How a health tech brand used VoC analytics to save a new product launch

Our client is a global health tech leader known for developing high-quality products across various consumer electronics categories. They mainly provide medical technologies that aim to improve the overall well-being of their consumers and patients.

With one of their new product launches, our client introduced a digital thermometer to help track and monitor an infant’s body temperature. The medical device looked like an adhesive bandage; adults could strap it safely on a child’s arms or legs. It also had a built-in sensor wirelessly connected to a smartphone app. Users were required to download the free app before using the device. Ideally, when the infant’s temperature falls or rises, the tool’s sensor should automatically detect this change and alert users.

Our client’s product development and marketing teams were ecstatic about this new product, especially after investing a hefty amount of resources into its launch. However, several weeks after introducing the new thermometer, the marketing team reported a significant drop in the device’s overall sales.

Although, it did have an overall positive amount of online ratings and reviews. The marketers just couldn’t understand precisely why sales were lower than expected. They could only guess that the thermometer had some unforeseen design flaws or a poorly written instruction manual.

Nevertheless, the company knew that the answer to their problem lay in the voice of the customer (VoC). It needed a solution that could help:

• Identify the correct consumer data to collect and analyze
• Pinpoint the exact reasons behind the new baby thermometer’s low sales and star ratings
• Support the marketers’ theory that the device failed because of poor technical design, not performance-wise

After evaluating several options, the health tech leader sought the help of Wonderflow’s text analytics platform.

As a unified text mining solution, Wonderflow utilizes artificial intelligence (AI) technology to automate the collection and analysis of consumer feedback. Because our client’s new infant device was sold mainly online, this allowed our AI to scrape relevant reviews across dozens of e-commerce platforms easily.

It primarily sourced data from parent-related blogs, community forums, articles, review sections of webshops, and more. Then, the data was sorted, cleaned, and translated into visual summaries. Our client’s employees across their various departments could easily access and share the data at any time.

Using Wonderflow’s granular insights, the firm learned that its new product had low sales because it did not truly meet the consumers’ expectations. It had nothing to do with poor technical design. The device’s app notifies users if and when the child’s temperature fluctuates (‘negative’ alert).

The app did not inform users when there was simply no change in body temperature (‘positive’ alert). This design was because the product developers initially assumed that any child caretaker would want to only know about a baby’s possible sickness. In other words, our client thought no news meant good news.

Additionally, no team member was a parent themselves, proving that they did not fully understand their target demographics. In fact, many of the device’s purchasers were the kind of parents or nurses who wanted constant updates on the child, even if he or she appeared to be in good health. Some users complained in their reviews that they felt the need to wake up during the night to check on the baby, believing that the thermometer was not properly functioning.

Overall, many felt it was a poorly designed device due to the absence of both good and bad app alerts.

By leveraging Wonderflow’s technology, our health tech client turned insights from nearly 500 consumer reviews into immediate results. 

Contact us to learn all the key results and the full story.

 

Wonderflow respects the client’s wish to share limited information in this case study.