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Tell us about your company’s core competencies? How will Germin8’s offerings become an effective tool for pharma companies?
Germin8 is a digital intelligence company focused on helping our clients become more customer-centric in terms of their products, services and communication. We do this by analysing conversations on social media from users, customers, employees, news media and other stakeholders where they publicly share feedback about a brand and its competitors. The analysis and insight generation is possible thanks to our proprietary AI technology for social media listening and engagement and a team of highly trained data scientists and domain experts.
In the case of the pharma industry, there are plenty of conversations taking place in social networking sites, blogs and online forums, where patients and their caregivers are discussing illnesses and diseases, drugs and medication, treatments, side effects, etc. They often discuss things that pharma companies care about like adverse events, side effects, effectiveness, pricing, adherence and availability. There are also conversations by doctors and pharmacists that will tell a pharma company a lot about how whether their drug is getting prescribed and if the prescription is getting fulfilled. Germin8’s offerings help pharma companies make sense of these myriad conversations from all these stakeholders (patients, caregivers, doctors, pharmacists and media) and help them understand what changes they need to make to their offerings and communication to boost sales and protect the brand’s reputation. Our clients include leading Indian and international pharma companies who use our technology and tools to understand conversations around their brands, competitors and therapeutic areas.
Germin8 has two products on which our various solutions are built. The first is called Germin8 Listening and the second is called Trooya, and both of these work in tandem. Germin8 Listening helps fetch conversations and analyse conversations from social media, while Trooya helps engage with the users who are starting these conversations. It is through these processes of passively listening to consumers using Germin8 Listening, and engaging with them on Trooya that we are able to understand what various stakeholders of a company need.
How do your solutions help pharma companies to understand public sentiments and help in enhancing brand outreach?
Germin8’s USP is that we focus on actionable data-driven insights that deliver some business outcomes instead of just chasing metrics that are difficult to attribute to business value. Two important ways we do this are aspect-based sentiment analysis and domain-specific ontologies. Aspect-based sentiment means that instead of just saying whether a post is positive or negative or neutral, we calculate the sentiment for each aspect of what was written separately. So if a post talks about two drugs, X and Y, and talks about how X was more effective than Y, our sentiment analysis algorithm would say that the post was positive about drug X’s effectiveness and negative about drug Y’s effectiveness. Most competitor tools would simply attribute the same sentiment to all the brands in the same post.
Domain-specific ontologies refer to knowledge structures that have been built by the Germin8 team for different domains or sectors. The pharma-specific ontology would include aspects like pricing, availability, effectiveness, side effects, adverse events, adherence and other features that are relevant to pharma. The effect of having these domain-specific ontologies combined with aspect-based sentiment is that every post is annotated in a very granular fashion. This means that when analytics is done on top of this finely annotated data, we get very rich, domain-relevant and actionable insights.
How can Germin8 help the pharma industry conduct market research?
Germin8 has several solutions that enable the usage of social media data for various market research goals. For the pharma industry, one common use case is when a company plans to launch an existing product in a new market. In such a scenario, we help them understand what patients, doctors, pharmacists and other stakeholders are saying about the drugs they will end up competing against. Pharma companies find this extremely valuable because it helps them understand the perceptions of patients and doctors about competitor drugs even before they launch. This helps them plan multiple aspects of their new product like their positioning, pricing, what their medical representative should be trained to say, how their product should be priced, and how their product should be promoted.
Apart from new product launches, social media research helps in product benchmarking and brand communication analysis. As per the internet laws and website policies, all our data collection is ethical and within the legal framework. We only collect public conversations and never reveal the identity of the person who has written something.
What kind of solutions/services do you offer to your pharma clientele? Do you see any particular challenge in the pharma sector? If yes, what are they?
We offer our pharma clients many different services like online reputation management (which would include things like adverse event reporting, negative news alerts, harmful posts by employees, etc.), competitor benchmarking, community management, and research for new product launches. There are two main challenges when it comes to pharma. Firstly, being a highly regulated industry, we have to make sure that our advice complies with the pharma regulations in the different markets in which we operate. Secondly, the pharma industry is quite conservative and relatively less mature when it comes to social media adoption, and so we have to be patient when introducing new ideas to clients.
Post COVID-19, how will the pharma industry see an AI-led transformation?
The biggest lesson from Covid-19 has been the need for speed. Never before have so many pharma companies had to work at break-neck speed to find vaccines and treatments while still adhering to safety and effectiveness testing protocols. This highlighted the need to reduce the time at each stage from drug discovery to trials to manufacturing and finally distribution. This is where AI can play a huge role in reducing the overall time to market.
There are many areas where AI can help. The first could be whittling down the search space of existing approved drug molecules to find candidates that might be effective for a disease that they had not been originally intended for.
Another possibility is figuring out the patterns in the virus’s pathology to understand how the virus actually works. This could lead to strategies relating to how the virus could be combatted.
A third possibility is that AI can help in the selection and management of the participants in a drug trial so that you can find participants who give the drug the highest chance of succeeding without compromising on the trial’s safety and effectiveness goals.
All of these possibilities would lead to shorter time to market for a drug, but these possibilities are just the tip of the iceberg. I foresee a future where AI will truly transform drug discovery and drug selection so that each patient can be given drugs that are best suited for their unique genome.
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