10 Ways to Boost Customer Services for Mid-Market Enterprises Using Machine Learning & AI

Artificial Intelligence systems are prevalent everywhere today! Programmes and algorithms are transforming practically every part of a business in this rapidly changing world. From hiring new employees through data collection, labelling, and filtering in an online business. Technology is used across all business sectors to reduce operating expenses and free up internal resources. However, customer experience is extremely important in a world where technology has excellent power and returns.

Customer service has emerged as a new form of brand identity. Companies today are testing the newest technology, like Artificial Intelligence and Machine Learning, to not only enhance the consumer experience but also to own it and differentiate themselves from their competitors. 

Around 27% of organizations believe enhancing their customer intelligence and data optimisation are their top priority when it comes to customer experience. Any bad experience causes around 30% of the customers to drop a company or switch brands. Nevertheless, intelligent technology will be increasingly effective in enhancing consumer interactions and will be able to provide transformative customer experiences.

The usage of AI is anticipated to grow quickly over the coming years. As per a report by Gartner, 25% of customer service and support firms will be employing chatbots or virtual customer assistants (VCAs) in the coming years. Machine learning (ML), natural language processing (NLP), and VCA technology have a very high potential to improve customer satisfaction by streamlining contact centre operations. 

The 10 main ways that AI technology is improving contact centres' efficiency and the calibre of customer care are listed below. 

Virtual Assistants for Customers

This technology, often known as "chatbots," can benefit both customers and agents in a few different ways. VCAs can assist with resolving more common client inquiries, freeing operators to concentrate on more complicated problems. In the context of live agent-to-human discussions, VCAs can also handle smaller customer request components to simplify customer engagements.

Machine Learning

Chatbots use machine learning technology, but it can also be applied in different circumstances to support actual contact centre agents. By providing live agents with intelligent recommendations based on a customer's history, including brand, product preferences, and previous difficulties, machine learning capabilities can assist agents in personalising customer encounters. These insightful recommendations can decrease average handling time (AHT) and help agents better grasp what clients want and need.

Customer Intelligence Analytics and Natural Language Processing

Consumer data from various sources may be gathered, interpreted, analysed, and shared using AI-led consumer analytics. Additionally, artificial intelligence (AI) software may identify components of impatience and rage in voice calls and, if necessary, escalate calls to live operators using natural language processing (NLP) capabilities. Real-time speech analytics (RTSA) support mid-conversational information provision to call agents and call quality assurance. Both of these help consumers have more engaging discussions now and in the future.

Intelligent Routing

AI can help improve the conventional interactive voice response (IVR) systems that guide customers through menu options and route calls or even replace them. Customers can often use an IVR to listen to a list of menu selections and choose the number that most closely matches the reason they are calling. However, a lot of clients frequently discover that the answer to their particular query or problem is not provided in the typical selections. By enabling clients to speak out loud about their call, intelligent routing improves traditional IVR. The software then evaluates their claims and refers them to the appropriate resource or agent. Intelligent routing can also access caller history and behaviour from the past to determine whether to transfer the call to a specific party.

Cost Reduction and Resource Optimization

As per a report by IBM, companies worldwide spend more than $1.3 trillion annually on 265 billion customer support calls. By accelerating response times, releasing people for more difficult work, and responding to up to 80% of basic questions, chatbots can help organisations reduce the cost of providing customer care. Call computerization is a prime example of this since it combines machine learning and enhanced speech recognition to enhance traditional interactive voice response systems while achieving cost savings of 60% to 80% compared to outsourced call centres run by humans.

24-hour Support All-Round the Year

Customers demand services that are flexible. All year long, brands must be accessible to consumers and be responsive to their needs, which can be made possible through automated customer support. It enables businesses to provide always-on customer care and handle problems as they come up. Customers may now have their questions answered 24 hours a day without having to wait a long time to hear back. This would not only significantly improve customer satisfaction and customer service, but it would also enhance brand reputation and foster more customer loyalty.

Personalised User Experience

Customers prefer dealing with customer care concerns on their own. AI has a significant potential to aid clients in more effectively finding the right information. Artificial intelligence aids in the analysis of consumer data and important metrics and makes product or service recommendations to clients based on their browsing and purchasing inclinations. Huge data sets may be analysed by AI, which can also be used to determine location and weather information and offer appropriate content to clients. Businesses can concentrate on unique buying habits and gain a better knowledge of each consumer by developing customer personas. They are then able to send customers relevant content via the appropriate channels and at the appropriate time. Customers can thereby resolve issues without contacting customer care.

Data Gathering

In order to generate a single customer perspective based on the customers' behavioural patterns, AI streamlines and unifies data collection. In its early stages, artificial intelligence was reliant on customer data that had already been collected and manually fed. The latest generation of AI-powered technologies is better at proactively asking clients for data without human interaction. They can quickly and easily identify patterns in client behaviour and react to their demands and feelings. They are prompt in their responses and are aware of when to probe further. After acquiring the necessary information, AI systems can analyse it and take further actions to assist clients as they make purchases. Organisations that have included AI in their systems are now reaping the benefits of data collection and archiving.

Predictive Insights

Businesses have to establish experiences that stick with their clients and become a part of their life. Customers who use predictive personalization feel that every brand, service, and product is created with them in mind. By giving customers information that is pertinent to them, companies that have integrated AI into their systems have strengthened their client relationships. This has improved communication and transparency. Their work has been improved by AI's predictive insights, which make it simpler to rapidly scan through prior products, inventories, or services to recommend goods pertinent to the customer. AI that incorporates emotions will simplify and streamline customer interactions and experiences across all channels.

Simplified Tasks

Bots with artificial intelligence (AI) or other systems used in customer service are capable of managing many jobs at once. The interaction between brands and consumers has been revolutionised as a result. As a result of having to wait so long for a customer executive to address their problem, 52% of consumers hung up on the customer support line, and about 50% of calls went unanswered, according to IBM. Companies are looking into ways to use AI in their operations to communicate with customers directly. AI can supply clients with automatic responses and FAQ information, while human agents typically burn out when handling tasks with a lot of data. AI can also assist in sorting through data, obtaining additional data, and analysing and responding to questions.

Conclusion

Businesses must incorporate AI into their operations if they want to attract new clients, keep existing ones, and cultivate brand loyalty. AI improves consumer engagement and keeps you one step ahead of the competition. The lives of customer service representatives are also made easier. With the use of AI, quick responses, personalisation, ticket prioritisation, data analysis, speeding up responses, identifying solutions, promoting goods and services, and more are all instantaneously automated, freeing up human attention for more difficult jobs.