Artificial intelligence is changing customer interactions with companies. You can see it everywhere, from voice to text communication.
For example, when calling customer service, you don’t need to wait in line to get offered customer support. More likely, the system will transfer you to an intelligent bot that is able to communicate in a human-like manner.
The voice solution is sophisticated enough to decipher emotions, interpret words, give context, and understand speech in noisy environments.
So, powered by AI and Machine Learning technologies, is able to carry out conversations as a human would. This increases operational efficiency and handles customer inquiries successfully.
The same applies to text messages. Two-way conversations can be automated on SMS and Rich Media Messaging. The power of automation allows instant responses via text messaging in a timely manner. You can also use chats on websites or messengers and get instant replies from a bot. AI-powered robots leverage different technologies to self-improve and provide a more sophisticated customer experience, such as machine learning (ML) and natural language processing (NLP).
The peculiarity of modern AI solutions is that you can’t tell whether it’s a human on the other side of the line or not. This fact leads to numerous benefits of AI in customer service, sales, marketing, education, healthcare, etc. You’ll look at more of them further in the article.
What Does Conversational AI Encompass?
Conversational AI is a set of technologies to empower chatbots and other traditional software to communicate with people naturally. One of the most popular spheres to use is in customer service. AI applications find their place on websites, social media, messengers, online stores, etc.
They boost conversion rates as customers don’t need to look for information themselves. You can see even more sales if you make the store mobile-friendly, incorporate crucial elements of eCommerce checkout UX, and ensure fast loading speed.
AI-based assistants perform various functions, including:
the ability to book a table or room
checking the order status
answering questions about shipping
giving personalized recommendations
They differ from traditional chatbots because they rely on advanced solutions. AI in customer service accelerates inquiry processing. As a result, a person enjoys a better experience without needing to talk to a specialist.
How Conversational AI Outperforms Traditional Chatbots
You may have seen traditional chatbots. Their biggest drawback is the inability to function outside prescribed scenarios. These scripts include specific keywords for a bot to process. Clients need to utilize these words as the system comprehends only programmed intents.
But the truth is that developers must come up with different situations and complaints and train the bot to respond accordingly. They should also consider synonyms, slang, irony, and other human-specific figures of speech. It sounds almost impossible, doesn’t it?
When clients’ questions don’t coincide with typical ones, the system can’t give an appropriate answer. It directs a person to a specialist or just returns the “I don’t know” phrase. Plus, it usually requires you to use buttons rather than text or voice. That’s why traditional bots are associated with high levels of frustration. Below is one of such scenarios.
Conversational AI takes the systems to the next level. It lets them decipher voice and text and employs ML, NLP, and automated responses to imitate human interaction.
Technologies Behind Conversational AI Experience
ML is basically the ability of the system to self-learn with time. The more it’s used, the more precisely it works. It remembers the insights from previous interactions and utilizes them for future cases. So it’s reasonable to implement such solutions in advance to reach their peak performance when the company grows.
The best part is that you don’t need to program all scenarios. ML improves the accuracy of the bot’s responses, leading to smarter systems able to handle complex questions.
The second technology is NLP. It enables AI to comprehend the language and generate replies to text or voice data. In essence, natural language generation is about simulating dialogue.
These technologies work together to pick the best variants in a particular situation. With their help, conversational AI chatbots offer remarkably seamless client experiences. Here’s an illustration from the Mobilia home decor store:
Six Ways to Leverage Conversational AI for Customer Service
1. Helping Prospects Outside Business Hours
One of the top reasons for customer churn is too much waiting time. Imagine calling customer service and having to stay in a queue. You may also need to repeat the question several times and jump from one department to another to resolve the issue. It results in dozens of minutes spent on customer service.
Statistics claim that not getting immediate responses can cost a business too much. 44% of respondents get annoyed for waiting 5 – 15 minutes. The negative experience diverts buyers from repeat sales. They can also leave unfavourable feedback on your website, damaging your reputation.
Similar situations may arise when someone needs assistance outside business hours. Suppose prospects want to purchase on the spur of the moment, but the team doesn’t work. They browse the website and see, “Leave your message, and we’ll get back to you.” They may forget about the desire to purchase when the store opens. That’s where you need to react quickly with AI chatbots.
AI in customer service works when the support personnel is busy or unavailable. Uniting an AI chatbot with other contact channels can help clients with frequently asked questions. The same report claims around 80% of consumers are ready to substitute human interaction with machines. 57% of consumers will choose a conversational robot if they have to wait for five minutes in line.
A case in point is the Heyday chatbot on Kusmi Tea. The solution reduced the waiting time from nearly 10 hours in August to less than 3.5 hours by September.
With the increasing number of interactions, you have more data about clients. What are the most popular channels they use to buy? What part of the day is the most occupied with calls? What regions generally face delivery challenges?
AI solutions can track this information and predict trends based on large volumes of data. It also tackles the data processing issue as manual operations may lead to human errors due to the lack of concentration. It becomes unnecessary to spend time on what you can easily automate. As AI in customer service is still far from replacing people in creative tasks, your personnel can focus on planning future strategies, not collecting data from all sources.
Insights processed by AI can become the basis for your marketing campaigns. They can also point at the weak points in the business to eliminate them in time. Another benefit of intelligent trend prediction is managing traffic spikes and avoiding stockouts during peak demands.
3. Providing Tailored Offers
As conversational AI brings numerous data, you can utilize it to personalize the shopping experience. This tactic increases conversions, as 75% of consumers prefer brands with tailored offers.
You can find examples of personalization on websites like Amazon, Netflix, and many others. They pick the most relevant products or films that might interest users based on the previous history. What if the website lacks information on new visitors? The system may take data from buyers with similar parameters.
AI plays a vital role in matching users with products and services. It lets brands create unique profiles of shoppers and store growing information there. What can they utilize? Location, browsing history, purchases, and many other assets help you display hyper-personalized content to users or segments united by much the same preferences.
Take inflatable swimming pools, sunscreen lotion, and fans as an example. These goods will be more appropriate if it’s summer in your prospects’ region rather than winter. That way, AI ensures a fantastic user experience with personalized recommendations.
Snapdeal has a convenient chatbot on Facebook Messenger. You can type the desired location, date, and price to find the best matching hotel in the region. The bot also lets you adjust the search to meet your requirements.
The initial investment in AI may scare you away. However, these costs will soon pay off as you won’t have to hire more employees to do repetitive tasks, manual data inputting, or analysis. AI is perfect for customer service automation, saving your employees precious time.
This modern technology not just assists with customer interaction. It increases productivity in a team. How? AI-powered software may analyze employee performance and spot burnout signals. Thus, you can give them time off before they decide to leave.
It prevents you from spending time and money on finding and onboarding new employees, as customer service is one of the most demanding departments in terms of satisfaction and positive attitude.
Look at Erudit AI. The company develops software to enhance employee wellbeing, engagement, and turnover metrics. AI uses anonymous data to inform you of possible risks with your staff.
Screenshot taken on the official Erudit AI website
Another way of cutting down on costs is the ability to spot inefficiencies. AI analytics provides KPIs, uncovering money-draining strategies for you to change something and increase return on investment (ROI). That’s how AI lets you focus on profitable strategies.
5. Using Sentiment Analysis to Understand Clients Better
Call centers and other businesses use AI in customer service to understand how people feel about their products or services. For example, clients may leave feedback on the website, comment on social media, or participate in surveys. That’s what sentiment analysis is about.
It’s an NLP technique to extract keywords from customers’ reviews and determine their emotions. It generally relies on positive, negative, or neutral gradation. Businesses analyze text data to figure out customer sentiments and needs. This information helps retain clients by proactively reaching out to them to resolve their issues, offer discounts, or send a thank-you note.
Types of sentiment analysis fall under the following categories:
Graded sentiment analysis. It involves separating feedback into groups from very positive to very negative. It may coincide with a five-star rating, where five is the highest satisfaction mark, and one is the lowest.
Emotion detection. This type is about classifying reviews based on emotions: happiness, satisfaction, resentment, anger, etc. It implies some challenges as people may utilize words with different meanings. For example, “bad” may not denote unfavourable feelings but a strong desire to get something, such as “I want this product so badly.”
Aspect-based sentiment analysis. It lets you grasp what features customers mention in a review. Combine it with graded or emotion detection analysis to get more information about the product.
Multilingual sentiment analysis. Here you can research customers in more than one language and expand your reach to international clients.
Here is how sentiment analysis enables Trustpilot to classify comments into excellent, great, average, poor, and bad.
Screenshot taken on the official Trustpilot website
6. Supporting Various Channels with AI in Customer Service
Prospects explore new channels to shop for goods. Consider the Internet of Things proliferation. Smart speakers, TVs, and other home appliances and electronics expand their possibilities. Now you can order Alexa to place an order and wait for the goods to arrive at your doorstep. Or you can browse products on the refrigerator screen and buy them without leaving your home.
This trend requires companies to switch to omnichannel marketing. According to Omnisend, using three or more channels increases the order rate by 494% compared to a single-channel strategy.
But it brings more challenges, such as recognizing prospects. A polished user experience stipulates that people don’t have to repeat their information from one channel to another but have an uninterrupted conversation with the brand.
How do online stores collect this data? With the help of artificial intelligence. It streamlines user authentication by analyzing biometric data (face and voice recognition, fingertips, etc.) AI in customer service can also expand the potential sources of customer insights. A case in point is facial expressions. You can extract sentiment data from them and route the dialogue in the desired direction.
Voice chatbots, image recognition, and intelligent search allow you to shop in various ways. To illustrate my point, I took a screenshot on the Castorama website. The store has a visual search function. You can upload or take photos to locate similar items from the catalogue.
Screenshot taken on the official Castorama website
AI is now indispensable for customer service automation, marketing, and sales. It assists various departments, analyzes tons of data, improves productivity, and reduces human error. AI doesn’t need rest or sleep, so it works 24/7, allowing businesses to save on hiring new specialists. It also lets companies utilize data to personalize the shopping experience.
That’s why AI is not just here to stay. It will continue to evolve and enhance communication between clients and businesses. Now it’s crucial to have a leg up on the competition, so AI-powered solutions are necessary for companies with long-term goals.
You’ve analyzed how AI can improve customer experience. Determine your goals and make sure you can achieve them more efficiently with AI.
Published on: 9th July 2022
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