PopSockets doubles profitability, boosts AOV by 25% with Fivetran

“Fivetran has been able to replace the role of one full-time data engineer. With Fivetran, our team of data analysts can now focus on tasks that are more valuable to the business rather than just massaging the data and making sure it’s in the warehouse.” — Kyle Chu, Sr. Business Intelligence Analyst, Popsockets

Key results:

2x increase in daily profitability from ad spend
25% increase in Average Order Value (AOV)
Saved at least 1 headcount by eliminating manual tasks of a traditional data engineering role

Background:

Industry: Retail & Consumer Goods
Company Size: 250+
Departments supported: Ecommerce, Marketing, Business Intelligence, Finance, Accounting, Supply Chain and Operations
Sources: Amazon Ads, Klaviyo, SQL Server, Google Ads, Bing Ads, MySql RDS, Google Analytics, Facebook Ads, Snapchat Ads, Pinterest Ads, Shopify
Destination: Snowflake Data Cloud
Cloud Platform: AWS
Use cases: Data Centralization, Ad reporting/analytics, Product Profitability Analysis, Customer Segmentation, Customer Lifetime Value
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Founded in 2014, PopSockets has sold over 230 million of its iconic phone grips in 75 countries and now has an expanding ecosystem of related products, including phone cases, wallets and mounts.
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Before Fivetran

Prior to Fivetran, PopSockets was struggling with reporting efficiencies and communicating insights cross-functionally. There were no strict timelines on when data would be refreshed and the team was enduring the tedious process of manually aggregating reports. As the company began to experience tremendous year-over-year growth and adopted an ERP system, the volume of data grew exponentially. PopSockets was looking for a scalable solution that would allow its small but mighty team of data engineers to build automated data pipelines for faster analytics and reporting. This led to the adoption of the modern data stack consisting of Fivetran and Snowflake.
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Negative consequences
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“In the past, we operated in silos. There was no real notion of data pipelines and of a centralized data warehouse to store data. We were manually pulling reports from various systems and platforms, and then trying to aggregate the data for meetings and company wide presentations.” — Kyle Chu, Sr. Business Intelligence Analyst

Struggling with data silos
Tedious and unscalable manual efforts to aggregate and store data in a single source of truth
Lacking visibility into marketing data to understand ROI of ad spend on various channels

Fivetran solution

As the company experienced explosive growth through various sales channels and increased its paid marketing efforts, Fivetran became a key solution in helping them centralize data and build models to uncover deeper insights — without having to grow its engineering team. When coupling Fivetran and Snowflake with their BI tool, PopSockets has been able to:

Support and unify data from additional ERP systems as they scale across the world
Automate reports with reliable and up-to-date data
Assess performance of individual ad campaigns on different channels more effectively

Outcomes

“By combining data across Salesforce Commerce Cloud, our ERP system and all ad platforms [with Fivetran], we've built a model that allows us to diagnose daily profitability and quickly shift spend across product categories and strategic initiatives. Layering in our COGS and fulfillment rates empowers us to understand true profitability, especially across product lines.” — Kyle Chu, Sr. Business Intelligence Analyst

Doubled daily ROAS
Increased AOV by 25%
Product segmentation - By pulling in ERP data, create leaderboards by SKU to show what products are contributing to not just sales and unit velocity, but also profitability
Saved hours of resources by no longer having to manually download reports from various platforms and aggregate for analyses
Centralized growing ERP data as PopSockets scales worldwide

Future roadmap:

Join ecommerce data and Klaviyo data to help segment customers and develop tailored marketing initiatives
Create a holistic view of product performance on Amazon by combining data from various Amazon ad channels and with Amazon Vendor central to see how products are influenced by ad spend on different channels

Leverage a modern data stack to help with AI/ML by aggregating data to understand purchase behavior of different products in different regions

Required capabilities

Cloud-based modern data stack
User-friendly, low code application
Access to broad range of SaaS based data sources
Plug-and-play solution that was both low maintenance and quickly implemented.
Adjustable data sync frequencies