Predictive Analytics
Predictive Analytics uses historical data and machine learning to forecast future outcomes. Rather than reacting to what happened, predictive analytics enables you to act before it happens—identifying at-risk customers, high-intent leads, or churn risk before they materialize.
What Is Predictive Analytics?
Predictive analytics answers "what will happen?" questions. Which customers are likely to churn? Which leads are likely to convert? Which products will sell best next quarter? Plura's conversation analysis powers predictive insights: analyzing past conversation patterns reveals which behaviors predict conversion, churn, or high lifetime value.
Predictive vs. Descriptive Analytics
These serve different purposes:
- Descriptive: What happened? ("30% of calls ended without resolution")
- Predictive: What will happen? ("This customer will churn in 60 days if we don't intervene")
- Prescriptive: What should we do? ("Offer this customer a discount and dedicated support to prevent churn")
- Timeline: Descriptive looks at past; predictive looks at future; prescriptive guides action
Why Predictive Analytics Matters
Predictive analytics transforms reactive teams into proactive ones. Instead of noticing churn after it happens, you predict it and prevent it. ROI appears quickly: predicting high-value customers lets you allocate resources strategically; predicting churn lets you intervene before losing revenue.
How Plura Enables Predictive Analytics
Plura generates predictive insights:
- Churn Risk Scoring: Identifies customers likely to cancel within 60/90 days
- Conversion Prediction: Flags leads most likely to close based on conversation patterns
- Upsell Opportunities: Detects when customers mention needs that match higher-value plans
- Optimal Timing: Predicts the best moment to reach out to prospects based on engagement patterns
Common Predictive Analytics Use Cases
Organizations use predictions for:
- Churn Prevention: Proactively reach out to at-risk customers with targeted retention offers
- Lead Prioritization: Route highest-conversion-probability leads to top sales reps
- Demand Forecasting: Predict future demand to optimize inventory and hiring
- Customer Lifetime Value Prediction: Identify high-value customers early to invest in relationships
- Fraud Detection: Identify unusual patterns that signal fraudulent activity
FAQs related to
Predictive Analytics
How accurate are predictions?
It depends on data quality and complexity. Simple predictions (will this customer churn?) can be 75-85% accurate. Complex predictions require more data and refinement. Start with simpler models.
What data do predictive models need?
Historical data relevant to your prediction goal. To predict churn, you need past customer data with churn outcomes. To predict conversion, you need past prospect data with conversion outcomes. Plura's conversation data provides rich behavioral signals.
How long does it take to build a predictive model?
With modern tools: 2-4 weeks to deploy a basic model. 2-3 months to refine accuracy. Continuous improvement happens indefinitely. Start with a basic model and improve iteratively.
What if I act on a wrong prediction?
Part of the risk. If your model is 80% accurate, 20% of actions based on predictions will be "wrong." But being proactive on 80% of true risks beats being reactive to all churn. Monitor results and refine.
Can I combine multiple predictions?
Yes. The most powerful approach combines multiple signals: churn risk + low engagement + support tickets. A customer matching all three needs immediate intervention. Layer predictions for better decisions.