Predictive analytics
AI-driven analyses that predict which leads, deals, or actions have the highest chance of success.
Detailed explanation
Predictive Analytics uses machine learning to find patterns in your historical sales data and make predictions about the future. This includes: lead scoring (which leads convert most), opportunity scoring (which deals will you win), churn prediction (which customers are at risk), next best action (what should you do now to win), and forecast accuracy (how reliable is your pipeline). Modern CRMs like HubSpot and Salesforce have predictive features built in, or you use specialized tools like 6sense or Clari. The advantage is that you no longer rely on gut feel but on data - you know where to invest your time.
Synonyms
Examples
You prioritize this deal based on the factors the model shows. Whether that also produces more revenue is measured during the pilot on your own pipeline.
When customer behaviour declines, the model places those accounts on an attention list with the factors that contributed. Customer success decides who is approached and with which offer.
When to use this?
Implement Predictive Analytics when you have sufficient historical data (minimum 500+ deals), want to know where to focus, or want to improve forecast accuracy. Works best in data-rich environments.
Sources & further reading
Your Company OS approach
Your Company OS helps you implement predictive analytics and actually use it. We first ensure your data is clean and structured (garbage in = garbage out), implement predictive features in your CRM, and train your team in interpreting the scores. More importantly: we build workflows around the predictions - how do you handle a high-risk churn account? What do you do with a hot lead? Predictions without action are worthless.
Related terms
Want to know more about Predictive analytics?
Want to know how to put Predictive analytics to work in your organisation? Get in touch with Your Company OS.