Time series forecasting: The new frontier of AI in wholesale telecoms

Facing constant demands for faster and more seamless connectivity and exceptional customer experiences, CSPs and MNOs are already turning to next-gen technologies and solutions.

The world is only becoming more digital, and telco is no exception. From investing in cloud infrastructure, offering increased digital services, and exploring the world of generative AI, CSPs are blazing forward and embracing the digital revolution. But that doesn’t mean there aren’t still opportunities being left on the table by providers. CSPs have only dipped their toe into the world of AI and the large variety of benefits it can deliver to organizations.

The current state of AI in telecoms

AI isn’t entirely new to telecoms. Many CSPs and MNOs have started to implement it into their operations. In fact, a huge 70% of decision-makers say they’re currently using generative AI. A prevalent use case has been in customer service, where chatbots are implemented to help improve the digital experience and lower the strain on agents. On top of this, network optimization and fraud detection are two other areas that have seen AI investment from CSPs.

Compared to other industries, telco is markedly ahead when it comes to embracing AI. This isn’t too shocking, given that it’s an industry founded on technological innovations. But still, you’d be surprised how many tech companies are notably poor at embracing technology internally (the irony isn’t lost on anyone).

While this attitude within telecoms is encouraging, it doesn’t mean it’s time to take our foot off the pedal. Now is the time to take further steps, look for the next opportunity, and reap the rewards of being at the forefront of this burgeoning technology. Many CSPs are still tied to traditional BSS systems, which simply can’t keep pace with the needs of a modern provider.

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The next step: Time series forecasting

Whether you haven’t explored AI at all or are looking for the next step in your AI journey, AI-enabled time series forecasting is the most practical way forward.

You’ll likely be familiar with time series forecasting, as it’s been an essential part of telecoms for years. Traditional time series forecasting has previously worked by analyzing past events with statistical methods to help predict and plan future trends. In telecoms, this has been used to forecast data usage, evaluate decisions, and verify trends. But this process can be lengthy, and calculations become more complicated as you analyze larger time periods. Looking back almost ten years ago, accuracies were only as high as 50-60%.

Now, using AI and large language models (LLM), this process can be sped up and made more accurate. By infusing forecasting with next-gen technology, CSPs can forecast longer periods and detect trends from subtle patterns, resulting in more precise and unique insights.

Use cases for time series forecasting

Where does AI-enabled time series forecasting fit within the telecoms industry? There are multiple applications to be explored, including:

• Call volume predictions—during peak hours, CSPs and MNOs can predict expected call volumes and automate resource redistribution. This makes sure call quality is maintained, outages are minimized, and customer satisfaction remains high. It also helps optimize cost efficiency by preventing over-allocation of resources during low-traffic periods.
• Adoption rate of new services—predicting uptake on new services such as bundles can help in planning and infrastructure. CSPs can better allocate network capacity, tailor marketing campaigns, and ensure they’re prepared to meet demand without overextending their resources. It also supports efficient rollout strategies, reducing the risk of underutilized or overextended network assets.
• Data usage patterns—CSPs can predict data usage, particularly during busy holiday periods when data usage is expected to surge. This enables you to ensure sufficient capacity is in place, avoid network congestion, and guarantee continuous service delivery to customers.
• Customer churn—detect customer events, such as reduced data usage, that may indicate potential churn. These customers can then be targeted with exclusive deals, discounts, or personalized packages to help increase customer loyalty and retention.
• Revenue forecasting—by applying time series forecasting to financial data, CSPs can predict future revenue trends, helping in financial planning, budgeting, and setting strategic targets.

These are just a handful of applications of AI forecasting in wholesale telecoms. The possibilities are vast, and the opportunities are huge. It’s vital to continue the momentum of the telecoms industry, ensuring that we continue to operate at the forefront of technological innovation and embrace the large business benefits organizations are set to gain. After all, time series forecasting is simply the next step. As AI develops, its applications grow, and there are even more use cases to be explored for the telecoms industry.

Finding the right AI solution

While we’ve outlined the benefits of AI-powered forecasting for telecoms, this doesn’t mean that every forecasting tool is equal. It’s important to evaluate different tools to ensure your investment goes into a solution that works for your goals and challenges.

Unsure how to approach finding the right solution? In our latest e-book, we cover everything you’ll need, from what telcos need to bring to the table up to our quick start guide for implementation.

The AI-powered future of BSS for wholesale telcos

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