Ratings and Reviews 5 Ratings
Ratings and Reviews 0 Ratings
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What is Webeyez?
Recognizing the fundamental challenges that affect online sales, site abandonment, and user satisfaction is crucial. Webeyez leverages real-time data from actual users to support sales and marketing teams in pinpointing technical problems that could hinder online revenue generation. With automatic notifications, you will receive immediate alerts if any issues arise and an assessment of the financial impact, enabling you to act quickly to mitigate revenue losses. To assist e-commerce and product managers in grasping the technical health of their websites effortlessly, Webeyez has developed a site score that requires no technical expertise. Utilizing Artificial Intelligence, this score offers an overview of significant technical obstacles affecting sales performance. The calculation of this score involves aggregating various elements such as goal success rates, durations, critical paths, page load times, server response times, and additional variables. Moreover, our sophisticated algorithm identifies anomalies and issues a warning when there is a decline in the score, along with insights into the underlying causes. This proactive approach not only enhances site performance but also fosters improved customer experiences and increased revenue potential.
What is VictoriaMetrics Anomaly Detection?
VictoriaMetrics Anomaly Detection is a continuous monitoring service that analyzes data within VictoriaMetrics to identify real-time unexpected variations in data patterns. This innovative solution employs customizable machine learning models to effectively pinpoint anomalies. As a vital component of our Enterprise offering, VictoriaMetrics Anomaly Detection serves as an essential resource for navigating the intricacies of system monitoring in an ever-evolving landscape. It significantly aids Site Reliability Engineers (SREs), DevOps professionals, and other teams by automating the intricate process of detecting unusual behavior in time series data. Unlike traditional threshold-based alerting systems, it leverages machine learning techniques to uncover anomalies, thereby reducing the occurrence of false positives and alleviating alert fatigue. The implementation of unified anomaly scores and streamlined alerting processes enables teams to swiftly recognize and resolve potential issues, ultimately enhancing the reliability of their systems. By adopting this advanced anomaly detection service, organizations can ensure more proactive and efficient management of their data-driven operations.
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Integrations Supported
Adobe Commerce
BigCommerce
Google Tag Manager
Magento
Shopify
VictoriaMetrics Enterprise
WooCommerce
Integrations Supported
Adobe Commerce
BigCommerce
Google Tag Manager
Magento
Shopify
VictoriaMetrics Enterprise
WooCommerce
API Availability
Has API
API Availability
Has API
Pricing Information
750
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Webeyez
Company Location
Israel
Company Website
www.webeyez.com
Company Facts
Organization Name
VictoriaMetrics
Date Founded
2018
Company Location
United States
Company Website
victoriametrics.com/products/enterprise/anomaly-detection/
Categories and Features
Categories and Features
IT Infrastructure Monitoring
Alerts / Notifications
Application Monitoring
Bandwidth Monitoring
Capacity Planning
Configuration Change Management
Data Movement Monitoring
Health Monitoring
Multi-Platform Support
Performance Monitoring
Point-in-Time Visibility
Reporting / Analytics
Virtual Machine Monitoring