Machine Learning
Machine Learning is technology that enables computers to learn patterns from data without being explicitly programmed. ML algorithms improve their accuracy automatically as they process more data. In business intelligence, ML powers predictive analytics, customer segmentation, lead scoring, and sentiment analysis.
What Is Machine Learning?
Machine learning trains algorithms on historical data to make predictions or decisions. Unlike traditional software where a programmer writes rules ("if revenue < $1M, route to junior rep"), ML systems learn optimal rules from data ("customers with 5+ support tickets are 3x more likely to churn"). Plura's conversation analysis uses ML to automatically detect intent, sentiment, and quality patterns from thousands of conversations.
Supervised vs. Unsupervised Learning
Two main ML approaches:
- Supervised: Train on labeled data ("these conversations converted, these didn't"). Model learns to predict which new conversations will convert. Used for lead scoring, churn prediction.
- Unsupervised: Find patterns in unlabeled data ("group these customers by similarity"). Used for customer segmentation, anomaly detection.
- Real-World Example: Train ML on 10,000 past conversations labeled "converted" or "didn't convert." Model then predicts conversion likelihood for new conversations in real-time.
Why Machine Learning Matters for BI
ML enables decisions at scale. Manual analysis of 10,000 conversations takes weeks. ML analyzes them in minutes and improves automatically as it processes more data. Organizations using ML-powered insights make decisions 10x faster and with higher accuracy.
How Plura Uses Machine Learning
Plura applies ML across the platform:
- Intent Detection: ML identifies buying signals in real-time conversations
- Sentiment Analysis: Classifies customer emotions (positive, negative, neutral) from text and tone
- Lead Scoring: Predicts conversion likelihood based on conversation patterns
- Quality Coaching: Identifies high-performing agent behaviors for team-wide adoption
Common ML Applications in Business
ML drives these business capabilities:
- Churn Prediction: Identify at-risk customers before they leave
- Lead Scoring: Rank prospects by conversion likelihood
- Customer Segmentation: Group customers by behavior, value, or needs
- Demand Forecasting: Predict future sales volume or seasonality
- Recommendation Engines: Suggest products/services customers will buy
FAQs related to
Machine Learning
Do I need a data scientist to use machine learning?
No. Modern ML platforms abstract away complexity. You provide data, the platform trains models automatically. You don't need to understand algorithms to use ML—just understand your business problem.
How much data do I need for ML?
More is better, but not always required. For simple predictions, 100-500 examples can work. For complex patterns, thousands are ideal. Start with what you have—ML improves as you collect more data.
How long does it take ML to improve?
First model: 2-4 weeks of data collection. Meaningful improvements: 2-3 months. Significant accuracy gains: 6+ months. The longer you run it, the smarter it gets.
Can ML be wrong?
Yes. ML models are probabilistic, not guaranteed. A model might be 85% accurate, meaning 15% of predictions are wrong. Monitor accuracy continuously and retrain as needed.
Is ML the same as AI?
No. ML is a type of AI. ML means learning from data. AI is broader—it includes any intelligent system. All ML is AI, but not all AI is ML (some AI uses rules, not learning).