Mountain House Modernizes Data Capabilities and Elevates Direct-to-Consumer Experiences in Google BigQuery
“By harnessing Reactor’s user-friendly interface and fully automated data pipelines, we can more effectively track consumer demand, optimize inventory, and identify emerging product trends to better serve our customers’ diverse needs.”
The company
- Company profile
- Direct-to-Consumer Brand of Oregon Freeze Dry, founded 1963.
- Industry
- Food Products
- Website
- mountainhouse.com
Data stack
- Pipeline & Transformation
- Reactor
- Destination
- Google BigQuery
- Data Sources
- Shopify, Google Analytics, Klaviyo, Meta, Google Advertising
- Business Intelligence
- Sigma Computing
Overview
Mountain House streamlines their data onboarding workflows by adopting Rector, an intelligent pipeline for modeled data, to unlock new efficiencies and real-time insights that will guide customer experience innovation and bolster its direct-to-consumer growth.
Challenge
Mountain House is the direct-to-consumer division of Oregon Freeze Dry and a trusted leader in freeze-dried meals for nearly 50 years. With the modernization and growth of the company’s ecommerce business, Mountain House needed to better understand the timing, unit economics and key performance indicators of its digital marketing and physical operations across both consumer and wholesale channels. Company-wide access to key operational data was the key.
Solution
At Mountain House, Reactor is used to prep, label, and map data into Google BigQuery for further analysis and activation, enabling the brand’s teams to quickly access unified, accurate data from e-commerce transactions, supply chain operations, and customer engagement channels.
Reactor combines transactional order detail from Shopify with digital marketing attribution data coming from Google Analytics. Detailed order and new and repeat customer data is tied back to digital marketing channels like Facebook and Instagram, and the ad spend driving each conversion.
With a complete picture of the product item master including costs of goods sold, fulfillment and shipping methods and status, and customer engagement over time, the BigQuery data warehouse at Mountain House can answer questions and enable data activation across the entire order and customer lifecycles.
As Reactor populates BigQuery with more operational data, the data warehouse can support a growing number of downstream use cases including generative AI analysis, expanded human-driven business intelligence and analytics, and shopper profiling, segmentation and activation.
Thanks to Reactor, Mountain House now has real-time access to mission-critical data and the latest emerging tools to act upon that data to optimize the business.
Impact
What changed for Mountain House.
Better business insights
Empowers their data teams to concentrate on delivering strategic, high-impact insights instead of grappling with data pipeline maintenance
Shared data understanding
Reactor’s powerful semantic layer and orchestration features present a strong alternative to older ETL solutions, which typically demand extensive engineering support just to maintain simple data pipelines.
Faster time to insights
Reactor’s flexible, low-code environment accelerates data transformations – applied as data streams into BigQuery – without sacrificing control or customization.
Reduced cloud data warehouse expenses
By transforming and defining data as it flows into BigQuery, Reactor helps reduce Mountain House’s total engineering and compute costs
Mountain House use cases
How they put Reactor to work.
- 01
Data unification
Combine data from various sources, including e-commerce transactions, supply chain operations, and customer feedback, into a single, unified view in Google BigQuery.
- 02
Streamline analytics
Model data in a dedicated cloud data warehouse, owned and controlled by Mountain House, to enable generative AI analysis, BI dashboards and data activation
- 03
Profitability analysis
Combine variable revenue and costs from marketing, operations and inventory to detail the profit drivers and detractors of each order
- 04
Customer profiling
Unify shopper order history with engagement data to better understand shopper behavior and purchase propensity
- 05
Demand planning
Utilize the unified data to predict future customer demand, enabling proactive inventory management and production planning.
- 06
Inventory optimization
Analyze sales and inventory data to maintain optimal stock levels, reducing carrying costs while preventing stockouts.
- 07
Product trend analysis
Identify emerging trends and shifts in customer preferences by analyzing sales data and customer feedback.
