Common Mistakes To Avoid While Developing Chatbots
3 Dec 2021
5 min read
A generic chatbot design may fail a chatbot at times of personalizing with users of a particular brand or a company. This is because such chatbots aren’t interacting with users in a way that makes them feel they are given a priority. And such absence of much-needed personalization will: create a poor customer experience. lead to no fulfilment of in-depth or domain-specific requirements of a company since generic chatbots lack data that can handle customer dealing. Thus, instead of keeping the design of your chatbot generic, you must get involved in such a chatbot development process which can blur the line between a human being and a chatbot not only handling customers of different sectors well but also understanding what they are talking about. All this will improve customer experience and it’s not bad for you from a business perspective!!
Now, your chatbot will end up doing something impractical as it won’t understand the user’s problem, nor communicate with relevant responses.
Also, if your company or you have adapted any marketing strategy and implemented it via chatbot, it won’t reap fruits in terms of revenue or sales growth as a chatbot isn’t willing to pay attention to metrics which matter the most in the business world.
So, from now, plan first why are you taking interest in deploying a chatbot for your business requirements and then launch it only after you find it capable enough to adapt to changing requirements of customers of variable preferences.
Customer service in which chatbots were able to replace 36 % of US customer service staff (according to business insider). Marketing automation in which chatbots have increased e-commerce sales. B2E i.e. Business-to-Employee in which chatbots have successfully automated certain HR responsibilities. Thus, instead of making the selection of wrong use cases while developing your chatbots, you must select anyone mentioned in the above points for better sales and boost up the satisfaction level of customers interested in the services your business is offering to them.
Keep the tone of your chatbot, when it is conversing, simple and less-complicated like using short and simple sentences instead of long ones. Aim towards including visual elements while designing the personality of a chatbot. These elements include GIFs, emojis, or video reels of shorter duration. Train your chatbots in a way that they can reach the core aspect of the user’s thinking as soon as possible. With this, they won’t be wasting time beating around the bushes. Rather than selecting chatbots that close the conversations with one-word answers, use voice chatbots that can mimic humans and are active towards welcoming and closing messages. Such communication develops a chatbot’s tendency to sound more human while interacting with an audience of variable preferences and problem statements.
To avoid the consequences of those unskilled chatbots, what businesses can do is release the beta version just like Amazon and Google. This will let those businesses know not only the common mistakes in chatbot design but also give them a picture of users’ opinions regarding the content they have accessed through those chatbots. And if possible, all such irregularities found in the beta version will be identified and corrected thereby managing the expectations of users in a time-saving and effortless manner.
Chatbot Response Volume – tells the number of responses your chatbot can produce for a certain period. Goal Completion Rate – specifies what the success rate of your chatbot is while accomplishing a set of goals. Retention Rate – signifies the number of users retained by your chatbot with its frequency of solving queries. Non-Response Rate – notifies how many times your chatbot (like button-based or AI-based) failed while responding to its target audience. With all the mentioned-above KPIs, it becomes much flexible to measure chatbot effectiveness at times a particular set of customers have some issues which need to be answered with appropriate and executable solutions. Or, identifying the customers who’re still not satisfied with the offerings your businesses propose in real-time.
Negative customer experience since they fail to personalize with customers on grounds of emotions. Less analytical capability and data security because of which they are likely rejected by businesses. Low-quality relevant responses that waste the time of businesses and customers.
Love to discover more, learn and create things.See other articles by Robin