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

• How to add a customer feature [more info]
• Grouping by a customer features [more info]

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