Telecom Analytics Using Artificial Intelligence

The telecommunications market is a mature industry with several key areas ripe for optimization and differentiation. Network service providers (NSP) have extreme amounts of connectivity data that could be used for advanced market analytics, dynamic product offerings or usage analysis, as well as service optimization, so it’s no surprise they are investing into AI. In this article, I cover some of the areas where AI analytics for telcos can create a competitive advantage.

Network Monitoring and Optimization With AI

Artificial intelligence creates the most value in areas that are easily quantifiable and require constant attention and rapid decision-making. Network optimization is a key area that stands to benefit NSPs the most: due to high-speed analysis and precision, AI can unearth opportunities to cut costs and improve the quality of service.

Network monitoring is also an appropriate area of AI focus, as outages, abuse, under-usage and other issues can cost a lot in terms of capital expenditures as well as customer satisfaction. AI solutions can be trained on historic data of failures and outages to discover weak links and risky areas in the network. Contemporary AI can be used as a predictive network management solution.

How AI Enables Self-optimizing Networks (SONs)

SONs are the next evolutionary step for networks that integrate AI solutions to their maintenance and optimization processes. A SON monitors and analyzes performance, dynamically manages traffic allocation for regions, time zones or other load spikes. SONs automate the analysis and decision-making steps in large network management to provide a higher-quality service by relying on traffic data available to the NSP.

AI Analysis Can Surface New Insights for Telcos

NSPs often struggle with finding actionable data insights from their vast oceans of data. This is an actual opportunity to use AI, as an advanced model could unlock and operationalize previously unavailable data by:

Artificial intelligence can do all this from aggregated meta data, which its analysis model can parse and evaluate quickly. In the long run, a solution such as CUJO AI Explorer provides NSPs with a lot of information about what devices use the network, how they use it, and whether there are any behaviors that had previously slipped under the radar.

Thanks to machine learning, robust algorithms can help operators create new plans or hardware solutions for a more optimized network. It also creates a more secure and transparent infrastructure for analytics.

Making Use of Huge Data Sets

The exponential growth of available data may seem daunting, but AI models thrive with each additional data point. There are certain key data points that telecommunications providers use for business and network performance analytics, such as:

  • – Service usage
  • – Billing data
  • – Networks
  • – Device types
  • – Software
  • – Location data

Each of these data points has a tangible impact on the way network density, speed, and quality are managed. Artificial intelligence is exceptionally good at operationalizing these datasets comprised of millions of hourly connections. AI’s rapid processing time and edge computing capabilities create a perfect tool for a data-saturated environment.

AI Improves Existing Analytics

When we talk about AI being extremely fast and useful, it is easy to forget a vital piece of the puzzle: the way algorithms learn and get feedback. This does mean that a module performs best when it has a narrow function with clear goals and quantifiable results.

But this does not mean that AI requires anything new of the industry. According to McKinsey & Company, more than two in three opportunities for AI in analytics build on existing use cases, meaning that AI has a solid groundwork of benchmarks and KPIs to estimate its usefulness and efficiency.

In our experience, companies that adopt advanced analytics solutions with AI see revenue gains through improved customer experience, more lines of business, and cost reductions. These include time to adoption, which might take up to a few years for some major providers. AI is also a lot faster than humans at most narrow analytical tasks, freeing up valuable human resources for larger, qualitative, and big-picture analyses.

AI and Telecom Churn Analytics

One of the essential business areas for most telecommunications companies is churn analysis. Artificial intelligence is a great tool for NSPs to discover patterns in huge datasets, combining customer usage, demographics, billing and other data can provide reliable indicators for a customer that is at a higher risk of churning in the next few months.

AI-driven churn analytics are a form of predictive analytics that are a bit more fragile and have a natural lag (or re-learning time) as circumstances change. This is because historical data might not apply to current circumstances, and the AI will need to learn from a larger number of current churn events to identify new patterns.

Threat Analytics Using Artificial Intelligence

Having developed several AI-driven cybersecurity solutions that monitor over 700 million devices, we see that many IoT devices are unsupported and vulnerable. Each of them creates and attack vector for malicious actors.

What we’ve found is that artificial intelligence solutions can successfully, quickly, and precisely identify and classify devices, thus exposing outdated IoT endpoints.

AI-driven device intelligence solutions such as CUJO AI Explorer can identify and classify devices, firewall vulnerable devices or simply monitor them for unusual behavior. Cybersecurity is a major area where AI creates new possibilities to analyze networks, in addition to making other existing areas more efficient.

Countless Other Areas for AI in Telco Analytics

Artificial intelligence can help analyze and improve many areas: efficient cloud management, speed management for VR/AR connections, predicting service issues before they happen, predictive issues with hardware: servers, towers, cells, home routers.

AI also helps to profile customers, analyze content consumption patterns, traffic classifications, offer better added value services to customers automatically. Marketers already use AI for lookalike audiences for more value-added service sales and higher lifetime value. A major trend in AI development is fraud detection.

However, there are also roadblocks for properly implementing AI in analytics, such as low data quality and standardization, the fear of AI’s impact on legacy systems, reliance on external vendors, waterfall process management, and the overall absence of ‘a single source of truth’ for data to feed into the AI. All these issues are fixable, but the process requires dedication at the company level.

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