In Cohorts, you'll create cohorts of people that are of interest to your organization. Cohorts represent specific groups of people, such as customers and leads. You can also create more advanced cohorts, like recent customers, or repeat buyers. As your data changes and updates, people will automatically flow in and out of your cohorts.
You'll use these cohorts as building blocks when you define the business objectives you want Faraday to predict.
Inside Cohorts, you'll find a list of your current cohorts if you have any, with columns for:
- Population: the number of people in the cohort.
- Event: the event that the cohort is based on; something that each cohort member has experienced.
- Traits: the traits (if any) that were included in building the cohort.
- Status: whether the cohort is ready, queued, or errored.
- Show in Explore: toggle to enable cohorts for geo-analysis in Explore.
Creating a cohort
- Select new cohort in the upper right of the Cohorts list view.
Select the event that will define your cohort. Your events are defined in Datasets. Click add event to add one as a defining parameter for the cohort. When selecting an event, you're optionally able to select recency, frequency, and value filtering options. When you've finished adding the event, click Finish.
Recency: allows you to set days-ago ranges on the event. An example of this would be if you're interested in creating a cohort based on customers who've placed orders between 30 and 90 days ago.
Frequency: allows you to set a range on the number of occurrences of the event. An example of this would be if you're interested in creating a cohort based on customers that have made more than 3 orders.
Value: allows you to set a range on the value of the event–generally orders. An example of this would be if you're interested in creating a cohort based on customers who've spent over $100.
Optionally, add properties to the event. For example, a “gift buyers” cohort could exclusively include people who experience “transaction” events with the “gift” property set to true. You can add custom properties to events in Datasets.
Next, optionally add any traits that you want to help define your cohort. Your user-defined traits are defined in Datasets, but cohorts can also be defined using traits from the Faraday Identity Graph.
A trait is a characteristic that you want the members of this cohort to exhibit, like household income or pet ownership. You can create trait-based cohorts purely based on traits from the Faraday Identity Graph by simply not including an event.
When you select a trait and click next, you'll be able to select specifically how you'd like the trait to apply to the cohort. Do you want to include people who do have this trait, or don't have it? Does it need to be within a certain range, e.g. for household income? Click finish when you're done adding the trait.
When selecting true/false traits, like dog ownership, you're given the option to check true, false, and missing. In these scenarios, true means that they currently own a dog, false means they don't, and missing means they haven't reported as owning a dog via the data in the Faraday Identity Graph. In some cases, it may be worth checking missing in a cohort's trait since there's a chance that they do fall into the selected trait, but haven't reported it.
When selecting age as a trait, always select one year beyond the year you're targeting. Looking to target customers 20-30 years of age? Select 20-31 year olds on the slider.
- With your events and/or traits selected, give your cohort a unique name and click save cohort to finalize the cohort. Population and status will display as calculating and building respectively until the cohort is finished building.