By James Ramey, DeviceBits
There’s no doubt that recent circumstances surrounding COVID-19 have reshaped the way retailers operate and connect with their customers daily. Shelter-in-place ordinances and stay-at-home mandates have forced retail brands to rely on artificial intelligence (AI), machine learning (ML), and chatbot technology to manage much of the workload required for servicing their customers’ needs. In fact, retailers saw an 86 percent increase in customers using self-support sessions from March to May.[i] Technology has played an increasingly important role, much of which will continue post-pandemic. Messaging apps, AI, and self-service customer support options such as adaptive FAQ’s with interactive guides have allowed retailers to scale down contact centers to rely more on chatbot technology and give customers the quick and reliable answers they need.
Customer Experience Fulfillment
Today’s customers have learned that retailers will go above and beyond to meet their needs and provide them with excellent customer service. When a question needs to be answered, customers are used to having troubleshooting assistance available at their fingertips. However, in the wake of recent events surrounding COVID-19, call center operations are limited, creating longer than usual wait times. Further, customers still expect the same level of service, creating an overload and backlog of calls with fewer agents.
In some instances, such as bill payment, access to customer service and answers to questions are even more critical. Agents can’t answer all customer service calls in a timely manner, considering thousands can pour in at any given time. Because of this, call centers have needed a solution that can manage much of their workload.
Retailers specifically are facing a series of challenges around fulfillment and support. Many were still working to move the majority of their business to an online environment when COVID-19 initially hit. The forced acceleration caused significant stress to fractured parts of their businesses. Many retailers who were predominantly online businesses experienced overflow well beyond what they had planned for and also fell short in the areas of fulfillment. To make matters worse, employees were not coming to work, following their own guidelines for mitigation response. The two aspects mixed together created a perfect storm of disruption in the retail supply chain. Now, retailers face major volume spikes which only prolongs the effects caused by fractures in their business.
During this time, tools that allow self-service customer response techniques are more important than ever. Most popular for call centers are AI and self-discovery methodology. AI, in the form of chatbots and other tools, is helping to bridge the gap in customer communications for retailers. Companies are giving their customers access to branded chatbots via SMS messaging, social media messaging, and live chat options. This allows customers to get their questions answered quickly and efficiently, without the complications of sending emails or waiting on hold for a customer care agent for lengthy periods.
Self-service is a rapidly growing customer care trend among consumers. Self-discovery tools such as interactive tutorials, adaptive FAQs, interactive guides, and videos contain the simple, DIY answers that customers are looking for. These tools allow the customer to solve most of their problems themselves, putting the power back into the customer’s hands. Instead of depending on a customer agent for help, a customer can use these self-support materials for productive learning. Other tools such as interactive tutorials and videos also can aid customers in their customer care journey by visually showing them how to resolve a problem.
Retailers would be wise to monitor self-serve channels to predict volumes for their call centers while expanding the capability for the customer to answer more of their own questions. One method for increasing customer satisfaction is providing Virtual Agent Coaches to assist new call center agents when they start taking interactions from customers. This means the agent is using their own chatbot to answer questions for the customer. According to recent data, this has proven successful in maintaining customer satisfaction with an improved conversion rate of 16 percent, and an average cart value increase of +7 percent.
The easiest way to implement chatbots to help alleviate call center agents is to create interactive guides with adaptive FAQs with the most common customer problems and questions included, allowing the customer to find answers themselves more quickly. These tools can help customers by providing answers to questions that other customers have previously asked and showing users step-by-step how they can reach a common goal. With less call volume coming in to call centers, retailers can contribute more vitality with the customers who need highly technical assistance for specific issues more capably.
Not only are customers relying on self-service and FAQ tutorials, but agents also can retrieve agent-assisted information from these support materials, creating a quicker and more personal experience for the customer rather than agents relying on scripts. And in some instances, customers are quickly redirected from a chatbot to a live agent on more technical questions where a higher skillset level of expertise is required to field questions.
In a time where temporary remote work has not allowed for many customer servicing centers to be opened, AI-driven technology and self-discoverable information can fill in the gaps and augment the work of call center agents.
About The Author
James Ramey is President of Tech Enabled Services at DeviceBits, a division of the Results Companies, providing an AI-powered customer experience platform to enable consumer self-service and agent assistance operating across all digital channels and supporting organizational digital transformation. For more information visit www.DeviceBits.com.
[i] Percentages based on comparing DeviceBits call center and customer inquiry activity during the period of Jan. 1- Feb. 29, and then comparing against activity from Mar. 1- May 31.