AI Analytics Engine
Smarter insights, faster and automated actions
Contentsquare uses billions of data points and behaviors to automate contextual analysis, segmentation and recommendations.
Use Contentsquare Artificial Intelligence to unlock the power of future digital experiences.
Benefits of the AI Analytics Engine
With Contentsquare your AI experience starts from the onboarding. We take pride in our cutting-edge AI solutions for onboarding and setup.
Our advanced technology streamlines the process, ensuring a seamless and efficient experience for all our valued users.
Most common use cases
Short and streamlined process
Enjoy a smooth onboarding process with automated mappings and labelling and privacy by design. Minimize human errors and improve accuracy to start your analysis confidently and faster. Streamline your data workflows with automated data capture and PII masking.
Save time and improve productivity
Know exactly where to start your analysis, follow the guide and run your analysis step by step. Uncover the why behind your metrics variations and identify next-best actions.
Data-driven prioritization
Get recommendations based on frustration scoring and potential ROI loss. Establish a baseline of customer frustrations during the user journey and prioritize actions to improve customer experience satisfaction.
Precision in action
Contentsquare learns how your business metrics normally fluctuate and delivers smart alerts when deviations occur from what is normal. Set up intelligent alerts anywhere within Contentsquare – from key points in the customer journey to specific interactions points with content.
Privacy
Contentsquare helps brands build healthy data relationships with their customers and AI is a new way for us to achieve this objective. The development of our AI didn’t affect our commitment to privacy and leads us to raise our standards for security and privacy.
Frequently asked questions
Automated analytics streamlines data processing and analysis, allowing businesses to make informed decisions quickly. By automating routine tasks, it reduces the time and effort required to extract valuable insights from large datasets, enabling more agile and data-driven decision-making.
Automated analytics tools automate repetitive tasks such as data cleaning, aggregation, and visualization, reducing the manual workload for analysts. This efficiency not only accelerates the analysis process but also enables data professionals to focus on more complex and strategic aspects of their work, ultimately boosting overall productivity.
Automated analytics facilitates real-time data processing, allowing businesses to monitor key metrics and performance indicators instantly. This enables proactive decision-making, as organizations can respond swiftly to emerging trends, issues, or opportunities in the dynamic business environment.
Machine learning algorithms integrated into automated analytics tools enable predictive analysis by identifying patterns and trends in historical data. This empowers businesses to forecast future outcomes, anticipate market trends, and make strategic decisions based on predictive insights.
Ensuring data quality and integrity is crucial for the accuracy of automated analytics. Implementing robust data governance practices, regularly validating and cleansing data, and employing quality assurance measures are essential steps to maintain the reliability of insights derived from automated analytics processes.
Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Applications of AI in digital experience analytics includes AI monitoring that predict customer experience issues and opportunities, AI Insights that deliver smart alerts to drive action, and intent-based scoring.
