Released over a year ago, OpenAI's ChatGPT immediately captured everyone's imagination, quickly becoming a popular byword for generative AI and AI in general. Thanks to its popularity and accessibility, last year, 45% of Contact Centre executives said that ChatGPT prompted a rise in AI investments within the Customer Experience (CX) industry and set a starting point for adopting Generative AI by mainstream business.
If 2023 was the year to get under the bonnet of ChatGPT and develop the first few AI applications, what can we expect to see in car manufacturers' and dealers' contact centres in 2024 beyond the usual automation use cases? Focusing specifically on enhancements in customer experience, here is my top 8 list:
1. AI Bots to boost agent expertise
When trained with internal documents and knowledge bases, AI bots would revolutionise agent expertise in the automotive industry. The bots could provide instant solutions to complex queries, elevating agents' expertise to rival experienced dealers. Offering detailed, accurate information about the products and services would significantly improve customer satisfaction while eliminating listening to the elevator music or waiting for live chat replies or callbacks from experts.
2. Adapting conversations to customer personality types
Integrating psychometric segmentation in automotive call centres could genuinely transform customer interactions. If the AI bot recognises a caller's number, it could retrieve a tailored script or call guidance based on the caller's personality type. Such a deep level of personalisation could drastically improve sales conversion and first-call resolution rates and raise customer satisfaction and loyalty to new levels.
3. Emotion detection and sentiment analysis
AI can gauge customers' emotions by analysing their tone, word choice, and speech patterns. This would help agents understand customer sentiments more effectively, enabling them to adjust their approach to be more empathetic and effective in sensitive situations. In cases of customer frustration or upset, the AI might suggest the most appropriate response to the agent or escalate the call to a supervisor if needed.
4. Personalising customer interactions
AI could personalise interactions by tailoring responses based on customer profiles, past interactions, purchase history, intent, and mood. This level of customisation would make the customer feel more valued and understood, potentially improving their overall experience.
5. Predicting customer needs
AI can easily analyse a customer's previous interactions, purchase history, and preferences to predict their needs and offer proactive support. This approach could resolve potential issues before they escalate, enhancing customer satisfaction. For example, if a customer frequently contacts support for EV charging billing inquiries, the AI bot could proactively offer billing information at the start of a call, potentially resolving the query more efficiently.
6. Automating after-call work and documentation
AI could automate the time-consuming after-call work agents typically handle, like updating customer records, filling out forms, and summarising calls. The AI might transcribe calls, highlight key points, and suggest follow-up actions based on the conversation. This would speed up documentation, ensure accuracy, and allow agents to focus more on customer interactions.
7. Creating knowledge content
AI bots can transform how knowledge is collated and shared. They could continuously enrich the existing knowledge base by automatically generating knowledge articles from interactive conversations. This process would add to the repository of information and feed into the AI's own learning mechanism, ensuring that the information remains up-to-date, relevant, and grows organically with each interaction, leading to an ever-evolving and improving resource for users and the AI itself.
8. Generating tailored training materials for agents
AI could actively analyse the interactions of customer service agents, examining various aspects of their conversations, such as the language used, problem-solving skills, and overall effectiveness in handling customer queries. From this detailed analysis, the AI would identify specific areas where each agent could improve and create personalised training plans for each agent.
They could include resources like targeted tutorials, skill-building exercises, and educational content focused on their specific areas of improvement.
The dynamic nature of this AI-driven training also means that as products and customer expectations evolve, the training content can adapt accordingly, ensuring agents always have the most current knowledge and skills.
These eight examples represent just a few of the potential CX applications of generative AI at its current level of maturity.
The year ahead
AI is now ready to personalise interactions on an unprecedented scale, and considerably boost workforce efficiency and productivity.
While this heightened efficiency may lead to a reduced workforce, organisations focused on enhancing customer experiences will likely maintain their numbers of skilled agents. They can redeploy them to areas of the customer journey where their impact on sales will be more pronounced and where they can improve customer satisfaction, reduce waiting times, and broaden operational reach.
In summary, in 2024, AI will likely streamline call centre operations and boost the customer experience.
I can't wait to look back at the end of the year and see how much of this actually materialised!
