This article provides a brief introduction to Google BigQuery along with key use cases for BigQuery in marketing data analytics. Growing opportunities for customer and marketing data analysis are compelling marketers to evolve from simple spreadsheets towards more specialized tools. Using BigQuery, Marketers can quickly migrate from spreadsheets to using the modern breed of full-fledged business intelligence from enterprise scale vendors. Tools such as Tableau, QlikView, and Google Data Studio enable marketers to easily convert raw data into actionable insights.

The articles reviews of some of the key scenarios that highlight spreadsheet software shortcomings, and explains why tools such as Google BigQuery have largely taken away that complexity and cost/time-related barriers to implementation of data analytics.

The limitations of using spreadsheets for advanced analysis discussed in this article are: the ability to handle large data sets, combining data from multiple sources, automated data refreshes, the ability to create custom metrics, dynamic reporting and the ability to trigger automated actions based on the data.

The article introduces BigQuery from a marketer’s perspective and the role of a highly scalable, cost-effective data warehouse, purpose-built for storage and querying of large datasets. It shows how a plug-and-play cloud-based solution can be built using much lower IT setup resources and expenses.

Three important use cases where BigQuery can help marketers are discussed which provide a specific value proposition as compared to spreadsheet-based data analysis. These are:

• Deep-dive analysis of Facebook Campaigns for a large performance marketing agency
• Econometrics and attribution analysis for ecommerce brands running integrated PPC Campaigns
• Combing Web Analytics with CRM data for B2B marketers looking for more targeted and timely communications