Customer journeys prove to be extremely useful to increase positive results in all customer related KPIs. One of the rules in large enterprises is that if it can’t be measured than it can’t be improved. However, it is tough to measure customer commitment to a brand. Customer journeys help in regulating customer commitment. Creating a customer journey map provides meaningful insights to all levels in a department. The map provides a clear understanding of the path and the channels the customers take to get to a product. It can be used to forecast the path of future customers as well.
According to McKinsey, a worldwide management consulting firm, customer journeys can enhance customer satisfaction by 20 percent. Customer journeys can be explained as distinct, unevenly sampled time series of customer events. Leading marketers agree that machine learning will prove to be critical in providing a personalized experience. By using data and machine learning side by side, marketers can explore new audiences that are similar to loyal customers. These capabilities empower an organization to reach and convert new customers.
Machine Learning Examines Data
Companies can use customer journeys to become more profitable. The first step would be a collection of a huge dataset. The larger the data, the better the result. A large quantity of data set creates numerous data points. The interaction between data points matters the most. It is humanly impossible to comprehend the pattern between billions of data points. Machine learning solves the problem by not looking at a single customer but looking at all customers, especially those with journeys close to the ideal customer to understand the events that are close to driving an outcome. By crunching all the numbers, the machine learning model learns.
Why Measure Customer Experience?
Some events lead to a purchase. The bigger the purchase, the events will be more valuable, and more such events lead to more significant purchases. Customer experience is critical in predicting growth. It is an approach that is easy to introduce using technology as a platform with a little bit of time and training. It provides a means to track a customer’s commitment, one subsequent event at a time. For example, in a case where the value goes up and down, we can gain visibility of which events that triggered the positive impact and which events triggered the negative impact. The companies can build a cohort of similar customers to see positive results.
Customer value as the most important KPI
It is critical for a business to anticipate customer needs and provide assistance along the way if they want to succeed. Customers expect highly personalized experiences with their interaction with the brand. Marketing leaders understand the absolute importance of building relationships and enhancing the experience of existing customers. Businesses must understand customers and to understand customers; enterprises need to interpret data from different sources. The data provide insights into customer needs to meet their expectations better and achieve business goals. An internal culture rooted in innovation and experimentation can achieve business goals effectively. Marketers with an understanding of reaching new customers at the right time will continue to build loyalty with the existing ones.