Customer Success

Hearst Reduces Time to Value for
Business-Ready Data in GCP BigQuery with Reactor.

Hearst-with-logo

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.

Annual Revenue

$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

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.

“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.”

Robert Gash Chief Technology Officer, Hearst
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PODCAST

What’s Driving Hearst’s E-commerce Aspirations?

How Robert Gash is approaching the strategic value of data and making this data useful for their distinct portfolio of brands.

Impact

01.

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.

02.

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.

03.

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

04.

Landed structured data

Landed only structured, well-formed data decreasing the cost of ingesting and storing unnecessary data

Hearst Use Cases

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.

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