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How to optimize Klaviyo queries

If you send tens of thousands of emails per day, you might face performance issues with the Klaviyo connector in Supermetrics. The queries might take a long time to process or time out.


You can optimize the queries by selecting a shorter date range and using only the dimensions you need.


Instructions

  1. Use a shorter date range for the query.

    The shorter the range, the less data there is to fetch. Shortening the range makes the query less heavy.
    If you're using Supermetrics in Google Sheets, you can use the "Combine new results with old" setting to append new data on top of your old data.

  2. Don't use dimensions that force the fetching of event-level data.
    Event-level data is used when you use any dimensions from the following categories:
    • Ad
    • Email client
    • Person
    • Purchase
    • Purchase item
    • Source
    • Website
  3. For lighter queries, use dimensions only from these categories:
    • Account
    • Campaign
    • Flow
    • List
    • Time
  4. Consider using a data warehouse solution instead.

    If you can't complete a query with a few days or week date range, a Supermetrics data warehouse product could be an option for you. In data warehouse solutions, the queries can run longer and there is a need to query only 1 day of data per day. Contact our sales team to get started.


Optimize data warehouse transfers

If you're having problems with data warehouse transfers using our Standard table group or your own custom table group, this likely happens because the data volume is too big for the Klaviyo API to handle.


In these cases, make your transfer lighter by using the readily available Lite table group or creating your own custom table group using dimensions only from these categories:

  • Account
  • Campaign
  • Flow
  • List
  • Time


We're working together with Klaviyo to improve the transfer capabilities and Klaviyo is working on a new API version with better data extraction capabilities. Once the new API is introduced, problems with big data volumes should be solved.

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