Integrate Klaviyo Data with BigQuery for Useful Analytics and Activation
A Step-by-Step Guide
Make the Most of Your Klaviyo Data in BigQuery
Klaviyo and BigQuery are a natural fit for data modeling, business intelligence and data activation – especially for retail brands interested in optimizing their overall business performance, acquisition and retention marketing programs, and merchandising and fulfillment operations decisions.
Klaviyo is intelligent marketing automation powered by customer data, and one of the most popular SaaS email, SMS and campaign automation solutions serving more than 130,000 customers in 80 countries. BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data. Use built-in ML/AI and BI for insights at scale. Together, the software application and cloud data tooling provide business and data practitioners with an opportunity to analyze and optimize email, SMS and campaign automation to drive profitable growth.
Connect to Klaviyo
The first step toward useful, modeled Klaviyo data in BigQuery is to connect the source and destination systems. There are many legacy tools available in market that handle the ETL or ELT transfer of Klaviyo data to BigQuery, and there are emerging tools that accomplish this transfer while providing value-added services like local data logging, and semantic data labeling and mapping along the way – making Klaviyo data modeling, analytics and activation easier once the data is landed in BigQuery.
To connect to Klaviyo, follow these easy steps
Open SoundCommerce in any browser. Open the “Intelligent Pipeline” application from the top right navigation menu. Select “Sources” from the left navigation menu. Choose “Add New Source” from within the Sources pane to open the data source library.
Search or browse to find “Klaviyo” within the data source library.
Complete the “Connection Setup” form with your credentials and token to securely connect to Klaviyo and begin collecting source data.
Log Klaviyo Data for Flexible Modeling in BigQuery
There are a few more considerations to address along the way. First, what happens if Klaviyo is unavailable for some reason, or the data you’re expecting has been purged by Klaviyo? What happens when Klaviyo changes their API schemas and data scope? What happens if you need to reinterpret your Klaviyo data for a new use case in the future?
You’ll want your Klaviyo data immutably logged locally, just upstream of BigQuery to ensure you have the data and data flow flexibility you need to future-proof your Klaviyo data and models. SoundCommerce provides permanent logging of Klaviyo data upstream of BigQuery to ensure failover and future-proofing. Regardless of how you connect your Klaviyo and BigQuery data, you’ll want a data lake or event log in the middle to ensure data integrity and modeling flexibility.
Define and Label Klaviyo Data for BigQuery
As new technologies arise and best practices evolve, traditional integration tools like ETL and ELT data pipelines are giving way to intelligent pipelines that help prep data for BigQuery starting at ingest. Simply moving JSON from Klaviyo to BigQuery leaves all the work for your data team in BigQuery.
As you onboard your Klaviyo data into BigQuery, you’ll want to create semantic labels and metadata that describe the Klaviyo data for easier unification and modeling across other systems and data in BigQuery.
There are third-party solutions that will catalog your Klaviyo data and generate semantic labels and mappings after you’ve landed it in BigQuery. With SoundCommerce, the Klaviyo data is defined and labeled on its way into BigQuery instead, to avoid this costly rework later. You’ll end up with business-ready entities like orders, customers, products and campaigns, making it much easier to model your Klaviyo data in BigQuery.
Map Klaviyo Entities to BigQuery
Once the raw Klaviyo data has been organized into useful entities, it’s time to map the Klaviyo data into useful tables in BigQuery.
Why do defined and labeled entities from Klaviyo matter so much? The main reason is that Klaviyo data needs to be combined with data from other SaaS and on-premise software systems in useful ways. Landing raw Klaviyo data in BigQuery without this semantic understanding means data engineering and analyst teams must do all of the heavy lifting regarding the meaning of the Klaviyo data and the standardization of the meaning of that Klaviyo data from scratch in BigQuery.
Defining, labeling and mapping the Klaviyo data on the way in means much less effort once the data is landed in BigQuery.
Materialize Klaviyo Data in BigQuery
Next, you’ll establish a secure connection to BigQuery:
Select “Destinations” from the left navigation menu. Choose “Add New Destination” from within the Destinations pane to open the data destination library.
Complete the “Connection Setup” form to securely connect to BigQuery to establish a secure destination for your labeled, mapped and modeled data.
That’s it! You now have logged, labeled and mapped data from Klaviyo flowing securely to BigQuery.
Model Klaviyo Data in BigQuery
Once you have well-formed entities from Klaviyo onboarded to BigQuery, it’s time to build useful analytical and behavioral models on the Klaviyo data – and combine the Klaviyo data with data sets from other systems in BigQuery for more advanced, cross-dimensional analysis.
You can build your own analytical models on the Klaviyo data in BigQuery using languages like SQL and Python, organized into model libraries in tools like DBT or Coalesce. With SoundCommerce, you get prebuilt analytical models for Klaviyo running in BigQuery, with ready access to the model source code in DBT.
Host the Modeled Klaviyo Data in BigQuery for Analytics
BigQuery supports reporting and visualization through a wide variety of analytics tools including Sigma, Tableau, Looker, Power BI and Microstrategy to name a few. You can build your own dashboards, tabular views and graphs in any of these tools to reveal insights about Klaviyo in your BigQuery models. SoundCommerce provides pre-built embedded reports in Sigma to reduce the time, cost and risk of BI reporting of Klaviyo data out of BigQuery – so you can start making better decisions and taking better action as soon as you’ve connected Klaviyo to BigQuery.
Host the Modeled Klaviyo Data in BigQuery for Campaign and Customer Activation
Whether your marketing team uses Klaviyo for activation – or uses other tools and channels or both to take action on the data – you’ll want to be able to easily move your modeled Klaviyo data in BigQuery to your most important marketing applications.
If you’ve followed the steps above to properly onboard and model your Klaviyo data in BigQuery, it’s easy to use reverse ETL (rETL) tools like Census or Hightouch to orchestrate the data from there, or use SoundCommerce native orchestrations to push data into common channels and applications like Facebook, Instagram, TikTok, Braze, Klaviyo, Insider or Dynamic Yield to put the Klaviyo data in BigQuery to use!
Getting Your Klaviyo Data Defined, Labeled, Mapped and Modeled in BigQuery is Easy!
SoundCommerce can automate the steps necessary to bring Klaviyo data into BigQuery, addressing the key functions of raw Klaviyo data logging, Klaviyo semantic definitions and mappings, and pre-built Klaviyo data models that are analytics- and activation ready in BigQuery.
Contact us today to get started with Klaviyo in BigQuery!
Technical Resources for Integrating
Klaviyo Data with BigQuery
More information and technical specifications for data collection from Klaviyo is available at:
More information and technical specifications for data ingest into BigQuery is available at: