REAL EXAMPLES. REAL RESULTS.

THREE QUESTIONS.
THREE OUTCOMES.

Finding a relationship, rejecting an unsupported claim, and knowing when the data cannot answer.

1WHEN THE DATA SUPPORTS A RELATIONSHIP

Does customer tenure affect annual revenue?

400k200k0Customer tenure (months)

STATISTICALLY SIGNIFICANT RELATIONSHIP

Longer customer tenure is associated with higher annual revenue.

Coefficient+4,280.31
Std. Error1,098.22
95% CI[2,110, 6,450]
p-value< 0.001
R²0.42
Rows847
✓Evidence supports a meaningful relationship.
2WHEN THE DATA DOESN’T SUPPORT THE CLAIM

Did discounting our enterprise customers in EMEA increase churn last year?

INSUFFICIENT STATISTICAL EVIDENCE

There is no statistically significant relationship between discounting and churn in this sample.

Coefficient-0.0029
Odds Ratio0.997
95% CI[0.922, 1.078]
p-value0.941
Rows312
×No evidence of a meaningful relationship.
3WHEN THE DATA DOESN’T CONTAIN THE ANSWER

Does customer satisfaction affect churn?

CANNOT ANALYZE

There is no customer-satisfaction measure in this dataset.

customer_id✓
region✓
segment✓
discount_pct✓
is_churned✓
annual_revenue✓
acquisition_channel✓
created_date✓
customer_satisfaction×
A plausible answer isn’t the same thing as an answer supported by your data.

ILLUSTRATIVE DEMO OUTPUT · NOT LIVE CUSTOMER DATA

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