All projects Consumer technology · internal data products

Product Analytics for a Voice Assistant

Amazon Alexa — Natural Language Understanding

Replacing manual CSV extracts with an automated Redshift and Tableau product analytics stack.

Amazon Redshift Redshift Spectrum Tableau Amazon SageMaker Amazon S3

The team

  • 1 Data Engineer
  • 2 BI Engineers
  • 1 Front End Engineer
  • 2 Software Development Engineers
  • 2 Technical Product Managers
  • 1 Senior Product Manager

Starting state

  • Custom SQL extracts written out to CSV files
  • CSVs combined into Tableau workbooks by hand
  • No automation and no monitoring

Use cases

  • Alexa feature launch monitoring
  • New language launch monitoring
  • Diving into poor NLU KPIs
  • Churn model for Alexa customers

Architecture

Layered architecture from source layers through staging and business layer to an access layer with Tableau Server
Source, staging, business and access layers feeding Tableau Server.
Data flow from source Redshift through a data extractor to target Redshift, then S3 into SageMaker and back out for campaign management
The churn model: Redshift and S3 consolidated, unloaded to S3, then trained and served through SageMaker.

What was built

  • Amazon Redshift as the analytics warehouse
  • Tableau for reporting, replacing hand-built workbooks
  • A custom data product built by SDEs for drill-down from Tableau dashboards
  • Redshift Spectrum to join warehouse data with the S3 data lake
  • Data unloaded to S3 and fed into SageMaker for the churn model

What changed

  • A manual, unmonitored CSV process became an automated warehouse with scheduled pipelines.
  • Analysts stopped assembling workbooks by hand and worked from shared, governed dashboards.
  • Drill-down moved into a purpose-built data product rather than more one-off extracts.
  • The same platform then supported a churn model, without standing up a separate ML stack.
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