The Challenge

Your NPS dropped — but from where? And why?
Dashboards show trends, but not truths. Without precision, improvement efforts miss the mark.

Our Analytical Approach

We apply structured statistical techniques to detect hidden patterns — and separate what feels important from what statistically is.

Here's how we do it:

• Driver Regression Analysis:
  Quantify which factors (e.g., onboarding clarity, support quality, usability) significantly influence NPS.

• Chi-Square Testing:
  Assess whether differences in promoter/detractor distribution are statistically significant across experience variables.

• Correlation Heatmaps:
  Visualize strength of relationships between experience scores and NPS.

• Text Mining + Sentiment Scoring:
  Analyze open comments using NLP to discover negative sentiment drivers (e.g., “confusing setup,” “slow resolution”).

Statistical Insights Example

We ran a multivariate regression for a mid-sized B2B SaaS client with NPS data and experience metrics from 800 customers.

Key Results:

Onboarding and usability were statistically significant predictors of NPS.
Support mattered, but less so — suggesting prioritizing product fixes over support changes.

Drilldown Using Chi-Square

We compared Detractors vs. Promoters on onboarding experience:

• 72% of Detractors reported onboarding as unclear or somewhat clear

• Only 19% of Promoters reported the same

Chi-Square p-value = 0.0002 → strong evidence that onboarding clarity affects promoter likelihood.

Actionable Takeaways

• Prioritize onboarding redesign: customers unclear on setup were 3.6x more likely to become detractors.

• Simplify UI elements where customers reported usability blockers.

• Refine support process — but not before fixing foundational journey gaps.