GenAI: Is it a game changer for customer relationship management?

TM Forum’s CEM collaboration team has been evaluating GenAI’s potential to radically change the way telcos interact with their customers, particularly when it comes to the delivery and support of hyper-personalized services.

Communications service providers (CSPs) are already harnessing rapid advancements in AI, Machine Learning, and real-time data processing to enhance customer experience through hyper-personalization, as well as to allow for a more systematic and objective evaluation of the outcomes of marketing campaigns. GenAI, however, opens the door to new capabilities.

One of the advantages of personalization is it supports the delivery of diverse modes of content exchange, contextualized across various channels. This is important given the development of multi-modal interactions (combining voice, image, video) along with the emergence of interfaces such as augmented/virtual reality which demand a quick and on-demand exchange of information.

The inherent capability of Generative AI can be applied to improve overall experience by feeding the right information and context more quickly to customer and business channels. In addition, generative AI based solutions can help CSPs to cater to the diverse requirements of hyper-personalization by enabling human-like rapid interactions, personalized multi-modal content generation, and contextual information exchange and many more.

The adoption of Gen AI helps CSP to progress towards hyper-personalization by leveraging the transformative abilities of large language models (LLM) and other Gen AI foundational models which are further enhanced with contextual insights extracted from business operations encompassing factors like demography, historical interactions, expressed preferences, and requirements, among others.

Example use cases:

1. Application of GenAI in Helpdesk applications

• GenAI offers valuable support for customer sentiment analysis and generating contextually appropriate responses.
• GenAI helps in rapid analysis of unstructured data and faster interpretation of context which enables identification of the issues faced by the customer reducing customer serving time for CSP

2. Increased efficiency of self-care applications

• GenAI can be a great tool in self-service where machine learning algorithms can create process driven response to particular communication service problem in easy and intuitive fashion
• GenAI may be highly useful for workforce dispatched to customer premise by offering virtual assistants that provide real time support in provisioning and maintenance.
• GenAI can provide better mechanism for providing assistance to premise-based network problem troubleshooting which can be further personalized based on demographic and other traits of customer. Hence, it can help in reducing operating cost for customer problem to a great extent.
• GenAI can provide highly contextualized information where customer can relate to situation unlike generic and less relevant information, thereby reducing dependency on contact center of customer support centres

3. Next Best Experience (NBx) and Decision Intelligence

• According to Forrester Research, NBX is an analytical paradigm that enables companies to identify and deliver the right experience to the right customer in real time based on everything they know about the customer.
• Compared to other Next Best paradigms like Next Best Offer, Next Best Action etc, NBX is more focused on the outside-in view and intended to enhance the customer experience at whatever engagement actions initiated by the CSP.
• Gen AI can enhance the NBX based decisioning scenarios by recommending the right decisions on actions that is influencing the specific customer experience metrics. Moreover, the Gen AI can be used to generate the right set of workflows, business rules and marketing content to realize the recommended actions.
• Integrating GenAI with Digital twin helps in offering more intuitive and intelligent twin that can interact with the consumers through natural language.
• Gen AI for the In-Service CEM which can generate proactive customer alerts on network issues, maintenance, coverage, or alternate choices.
• Gen AI for billing such as bill explanation.

Figure: a variety of GenAI applications for the customer context

CSPs and their customers are almost always very closely connected to each other, , which creates a high need for interaction, as well as a large opportunity to use data to manage the experience journey. CSPs use many mechanisms to connect and serve customer e.g., Online channel, call center, Chatbot, user education session and so on.

CSPs need to apply technology that fit to the choices made by customer for device, application, and lifestyle, through:

• mechanisms to connect and serve customer
• choosing communication services driven by customer choices
• understanding how to build or maintain collaborative engagements with the customer
• applying channel and service format that react on the customer’s demand

In summary, GenAI can help making the decisions based on the created knowledge through the AI for the distributed decisions in the journey engagement. To apply GenAI correctly, each (sub-) process asks for a deep understanding of the business process. The different aspects in the business process must be coherent designed across the engagements. Namely:

• which decisions are necessary for creating the business value and optimizing the journey
• which decision modelling is helpful and must be created and embedded in the GenAI
• designing the AI for all the different decisions that deliver the business value in the journey
• define what data it is necessary to collect, so that the AI produces the proper knowledge for the defined decisions
• what data must be accessible and gathered during the operational ingest period.

GenAI can be “generic” AI in terms of strategy, organization, technology, but in terms of the customer, business and operations, the gathered AI knowledge is very different and comes from very different data objects of the sub-business processes.

The study highlights the challenges of AI-data-driven innovation in the communication service provider environment and the key role of coherence in promoting new business processes & technologies. Please join us in creating effective GenAI - customer context - that drives our business forward! Join the project here to find out more or contact the CEM Team Chair, Arnold Buddenberg (arnold.buddenberg@orange.com).