Signal Misread

Incorrect tracking codes and linguistic gaps between customer language and system categories lead to misinterpreted escalation trends. Conclusions drawn from this data may not reflect actual complaint patterns.

## Trigger Signals

• Tracking codes don't match actual complaints
• Customer language differs from system categories
• Trends conflict with frontline observations
• Data-driven conclusions feel disconnected from reality

## Why It Persists

Building the tracking system is someone's job. Questioning its accuracy threatens that work. The gap between data and reality widens slowly enough to normalize.

## Common Misdiagnosis

• "We need better reporting"
• "The data is incomplete"
• "Teams aren't logging correctly"
• "We need more training on the system"

## Cost of Ignoring

Decisions get made on distorted signal. Resources flow to the wrong problems. Actual pain points remain invisible while reported trends get attention.

• Tracking codes don't match actual complaints
• Customer language differs from system categories
• Trends conflict with frontline observations
• Data-driven conclusions feel disconnected from reality

## Why It Persists

Building the tracking system is someone's job. Questioning its accuracy threatens that work. The gap between data and reality widens slowly enough to normalize.

## Common Misdiagnosis

• "We need better reporting"
• "The data is incomplete"
• "Teams aren't logging correctly"
• "We need more training on the system"

## Cost of Ignoring

Decisions get made on distorted signal. Resources flow to the wrong problems. Actual pain points remain invisible while reported trends get attention.

Building the tracking system is someone's job. Questioning its accuracy threatens that work. The gap between data and reality widens slowly enough to normalize.

## Common Misdiagnosis

• "We need better reporting"
• "The data is incomplete"
• "Teams aren't logging correctly"
• "We need more training on the system"

## Cost of Ignoring

Decisions get made on distorted signal. Resources flow to the wrong problems. Actual pain points remain invisible while reported trends get attention.

The data captured doesn't match the reality experienced. Tracking codes, categories, and system language diverge from how customers and frontline teams describe problems.

## What It Looks Like In the Wild

Incorrect tracking codes and linguistic gaps between customer language and system categories lead to misinterpreted escalation trends. Conclusions drawn from this data may not reflect actual complaint patterns.

## Trigger Signals

• Tracking codes don't match actual complaints
• Customer language differs from system categories
• Trends conflict with frontline observations
• Data-driven conclusions feel disconnected from reality

## Why It Persists

Building the tracking system is someone's job. Questioning its accuracy threatens that work. The gap between data and reality widens slowly enough to normalize.

## Common Misdiagnosis

• "We need better reporting"
• "The data is incomplete"
• "Teams aren't logging correctly"
• "We need more training on the system"

## Cost of Ignoring

Decisions get made on distorted signal. Resources flow to the wrong problems. Actual pain points remain invisible while reported trends get attention.

• "We need better reporting"
• "The data is incomplete"
• "Teams aren't logging correctly"
• "We need more training on the system"

## Cost of Ignoring

Decisions get made on distorted signal. Resources flow to the wrong problems. Actual pain points remain invisible while reported trends get attention.

Decisions get made on distorted signal. Resources flow to the wrong problems. Actual pain points remain invisible while reported trends get attention.