Generative AI is one of the hottest tech topics in 2023. In telecoms, several CSPs have already started to deploy Generative AI into their operations. Almost all CSPs are investigating where and how Generative AI could be used and consensus is that the first use of Generative AI will be to support customer operations, and specifically customer care.
If we look at the current performance of customer care in the telecoms industry, then it’s fair to say that it’s not bad, but some help would be welcome. Each year, the UK regulator, Ofcom, surveys telecom customers to compare customer service. In the mobile market customers are largely satisfied with their CSPs, but when customers do need to get in touch with their CSPs some major areas for improvement appear. The most recent report, Comparing Customer Service: Mobile, Landline and Home Broadband published in May 2023 covered customer data from 2022. Although the Ofcom survey only covered customers in the UK, it could be a fair assumption to say that the results would be typical of a mature and competitive telecoms market.
When mobile customers want to get in touch with their CSP 77% make a call and only 17% use webchat. In 2022, 12% of mobile customers said they had cause to make a complaint to their CSP, and of this number, 71% went on to get in touch with their CSP. In an ideal world, all these calls would be dealt with quickly and to the customers’ satisfaction, but the reality is that 57% of customers did not have their problems resolved on the first call and 10% of calls to mobile CSPs were abandoned with people hanging up before even speaking to an agent. In 2022 the average call waiting time was 2m 23 seconds.
There are many varying factors that can contribute to the cost of each CRM call. Analysts have calculated that the average customer care call can cost anywhere between US$2.70 to $5.60 per call. Even at the lower end of the cost scale, there is a significant financial imperative for CSPs to improve customer care by improving the throughput of calls and increasing the first call resolution rate. There is also a positive impact on NPS and customer satisfaction by dealing with customer care calls quickly, professionally, and to the customers’ satisfaction.
Customer care needs a helping hand to improve performance – this is where Generative AI can help.
Generative AI has been the major technical talking point of 2023 and will impact almost all industries and deliver significant economic results. Leading analyst firm McKinsey produced a report in June 2023, The economic potential of generative AI: The next productivity frontier, which highlighted that Generative AI could add the equivalent of $2.6 trillion to $4.4 trillion value annually to the global economy. McKinsey analysed 63 use cases and believes that the impact of AI would be to improve productivity by 15% to 40%. Their research also reported that 75% of the value that Generative AI use cases could deliver comes from just 4 areas: customer operations, marketing and sales, software engineering, and R&D. In a 2023 Gartner webinar poll of more than 2,500 executives, 38% of business executives indicated that customer experience and retention is the primary purpose of their Generative AI investments, followed by revenue growth (26%), cost optimisation (17%) and business continuity (7%). This business executive viewpoint is aligned with the McKinsey report but also indicates that businesses are not only after AI-enabled cost savings, but perhaps also after driving better customer experience, loyalty, and NPS score via improved quality of customer experience.
These reports by McKinsey and Gartner focussing on customer operations and customer experience as the primary drivers for AI investments and initial use cases reflect what we’re seeing in telecoms. There are many use cases for AI in CSPs, ranging from network management to marketing, but the first use cases of Generative AI in telecoms will be supporting customer operations and improving customer care.
Customer operations, specifically customer care, is a critical element in telecoms. This is important to help maintain satisfaction levels in customers and drive new revenues by upselling new offers. However, it is also a major source of costs for CSPs. This is one of the reasons why CSPs have been digitalising operations in the past few years and providing digital self-care channels.
While CSPs often prefer chatbots and other digital assistant services to be the first line of support for end-users, quite often human customer service is also still wanted by end-users. Generative AI has the potential to further improve the chatbot experiences, and drive efficiency and quality improvements for customer care agent tasks when the end-user is reaching out for human assistance. When an end-user reaches out to a customer care agent, Generative AI’s ability to summarise and process data (such as interactions in chatbot and earlier in other channels) can power a co-pilot experience for the care agent, providing both sentiment analysis, summary of previous interactions and possible resolution to the identified issue the customer is likely to have. The analysis of the customer problem and a recommendation (resolution to the problem or next best activity) is made available for the care agent to refine (if needed), discuss, and present to the customer. This reduces the time for the customer care agent to successfully resolve the customer issue and enables the agent to possibly use the opportunity to engage the customer for possible upselling scenarios.
By using a combination of Generative AI-based co-pilot and human customer care agents, first-call resolution rates can increase, which has a positive impact on NPS, reducing the propensity to churn, and driving customer care costs down. Telecom customers still want to talk to humans for customer care. Providing an AI Co-pilot can make the process better for the end-user customer, the care agent, and the CSP.
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