Repeat purchasers from Salesforce
Introduction
Planning to use data about your repeat purchasers from Salesforce to start making predictions in Faraday? Great!
Cohorts are the building blocks of your predictions in Faraday, and your Repeat purchasers cohort will enable you to predict which customers are most likely to come back for more. Follow the steps below to create your Repeat purchasers cohort.
Getting started
Make sure you have a Faraday account (signup is free!) and that it's not in test mode.
Prerequisites
You'll need data representing one of the your repeat purchasers available in a CSV file. This data must take the form of one of the following:
- Each row represents a single transaction event that a person could experience in order to become a repeat purchasers
- Each row represents a fully qualified repeat purchaser
Exporting data from a CSV file
In order to get started, you'll first export data from a CSV file to CSV.
- Navigate to Salesforce Reports.
- Click new report in the upper right of the reports dashboard.
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In the report type selector, choose Contacts & Accounts, then click start the report.
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Next, customize the report to include key identifiers like email, first name, last name, and the identifier your organization uses to define repeat purchasers, such as contract signed date or order date. Once finished adding columns, refresh the report via the prompt.
Including Contact ID will allow you to use that as your identifier when you import your predictions back into Salesforce.
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Click run in the upper right to run the report.
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After running the report, click the down arrow next to edit in the upper right, then export.
- In the export popup, ensure details only is selected if given more than one option, and that the format is .csv. When ready, click the export button to finalize the export, which will download the report as a CSV.
Shortcuts
Your account may already have a cohort representing your repeat purchasers. Before following the instructions below, check your existing cohorts.
Your account may already have a dataset with Repeat purchasers or Transaction data. If you see one listed among your existing datasets, you can skip the "Dataset" section below and proceed directly to the "Cohort" section.
Dataset
Connecting your data
First, you'll add data representing your repeat purchasers from a CSV file to Faraday using a Dataset.
- In the navigation sidebar, choose Datasets.
- Click the New Dataset button.
- Fill out the form
- Wait briefly while Faraday analyzes your data. It shouldn't take long.
Describing your data
Next, you'll help Faraday understand what your data means, starting with recognizing people in your data.
Identities
Your data may contain multiple identities per person, like billing and shipping. Choose one to start with.
- Click the Add identity set button.
- Fill out the form
- Provide a memorable name for this identity set, like "billing," "shipping," or "customer." You can only use lowercase letters, numbers, and underscores here for technical reasons.
- For each identity property listed on the left side of the table, use the dropdown on the right to see if there's a column in your data that contains this kind of data for your chosen identity set. (If not, you can leave that dropdown blank.)
- Click the Finish button.
If there are other identity sets in your data, feel free to use the Add identity set button and repeat the process.
Behavior
Now you have to make a decision about your data:
A. Does each row represent a single transaction event that a person could experience in order to become a repeat purchaser?
B. Or, alternatively, does each row represent a fully qualified repeat purchaser?
Option A: each row represents a transaction event
- Click the Add event button.
- Fill out the form:
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Check the dropdown for an existing event that resembles a transaction. If you find one, choose it.
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Otherwise, choose Create a new event stream.
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Click the Next button.
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If you're creating a new event stream, enter a memorable name, like "transaction." You can only use lowercase letters, numbers, and underscores to name event streams.
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Under the Timestamp section, use the dropdown on the left to choose a column in your data that contains a "timestamp" for when that row's Transaction event occurred. You may have to use the dropdown on the right to choose the correct format for your timestamp.
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Skip the Value section.
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- Click the Finish button.
Option B: each row represents a single repeat purchaser
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Skip the Events section
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Click the Add trait button.
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Fill out the form:
- In the Trait name textbox, enter a memorable name like "count".
- Choose the column best representing the number of occurences.
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Click the Finish button.
You're done connecting your data to Faraday! Now let's use it to define a Repeat purchasers cohort.
Cohort
Your Repeat purchasers cohort is a formal, fluid representation of your repeat purchasers. You'll use this cohort, along with others, as building blocks to configure Faraday to make powerful predictions about your repeat purchasers and others.
Filtering
Here you will specify the qualifications a person needs to meet to be part of this cohort. If everyone in the dataset should be a member, you can skip this section.
Follow the instructions for option A or option B - whichever one you picked above.
Option A: each row represents a transaction event
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Click the Add event button.
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Fill out the form:
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Click the Finish button.
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Skip the Traits section.
Option B: each row represents a single repeat purchaser
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Skip the Events section.
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Click the Add trait button.
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From the list of traits, choose the count trait you created above (you can filter by "User defined" or the search bar to find it easier).
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Click Next
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Fill out the form:
- From the dropdown, select Greater than (>). (If this option does not appear, then double check that the column you chose to represent count is an integer.)
- On the right, enter the value 2.
Finishing touches
Your new cohort is now ready to use!
Conclusion
With your Repeat purchasers cohort created from a dataset based on Salesforce repeat purchasers data, you're ready to use your cohort in a prediction!