Customer experience and customer engagement are an essential part of e-commerce companies with figures showing that almost 50 percent of online consumers abandon their shopping carts if they are unable to get immediate responses to questions about the product or service. With many companies turning their attention to the possibilities AI has to offer, over the next few years, we can expect to see almost all customer interactions managed by smart machines.
Throughout this article, we will address why artificial intelligence is necessary to improve customer interaction and customer experience, how it is being applied, and some examples of businesses making huge profits using this technology to enhance their customers’ experience.
Is artificial intelligence required for enhancing customer experience?
Even as people accept that customer service is a significant factor in purchasing decisions, today, only 49 percent of customers in the US claim that businesses provide reliable customer support. Studying consumer data trends and attitudes can help companies develop their customer experiences. However, it is difficult for employers to view massive quantities of data. This is why customer relationships and expertise involve AI systems.
Sales and customer service agents are unable to keep up with each customer’s entire past with the business, let alone draw valuable lessons from these data in real-time. Yet Automated Systems operated by AI can:
- Identify buyer trends with greater precision.
- Enhanced customer support is provided at multiple client touchpoints.
- All the time, respond quicker to customers.
Is the technology used to enhance customer service?
Technology allows you to communicate regularly with customers. The consumers leave online footprints will also give you useful insight into what motivates them, and why they’re likely to buy from you or a rival. It helps us to attract customers with considerate gestures.
Any organization planning to use neural networks to improve customer experience should consider data integration, real-time insights into distribution, and market context.
Data Integration (DI) with Artificial Intelligence (AI) technology is the ideal fit technique to accomplish automation of data preparation activities while also integrating into its core competency the agile and effective process analysis of big data.
Data Integration, in the past, when it would take weeks to put together vast quantities of data (data consolidation), now it just takes days. AI-based data aggregation tools make the repetitive process of conducting analytics on customer travel more comfortable, cheaper, and less stressful.
Insights into Real-Time
To make tangible impacts on intelligent devices’ consumer interactions, data insights need to be communicated in real-time. SaaS systems, while far from being standard, can use real-time insights to provide better customer experience with APIs at their various touchpoints. Customer insights systems now give companies these choices.
The background must be provided to neural networks, that is, the importance of particular events in shaping or predicting consumer actions. To determine the next best move, they must be conscious of each customer’s specific path and subsequent behavior.
The more the service is customized, the more happy the clients are. Artificial intelligence technology can deliver more personalized services and help you better connect with consumers through data and behavioral analytics. Here smart systems will help:
Gather as much useful data as you can
Before you can respond to their needs and demands, you have to know your clients. That is why it is vital to collect customer data. To succeed, a company has to continually engage with its customers and recognize their desires, concerns, and brand experience. It can be difficult and often impossible to connect with and get input from customers when the customer base is high. Nonetheless, the good thing is you don’t have to hire staff to do these jobs. Big data is the location where systems based on AI flourish. And a company collects this data every day by communicating with potential and current customers via computer.
Acquaint the customers and consider them
After the data is collected, useful information needs to be processed for extraction. However, a study carried out by Forrester Research found that many businesses evaluate just 12 percent of their total data and lose out on useful lessons from 88 percent of the overlooked data. The companies that collect data but don’t know what to do with it or lack the right expertise or systems for interpreting it. AI-enabled customer analysis will help you make better use of vast quantities of otherwise incomprehensible data. Those systems can bundle and interpret data obtained from customer interactions. Through these observations, you can see the bigger picture more clearly and understand better how to improve your customer relations.
Advise your desires
“Ninety-five percent of customers share their bad experiences with another brand. Sixty-seven percent of customers will be willing to pay more for a better experience. To deliver better service faster for companies or advertisers, they need to gain detailed information about consumer desires, tastes, and habits, “says Andrew Ortiz, a marketing professional at Skillroads.
Understanding what your various client journeys are going to help you gain control over your rivals. You will devise smart marketing plans to attract and maintain them when you know your customers. Besides, as you continue to communicate with them via AI-powered chatbots, it will be easier to predict their expectations, create customized insights, and develop engagement. Know if they had a poor experience, 48 percent of consumers are unlikely to buy from a company again.
AI services transform the face of customer relations
Until now, the most significant effect of smart systems in industry is automating much of the routine activities traditionally performed by customer service workers, sales and marketing personnel, and other customer-dealing employees. Two smart applications which have revolutionized customer experience here:
Chatbots – also known as “conversational agents” – are software programs that replicate written or spoken human communication to visualize an interaction or conversation with a real person. Today, chatbots are most widely used in customer service space, performing positions typically performed by living, support operators such as Tier-1 and customer satisfaction teams.
For example, you can create your chatbot that can be used in Facebook Messenger; and many places provide the ability to build basic chatbots using accessible drag-and-drop interfaces.
A virtual assistant is an individual from a remote location offering various services to entrepreneurs or enterprises.
Virtual assistants can do a variety of things, including social media management, event management schedules, meetings, emails, gearing up reports, and personal activities such as arranging restaurants and hotels and easy digital marketing activities.
Examples of businesses taking the lead in AI at CX
Companies in different industries are continually experimenting with AI-enabled systems to gain insight into the consumer and, thus, predict their actions and improve their experience. And it’s fair to assume that their efforts are reaping great rewards. Such advantages include strengthened consumer relationships, increased value over the customer’s lifetime, and decreased sales funnel leaks.
Pizza bot at Domino’s on Facebook Messenger
The latest brand to hop on the chatbot bandwagon is Domino’s Pizza, announcing a new app that enables customers to order directly from Facebook Messenger. Customers will be able to contact Dom, the Domino’s pizza bot easily, and request food with a single word or emoji instead of calling up or ordering online.
To start the ordering process, their customers just need to give the bot a message with the word pizza. The chatbot takes the customer’s information immediately and places an order. With the bot in place, it has become easier and faster to complete an order than to visit the restaurant or call a customer service representative.
Sephora’s Virtual Artist
The latest app update introduced the new functionality of Sephora Virtual Artist, developed explicitly with facial recognition software to allow users to check lip products and purchase them directly. Recent integration by Sephora may further push the strategy into the world of mainstream makeup.
With smartphone users becoming more and more familiar with facial recognition apps and the technology increasing in quality, the strategy is being embraced by many retailers. Sephora has experimented quite a bit with Virtual Reality in the past, and its new update is a perfect way to boost sales by letting consumers see what attraction items would look like on their faces.
The app quickly puts together all of Sephora’s goods and can give consumers real-time deals and suggestions. When the consumer wants to buy, they have a smooth and fast checkout process.
Spotify music recommendations enabled with AI
With tens of millions of users listening to music every minute of the day, brands like Spotify collect a mountain of tacit consumer data that includes song preferences, keyword preferences, playlist data, listener geography, most used devices, and more.
Spotify uses AI and machine learning to discover and act on insights from external data and user behavior, maintaining its position as one of the web’s most popular music streaming sites.
To generate the personalized music list ‘Discover Weekly’ for Spotify, the team uses three models, i.e., Collaborative Filtering, Natural Language Processing, and Audio Models.
The company has brought user experience to the next level with AI-based technology. Their website will store and analyze substantial consumer data to identify trends and predict what kind of music individual consumers want. Spotify users get suggestions from their data to create playlists every week.
Black Diamond equipment’s predictive AI-based system
Black Diamond Equipment, an online retailer of high-end skiing and hiking gear, can create an online shopping experience that is personalized and intuitive. Their platform relies on sophisticated analyses of past consumer transactions, current weather conditions, and other data to deliver highly customized product suggestions.
By using intelligent technologies to deliver tailored consumer experience and customer reviews, the retailer has made a name. The business uses sophisticated analytics to extract insights from clients’ buying experience and blends them with other related information to make personalized recommendations in real-time. Our network has seen a decline in abandoned carts and a rise in the number of conversions.
Main takeovers on machine learning to boost customer service
Machine learning algorithms can enhance efficiency, interaction, and experience for customers. Organizations can understand, remember, and respond to customer requests and feedback in a meaningful way with little effort and investment. Nevertheless, the challenge remains to develop such systems quickly enough to reap the benefits. With the lack of expertise in data science and the problem of getting multiple applications ready for this emerging technology, deployment has become troublesome for many companies.
- In 2020, 80% of businesses will seek to compete with Customer Experience (CX), but only 50% will truly understand how consumers feel, what they want, and what they need. Over 100 + metrics for assessing customer service, including NPS and customer satisfaction overall.
- Marketing – 53%, distribution – 49%, and Customer support – 53% are main divisions with responsibility for CX. Brand experience is progressively being co-created, given the ever-expanding portfolio of marketing activities, which now includes engagement and consumer loyalty and analytics.
- Collecting & evaluated consumer reviews – 57% and applying and assessing CX metrics – 53%. These goals are interconnected. Customer experience specialists would need a data plan to define and collect both structured and unstructured data from each customer’s touchpoints to accomplish both. You can help create a 360 view of the application by mapping multiple data sources. A structure must be developed to establish the accuracy of the measurement across these touchpoints. Emotion can be linked with structured data such as NPS or CSAT to connect and take action on drivers.
- AI – Almost Intelligent: In an AI debate, an important distinction is regarding conversational AI-how computers can communicate with people. Machine learning is already being implemented for CX, particularly in customer analysis.
How does CX-enhance AI? – Much research needs to be done here. Take conversational AI, 68 percent have no plans or will execute them in the next three years. Study at Adoreboard, in the UK, asked 1,000 millennials if they would prefer to assist a human or a chatbot. Results showed that 76 percent of people favored chatbot versus 12 percent. It means that by 2020, 40 percent of applications for bot / virtual assistants launched this year would have been abandoned. The main question that should be asked is who serves AI- boost the customer’s life, or is it merely a business efficiency?
- Getting to CX: Customer Analytics overtook Voice of Customer by 14 percent in the last year as the critical tool to invest in CX ventures. The difference would widen. It was knowing why and how you feel about customers will provide valuable insight.
This article is co-authored with K.Praveen Kumar.