CSV Upload for Custom Traits

CSV Upload

  1. Head over to Account Settings > Data Settings > Manage My Data
  2. Upload your CSV with the following three column headers
    1. "email" or "customer_id"
    2. "category" (i.e., Acquisition channel, NPS, Sales rep)
    3. "trait" (i.e., Twitter, 5, John Arancini)
    Example using email:
    Example using customer_id:
  3. You can chose to override existing customer trait data, or ignore duplicate data
  4. Depending on your volume of data, your traits should be uploaded in 2-3 hours.

Uploading a csv containing any of the following category names will produce an error:

  • Age of Account
  • Age Range
  • Country
  • Location
  • Company Industry
  • Company Size
  • Gender
  • Job Category
  • MRR Quartile
  • Twitter Followers
  • Activity Level

These are all pre-loaded categories that we populate for you.

What if I don't have complete data for my customers?

We know that often times, you might not have complete data on your customers for custom traits. For example, may be you only started tracking marketing channel a few months ago. There might also be delays between signing on a new customer and assigning it a trait.

To address these situations, ProfitWell will extrapolate your trends across unlabeled customers for you.

Example: Let's say you have Gender data for 20% of your customers and half of those customers are labeled "Female." ProfitWell will extrapolate that information and assume that 50% of your overall customer population is Female.

What if some of my custom traits don't apply to all of my customers?

We recognize that some of your custom traits may not apply to all of your customers and extrapolation doesn't make much sense. For example, you have a segment of customers that are enterprise customers and have dedicated account managers. The bulk of your self-serve customers don't have account managers assigned to them.

In these cases, we recommend assigning an N/A trait to these customers.

If customers are unlabeled, ProfitWell will extrapolate existing data onto those customers.

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