Your team only has so much time on its hands, and chasing down every lead for a conversion, and every customer to keep them engaged, all while finding new ways to scale acquisition, isn’t a realistic–or sustainable–approach.
That’s where Faraday comes in. Faraday uses AI to predict the consumer behaviors you need in order to help your brand reach its goals. In just a few clicks, you can discover what personas dominate your customer base, score customers for churn risk, score leads to find the ones most likely to convert, and more.
No code–and no PhD–required.
Let's peel back the curtains a bit. Your workflow in Faraday can be distilled down into three basic concepts:
- Connect your data
- Define your business objectives
- Deploy predictions back to your stack
For example, say your business has a churn problem–your team is having no trouble getting people on board, but they're not staying on board. With Faraday, you can jump into connections to connect your data. Whether your data sits in a data warehouse like BigQuery, a cloud bucket like Amazon S3, or a CRM like HubSpot, you connect the dots here.
Next, you'll define your identities and events in your data in datasets, from either a connection you've made to your stack, or a direct CSV upload of customer data.
With your data connected, you'll define your business objectives in outcomes by specifying cohorts of people that you'd like to predict for.
In the above churn scoring example, you'd specify that you'd like to score your current customers (eligibility cohort) based on how much they're likely to end up like your churned customers (attainment cohort).
Now that your business objective is defined, the final step is to deploy predictions back to your stack in pipelines.
When you're creating your deployment, choose the format that's appropriate for wherever you're deploying to, be it your database, CRM, or some other activation platform, enable your pipeline, and voilà!