AI to enhance customer experience (Part 1)

 AI is expected to help improve digital customer experience during the pandemic.

Virtual waiting rooms and texting chatbots

In virtual waiting rooms, Mobile chatbots connect with patients in a manner that help them with digitized forms, remote control capability for both telehealth and in-person visits. The initiative is part of the intention to provide treatment to patients in response to the pandemic. Healthcare companies need to rapidly evolve and make sure that all patients can see their physicians through secure, private and accessible networks, said Jeff Johnson, Vice President of Digital Business at Banner Health.

The mobile chatbot assistant has replaced the traditional pre-visit process of staying in waiting rooms, filling out forms, etc. Chatbots can now interact with patients via their mobile devices and desktop monitor to substitute tasks that were previously done by humans. Digital triaging is another application of AI in healthcare. While people engage with the symptom checker, AI will present them with the potential causes of the symptoms and how they can better be treated, whether it is at one of the hospital emergency locations, a primary care practitioner in their medical network, emergency departments, or by self-management.

Chatbots are being applied in hospitals to aid with the overload situation (Photo: Take)

Analyzing online actions to enhance service

The digital transformation has pushed businesses to transform their customer loyalty through e-commerce strategy. Retailers have begun using AI-based tools to understand their customers through online behaviors. But it was the pandemic that drives the customers to online shopping, urging vendors to try even harder to examine how their customers interact with the website.

The adoption of AI technologies has been integrated into every step of the customer journey, including all the details, actions on the user interface. 

Though the idea seems promising in the retail industry, it is often perceived doubtful by vendors since there is little real-life application of these technologies, especially AI and deep learning that bring measurable outcomes. However, more and more retailers are now willing to take risks with the hope of transforming their business or, better yet, reverse the impacts of the pandemic on their operation in general.  

Source: CIO ASEAN

About us: TMA Solutions was established in 1997 to provide quality software outsourcing services to leading companies worldwide. We are one of the largest software outsourcing companies in Vietnam with 2,500 engineers.

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5 Steps To Make Chatbots More Intelligent With Contextual Intelligence

Chatbots need to have contextual awareness if they have to sufficiently resolve a query. Conversational UX relies on effective contextual intelligence to handle millions of queries over periods to create more meaningful relationships with customers. 

Designing a Contextual Chatbot

Embedding contextual analysis is an important first step from the beginning. Besides, designing a contextual chatbot requires a strategic plan about key characteristics and use-cases for the technology. 

Planning can be optimized by analyzing existing features & area and mapping out future requirements for specific use cases. Through this process, various technology integrations can be put in place to ensure that there is congruence. 

Added to that, a contextual chatbot requires an integrated approach, massive data lakes and the right analytics methodology to design and development. 

Training With the Right Data Sets

To make chatbots more contextually intelligent, you need to provide the right data sets. It can source the true meaning behind critical keywords to strengthen its neural network. This makes the context richer, especially in the case of customer-oriented chatbots.

The probability model needs the right type of data to adequately perform significant functions. The chatbot needs to understand real conversations to be able to derive real responses to future queries. A high-quality intent classification model needs to be designed keeping focused raw data at the center of the process.

Integrating the Right Technologies

Whether that’s open-source technologies or vendor-driven systems, you need to find the right integrated technologies. The contextual chatbot needs complex technology such as Tensorflow or Dialogflow to handle complex queries.

Chatbots must be able to store and retrieve important information related to the context being analyzed. Moreover, you can even timecode the interaction to extract information using for analysis. For example, you can understand the type of action that is being conveyed in a query or a sentence. Then it conveys the right response based on this action-oriented model. 

Applying NLP and Deep Learning

Traditional chatbots rely on a retrieval-based model that works within specified parameters. But the contextual chatbots require a more powerful NLP network and greater access to deep learning resources so that the system can learn key phrases to implement to create new chains of responses.

When chatbots are having thousands of simultaneous conversations, they automatically iterate on the best responses by using NLP. NLP helps to understand new information as it enters the systems and you can test out various responses

Sophisticated data mining tools are also required to ensure that there is a deeper context for every dialogue had. Through greater resource allocation and complex analytical models, the incoming data can be parsed and categorized effectively into segments that are run independently through the network. After that, nodes can act as connectors to ensure that there is a contextual response given to each query.

Differentiating between Sessions Context vs User Context

The chatbot needs to be complex enough to capture bigdata about the conversations as well as the customer’s profile for session context, which helps it to be more contextually intelligent and relevant for customers. 

When moving on from subject to subject, chatbots are contextually aware and create the most suitable responses on the context detected. As a result, this leads to the retention of the customer while engaging them in a more meaningful conversation.

In conclusion, to make chatbots become more intelligent, you need to analyze large data sets through a more complex model in real-time to understand the context. Chatbots also need constant training to enhance the ability to respond appropriately because each industry has unique requirements.

Source: DZone

About us: TMA Solutions was established in 1997 to provide quality software outsourcing services to leading companies worldwide. We are one of the largest software outsourcing companies in Vietnam with 2,500 engineers. Visit us at https://www.tmasolutions.com/

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