Online Review Analysis: Why It Matters and How Advanced Analytics Can Help

Patients today do much more than just follow a doctor’s advice. They search online, and share their experiences on review sites and health forums. These conversations have created a rich source of real-world feedback that pharma companies can no longer ignore. And by using online review analysis, organizations can study patient opinions at scale, understand what works well, identify gaps, and see how their products compare with others. Let’s discover how.

Why online review analysis matters

Think about it: How often have you looked up your symptoms on Google, identified possible reasons for your ailment, and researched treatment options for specific conditions? Most of us do it. If not for ourselves, for someone we know.

Data shows that 66% of internet users have looked for information about their medical conditions online and 55% of them have researched treatment options¹. When it comes to purchasing medications, 43% of them leverage online information to inform buying decisions². They often rely on reviews and recommendations to better understand the product's effectiveness, complications, and possible alternatives.

As more patients turn to these review platforms to share or understand treatment experiences, platforms like Ask a Patient, OTC online stores, disease-specific communities, and patient discussion forums automatically become one of the most comprehensive sources of data - offering pharmaceutical organizations gold nuggets of information that can tell them what is working, what is not, and how their products compare with those of their competitors.

In addition, pharmaceutical organizations can identify trends and patterns in customer opinions over time and use that information to improve their products and grow their market presence.

For example, the following online review tells an eye solution manufacturer that they need to invest in better packaging, ergonomic design, and provide better discounts.

Online review analysis is a primer for market research

Primary market research (PMR), such as Awareness Trial and Usage (ATU) studies, has been crucial for life sciences organizations for a long time. They track how customers become aware of a product, when they start using it, their experience, and if their usage continues – all critical metrics to monitor and improve brand performance over time.

However, while PMR can yield rich data, organizations typically need 6 to 12 months to carry out the extensive process of planning and coordinating personal interviews with hundreds of customers. In parallel, online review analysis can serve as a faster complement, capturing real-time patient feedback while formal research is in progress.

Online review analysis can offer preliminary insights in the interim, helping organizations keep track of the daily market sentiment while their PMR is underway. These insights can be leveraged to understand customer experience and challenges on the go. Additionally, they can help organizations deep dive into specific pain points and challenges during their one-on-one PMR interviews.

Automating online review analysis process for faster insights

Patient or customer review analysis on its own can be agonizing with a manual process in place. When analysing reviews, you'll want to consider questions like:

Accurately answering these questions through manual analysis can be extremely time-consuming. This is where online review analysis powered by automation becomes critical, helping pharma scale insights quickly and efficiently. Fortunately, new automated technology, like Natural Language Processing (NLP), can speed up the analysis, ease resource bandwidth, and more accurately generate review insights.

Leveraging NLP

NLP models can convert unstructured text into a structured format, enabling you to recognize sentiment in customer conversations by identifying language patterns that reflect their opinions and expectations about a certain treatment.

Figure 4: A workflow of Natural Language Processing

Machine learning models are trained to mine data and classify text by polarity of customer opinion (positive, negative, neutral, and everywhere in between). This is called sentiment analysis, and it helps pharmaceutical organizations understand how customers are feeling about their brand or if there's a change in brand sentiment over time.

Case study: Online review analysis in action

For example, a skincare brand is selling an acne liquid solution in 2.5 ml, 5 ml, and 10 ml bottles. Thorough customer online review analysis showed that while customers found the formulation most effective than competing brands in the market, they were more inclined to purchase the 10 ml bottles owing to cost efficiencies.

In addition, the reviews also indicated that the 2.5 ml bottles are not likely to see a rise in sales because a majority of the customers felt that the price was too expensive for the quantity provided. However, overall, positive reviews outweighed the negatives - allowing the company to work on better packaging and pricing, without having to worry about its key formulation for now.

Figure 5: An example of customer sentiment analysis

Figure 6: A deep-dive into customer opinions across negative, positive and neutral categories

In addition, trained machine learning algorithms can also be applied to a dataset of product reviews in an effort to analyze customer star ratings on the platform and understand what the majority of the sentiment points to.

Figure 7: An example of customer star rating analysis

Driving value and impact in life sciences

At its core, online review analysis is about putting the customer at the center, helping pharma companies make smarter product and engagement decisions based on what patients truly value. By applying advanced methods, organizations can move beyond surface-level reviews to uncover deeper insights around preferences, pain points, and expectations. Moreover, with Natural Language Processing (NLP) for real-time interpretation of vast review data, life sciences companies gain faster access to actionable intelligence. This not only supports product improvements but also strengthens trust, sharpens market strategies, and builds long-term competitive advantage.