For any business, good customer experience is a key factor of repurchase, word-of-mouth, and loyalty, and is strongly tied to a company's long-term earnings. While knowing customer behaviors is essential to help businesses improve their customer experience, the complexity of data collected via social media, call centers, websites, and other channels growing on the daily basis keeps businesses far reaching from valuable customer insights. Machine learning solutions can overcome such adversity with its capability to handle and analyze massive amounts of data effectively. What is unearthed in the customer journey can be discovered by using machine learning to assist making-decision process for improving their service and product quality. Here are the four approaches how machine learning can help organizations optimize their customer journey:
1. Improved Efficiency in Customer Support
Customers these days have to wade through IVRs and wait on hold before speaking with a customer representative. It can get too overwhelming for the customer support with a wider range of business offerings and customers have now come to expect more out of companies. Such tasks should thus be aided by machine learning without human intervention, which can speed up and improve the customer service process. ML-driven chatbots are a common and effective application in machine learning to meet the increasing demands from customers and business. According to a 2020 MIT Technology Review poll of 1,004 corporate executives, machine learning is most often used in customer support through chatbots. With 73% of those polled, chatbots will still be the leading use of AI in companies, followed closely by sales and marketing at 59%.
With machine learning solutions, organizations’ service quality can be brought to the next level by providing timely customer support. Adnovum can help your business to employ such round-the-clock and intelligent assistants with our machine learning solutions allowing your business to save costs and to boost customer satisfaction.
2. Enabling personalized experience for individual users
By analyzing real-time data, machine learning can produce tailored content and suggestions. This offers customised and self-service experiences that are quicker and more convenient than conventional techniques, such as perusing websites for information. For example, when customers visit an e-commerce website, they will receive suggestions based their past behaviors on the site. Machine learning algorithm is being leveraged for such recommendations. Providing customers with a personalized experience will result in higher satisfaction and encourage purchases. Machine learning algorithms are constantly evolving. The more data the ML algorithm consumes, the more accurate their analytics and prediction would be.
Natural language processing (NLP) is an area of machine learning that has been getting a lot of attention lately because of its potential to help businesses extract insights from customer intent by deciphering their spoken and written interactions. NLP is, for instance, beneficial in marketing and purchasing recommendations since it can be used to monitor user behaviors and tailor campaigns with a greater possibility of reaching business goals. Organizations can thus optimize their ROI from their marketing campaigns by predicting and recommending products or services to customers who are likely to purchase.
3. Better Understanding of Customers’ Behaviour
Regardless of the complexity of data collected, machine learning solutions can make data analytics efficient to provide more insightful information about the customers allowing businesses to make impactful decisions on improving their customer relationship. Besides that, customer insights can be updated continuously as machine learning algorithms consume new data constantly to produce the most relevant insights, despite the evolving customer behaviors.
Business owners and marketing teams can perform well by forecasting the behavior of their previous, present, and prospective consumers thanks to these ML-driven initiatives. For example, leveraging machine learning for better understanding customer behaviors will allow organizations to discover the key causes of high churn rate, and which variables in their efforts have the most influence on customers' purchasing behaviors. This will inform organizations to take remedial actions to keep their customer engaged in their services and facilitate business growth.
4. Build Trust in Customers with Machine Learning in Cybersecurity
Debit and credit cards are commonly used whenever users purchase services or items on digital platforms. Customers should have a peace of mind with their online purchases on a platform with good IT security. With a reliable digital platform, customers can feel more confident while transacting and, as a result, more satisfied.
Machine learning models can be trained to assist organizations to strengthen their cyber security postures by continually recognizing and responding to vulnerabilities in the security networks. Organizations' capability to swiftly respond to emerging cyberattack techniques can be improved over time as machine learning algorithms will be trained endlessly to spot new patterns of threats. From there, network security will eventually become more intelligent, effective moving from a reactive defense state to a proactive one.
Keeping customers is cheaper than attracting new ones, and businesses know this to be the case. Leveraging machine learning solutions can optimize every touch point in a customer journey with personalization, more secured and faster delivery of the user experience. Adnovum can assist your organizations in incorporating machine learning applications into your services with our solutions customized for your business requirements and objectives.