The emergence of technology has enabled businesses to attain new pinnacles of providing better services to customers. We utilize technology in every aspect of a business from understanding the need for a service to delivering one. Recently, the significance of customer feedback has increased as companies are bound to offer the best of services at reasonable prices, thanks to the cut-throat competition in the market. And customer feedback works wonders to survive that competition.
Therefore, the focus shifts towards the aptest medium of interacting with customers. And that is Contact Centers. However, Contact Centers also play a major role in acquiring new customers. Now, while reading the name, one cannot paint a picture beyond a call center where employees receive calls from customers. Which is true but we must look at the bigger picture. Bygone are the days when contact centers were limited to just one type of medium through which they could reach customers.
The real estate industry is one of those in which the buyer has to make the purchase even before he receives the final product. Therefore it is very important for the seller to seal the deal with a convincing prototype model. However, it has been a long journey for the industry to perfect the sample design for their customers. From blueprints of the design to 3D models to the original sample house, everything has been shown and done to help the customer imagine the final project. However, there is always room for improvement.
The introduction of Virtual Reality (VR) into the world of real estate has brought numerous opportunities for the sector. To start with, it resolves the issues regarding property visits. It is a time-consuming process to visit multiple properties before making a choice. However, one cannot take any decision without being completely sure, making the tiring task mandatory. Using virtual reality, buyers can visit the property sitting at their homes by just putting on the VR headsets. This saves a lot of time for the seller as well.
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.