Savant's automation saves 500+ hours/month and cuts costs by 40% for Million Dollar Baby

About Million Dollar Baby

Based in Los Angeles, Million Dollar Baby (MDB) is a prominent nursery furniture company. They offer a wide range of products, from cribs to gliders, serving both major retailers and direct consumers. Their product lineup includes everything from budget-friendly options on platforms like Amazon to high-end collections popular among celebrities.

The challenges

MDB's primary challenges were inventory management and forecasting. As the supply chain became increasingly complex and data requirements grew, MDB needed to modernize its analytics capabilities. 

Manual inventory management was complicated

After the COVID-19 pandemic, MDB faced increasingly complex supply chains with new distribution centers and diverse retail partners. MDB struggled with manually managing inventory and orders across distribution centers, which was crucial but time-consuming.

Needed to modernize its analytics capabilities

MDB identified that their toolkit was fragmented, lacked a central source of truth and was difficult to use. Performance was an issue, with files taking hours to refresh and frequent crashes due to complex formulas. These challenges led to isolated information, duplicated efforts and other inefficiencies.

Wanted to avoid the need for a large IT/data team

MDB’s data stack evolved quickly, requiring a streamlined approach to manage data sources, ingestion, storage, delivery and analytics. They aimed to avoid extensive migration and high costs associated with using multiple tools. The goal was to transform their data management without the burden of a large IT team and inflated costs.

Existing tools lacked predictive insights

There was no shortage of historical data for MDB, as is common in retail and CPG sectors. But, the data lacked the predictive insights needed for informed decision-making. MDB continuously onboarded new applications to manage increasing data demands and complexities. This created an imbalance where business leaders constantly sought more data for valuable insights while analysts struggled to keep up with these growing demands.

The solution

Savant’s no-code, low-code platform helped MDB build self-service analytics capabilities without relying on a large engineering team. It supports MDB’s operations across supply chain optimization, finance and marketing analytics.

Data replication with ETL/ELT to Databricks: Synced 40M+ rows daily

With Savant, MDB synchronized data hourly and daily from over a dozen critical sources to Databricks. Thanks to pre-built connectors and low-code API hooks, data migration and integration with Databricks were completed in one week instead of months, saving hundreds of hours. Savant’s point-and-click connectors streamlined data replication, syncing over 40 million rows daily from 15+ tables in the ERP system to Databricks. This process includes data cleansing, blending, and stacking, all without complex SQL or Python scripting.

Gen AI automated data transformation tasks: Delivered 3x faster analytics

Savant’s Gen AI capabilities automated tasks previously done manually with Excel or SQL, which saved hundreds of hours each week and minimized human error. By generating simple commands, Savant enabled team members of all skill levels to perform complex data manipulations without needing advanced coding skills. This made data transformation more accessible and efficient, reducing reliance on top-tier data engineers and speeding up the analytical process by 3x. 

Automated marketing dashboard updates

Savant automated data pulls from multiple Google Analytics sites, social media and ad platforms for MDB, significantly reducing manual data handling. Analysts no longer needed to log into each site, download files and stitch them together. Instead, Savant’s platform managed daily data updates and ETL processes, syncing cleansed datasets to Databricks. This automation enabled real-time updates to Power BI dashboards, providing instant insights into marketing performance and drastically improving reporting efficiency.

Built supply chain operational alerts for inventory depletion

Savant changed how MDB manages exceptions and data monitoring with alert bots. These bots integrated inventory data from ERP with forecasts from Databricks. They acted as digital detectives who identified SKUs at risk of depletion and sent real-time alerts to the operations team. This shift from reactive to proactive monitoring replaced hours of manual effort that often got deprioritized due to team overload. 

Streamlined supply chain updates to Shopify storefront

Savant automated the process of updating its business apps like Shopify with inventory and ETA information. For instance, Savant extracted replacement part ETA updates from the ERP system and integrated input from the warehouse team via Google Sheets for non-inventory hardware. This data was promptly synced to Shopify, ensuring accurate stock estimates and delivery times. What was once a weekly task managed by three or more employees was now performed daily without human intervention. Customers received up-to-date information on product availability, enhancing their shopping experience and reducing manual effort.