Customer experience is now at the center of modern business strategies. Such a customer-centric approach is growing in our times due to massive competition and options available to consumers. But how do we get an edge to offer an exceptional customer experience? Technologies allow us to hit that sweet spot and deliver impeccable customer service.

Among these technologies, Artificial Intelligence is a frontrunner choice for any customer-oriented business. AI enables consumers to get a hyper-personalized experience that is quick and efficient. Particularly, Conversational AI is a branch of AI that can help us transform how we deal with our customers.

As the name suggests, Conversational AI is an area of AI where AI is used to communicate with humans as a medium. But do you know how Conversational AI works? Excellent data quality is a key to the successful implementation of Conversational AI. This article will give you an idea of the ins & outs of Conversational AI along with the vitality of data quality.

When you consider it from a customer’s perspective, creating a better experience is all about simplification. Conversational AI can help simplify the entire consumer experience from product understanding to purchase and from post-sales services to customer claims. Natural language processing (NLP) is the key behind Conversational AI. It allows brands to understand customer inquiries along with their questions, intent, and sentiment.

Conversational AI doesn’t just understand the customers but it can also help them answer or resolve queries on time. In order to understand your customers and their needs, Machine Learning algorithms learn from massive data sets. These Machine Learning models are often trained upon datasets that are collected over time across different industries.

To be accurate, data is the building block behind Conversation AI. Through the help of available data and patterns, AI defines the understanding of a customer's message and looks for the desired information. Here are several common scenarios where you can leverage conversational AI for your needs:As shown in the generic messages listed above, any inquiry consists of several keywords and sentence fragments. These indicators can allow Conversational AI to look for similar indicators in the database. Upon finding relevant matches, Conversational AI can identify the intent based on the best match within the data.

Once understood the intent behind those messages, Conversational AI can define the course of response with trained data sets. Therefore, data acts as a key to training Machine Learning models and forms a link between inputs and required actions. As you can see, data is a vital element of any Conversational AI solution.

While the quantity and availability of data is a necessity to train a Machine Learning model, it is equally important to have accurate, precise, and useful data. As you can predict by now, the quality of the data is a direct parameter that determines the effectiveness of any Machine Learning solution.

Data that is inaccurately collected or extracted would make the Conversational AI inefficient. As such poorly collected data can actually defeat the purpose of quick, efficient, and automated customer support. Therefore any solution that we build with Machine Learning has to use a verified and accurate set of data in order to be useful and improvised over time.

Effects of poor datasets can be catastrophic when implemented on a customer front. However, on the other hand, quality data can lead to numerous performance advantages:The high-level model design is shown below and each of the blocks will be explained in the later sections. Throughout the sections, question and user expression refer to the same. Likewise, intent and category refer to the same.[vt_useful_link link_title="Use tags, fields, and labels the right way as per your specific needs. These can be specific for your business or industry and should address most of the input queries. Properly tagging and labeling information can assure accurate use of the data.

"]On top of the collection of data, it is important to follow a well defined data preparation and feature engineering process in order for it to be useful. Conversational AI carries a great potential to transform the customer experience, and we might get to experience it commonly very soon. However, it is essential to understand that such a solution can only be beneficial to you if it is built upon the right dataset.

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Also read how to collect data for your chatbot here.