usage needs and preferences beyond mobile. For example, Nova Energy in New Zealand is offering mobile services bundled with utilities, meaning customers get a good deal on their energy prices as well as their mobile services via a simple, unified bill at the end of each month. Another example is Huawei’s Northern Africa carrier business which powers Ethio Telecom’s Telebirr and Safaricom’s M-PESA with mobile service packages tailored around their customers’ needs.

3. Reducing customer churn

Every MVNO knows the importance of churn management. Just a small increase in customer churn will have a disproportionate impact on profits due to high customer acquisition costs. Furthermore, customers don’t churn out of the blue. Pre-churn indicators – failing to pay bills on time, price sensitivity, no longer responding to messages or deals, customer service calls and complaints, or otherwise using negative language – form vital intelligence that BSS data analytics can reveal and MVNOs can use to improve customer engagement. AI can also identify clusters of changing behavioral patterns among users, allowing them to take a more proactive approach to retention and cross-selling. Operators have traditionally waited until a customer calls to cancel their service, and then an offer is made. But now, proactive actions before churn based on each customer’s data power AI tools to produce personalized solutions to proactively address their concerns, reducing churn before it happens. Depending on the pain point, MVNOs can take appropriate action, whether that is introducing a loyalty program, offering discounts, solving a particular customer challenge, or understanding where to target operational improvements. For example, a LatAm MVNO owned by a leading retail chain was able to grow its customer base in a highly saturated, competitive market with low portability barriers through a combination of smart moves powered by AI analytics and automation, like leveraging the synergies of the parent retail group, improving availability and operations, targeting new growth segments, and enhancing and personalizing loyalty offerings.

Upgrading Infrastructure to Leverage AI

AI only exists today because we have the volume and real time velocity of data needed to drive accurate and proactive actions. But BSS data, at least in traditional systems, is heavily siloed. It’s one of the biggest challenges hindering the efficiency of AI activation. To get the most out of powerful, advanced AI analytics, AI needs access across these silos. As a workaround, AI tools are often bolted onto these systems. This works – to an extent – but can lead to greater inefficiencies down the line in lifecycle costs and information transformation issues. For many MVNOs considering overhauling their outdated business infrastructure, AI is perhaps the strongest reason to modernize towards a cloud-native BSS system that can provide levels of insight that a legacy, siloed system can’t compete with. If AI doesn’t have a comprehensive view of telco data en masse, such as network performance or call dropout times, these changes to core infrastructure can be difficult to initiate.

Data Privacy, Security and Regulation

Pivotal to AI’s success in telecoms is trust, transparency, security, and robust regulation. MVNOs already abide by existing data privacy laws (e.g., GDPR) and are currently watchful of the rapidly evolving regulatory response to recent AI advances. Data pseudonymization and anonymization both address the need to protect user privacy and are useful in identifying trends and actions applicable across many territories or globally. However, when it comes to tailoring the findings into actions for specific customer segments in specific geographies, AI-powered BSS solutions need to have local algorithms and customer mappings to enable efficient targeting, while still meeting all the local laws governing the data.

What’s Next for AI in BSS?

AI-powered automation built on unprecedented data scale and speed is poised to revolutionize the business operations of MVNOs. Telcos, armed with invaluable customer data, must adapt or risk being left behind in the face of digital disruption. Advanced AI techniques, including ML, LLMs, NLP, and automation are enabling more personalized customer experiences, operational efficiencies, and value creation. Generative AI can also offer a path toward individualized offerings or even a world where customers themselves describe what they need, and AI systems create bespoke packages spanning the breadth of an MVNO’s services. However, to unlock AI’s capabilities fully, AI needs to integrate seamlessly with the underlying BSS data infrastructure. For many MVNOs, this interoperability can only be fully realized by modernizing the outdated, frequently siloed, legacy systems.