Datasets is where you plug your customer data into Faraday, then organize it to make it usable for predictions. Here, you'll organize your data into identity sets, events, and traits that can be used throughout the Faraday platform. By plugging your data into Faraday, you're able to use it to target specific desirable outcomes that your customers take–such as purchases in leads.
Inside Datasets, you'll find a list of your current datasets if you have any, with as columns for:
- Source: the source type of this dataset, such as CSV.
- Row Count: the number of rows in the dataset.
- Identities: the number of unique identities, or people, in the dataset.
- Matches: the number of people in your dataset that Faraday found matches for.
- Match rate: the number of matches divided by the number of identities.
- Events: the event type of the dataset, such as orders or churns.
- Status: whether the dataset is ready, queued, or errored.
Creating a dataset
- Select new dataset in the upper right of Datasets.
- Next, choose to create your data set from either a connection or CSV. The connections that populate here are pulled from the Connections, which is where you can add new ones.
- Regardless of whether you chose a connection-based or CSV-based dataset, after clicking finish, you'll receive a notification that your dataset has been created, and you'll be moved to the edit dataset view where you can customize it.
Adding an identity set
Identity sets are used to help Faraday identify people in your data. With this information, you can create cohorts of your customers (or anyone else identified in your data) and outcomes to make predictions about these individuals.
To add an identity set:
- In the dataset's definition (default) tab, click add identity set.
- Give your identity set a name.
- Next, match the properties that exist in your data in the field in dataset column with the Faraday property names in the left column.
Not all property fields are required, but email and address are the most useful for identifying people. The more fields you include, the more likely to match the people are.
- Once you're done matching your properties, click finish to save the identity set. If you need to edit or delete the identity set at any point, click the three dots (...) on the right.
Events show Faraday how to recognize actions taking place in your data, such as purchases, renewals, click events, upsells, etc. Dates are often the most useful piece of data for events.
Event streams that you define in datasets are available for selection when creating cohorts, which are then used to create outcomes, which then go on to help build your predictive pipelines.
- In the dataset's definition (default) tab, click add event to get started, which will open the new event window.
- Next, choose whether to add the event to an existing event stream, or create a new one.
- If you're using this data to add onto an existing event stream, select the appropriate event stream from the dropdown.
- If you don't have an existing event stream, or want to create a new one, select create new event stream.
Unsure which option to select? Generally, if the new dataset you're creating contains event data that a previously-made dataset also includes, such as order or churn dates, you'll want to add this event to that existing event stream to keep your data clean.
- In either of the above cases, click next to be taken to the event definition screen.
- Select the timestamp property for this event from your data in the left column.
- Optionally, select the format of the timestamp in the right column. This is generally auto-detected and if it is, it won't be modifiable.
- Define the value of the event, if applicable.
- Optionally, map any relevant properties that exist in your data by entering a name, associated field in dataset, and format.
- Click finish to save the event. If you need to edit or delete the event at any point, click the three dots (...) on the right.
Traits are interesting data points that can enhance the usefulness of your data in Faraday, but aren't used to identify a person or an event. For example, whether a person owns or rents their home, hobbies, income, etc. These traits can be appended to pipelines, used to create cohorts, used for analysis, etc.
Traits are completely optional, and are generally an edge use case. If you're unsure whether or not you should include them, it's best to avoid doing so.
- In the dataset's definition (default) tab, click add trait to get started, which will open the new trait window.
- Next, give the trait a name.
- Lastly, choose the corresponding field in the dataset. For example, you may have a field in your data called category that lists the customer's first purchase category.
Once you've finished adding an identity set, event, and/or trait, click Save dataset to save it for use throughout Faraday.
Refreshing data in a dataset
Sometimes it might be beneficial for you to add additional data to a dataset. For example, if your original dataset was a manual upload of order data from the previous month, and you'd like to append this month's order data.
If you've configured a dataset via connection to your data warehouse, it will automatically be kept up to date. As such, this section is focused on manual, CSV uploads.
- To start, you'll want to head to the dataset you'd like to configure, expand the advanced tab, and find replace all with latest file. By default, this setting is set to false, so each time you upload data via the below steps, the new file is merged into the dataset. If the value is changed to true, the entire dataset is replaced with a new file upload.
- Once in the data tab of your dataset, drag your new file to the upload prompt or click to open the file picker. When your additional file's upload is complete, it will appear in the files in dataset list and the dataset status banner at the top of the dataset (green for ready, red for error) will display the upload's refresh date.
Note: Your additional file upload must be in the same format as the data uploaded previously in this dataset. Columns in the new CSV must exactly match those in the original CSV.
- With your new data uploaded, you can now dig back into your predictive building blocks–your cohorts, outcomes, personas, and more–and make any required edits. For example, your newest upload may have included second purchases from a customers who were in your first upload, so you can now jump into cohorts to create or update a repeat purchaser cohort.
Standard fields to send to Faraday
Faraday matches your customers into our database at the individual level, so the more info about each individual in your data, the more likely you are to have a good match rate. Date fields are extremely important when building predictive models. As an example, we like to know if someone is a customer, but more importantly, we need to know when they become a customer, or when they purchased a certain product, or took some other specific action. Often, many of the key date fields in your data might live in the orders table in your database.
|First name||Customer first name|
|Last name||Customer last name|
|Street address||Customer street address|
|Customer email address|
|Phone||Customer phone number|
|Customer||The field in your data that determines a customer|
|Lead data||The field(s) in your data that determines a lead. Do you have various lead categories? What determines when a lead converts? Lead status?|
|Product data||Date of purchase, item purchased, price of item, product types, number of orders|
|Subscription data||Date the subscription started and/or ended|
|Customer ID||The field that will be used to match your predictions back to the appropriate customer in your stack (e.g. Salesforce ID)|