Complanits Per Million - bharath143775/Executive-Metrics GitHub Wiki

๐Ÿ“Š Complanits Per Million(CPM)

๐Ÿงพ Definition

CPM (Consumer Product Monitoring), also known as Complaints Per Million, measures how many consumer complaints were received for every one million units of product sold or produced.

This quality KPI gives visibility into product issues from the customer's perspective. A lower CPM means better product quality and fewer customer complaints, helping organizations maintain brand trust and loyalty.


๐Ÿงฎ Calculation (DAX/SQL)

๐Ÿง  Formula:

CPM = (Number of Complaints * 1,000,000) / Total Units Sold

Why 1,000,000?
This normalization enables fair comparison across different product categories or time periods, regardless of sales volume.


๐Ÿ“Œ Example Calculation:

Complaints in April: 25
Units Sold: 10,000,000

CPM = (25 * 1,000,000) / 10,000,000 = 2.5

So, your CPM for April is 2.5, which means 2.5 complaints per million units sold.


SQL Example:

SELECT (CAST([Consumer_Complaints] AS FLOAT) * 1000000.0) / NULLIF([Units_Sold], 0) AS CPM FROM QualityMetrics WHERE [Month] = '2025-04'

DAX Example:

CPM = DIVIDE( [Consumer Complaints] * 1000000, [Units Sold], BLANK() )


๐Ÿ’ผ Business Context

Why is CPM important?

Acts as a customer-facing quality indicator.

Helps identify recurring defects or packaging issues.

Assists in targeting root causes to improve manufacturing processes.

Enables quick response before brand reputation suffers.

Used by:

Quality & Food Safety teams

Production leadership

Brand managers

It is often reviewed during monthly and quarterly performance reviews.


๐Ÿšจ Thresholds and Alerts

Level CPM Value Alert Type
โœ… Good 0 โ€“ 1.0 No action needed
โš ๏ธ Warning 1.1 โ€“ 3.0 Review complaints and take action
โŒ Critical > 3.0 Launch root cause investigation

Alerts should be triggered when CPM increases sharply or crosses threshold levels consistently.


๐Ÿ“ˆ Historical Insights

Trend Analysis: Track monthly CPM to spot emerging issues.

Benchmarking: Compare against past data or industry benchmarks.

Complaint Category Correlation: Dig into top complaint types (e.g., taste, packaging, spoilage).

Preventive Actions: Evaluate if past interventions led to sustained improvements.