Artificial Intelligence (AI) and, more specifically Generative AI (GenAI), are transforming the telecom industry for the better. Nearly 100 percent of telecommunications organizations are actively exploring or implementing these technologies, according to a recent NVIDIA industry survey, with 50 percent focusing on GenAI. Of the survey respondents, the highest priority after employee productivity was customer experience, surpassing network optimization. This indicates a shift from billing and network โ a long-time revenue focus of operators โ to the customerโs experience via the mobile device.
In practice, GenAI has the capacity to radically transform customer engagement โ from pushing product (product-focused) at the operatorโs convenience in a static, algorithmic-like fashion to catering to customer-specific needs (customer-focused). This includes limited device or account support to total resolution for device care or proactive commercial upsell.
This kind of transformation, however, requires the right fuel. To achieve meaningful customer engagement, mobile operators must focus on data readiness โ not just any data, but the right data. This allows AI solutions to deliver precise, contextualized and personalized experiences to customers in a way that positively impacts the customer lifetime value (CLV) triad (recognized as operational efficiency, loyalty and monetization opportunity). In this article, we will walk through the key data points that operators require to positively impact customer journeys.
GenAI thrives on high-quality, contextualized data in order to deliver meaningful and accurate outputs. In the telecom industry, where customer interactions are highly dynamic and multi-faceted, ensuring AI has access to numerous data inputs is imperative. It can make the difference between algorithmic-like, limited-resolution capabilities that yield poor NPS to providing creative and hyper-personalized solutions with markedly better customer responsiveness.
The Triad of Data Fueling GenAI for Mobile Operators
For GenAI to effectively manage interactions and deliver customer-centric outcomes, it must be fueled by three essential types of data:
Conversational Inputs: Understanding Customer Intent
When customers engage with GenAI, their language, tone and expression style provide valuable context. GenAI can analyze these inputs to gauge sentiment, urgency and even demographic indicators like age or familiarity with technology. This enables the AI to respond more naturally and effectively, personalizing interactions in real time.
CRM Information: Leveraging Historical Context
A customerโs past interactions, device history, billing records and contract details, which are stored within an operatorโs Customer Relationship Management (CRM) system, are vital for AI-driven decision-making. This layer of historical and behavioral data ensures that interactions with GenAI can become proactive, fostering more opportunities for GenAI to more optimally select from the recommendation engine in any given scenario.
Mobile Device Information: The Key to Real-Time Optimization and Precision
Perhaps the most pivotal data input in AI-powered telecom customer experiences is mobile device intelligence. By analyzing real-time diagnostics, device configuration and condition, GenAI gains a true, up-to-the-minute understanding of the customerโs tech environment.
Without each of these three data points (CRM, conversational and mobile device intelligence), any GenAI solution designed to engage customers will come up short, unable to deliver optimal results at different points of the lifecycle. Because the mobile device is a key identifier of the customer and their disposition, mobile device intelligence is the make-or-break data point. Sourcing mobile device intelligence should be done proactively, meaning the operator should identify opportunities to encourage customers to execute health checks outside of device troubleshooting events.
Diagnostic data that is extracted and collated is executed by โDeviceAIโ โ the game-changer in AI for mobile operators. It gives operators a complete composite of a customer and thus the capability to direct them to the optimal resolution for a variety of journeys.
Impact on the Lifecycle Journey
The table below illustrates how different data points contribute to GenAIโs decision-making, enabling smarter, context-aware chatbot interactions that enhance customer satisfaction and business outcomes.
Chatbot: The Primary GenAI Conduit and How Data Influences It
GenAI needs a focal point to engage customers. In the telecom space, that focal point has become the chatbot. While more than 80 percent of customers have interacted with a chatbot at some point, only about a third find them effective in resolving issues. The challenge isnโt the chatbot itself; itโs the intelligence behind it.
By driving the right data inputs, chatbots can move beyond scripted, algorithmic responses and evolve into dynamic, problem-solving agents. A GenAI-powered chatbot within a mobile app becomes a centralized engagement hub, capable of managing multiple customer journeysโwhether itโs troubleshooting a device issue, recommending an upgrade, or streamlining an insurance attachment process.
With access to conversational inputs, CRM data and real-time mobile device intelligence, a GenAI-driven chatbot anticipates customer needs, personalizes interactions and drives more value-driven engagements instead of merely resolving the immediate issue, benefiting both the customer and the operator. Below is a timeline of how the key data points influence decision-making of the chatbot.
Arguably, the most critical cog in this machine is the real-time mobile device data, which provides apinpoint accuracy to GenAI to determine which offer from a recommendation engine would best suit the customer, i.e. between repair and or upgrade/trade-in.
In practice, the ability to proactively source diagnostic information triggers commercial opportunities, as the TELUS Mobile Klinik (MK) pilot demonstrated. By initiating a conversation with the customer and then enticing them to engage in a proactive health check on their mobile device, GenAI can expand its base of offers available in the recommendation engine and select the best choice that will drive a customer to convert. Using this process, TELUS MK was able to triple customers actioning a marketing promotion and then nearly double retail finder tool openings, showing intent to visit the store to complete the conversion.
As AI continues to evolve, the key to unlocking its full potential lies in data readiness. Operators who invest in the right data, at the right time, for the right customer journey will not only improve customer experience but also gain a competitive edge in the era of intelligent connectivity. MCEโs dDLM platform allows operators to both digitize customer journeys and appropriately improve access to the right data, ultimately enabling them to unlock greater CLV.
