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.
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.
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.
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.
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.
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.
Machine learning can identify anomalies such as unusual patterns in trading data, and alert risk managers to investigate or trigger automatic remediation.
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.
