Circles and OpenAI: +22% ARPU and -9% churn in the telecom sector

Circles: chronology of an AI integration built to last

Circles is a digital telco operator founded in Singapore, operating today in several Southeast Asian markets. Unlike traditional operators, it built its infrastructure on a cloud-native and data-driven model from the start. This choice has made integration with OpenAI technologies smoother than what happens in large legacy telcos.

Over the past few months, Circles has adopted the OpenAI API and the Codex model to power an AI-native personalization system. In particular, the objective was twofold: to improve the user experience throughout the entire customer lifecycle and to accelerate the internal development of new features.

Therefore, the project was not limited to an isolated chatbot or recommendation engine. Instead, it redesigned the way the platform generates, tests, and distributes personalized experiences at scale.

The numbers that define the case: ARPU, churn, and development speed

The results published by OpenAI highlight three distinct metrics. First, ARPU—Average Revenue Per User—grew by 22%. This figure indicates that existing customers are spending more, likely because they are receiving offers that are more relevant to their consumption profiles.

Subsequently, the churn rate fell by 9%. In addition, the efficiency of the software development process improved significantly, thanks to the use of Codex for code generation and testing. Thus, the impact of AI is felt across multiple levels of the company simultaneously.

These numbers deserve a critical reading. According McKinsey Global Institute, companies that systematically integrate AI into their core processes achieve results up to three times higher than those that use it in a fragmented way. The Circles case confirms this thesis.

Architecture of the approach: what Circles actually did

Circles' operating model is based on three main components. First, a unified data layer that collects real-time behavioral signals—data usage, contact frequency, purchase history. Then, a personalization engine powered by OpenAI APIs that generates dynamic offers, messages, and recommendations for each user segment.

Finally, Codex is used internally by development teams to accelerate code writing and testing. In this way, the time-to-market for new features is significantly reduced. Similarly, teams can iterate faster on the customization logic itself.

This approach — data, customer-applied AI, development-applied AI — represents a replicable structure even outside the telco sector. Therefore, it is useful to consider which elements are transferable to Italian B2B or retail contexts.

Winners, losers, and those who watch

The most obvious winner is Circles itself, which consolidates a measurable competitive advantage in an industry — telecommunications — historically characterized by compressed margins and high competition. Furthermore, OpenAI strengthens its narrative of business-applied AI with a concrete and documented case study.

However, the real losers are the operators and companies that continue to postpone AI integration. According Gartner, by 2027, more than 60% of companies that have not adopted generative AI in their customer-facing processes will lose market share to their more advanced competitors. The gap is widening every quarter.

Those who stand by and watch—and there are still many Italian SMEs—risk finding themselves in a difficult catch-up position. Despite this, the good news is that the tools are accessible and integration models are now well-documented.

Reading SHM Studio: What it Means for Italian Marketing

The Circles case is relevant for Italian marketing managers for at least three reasons. First, it demonstrates that generative AI produces measurable results on real business KPIs—not just superficial engagement metrics. Second, it shows that integration must be systemic, not episodic.

Thirdly—and this is perhaps the least discussed aspect—it highlights how AI can act simultaneously on the customer front and the internal front (development, operations). This dual impact is what generates the greatest return on investment.

In SHM Studio We work with B2B and retail companies that want to apply these principles to their own context. For example, AI content personalization is one of the pillars of our projects in digital marketing e SEO copywriting. Similarly, we integrate advanced segmentation logic into our campaigns Google Ads e LinkedIn.

What numbers do not say: enabling conditions

A case study like Circles tends to highlight results. However, it is useful to ask what conditions made it possible. First of all, Circles already had a well-established data-driven culture. Second, it had a flexible cloud infrastructure. Finally, it had internal teams capable of integrating external APIs autonomously.

For many Italian SMEs, these conditions are not yet fully present. Consequently, the path toward similar results requires a preliminary phase of data readiness and process revision. Therefore, it is essential not to simply copy the model, but to adapt it to one's own digital maturity.

According to Harvard Business Review, companies that get the best results from AI are those that invest first in the foundations — clean data, clear processes, trained teams — and then in applications. Circles is a consistent example of this.

Next moves: three operational directions for 2026-2027

In light of this case, it is possible to identify some concrete directions for Italian marketing and digital managers. First of all, it is worth conducting an audit of your customer database: data quality, completeness, and accessibility are the prerequisites for any AI project applied to personalization.

Furthermore, it is advisable to explore the integration of AI APIs into marketing automation and CRM tools. Many platforms already widespread in Italy—from HubSpot to Salesforce—offer native connectors with OpenAI and equivalent models. Therefore, the starting point is not necessarily custom development.

Finally, it is strategically relevant to begin measuring the impact of AI on real business KPIs — ARPU, churn, conversion rate — and not just on operational metrics. This shift in perspective is what turns a pilot project into a scalable investment. SHM Studio AI Services They are designed precisely in this direction: from strategy to measurable implementation.

For those who want to learn more about how to structure this path in their own company context, the team at SHM Studio is available for a dedicated consultation. You can contact us through the Contact Us to explore our blog for further insights on AI, SEO, and digital marketing. Among the available services, we also highlight the activities of SEO e web development performance-oriented.