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+---
+template: '../@theme/templates/BlogPost'
+title: Beyond anomaly detection — striving with metrics
+description: Metrics shouldn't only exist for quick error detection. They should exist to help you strive — to set goals, accelerate learning, and achieve meaningful improvement.
+seo:
+  title: Beyond anomaly detection — striving with metrics
+  description: Metrics shouldn't only exist for quick error detection. They should exist to help you strive — to set goals, accelerate learning, and achieve meaningful improvement.
+  image: ./images/beyond-anomaly-detection.png
+author: adam-altman
+date: 2025-11-11
+categories:
+  - metrics
+  - goals
+image: beyond-anomaly-detection.png
+---
+
+# Beyond anomaly detection — striving with metrics
+
+Recently, I wrote about [**Orion’s Belt** — three guiding stars for measuring success: *Acquisition, Adoption,* and *Deflection*](./key-metrics-for-docs.md). Those metrics help you navigate.  
+But navigation alone isn’t enough. You also need instruments that help you *strive*.
+
+Too often, we use metrics like smoke alarms: they tell us when something is wrong. A graph spikes or dips, we react, we fix, and we move on.  
+But metrics shouldn’t only exist for *quick error detection and resolution*.  
+
+Metrics should exist to make us better — as teams, as companies, as individuals.
+
+## When metrics only measure what’s broken
+
+Many dashboards are built for “what’s wrong?” questions.  
+- Did the API error rate spike?  
+- Did a signup funnel break?  
+- Did conversion suddenly drop?  
+
+These are important. You can’t operate without anomaly detection.  
+But if that’s all your metrics do, you’re flying with warning lights — not a navigation system.
+
+> Reactive metrics keep you alive.  
+> Proactive metrics make you improve.
+
+## A personal detour: Returning to competitive tennis
+
+Two years ago, I decided to return to competitive tennis after 25 years of "riding a desk."  
+My body didn't exactly appreciate the nostalgia. Within weeks, I was injured — sore shoulder, tight back, and a frustrating sense that my brain remembered how to play, but my body didn't.
+
+After a year working with a biomechanical *Functional Patterns* trainer (thank you, Dennis Nel), I could finally play matches pain-free again.  
+That alone felt like a miracle. But now I was losing matches that I believed I should win.
+
+My instinct was the same as it had been as a teenager: *just play more matches.*  
+More reps equals more improvement, right?
+
+Not anymore.
+
+My body could handle maybe two or three matches per week. Any more and I’d feel the toll — slower recovery, reduced power, fatigue.  
+Worse, progress seemed random. Some weeks I’d play brilliantly; others, I’d regress.  
+
+I was collecting experience but not *learning* efficiently.
+
+## The feedback revolution
+
+Then I discovered **SwingVision**, an app that records your matches, automatically tracks shot stats, and cuts out dead time. You can rewatch a full match in ten to twenty minutes.  
+
+Tennis at any competitive level is a game of **error rate** — unforced errors decide almost every outcome.  
+With SwingVision, I could measure my **forehand and backhand success percentages**, **ball speeds**, and **placement patterns**.
+
+Now I could *set goals*:
+
+- Increase average forehand speed by 5 mph  
+- Raise my forehand-in-play percentage by 5%  
+- Do both without increasing unforced errors
+
+Once I started tracking these, my improvement was no longer random.  
+I could correlate technical changes to results.  
+
+But the *real* breakthrough came not from the stats, but from the **video**.
+
+Watching myself play revealed **decision mistakes** — tactical errors I’d never remember afterward.  
+Without the video, I might recall one or two bad decisions per match. The video exposed ten or twenty.  
+That’s ten times more information.
+
+It wasn’t always fun watching myself make those mistakes, but the visibility turned randomness into feedback.  
+My pace of learning accelerated dramatically. I could identify *why* I was losing points and deliberately work on those patterns.
+
+## Anomaly detection vs. Performance metrics
+
+What SwingVision did for my tennis is what good metrics can do for a product, a campaign, or an engineering system: they turn random progress into deliberate improvement.  
+
+Metrics shouldn’t just yell when something’s broken.  
+They should *teach you* where and how to improve.
+
+| Tennis example | Business equivalent |
+|----------------|--------------------|
+| Forehand error rate spikes | Conversion rate drops |
+| Shoulder strain flares up | Latency alert triggers |
+| Average rally length decreases | Session duration declines |
+
+All of these are anomaly detections — *reactive* signals.  
+They tell you to stop, fix, or recover.
+
+But here’s what makes you better:
+
+| Tennis example | Business equivalent |
+|----------------|--------------------|
+| Increase average rally length by 20% | Increase time-to-value for new users |
+| Maintain serve speed while reducing errors | Improve API latency without losing reliability |
+| Win more points by decision quality | Improve campaign ROI by targeting better audiences |
+
+These are *goal metrics* — metrics you pursue deliberately.
+
+## Two kinds of metrics
+
+### Reactive (keep the lights on)
+
+Reactive metrics are essential for monitoring stability and catching regressions early.  
+They should:
+- Detect anomalies fast  
+- Trigger alerts automatically  
+- Be easy to interpret under stress  
+
+Examples:
+- API error rate  
+- Conversion anomalies  
+- Traffic drops  
+- Payment failures  
+- Downtime minutes  
+
+They’re your smoke detectors and tripwires.
+
+### Proactive (help you strive)
+
+Proactive metrics aim for **improvement**, not survival.  
+They’re tied to goals, experiments, or campaigns.
+
+Examples:
+- Increase documentation engagement  
+- Reduce time-to-integration for new developers  
+- Increase activation rate of trials within seven days  
+- Improve adoption of a feature across existing customers  
+
+These metrics require intention.  
+You can’t automate *striving*.  
+You define what *better* means and work toward it.
+
+## Metrics and meaning
+
+A metric without a purpose is noise.  
+To be useful, every metric needs context — *what goal is it serving?*
+
+At Redocly, we often talk about **orbits**: Acquisition, Adoption, and Deflection.  
+Those are outcome metrics.  
+But orbiting them are smaller measurable inputs — like satellites.
+
+For instance:
+
+- **Acquisition:** site impressions, trial signups, referral sources, search ranking  
+- **Adoption:** successful integrations, API calls, doc interactions, feature usage  
+- **Deflection:** reduced support tickets, improved self-serve rate, feedback scores  
+
+Each can have *reactive* and *proactive* dimensions.
+
+| Metric | Reactive use | Proactive use |
+|--------|---------------|---------------|
+| API uptime | Alert if < 99.9% | Aim for 99.99% across all regions |
+| Docs page views | Detect traffic loss | Improve funnel from page → trial |
+| Feature usage | Catch broken rollouts | Drive adoption of core features |
+
+The distinction isn’t the data — it’s the *intent* behind using it.
+
+## What gets measured, improves (but only if you watch the replay)
+
+There’s an old business adage: *what gets measured, improves.*  
+That’s not entirely true.  
+
+> What gets measured **and reviewed regularly** improves.
+
+I was measuring my tennis for years — points won, games won —  
+but until I started *reviewing the video*, my improvement was inconsistent.
+
+In business, dashboards often accumulate metrics without reflection.  
+Teams glance at charts in a weekly meeting, nod, and move on.  
+
+Improvement comes when metrics feed back into *decisions*:
+- What will we do differently based on this data?  
+- How will we know it worked?  
+- When will we review again?
+
+That loop — measure → reflect → adjust → remeasure — is the engine of progress.
+
+## Campaigns: the perfect metric playground
+
+Campaigns are experiments.  
+You’re testing hypotheses:
+
+- If we run this campaign, will we acquire more users?  
+- Will they activate faster?  
+- Will they adopt a feature more deeply?
+
+Each campaign has two metric layers:
+
+1. **Exposure metrics** — how many people saw or interacted  
+   - Impressions  
+   - Clicks  
+   - Page views  
+   - Video completions  
+   - Reach  
+
+2. **Goal metrics** — how many achieved the intended outcome  
+   - Trials started  
+   - Features adopted  
+   - Integrations completed  
+   - Docs feedback submitted  
+
+Knowing only one layer is like knowing your shot count but not your accuracy — you're missing half the story.  
+Knowing both gives you *conversion ratios* — the real performance signal.
+
+### Setting expectations before measurement
+
+Before running a campaign, define the **target metric** and **expected outcome**.
+
+> Goal: Increase trials started from our docs site.  
+> Metric: Conversion rate from "view docs" → "start trial."  
+> Baseline: 1.8%.  
+> Target: 3.0% within 30 days.
+
+Once defined, every decision — content, design, placement — supports that goal.  
+After the campaign, compare actual to target, learn, and iterate.
+
+If you miss the target but learn something actionable, that’s success too.  
+Progress often looks like a string of useful failures leading to insight.
+
+## Choosing better metrics
+
+Good metrics have three qualities:
+
+1. **Controllable** – You can influence them through your actions.  
+2. **Comparable** – You can measure change over time.  
+3. **Comprehensible** – Everyone can understand what they mean.
+
+When metrics are clear, teams stay aligned.  
+You can say "we're aiming to increase activation by 10%" and everyone knows what success looks like.
+
+## Example: Applying this at Redocly
+
+Suppose we’re running a campaign around **AI Search** — our intelligent search across developer documentation.  
+
+| Stage | Metric | Goal type | Purpose |
+|-------|---------|-----------|----------|
+| Acquisition | Visitors to AI Search landing page | Reactive | Detect if campaign traffic drops |
+| Adoption | Activated AI Search feature within 7 days | Proactive | Increase activation rate |
+| Deflection | Reduction in support tickets about “can’t find docs” | Proactive | Measure success outcome |
+
+If we only watched traffic, we’d miss the full picture.  
+Pairing reactive and proactive metrics lets us see both health **and** progress.
+
+## The psychology of striving
+
+Reactive metrics motivate fear: *avoid failure, prevent downtime, stay safe.*  
+Proactive metrics motivate *growth*.  
+They create purpose and momentum.
+
+People are naturally better at striving than maintaining.  
+When you give a team a clear goal — like increasing integration success by 15% — you invite creativity.  
+The focus shifts from "don't break it" to "how can we make it better?"
+
+That shift changes company culture.  
+Instead of waiting for anomalies, teams look for opportunities.
+
+## What tennis taught me about metrics
+
+Returning to tennis taught me a few truths that apply to metrics:
+
+1. **You can’t play unlimited matches.**  
+   You can’t just throw more campaigns or features at a problem.  
+   You need feedback to make the limited attempts count.  
+
+2. **Progress isn’t linear.**  
+   Metrics fluctuate even as you improve. Focus on trends, not single points.  
+
+3. **Decision errors cost more than technical errors.**  
+   A bad shot is fine; a bad decision sets up the opponent.  
+   Focusing on the wrong metric can have the same effect.  
+
+4. **Feedback shortens the learning curve.**  
+   Video review made my improvement predictable.  
+   Structured reviews of campaign metrics do the same.
+
+## From anomalies to aspirations
+
+Metrics evolve through three stages:
+
+1. **Monitoring:** track data to catch issues  
+2. **Managing:** react quickly to anomalies  
+3. **Mastering:** use data to *strive* toward goals  
+
+Most teams stop at stage two.  
+Mastery means using metrics not just to protect what you’ve built, but to *grow* it.
+
+When I started tracking my tennis performance, I stopped feeling like progress was random.  
+It wasn't about fixing what went wrong — it was about understanding what could go *better*.
+
+That’s what great metrics do.  
+They make your work visible, measurable, and improvable.
+
+## A closing thought
+
+If you only use metrics to detect anomalies, you’ll spend your time firefighting.  
+If you use metrics to set goals, you’ll spend your time improving.
+
+> Alarms tell you when something breaks.  
+> Goals tell you where to go.
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