Today’s post about how BigML helps B2C companies optimize revenue-building initiatives is written by Seamus Abshere, Chief Technology Officer at Faraday.io, as the fourth of our blog post series authored by the speakers at the upcoming 2ML event. Below is a sneak peek on how Faraday.io takes customer data and combines it with a proprietary national database and ML templates to help other companies acquire, upsell, and retain more customers. We invite you to hear Seamus’s stage presentation at 2ML on May 8-9, in Madrid, Spain.
Helping consumer-focused companies optimize demand generation
The CPO of a leading online furniture company recently shared, “Thanks to Faraday, 1 in 3 of our sales is coordinated using AI”. But, what does that really mean? Faraday.io is in essence “AI for B2C”. We employ reusable Machine Learning templates for all stages of the B2C revenue lifecycle from customer acquisition to upsell and retention. Our customers have seen social media advertisement performance comparable to the best targeting that Facebook ML models can support, all the while benefiting from cross-channel engagement and revenue attribution.
This is possible due to major advances in computing power coupled with improved algorithms over the last decade. As a result, the barriers to leveraging Machine Learning have diminished significantly. At Faraday, we’re now seeing a wealth of companies fundamentally altering their industry dynamics with innovative, data-driven actions handled by advanced Machine Learning algorithms rather than stale, hard-coded business rules.
Common challenges in operationalizing ML systems
Although there are success stories to celebrate and Machine Learning tools are becoming more accessible than ever before, many companies still struggle to successfully operationalize the technology for a number of reasons:
- Machine Learning thrives on large datasets, which can get pretty expensive depending on the subject domain.
- Companies that have the right data still need data scientists and Machine Learning experts. These professionals are in high demand with salary ranges off the scales in most geographies, especially for those that are more experienced.
- Once models are built, engineers must develop systems to feed predictions to various destination applications and track the accuracy of those predictions to further refine the models.
- Due to talent scarcity, companies struggle in applying Machine Learning resources to all business functions that can benefit from it. Surprisingly, consumer-facing functions like marketing, sales, and customer experience aren’t always the highest priorities when allocating Machine Learning resources, despite their immediate impacts on revenue.
Faraday’s unique approach to melding ML with the B2C value chain
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 household
- BigML’s Machine Learning engine integrated into the platform
- A dedicated data-science team constantly working to validate and improve model accuracy and other performance metrics
- An automated campaign delivery system enabling companies to take direct action on their Machine Learning-based insights and predictions
Want to know more about B2C revenue optimization and demand generation?
Join the #2ML18 event on May 8-9 in Madrid, Spain. Get your ticket today so you can meet all the speakers as well as the BigML and Barrabés.biz teams, the co-organizers of 2ML. Looking forward to seeing you there!