Show Menu
TOPICS×

Summary & benefits

Congratulations and thank you for investing your time in learning about Query Service and Adobe Experience Platform! In this module, you've learned how to use Query Service to ask questions and get answers from the data in real-time. The context of this journey was an analyst's journey trying to understand how the customer journey around the topic of churn is happening today, to then try to optimize it going forward.
Query Service allows you to efficiently analyze your end to end customer journey from a single platform in real-time. The ability to put together an end to end view of the customer will allow you to understand where customers are dropping off and address any pain points to ultimately mitigate churn and increase engagement for upsell/across-sell opportunities across any point of the journey.

Benefits

Let's highlight the benefits of using Adobe Experience Platform and Query Service to interact with data :
  • Query Service is a serverless tool so it doesn't require any additional infrastructure
  • Query Service provides analysts with near real-time access to live data as data is available for querying with a 15-minute latency
  • It's no longer needed to export data from Adobe and import it into non-Adobe solutions in a lengthy batch export mechanism. You can query the data directly, without having to move it
  • All interactions with data can be done using standard SQL language, making Query Service very accessible to any data engineer or analyst
  • Query Service easily integrates into an existing Business Intelligence enterprise ecosystem. Any application that supports the PostgreSQL protocol can consume data from Adobe Experience Platform. This means that applications like Microsoft Power BI, Tableau, Qlik, Looker and many more are integrated with Adobe Experience Platform out-of-the-box.
You can now:
  • Eliminate data silos and perform queries and analysis across the entire customer journey in a single platform.
  • Eliminate repetitive, manual, time consuming tasks such as exports of proprietary and third party data into a separate analytics platform.
  • Turn insights into actions by identifying and creating high value audiences for activation or for data science driven use cases, such as lookalike modeling for acquisition purposes.
  • Use standard SQL, which will result in faster time to value and adoption.
  • Continue to use your existing technology stack as several turnkey integrations exist for the most widely used analytics tools such as Microsoft Power BI, Tableau, and Qlik.