How Soundboks streamlined data integration with Weld, S3, and Databricks

Soundboks, founded in 2015 by three high school friends from Copenhagen, has grown into a globally recognized brand known for its powerful Bluetooth speakers. With both direct-to-consumer and retail sales, they needed a centralized data infrastructure to streamline reporting and eliminate time-consuming manual processes.

Before implementing Weld, their retail sell-out data was received via email attachments, requiring the team to manually extract and input the data into spreadsheets. This process was slow, prone to errors, and limited accessibility across teams.

By integrating Weld, Amazon S3, and Databricks, Soundboks built a modern data pipeline that automates data ingestion, improves reporting, and provides up-to-date visibility into sales performance.

Results with Weld

• Automated retail sell-out reporting, eliminating manual data entry
• Up-to-date insights in Power BI, improving visibility into retail performance
• E-commerce and retail data consolidated, enabling accurate forecasting
• Faster decision-making, reducing time spent on manual reporting

Fragmented data and manual processes

Soundboks’ sales data was spread across multiple platforms, including Shopify for e-commerce, Amazon Selling, and various marketing channels like TikTok Ads, Meta, Google Ads, and Snapchat. Additionally, they relied on Google Sheets and Excel for manual data handling, making it difficult to track performance across channels.

Their team had to manually copy-paste data from CSV and Excel files into spreadsheets, which slowed down decision-making and increased the risk of inaccuracies. As their business grew, they needed a scalable solution that could automate data ingestion, improve accessibility, and enable more frequent updates to their analytics.

Building an automated data pipeline with Weld, S3, and Databricks

To streamline data operations, Soundboks implemented Weld to sync data into Amazon S3, where it is stored in Avro file format for efficient processing. Databricks then enables advanced analytics and transformations, allowing for more sophisticated modeling and reporting.

One major efficiency gain was automating the processing of retail sell-out reports. Instead of manually handling email attachments, Weld’s email connector now extracts and processes this data automatically, making it instantly available in Power BI dashboards.

Additionally, Soundboks has integrated OneDrive and Excel for financial forecasting. Stakeholders can input budget forecasts directly, which are then synced into their data warehouse. This enables real-time comparisons between forecasts and actual sales data.

Beyond sales and forecasting, Soundboks aggregates marketing data from multiple ad platforms (TikTok, Meta, Google, Snapchat) into a single source of truth. This allows them to analyze campaign performance holistically and optimize advertising spend based on real revenue impact, rather than just platform-reported conversions.

Soundboks also leverages Klaviyo for email marketing analytics, syncing data to better understand customer engagement and retention metrics.

Conclusion

By integrating Weld, Amazon S3, and Databricks, Soundboks has eliminated manual processes, improved reporting efficiency, and built a scalable data infrastructure that supports better, faster decision-making. With sales, marketing, and email data consolidated, they now have a holistic view of their business performance, enabling smarter growth strategies and data-driven optimizations.

With a robust data foundation in place, Soundboks continues to scale while ensuring their data infrastructure keeps pace with their expanding global presence.