> For the complete documentation index, see [llms.txt](https://docs.live-eo.com/surfacescout/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.live-eo.com/surfacescout/measuring-success.md).
# Measuring Success
Context
We have developed a solution to address a common challenge for all our customers: mitigating risks around their pipelines. However, customers may perceive the quality of our analytics differently.
Several factors can influence this perception, including the varied geographical environments that require fine-tuning of our machine learning model and QA processes—for example, the contrast between arid desert landscapes in the Middle East and lush, forested regions in Northern Europe.
* **Geographies**
* **The level of interest can vary based on the type of detected change.** For instance, one customer might prioritize insights on agricultural or plowing activities differently than another.
### We need your feedback!
We have showed you in the [Review Cases](/surfacescout/review-cases.md#reviewing-insights) section that you could evaluate the quality of an Insight:
Feedback dialog box
This is essential to fine-tune the analytics so you can prioritize the risks that matter most to you, minimize false positives that create operational overhead, and assess whether SurfaceScout could become a valuable information source (we think it is! But we need your data to confirm it ;)).
We typically track 3 key metrics:
* **Precision**: The percentage of actual changes detected out of all predictions delivered.
* **Relevance**: The percentage of relevant predictions out of all predictions delivered.
* **Uniqueness of Information**: The number of unique detections not captured by other methods (like helicopters), compared to everything we’ve detected.
* **Uniqueness & Relevance combined:** The number of unique and relevant detections compared to everything we've detected
With your feedback, these metrics tend to improve significantly—often reaching over 80% relevancy, for example after a few deliveries. Without your input and domain expertise, however, results are less reliable (see the orange section for details).
Analytics performance VS feedback
{% hint style="info" %}
For **Precision**, which measures the rate of false alerts, we typically begin around 90%.
For **Relevance**, we usually start lower, around 40%. This is where your feedback makes a significant impact: after 4-6 deliveries, we often achieve scores above 80%!
{% endhint %}
{% hint style="info" %}
For some customers, we can directly correlate detected Insights with tangible risk reduction in financial terms (€ or $). Recently, with a customer, we were able **to reduce risk exposure by 4M€ annualy for every 1,000 km of pipeline!**
{% endhint %}
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