Large language models can revolutionize customer service
As ChatGPT brings artificial intelligence to the next level, more forward thinking organisations are interested in implementing the technology into their business processes. Large language models (LLM), such as GPT-3.5 (the ‘free’ tier) and GPT-4 (commercial tier, as of the time of writing), are proving to be capable of solving a lot of pain points organizations experience today, especially when it comes to offering superior customer experiences.
According to a recent poll by Gartner, 45% of executives surveyed said the publicity around ChatGPT has prompted them to increase their AI investments. Despite concerns about risks from the technology, 68% of executives believe that the benefits of generative AI outweigh the risk. Interestingly, the poll also indicated that improving the customer experience is the primary objective when investing in AI.
An LLM can bring value to a business by automating the mundane administrative tasks allowing the ‘humans’ time to improve the customer experience. Their naturally learned conversational style fits the text-based medium and can provide efficient, accurate, and personalized customer support. The technology may just be the answer to the problem of achieving personalization at scale.
The customer service industry already uses chatbots programmed with templated replies to common customer queries. Unfortunately, without extensive journey mapping and testing, most of these chatbots were inefficient in providing detailed solutions to customers. As new services come online or the details of existing services change, manual chatbot programming has to be done again – once more, there is a scalability problem.
The three areas in which LLMs can make a difference in customer service include assisting customers more conversationally. That might involve answering frequently asked questions, helping with product recommendations, and resolving customer complaints (like Where is my order? & missed deliveries). Plus, with the correct interconnections between systems, intelligent automated algorithms could take actions, too, like putting through a re-order or issuing a refund.
Sentiment analysis algorithms running in text and voice channels help an organization respond to customer complaints more quickly and show companies where there’s friction.
Finally, personalized customer support interactions (based on the body of customer data accrued by the business) can provide highly-personalized product recommendations, individual promotions and offers, and tailored customer support.
Implementing LLM into customer service
When AI started making headlines several years ago, many organizations invested in the technology but realized that without significant integration work with existing systems, any benefits would be difficult to achieve. Machine learning algorithms require a learning corpus, and with no access to data from other systems, the results from any AI innovation remained promising but firmly with the R&D department.
LLMs could change customer service, but businesses must fully understand that AI is not just a simple “plug and play” solution that can be easily implemented. Companies need to work with the right technology partner who can help them plan and execute their AI journey, much of which involves work outside the AI ‘core.’
Planning begins with businesses needing to understand their data, its sources, methods of ingress, and use cases. If a customer wants to know something about the order they just placed, the tool needs to be able to extract the relevant information – order numbers, shipping tracking links, and discount codes – before it answers. That data could be housed in the company CRM, its e-commerce platform, third-party systems (like those of last-mile delivery services, for example), warehouse management systems, logistics, and so on.
DigitalGenius can help organizations with their AI journey in the specific area of customer service. The company has already developed and leveraged AI for multiple use cases, such as intent detection, entity detection, sentiment analysis, and foreign language translation.
DigitalGenius created Flow Builder to help businesses build workflows that map their existing processes on a user-friendly platform so that data paths and silos are apparent. Then, a conversational model based on an LLM can operate in customer interactions, using the data it needs to provide better service.
The same AI-powered technology can respond instead of customer care agents to repetitive, basic queries. Many queries in customer service are similar and can be handled by automated replies. If a query gets more complicated or requires a human, the chatbot can transfer the customer to where they can get the help they need. For example, an AI-powered chatbot for an e-commerce company can provide information on price changes but might transfer a call to a human agent if the customer requires advice on which items of clothing would go well together.
Human agents, therefore, are not replaced by AI-powered chatbots; instead, they’re given more complex (and, therefore, interesting) challenges where they can add value.
Businesses are helped to integrate AI-powered chatbots into their systems as many ‘bots come pre-programmed for everyday use cases. Chatbots come with pre-made integrations, API tools, and developer documentation for bespoke interfacing with the existing technology stack (the company CRM, finance systems, etc.). For organizations that lack the resources in respect of technical abilities in-house, DigitalGenius is happy to supply a readymade system that’s simple to fine-tune for specific needs.
DigitalGenius continues working with AI models and helps businesses with their journey to improve their customers’ experiences by using large language models and other advanced technologies. To find out more about how DigitalGenius can improve customer service and other business functions in your organization, book a demo.
16 February 2024
15 February 2024