When business metrics drop but engineering can’t explain why

KPIs are outcome metrics. They tell you what happened, not why it happened.

A 2% drop in checkout conversion might indicate:

• A backend error introduced in a recent release
• A third-party payment provider slowdown
• A crash affecting a specific OS version
• A performance regression on older devices

From a KPI dashboard alone, all of those failures look identical.

This is why teams often end up in reactive mode—paging engineers, combing through logs, and adding more instrumentation after customers are already impacted. The KPI did its job by sounding the alarm, but it didn’t provide a path to resolution.

Without a way to connect KPIs to user-level behavior and technical signals, outcome metrics become lagging indicators rather than protective ones.

Why KPIs and engineering metrics rarely line up

In most organizations, KPIs live in one world and engineering metrics live in another.

• Business teams rely on analytics tools and revenue dashboards
• Engineering teams rely on logs, metrics, and traces
• Product teams toggle between the two, trying to infer meaning

This separation creates three major blind spots.

1. Sampling hides the failures that matter most

Many observability and monitoring tools rely on sampling to control cost. That means only a fraction of user sessions are captured. Unfortunately, the sessions most likely to be sampled out are often the ones teams most need to see: rare crashes, edge-case failures, device-specific bugs, and intermittent issues.

When even 1% of transactions fail, the business impact can be massive, but sampled data makes those failures easy to miss.

2. Infrastructure signals don’t equal user impact

A spike in CPU or latency doesn’t automatically mean customers are suffering. Likewise, a system can look “healthy” while users quietly fail to complete critical flows like checkout or login.

Without a direct connection between infrastructure metrics and user outcomes, teams are forced to guess which alerts actually matter.

3. Engineering gets blamed without evidence

When KPIs dip and engineering can’t explain why, trust erodes. Leaders face pressure to respond quickly but lack the visibility to provide clear answers. This often leads to finger-pointing, defensive reporting, and delayed fixes.

KPI translation: turning business metrics into user journeys

To make KPIs actionable, teams need a practical way to break business metrics down into real flows, dependencies, and technical signals that can be observed, alerted on, and resolved in real time.

A KPI like “successful payments” isn’t abstract. It represents thousands, or millions, of individual users moving through a specific flow.

A translated KPI has three parts:

1. The business metric – what leadership cares about
2. The user flow – the exact steps a user takes
3. The technical definition of success – the signals that indicate success or failure

When KPIs are translated this way, teams can move from observing metrics to protecting outcomes.

A practical example: translating checkout conversion

Consider a common KPI: checkout conversion rate.

On its own, the KPI tells you how many users completed checkout. It does not tell you:

• Which step users failed at
• Which devices or OS versions were impacted
• Whether failures were caused by crashes, errors, or slow performance

A translated view reframes the KPI as a collection of real user sessions:

• Users who reached checkout but never completed payment
• Sessions with payment API errors
• Crashes occurring after a recent app update
• Slow network responses causing abandonment

Instead of asking “Why is conversion down?”, teams can ask:

• Which users failed?
• Where did they fail?
• What technical signals explain it?

What KPI ownership looks like across teams

Engineering leadership

Leaders are accountable for business outcomes but often lack the visibility to explain KPI changes confidently. Translated KPIs provide a direct line between engineering work and business impact, enabling faster decisions and clearer communication.

DevOps and platform engineering

Instead of reacting to endless alerts, platform teams can prioritize issues based on real user impact.

Product managers

Product teams no longer have to wait for complaints or guess which bug is hurting conversions.

Conclusion: clarity is what turns metrics into action

Business metrics will always matter. They are how organizations measure growth, revenue, and customer trust. But without clear technical understanding, KPIs become alarms that trigger pressure instead of progress.

This challenge—and the need to close the gap between outcomes and technical reality—is explored more deeply in Rethinking KPIs and The KPI Translation Toolkit.

If this blog resonates, those resources go further into why outcome metrics break down and how teams can translate KPIs into defined flows, success criteria, and actionable signals.

Because if you can’t explain a KPI, you can’t protect it.