Loop
Automated anomaly monitoring helped avoid a $20K cost increase
SELECT's monitoring capabilities proved critical when Loop experienced a sudden $20K cost increase. The platform's automatically alerted the team of the cost spike and the deep visibility enabled rapid identification of the root cause and immediate resolution.
“We had an instance where suddenly we had a $20K increase that was picked up by SELECT's alerting. In SELECT, we were able to quickly determine that one dbt view with an expensive window function was pulled into an hourly report, triggering the sudden cost increase, and were able to implement a fix accordingly.”
Ian Fahey
Senior Analytics Engineer at Loop
Cross-team cost conversations drive strategic alignment
Perhaps the most significant benefit has been SELECT's role in enabling data-driven conversations across different teams within Loop. The platform provides the quantitative foundation needed for productive discussions about resource allocation and business priorities.
“I really like having numbers to go talk to other groups within the company, whether it's on the business side, like, hey, can you tell me that this field is that valuable to the business because it costs us this much on the Snowflake spend, or upstream, saying, hey, we have this data cleaning step in dbt, it costs us this much. Should we instead be moving this transformation upstream to lower costs?”
Ian Fahey
Senior Analytics Engineer at Loop
This quantified approach has transformed how Loop prioritizes technical work, moving optimization projects from "nice to have" tech debt to strategic initiatives based on concrete savings opportunities.
“It takes something from tech debt to business critical. There's a whole list of things on our tech debt list that we're like, well, if we have world enough and time, we could get to that. And if you can be like, hey, it's going to save us 10K, you can move that to the top of your list for the week.”
Ian Fahey
Senior Analytics Engineer at Loop
Looking ahead: ML workloads and advanced integrations
As Loop expands their machine learning initiatives and explores new technologies like Hex semantic authoring for natural language queries, SELECT will continue providing essential cost visibility for these evolving workloads.
Loop is also exploring SELECT's upcoming features including Streamlit integration for ML cost tracking, row count and freshness monitoring to integrate their data observability tooling, and AI copilot functionality for natural language cost queries.
The team's commitment to proactive cost management through daily SELECT monitoring ensures they can confidently scale their data platform while maintaining cost discipline as their business continues growing.
Up next.More Customer Stories.
Get up and running in less than 15 minutes
Connect your Snowflake account and instantly understand your savings potential.
