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About multimodal.dev (Developer Tools)
This page provides details about multimodal.dev (Developer Tools) which was ranked #99 out of 266 in the list of sources (7 citations (0.1% share)) in answers from AI models (Perplexity Sonar (with search) (latest)) when they were asked the following 1 question: "What metrics or KPIs should be tracked to measure success in AI Tools for Marketers?" on Oct 24, 2025 by AI Chat Watch. This source is referenced for brands: Segment.
Used by AI Models
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Pages from multimodal.dev (2 links)
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| /post/ai-kpis | 7 | 28 |
Brands Referenced By This Website (1 brand)
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Citations from AI Responses
Perplexity Sonar (with search) (latest)
(5 citations)
What metrics or KPIs should be tracked to measure success in AI Tools for Marketers?
[...] elling[[7]](https://cloud.google.com/transform/gen-ai-kpis-measuring-ai-success-deep-dive). ### AI-Specific KPIs for Marketing Tools 1. **Model Accuracy** How often the AI tool correctly predicts or classifies outcomes (e.g., audience segmentation, customer intent) to ensure reliability[[3]](https://www.multimodal.dev/post/ai-kpis). 2. **Precision and Recall** Precision measures the relevancy of positive predictions (true positives vs false positives), while recall measures the model’s ability to find all relevant cases (true positives vs false negatives). Both are key when AI predictions impact targeting or recommenda [...]
[...] ecision and Recall** Precision measures the relevancy of positive predictions (true positives vs false positives), while recall measures the model’s ability to find all relevant cases (true positives vs false negatives). Both are key when AI predictions impact targeting or recommendations[[3]](https://www.multimodal.dev/post/ai-kpis). 3. **F1 Score** Balances precision and recall into one metric to evaluate overall model performance[[3]](https://www.multimodal.dev/post/ai-kpis). 4. **Area Under ROC Curve (AUC-ROC)** Assesses the model’s capability to distinguish positives vs negatives, important for classification [...]
[...] ability to find all relevant cases (true positives vs false negatives). Both are key when AI predictions impact targeting or recommendations[[3]](https://www.multimodal.dev/post/ai-kpis). 3. **F1 Score** Balances precision and recall into one metric to evaluate overall model performance[[3]](https://www.multimodal.dev/post/ai-kpis). 4. **Area Under ROC Curve (AUC-ROC)** Assesses the model’s capability to distinguish positives vs negatives, important for classification tasks like lead scoring[[3]](https://www.multimodal.dev/post/ai-kpis). 5. **Model Robustness and Data Quality** Metrics ensuring AI is stable acros [...]
[...] nces precision and recall into one metric to evaluate overall model performance[[3]](https://www.multimodal.dev/post/ai-kpis). 4. **Area Under ROC Curve (AUC-ROC)** Assesses the model’s capability to distinguish positives vs negatives, important for classification tasks like lead scoring[[3]](https://www.multimodal.dev/post/ai-kpis). 5. **Model Robustness and Data Quality** Metrics ensuring AI is stable across data changes and working with clean, representative data sets[[3]](https://www.multimodal.dev/post/ai-kpis). 6. **Integration and Automation Efficiency** Measures how well AI tools automate workflows and int [...]
[...] apability to distinguish positives vs negatives, important for classification tasks like lead scoring[[3]](https://www.multimodal.dev/post/ai-kpis). 5. **Model Robustness and Data Quality** Metrics ensuring AI is stable across data changes and working with clean, representative data sets[[3]](https://www.multimodal.dev/post/ai-kpis). 6. **Integration and Automation Efficiency** Measures how well AI tools automate workflows and integrate into marketing processes, improving operational efficiency[[13]](https://corporatefinanceinstitute.com/resources/data-science/ai-kpis-tracking-performance/). ### Implementation Best Pra [...]