Tactic 5:
AI-Assisted Results Analysis & Insights
Tips:
•
Double-check the summary against the data.
AI might occasionally misstate a number or misinterpret significance.
Always verify critical claims (e.g., whether something is truly statistically significant) with your analytics source.
•
Use the AI to create different outputs for different audiences.
A detailed analysis for the team, a one-paragraph executive
summary for leadership, and even a catchy headline for an internal newsletter (“Test X increased sign-ups by 10%,
driving $Y in potential annual revenue.”). This way, communication is tailored but consistent, and you only spent minutes
generating it.
•
Ask for visual suggestions.
While a text-based AI won’t create charts, it can recommend what kind of chart or table would
best illustrate the results (“a bar chart comparing conversion rates with error bars for confidence intervals”). This can
guide you or a designer to quickly produce supporting visuals.
•
Leverage AI to combine experiment data with other context.
For example, “Our A/B test showed a 10% lift. How does this
compare to typical results in our industry?” The AI might know or infer that 10% is quite high for, say, retail e-commerce
tests, which you can use to add color in your report.
GenAI Quick-Win Playbook for Experimentation
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