Yet the scale and speed at which digital transformation is required after this pandemic is unprecedented and will leave permanent change across all industries. For the beginning part of this article, you would have come across machine learning several times, and you might be wondering what exactly machine learning is and why it’s so deeply rooted in AI chatbots. The technology behind standard chatbots does not support interpretation of user intent, preventing the suggestion of personalized solutions, as smart bots do.

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This in itself is an asset, but in exceptional circumstances like Covid-19, the ability for digitalized companies to not stop or slow production has been a competitive advantage. Consequently, companies rely on the support and collaboration with external parties to assist in starting new projects. We have seen the high number of companies adopting digital-first business strategies and new business models to stay ahead of the market. Digital transformation is all about applying digital technology to reinvent and improve how companies work, engage and do business. It also affects how enterprises use their talent, processes and culture to make the most of these technologies to meet their goals. Businesses are seeking to become lights-out businesses that can provide constant support to their customers and allow agents to focus less on administrative tasks and more on top-tier supports.

How a smart chatbot works

Despite existing for several years, telemedicine did not have full support from healthcare patients or providers. However, to prevent patients with Covid-19 symptoms from overwhelming hospitals and clinics, the need for virtual health advice is accelerating this option. There is growing concern over intelligent created machinelearning chatbot the possibility of future spates of Covid-19 or any other crisis of similar magnitude, and industry leaders want to be prepared the next time around and take these steps when there is a lull in the storm. Businesses are making their moves to mitigate damage and increase assurances for customers.

After the successful training, the model is able to predict the tags that are related to the user’s query. Together with Artificial Intelligence and Machine Learning chatbots can interact with humans like how humans interact with each other. The implementation of chatbots is helpful in many cases from customer support to personal assistants. So building your own chatbot for your personal uses or for business makes sense. In this article, we are going to build a simple but efficient AI Chatbot using Python, NLTK, TensorFlow, and Neural networks.

Model Training

People want an experience over a variety of channels that are seamlessly connected, so that if they leave one channel they can pick up where they left and continue their journey on another channel. Businesses must cope with a wide range of customer interaction touchpoints whilst focusing on the entire customer journey. Companies with the strongest omnichannel experiences see the benefits, as they retain 89% of their customers on average, compared to 33% retention for companies with weak omnichannel customer experience. There is a correlation between technological innovations, digital transformation and better customer experience. When we mention Spotify and Netflix, we need to remember how they were disruptors in their markets that boosted customer experience. Algorithms that detected music and movie preferences were of no use with CDs and DVDs.

intelligent created machinelearning chatbot

We have mentioned the importance of big data in the post-Covid environment. Data and advanced analytics are going to play a key role in organizational cultures and in decision making. Business that deal with vast amounts of customer data can leverage it to provide real-time business intelligence. We will see more businesses investing in Business Intelligence software. Consumers know that their data can be used to provide personalized experiences and expect tailored responses from brands to ensure that customer journeys are of the best quality. Canned or negative interactions can put their loyalty to a brand on the line.

Digital Transformation strategy tips

For example, the words ‘running’, ‘run’ , ‘ran’ , ‘runs’ all have the same lemma, which is ‘run’. Lemmatization saves a lot of time and unnecessary errors while we process these words for our machine learning model. An intent-based chatbot looks for key terms and ‘entities’ in the messages it receives. If the user were to ask what the weather is like in a location on a certain day, the intent is to know the weather.

Chatbot interactions are categorized to be structured and unstructured conversations. The structured interactions include menus, forms, options to lead the chat forward, and a logical flow. On the other hand, the unstructured interactions follow freestyle plain text. This unstructured type is more suited to informal conversations with friends, families, colleagues, and other acquaintances. Though it sounds very obvious and basic, this is a step that tends to get overlooked frequently.

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This does more than simply make a customer feel special, it encourages cross-selling. 49% of customers bought items they did not intend to purchase due to a personalized recommendation from a brand. Digital transformation and technologies allow businesses to understand consumer behavior and preferences and can influence them by creating personalized, predictive and dynamic experiences that will affect their products and channels. Being able to define and measure an initiative is easier said than done.

intelligent created machinelearning chatbot