Diminishing barriers to entry for machine learning
Thanks to major improvements in computing power and network speed over the last decade, the barriers to leveraging machine learning have diminished significantly. We’re now seeing a wealth of companies fundamentally changing their industries with innovative data-driven processes optimized with advanced machine learning algorithms.
Common challenges in successful operationalization of machine learning
While machine learning is more accessible than ever before, several companies still struggle to successfully operationalize the technology for a number of reasons.
- Machine learning requires huge datasets to be successful. Companies often lack the volume, breadth, or depth of data needed, so they have to purchase 3rd party data which gets pretty expensive.
- Companies that have the right data still need data scientists and machine learning experts to clean and organize the data, define desired outcomes, and write queries to tell the machine learning engine what to look for. These individuals are in high demand and salaries are through the roof.
- Building accurate models is just the first major hurdle. Once models are built, engineers must develop systems to feed predictions to various destinations and track their accuracy to further refine the models.
- Due to the scarcity of talent, companies struggle to apply their resources to all business functions. Data science teams have lengthy backlogs and tend to prioritize optimizing complex back-office processes like demand forecasting and supply chain management. Consumer-facing functions like marketing, sales, and customer experience are generally lower priorities when allocating machine learning resources, despite their immediate impacts on revenue.
How Faraday helps B2C companies overcome these challenges
We understand that acquiring, managing, and implementing the resources and processes needed to operationalize machine learning can be daunting, so we bundled it all up into a simple, user-friendly platform designed for non-data scientists. With the Faraday platform, B2C companies have access to:
- 240+ integrations to seamlessly upload their prospect and customer data,
- a massive national database containing over 235 million U.S. consumers in over 125 million households,
- a built-in machine learning engine that automatically builds predictive models for each and every desired outcome,
- a dedicated data-science team constantly working to validate and improve model accuracy,
- and an automated campaign delivery system enabling companies to take action on their machine learning-based insights and predictions.
"Thanks to Faraday, 1 in 3 of our sales is coordinated using AI" – CPO, leading NYC-based direct-to-consumer furniture company.