Date Published: June 21, 2022 - Last Updated 1 Year, 256 Days, 2 Hours, 13 Minutes ago
As consumer demand continues to move away from traditional brick and mortar stores to retail-specific applications that enable 24/7 buying, new formats of communication between businesses and customers have emerged. Customers now require round-the-clock assistance at their fingertips.
Businesses have begun adapting to this need by introducing chatbots with artificial intelligence (AI) capabilities to their contact centers. Chatbots are very specific to a business function so the choice of chatbot manufacturer will be critical to its success.
By 2024, Insider Intelligence predicts that consumer retail spend via chatbots worldwide will reach $142 billion — up from just $2.8 billion in 2019. No matter the industry or use case, the customer experience should be front and center when designing a tool to meet their needs.
Effective vs Ineffective Chatbot Use Cases
Chatbots often serve a specific function for an organization, and for them to work more effectively, the tool needs to be tailored for that function. Training generic machine learning to solve a chatbot problem will not even cover the edge cases of what people say and will likely miss out on a lot of context. Because of this, ensuring that the scope is defined and somewhat narrow is critical.
When evaluating what to put into a chatbot, a company should look at their existing contact load for the channel. If the chatbot is used for messaging services, then the type of things people ask on messaging should be reviewed. If the chatbot is actually a voicebot, then the contact reasons for voice should be reviewed.
To measure a chatbot’s performance, look for customer contacts that are short and easy to resolve, as indicated by low handle times and high first contact resolution (FCR). Also, you should gauge whether the function that is being automated is experiencing significant volume, as there is no sense automating something that no one uses.
However, companies need to be careful not to overcomplicate the use of chatbots. They can become basically useless if they’re used on complicated materials. Chatbots are good for providing slightly better self-service and improving speed of access. They can query account balances and peruse knowledge bases or FAQs. They simply help guide the end user through the process.
Deciding If, When, and How to Build Your Chatbot
First, companies need to differentiate between a chatbot, voicebot, or generic conversational AI. A chatbot can offload some agent activities and ultimately provide better response times for the simple interactions that are automated, or faster access to the agents who are dealing with fewer interactions. Functionally, a chatbot provides as much customer benefit as an interactive voice response system (IVR).
A strict chatbot would be built if interactions with customers are simple and short over a messaging interface. However, if chat volumes are low, it may not make economic sense to do this. A voicebot application may provide more value for simple interactions or data collection (Customer ID, skill, etc.) if there’s a greater voice interaction volume.
Conversational AI is based on machine learning. What differentiates it is the training. Therefore, if your company works with a vendor that is industry specific, it will have a good base to tailor that application to the business needs. Generic chatbots will require significantly more programming effort.
A good analogy for this is when speech recognition was introduced. It quickly became better than the human ear at identifying words and phonemes. What it lacked was training. If it didn’t know a word or couldn’t understand, it resulted in a huge backlash against speech recognition IVRs from customers. However, when properly trained and verticalized, speech recognition actually worked quite well. There were multiple companies that specialized in newspaper customer service, and their speech recognition worked really well because they had thought about all the different ways of saying things. Chatbots are similar. In a vertical market, they will be better able to interact with a customer than a generic chatbot or base machine learning engine.
Chatbots Boosting the Customer Experience
Chatbots benefit the business by helping offload customer interaction volumes from agents, but they must be built properly to maintain good customer satisfaction rates. Chatbots can automatically collect customer validation information, which is useful and removes that task from the agent – ultimately freeing up agents to work on other tasks but also keeping that information handy if an agent needs to intervene. Additionally, chatbots will continue to evolve, learn, and grow. With proper training, chatbots will only get better when used, evaluated, and further trained.
Customer experience chatbots work differently on the front end, but generally simplify any self-service function. Rather than navigating through a set of forms, users are prompted for each new data set. This also helps with anything that has a tree associated with it.
Secondly, users might have an easier time inputting information about themselves to get routed properly or to be pre-validated prior to reaching an agent, rather than trying to use a conversational tool or inputting information over the phone. This ultimately saves people time, and it can improve first contact resolution rates – boosting customer satisfaction.
So, as the market for chatbots continues to grow, customers across industry verticals should expect to use chatbot functions to help solve their needs more frequently.