With summer in full swing, Faraday’s product team and developers have been working hard to create some incredibly exciting updates to the Faraday platform. The why behind these changes is rooted in two of our core product pillars:
- AI for all. The benefits of machine learning should not be reserved solely for the Fortune 500 or those with advanced degrees. The promise of easy AI, scalable cloud-based infrastructure, and a good UI should unlock the benefits of consumer behavior predictions for all brands.
- Making data science fun. Using Faraday should be intuitive and easy. You should feel empowered to ideate, experiment, and deploy, all on your own.
We're thrilled to announce that as of today, four major updates are live. In this blog, we'll walk you through how each one works—here's what we'll cover:
- Build your own predictions in Outcomes: Describe the business outcomes you want predictions for, and Faraday will build and maintain predictive models for that outcome, with models getting more accurate over time as they learn from your data.
- Deploy your predictions with ease in Pipelines: Choose your likely-to-buy outcomes, your personas, and more; pick a specific population of customers; and target a destination for your deployment, like your data warehouse, cloud bucket, CRM, ESP, etc.
- Plug into Faraday with Connections: Plugging in your data is now a simpler process with fewer steps required. Plus, Connections paves the way for a future update where we'll implement true bi-directionality for your data and predictions in the Faraday platform.
- Create more powerful Cohorts: Cohort building now features the ability to specify certain trait requirements from your data or the Faraday Identity Graph in order for a customer to enter a cohort, adding an extra layer of depth to your predictions.
Let’s dive in.
Back in December, we released an update that enabled Faraday users to build their own predictive personas within a cohort of their choosing based on various attributes from the Faraday Identity Graph.
That update had been the first major in-platform step toward user autonomy, and, more importantly, toward making AI accessible for all. Continuing on that path, Faraday users can now access an additional self-serve interface, this one for defining propensity (likelihood) predictions: Outcomes.
Outcomes allows you to easily describe the business outcomes you’re trying to predict in just a few clicks. Likely to buy? Sure. Likely to churn? You bet. Likely to buy again? That, too. Behind the scenes, Faraday takes over from here, applying dozens of strategies to build a range of candidate models, then selecting the one that most accurately predicts your outcome. Over time, Faraday automatically builds and rebuilds progressively more accurate models as you accumulate more data.
Our new Outcomes interface puts the power of experimentation in your hands, allowing you to easily try out different outcomes and view how effective the model will be across your use cases—all without needing a data scientist at your shoulder.
Once you’ve defined your own prediction objectives, make them actionable with Pipelines.
Pipelines completely streamlines the prediction deployment process in Faraday. Previously, you may have used some combination of Reach, Monitor, and Inform to deploy predictions on-demand, on a scheduled basis, and in real-time via API. These three products have been unified into Pipelines for a more accessible and intuitive user experience.
Jump into Pipelines and choose your:
- Payload: a combination of your desired propensity outcomes, your personas, and more.
- Population: the people you want the predictions on.
- Targets: where you want your predictions deployed, including your data warehouse, cloud bucket, or anywhere else in your stack (CRM, ESP, etc).
Faraday takes over from here, keeping your predictions constantly up to date by refreshing scores and persona assignments when new data comes in, ensuring you’re never left wrong-footed when delivering personalized experiences to your most likely-to-buy customers.
With Pipelines, for the first time ever, you’re able to both describe and deploy your own predictions completely autonomously—without a data science degree.
Starting today, you’ll notice the Sources and Destinations areas of the Faraday platform have been combined into a single Connections view.
With Connections, you’re able to plug Faraday right into your data warehouse or cloud bucket by entering your location-specific details. Once done, it’ll be ready for selection as a deployment target in Pipelines. Dropping the number of setting screens required to complete your end-to-end connection from two to one should save you some clicks, and get your first predictions deployed that much faster.
Keep an eye out for more news here, because Connections is a major building block for a future update that will enable every Connection to be bi-directional—your predictions, wherever you want them, whenever you want them.
Previously, Cohorts in Faraday were defined by event streams, or an event that each person in a cohort has experienced. This included the type of event (such as an order), when the person experienced the event (all orders, last X days, etc), the frequency of the event (such as number of times an order was placed), and finally, the value of the order.
We introduced the Segments console last year to allow users to define a group of people based solely on the Faraday Identity Graph (FIG)-based traits, like geography or income. While this was a great step for offering flexibility in terms of the types of population you could define in the Faraday platform, it was cumbersome having to navigate through both Cohorts and Segments to do so.
With today’s update, Segments and its functionality has been folded into Cohorts. Now, in one console, users can define their cohorts by selecting specific traits from either their customer data, or exclusively from FIG. Event streams are no longer required to build a cohort.
Prior to today, your "Recent bulk purchasers" cohort might’ve been simply based on the number of orders in the last 30 days. You can now refine this cohort further to say "Recent bulk purchasers in CA" or "Recent bulk purchasers with income >$100k." On the flip side, you might also build a cohort without an event stream, such as "Dog owners in CA with an income >$100k."
We hope that by allowing you to define your preferred population of people—whether that be based on an event stream like orders, FIG traits, or a combination of the two—gives you another building block upon which you can easily experiment with predictions through Outcomes, Personas, and Pipelines.
Coming up, we’ll be revamping data ingress to make it easier to map your data into Faraday. We see this as a critical step towards democratizing data science and empower you to create and maintain a powerful predictive layer atop your martech stack.