Siri, from Apple, performs one task at a time (narrow AI), such as answering a question or sending a message, demonstrating the power of narrow AI. Other examples include e-commerce recommendation engines and beauty product demonstration apps (eg, Sephora’s virtual artist tool).
Customer support chatbots driven by AI are not only available 24/7 and extremely scalable, but also can improve efficiency by using data-driven insights. AI chatbots also support churn prevention through retargeting high-risk customers, and automated reminders for renewal.
Automate Routine Tasks
Automating ticket tagging, routing and data capture processes keeps your average ticket price low and allows your agents to focus on things like solving customer problems, greeting repeat customers and encouraging social media chatter. Albus is a highly skilled bot that can find answers directly to a customer’s question, so they don’t need to search across all sorts of channels and knowledge portals, which lets a customer service rep use the time saved to provide improved and more rapid customer service. Smart triaging and content clues can help teams answer fuzzier customer service cases that require some extra customer empathy because AI can pick up intent, language and sentiment in every interaction. The renewable energy company Rhythm Energy utilised Zendesk AI to route more inbound tickets to their in-house team, which ultimately lowered escalations rates with 46 per cent less ticket volume and 50 per cent less tickets escalated.
Reduce Average Response Times
Generative AI automates time-consuming tasks, like a search bar or typing up canned responses. This frees up the agent’s time, allowing them to help more customers in a deeper (rather than efficient) manner, for instance, one-on-one discussions, recommending the most suitable product, or officially filing and following up on a return. In this way, generative AI enables customers to have very personalised support interactions, and agents have time to resolve problems in a knowledgeable manner. Narrow AI tools like this, whether built into a chatbot or Netflix’s film-recommendation system, or whether they are behind any other kind of computerised decision-making, are limited to following explicit instructions programmed by human beings or to learning from their experience (from the examples they are provided with, or from data about what has worked previously). They are unable to expand what they know or can do to any area outside those programmed rules. A chatbot might allow a business to greet their online customers nicely on websites and messaging channels; use pronouns in the way that they would be used by the way that they’d be used when addressing them; field easy questions; and distribute complicated questions directly into the proper groups to triage and work on them – thereby lowering average response times while increasing accuracy; make sure that urgent calls don’t slip through the cracks, and present all the proper data to an agent so that he or she can perform when they get through to an actual human to have their way or solve their problem.
Boost Customer Satisfaction
These can be automated allowing AI to extend customer satisfaction and retention through increasing the speed and efficiency of agents. AI can answer the questions that are most commonly asked, which means that average response times decrease and agents get more time to offer personalised help for complex issues or when volumes are higher. From ensuring that customer support and development teams are informed about recurring issues that come up in help forums or feedback forms, to helping onboard new customers and CSMs by providing instant answers and surfacing articles to them quickly, to renewals – identifying at-risk accounts that are likely to churn, and sending the correct reminders – it is all about helping product management grow businesses and reduce churn.
Enhance Multi-Channel Support
While you can use an AI tool to do it quicker, the ultimate goal is not to develop a query-and-response rulebook. That won’t grow as you do. To scale, response times should be faster, common requests should be handled better, and the underlying relationship could feel even better. That is the promise of AI – to improve customer experience. AI can automate tagging and routing by understanding the meaning of the query, the language and tone, and sending the ticket directly to the right agent, including a full context report. You may find yourself interacting with the equivalent of sophisticated chatbots on hired help or tunnel guides, as advanced chatbots make for comfortable first lines of support on websites and across messaging channels. An advanced chatbot will have you covered if it has been pre-trained to understand customer issues and respond in a friendly and colloquial manner to customer requests or statements. The quality of service remains consistent across channels, and communication is omnichannel enough to enable transitions as customers switch channels for convenience. Feedback can be gathered more efficiently as well, and centrally managed data across all touchpoints enables more accurate analysis of the sentiments of customers. As a result, businesses can ensure that they maintain high customer satisfaction ratings without allocating additional resources to handle peaks in volumes.
Automate Routine Workflows
Although AI can quickly reply to a simple client questions, complicated requests can still require more human expertise and emotional intelligence to find a solution. In this case, AI can help locate the customer and connect him or her with the best available resource among different communication channels. Customers can receive timely responses to their issues without having to wait on hold (or worst of all, being transferred) by using AI to automatically tag and route tickets; staff can get more efficient with information requests, paperwork, service follow-ups and updates; and companies can realise major savings from costly back-office operations that can be automated. This kind of response measures can assist AI in identifying efficient strategies to address the most common customer inquiries (depending on sentiment and intent), and help to serve up highly tailored and effective self-service solutions towards empowering customers to self-serve more, with customers’ own data, while directing support agents to be focusing on the exceptions that require human expertise.
