So, you want to leverage your first-party data in the most efficient way possible? You need to build the right marketing technology stack. First up? Ensure that you’re collecting the right data and storing it in a way it can be used for modeling and then exported for activation.
Here’s what that might look like for a project like predictive LTV modeling:
To calculate LTV, you want to identify the data that will help you the most. You might pull behavior, audience, event, time and page behavior data from Google Analytics, while you might gather demographic, financial, scoring and custom dimension data from your CRM.
Now you need to move your data from your various platforms to a marketing data warehouse like BigQuery where you can securely store and analyze it. BigQuery, part of Google Cloud, allows you to query massive petabyte-scale datasets using SQL and access results in seconds.
Here are three ways to move your data into BigQuery:
BigQuery also offers a Data Transfer Service with multiple connectors to help you import your data. Essentially, if your first-party data is in a Cloud, you can use it in BigQuery.
BigQuery has built-in compute and machine learning (ML) capabilities that use real-time data to predict business outcomes like LTV. The best part is that to use them, you don’t need to be familiar with Python ML frameworks like TensorFlow or SK Learn, you just need to know SQL. Once you model your data, you can iterate and improve on that model so you’re ready to confidently activate the results.
When you have the LTV information from your model, you can export it back into Google Analytics and use it in your Google Marketing Platform tools and Google Ads to inform your campaigns.
To get the whole story, download our guide, “Take Your First-Party Data Further with Google Cloud for Marketing.” Questions? Contact us.