While two very different industries, healthcare and telecom do share many issues and problems. From keeping customers satisfied, maintaining excellent customer service in all areas, and ensuring companies are run as efficiently as possible, it is these and other issues that often result in problems for companies that lag behind in the use of technology. When this occurs, partnering with CloudWick can solve virtually all of a company’s production issues.
When it comes to using CloudWick machine learning models in the healthcare industry, most experts agree the sky is the limit. With more and more emphasis being placed on having the patient’s experience be as good as possible from start to finish, many healthcare companies are now embracing the use of analytics, artificial intelligence, and algorithms to examine data and make changes as necessary. For example, one of the biggest problems for many private practices and hospitals is patients who fail to show up for appointments. According to the latest data, nearly 30 percent of appointments scheduled each day are no-shows. As a result, this leads to more than $150 billion in wasted costs industry-wide on an annual basis. But by using CloudWick machine learning models, it is believed the rate of no-shows can be greatly reduced.
And while telecom companies do not have a problem concerning no-shows for appointments, they do have issues when it comes to retaining customers. Since there are numerous telecom companies from which to choose, customers often have the upper hand in these situations. As a result, when a telecom company loses even one customer, it can become a costly problem. For example, along with no longer having regular monthly revenue from that customer, the company also loses out on potential future sales. Known in the industry as customer churn, it is a problem all telecom companies want to reduce as much as possible. By using CloudWick machine learning models, this can happen. By using analytics and artificial intelligence, these companies can spot trends that led to customer dissatisfaction, and thus learn how to more accurately predict customer churn and how to obtain new customers.