Intelligent Automation changed Customer Care, for good. Learn how.
Brands were forced to change the way they do Customer service. Retail, e-commerce, banking and telco are the ones more impacted.
Learn what changed and dig into the most relevant customer service trends.
#1 | Productivity and processes
Every online purchase a consumer makes necessitates several steps that must take place behind the scenes before the item arrives at its destination. Unfortunately, for both the merchant and the customer, each manual step during and after a customer transaction introduces a considerable chance of human error, leading to higher operational costs. Unintentional mistakes are frequently the cause of shipment delays and result in customer dissatisfaction.
But what if these back-office tasks could be assigned to an AI-powered chatbot that operates in the background? Automation boosts efficiency, accuracy, and speed. It is ideally suited for repetitive tasks because it eliminates the possibility of human error when data is entered into a system. AI can enhance the numerous procedures involved in an online transaction, such as currency conversion, product recommendations, order tracking, refunds, and discounts.
#2 | Seamless customer experience
Retailers should leverage the various advantages of AI and automation beyond the back office to offer a more seamless consumer experience. Digital assistants can be integrated with front-end communications to keep customers up to date on their purchases. Automation can also empower customer support workers to swiftly gather essential information about a client while conversing with them on the phone or through chat, enabling them to provide timely assistance. This helps businesses display their concern for the consumer while also assisting the agent in resolving difficulties more efficiently.
AI-powered chatbots may also be configured to react to signals supplied by back-end systems to foresee problems and send a message to the consumer. Intelligent chatbots, if activated, can intervene with information or a remedy to a delivery delay before customers are even aware of it. For example, a digital assistant could proactively understand if a customer has failed to log in and offer password support.
Along with showing the customer that they care, the retailers also remove a potential support ticket from call center agents and allow them to focus on more valuable tasks. Moreover, customers today prefer to use self-service options, making it even more critical for retailers to have these solutions available.
#3 | Prioritizing support tickets
Sentiment analysis — a type of AI that employs NLP to evaluate whether a block of text is good, negative, or neutral — may be used by AI customer service systems to recognize when consumers are frustrated and automatically escalate issues to live agents. Agents could prioritize open tickets in this manner, resolving disgruntled customers first and lowering their risk of churn.
#4 | 24/7 customer care
According to HubSpot research, 90% of customers expect a response within ten minutes after raising a support issue. Immediate gratification is the driver behind leading e-commerce retailers such as Amazon (Amazon Prime).
Retailers must leverage AI automation to negate the substantial financial investment of staffing call centers to meet the demand. However, virtual agents are more than a cost-saving.
AI customer care automation allows 24/7 service without breaks, meaning customers can speak to you wherever they are, whenever they need you.
#5 | Personalizing customer care
Retailers may personalize dialogues with consumers by using AI to evaluate and learn from previous conversations. This allows them to draw on issues or preferences that those customers have stated in the past.
An AI application, for example, can provide notifications for customer service professionals detailing previous problems with a certain client when a new discussion with that customer is starting.
Natural language chatbots may also be trained to interpret this historical data, allowing customers to have individualized interactions with customer support “reps” at any time of day or night, without having to wait on hold.
Gartner stated that much AI investment will focus on customer service. Machine learning applications and other emerging technologies will drive 70% of customer interactions by the end of 2022.