Having your raw data stored in one central location accessible through code and dashboards and using a platform that can manipulate data, many marketing decisions to personalise communications become suddenly possible. These include:
- Descriptive analytics on how frequency affects conversion per user per campaign. Having this information helps when you build re-marketing campaigns to adapt frequency on a specific list of users. BigQuery's access to raw Campaign Manager data makes this information possible.
- Diagnostic analytics to understand the impact of a campaign and website behaviour on your sales. To activate these analytics, you use SQL statements to create joins of IDs over big data.
- Predictive analytics on LTV for specific users. By predicting the value of specific groups of users, you can run marketing campaigns to increase sales. You gain this insight through joining data and using machine learning to build customer segments and predict an LTV amount.
- Prescriptive analytics on product sentiment. By analyzing the evolution of text comments and ratings, you can help prevent inaccurate targeting by predicting how a certain group of users will receive a product that has certain characteristics. You might do this task by using sentiment analysis and customer segmentation, for example.