Data and AI: A game changer for the telecom customer experience

We are in an exciting era of AI in telecom, especially with the latest leaps and bounds from generative AI promising a transformation and modernization of human and machine interactions across countless applications. Generative AI’s new foundational model brings interesting capabilities for both content generation (i.e. generating a go-to-market strategy for a new product launch) as well as content summarization (i.e. highlighting key points from a 10,000-word video conversation between a subscriber and a contact center agent).

The communications industry, with its highly active customer base and constant customer communication, generates high volumes of data sets perfect for training generative AI’s large language models (LLMs). This data varies from marketing leads, sales pipelines, sales quotes, contracts, orders, invoices, usage transaction records, payment records, omni-channel trouble tickets, to name a few. Generative AI uses these data sets to self-learn, gather a 360-degree view of the customer and personalize recommendations and messaging.

LLMs enable communications service providers to intelligently interact with their customers, partners and the broader ecosystem, based on deeper insights from prior customer interactions across multiple channels or network events such as service outages. While some of these interactions will bring direct and immediately noticeable benefits (topline increase), there are others that will bring long-term business benefits (enhanced customer experience, and increased business efficiency as standard examples).

Given the power that generative AI holds in influencing business outcomes, it’s imperative for communications companies to set clear goals for business outcomes and a framework for how those will be accomplished. Here are four distinct categories that communications companies can structure their AI around:

While the general awareness about online scams and fraudulent events has increased globally, technology advancement enables new sophisticated tactics to lure the unprepared. PwC’s Global Economic Crime and Fraud Survey indicates that organizations in the telecom, media and technology (TMT) and financial services sectors are more exposed to fraud.

Generative AI-powered fraud and anomaly detection systems can enable service providers to protect subscribers (both businesses and retail) from such unexpected events. Typical examples include alerting business customers when payments are made during off-peak hours, detailed reporting on fraudulent transactions by subscribers through LLMs on past cases, or finding common configuration errors that result in revenue losses.

These actions have direct topline impact for communications service providers. Introduction of the telecom marketplace enables communications companies to create a digital ecosystem where they can partner with producers of different goods and services and offer those to their customers. AI-powered product bundles, from telecom as well as non-telecom product catalogs, enables companies to tap into new up/cross sell opportunities. Examples include:

• For enterprise businesses, offering sports lovers augmented reality experiences at events.
• For the retail segment, offering highly-valued, frequently-roaming subscribers travel insurance plans and travel accessories.

• Searching for new partners to include in the marketplace.
• Finding new customer segments that you haven’t used before.
• Creating and testing new offers, writing marketing copy, and creating artwork.

Generative AI in this category may have little topline impact, but it can amplify customer experience in the long term. Combined with omnichannel outreach over email, SMS, and app notifications, AI can provide timely updates to subscribers to create that ‘aha!’ moment for a better customer experience.

• Detailed descriptions of marketplace content.
• Creating trending news summaries sent as an instant message.
• Contact center and customer service applications by either helping agents or through chat bots.
• Answering product inquiries and assisting in issue resolution.

This category could potentially also open a new revenue stream through value-added service where content from multiple external and internal sources could be combined to offer “information-as-a-subscription.”

AI can utilize customer data to identify who to upsell a data upgrade package to based on usage patterns. To do this, an AI workflow identifies users who are frequently exceeding their data limits. The workflow can then send a personalized message to the customer with an upgrade offer.

This step also highlights the need for a layer of trust and data security to be built in. The message should also include clear explanations of why the company believes these services are relevant to them and ensures that the customer understands how their data was obtained along with how their data was masked, anonymized, and encrypted for their safety. Taking these transparent steps ensures trust with the customer while providing them with the personalized service they expect.

There’s little doubt that the future role played by AI in telecom will be crucial in the years to come. Whether it’s enhancing crucial security functions to helping to create compelling marketing campaigns, these four areas highlighted are a good foundation to build your company’s AI around. The sheer volume of data telecom companies possess make the industry a clear front runner in AI implementation. It’s how the industry will build more profitable growth in the years to come.