The insights and benefits of unified communications analytics

1. Connectivity analytics

Large-scale enterprises run thousands of internal and external calls simultaneously and organize video conferences every day. UC systems must run smoothly to maintain optimal audio and video quality. UC analytics monitors the performance of calls, conferences and endpoints in enterprises.

Insights incorporate performance metrics, like latency, jitter, packet loss, bandwidth utilization, echo, distortion, call quality, resolution, frame rate and various other measurements. These insights help enterprises implement actionable business intelligence, like troubleshooting issues and optimizing resource allocation to improve connectivity.

2. Real-time monitoring

UC analytics draws insights from enterprise group inventories, mail contacts, mailbox usage patterns, call detail records, video conferencing activities, inactive users, file sharing and meeting summaries. Simply put, UC analytics provides actionable insights into UC system health and performance.

Enterprises use UC analytics to monitor real-time calls to track which codecs are being used and assess their quality and efficiency. These insights help identify the best codec performance and perform multicodec transcoding.

3. Security insights

UC analytics provides insights about user behavior during calls, access control, unusual call patterns, login locations and suspicious activities, like repetitive failed login attempts, frequent password updates, excessive file sharing, extension additions, multiple quick calls, message flooding and Zoombombing fraud. Alarms are integrated into some UC analytics platforms.

UC analytics can operate on historical data to audit logs for threat detection. Data loss prevention matches in UC analytics identify channels that contain business-sensitive information. As a result, UC analytics prevents data leakage and adheres to regulatory compliance. Enterprises are able to take quick counteractions in compromising UC situations.

4. Migration information

UC analytics can provide valuable insights to enterprises that plan to migrate from one UC platform to another -- between different vendors. Before migration, enterprises could create inventories and reports to prepare for migration.

After the migration, UC analytics can provide insights like platform comparison, user activities and adoption. Hence, enterprises can use business intelligence to select UC platforms for migration to the cloud or a platform migration.

Benefits of UC analytics

Clearly, UC analytics tools provide valuable insights into communication data and platforms. Aside from comprehensive and effective insights, UC analytics provides additional functionalities and benefits.

1. Large data handling

UC analytics is independent of a vendor-centric approach. Powerful features enable organizations to extract meaningful information from large volumes of complex data in single-vendor UC services. For example, advanced search features help to extract historical call data.

In the case of multivendor products, UC analytics integrates and analyzes undistributed fragments of data from different UC platforms, including legacy devices. UC analytics promotes cross-platform collaboration through insights from multiple vendors into one dashboard.

2. Boosting employee adoption

UC analytics records and analyzes employee behavior when they use UC tools, such as what percentage of users are actively using a particular app, average usage duration and how frequently they open applications. UC analytics can analyze team collaboration patterns, including preferred tools and channels. As a result, UC analytics can identify users who do not fully use the available and critical features of UC tools.

Enterprises could use this data to determine common reasons behind communication, boost employee productivity and colleague transparency, and provide targeted support and training. In simple words, UC analytics can boost on-premises or remote employee adoption and engagement rate, enhancing the overall UX of different UC platforms and tools.

3. Enhancing customer experience

UC analytics can help enterprises study the behavior of employees and customers alike. It can help an enterprise understand customer profiles and create a personalized customer experience strategy. Data consisting primarily of call logs, response times, resolution rates and customer ratings during calls can identify service limitations.

UC analytics can provide information about mounting support tickets. After office hours, UC analytics can report to enterprises about the performance of VoIP bots in contact centers. In addition, UC analytics platforms can be integrated with CRM software.

4. Proactive analytics

UC analytics relies on a proactive approach to evaluate UC system health. A proactive approach prevents incidents from happening rather than fixing them. Real-time monitoring prevents potential issues. Historical and real-time data are analyzed to detect failures. However, UC analytics enables enterprises to perform faster troubleshooting, resulting in minimized UC downtime.

5. Optimizing budgets

UC analytics provides data-driven insights to reduce unnecessary UC expenditure. For example, reports can show the number of users on each UC platform or tool. Enterprises can identify areas of improvement in heavily used UC platforms or tools. A low budget can be allocated to less frequently used UC tools and unused paid features. UC analytics can determine system capacity for better resource allocation and planning.

The role of machine learning in UC analytics

Machine learning is a global forecasting approach. Machine learning models are the basis of analytics -- UC analytics, for that matter. These models can help managers forecast UC needs to design and optimize UC infrastructures. This section describes the role of machine learning in UC analytics.

1. Forecast reports

Analytics reports are structured insights supported by interactive data visualization. These reports are generated daily, weekly, monthly and even yearly. Machine learning models generate these reports to provide meaningful insights, actionable steps and forecasts. UC analytics platforms generate long-form reports and dashboards arranged in an understandable and presentable format under different sections -- something a non-IT professional can comprehend.

2. Predictive maintenance

Based on historical data, machine learning can estimate when UC maintenance should be performed. Machine learning models use complex data generated by UC hardware and software endpoints. Cloud-based machine learning platforms use feature engineering to train, refine and validate UC data. Unstructured data is transformed into meaningful insights to enable timely maintenance, predict failure and prevent UC downtime.

3. Anomaly detection

Anomalies occur in UC platforms when real-time data deviates strongly from historical data. Anomaly detection is universal in machine learning. It relies on some AI algorithms, like deep statistical and neural methods, to detect issues. Security and regulatory insights help machine learning and AI models detect unusual data patterns and threats before they escalate.

4. Reduced manual workload

At the end of each analytics report, machine learning and AI models generate actionable steps. Corrective actions can help UC admins and managers with faster troubleshooting. UC analytics sends regular automatic alerts, notifications and updates to administrators and relevant professionals. Automatic troubleshooting reduces manual workload and improves UC system efficiency.

5. Behavioral analysis

Advanced machine learning and AI models can extract facial features to determine emotions during video conferences. In voice calls, these models can assess the tone, volume and usage of certain keywords to measure emotion. Combining technical data with voice and facial analysis can promote employee well-being and collaboration. As a result, UC analytics could optimize the workforce and eventually lead to revenue growth.

6. Business intelligence

Recording and analyzing interactions between employees and customers or bots and customers can shape hidden sales data. For example, UC analytics can provide a list of the most discussed products and services, peak calling hours, busiest days and much more. Such data is critical to decision-makers to predict market performance and improve ROI.

The future of UC analytics

UC analytics are often categorized into system-oriented UC analytics and end user-oriented UC analytics. System-oriented UC analytics covers how UC platforms are used and by whom to understand usage trends and guide training efforts. End user-oriented UC analytics is a type of business-oriented analytics that uses big data analytics to boost employee productivity and cut costs.

End user-oriented UC analytics is gaining momentum in the business world. According to Gartner, global UC spending will hit $53.5 billion in 2026. In the future, UC analytics is likely to scale through machine learning and AI models. Newer UC analytics platforms could rely on popular technologies, like digital twins, IoT devices and causal AI systems.

Venus Kohli is an engineer turned technical content writer, having completed a degree in electronics and telecommunication at Mumbai University in 2019. Kohli writes for various tech and media companies on topics related to semiconductors, electronics, networking, programming, quantum physics and more.