- Discovers Intelligent Insights in Minutes
automated website analysis generates user stories on your web and app analytics data in a matter of minutes.
- Expertise in Machine Learning
Utilizes machine learning models to generate stories on the most significant changes in your business.
- Automates Tons of Hours of Analysis Effort
Process your analytics data using in-built models with automated website analysis to reduce long hours of data collection, processing and further analysis.
- Integrates with Any Data Source
Built on open architecture to integrate multiple web or app analytical sources without any complex configuration.
- Generates Stories on Combined Data Sources
Badger processes data from multiple data sources and generates automated user stories on the combined data sets.
- Explains the Cause & Effect of Change in Metrics
Badger attributes the significant change in your business metrics (e.g. Revenue) to related causal variables (e.g. average order value, conversion rate, sessions etc). It further drills down on every other variable that has contributed towards the overall change.
- Drills Down on Significant Change Variables
Badger digs on a particular metric (e.g. Sessions) as a part of automated website analysis to uncovers all change variables that affect the metric (e.g. Traffic from Metro/ non-Metro, Mobile device/ Desktop device Users, etc). It pinpoints the variables that need you to take quick actions.
- Detects Real Time Anomalies
Badger uses Anomaly Detection to report in real time all significant anomalies in your business metric (e.g. deviation in New Users traffic from USA). It diagnoses the actual cause of the deviation and the frequency of occurrence.
- Analyzes the Competitive Scenario
Badger analyzes the competitive scenario of your business metrics and delivers a snapshot on how your business has performed with respect to industry benchmarks (e.g. App Store Rankings for the app category).
- Monitors Composition Change of Business Metrics
Badger performs a Composition Change Analysis on any particular attribute. It provide insights on change in composition mix of the attribute (e.g. E-commerce product category) with respect to business metric contribution.