Artificial intelligence has revolutionized many industries. The use cases of AI in retail sector is quite compelling. AI in retail sector has become a disruptive force with it being deemed as “weapon to achieve profitable growth”. AI empowers both backed and fronted of e-commerce business. It can help retail business to estimate demand forecasts to drive higher margins, can increase the efficiency of supply chain, give valuable insights for making smart business decisions. On the customer side AI can personalize customer’s shopping experience by providing personal recommendations, 24×7 chat bots etc. This article main focus is on how artificial intelligence enhances customer experience. According to market survey conducted by fortune business insights , the global market size of artificial intelligence in retail is expected to grow from 3.1 billion USD in 2019 to 23.2 billion USD by 2027, thus registering a compound annual growth rate of a whooping 29.6%. The market adoption of artificial intelligence in retail is expected to expedite due to ongoing COVID-19 pandemic. Well established retailers are trying every possible way to improve engagement of their customer increase brand royalty and customer retention. However certain factors are derailing their efforts. Factors such as lack of infrastructure, legacy code, or higher cost of implementation are limiting AI adoption in retail.
As per a survey conducted by Capgemini, AI in retail could help save $340 billion annually by the year 2022. By automating several processes and operations and making them more efficient, AI in retail isn’t just a stepping stone – using it right could help you ‘leapfrog’ your competitors. AI has moved past being the buzzword for retailers to a tool for revolutionizing retail sector.
According to market survey conducted by fortune business insights , the global market size of artificial intelligence in retail is expected to grow from 3.1 billion USD in 2019 to 23.2 billion USD by 2027, thus registering a compound annual growth rate of a whooping 29.6%.
Retail organisations who have adopted artificial intelligence are already seeing a whooping 16 percent to 19 percent in customer engagement, business intelligentsia, competitiveness and innovation and ultimately profit margins. This is reported by a study conducted by Microsoft Asia and IDC Asia/Pacific released this week at the NRF show in New York. Microsoft’s experience center located in Singapore has AI-driven technology is driving in-store technology driven by Microsoft-accredited partners Hong Kong-based Tofugear and Singaporean startup Trakomatic. Another research by Future Ready Business conducted for Asia-Pacific’s Retail Sector, predicted a 37 to 44 percent increase in key business matrix for retail organizations who have started using artificial intelligence to their advantage.
However, many retail businesses still that “Artificial Intelligence” is just a “large hype” with no actual contributions to increase in profit margins. About 29 per cent of Asian Retailers do not believe in AI solutions for improving their businesses. Furthermore, 67 per cent of Asian retailers in the survey have not started implementing any AI solutions or are waiting for the technology “to mature” before incorporating it into their business strategy.
Many businesses are looking to democratize use of artificial intelligence and its benefits in retail sector by bringing it small medium sized business instead of just limiting its usage to big multi-national corporation.
How can Artificial Intelligence Improve Retail Sector?
Personalised online shopping
Artificial Intelligence can make shopping very personalized. In fact, it is the most sought after thing in the online retail industry. These can include personalised search results, personalised offers and personalised product recommendations. Another innovation in personalization of shopping experience is – personalized pages. For every user the landing page of a retail website can be served differently and according to their past view history, clicks, liked items, items in their carts and purchase history. Thus, in essence giving customers what they would like to buy on the first page itself. By tracking users past activities in the website, the artificial intelligence solutions can predict the taste of user: their likes and dislikes. All the data points generated from like purchase history, view history, clicks, products added to the wish list and cart etc. are given as data points to suitable algorithm for generating product recommendations which are essentially the products the users are most likely to buy.
24×7 FAQ chat bots
Intelligent chat bots can answer many queries of customer’s this reduces customer’s waiting time to reach out to a customer care executive. Also these chat bots are available throughout the year, and at all hours of the day. Thus implementation of chat bots for online retailers makes answering queries convenient for both – customer care executives and customers. For customer care executives it can give them more time to deal with more complex queries. For customer, it can provide instant answers to smaller queries. Organizations report a reduction of up to 70 percent in call, chat and/or email inquiries after implementing a virtual assistant. They also report increased customer satisfaction and a 33 percent saving per voice engagement. Chat bots are definitely changing the way the companies interact with their customers. Integrating such FAQ chat bots to one’s website goes a long way in improving customer engagement and brand loyalty. Apart from providing faq related to orders, they can also provide personal recommendations according to customer’s taste. In fact, H&M’s chatbot on the messenger app Kik, allows users to make a choice from recommendations, filters and style preferences, set the fashion brand ahead of its competition way back in 2016.
This solution can enhance customer experience in offline stores. Imagine that you have an image of a particular product like a dress, you can send the image via Bluetooth/ Wi-Fi and the kiosk pops up with the exact dress/similar dress present in the store. Also, it will guide you to the exact location of the dress in the store. This will lead to ultimate shopping experience leading customer’s to spend more in offline stores.
Virtual trial mirror
Stand in front of the mirror and you would be surprised to see the entire catalogue present in front of the big mirror. You can actually see a different product on yourself, change the background, the colour of the dress, take a selfie, match with the complementary products and see the entire look without actually wearing the dress. Also, you could change the background and see how will you look like in different lighting conditions.
Walk Out Shopping Experience
Just pickup the stuff you like, add it to cart and walk out without going to billing desk. This erases the need of long queues at billing counter. This kind of technology has been implemented by amazon by amazon in select stores as “Amazon Go”. More big retailers like walmart are looking forward to using AI to augment their offline store customer experience. Their Intelligent Retail Lab, is an inventory tracking solution which makes sure most in demand in that location are always in stock. This helps in reducing the number of customer returning disappointed and empty handed on not finding their desired products.
Flexibility Of Price Adjustments
In a very competitive and price sensitive market akin to retail, artificial intelligence can provide a dynamic pricing strategy which uses valuable information from customer’s. These applications help enterprises to analyze the efficacies of multiple pricing models before arriving at the optimal price for their products. Retailers can also adjust prices by seasonal trends, competitive products and consumer demand. If we consider the long-term revenue it can generate, it is surely an investment worth making.
Artificial Intelligence has the power to transform the way customer’s shop in both online and offline store. According Microsoft Asia’s regional lead for retail and consumer goods, Raj Raguneethan, “Every … business should become a technology company to really compete in the modern world”. The companies who would like to survive and thrive in this cloud landscape powered by data must adopt AI solutions to cater to their customer experience as well as ensure smooth functioning of back end offices.
Need help with Emotion recognition and detection software?
If you want to utilize Artificial intelligence for your retail business need help, advice, or developers, feel free to contact us at email@example.com or on our LinkedIn page or visit our company website, www.quantiantech.com
References https://www.fortunebusinessinsights.com/artificial-intelligence-ai-in-retail-market-101968  https://insideretail.asia/2020/01/15/ignoring-ai-in-retail-will-cost-market-share-and-sales-warns-expert/  https://www.forbes.com/sites/forbestechcouncil/2020/08/21/seven-ways-artificial-intelligence-is-disrupting-the-retail-industry/?sh=11d6b16a56ae  https://yourstory.com/2017/01/impact-ai-retail-industry
At the peak of COVID-19 pandemic, government launched a chatbot called MyGov Corona Helpdesk (for whatsapp) to provide constant updates and eradicate fake news about Novel Coronavirus. People could converse with the chatbot on platforms like Facebook’s messenger, Whatsapp and Telegram. About 17 million people used it within 10 days of its launch. This chatbot is an example of “rule based chatbot”. However this chatbot can only handle pre-defined inputs. Conversational bots are a level up from these rule based or FAQs chatbots which can recognize context in a conversation.
MyGov Corona Helpdesk Whatsapp chatbot
What are conversational bots and how do they differ from FAQ chatbots?
A massive improvement over rule based chatbots are conversational bots. If you think about how human converse, context matters. What the user said before, how, when and where should influence how the conversation goes. Conversational bots powered with AI can understand the context. Understanding the context also means that the bot are capable of answering new and unexpected inputs from user. These conversational bots consider the context of what has been said before, gracefully handle unexpected dialogue turns, drive the conversation when the user drifts from the regular conversation path and improve over time.
Apollo Hospitals launched an app with a conversational bot called Apollo247 which analyses dialogue with its user to tell whether they need to visit a hospital for COVID 19 related symptoms or not. The app has a bot which asks the user gender, his/her age, what ailments one is suffering from and advises whether to visit a hospital or not. However, it states that the bot’s analysis “should not be taken as a medical advice”. It can also tell whether one should get a scan done or not .
Apollo247 conversational bot
How conversational bots can help your business?
As per survey published by Statistia in 2019 , about 78% has leveraged conversational bots powered by AI in simple self-service scenarios. 77% enterprises have reported to be using bots to try and assist with a query before passing it onto a customer care personnel. Another 70% companies, reportedly use bots to retrieve information and offer recommendations and answer to queries quicker. According to Statistia, conversational AI bots are most used in customer service. The second most common area where conversational bots are used are Customer Relationship Management (CRM) .
Most common areas for conversational bot implementation in organizations worldwide
A user can be dissuaded from using a certain online service/product if they find the site hard to navigate, or cannot get answers to simple queries, or find it too hard to get basic services. These hurdles can be overcame by conversational chatbots which are fast, intuitive and convenient. AI chatbots offer a way to increase customer engagement by providing a personalized experience. They can retrieve high value content from customers.
Listed below are 10 key areas where business can take advantage of conversational AI bots:
Chatbots provided customers with a sense “immediate response”. They always want their queries “now” service within five minutes of making contact online. Conversational chatbots enables a similar kind of response and behavior akin to talking to customer care personnel.
Drive More Revenue
Intelligent chatbots acts as a guide for customers and take them on a buying journey. This makes sales conversion and revenue. Advanced chatbots can remember “context” and thus provide customers with preferences and provide advice, tips and help, while gently providing recommendations that results in upselling of products.
According to reports use of virtual assistants cut need for queries handled by human agent by 40%, and often deliver first call resolution (FCR) rates far in excess of live agents. Chatbots will reduce costs by handling more customers at a time.
Maximize Staff Skills
Conversational bots can be used to automate a portion of call, email, SMS etc, that would have required human involvement. This gives time to employees to engage in higher-value customer engagements.
Reach New Channels
Chatbots provide personalized customer experience and try to solve customer’s query before initiating a human involvement. These bots can be simultaneously deployed on various platforms like social media, calls, SMS etc. This reduces the overhead required to deploy a support to team on each new channel or network.
Conversational bots can increase brand loyalty as well as customer retention. This happens due to fast and frictionless answering of customer queries. Use of conversational AI reduces the cost overheads of the company as well as increases customer retention.
Customer want service 24/7 and 365 days. Delivering this kind of support by human agents seems impossible. However, conversational bots can be available all the time. The customers can get their queries solved using conversational bots anytime and even on holidays.
Customers tend to spend 60% more per purchase and also report an increased frequency of purchase. As customers start to favor online methods of communication, chatbots provide an opportunity to reignite the customer experience with increased engagement, personalized customer service and improved customer satisfaction.
Understand the Customer Better
Apart from providing customers with quick query solving, these bots can also provide meaningful insights into the company’s customers. They can be used to understand trends and better interpret customer sentiment, providing invaluable insight that informs product and service development.
Furthermore, this data can be accessed by for a single product or for multitude of products.
Conversational bots can be a key factor for customers choosing your company over your competitors. These bots can deliver frictionlesss user experience that derives higher customer brand loyalty and higher customer retention.
Use cases of Conversational Bots
Chatbots are being used by multiple industries to provide seamless experience to their customers. Some of these are covered here.
Banking, Insurance and Financial Services
These chatbots can guide customers to perform a variety of financial operations without making it feel like they have to fill too many forms. The information shared with these chatbots are completely safe. From checking an account, reporting lost cards or making payments, to renewing a policy or managing a refund, the customer can manage simple tasks autonomously. These chatbots can provide immediate support to a customers. These can also be used to train customer care personnel.
These chatbots can take information from humans and then accordingly recommend car for the customers according to their needs and wants. They intake various needs of customers such as the features they want to have, their budget etc. These feel lie human-like interaction and is bound to drive the conversion rate upwards. Apart from recommending cars these chatbots prompt to schedule a test drive at the nearest car dealer.
Retail & Ecommerce
Adding conversational bots to your existing retail channels increases customer engagement as they can answer clients queries and requests instantaneously. These also provide various updates on shipping details, discount etc. in a human-like fashion. They can also help customer navigate the website to pages where they can find the product they are looking for. The data collected by chatbots can further be analyses by the marketing team understand customer behavior and make strategies to increase customer engagement and retention.
Here conversational bots are used to provide self-help FAQ and knowledge forums to find a customer’s answer to any technical issues they might be facing. Customers can also use these bots to find best deals for them, and even change their personal information such as address just by chatting to bots! Further, chatbots can come up with personalized plans for customers. At the same time, chatbots can assist potential customers in choosing the right product for their needs. This will also allow customer care employees to only address the complex issues rather than getting involved in trivial issues.
Smart Homes & IOT devcies
These conversational bots enable customers to access various functionality of smart homes via every day human speech. They can also be used as guide in smart cars to tell directions, set desired temperatures etc.
Chatbots in healthcare industry can avoid unnecessary visits to hospital. This technology seems very useful especial during pandemic when there is massive stress on hospitals and doctors alike. Further, they can also be used to schedule appointments and scans for the patients.
When bots were launched first time into the market, their were predictions that these bots will replace customer care employee requirements totally. However, this did not happen as the current state of bots have its own limitations. One of the biggest limitations of conversational bots are they fail to understand complex queries. Secondly, bots can only handle the situations fr which they have been trained. On failing to recognize an incoming message they generate messages like – “Sorry I could not understand your question” which might lead to the customer becoming frustrated. If the bot is supposed to handle high volume queries, the cost of building and deploying such a bot might be higher. The chatbot may make the conversation feel repetitive which might again frustrate the customers.
Developers understand the above limitation and thus are careful while developing bots. There are a lot of tools available to build bots for commercial purposes such as Keras, TensorFlow, and PyTorch. Apart from this various frameworks like Google’s diagflow and RASA can also be used to develop effective conversational bots which can handle context. These chatbots can act as first point of contact and reduce the overhead of backend office by solving simple queries and requests of customers. A lot of companies have already integrated chatbots to their websites, apps or other channels to drive customer engagement higher. So are your ready to step up your customer experience with these conversational bots capable of recognizing contexts?
Need help with Conversational Bot?
If you want to build Conversational bot or integrate to your existing products and need help, advice, or developers, feel free to contact us at firstname.lastname@example.org or on our LinkedIn page or visit our company website, www.quantiantech.com
References https://gadgets.ndtv.com/apps/news/coronavirus-mygov-corona-helpdesk-chatbot-whatsapp-indian-government-total-users-haptik-2204458  https://yourstory.com/2020/03/apollo-hospitals-launches-24-7-ai-free-app-coronavirus  https://www.statista.com/statistics/966909/worldwide-conversational-bot-implementation/
Artificial Intelligence in Emotion Recognition
Emotions serve as a source of information to perceive how a person is reacting to a particular scenario. Recognizing emotions can help us take actions for getting desired outcomes. Humans use a variety of indicators such as facial expression, voice modularity, speech content, body language, and historical context to gauge the emotions of others.
Emotion recognition using AI is a relatively new field. It refers to identifying human emotions using technology. Generally, this technology works best if it uses multiple modalities to make predictions. To date, most work has been conducted on automating the recognition of facial expressions from video, spoken expressions from audio, written expressions from the text, and physiology as measured by wearable devices.
As per the reports by marketsandmarkets  published in February 2020 the global Emotion Detection and Recognition Market is expected to grow from USD 21.6 Billion to a staggering USD 56 Billion by 2024. Many technology companies like Amazon, Microsoft have already launched emotion detection tools for predicting emotions with varying accuracy. So, are you ready to utilize this upcoming technology to your business advantage?
This article will take a look at use cases of such technologies, an overview of how it is achieved, and concerns related to this technology.
Emotion Detection and Recognition Market
The ongoing pandemic has led to many day-to-day activities being carried out in online mode. These include online classes, hiring processes, work from home scenarios, etc. This online adoption has led to an increase in the demand for emotion detection and recognition software. The market size for software related to this technology is expected to grow at a Compound Annual Growth Rate (CAGR) of 21.0% till 2024. Factors such as the rising need for socially intelligent artificial agents, increasing demand for speech-based biometric systems to enable multifactor authentication, technological advancements across the globe, and growing need for high operational excellence are expected to work in favor of the market in the near future. Also, the pandemic situation has reinforced the need for such a technology.
The advancement in technologies such as Deep Learning and NLP (Natural Language Processing) have further accelerated the development and adoption of emotion recognition software. Deep Learning uses neural networks to classify images into several classes. For instance, neural networks can be applied on the face to detect whether their expression denotes sad, happy, shock, anger, etc.
Applications Of Emotion Detection and Recognition Technologies
FER (Facial Expression Recognition) software can be used in focus groups, beta-testing for product marketing, and other market research activities to find how the customers feel about certain products. Here, the participants have already consented to the use of FER software on them, thus having no legal ramifications. FER technologies have become quite infamous for using data by stealth. This application of FER does not involve any such malpractices.
Another novel experiment in marketing was back in 2015 by M&C Saathchi where advertisement changed based on their people’s facial expressions while passing an AI-Powered poster.
The ongoing coronavirus pandemic has led to the shifting of most in-person interviews to video call interviews. The emotion detection software can analyze expressions such as fear, shock, happiness, neutral, etc. However, this remains a controversial use case of Emotion recognition software as it has the following caveats –
• The AI model used for it might have a racial bias. For example, black men are usually classified as having an “angry” facial expression
• It is not legal to use such technologies in the EU and few other nations
• This usage will be subjected to further regulations.
Deepfakes are AI-generated fake videos from real videos.  It takes as input a video of a specific individual (’target’) and outputs another video with the target’s faces replaced with those of another individual (’source’). 2020 US election saw a surge in such videos, with politically motivated videos. A research was conducted by Computer Vision Foundation and in partnership with UC Berkley, DARPA and Google which used facial expression recognition to detect deep fakes.
Medical Research in Autism
People who have Autism often find it difficult to make appropriate facial expressions at right time. As far as  39 studies have concluded the same. Most autism-affected people usually remain expressionless or produce facial expressions that are difficult to interpret. Machine Learning can be applied for the early detection of Autism spectrum disorder (ASD), where people who are diagnosed with this disorder have long-term difficulties in evaluating facial expressions.
There are a number of Machine Learning projects and research that were conducted to help people on Autism Spectrum.  Stanford university’s Autism Glass project leveraged Haar Cascade for face detection in images. Google’s face worn computing system was then applied to these images to predict emotions. This project aimed at helping autism-affected people by suggesting them appropriate social cues. Another project used an app for screening subject’s facial expressions in a movie to identify how their expression compared with non-autistic people. The project utilized Tensorflow, PyTorch, and AWS (Amazon Web Services).
There are much more applications of emotion detection technologies that can help people suffering from autism.
Virtual Learning Environment
A number of studies have been conducted using emotion detection technologies to determine how well students understand and perceive what is being thought in an online class.
One of the research based on the same applies neural networks to classify emotions in six kinds of emotional categories . For this, they have used the Haar Cascades method to detect the face on the input image. Using face as the basis, they extract eyes and mouth through Sobel edge detection to obtain characteristic value. Neural networks are then used to classify facial expression in one of the six emotion classes.
How does Emotion Recognition Works?
Emotion Recognition using images
In most emotion recognition software, emotions are usually classified in one of these 7 classes – neutral, happy, sad, surprise, fear, disgust, anger. The first step to any facial expression classifier is to detect faces present in an image or video feed.
The next step is to input the detected faces into the emotion classification model. The classification models usually employ CNNs (Constitutional Neural Networks) to detect various classes of facial expression on the training dataset. Essentially, a CNN is able to apply various filters to generate a feature map of an image which can then be applied to ANNs (Artificial Neural Networks) or any other machine learning algorithm for further classification.
Detecting emotions in audio clips
In emotion recognition from audio, different prosody features can be used to capture emotion-specific properties of the speech signal . The features such as pitch, energy, speaking rate, word duration are applied to suitable machine learning models to detect possible emotion.
Another method to detect emotion in audio clips is using Mel-frequency cepstral coefficients (MFCCs)  on audio clips and then applying CNN to the input generated using MFCCs. This is so far one of the most famous techniques in emoticon recognition using audio.
Putting it to use to analyze video
Emotion recognition using images and audio is combined using complex mathematical or machine learning models to produce accurate results.
Limitations of Emotion Recognition Technologies
Emotion recognition shares a lot of challenges with detecting moving objects in the video: identifying an object, continuous detection, incomplete or unpredictable actions, etc. It might also suffer from lack of context of the conversation, lighting issues for images, and disturbances in form of noise for audio inputs.
Depending on the datasets used, the Machine Learning models for emotion recognition can have an inherent bias. Even google photos suffered from racial bias, where google photos could not identify dark-skinned people. Emotion recognition often suffers from biases such as classifying black men as angry etc. Thus it is very important to use diverse datasets for emotion detection and recognition software.
Political and Public Scrutiny
Facial recognition and systems built on this technology have often drawn criticism from politicians and people alike. These are usually privacy concerns, and the use of data without a person’s knowledge. European Union (EU) has already banned Facial recognition-based software. More regulations are expected to follow for emotion recognition technologies.
Emotion detection and recognition systems are under constant political scrutiny. In spite of this, the market for these systems are expected to have a compound growth rate of 21%. It is also expected to have a revenue of USD 56 Billion by year 2024. Apart from the outstanding economic projections for emotion detection and recognition software, the use cases of this technology are rather compelling. If hurdles like privacy, laws regulations, racial bias can be overcome this technology can be integrated in various products to enhance the user experience.
Need help with Emotion recognition and detection software?
If you want to build Emotion recognition and detection and need help, advice, or developers, feel free to contact us at email@example.com or on our LinkedIn page or visit our company website, www.quantiantech.com
References “Emotion Detection and Recognition Market,” Market Research Firm. [Online]. Available: https://www.marketsandmarkets.com/Market-Reports/emotion-detection-recognition-market-23376176.html. [Accessed: 02-Mar-2021]  Sackett Catalogue of Bias Collaboration, E. A. Spencer, K. Mahtani. “Hawthorne bias.” Catalogue Of Bias, 2017  Li, Y., & Lyu, S. (2018). Exposing deepfake videos by detecting face warping artifacts. arXiv preprint arXiv:1811.00656.  Trevisan, D. A., Hoskyn, M., & Birmingham, E. (2018). Facial expression production in autism: A meta‐analysis. Autism Research, 11(12), 1586-1601.  Google Glass may help kids with autism – Stanford Children’s Health. [Online]. Available: https://www.stanfordchildrens.org/en/service/brain-and-behavior/google-glass. [Accessed: 02-Mar-2021]  W. I. R. E. D. Insider, “Researchers Are Using Machine Learning to Screen for Autism in Children,” Wired, 23-Oct-2019. [Online]. Available: https://www.wired.com/brandlab/2019/05/researchers-using-machine-learning-screen-autism-children/#:~:text=Studying%20ASD%20at%20an%20Unprecedented,children%20in%20a%20single%20study. [Accessed: 02-Mar-2021]  Yang, D., Alsadoon, A., Prasad, P. C., Singh, A. K., & Elchouemi, A. (2018). An emotion recognition model based on facial recognition in virtual learning environment. Procedia Computer Science, 125, 2-10.  Štruc, V., Dobrišek, S., Žibert, J., Mihelič, F., & Pavešić, N. (2009, September). Combining audio and video for detection of spontaneous emotions. In European Workshop on Biometrics and Identity Management (pp. 114-121). Springer, Berlin, Heidelberg.  R. Chu, “Speech Emotion Recognition with Convolution Neural Network,” Medium, 01-Jun-2019. [Online]. Available: https://towardsdatascience.com/speech-emotion-recognition-with-convolution-neural-network-1e6bb7130ce3. [Accessed: 02-Mar-2021]
The history of chatbots dates to Joseph Weizenbaum’s ELIZA program, which was released in 1966. Weizenbaum, a professor at the Massachusetts Institute of Technology (MIT), named the program after Eliza, a character in Pygmalion, a play about a Cockney girl who learns to speak and think like an upper-class lady. Weizenbaum’s computer program convinced many users that they were talking to a human being and not a machine at all.
In the half century since ELIZA was released, chatbots have come a long way. The introduction of machine learning capabilities in bots has vastly improved the human-like experience in their conversations. Most bots, though, still behave like machines over short interactions.
Interacting with the earliest versions of chatbots was frustrating and time-consuming process. These bots would often respond to very specific request and could not answer anything beyond a fixed script. It was essentially the text equivalent of calling a customer service call center. As a result, communicating with a bot was an irritating (if not painful) option most consumers than speaking with a human customer service agent. Artificial intelligence has changed this.
AI-powered Natural Language Processing, or NLP, enables chatbots to mimic human conversation. They can identify the underlying sentiment and intention behind the communication, then deliver a response that is similar to what a human would have done. In addition, chatbots with NLP can now learn from past conversations and improve their ability to provide appropriate responses and solutions.
How are these different from regular chatbots?
AI bots Use Contextual Information
Chatbots that aren’t powered by artificial intelligence essentially deliver a one-size-fits-all experience to all of the users. They begin with a generic greeting, offer a standard list of menu options, and can only to deliver a fixed list of issues and questions.
AI bots can however use information around the content users access and read on your company’s site. If a user visits several pages focused on a specific service you offer, for example, the AI Bot understands that this user is primarily interested in that service. Then, it can begin the conversation around that service.
2. They do lot of pre-work for human interaction
A chatbot doesn’t have to hold an entire conversation with a customer from start to finish. They can initiate conversation, then ask for the details a support agent would need to assist the user. These details might include the user’s account number, order number, payment details, and contact information. This way, when a support agent steps in, they’ll already have the background information they need to assist the customer — and they won’t need to spend any additional time asking those basic questions.
Al, more advanced bots can even tell when it’s time to escalate a conversation. As technology advisor Bernard Marr explains, “AI-powered chatbots, can raise an alert when they detect, for example, a customer becoming irate – thanks to sentiment analytics – prompting a human operator to take over the chat or call.”
3. They can Route Inquiries intelligently
For large support teams, routing each ticket to the right person can be challenging time-consuming. AI-powered chatbots can help. AI chatbot can determine the need behind a user’s request. It can “understand” what that user is looking for, and what information they’ll need before their issue can be resolved. Then, it can intelligently determine which agent to assign your ticket.
If a user’s query is relatively simple the chatbot might opt to assign it to a newer member of your support team. If the inquiry is more complex and will require a subject matter expert, on the other hand, the bot would likely assign it to an agent with experience in that particular area. Plus, if the bot has access to each agent’s list of tickets, it can also take workload into account, and route inquiries to manage capacity efficiently.
This way, each customer will be directed to the person most qualified to resolve their issue — and to deliver that resolution in the least amount of time.
4. They can understand change in conversation
Conversational chatbot solutions powered by AI also support multi-turn dialogue. This is the ability to switch between various user questions within a single conversation. This is what sets apart a human-like AI versus building chatbots. An AI-powered virtual agent responds without getting confused if a person pivots the conversation. For instance, a person can ask about the price of checking a bag in the midst of checking flight status. In conclusion, AI can also understand more short-form and slang than chatbots.
5. Voice recognition
Voice recognition enables faster, hands-free interactions for users, making AI bots even more convenient. Examples of voice recognition can be found in a number of personal assistants, including Google Assistant, Siri and Alexa. Companies, including WestJet, are also launching skills on voice platforms to provide yet even more choice with how customers receive support.
Qualifying leads: A significant portion of leads to car dealers come from online channels. Therefore conversion optimization is crucial for automotive companies. As a result, automotive companies are using chatbots like Kia’s Kian which can answer customers’ complex questions and dramatically increase conversions.
Chatbots can add a new layer of interactivity to e-commerce, allowing customers to interact beyond menus and buttons. Major use cases are:
- Set price alerts: Bots that give price alerts notify when there is a change by observing the change of prices on different websites. Settings can be made according to the person and give alarm in desired situations.
- Order physical goods like clothes with conversational commerce unlock more options
- Buy gifts: Similar patterns in the users’ behavior can be analyzed and the product they are looking for can be recommended by chatbots. this facilitates the search for gifts.
- Track orders: Order tracking, which is one of the most common features of e-commerce platforms, can be done quickly via chatbots. Tars provides a chatbot solution for payment and order tracking by integrating on e-commerce websites.
Travel & Hospitality
All the way from booking travel to solving travel-related problems, chatbots have the potential to help.
Vacation planning: While most parts of travel bookings are already self-service, it is time-consuming to plan a vacation. Travelers need to discover the sights and experiences they would be interested in, plan an itinerary, pick hotels to stay in based on numerous criteria from kid-friendliness to location. While these tasks are frustrating for travelers, clever chatbots can make them much more pleasant experiences.
- 2Reservations & handling menu related questions: Chatobook aims to become OpenTable of chatbots. Beyond reservations, it can share menus and promotions, collecting feedback.
- Queries & complaints: In the long run, it is not good for business to make complaints more difficult to make. It frustrates customers and deteriorates a company’s reputation. Chatbots can service most queries and complaints fast, improving the satisfaction of your most dissatisfied customers.
- Information service: Most banks chatbots are capable of informing users about their balances, recent transactions, credit card payment dates, limits and so on.
- Credit applications: Just as robo-advisor chatbots are taking over investment advisors, chatbots are also capable of collecting necessary data for credit decisions
- Money transfer: Chatbots can easily handle money transfers via SMS, Facebook messenger or other popular chat platforms. Western Union has a money transfer bot that enables money transfer requests through Facebook Messenger.
- Bill payments: BillHero is an example of a bank-agnostic mobile application and it brings bill-paying capabilities via chatbots on Facebook Messenger.
- Handling healthcare & insurance coverage related inquiries: Applications such as HealthJoy, HealthTap and Your.MD help customers navigate the complex healthcare landscape in the US.
- Diagnosis: MedWhat and Ada Health are AI-powered chatbots that can serve as a medical assistant by gathering information via conversation with the patients. It seems chatbots are on the way of becoming the first contact point on diagnostic healthcare. After the SARS CoV-2 outbreak, people have become more aware of the dangers of infection. Diagnostic bots, like telemedicine, facilitate remote diagnostics, reducing potential infections.
- Therapy: Since therapy is almost completely text-based, it is a great area for chatbots to work in. Woebot is one of the leading chatbots, providing cognitive-behavioral therapy in the treatment of depression.
23- Agent inquiry handling: Allstate developed Allstate Business Insurance expert (ABIe) to handle questions from 12K agents. Agents inquire ABIe about policy details sales quotes.
24- Customer inqury handling: Allstate’s Allstate Business Insurance expert (ABIe) was expanded to serve end users as well.
Lead qualification: Qualifying leads take significant time in real estate. Chatbots such as Apartment Ocean greet potential clients and understand their level of interest, helping human agents prioritize who they will serve. Roof.AI is a chatbot platform that helps to create communication between real estate businesses and their customers via social media.
These are just a few samples. Some more success stories can be viewed here – Top 30 successful chatbots of 2021 & Reasons for their success (aimultiple.com)
As brands find new ways to incorporate artificial intelligence into their daily operations, it’s certain to establish an increasingly large presence in the business world. But for now, chatbots are an excellent way to become an early adopter of this technology and start delivering better customer experiences than ever before.
Interested in learning more about artificial intelligence and chatbot technology? We’d love to discuss how we can help provide you a powerful AI customer service platform which in turn provides a conversational experience your customers expect. Let’s chat! Or if you prefer to write then reach us at firstname.lastname@example.org