We live in an age of informatics and robust data exchange. Dealing with huge amounts of information; understanding and processing them is vital for any business. Companies use this data to build better relationships with clients. The global scale of data management and interpretation thus needs continuous makeovers. In such a scenario, the traditional Customer Relationship Management (CRM) software falls short. Rather, this dynamic market needs something just as ubiquitous – Real Estate CRM is one such tool that effectively fills this niche.
Why Automation and not Management?
CRM or Customer Relationship Management software is a tool that analyzes and organizes data according to the company’s criteria. However, CRM has rarely progressed past its infancy, making it unwieldy and difficult to use. Data has traditionally been manually entered into database systems. And other than moving from on-site to the cloud, CRM has not changed much since its inception in the 1990s.
With the advent of machine learning and predictive data engines, however, we now have the tools to create a much more encompassing management system to better analyze and predict large amounts of data, supply information to clients and in general, manage information more efficiently. Customer Relationship Automation is, thus, the future of CRM.
Without automation, employees have to waste time manually entering data, and then waste even more time searching through it. While the ability to monitor employees through CRM is a positive functionality, the vast majority of salespeople loathes the repetitive tedious extra work. This lowers office morale. Moreover, it hurts productivity since, on average,reps spend only 11% of their time actively selling.
Automating data entry and using decision making algorithms to streamline menial tasks would not only increase workers’ motivation, it would also reduce errors and free up the time to produce results that would add to the bottom-line.
While tracking tools are useful, only tracking will hardly produce useful data, especially when hundreds of thousands of terabytes of data are in question. Say, you are searching for a product on Amazon. The site will immediately suggest related items based on your current viewing list and your previous purchasing and viewing history.
Such an instantaneous response would have been impossible if CRM had only been tracking the data and a human employee had to interpret it. Automation allows a company to apply clustering algorithms and linear algebra to teach the machine to predict a customer’s needs with a high degree of success.
As the interaction between sales executives and customers gradually become more digitized (be it via chat, Facebook or Twitter), it is largely becoming such that no single representative can follow through on one customer. Thus, it is important that the machine must know the customer and not the employee. By tracking and analyzing all interactions between reps and customers, the Customer Relationship Automation environment can not only solve a customer’s problem, but also predict the needs of future customers based on this model.
The traditional CRM software, while robust, fails to meet the expectations of a largely changing market. Data driven algorithms and dynamic programs are the key to the future, and, it has become evident that the CRM too, must evolve to survive. The future of CRM lies in harnessing predictive data and proactively using these suggestions to pre-empt customer needs. Hence, Customer Relationship Automation must be the new CRM in this coming future.