Edsger. W. Dijkstra, a pioneer in many research areas of computer science, has said: “The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.” The truism of his statement is unfolding in front of us now amidst the exasperating modernization that is happening in the world of computer science. As this advancement has helped businesses in inventing and reinventing most of their operations, e-commerce is no exception. A partial, if not total, ouster of live chat because of chatbots is a likely possibility today. An analytical understanding of live chat vs chatbots will help in measuring the actual feasibility of that possibility. However, before we enter into the terrain of live chat vs chatbots, it’s important to note that during the teething days of chatbots, their detractors complained that chatbots were too mechanical. They were too robotic and lack the human intuition and warmth that is key to conversational commerce. Many also pointed out that the algorithmic nature of chatbots made them completely inept at understanding words that are not programmed in them as keywords. Moreover, they also pointed out that the lack of natural intelligence, with which a human is born, doesn’t allow chatbots do grasp the meaning of a sentence by strictly staying within the context. For example, chatbots will not understand if the term “St. Louis” is the name of the person, city, or school. On the other hand, as live chat actually has a human presence on the other side of the screen, it’s devoid of contextual errors and emotional coldness of the chatbots. Without a doubt, there was truth to the views of chatbots’ detractors. But that was a few years ago, and times have changed.. Technical advancements, such as natural language processing (NLP)– a field of artificial intelligence, computational linguistics, and computer science concerned with the interactions between computers and human (natural) languages–have been successful in bridging the gap between human language and chatbots. NLP makes chatbots think and learn. They work with high end rules of synonyms, ontologies, and spell checker. As they now don’t get distracted by the misspelled and new words due to the machine learning process, they work by searching for the meaning of the sentences, and not just seeking for specific keywords–just like human reasoning. This has given a clear upper hand to chatbots in the live chat vs chatbots debate. As far as operational efficiency and accuracy of the chats, which are absolutely necessary for indelible customer experience, chatbots have more advantages. The following image explains why that is the case. We can see the immediate benefits of chat bots over live chat with an actual customer service agent in the above image. As chatbots can have unlimited concurrent conversations (humans can only realistically focus properly on a few at the same time) and provide coverage 24/7, the cost per conversation becomes significantly less. Plus, with a live agent, it’s almost impossible for him/her to provide a consistent conversation experience and professional manner across multiple different customers. Chatbots are structured in a way to ensure that the conversation will always take the same format and tone. It’s then totally comprehensible why research has shown that people engage and reply “Thank you” to an order confirmation delivered via a chat bot message, but tend to ignore an email. The need, thus, is to go beyond the live chat vs chatbots debates, as the latter has far more capabilities and usage possibilities.
Everyone knows that chatbots are already used for ordering purposes. By collaborating with Slack, Taco Bell allows users to order food in the instant–messaging application used by companies for internal communication. It looks something like this- Though they have undoubtedly accelerated the food ordering process, chatbots can go a step further. Through tracking a person’s preferences, chatbots may assist customers with complex purchasing decisions. When a customer is in discovery mode, chatbots can rummage through product catalogs and send personalization recommendations. Even after personalized suggestions, if that customer is in no mood to buy right now, you don’t hit a dead end. This is because interacting with chatbots is not a one time experience. In their next interaction with that same customer, they will remember former conversation and preferences.
Mobile has emerged as the preferred medium for buyers to do product research and complete purchases. If they find that the sales path is not mobile-optimized, it adds up to their frustration. Chatbots can negate this frustration, as they eliminate the necessity to contact a company for additional support. They let customers begin and finalize their transactions within a chat interface or a single messaging app. Through its “Easy Order” pizza-ordering bot for Facebook Messenger, Domino’s Pizza has established a mobile link with its customers. All you have to do is pair your Facebook account with your Domino’s account and type “PIZZA” to the Messenger bot. Depending on your ordering history, Easy Order automatically selects the pizza. You can manually configure your preferred pizza within your account interface as well. The order will be placed with the tap of a button within the mobile app or through an Apple Watch. Here’s how it looks.
Clearly, the prowess of chatbots goes beyond answering questions. They increase sales by bringing tailor-made communication to a new visitor, and become a first point of contact with him/her. And all this can be realized with overall efficiency. Note that by assisting chatbots, companies can automate their support up to 90%. The saved time and money can be put into solving much complex problems and innovative drives. Nordstrom, for example, recently cut customer service jobs due in part to automation and operational changes. Even after believing that there is plenty of room for improvement, that’s what makes them a “perfect tool” as they spur user engagement, sales, marketing, customer support, and retention.
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