Machine Learning (ML) and Artificial Intelligence (AI) are no longer futuristic — they are essential tools for data-driven organizations. But the barriers to entry often seem high: complex programming, expensive data science teams, and months-long projects. What if your business analysts and domain experts could harness the power of ML themselves, without writing a single line of code?
That’s exactly where Qlik Predict comes in. It’s a no-code ML platform integrated directly into Qlik Cloud that lets you generate precise predictions from your existing data — see the Qlik AutoML product page for an overview of capabilities. This guide explains the fundamental concepts behind Qlik Predict so you understand how it works and what enormous potential it holds for your organization.
The Core Idea Behind Qlik Predict: From «What Happened?» to «What Will Happen?»
Traditional Business Intelligence (BI) excels at analyzing the past. Qlik Predict extends this horizon by recognizing patterns in your historical data to predict future events.
- Classic BI question: «How many customers churned last month?»
- Qlik Predict question: «Which of my current customers are likely to churn next month?»
Key Concepts of Qlik Predict
For the full technical reference, consult the Qlik AutoML (Predict) documentation.
1. What Is an «Experiment»?
An experiment is the workspace for a specific prediction project. For example, if you want to predict customer churn, you create a «Churn Prediction Experiment.» Here you upload your data, define your target, and let Qlik find the best prediction models.
2. What Types of Predictions Are Available?
Qlik Predict helps you answer the right type of question by offering different experiment types:
| Experiment Type | Typical Question | Use Case Example |
|---|---|---|
| Binary Classification | Will event X happen? (Yes/No) | Will a customer churn? Will an invoice be paid on time? |
| Multi-class Classification | Which category does X belong to? (A, B, C…) | Which product segment is most relevant for this lead? What’s the most likely reason for a support ticket? |
| Regression | What will the value of X be? (Numeric value) | What will revenue be next quarter? How many days will delivery take? |
Understanding Model Quality Beyond Accuracy
After Qlik trains multiple models, you need to select the best one. Here are the key metrics explained:
- ROC AUC: Think of this metric as an overall quality score for the model (on a scale from 0.5 to 1.0). A value of 0.5 is pure guessing, while 1.0 would be a perfect prediction. A good value is typically > 0.85.
- Precision: When the model flags a customer as «at risk,» how often is it correct? High precision avoids unnecessary work for your team.
- Recall: How many of the actual risk cases did the model successfully identify? High recall ensures that as few important cases as possible slip through the cracks.
What Is Explainable AI (XAI) and Why Does It Matter?
A prediction is only valuable when you can trust it and act on it. Qlik Predict is not a «black box.»
Understanding SHAP Values:
For every individual prediction, Qlik uses SHAP values to show exactly which factors (features) most strongly influenced the model’s decision. For example, you might see: «The churn probability for this customer is high because usage duration dropped by 70% in the last 30 days and they opened three support tickets.» This makes predictions explainable and actionable.
Practical Use Cases for Qlik Predict
Churn prediction is just the beginning. Once you understand the methodology, you can solve a wide range of business problems:
- Sales & Marketing: Lead scoring (prioritizing leads with the highest close probability), identifying cross- & up-selling opportunities, predicting customer lifetime value.
- Operations & Logistics: Demand forecasting (demand planning), predictive maintenance, delivery time prediction.
- Finance & Controlling: Revenue forecasting (sales forecasting), fraud detection, predicting payment defaults.
Mastering Churn Prediction with Qlik Predict: The Ultimate Practical Guide
How to Apply Your Qlik Predict Knowledge
You now understand the theoretical foundations of Qlik Predict. The best way to solidify this knowledge is through hands-on practice.
We’ve created a detailed, hands-on guide that walks you through the entire process step by step — from data preparation to a finished dashboard. Follow our tutorial and build your first prediction model.
Ready to Build?
Read our step-by-step tutorial: Mastering Churn Prediction with Qlik Predict: The Ultimate Practical Guide
There you’ll learn how to apply these concepts to proactively prevent customer churn.
Qlik Predict is part of a broader AI ecosystem. Qlik Answers uses agentic AI to let business users ask natural language questions about their data — see how these AI capabilities complement each other for a complete analytics intelligence stack.