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Funnelytics vs QlikView

Professional comparison and analysis to help you choose the right software solution for your needs.

Funnelytics icon
Funnelytics
QlikView icon
QlikView

Funnelytics vs QlikView: The Verdict

Last updated: May 2026 · Comparison by Sugggest Editorial Team

Feature Funnelytics QlikView
Sugggest Score
Category Business & Commerce Business & Commerce

Product Overview

Funnelytics
Funnelytics

Description: Funnelytics is a marketing analytics software that helps businesses track customer journeys and analyze conversion funnels. It provides insights into where customers are dropping off in the sales process so marketers can optimize their campaigns.

Type: software

QlikView
QlikView

Description: QlikView is a business intelligence and data visualization software that allows users to create interactive dashboards and data apps. It provides data integration, analytics, and reporting capabilities in a single platform.

Type: software

Key Features Comparison

Funnelytics
Funnelytics Features
  • Funnel Visualization
  • Conversion Tracking
  • Funnel Analytics
  • User Segmentation
  • A/B Testing
  • Heatmaps
  • Integrations with Popular Marketing Tools
QlikView
QlikView Features
  • Drag-and-drop interface for data modeling
  • In-memory associative analytics engine
  • Interactive dashboards and visualizations
  • Self-service BI capabilities
  • Data integration from multiple sources
  • Mobile BI support
  • Collaboration and sharing capabilities
  • Advanced security features

Pros & Cons Analysis

Funnelytics
Funnelytics
Pros
  • Comprehensive funnel analysis
  • Detailed conversion tracking
  • Customizable dashboards and reports
  • Collaborative features for teams
  • Intuitive user interface
Cons
  • Limited free plan features
  • Can be expensive for small businesses
  • Learning curve for non-technical users
QlikView
QlikView
Pros
  • Powerful data visualization capabilities
  • Intuitive and easy to use
  • Fast in-memory performance
  • Flexible self-service BI
  • Scalability to large data volumes
  • Strong data integration features
  • Collaboration capabilities
  • Broad platform and device support
Cons
  • Steep learning curve
  • Expensive licensing model
  • Scripting language can be complex
  • Limited advanced statistical/analytic functions
  • Less flexibility than open-source options
  • Dependency on proprietary file format (QVD)

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