How is the “Suggested” target value calculated in Proactive Analytics?
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3 weeks ago
Hello ServiceNow Community,
In Proactive Analytics target configuration, the Value field is pre‑populated as “Suggested”, as described in the documentation below:
https://www.servicenow.com/docs/r/now-intelligence/proactive-analytics/proactive-analytics.html
However, I could not find details explaining how this suggested value is calculated.
Could you please clarify:
- What logic or statistical method is used to derive the “Suggested” value?
- Is it based on historical data (average, trend, percentile, etc.)?
- What time range of data is evaluated?
- Does this value adjust over time or is it fixed at creation?
Understanding this would help administrators decide whether to use the suggested value and explain the rationale when questions arise.
Thank you for your support.
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3 weeks ago
Hi @Kohei Tominaga1 ,
From your shared documentation, it seems it is using Predictive Intelligence at backend .
- Machine Learning Models: PI uses classification and similarity algorithms trained on historical data, such as closed incidents.
- Precision and Recall: The model's effectiveness is calculated based on:
- Precision: How many of the "Proposed" (suggested) values were actually correct (reducing False Positives).
- Recall: How many of the total records that should have been classified were correctly identified (reducing False Negatives/misses).
- Confidence Thresholds: The system only suggests a value if the probability of accuracy exceeds a predefined threshold
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3 weeks ago
Hi, @Tanushree Maiti
Thank you for the explanation — I agree that Predictive Intelligence and machine learning techniques are likely used in the background, and your description matches my understanding.
What I am trying to clarify further is how the “Suggested” value is calculated specifically as a target.
If the suggested value is simply a reflection of historical performance, that makes sense.
However, if there is logic that proposes an improved or aspirational target derived from past trends and model outputs, I would like to better understand the underlying approach or formula used to arrive at that value.
For example:
- Is the target adjusted upward or downward based on historical trends?
- Does it incorporate optimization logic beyond a simple prediction?
This level of detail would help administrators confidently explain the rationale behind the suggested target and decide when it should be adjusted.
Thanks again for sharing your insights.
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3 weeks ago - last edited 3 weeks ago
Hi @Kohei Tominaga1 ,
I guess you are looking for how to finetune the predictive intelligence models. Though I have never worked on this deeper level, but following Community articles can help help you.
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3 weeks ago
No, I don't want to make any change but want to know the details how OOTB calculate suggested value...
I have searched a lot of sites but could not find any resources
