Real-time hypothesis exploration

Learn how to check and validate any hypothesis about the factors that are influencing your customer's behavior and business KPIs.

Outcomes

  • Quickly find the driving factors of any KPI you’re trying to optimize
  • Automatically self-serve insights using approaches that have been tried and tested by data scientists




Part 1: Building your dataset





Adding customer features to a dataset

Using aggregations to do quick exploration

  • Using column shortcuts to create group by tabs [more info]

Adding activity features to a dataset

  • How to add a features from an activity [more info]

📝 Practice: Start with a KPI (conversion rate or average) and segment (break it down by a customer attribute)

  • Understand the impact on your KPI in aggregate
  • Understand the impact on your KPI over time




Part 2: Generating your Analysis





Using the Analyze button to do real-time hypothesis exploration [more info]

  • Specifying the inputs
  • What is time between?

Understanding the analysis [more info]

  • Understanding the baseline
  • Assessing the impact of your features

Tracking the influence over time [more info]

  • Scheduling analysis to be re-run

📝 Practice: Use the same KPI/customer column as before to generate a full analysis

  • Does that customer feature matter?
  • What action would you take from this analysis?




Part 3: Asking Good Questions

Tips for coming up with good hypotheses

  • Starting with KPIs
  • Starting from Customer journey
  • Starting from another Narrative

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