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How Sentiment Analysis Can Transform Chatbots for Better Customer Experience

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Chatbot Customer Experience

Here, we will take a look at sentiment analysis and how its development is revolutionizing customer experience.

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The boom of big data has swept everything in its path, and as AI keeps gaining traction in customer service, virtual assistants, like chatbots, are increasingly becoming an inseparable part of customer experience. And with over 40% of large organizations deciding to deploy one or more AI-based chatbot(s) for their operations, businesses are seeing improved workflow and reduced costs.

Typically utilized for support purposes, chatbots are hugely favored by customers as they provide instantaneous information on services and products, improving customer experience in the process.

The adoption of chatbots hasn’t been slow too. As per studies, 67% of consumers worldwide interact with chatbots for user support, and it has been predicted that 85% of customer interaction will be handled by virtual agents like chatbots by 2020.

The employment and heavy use of chatbots across industries has led to them becoming more advanced and sophisticated and having developed new artificial intelligence features that enhance the customer experience. One feature among them is sentiment analysis.

Also known as conversation mining, it allows chatbots to ascertain the emotion of a customer’s message and help businesses determine if customer conversations are going well with their bots or not.

Revolutionizing the way organizations conduct service operations, sentimental analysis is highly revered by companies as 72% of them believe that it enhances customer experience, while 68% agree that it lowers overall costs.

As sentiment analysis catches upon quickly, it is increasingly encouraging user adoption of enterprise chatbots.

Here, we will take a look at sentiment analysis and how its development is revolutionizing customer experience. However, before that, we’ll first deep-dive into sentiment analysis and what it is.

Sentiment Analysis

It’s been estimated that unstructured data amounts to 80% of the global total. New data is generated every day in the form of chats, social media conversations, emails, customer support, articles, surveys, and documents. However, it’s very tough to analyze and sort through this data. This is where sentiment analysis comes in, helping organizations make sense of this unorganized data by tagging it.

Being a subcategory of machine language and natural language processing (NLP), sentiment analysis mines opinions, thoughts, or sentiments from audio and textual data. Appended as an additional veneer on top of a chatbot’s natural language understanding (NLU) engine, it helps a chatbot ‘understand’ the mood of the customer by scrutinizing sentence structuring and verbal cues.

Sentiment analysis is being predominantly employed in customer support and marketing to parse user data from social media, feedback, surveys, and reviews to help businesses know how well their products/services are being received. When used in the context of chatbots, sentiment analysis is aimed at developing a bot’s emotional intelligence.

Let’s see how sentiment analysis works in chatbots:

  • The type of sentiment is identified, and the emotion behind it is gauged to see if the mood of the dialogue is positive, negative, neutral, or objective. It detects emotions like frustration, anger, sadness, positivity, happiness, and other emotions.
  • ML and NLP work in harmony to calculate the magnitude of the emotions and provide a numerical score to the core emotions.
  • Post this, sentiment analysis provides an output that helps chatbots drive the conversation in the right direction. For example, in a dialogue with a positive score, the chatbot can use the opportunity to recommend a complementary product. And in dialogues with a high negative score, the chatbot can transfer the complaint call to a real support agent.

This is what sentiment analysis is ultimately trying to achieve. Apart from learning that people need help with something, a chatbot can also understand their mood and accordingly deliver the best customer experience it can by using this extra layer of information.

Sentiment Analysis + Chatbot

“Healthcare and banking providers using bots can expect average time savings of just over 4 minutes per inquiry, equating to average cost savings in the range of $0.50-$0.70 per interaction. As Artificial Intelligence advances, reducing reliance on human representatives undoubtedly spells job losses.”

Industries worldwide are waking up and reacting to the use and employment of chatbots. According to Business Insider, 80% of businesses will use chatbots by 2020. Another study by Juniper Research reveals that chatbots will be majorly responsible for cost savings by over $8 billion per annum by 2022, up from $20 million in 2017.

From the banking, insurance, and hospitality sectors to healthcare, travel, and eCommerce, customer-facing industries are increasingly using AI-powered chatbots integrated with sentiment analysis.

One example is the cosmetics brand, CoverGirl. By deploying an influencer chatbot augmented by sentiment analysis, they were able to see improved mCommerce performance.

CoverGirl

The results included 91% of the conversations driven by chatbot earning positive sentiments and 17 messages being exchanged per conversation on average, reflecting a high rate of engagement. Additionally, 48% of them led to coupon delivery as the click-through-rate of them stood at an impressive 51%.

CoverGirl’s example shows how efficient sentiment-analysis-powered chatbots can be in recognizing customer intent, driving conversations, relaying useful information, and providing a solid customer experience.

With sentiment analysis, chatbots can sense the conversation going well or otherwise and react to customer emotions accordingly, ultimately providing them with a delightful experience.

The reason sentimental analysis is so invaluable is because of how it can correctly perceive social conversations. Imagine how useful an algorithm could be that showed you how consumers saw your products and services, why they think the way they do, and what you can do to improve their experience.

To show you how this happens, let’s take a look at some ways sentimental analysis can transform chatbots for better user experience.

1. Customer Assistance

Organizations spend about $1.3 trillion USD on about 265 billion customer service calls each year. For a terrifyingly high volume of calls, the emotions of the customers making it are even more diverse.

This is where a chatbot can help support personnel immensely by identifying critical issues and keep them from worsening. These chatbots can change their responses so that they sympathize with the emotions of the customer.

In this way, sentiment analysis can aid in recognizing troublesome situations beforehand and help your support team take appropriate action right away.

2. Seamless Autotransfer to Live Agents

It’s true that chatbots are the next best thing since sliced bread. However, as equipped as they are, they are still not intelligent. As such, it is crucial to understand the importance of timely transfer of problems to live agents in customer service.

With sentiment analysis, a chatbot can sense the tone and mood of the customer, and if they are aggrieved beyond the persuasive capabilities of the chatbot, route them to a human agent. Since chatbots can already automate up to 80% of customer inquiries, support agents can always be at hand to focus on disgruntled customers.

3. Instant Support Personnel Performance Feedback

While sentiment analysis is generally used to enhance customer experience, it would actually be more prudent if the first step organizations took was to use it on their employees.

By implementing automated scoring to provide real-time feedback of customer support after each interaction, you can make use of the newly gained knowledge and train your support team on how to handle similar situations in the future.

This feature can also be used during the induction phase of new hires to train them for a specific area of customer support.

4. Upselling and Onboarding New Users

As demonstrated by CoverGirl’s bot, chatbots can aid businesses in recommending and upselling their products to existing customers. It can also result in the enhancement of customer acquisition metrics by preserving the interest and curiosity of a new customer in its services by analyzing their emotions.

As seen above in the article, sentiment analysis can impact the emotions of a customer by helping chatbots recognize the user’s sentiments and react to them accordingly, ultimately influencing their decision-making process.

Hence, by investing in and employing this technology on an enterprise-wide scale, organizations can enhance customer experience, leaving human agents free to work on really challenging issues and improving productivity as a result.

5. Gauge Customer Satisfaction

Apart from the segmentation of audiences, sentiment analysis can also help businesses become aware of how their brand, products, and services are received by customers. This gives chatbots immensely deep insight into how users feel and which frame of mind they are in before interacting with them.

The future of sentiment analysis

As with other forms of AI, sentiment analysis is only going to increase in intelligence to the point when it can be done in real-time. Further advancement could one day result in capabilities such as automated and intelligent responses being developed with the day where sentiment analysis helping support teams to provide better and quicker resolutions to customers not being too far away.

The information provided by sentiment analysis tools will help businesses paint a better picture of their customers’ needs. This knowledge can then be used by them to upsell their products.

As an increasing number of organizations make the switch to these tools, sentiment analysis could end up having a tremendous impact on customer satisfaction and improve customer retention by an unprecedented margin in the times to come.



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