Can you imagine registering your name, phone number, email, and address every time you order anything online? Me neither. No matter how big the online market gets, it can’t grow longer without customer data. Forget the online market, huge brands that are dependent on the bricks and mortar model are also updating their methods of collecting and storing customer data in order to offer them a better experience. In which case it shouldn’t be overemphasizing to say that Customer Data Management has grown to become one of the most important tools for businesses in the present time.
But how can managing the information of your customer be so important? Take the example of a company that has recently acquired a new customer. However, the digital marketing team of that company isn’t aware of this acquisition and is still approaching the same customer via text and email. This isn’t just a waste of resources but also a threat to the customer experience. Having your database managed by a customer data management system makes the process of acquiring and retaining customer efficient. It also helps in understanding the needs of customers and deliver a better quality of products and services by analyzing the feedbacks which leads to higher sales numbers.
We live in an era where businesses are driven by the force called data. With the help of the same, enterprises are able to efficiently acquire potential customers and sustain the existing ones as well. It can also be used to understand what customers need in a product or a service which leads to a satisfied base of customers with exemplary solutions and optimization of available resources. Now, one might ask, how can random facts and figures avail enterprises such significant insights? The answer to that is data analysis.
There are numerous methods of analyzing data and the most popular among them are Statistics and Machine learning. Both the terms are often considered as one and the same but in reality, even though their purpose is the same, their use cases are different. Statistics is the science of classifying and analyzing data by using mathematical equations. On the other hand, Machine Learning is one of the applications of Artificial Intelligence which enables the system to learn on itself with the use of data. While Statistics is used to collect, analyze, summarize, represent, and organize the data and transform it into useful insights, Machine Learning uses data to learn, predict, and enable the system to perform cognitive tasks in an unsupervised setting.