Chatbot customer service is a multifaceted sector, filled with customer purchases, requests, and appointments. With such a wide variety of tasks involved, professionals often wonder how to make customer service more efficient.
Luckily, AI chatbots can help. Not only could implementing AI customer service solutions take some work off your plate, but there may be a significant demand for them as well. According to Zendesk, a cloud-based customer service platform, 51% of consumers prefer interacting with AI agents rather than humans for instant service.
With that said, we’ll cover:
- How AI is used in customer support services.
- Why you should use a chatbot for customer service.
- How Psycray has helped clients with customer support using AI.
- Ideas for how to best use AI customer service.
- Concluding Thoughts.
How is chatbot customer service used in customer support?
AI is often used to handle basic questions and concerns customers have in business settings across industries. For example, if a customer wants to know when they can expect their product to be shipped to their home, AI can give them an estimate based on the data they have about when the product was sent out, where it came from, where it’s going to, etc.
In addition, AI support systems can identify questions or concerns that they cannot handle on their own. For instance, if a customer provides an inquiry that is too complex to understand, or if the AI agent doesn’t have the appropriate data to answer the question, AI can refer customers to a human representative for the support they need. This ensures that every customer gets the high-quality customer service they deserve.
WHy use chatbot customer service?
Chatbot customer service is incredibly useful because no staff member has 24 hours in a given workday … but AI does, so you can use that to your advantage when you set up a 24/7 chatbot.
How Psycray has helped clients with customer support using AI
Psycray has improved business’ customer experience with AI in several ways. Here are a couple of examples:
Psycray developed an email triage system that allowed a trade association that managed over 1,500 companies to handle a high email load.
If the AI agent could detect the company the customer was referring to, they sent them a reply with that company’s contact details. If they could not, they asked the customer to either verify their public contact or reach out to their point of purchase.
This allowed the association to go from having a clogged inbox to having a smooth email flow and system.
We also developed a three-part predictive insights model for a national plumbing company. To complete this project, we first built a structured learning layer filled with data on reviews across the Internet. Then, we built an AI agent layer that could predict negative customer reviews, upcoming surges in total review volume, and sentiment recovery patterns following interventions. Lastly, we built an AI agent layer that turned data insights into actionable next steps (Ex: Dallas is expected to see a 20% increase in timeliness-related complaints – consider adding a float technician).
This predictive insights model — which analyzed customer sentiment data — facilitated improved decision-making.
Ideas for how to best use chatbot customer service
- A chatbot that takes care of FAQ, transactions, and information requests.
An AI agentic chatbot can handle basic customer questions, purchases, and requests for information. Any complex inquiries that the chatbot can’t answer can be relayed to a human representative so the customer can get their problem resolved.
- Customer service kiosks.
An AI-powered kiosk can handle cash, credit, and check payments, which can speed up customer service in public settings. This not only makes the service more efficient, but it also lets employees focus on truly connecting with customers. Ideally, this leads to more personalized, effective service.
According to Forethought, an AI agent company, AI can also be used for 24/7 service, helping customers around the clock through embedded help desk information, past ticket history, internal company wikis, external-facing knowledge bases, agent notes, and more. These tools allow companies to stay more on top of customer needs than they ever have before.
What’s more, companies can even use sentiment analysis technologies within their AI systems to detect when a customer is happy, sad, angry, etc. This way, the AI can provide the proper agent to respond to the customer’s emotion, which facilitates a more appropriate, sensitive response.
- Basic patient support.
In healthcare settings, agents can help patients schedule appointments, request medication refills, and retrieve healthcare forms. This facilitates a smoother patient service experience and enables healthcare professionals to prioritize their patients rather than getting bogged down in administrative work.
AI can even be used to manage patient flow in hospitals. For example, according to Phillips, a healthcare technology company, AI technologies can be used in predictive ways to determine who should get an ICU bed first, who is ready to be transferred to a step-down unit, and more.
This can work by having an AI model set up with current and historical hospital data. Following initial validation, AI models can update their information based on the changes that occur daily, helping healthcare professionals obtain and make decisions off of accurate data.
Take, for example, Rosa, a 66-year-old patient admitted to the hospital with heart palpitations and shortness of breath. The patient flow coordinator, Jennifer, can get a message that Rosa is on her way to the hospital in an ambulance so that she can make an appropriate decision about where to direct her. If she notices the model showing that specific hospitals will be over-filled with patients in the next 24 hours, Jennifer can take that into account and start helping patients get to lower-census hospitals. Or, she can contact various hospital providers to inquire about open overflow beds, activating surge plans, and planning for additional staffing.
Once Rosa is admitted, Jennifer can see machine learning algorithms that measure physiological data and vital signs that predict risk of health deterioration to help the care teams prioritize clinical evaluation based on who needs care the most and what kind of care they need. Also, Jennifer can view how many ventilators will be needed by each care unit for the next 48 hours. From there, smart algorithms can assess when Jennifer will be ready to transfer Rosa to a lower-acuity unit. Once she’s ready to stay in the medical-surgical unit, predictive algorithms can again analyze when she’ll likely be ready for discharge.
By having AI/ML models monitor patients closely throughout their stay at the hospital, healthcare providers can make more informed decisions about when to let patients go home. This allows more patients to be welcomed in to receive the critical care they need.
Phillips offers much more information on how AI/ML models can help the entire patient coordination process, but we hope this gives you a glimpse into how much of a lifesaver these models can be for patients.
- Basic insurance support.
In addition to optimizing customer service and healthcare, AI agentic chatbots can handle simple insurance claims, beneficiaries, and more. According to Salesforce, companies can use AI to process data claims, detect fraud, automate underwriting, schedule meetings, assess risk, and more, allowing for a seamless insurance process. Since insurance professionals often have many complex and mundane tasks to do, having AI take care of these tasks frees them up to focus on higher-order priorities like building relationships with clients and helping them with challenging inquiries.
Conclusion
AI can help many businesses ease the administrative burden that often comes with customer service-related tasks. If you’re interested in our chatbot customer service solutions, feel free to contact us. We’d love to book a one-on-one session with you to learn more about your business and how we can help you.