Medical Wearable: Translating Big Data into Product Design Information

Medical Wearable: Translating Big Data into Product Design Information

Technology blessed us with wearable devices that capture real time data related to vital signs and symptoms. But making improvements to products based on customer usage pattern is a different ball game altogether. In the medical domain, it is important to design wearable from user perspective since the health industry is critical for the users. The blog presents an approach to use analytics to design products that can match health-conscious customer's expectations and needs.

Product design is one of the most important parts of the product roadmap. Success of any product depends on the design of the product to a very large degree. Design is not just about what the product looks like and feels like but also about how well it works. One of the critical questions that need to be answered is to determine the problems that need to be addressed while designing a product.

There are two types of problems in Product design:

• Design problems which bother users and compromise on comfortability, functionality or usability of the product
• Design problems which do not affect usability but affect user experience
• The wearable devices market is getting crowded with loads of products that provide basic functionality but one needs to be sure about the USP of the product compared to the market. Most players in the market provide solutions through better designed products but it is essential to engage the customer with the product and leverage its benefits in a better way.

For medical wearable, customers won't continue using a product for long unless it proves to be beneficial and usable both in the short run and long durations. In order to ensure continuous usage, users must be engaged with their medical wearables.

The devices need to extend beyond medical benefits that include convenience, time-saving, and above all improvement of self-image.

In order to ensure higher level of engagement and usage of device, organizations should use Big Data for research on usage pattern of the product. Here, the intention is to find out ways to enhance the usage of the product. For this, we can mostly rely on data from end users, e.g. customer insights, social media interactions, forums and most important telematics data - which includes sensor generated data from devices. Insights from this data could provide us critical information that will aid in designing products. This will not only provide insights about how product is being consumed but also help in finding out areas to focus attention.

Improved products will help end users to be engaged with devices rather than just delivering functionality benefits. A user-friendly and optimized product design will always promote high error reduction, better adaptability and productivity, leading to increased customer satisfaction and retention.

A simple example that could be considered when using data analytics for designing a product - assume two companies providing fitness services using their wearable devices. Company A provides usage statistics monitoring all health parameters like heart rate, blood pressure and other vital signs. Company B on the other hand also tries to provide insights through the reports, like asking the users to exercise since it does not see a rise in heart rate any point of time in the day. Even though both the companies measure the same set of parameters, company B tries to engage with the customer through predictive and prescriptive analytics. Customers would see both these products differently and gauge their performance based on the interaction that the device provides. Customers of company B would be more than willing to use the device regularly for improve their lifestyle and would recommend it to their peers as well.  A product designed to act on insights generated through monitoring will go a long way in gaining customer trust and help them live a healthier life.

Written by

Anand Borad