High spenders from Klaviyo

Introduction

Planning to use data about your high spenders from Klaviyo to start making predictions in Faraday? Great!

Cohorts are the building blocks of your predictions in Faraday, and your High spenders cohort will enable you to predict for not just those likely to become customers, but those likely to spend big. Follow the steps below to create your High spenders 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 high spenders 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 high spenders
  • Each row represents a fully qualified high spender

Exporting data from a CSV file

In order to get started, you'll first export data from a CSV file to CSV.

  1. Navigate to Audience → Lists & segments in Klaviyo.
  2. Select your list–"High spenders," for example.
  3. On the far right, select manage list, then export list to CSV.

klaviyo export

  1. On the export review page, select the columns to include in your export. We recommend including email at a minimum, and any customer data that you'd like to leverage in your predictions, such as address.

klaviyo export review

  1. When ready, click start export and your list CSV will be downloaded.

Shortcuts

Your account may already have a cohort representing your high spenders. Before following the instructions below, check your existing cohorts.

Your account may already have a dataset with High spenders 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 high spenders from a CSV file to Faraday using a Dataset.

  • In the navigation sidebar, choose Datasets. Screenshot of the datasets list
  • Click the New Dataset button.
  • Fill out the form
    • Choose a CSV file and click the Next button.
    • Drag and drop a CSV file representing your high spenders to the indicated area.
    • Enter a memorable name, like "Transaction data" or "High spenders data".
    • Click the Create dataset button. Screenshot of the new dataset form, filled out
  • 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.

Screenshot of the dataset details, blank

  • 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.) Screenshot of the new identity set form, filled out
  • 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.

Screenshot of the dataset details, with identity set

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 high spender?

B. Or, alternatively, does each row represent a fully qualified high spender?

Option A: each row represents a transaction event
  • Click the Add event button.
  • Fill out the form:
    • Check the dropdown for an existing event that resembles a transaction. If you find one, choose it.

    • Otherwise, choose Create a new event stream.

    • Click the Next button.

    • 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.

    • 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.

    • In the Value section, use the dropdown on the left to choose a column in your data that contains a "value" for each Transaction event. You may have to use the dropdown on the right to help Faraday understand your data's format.

    • Skip the optional Properties section. Screenshot of the new event stream form, filled out with eventlike details

  • Click the Finish button.
Option B: each row represents a single high spender
  • Skip the Events section
  • Click the Add trait button.
  • Fill out the form:
    • In the Trait name textbox, enter a memorable name like "value".
    • Choose the column best representing each high spender's value.
  • Click the Finish button. Screenshot of the new event stream form, filled out with traitlike details You're done connecting your data to Faraday! Now let's use it to define a High spenders cohort.

Cohort

Your High spenders cohort is a formal, fluid representation of your high spenders. You'll use this cohort, along with others, as building blocks to configure Faraday to make powerful predictions about your high spenders and others.

  • In the navigation sidebar, click Cohorts. Screenshot of the cohorts list, empty
  • Click the New cohort button. Screenshot of the new cohort form, empty

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

  • Click the Add event button.

  • Fill out the form:

    • Choose the event you created (or selected) in the "Dataset" section above. Screenshot of the new cohort form with event selected
    • Click the Next button.
    • Expand the Lifetime value section.
    • Under Minimum value, enter 1000. Screenshot of the new cohort form with event property details
  • Click the Finish button.

  • Skip the Traits section.

Option B: each row represents a single high spender

  • Skip the Events section.

  • Click the Add trait button.

  • From the list of traits, choose the value trait you created above (you can filter by "User defined" or the search bar to find it easier).

  • Fill out the form:

    • From the dropwn, select Greater than (>). (If this option does not appear, then double check that the column you chose to represent value is an integer.)
    • On the right, enter the value 1000.

Screenshot of the new cohort form with trait property details

Finishing touches

  • Enter a memorable name, like "High spenders." Screenshot of the new cohort form filled out
  • Click the Save cohort button.

Your new cohort is now ready to use!

Conclusion

With your High spenders cohort created from a dataset based on Klaviyo high spenders data, you're ready to use your cohort in a prediction!