Using AI to Extend Customer Care to Deliver Additional Business Value
This article was first published in The Fast Mode.
At the recent AT&T analyst and investor day, AT&T CTO, Jeremy Legg outlined how to measure the success of AI and GenAI with five simple guidelines. They are:
- Increase in number of customers taking convergent offers
- Increase in net promotor score (NPS)
- Increase in penetration
- Increase in ARPU
- Increase in customer lifetime value
Taking a business value approach to measure the success of any new technology always makes sense. All the five key measures listed above can be impacted by customers’ experiences when engaging with their CSPs. This would explain why the leading use of AI in telecoms is focused on the continuous quest to deliver a better customer experience.
According to Nvidia’s 2024 State of AI in telecoms report 49% of respondents are using AI to deliver a better customer experience. However, customer experience is a very wide term and could cover every touch point (from website to app to customer care to the network) where a customer interacts with their CSP. What is interesting is to look at where CSPs are investing in GenAI to identify which specific areas of customer experience optimisation are highest up the priority list for CSPs. The report showed that the majority of CSPs (57%) invest in generative AI to improve their customer service and support.
#1: GenAI Supporting Self-Care and Assisted Customer Care
GenAI customer care copilots are being rolled out to support customer self-care and assisted (human) care. Chatbots and other digital assistant services are the first line of customer support and a GenAI copilot enables a rich set of data to be analysed and used to answer questions and resolve issues via the chatbot. However, often human (assisted) customer care is also needed, and GenAI customer care copilots can increase efficiency and deliver quality improvements for assisted customer care.
When a customer engages with a customer care rep, the GenAI customer care copilot can summarise and process data (such as interactions in the chatbot and other channels) and provide help for the care rep. This can include providing routing of the call to the correct care rep, sentiment analysis (e.g. based on the tone of language used on the chatbot), a summary of previous interactions, intent recognition and templated responses / smart suggestions to the identified issue the customer is likely to have. This provides an analysis of the customer problem and a recommendation (resolution to the problem or next best activity) and is made available for the care rep to refine (if needed), discuss and present to the customer. This reduces the time for the customer care rep to solve customer problems and to enable the rep to possibly use the opportunity to engage the customer for possible upselling scenarios.
By using a combination of GenAI-based copilot and human customer care agents first call resolution rates and call throughput can increase, which has a positive impact on NPS, reduces propensity to churn, increases upsell opportunities and drives customer care costs down.
#2: AI Technical Customer Care – The New Frontier
The next level of AI-driven customer care is technical customer care, where care reps can answer questions about complex network issues. Currently, in many CSPs when a customer calls a care rep to complain about a network or service issue, the customer service rep typically sends a ticket to 2nd level care. This is typically an engineer who works in the network operations centre who will find out what the problem is and when it can be resolved. This can often be a complex and time-consuming process, involving a multitude of data sources and root causes that may need to be investigated to find out the source of the customer’s problem. This means that the customer service rep cannot provide the answers to the questions that the customer has.
Extending customer care with AI technical care solves this problem. AI technical care analyses network elements to identify network issues and identifies the customers and services impacted. The customer care copilot analyses the customers affected and checks if any SLAs (service level agreements) have been impacted and sends a notification to the relevant customers. AI technical care then identifies the root cause of the network problem and decides on what next best action to take. This action is then carried out via AI technical care and OSS, and then the impact of the action is verified. When AI technical care sees that the issue has been resolved the customer care copilot then notifies the customers that the network issue has been resolved. Also, the care reps are supported by an AI-driven customer care copilot to provide answers for any customers who get in touch to ask about this network issue.
Using GenAI customer care copilot to improve customer care performance can deliver the business value needed to help measure the success of AI. Adding AI-driven technical customer care to the overall care process can add even more business value and deliver a better care experience for customers.
Martin Morgan
Head of Digital Marketing, Qvantel