Top 10 KOLS in Oncology

Top 10 KOLs in Oncology

The pharmaceutical industry is a fast-paced and competitive environment. So, a pharma company needs a way to stay on the cutting edge of research, while also investing in new drugs that can generate a profit. One strategy they have developed is to engage key opinion leaders. These are doctors and researchers who have been identified as being respected by their colleagues within their field. Pharma companies work with these KOLs to promote their products and ensure they stay up-to-date on the latest developments in medicine.

Here are the top 10 KOLs in Oncology identified using our PharmaSignals platform. 

Mark Lewis

Director, GI Oncology,
Murray, UT


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148 Studies

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48 articles

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Tatiana Prowell, MD

Maryland, USA

34K Follower

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14 articles

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Narjust Duma, M.D.

Assc Director & Thoracic Oncologist
Boston, MA


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54 articles

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Nathan A. Pennell MD

Professor and thoracic oncologist
Cleveland, OH


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129 articles

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Vivek Subbiah, MD

Oncologist Houston, TX


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405 articles

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Sumanta Pal

Professor of Medical Oncology, Los Angeles, CA


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12 CT Studies

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925 articles

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Stephen V Liu

Thoracic Medical Oncologist, Associate Professor Georgetown, Washington, DC


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126 articles

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Lecia Sequist, MD, MPH

Lung cancer oncologist & early cancer detection researcher Boston, MA


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8 CT Studies

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246 articles

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Charu Aggarwal, MD, MPH

Leslye M. Heisler Associate Professor for Lung Cancer Excellence, Medical Oncologist Philadelphia, PA


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1 CT Studies

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160 articles

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Nicole Kuderer

Translational Oncologist Seattle, Washington, USA


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106 articles

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While this data helps you to get to know on what Pharma Influencers are talking about, our AI powered solution can help to gain deep insights from these social mentions.

Voice of Customer through Data Harvesting and Storage in Data Lakes

Consumer Research of the Future

Imagine a world where you have access to all of your consumer reviews data in one single place. A simple logon, query, and review of what was posted in the last 24 hours across all of your e-commerce channels for all of your brands and products, review star ratings rolled up in comments, consumer challenges, and consumer wins all in a matter of 10 minutes. This is the future.

Brands operating in retail are highly consumer-focused as they operate in a hyper-competitive environment. In such an environment it is critical to understand the voice of customer analytics for consumer brands. To enhance their understanding of consumer behavior, they constantly need consumer-generated data about their products. The traditional approach to getting such data has been to use surveys or research reports from companies such as Nielson, Kantar, and other market research firms. However, the emergence of e-commerce websites, social media platforms, and other online sales channels has significantly increased avenues to understand the consumers. To ensure more seamless access to such large amounts of data, brands can harvest data from these websites and store it in data lakes for future use.  

Benefits of Data Harvesting and Data Lake Storage

With consumer data scattered across various online mediums, the starting point for accessing such data begins with harvesting the data across a wide variety of sources. Second, once collected, data is then housed in one central repository available for further refinement and significantly reduces the time required to source data as the need arises by various cross-functional teams. Finally, these established data pipelines provide access points for continuous data flow from source to data lake storage destination.

The diversity of such data is high yet relevant for various internal teams. For example, likes on social media validate the marketing message, dislikes and negative reviews act as a metric to improve customer experience & product quality. 

Challenges in Building a Data Lake

While data lakes provide instant access to relevant data, data harvesting for data lakes that hold consumer-generated data is not without their challenges. Three key things to note when planning for a consumer reviews data lake include:

  • Data Dispersion: Consumer commentary is dispersed across thousands of public sources, making it difficult to collect relevant data efficiently. Such sources include review sites, discussion boards, blogs, social media comments, and private sources, including CRM systems, survey platforms, and chat systems.
  • Limited API Access: While many sources provide APIs, others need to be scraped with custom-built extractors.
  • Schema Variety: Each website has a different schema, and the scrapers need to be adjusted for these schemas and then set up for data scraping. The entire collected data is stored in a Data Lake to make them usable for downstream processes.

A Consumer Reviews Data Lake in Play

A large CPG company that has leaped the future of consumer reviews market research is the world’s leading consumer health and hygiene company with over 100+ brands and operations in 60+ countries. The key motivation for the company was to improve consumer satisfaction and engage directly with dissatisfied consumers to address critical concerns.

Operating geographies

The team at SetuServ harvested consumer reviews data at the product level from 80+ sources across 29+ countries. The extracted data was then cleansed and aggregated into MongoDB and Postgres SQL databases. In addition, the Non-English reviews were translated into English. The collected information was then disseminated through APIs and PowerBI dashboards with access across the global organization.

Data Sources for harvesting

Various country managers used the extracted data in the organization to:

  • Assess trends over time by brand/product
  • Identify low rated reviews that they could respond to

Because collecting data is an ongoing process, the company is able to mitigate negative sentiment faster and stay updated with market issues in real-time. The above project gives a sneak peek into how powerful data harvesting and data lakes can benefit consumer brands. To know more about data harvesting and building data lakes reach out to us at [email protected] or visit for more information.

Solve e-commerce market share conundrum using reviews data

Market Share

‘Market Share’ is one of the key performance indicators for brands as it helps them understand where they rank vis-a-vis competitors and what they can learn from competitors. In addition, market share data allows brands to decide how they want to position themselves and develop their business plan. Market share is such an important metric that it is specified explicitly in their annual reports, exchange filings, and investor presentations for listed companies. Now that we have established the importance of market share data, let us understand how brands acquire this data.

The traditional approach of accessing market share data involves getting data from market research companies like Neilson and IRI. However, the information is restricted to offline sales of brands and their competitors. The last decade has seen the advent of many digital-only brands, and the covid-19 pandemic has led to accelerated online adoption as an important sales channel. Like Neilson and IRI, no equivalent data sources are available that provide market share data for brands sold online. Therefore, brands need to combine offline and online market share data to have a holistic market share picture while planning future strategies.

Challenges in computing online market share data

Almost all brands have their products listed on major e-commerce marketplaces and aggregators. Besides being listed on such marketplaces, brands are setting up their websites to establish direct relationships with consumers and to garner higher profit margins. While the presence of multiple sales avenues aides in brand visibility and increased sales, they inherently complicate the process of market share computation as,

  • Most e-commerce marketplaces and data aggregators do not disclose online e-commerce sales for all brands in a category.
  • Most countries have multiple e-commerce platforms, making estimation of units sold across all the platforms difficult.

Some e-commerce platforms like Shopee, which has operations in Southeast Asia, and HKTV Mall in Hong Kong provide data about ‘units sold’ at the SKU level. However, these platforms makeup only a fraction of the entire market, thus not fulfilling market share data computation.

A data-driven approach for getting online market share data

While most e-commerce platforms do not explicitly share the units sold for each SKU, they provide ratings and reviews for each SKU as they add credibility to the platform apart from influencing purchase decisions

With the hypothesis that customer ratings and reviews can be used as a proxy to units sold, we started analyzing data in the geography of Indonesia. The logic behind the usage of customer reviews was that more sales trigger more reviews and more reviews convince more users to buy products.

During our analysis of e-commerce products in Indonesia, we saw a strong correlation between monthly new units and monthly new reviews. As illustrated in the following graph, the correlation between new units sold and new reviews is very strong (as indicated by an R-square of 75%).

Product Level Correlation


As we roll up to brand level, correlation strengthens further, as indicated by a higher R-square of 95%, due to reduced fluctuations in new units sold and new reviews.

Brand Level Correlation



Such strong correlations between reviews and units sold to justify the hypothesis that ratings and reviews help quantify market share in a given category. Additionally, reviews explain why a brand is leading or trailing competitors. Finding such root causes helps brands decide the actions they need to take to improve their market share.

At SetuServ we use our proprietary machine-learning algorithm to find such root causes from large amounts of e-commerce data across multiple geographies.

Identify the top unmet needs of patients and physicians

As patients and physicians use pharma drugs, they share their experiences and challenges in social media and discussion forums. For example, they share how well a drug is working for them, post questions on how to handle side effects, and seek answers on payer coverage. These discussions are a rich source to understand unmet needs of patients and physicians. 

However, the patient and physician posts are dispersed across a variety of discussion boards, Twitter and other social media sites. These posts need to be aggregated across all such sources. Posts containing unmet needs need to be segregated from the rest of the posts, and detailed discussion topics need to be mined from such posts.

Once unmet needs are identified, Pharma companies can act on them to improve the patient and physician experience. Below are 3 examples of how Pharma can act on the unmet needs mined from social conversations.

  1. Content strategy for drug websites

Pharma is a heavily regulated industry. Prior to publishing any new content on the drug websites, compliance, ethics and legal teams review it to ensure that the content adheres to what the drug is approved for. Such a review process elongates the timeline and the effort required to develop new content. As a result, pharma brands need to be selective about the content they are developing, and make sure that the content will be helpful to patients and physicians.  

Unmet needs and recurring questions discovered from physician and patient discussions can form a basis for creating relevant content on websites. As shown in the following example, a common question typically asked for most drugs is how CBD oil interacts with drugs.

Creating and adding relevant content to the drug’s website would help the patients and physicians get answers to their key questions.

  1. Work with payers to address coverage & pricing issues

Patient conversations often contain the issues they are facing in getting the drugs covered by payers. Mining the conversations for such insights can help identify the payers and geographies where patients are facing such issues. Following patient post is an example of how a patient could not be covered for getting treatment using Revlimid.

Using above mentioned posts, pharma companies identify the payer coverage gaps, and work with payers to cover their drugs. Additionally, Pharma companies can communicate how patients can get support related to payer coverage and post FAQs on the drug’s websites. This helps patients get access to the key drugs they need for their treatment.

  1. Field force training

Patient and physician discussions can uncover key questions asked by patients and physicians, and other challenges such as drug availability and pricing. Sizing these discussion topics help prioritize the key issues that field reps can be trained on. Such training on key issues empowers Reps to inform physicians on how to address key challenges.

The following example illustrates a common side effect that patients taking Imbruvica experience. 

Continuously finding updating the list of frequently asked questions and training Reps on them would better equip the field force when they face the same questions from physicians.

As illustrated in above examples, patients and physician conversations contain a rich trove of unmet needs. PharmaSignals platform uses AI to uncover such unmet needs.


Competitor Intelligence on Messaging Changes using Automated Website Monitoring

Pharma companies maintain patient and physician websites for each of their drugs to provide information such as efficacy and safety of their drugs. These web pages provide rich insight into how they are positioning and messaging their drugs, and how it is changing over time. Pharma companies can monitor websites of their competitors to glean competitive intelligence on key messaging changes.

However, a pharma drug typically has half a dozen key competitors to track and analyze. Each competitor drug has a patient and physician website, with each website containing 10 to 20 pages to monitor. This implies that a drug company needs to monitor 120 to 240 webpages on a daily basis. The changes identified from daily monitoring are not always relevant to messaging. Hence, valuable changes need to be separated from noisy changes. Performing these activities on a daily basis and identifying key messaging changes can be tedious, resource-consuming and prone to errors. This blog post discusses how automated website monitoring can overcome these challenges and produce valuable competitive intelligence.

Automating tracking of changes in competitor websites

Web crawlers can collect content from webpages on a daily basis. However, the crawlers need to be customized to overcome several nuances such as scraping dynamic content, images & data from pop-ups. Additionally, the scrapers need to overcome blockers and navigate through page hierarchy. Such customizations ensure reliable and comprehensive data collection. Once the data is collected on a daily basis, text mining & pixel mining programs can be configured to identify the daily changes and quantify the percentage of data that changed on the website.

Parsing out key messaging changes

Webpage changes can range from not-so-important changes such as footnotes to very valuable changes such as key messaging changes. A well trained Natural Language Processing (NLP) classifier can parse out the valuable changes from the noisy changes. Following graphic illustrates an output –

Distributing the detected changes to key stakeholders

Once the key changes are parsed, they can be sent to key stakeholders as daily alerts so that they take timely decisions related to their own brands. Following graphic illustrates an example email that can be sent to the key stakeholders.

.SetuServ’s AI-Powered PharmaSignals platform uses the above mentioned technologies to monitor competitive websites for valuable content changes. It monitors the competition through one central interface and notifies the concerned team about new insights, amendments, and additions via e-mail. 

Patient Discovery For Rare Diseases Using Social Listening

Sources mined for rare diseases

The critical challenges faced by the Pharma Companies in developing drugs for rare diseases are mainly inadequate clinical information and difficulty in locating patients. The key factors leading to this challenge is the number of patients affected by rare diseases is scarce and geographically dispersed. Furthermore, the lack of high-level awareness among the medical community and patients has often led to misdiagnoses. All the above factors make it difficult for the Pharma Companies to understand the natural history, epidemiology, and progression of rare diseases that affect small and often highly diverse patient populations, thus impacting drug development progress. Before diving deep into how text mining of patient discussions can help, let us understand what qualifies as rare diseases, let’s understand rare diseases and their criteria.

What is a Rare Disease and its criteria?

Countries have their own official definitions and criteria for a rare disease. In the US, the Orphan Drug Act of 1983 defines a rare disease as a condition that affects fewer than 200,000 people. Whereas, in the European Union, a disease is categorized as rare when it affects fewer than 1 in 2,000 people.

According to the National Institute of Health, there are around 6000 rare diseases. This includes certain types of cancers, autoimmune disorders, digestive disorders, infectious diseases, neurological disorders, and other illnesses. Currently, these rare diseases are found to affect around 3.5% – 5.9% of the worldwide population, which sums up to a conservative estimate of roughly 300 million people worldwide. The foremost cause for 72% of the rare diseases is genetic, whereas the others are caused due to infections (bacterial or viral), allergies, and environmental causes.

Drug Development Challenges

Developing drugs for rare diseases involves challenges beyond those typically observed in large trials for more prevalent conditions. One of the primary challenges is the diagnosis of rare diseases. Identifying the underlying cause and symptoms can be difficult, as it tends to differ for each patient. This challenge in diagnosis boils down to the difficulty in finding adequate patients who meet inclusion and exclusion criteria for a particular drug or therapy trial, as patient populations are often widely dispersed and heterogeneous in disease subtypes, symptoms, stages, and exposure to prior treatment. 

Traditional approaches in rare diseases patient discovery such as the patient survey and extracting data from Electronic Health Record (EHR) physician notes often tend to be ineffective due to a lack of appropriate medical codes and integrity of the data sources. These hurdles in patient discovery call for a novel solution.

Social Listening – A Potential Solution

Patients with rare conditions and their caregivers tend to scatter geographically due to the low prevalence of the diseases. This geographical dispersion makes it difficult for them to connect with other patients and specialists to seek advice.  Their need to connect and find support and growth in global internet penetration has led them to the social media platforms to connect, communicate and garner valuable insights on the diseases.

By leveraging Social Listening, the Pharma Companies shall mine patients/caregiver conversations from Social Media Platforms such as Twitter, Reddit, and rare diseases-related medical discussion forums such as NORD, Inspire & Genetic Alliance to identify them.

Sources mined for rare diseases

The following are some of the social media posts or forum discussions where the patients/caregivers are discussing about rare diseases such as Myasthenia Gravis, Duchenne Muscular Dystrophy, Primary Sclerosing Cholangitis, etc. Mining the text of patient discussions can uncover thousands of patients who are seeking treatments or educational content.

Myasthenia Gravis:

Myasthenia gravis (MG) is a long-term neuromuscular disease that leads to varying degrees of skeletal muscle weakness. The most commonly affected muscles are those of the eyes, face, and swallowing. It can result in double vision, drooping eyelids, trouble talking, and trouble walking.

Myasthenia Gravis Output

Duchenne Muscular Dystrophy:

Duchenne muscular dystrophy (DMD) is a severe type of muscular dystrophy that primarily affects boys. Muscle weakness usually begins around the age of four and worsens quickly. Muscle loss typically occurs first in the thighs and pelvis followed by the arms.

Duchenne Muscular Dystrophy Output

Primary Sclerosing Cholangitis:

Primary sclerosing cholangitis (PSC) is a long-term progressive disease of the liver and gallbladder characterized by inflammation and scarring of the bile ducts which normally allow bile to drain from the gallbladder.

Primary Sclerosing Cholangitis Output

However, given the sheer scale of social media, precisely identifying relevant posts among the noisy posts is equivalent to finding a needle in the haystack and highly resource-consuming when done manually. To overcome this problem, Pharma Companies shall leverage AI/ML-powered text mining solutions that can monitor, crawl and scrape data from various sources, and convert those raw data into actionable insights. Pharma companies with drugs for rare diseases in production/pipeline can use these insights to identify patients with rare diseases and engage with them for drug trials or sale of the product.

SetuServ’s PharmaSignals helps Pharma Companies discover patients suffering from rare conditions by leveraging state-of-the-art text mining and social listening algorithms to extract sheer volume of posts or discussions from social media and relevant discussion forums, process the raw data, and extract the insights about patients/HCP related to the rare diseases.

How Customer Reviews Influence Purchasing Decisions in E-Commerce

Customer Reviews

The growth of e-commerce has been absolutely staggering over the last decade and has become an indispensable element of the global retail industry. E-commerce penetration has been growing steadily and now accounts for more than 21.3% of the total retail sales globally. As this growth in e-commerce penetration continues, NASDAQ predicts that by 2040 almost 95% of the total retail sales will be via the e-commerce channel. This astonishing growth has also created perpetual competition among e-commerce businesses in acquiring and retaining customers. In order to stay successful, it’s critical for e-commerce businesses to solve the never-ending mystery of understanding customer purchase behavior and identifying determinants.

E-commerce customers have become more comfortable in buying products preferred by like-minded consumers in the same demography. This tendency makes customers accustomed to reading online reviews before purchasing from an e-commerce website, this research in some cases extends beyond reviews on the e-commerce platforms to social media posts, blog posts, and news articles. After purchasing, customers also tend to post their own comments and reviews about the products/services online, helping other customers. This makes customer reviews one of the most critical influencing factors to consumer purchase behavior. In this blog post, we shall briefly discuss how customer reviews drive customer’s purchase decisions.

Exciting Customer Review Stats

  1. 91% of customers regularly or occasionally read online reviews.
  2. 84% of customers trust online reviews equivalent to a personal recommendation.
  3. 33% of e-commerce customers choose a product or service based on other customer’s reviews.
  4. Their friend’s social media posts have influenced 81% of customer’s purchase decisions.
  5. 72% of customers don’t take any step towards a purchase until they have read reviews.

How customer reviews matter to a business?

In the current digital world, it’s quintessential for businesses, especially e-commerce businesses, to empower and encourage their customers to write reviews, frequently monitor, review and respond to them. The following key points outline the benefits of online reviews on businesses.

Product Insights: The decision-making process of online shoppers has become extremely complicated. They spend more time evaluating and comparing products before making the final purchase decision. Customer reviews act as a catalyst to this process by helping customers get a better insight into the product and shop at ease.

Online Reputation: With numerous brands offering multiple products in the same category, it’s quite challenging for customers to trust and choose one. Reading positive reviews on the product/service and responses to the negative reviews create an online reputation that makes the customers trust the brand and the product.

Consumer Engagement: Reviews are not just a way for the customers to accumulate information on products or services but also a powerful consumer engagement tool. Customers love their views to be acknowledged and heard by the brands, including responses to their reviews. Apart from thanking customers for positive reviews, following up on the negative reviews and solving the customer’s problem would help the brand go a long way.

Product/Service Feedback: Customer reviews are a great source of product/service feedback. For brands, it helps in identifying customer pain-points and scope for improvements directly from the end-users. Not just the product/service, it also helps in other process-related pitfalls such as checkout, logistics, and delivery that would help in improving the overall customer experience.

SEO and Conversion: It’s quite evident that customer reviews help in improving conversion and customer experience. In addition, reviews have significant SEO benefits. Along with standard product descriptions and product specifications, customer reviews add new user-generated content that helps in improving keyword density and serve to differentiate the product page in the search results.

SetuServ’s Voice of Customer Insight and Signals (VOCIS) platform provides actionable insights on the customers and products derived automatically from customer reviews and social media mentions. VOCIS leverages state-of-the-art natural language processing, text mining, and sentiment analysis algorithms in generating insights that can help e-commerce businesses identify opportunities to drive product/service innovation and sales.

How Pharma Companies Can Benefit From Conference Analytics

Conference Analytics

HCPs desire to keep themselves updated about the latest happenings in their respective therapeutic areas by following KOLs on social media, various journals, blogs, newsletters, etc. One of the key channels which are considered critical by HCPs is attending medical conferences. Medical conferences are not merely a gathering for imparting education but a medium to interact with peers and KOLs, discussing about new drugs, evolving novel, and locally relevant ideas leading to improvement in health-care delivery, and patient outcomes.

According to a survey by Ogilvy Common Health World Wide, most HCPs attend conferences to learn about new clinical information (79%) and keep up with current trends (78%). Thus, reiterating the importance of medical conferences.

Conference Analysis

Most brands use these conferences to present their latest clinical trials and promote their products through promotional booths. Pharma marketing and sales professionals use these booths as a platform for interaction with HCPs and KOLs. They share relevant information in the form of brochures, handouts, and e-mail newsletters. 

Conference Analytics

Many HCPs and KOLs are very active on social media and tend to share their opinions on drugs presented at conferences through these mediums. The gained knowledge during a conference is shared across multiple media sites and journals where they contribute content in the form of subject matter experts. Such data across various channels when collated and analyzed can give amazing insights into what the HCPs and KOLs perceive about various clinical trials presented at the conference. 

With the current COVID-19 pandemic curtailing the ability to freely conduct large gatherings or events, many conferences have shifted to digital gatherings and events. This has significantly increased the online interactions between HCPs and KOLs. Subsequently, KOLs have started disseminating their learnings about clinical trials and new products on their respective social media profiles and other media sites. Aggregating these conversations and analyzing them can give actionable insights.

How does SetuServ’s Pharma Signals Platform help?

SetuServ’s Pharma Signals platform is specifically designed to analyze large amounts of data using contemporary technologies like AI and NLP thus achieving speed, accuracy, and scale. The platform has the ability to aggregate data from various social and media sources, separate noise from insightful signals, extract entities such as study name, disease area, drug and mechanism of action, and mine insights of interest.  The derived insights are used by various teams in different ways to provide insights on KOL, PCP, and patient conversations. 

Competitive intelligence teams can use these insights to understand various aspects, vis-à-vis competitors while the medical reps can use the platform to collate rich quality content which enables quality interaction with HCPs or KOLs.

To illustrate the richness of the opinions expressed at conferences, we extracted 5 insightful KOL tweets from the American Academy of Allergy Asthma and Immunology (AAAAI) conference. The output is shared below

HCP Tweets From Conference

The output represents the quality of KOL opinions. Identifying and mining such insights provide actionable intelligence to pharma companies, especially on competitors’ drug pipelines. 

SetuServ’s PharmaSignals platform helps pharma companies in extracting actionable insights by leveraging state-of-the-art text mining algorithms and industry-specific workflows, data pipelines, and AI modules linked to the specific organization’s KPIs and goals


Understanding Customer Perception on Vitamins, Minerals and Supplement Products – A Voice of Customer Study

Vitamins Competitor Insights

The constant shift in consumer needs is one of the pivotal factors that drive the Fast Moving Consumer Goods (FMCG) industry. Most notable are the constantly changing health trends and vastly available information on health and nutrition that are majorly influencing the types of products consumers are using to maintain their health. Additionally, the increase in health concerns due to changing lifestyles and dietary habits has led to the evolution of one of the fastest-growing CPG categories, VMS. Vitamins, Minerals, and Supplements (VMS) come in the form of tablets, powders, or liquids that deliver vital nutrients to the body and preserve or boost an individual’s health. The products under the VMS category are typically available Over the Counter (OTC), where one doesn’t need a prescription to buy them.

Vitamins, Minerals, and Supplements (VMS) – Market Overview

Growing inclination towards preventive healthcare remains the key driver for over-the-counter VMSs. In addition, the increasing aging population, sedentary lifestyle changes, and emerging trends of self-directed nutrition tend to be the additional factors driving the growth of global VMS. A recent report by Grand View Research highlights that the global VMS market size was approximately $123.28 billion in 2019 and is projected to grow at a CAGR of 8.2% between 2020 and 2027.

The use of VMS skyrocketed during the recent unprecedented COVID-19 pandemic as VMS emerged as a preventive measure and have been used by many to build immunity and to maintain health. To substantiate the same, a recent Civic Analytics Consumer Survey has shown that there is a 10% -15% surge in spends on VMS globally since the pandemic began. Another critical factor that is contributing to the increase in consumption of VMS is the growing e-commerce revolution. Even before the current COVID-19 situation, VMS sales through online channels had been growing at double-digit rates since 2015, which makes e-commerce portals one of the preferred modes of purchase.

Vitamins, minerals and supplements sales in US


Consumer Reviews and VMS: Our Methodology

Understanding customer sentiments towards a product is of paramount importance in businesses today. As E-Commerce platforms are one of the most preferred ways to shop VMS, we recognize the importance of customer reviews in gaining insights on customers’ likes, dislikes, and needs on VMS products. In this study, we mined 105K+ reviews from Further, the unstructured and textual review data was then processed by Text Mining and Natural Language Processing (NLP) algorithms in performing multi-dimensional sentiment analysis. This process uses AI models that are custom trained for the specific use cases with industry-specific taxonomies and hierarchies evolved from raw data. The AI models help in surfacing accurate insights from customer reviews at scale.

Data Mining Pipeline

Consumer Insights & Analysis

The following are the insights automatically derived by the Voice of Consumer Insights and Signals (VOCIS) platform by aggregating more than 105K customer reviews for the products under the “Vitamins and Dietary Supplements” category from

Brand Insights

This analysis focuses on providing insights into the brand perception of the customers. With the use of sentiment analysis, we can compare the perception of a brand with that of the leading competitors and use the same to create a data-driven brand strategy.

Vitamin Brands Comparison

Though the customer ratings (4.77) are marginally lower compared to its close competitors such as Dacha and Wellabs, Sports Research emerged as the most popular and preferred brand with the highest Share of Voice (SoV) of 16%.

Product Level Insights

With sentiment analysis, it is possible to compare key product characteristics to find features that resonate positively with the audience and the ones that don’t. These insights help brands in understanding key need gaps and allow for product optimization in appealing to a broader consumer segment.

Vitamins Areas Of Concern

Out of the customer reviews analyzed, the above topics evolved as the key areas the customers expect all the brands to improve on. For example, taste emerged as the key influencer on overall ratings with a 0.12 impact in rating, the general performance of the VMS turns out to be the most discussed factor at 16% mentions.

Competitor Insights

Competitor Benchmarking is quintessential as it helps in developing a deep understanding of the market ecosystem and builds a picture of gaps to be fixed and opportunities to leverage. It also helps in streamlining the product and marketing efforts by emulating your competitor’s success elements such as price and key product features.

Vitamins Competitor Insights

The above graph highlights areas of strength and areas for improvement for the brand Sports Research and the competition. Topics such as taste, packaging, and addressing them through targeted marketing communications will help in improving the impact on customer preference towards the brand. Likewise, when it comes to VMS, side-effects such as headaches, allergic reactions, and itching can have a negative impact on product sales.

SetuServ’s Voice of Consumer Insights and Signals (VOCIS) helps businesses in extracting valuable insights from customer reviews and conversations online, such as E-Commerce platforms. VOCIS is highly capable of rapidly building custom models specific to the use cases to render highly accurate and relevant insights linked to the organization’s KPI and goals.

How Financial Services Companies Can Improve Their App Performance by Mining App Reviews – A Voice of Customer Study

Amex app competitive benchmarking

The Banking, Financial Services, and Insurance (BFSI) industry across the world has made significant efforts in navigating digital disruption and increasing mobility in the past decade. From being a cost center, digital banking channels have now evolved as revenue generators and cost savers by reducing the enormous operational costs of running brick and mortar branches. With the increase in smartphone and mobile internet penetration, technology has enabled customers to transact on the go. The convenience of swiftly making financial transactions anytime and anywhere with a few clicks has made mobile banking the most preferred banking channel among retail banking customers. Additionally, the power of mobile banking has paved the way for the digital-only challenger and neo banks, and the features offered on mobile banking apps have become key decision factors for customers selecting a bank.


Mobile Money – Market Overview

Mobile banking is already a pervasive trend. According to a recent study by Juniper Research, the total value of financial transactions via mobile devices would exceed $1 trillion by 2024 from $580 billion in 2019. It also predicts that nearly 3 billion people, roughly half the world population, would be transacting via mobile by 2021. This massive growth is fuelled predominantly by the increase in mobile and data penetration in the emerging markets, tech-savvy millennials, and digitally native Gen-Z.

Mobile Banking Competitive Intelligence

Speeding up this growth of mobile banking is the recent COVID-19 situation. The lockdown measures have encouraged consumers to bank from home and have contributed to significant growth in mobile banking adoption globally. Recent research by PYMNTS has highlighted that 8.7% of customers are more likely to use mobile banking apps than they were before the pandemic outbreak.

Our Methodology

Financial Institutions hold petabytes of structured and unstructured data that can be leveraged to anticipate customer behaviors and create strategies. However, the regulatory bodies are becoming particularly strict about how the data gets managed and shared. These restrictions make it difficult for the efficient 3rd party analytics team to access the data to generate key decision-making insights, and they have to rely on publicly available data.

User reviews in open app distribution services such as Google Play Store and Apple App Store are critical in understanding the elements of customer experience. These app markets allow users to submit feedback for downloaded apps in the form of star ratings and text reviews. Mining and processing these text reviews will help in getting deep insights into elements of customer experience on the app.

In our VOCIS solution, thousands of reviews are mined based on the app category from the Play Store and App Store as the primary data source. Additionally, textual data from secondary sources such as blogs and review forums were also extracted for better insights. Further, the unstructured and textual review data is processed by Natural Language Processing (NLP) algorithms in performing multi-dimensional sentiment analysis to understand the user perception towards the app against a variety of critical factors. The insights from sentiment analysis guide the product team in optimizing the app.

Data Mining Pipeline

Analysis Insights

The following are three key areas the insights derive via the Voice of Consumer Insights and Signals (VOCIS) module through aggregating thousands of customer reviews for the apps under the “Finance” category in Google Play Store and Apple App Store.

Brand Insights

This analysis focuses on providing insights into the brand perception of the customers. With the use of sentiment analysis, it is possible to identify the most preferred brand and compare the perception of the brand with that of the leading competitors and use the same to create a data-driven brand strategy.

Amex app comparision

While Chase emerged as the most preferred banking app with a higher rating (4.8) and Share of Voice (33.5%). American Express can now drill down to assess areas for improvement and specifically target these areas as part of their overall strategy.

Product Level Insights

With sentiment analysis, it is possible to compare key product characteristics of similar apps to find features that resonate positively with the audience and the ones that identify areas for improvement. These feature insights guide banks in understanding the key gaps and how to modify the apps to appeal better to their customers.

Analysis of AMEX Ratings

Based on the reviews analyzed, it is evident that the features that Amex should focus on to improve the rating and customer experience are “ improvement in payments and notification”.

Competitor Insights

Competitor Benchmarking is quintessential as it helps in developing a deep understanding of the competitor apps and builds a picture of gaps to be addressed and opportunities to leverage. It also helps in streamlining and prioritizing the product development efforts by emulating your competitor’s winning app features.

Amex app competitive benchmarking

While Amex is leading in the logon by fingerprint sensor theme with .22 vs. competition at -0.4, many people value this feature (ie. big bubble), and improving on this feature would significantly increase AMEX position overall.

SetuServ’s VOCIS platform augments consumer insights by extracting valuable insights from customer reviews in the app distribution platforms, such as Google Play Store and Apple App Store. VOCIS is highly capable of rapidly building custom models specific to use cases to generate highly accurate and relevant product insights for a better customer experience.