Hearst Reduces Time to Value for Business-Ready Data in GCP BigQuery with Reactor.
“SoundCommerce’s composable solution reduced our Time-to-Value for robust and in-depth retail analytics, while Reactor simultaneously enabled our internal data teams to focus on integrating our proprietary data to provide a more complete picture of our audience’s interests and commerce needs.”
The company
- Company profile
- $12B
- Industry
- Consumer Media
- Website
- hearst.com
Data stack
- Pipeline & Transformation
- Reactor
- Destination
- Google BigQuery
- Data Sources
- VTEX, AfterShip, Meta Ads, Facebook/Instagram, Bing, SailThru
- Business Intelligence
- PowerBI
Overview
By integrating the Reactor intelligent pipeline into their modern data stack, Hearst was able to land clean, well-defined data from their newly launched e-commerce stores directly in GCP BigQuery for immediate modeling and activation. This strategic approach ensures that Hearst remains at the forefront of the digital transformation in the media industry, leveraging technology to maintain its leadership position and drive sustainable growth.
Challenge
With 165 million unique users each month according to Comscore, Hearst has the opportunity to enable digital shopping experiences for these users across its consumer media businesses. This essentially means Hearst is building a new ecommerce business for every media title in its catalog.
Solution
The Reactor intelligent pipeline onboards, cleans and defines data from Hearst’s disparate systems and brands providing a comprehensive view of digital activities, crucial for optimizing marketing strategies and operational efficiencies.
Impact
What changed for Hearst.
Launched new e-comm stores
Launched new e-commerce stores for each Hearst media title starting with Men’s Health then expanding to Women’s Health, Cosmopolitan, Good Housekeeping, and more.
Deployed data models
Deployed data models that transform raw data into actionable insights across its multiple consumer publications, providing a more detailed understanding of customer interactions and profitability metrics, ensuring each segment contributes to overall profitability and enhanced customer lifetime value.
Accessed real-time metrics
Accessed real-time metrics and performed in-depth analysis of digital behaviors, which help in predicting trends and making adjustments to marketing and operational strategies
Landed structured data
Landed only structured, well-formed data decreasing the cost of ingesting and storing unnecessary data
Hearst use cases
How they put Reactor to work.
- 01
Generate granular profit and loss profiles for every order and shopper, modeled alongside order event and customer engagement data to better segment, target, engage and serve ecommerce shoppers from first click to doorstep delivery.
- 02
Gain visibility into fine-grain customer order details including order items across the catalog item master (product assortment), order cancellations and returns, and order fulfillment status.
- 03
Consolidate order and customer data including shoppers’ repeat purchase behaviors spanning years of order history to calculate shopper lifetime profitability.
- 04
Track and model customer loyalty, order history, demographic, repeat, purchase propensity and marketing engagement data using Reactor’s expanded customer profiles.
