What value can social listening bring to marketing activities in the biopharmaceutical space?

It’s no secret that social listening holds immense value in terms of brand awareness. Generally speaking, it allows brands to gauge customer opinion through a variety of social media channels, taking consumer voices into account and altering their products or marketing strategies as a result. But how is this relevant to the healthcare industry, and what value does it add to the actual patient experience?

At Permea, healthcare-based social listening focuses on highlighting the positive and negative aspects of a certain medication or treatment, according to real-world patients. Users will voice their opinions and experiences on social media channels such as Facebook or Twitter, which will be analysed by a team of data analysts in order to extract only the most useful and relevant information.

The goal of this process is to create a more personalized and data-driven approach to healthcare. Gone are the days in which The Patient is a homogenous, single-driven hivemind: they are real people, with legitimate thoughts and concerns that are worth listening to. What they say matters, and their opinions can make or break the success of a treatment – so it’s best to nip any potential problems in the bud during the initial clinical trial process.

The clinical trial stage is already a lengthy, arduous, and often expensive step. By presenting biotech and pharmaceutical companies with concrete data concerning what patients do and do not want in a particular medication, research and development teams are able to avoid any unintended pitfalls and increase the likelihood of their treatment being widely accepted and economically successful.

Additionally, by tweaking the treatment in question to better adhere to customer preferences, the research and development teams can avoid investing huge amounts of time in particular drugs that ultimately will not perform well in the competitive healthcare market.

When it comes to drug development, there are countless decisions that need to be made, and it’s quite a challenge for the human brain to process and respond to each one. Using artificial intelligence and predictive data analytics takes the guesswork out of this process, improving the chances of a treatment passing all three stages of the clinical trial step.

Social listening in the healthcare industry is by no means the haphazard and chaotic harvesting of data, fueling marketing departments in their desire to sell you products you probably don’t need. In this context, social listening does what it says on the tin: algorithms acknowledge your thoughts and opinions on your treatment, and relay this feedback to the biotech company so they can actively work on improving your quality of life. Using predictive analytics saves huge amounts of time, letting research and development teams focus on what matters most.

When the word “data analytics” is thrown around in conversation, a reflex that many people have is that of suspicion. Why is my data valuable, and why are big pharma companies so keen to get their hands on it?

One of the driving forces of this distrust stems from a large amount of misinformation surrounding data analytics – but the reality is that data analysts simply aren’t interested in knowing every detail of your life that you decide to share on social media, as this information just isn’t relevant or useful to them in any way.

What data analysts are interested in, however, is hearing patient voices concerning specific medications or treatments. For example, the patient might share their experience of a certain type of diabetes medication in a Reddit forum, highlighting both the positive and negative aspects of the drug, and any potential or unexpected side effects that they encountered. Only this content is even slightly useful to data analysts, who use algorithms to collect this data before breaking it down into digestible, understandable chunks. By relaying openly-published patient opinions back to the pharmaceutical company producing the drug, the makers of the medication might tweak it accordingly – perhaps getting rid of certain side effect that patients mentioned during this process.

The main purpose of data analytics in healthcare is simply to improve the lives of real-world patients – making their voices count.

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