Balsam Brands Reduces Cost and Complexity Integrating and Modeling Business-Ready Data in Snowflake
“At Balsam Brands, we know the importance of using trustworthy data to drive world-class experiences for customers and better company performance. With Reactor and Snowflake, we’re building future-proof data infrastructure for machine learning, advanced analytics and data activation. The goal is to make the data and the tools easy and effective for every business stakeholder across our company.”
The company
- Company profile
- Founded in 2006, privately-held
- Industry
- eCommerce Retailer
- Website
- balsambrands.com | balsamhill.com
Data stack
- Pipeline & Transformation
- Reactor
- Destination
- Snowflake
- Data Sources
- SAP Hybris, IBM Sterling Commerce, Amazon Marketplace, Google Analytics, Various Digital Marketing Channels, ContentServ & Klaviyo
- Business Intelligence
- PowerBI
Overview
Balsam Brands uses Reactor to unify and catalog data for AI modeling, cross-functional business analytics, and customer activation, reducing the time and complexity of integrating and modeling useful business data for the entire organization.
Challenge
Founded in 2006, privately-held Balsam Brands designs and sells holiday and home décor consumer products via its flagship website BalsamHill.com, selling direct-to-consumer (DTC) and shipping direct to the shopper doorstep. Balsam operates mostly online with a growing footprint of physical stores. Nearly all of the brand’s revenue is generated during the Christmas season, and their top selling product category is artificial trees.
With a heavy focus on the holidays, Balsam needs in-house data and analytics teams to do more than just pull reports. The team needs to easily access key metrics and help business teams make data-driven decisions, like identifying high-value customer segments and marketing to them appropriately. The company’s legacy SQL database took days or weeks to process mission-critical data for marketing, merchandising or operations decisions and actions. The legacy data infrastructure consumed time and resources during the holiday season that Balsam could not afford to waste.
Solution
With Reactor (ReactorData.com), the intelligent data pipeline, Balsam Brands gains a resilient, flexible and fully observable path for all enterprise data, from source system to destination table in Snowflake. Reactor provides non-engineering users the ability to collect and log data, transform it from source schema into common entity models (business objects like orders, customers and products), and flow these normalized, harmonized entities business-ready into Snowflake. With Reactor, Balsam knows the exact lineage and state of every processed record. With extended semantic metadata definitions for every field, exact definitions and calculations are created and documented within Reactor at ingest, providing the context and labels for downstream consumers of the data. By the time data is landed in Snowflake, it is prepped, mapped, labeled and ready for Balsam analysts and business end users to go to work.
On top of these Reactor-landed tables in Snowflake, Balsam can access business-ready, direct-to-consumer retail data models. Ready-to-go data models are available in Snowflake for immediate analysis, allowing the Balsam Brands team to avoid time-consuming, resource-intensive data transformation tasks, for faster time-to-value on the data.
With Reactor, Balsam’s teams can now work as they see fit with useful data structured for easy access and integration with downstream systems. Reactor can connect to any source system, model that data, and land it directly in Snowflake. This gives Balsam Brands the capabilities that, in the past, would have been very difficult to bring in-house and even more difficult to bring that up to the users in a way that they can see it and access it easily.
Impact
What changed for Balsam Brands.
Balsam Drives Greater Conversion
Balsam drives greater conversion and revenue using real-time feedback on ‘million-dollar days’ during the holiday seasons.
No Data Loss or Downtime
The brand replatformed its order management systems with no data loss or downtime given Reactor’s ability to ingest, cleanse and semantically map data into common entities regardless of source system.
Balsam Tests New Data Sets with Ease
Balsam tests new data sets with ease to understand correlations and impact on their customer journeys and profitable sales.
Balsam Brands use cases
How they put Reactor to work.
- 01
For Executives & Operations Pros
Reactor unifies high fidelity order data – including order lifecycle and profitability – across owned commerce (SAP Hybris), marketplace (Amazon) and ERP (Sterling Commerce) systems.
- 02
For Marketers
Reactor unifies digital marketing data, combining shopping cart conversions, digital marketing attribution, paid media spend and activity – all collected and unified from system-of-record APIs rather than browser cookies and site tracking tags.
- 03
For E-commerce Pros
Reactor consolidates shopper engagement data from all transactional and campaign sources including website, shopping cart, email and SMS; materialized in Snowflake for customer segmentation, marketing activation and higher relevance campaign automation.
- 04
For Data Analysts
Reactor lands data in Snowflake for easy access by generative AI large language models (LLMs), BI, analytics, activation tools, and other downstream applications across the enterprise.
