One-to-one engagement powered by machine learning
With mParticle, Overstock is able to deploy machine learning models rapidly on a solid data foundation.
Since 1997, Overstock has provided customers with an easy way to purchase both everyday and hard-to-find furniture and home decor items at the best prices. A decade after its founding, faced with increased competition and rising customer acquisition costs, the company embarked on a company-wide initiative to optimize its return on marketing spend.
To harvest insights from Overstock’s massive customer dataset, the team went in search of an agile, scalable data modeling solution, but quickly encountered a more foundational challenge: their customer data sets were stored in numerous disparate systems, with no universal linkage across them. Without clean consistent data readily available, even the best machine learning would be of little use.
After an exhaustive review, Overstock selected mParticle as the customer data platform to collect, cleanse, and normalize their data before feeding it to their machine learning engine.
After training the models and scoring individual records, Overstock’s machine learning engine then writes the modeled values as user-level attributes, back into mParticle, where they can be leveraged to drive marketing actions.
mParticle has enabled Overstock to model customer data and score individual customers across hundreds of dimensions. The machine learned attributes include: channel propensity, category and product affinity, expected conversion propensity, expected purchase value, and expected lifetime value, among many others.
With mParticle, Overstock was also able to decrease machine learning model deployment cycles from three months to one day, enabling them to continue pushing the boundaries of machine learning powered AI, while creating highly differentiated customer experiences.
Using the scored values, non-technical marketers have created and deployed custom segments that have led to a 10% increase in overall marketing efficiency.
Latest from mParticle
GOAT: Lifecycle marketing for scalable growth
Learn how GOAT uses mParticle to streamline their data pipeline and increase Customer Lifetime Value.
mParticle launches new features to help brands create ‘data flywheel’
New features for seamless data quality management, and transformation to serve as a foundation for improved customer experience and better insights.
Better data, better insights, better results: Helping brands create a data flywheel
Introduce total quality management and enforcement into your customer data pipeline with new Data Master features and Calculated Attributes. With Data Master and Calculated Attributes, establishing a source of reliable customer data that will create a customer data flywheel, where the data quality and data’s impact on the product cycle will continuously improve over time.