Data Science Platform for Retail, eCommerce & Consumer Products

Technology innovation, digitalization, and machine learning models have fundamentally changed the landscape of retail, ecommerce and consumer products businesses. Making model-driven decisions that apply machine learning with best-of-breed tools is the key to success in these rapidly changing industries.

“The path that we’re taking at Gap is to leverage third parties like Domino… to help us quickly solve problems that we couldn’t solve on our own”

— Senior Director of Data Science Operations and Tools at Gap

Challenges

One-Off Requests
Ad Hoc Requests

Competitors, consumer preferences, fashion trends, distribution strategies, and market dynamics are constantly changing. Data scientists are often asked to work on one-time analyses or meet urgent ad hoc requests, which is inefficient, frustrating, and not reproducible.

Legacy Systems
Legacy Systems

Retail and consumer products industries have many legacy systems and they face complex data access and management issues around consumer privacy and omnichannel integration, making it difficult to deploy and integrate new technologies.

Deploy models into production
Deployment

Putting models into production is difficult due to operational and data infrastructure complexities, global scale and supply chain challenges, large networks of staff, numerous touch points, and increasingly high expectations from consumers.

Benefits for Retail Companies

Personalized Experiences
Personalization

Consumers now expect brands to anticipate their needs, to recommend the products they want, and to communicate through preferred channels with the right price and availability in real time.

Inventory Planning
Inventory

Machine learning algorithms fit nicely with the needs of inventory planning and supply chain systems. Data scientists can train models based on high volumes of behavioral data to help planners work much faster and deliver more precise results. And when the data informing those models changes, the models can be iterated on and updated quickly.

Fraud Detection
Fraud Detection

Machine learning can identify anomalies such as unusual patterns in transactional data, and alert risk managers for further investigation or trigger automated remediation.

Trusted Throughout the Retail, eCommerce and Consumer Products Industries

Apply data science to understand the rapidly shifting attitudes and sentiments of highly informed buyers across channels. Utilize the enormous amount of data available to increase conversion rates and revenue.

Domino provides data scientists with an open, central platform to improve collaboration across teams while automating reproducibility and DevOps tasks so they can easily access the tools they want to use, in whichever cloud or on-premises environment the business prefers. Once models are ready for production, they can be quickly deployed and results are easily shared with business stakeholders such the marketing team, planning team, store or channel managers.

Gap
Bluestem Brands
FabFitFun
FlexShopper

Lessons learned from modernizing data science and analytics at Gap (PDF) →

Dun & Bradstreet seal