Importance of Understanding Consumer Emotions since COVID-19

corona virus word cloud

People’s lives across the globe have been disrupted and impacted in unprecedented ways due to the current COVID-19 pandemic situation. The spread of this pandemic has meddled with the lives, livelihoods, communities, and businesses worldwide. The Asian Development Bank (ADB) has forecasted that the COVID-19 induced economic loss could go up to $8.8 Trillion, i.e., 9.7% of global GDP. The International Labour Organization (ILO) has pointed out that more than 400 million full-time jobs have been wiped out globally due to this pandemic.

corona virus word cloud


This pandemic has also brought in a unique set of social and economic challenges that have emphasized the actual value of identifying a business’s strengths and weaknesses, as well as recognizing and mitigating associated risks. At this critical juncture, organizations around the world have pushed themselves in innovating solutions to limit the economic disruptions and put them back in the path of recovery. In order to do that, it’s critical for the organizations to empathize with their customers at the emotional level. In this post, we shall discuss the importance and ways of understanding customer emotions during these times.


Importance Understanding Customer Emotions

With most of the customers restricted within their homes due to the COVID-19 lockdown, delivering experiences and services that meet their new needs with empathy and care would be the influencing factor in the survival of the business during this pandemic. The companies that are capable of delivering the same would create a long-lasting positive customer experience thus leading to better customer retention and sales. 

Currently, the key challenge for marketers is to get a pulse on the customer preferences in real-time and rapidly innovate to optimize the product and identify the customer journeys of the target segment. In order to achieve the same, along with agility and responsiveness, organizations must also ensure that their decisions are backed through data and real-time research.


Ways to Understand Customer Emotions

The traditional marketing channels have gone obsolete during this pandemic, and the marketers have had to rely solely on  digital channels for  insights to influence their target audience. The following are the sources that can be leveraged to efficiently understand the customer’s emotions.


  1.     Search Queries

Google, the leading search engine that processes around 70,000 searches every second and 5.8 billion searches every day. All these searches are customers trying to find solutions for their problems. That is a lot of data that can be leveraged in driving business results. A systematic keyword research shall help in understanding the following.

  • Who is your Target Audience
  • What are their Areas of Interest
  • What are their Needs
  • What is their state of mind


Analyzing the search queries provides insights on the customer’s emotions and needs and adds more value to the business.


  1.     Social Sentiment

Social media has become a prominent digital platform where customers voice  both their concerns and satisfaction about the product or service. Text mining the social media posts of the target audience and performing sentiment analysis on the same  provides valuable insights about their current emotions. These relevant insights help in identifying product/service gaps and anticipated features that can be funneled to improve the customer experience leading to better business growth.


Setuserv’s Customer Review Insights and Signal (CRIS) platform can help businesses in improving their marketing content reflecting customer’s emotions and needs based on the automated search query analysis, and also perform sentiment analysis that helps in a deep understanding of their customers and market ecosystem. Read more about the CRIS platform here.

Role of Sentiment Analysis in Market Research

Social Media Sentiment Analysis

Technology is radically transforming the customer journey, making customers more connected and more empowered than ever before. With more than 59% of the world’s population connected via the Internet, customers are now actively voicing out their opinions on social media and other online platforms, leading to an enormous volume of user-generated content. For brands, these immense data provide deep insights into consumer behavioral trends and help companies learn about customer perceptions of their products, services, or events. Here is precisely where techniques such as Sentiment Analysis plays a pivotal role in Market Research.

What is Sentiment Analysis?

Sentiment analysis refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.

In a nutshell, Sentiment Analysis is a text analysis technique that enables interpretation and classification of human emotion and intent within the text data.

Social Media Sentiment Analysis


Need For Sentiment Analysis

Most of the Market Research focuses on quantitative metrics such as market share, market penetration, and SOV. However, there is a gap in performing qualitative analysis that helps in understanding the real pulse of the customers. The real challenge causing this gap is most of the customer-generated data such as social media posts, chat conversations, blog posts, and emails are unstructured data. This type of data is not only hard to analyze and understand but also time and resource consuming.

Sentiment Analysis employs text mining and Natural Language Processing algorithms that analyze unstructured data and derives insights that help businesses in deeply understanding their customers and competitors.

Sentiment Analysis Methodology

Below is the process flow of a generic Sentiment Analysis process

sentiment analysis process flow

  1.     Data collection

As the Sentiment Analysis leverages vast user-generated content, the raw data from various data sources such as Social Media, Blogs, Discussion Boards, Review Sites, and E-Commerce are collected and stored in a Data Lake. At this point, the data is highly disorganized and unstructured.

  1.     Text preparation

This phase involves the cleaning of the extracted data and prepares it for further analysis. For efficient analysis, irrelevant information such as noise, non-contextual content, metadata, and stop words gets identified and removed.

  1.     Sentiment Detection

Before leading to the next phase, it’s essential to filter the data further for efficient analysis. In this process, all the textual sentences in the dataset get tested for subjectivity. Only the sentences with subjective expressions are retained in the data set for further analysis, whereas the rest are discarded.

  1.     Sentiment Classification

In this phase, the subjective text data gets classified into various sentiment classifications. After sentiment classification, the polarity of the sentiment is detected and determined whether the text expresses positive, negative, or neutral emotion. Broadly, there are three types of sentiment classification algorithms.

  • ML-Based: This method uses Machine Learning algorithms, which leverages prediction models extensively trained using pre-existing labelled data.
  • Lexicon-Based: This approach uses a dictionary of words in which each word gets mapped with its emotional polarity and sentiment strength. Then, the dictionary is matched with the data to calculate the overall polarity score.
  • Hybrid: This method leverages the best of both above. It uses Lexicon-Based algorithms for training the prediction model, where Lexicon is proven to be more efficient. The same prediction models are used to analyze the data using ML for a quicker turnaround.
  1. Analysis Presentation

Conventionally, the analysis insights were published as standalone reports and graphs. But with the growth in technology to process a large amount of data in real-time, interactive dashboards with detailed data visualization features are currently used.

Sentiment Analysis Classification

Application of Sentiment Analysis in Market Research

Sentiment Analysis not only helps businesses monitor their customers’ perception of their brand but also provides valuable market intelligence on the complete ecosystem. The following are some of the secondary market research use cases of Sentiment Analysis.

  •       Competitor Analysis 

Sentiment Analysis not only provides insights about your online reputation but also allows for an understanding of what customers in the target segment think of your competitors. These deep insights help in bridging the gaps in products and services to resonate with the target audience and also aid businesses in staying ahead of the competition.

  •       Product Optimization

With the development of deep-dive analysis algorithms, it is possible to understand not only the sentiment of the customers towards a product but also the factors, features, and attributes.. Such insights help businesses optimize their offerings and make it attractive to their target segment.

  •       Brand Monitoring

Brand monitoring is one of the most common applications of Sentiment Analysis. It helps in assessing how a brand, product, or organization is perceived by the public, especially in target segments.

  •       Customer Experience

An immense amount of customer data such as chat transcripts, voice recordings, and e-mails get generated while customers interact with customer support. Measuring the customers’ reactions in these interactions would give us a clear picture of their level of satisfaction, and also reflects the performance of the support teams.

SetuServ’s VOCIS platform can help businesses perform efficient Sentiment Analysis that helps in a deep understanding of their customers, marker ecosystem, and help stay ahead of the competition. 

Scaling up your Share of Voice in a Competitive Market


Share of Voice (SOV) is a critical metric in evaluating the overall marketing efforts as it reflects both marketing efficiency and product performance. In order to efficiently gauge brand visibility, it is vital to understand this metric and leverage the same to gain an edge over your competitors.

What is Share of Voice?

SOV is a brand’s share of the total spending for the category for a specific time period. In short, it’s the measure of the market the brand owns compared with its competitor. The below is the generic share of voice formula

Share of voice

Though there is a relationship between both metrics, SOV is often confused with Share of Market (SOM). To clarify, SOM is the brand’s share in the total sales in that category in a given time period, whereas SOV is the brand’s share of resonance in that category in the same period.

Share of voice by brand


Why is Share of Voice Important for the business?

SOV has always been an important metric for marketers as it helps them in assessing the visibility of their brand in the marketing place. Traditionally, SOV echoed the voice of the brands and their ability to gain media exposure. But, with the increase in social media penetration and customers sharing their viewpoints on it, SOV has become the voice of the customers that brands must perceive.

 The following are the reasons why SOV is critical to the brand & business:

 Acts as a barometer to the reputation and visibility of a brand

  • Measures the success of the marketing campaigns
  • Helps in strategizing and optimizing marketing spends
  • Helps in understanding the market and the competitor landscape
  • Provides insights into the customers, competitors, industry trends and issues

How to increase Share of Voice?

If the  objective is to improve the performance and market share of your brand, it is essential to scale up the SOV.

  1.   Know Your Share of Voice

The first step forward is to accurately determine the SOV, not only at the brand level but also on the Segment, Category, and Product levels. As it may sound simple, it requires a robust tool with lexical text analysis and Machine Learning algorithms that mines the following data from Social Media, Search Engines, Competitor, Review, and E-Commerce websites and transforms into meaningful insights.


  • Brand Mentions
  • Hashtags
  • E-Commerce Listings
  • Customer Reviews
  • Ad Impressions
  • Search Results

Accurate SOV not only helps to understand the brand’s position in the market landscape but also assists in better understanding the competitors and device further strategies in winning the market.

  1.   Leverage Allied Metrics

SOV used in conjunction with Brand Development Index (BDI) and Category Development Index (CDI) can help marketers focus on markets that have potential and optimize their marketing spends. Growth in both BDI and CDI eventually contributes to an increase in SOV.


The Brand Development Index (BDI) quantifies how well a brand performs in a market, compared with its average performance among all markets. That is, it measures the relative sales strength of a brand within a specific market [Source: Wikipedia]

 The Category Development Index (CDI) measures the sales performance of a category of goods or services in a specific group, compared with its average performance among all consumers. By definition, CDI measures the sales strength of a particular product category within a specific market. [Source: Wikipedia]



Go With The Trend

It’s pivotal for a brand to keep up with the ever changing social trends. The best way to stay afloat as a brand is by understanding the content that’s consumed by the Target Audience. By tracking the flash trends in the market and by creating content with trending keywords and phrases shall contribute to brand awareness, thus boosting the SOV.

Influencer Marketing

Partnering with the right influencer in the niche can help to build trust in the brand. Influencers also help in creating compelling content that connects well with the Target Audience and boosts brand awareness. Knowing your influencers and the right way to connect with them and their followers is key in boosting SOV.


The Market Landscape module of the Setuserv’s Customer Review Insights and Signal (CRIS) platform can help brands understand their effective SOV and industry trends and help them boost their market share and stay ahead of their competition. Read more about the CRIS platform here.



5 Ways in which Customer Sentiment Analysis can Improve Your Customer Experience

customer journey

Customer Experience (CX) has become an indispensable factor in running businesses today, with more than two-thirds of the companies primarily focusing on CX to stay ahead of their competition. Also, recent research by PwC shows that 86% of adult customers are ready to spend more for a better customer experience, and this ground reality makes enterprises prioritize CX.

Getting Started – Data is the key

The best way to start is to focus on collecting customer data and feedback from multiple channels and leverage the same in deriving valuable and accurate insights. Then, use it in improving the acquisition, satisfaction, and retention of the customers, thus increasing the overall revenue. The more the number of customer touchpoints, the more data is generated and the generated data can be analyzed for superior actionable insights. Each stage of the customer journey has multiple touchpoints and it is important to have a great user experience across these stages. For example, in the awareness stage the ease of participating in NPS surveys, discussion forums, chats on the website, etc becomes key to a good quality customer experience. Likewise streamlining premium user experience across other stages like purchase, usage, customer support, and loyalty rewards the brands with higher customer affinity if the brands proactively use the data to gain deeper insights.

customer journey

Customer Sentiment Analysis

Customer Sentiment Analysis is an automated technique that empowers us in the emotional understanding of how customers feel about a product, brand, or service. This process leverages advanced Natural Languages Processing (NLP) algorithms in ingesting a large number of online communications from multiple channels and converting them into meaningful insights that help in optimizing various use cases.

Below are the five ways in which Customer Sentiment Analysis would help in enhancing the Customer Experience

Understand Social Sentiment

In today’s context, Social Media is the go-to place for customers to bounce their feedback, either be a shout-out or a concern regarding a product or service. By continuously monitoring the brand mentions in social media, and streaming them into text mining algorithms, it is possible to build an overall sentiment index to measure the customer perception and reputation of the brand/product in the social spaces. The sentiment index is then further clustered into a topic-wise sentiment index increasing the granularity of the analysis.

Improve Products and Services

Performing sentiment analysis on the product reviews on e-commerce sites and market places, surveys, support tickets, and transcripts of focus group interviews assists in identifying bugs and gaps between product features and customer expectations. This data guides us to strategize a stellar product road-map and service improvements that help improve customer experience.

Enhance Customer Service

The efficiency of customer service is one of the crucial factors determining customer experiences. There are multiple channels such as e-mail, chat, and calls where the customers interact with customer service to solve the problems related to the offered product/services. By running, sentiment analysis on the customer and agent interaction uncovers gaps in customer service, thus improving the overall customer experience.

Knowing The Competitors

A comprehensive competitor analysis aids in identifying influential competitors and provides valuable insights into their product/services and marketing strategies. By also running sentiment analysis on the competitors would benefit in understanding market gaps and opportunities that provide you an edge in selling your product. This analysis also improves the customer experience by learning from the mistakes of the competitor.

Optimizing Marketing Efforts

Sentiment analysis also acts as a guide to accurately derive the campaign performance by analyzing how customers react and engage with marketing campaigns. These insights also support in segmenting customers and deliver hyper-personalized messaging that improves conversion rate and customer experience.

 SetuServ’s AI-powered Voice of Consumer Insights and Signals (VOCIS) platform is a proven solution when it comes to Customer Sentiment Analysis and helps you to provide a better customer experience to your customers.


How Deep Dive Insights Help Organizations Stay Ahead Of Competition

Obtaining insightful information about markets and competitors and transforming such data into valuable insights continues to be a challenge for most organizations today. Nevertheless, a majority of organizations rely solely on surveys, experience and intuition when making strategic decisions. As industries become increasingly complex and organizations are overwhelmed with data from a spectrum of digital channels, they must leverage both broad based data sources as well as Deep Dive Analysis to stay ahead of their competition. The deeper and more accurate the understanding of the market, customers, ecosystems, and competitors, the more the products and services shall be precisely designed to appeal to the target market and outweigh the competition.


Shortfalls Of Survey

Conventionally, businesses use surveys, most often with close-ended questions, as a primary research tool to acquire market intelligence. While surveys can efficiently target specific questions, attitudes and products to reveal details about specific areas, they fall short in uncovering the organic, emotional responses from consumers to a product or service. The following are the shortfalls in solely relying on surveys in acquiring competitive intelligence:

  • Surveys depend on the consumer’s motivation, honesty, memory, and ability to respond, and they are very susceptible to bias.
  • Respondents may not feel comfortable providing answers that unfavorably present themselves.
  • Questions are pre-selected and may drive a respondent to provide opinion where they may have been once indifferent
  • Inaccuracies in data due to discrepancies between the respondent’s stated opinions and their actual experiences and opinions.
  • Structured surveys with closed-ended questions may have low validity when researching effective variables.
  • Data aggregated from limited response shall not be generalized for the whole Target Audience group, as the number of respondents who choose to respond to a survey may be different from those who didn’t, thus creating bias.


Bridging The Gap With CRIS Deep Dive Analytics Module

With the continued growth in digital consumption and online competitive influence, businesses need to consider how to evolve to meet the changing demands of both the consumers and competition by moving beyond the limited scope of surveys. SetuServ’s Deep Dive Analysis module of Customer Review Insights and Signal (CRIS) provides a turn-key solution to uncovering organic consumer market research and competitive intelligence.

Deep Dive is a real-time analytics module (part of CRIS) that mines customer and competitor information from a variety of sources such as social media, review websites, and competitor websites and can also incorporate a company’s internal data sources such as e-mails and open ended survey results. Insights views showcase how a category, brand and/or product is trending, what themes are most impactful to performance and how these product feedback themes compare vs. competition Deep Dive employs a hybrid system of Machine Learning and Contextual Text Mining algorithms and augments the same with human curation in delivering competitive intelligence not just at the brand level, but drilled down to Category, Sub-Category, Product and Feature levels.

Listed below are some of the real-time insights that Deep Dive module is capable of delivering in a more granular level via an interactive and dynamic Tableau Dashboards.

Brand Performance

  • Fundamental emotion that a customer associate with the brand and the competitors
  • The sentiment of the brand against the competitors
  • Bench-marking new products and innovation of brand and the competitors
  • Attributes of the brand influencing purchase decisions

Segment Performance

  • Top selling products in the segment compared to competitors in the same category
  •  Attributes influencing customers buying decision in the segment
  • Opportunities for innovation in the product/service category

SKU/Product Performance

  • Overall customer rating of the product against its competitors
  • Market share analysis of competitors by product/service
  • Customer sentiment attributed to product features


SetuServ delivers bespoke deep-dive insights into the consumer decision-making process based on the specific needs of the organization and helps brands gain an edge against competitors. Learn more about Deep Dive here.

How To Leverage Machine Learning And AI For Market Landscape Analysis

Market landscape

Market landscape involves the broad mapping of various players in a specific industry, segment or geography with characteristics like business health, drawbacks, and opportunities. Market landscape analysis helps to understand competition and create broad business strategies using specific business insights. 


Why and when to use market landscape analysis?

Market landscape analysis gives a wide canvas for brands, so the boundaries have to be defined and purpose-driven. For example, it involves the mapping of existing players in a segment, competitor’s strengths and weaknesses, demographics targeted, etc. The availability of tons of data helps to understand where a brand stands vis-à-vis competitors.

With such an analysis, making data-driven decisions that are in sync with business objectives and emerging trends becomes easy. It allows organizations to build holistic go-to-market strategies or leverage gaps left unexplored by competitors.  

The process of mapping various characteristics is useful when considering significant expansion or planning for diversification from core business. Market landscape analysis helps in building a superior strategic plan apart from playing a key part in setting up a collective impact collaboration.


The traditional approach to market landscape analysis

The traditional approach to market landscape analysis involves a combination of qualitative and quantitative techniques to understand the size of the market, needs of the consumer, and competition in the market. 

Qualitative research involves ethnographic study, focus group discussions, in-depth interviews, etc whereas, quantitative research involves survey and statistical analysis of secondary data. Such a research project is considered to be time-consuming and the sample size plays an important role in defining the accuracy of resultant insights.

The time consumed by similar research projects is very high thus, the chances of missing newer trends with the rapidly changing technological landscape might arise. To mitigate such unforeseen risks, we at Setuserv have created a platform that helps in being dynamic with your market landscape analysis, the platform is called VOCIS. It uses customized machine learning algorithms as per the requirement of a brand to ensure actionable insights are mined.


VOCIS Platform: Dynamic approach to market landscape analysis

The market landscape module of the VOCIS platform augments the traditional approach of analyzing data with a specialized machine learning algorithm. The algorithm helps analyze data at a granular level and gives easy to understand information about:


  • Share of Voice
  • Ratings of specific brands and their products
  • Customer sentiments
  • Trending Brands


Market landscape


The above diagram depicts the data from the health, household, and wellness segment. 

Advantages of the VOCIS platform

VOCIS platform is robust and allows to capture accurate insights by mining data about new entrants without dislodging existing information. Gathering similar data helps in effective competitive bench-marking.

The platform’s true potential is in the ability to take more structures of data, this helps it to be scalable. i.e. The brands have their own proprietary data can interlace with existing data being mined by the platform thus, creating highly customized and brand-specific solutions. 

By combining the specificity of human intelligence which understands the emotional aspects of consumers with data mined from the abundant unstructured data using AI algorithms you have a strategy that is validated using data. This approach helps increase the confidence in actions taken as the insights of the human brain are validated through data from artificial intelligence.

Adopting the Amazon way of creating bestsellers through actionable insights

We often tend to romanticize the stories about intense rivalries like Steve Jobs vs Bill Gates, Arnold vs Stallone, Messi vs Ronaldo, etc, and if the rivalry has a bit of an underdog story similar to that of David vs Goliath then the interest levels peak. Well, when we correspond the same to various brands and their fierce competition among each other, there are few aspects which pop-up evidently. Every successful brand builds efficiencies in particular aspects of their business and rework them continuously to gain more insights. For some businesses, these insights might be intuitive but in this age of information overload and clutter of data, extracting insights that are actionable involves building complex analytical models and queries.

Gathering business insights the traditional way

The traditional approach of getting business insights involved a combination of focused market research campaigns, brand study, and intuitive hypothesis basis limited customer interaction. Such an approach might not elicit the actual emotional thought process and expectations from a consumer which in turn can be used to create efficiencies in various processes or innovate new products. This approach lacks the flexibility for the current age as the trends, customer preferences, and economic climate changes happen at a rapid pace. Adding these data points to the research increases the complexity and at times the analysis might become obsolete by the time the report is published.

For example, assume that you were launching a shopping website optimized for Internet Explorer and you had authorized a study on the same. While the study is underway, the consumers start shifting to Google Chrome and Mozilla Firefox. The change happens in a rapid and stealth manner that you as a firm might have an optimized website for Internet Explorer but the greater business opportunity would be lying with Chrome and Firefox. It would be a double whammy of sorts as the study would be redundant and the first-mover advantage too is lost.    

Gaining deep customer insights

 Deep customer insights is a granular and intimate shared understanding of the customer psyche which involves deep inspection of the spoken and latent, current, and future needs. Such depth may not be possible through traditional methods especially market research through surveys and questionnaires as the number of data points to be recorded would be very high and customer fatigue would creep in while answering long questionnaires. The other factor is that traditional methods can’t be implemented at a scale unlike building analytics models or data capturing infrastructure while mining deep insights. Creating a framework where data can be used for decision making will help small and medium enterprises to create superior products with better resource allocation and for large-scale companies, it helps optimize their marketing and R&D budgets. 

Doing things the amazon way

Amazon’s advantage is the scale not only with product sales, but also product feedback on what sells. The company uses data on its portal to develop and launch private labels for products with high sales. The information available with amazon helps them understand how to price a product, at what margins they need to operate, features that can be included, and whether the segment is a viable business opportunity. According to an article in wall street journal, Amazon used data about a niche category of bestsellers sold by a third-party vendor like car trunk organizer. The information accessed included total sales which give an idea of the opportunity, marketing and shipping spend, and the fee charged as commission. The data was used to introduce Amazon’s own car-trunk organizers thus helping Amazon achieve higher margins when compared with third-party retailers. Most retailers introduce their own brands using data to some extent but the information they have is far less than what is available to Amazon. This allows Amazon to be more dynamic in terms of pricing the product as per current economic conditions. 


Can the Amazon data mining model be replicated?

It might not be possible to replicate the process in its entirety as Amazon has access to proprietary data and the amount of information available is vast. However, we can use publicly available data on Amazon and augment this feedback with other public and private sources to build a robust solution. This is precisely what SetuServ’s VOCIS platform offers. The platform mines consumer feedback from multiple online sources including eCommerce platforms (this includes Amazon bestsellers data), business listing sites, social media portals, travel sites, and all other major sources for reviews. The platform uses machine learning to mine through millions of data points and serves up insights. The two biggest advantages of the platform are 1) it has the ability to integrate data and insights across channels, both public and private, and 2) it is fully customizable per domain, brand, company, and/or business solution. The ability to customize helps in avoiding redundancy and aides in scaling up the solution for future use and integration to business operations.

Existing solutions vs VOCIS

SetuServ’s VOCIS platform is modeled on the basis of granular insights to uncover the WHY on consumer’s behavior to purchase. Granular insights and the ability to benchmark against competition separates VOCIS from existing platforms. In simple terms, AI models can be customized as per client’s data and use cases, thus, helping deliver insights that are actionable. 

As popularity for VOCIS grows, so do the ready-made available live data portals. Two such industry-specific solutions are:

Sneakpeek: Insights offered by VOCIS solution

VOCIS solution mines millions of consumer review data points via multiple channels and integrates them to serve upmarket research insights. The comprehensive approach was taken by the VOCIS platform while mining data gives the user a holistic understanding through three key modules:

  • Market Landscape 
  • Deep Dive Analysis 
  • Flash Trends 

CBD Insights From Public Comments to FDA

FDA recently asked for public comments on Cannabis-related products with the intent to understand public perception on the effectiveness of CBD products and to gauge public interest in legalizing CBD. This request received ~4,000 comments from April to July. We analyzed this data to quantify the public perception on CBD.

Overall, the commenters were very bullish on CBD. Their sentiment on CBD products was quite high as shown in the following graph.


This high sentiment was driven mostly by topics on “Advocacy for CBD Legalization” and a variety of effectiveness related topics as shown in the following graph. There is an ongoing debate on the legality of CBD. In this source, commenters have predominantly argued for making CBD legal.


“Pain Relief” which was the second biggest positive topic has a broad range of sub-topics under it, people are using it for all the following use cases –


Under “Preferred To Other Medicines/ Drugs”, below are the common products for which CBD is used as a substitute


Comments were written by both males and females, with males writing more comments than females, as shown in the graph below:


Emerging Trends In Cannabidiol(CBD) 2019


The CBD industry is growing rapidly and is expected to grow to $20 Billion by 2024 (Forbes). This growth is driven by CBD’s adoption in industries as diverse as cosmetics, food & beverage and pharmaceuticals.

Though there is a strong interest in medicinal benefits of CBD, they haven’t yet been understood fully (Economist). However, consumers have started adopting CBD based products and have started reviewing them on various media. Just in the past 3 years, ~27,000 reviews were written in ~25 sources across the US, UK, and Germany. We have mined this rich source of user experiences to quantify how CBD is benefiting consumers and to uncover the emerging trends. This blog article summarizes the emerging trends over Quarter 2 (Q2) vs. Quarter 1 (Q1) of 2019.


More than a quarter of CBD reviews talk about how effective CBD is in addressing pain relief, stress relief, Insomnia and a dozen other benefits. Among these, Pain Relief and Migraine are emerging benefits in Quarter 2. A higher proportion of customers have used them in Quarter 2 than in Quarter 1.


Also, Customer Loyalty for CBD products has increased (from 9% in Quarter 1 to 11% in Quarter 2). Not only do customers intend to buy more, but more customers are recommending CBD to others.



CBD products are made in multiple formats – pills, powders, tinctures etc. In 2019, products in Tincture liquid format proliferated the market. However, in 2019 Quarter 2, there has been a considerable drop (vs. Quarter 1).



Among all the brands sold in the market, “CBD Armour” and “StarKit” has garnered a higher proportion of  reviews in Quarter 2 than Quarter 1. For “Star Kit”, a specific product, “Startkit CBD E-Liquid”, has grown from 2% on Q1 to 4% in Q2.


Among the existing players, “Pure CBD Exchange” and “White and Fluffy” have witnessed a decrease in the number of Brand level reviews.



Among ~25 sources we covered, “Trustpilot” had the highest percentage increase in the reviews count while “Healthy Hemp Oil” had the highest percentage decrease in reviews volume.



 “Ice Head Shop” is the review site that had the highest number of products being launched coming up every quarter. In Q2 alone, it had ~80 new products being launched.


Are You Listening To The Voice Of Your Employees ?

“The real competitive advantage in any business is one word only, which is ‘people’ “ – Kamil Toume

 Businesses realize the value of mining Voice of Customers (VoC) to derive actionable insights. However, they do not pay much attention to generating and mining Voice of employees (VoE).  Mining VoE and acting on these insights can help create a very engaging workforce. This recent article from economist estimated that a one-point gain in employee satisfaction raises that of customers by 3.2 points in industries where workers have the most direct contact with customers, such as retail, restaurants and tourism.


To practice what we preach to our clients, we at SetuServ aimed to generate and mine feedback from ~50 employees we have. When we initially requested employees to provide constructive feedback, we generated some data but not enough to derive systematic insights. We then ran a contest to write kudos on anyone that they interacted with. This generated ~10x more data. Combination of a variety of such tactics and incentives helped us generate sufficient VoE data to analyze.


We then analyzed the comments using our proprietary text analytics solution to understand the areas our employees were strong and the areas they needed to improve. The following chart shows all the topics that our employees were strong at. We were glad to see that these strengths are what we need to build innovative solutions for our customers.

Analyzing the constructive feedback comments and performance evaluation comments showed that our employees needed to get better at communication and handling high pressure situations. We came up with a Learning and Development plan to improve these skills through training sessions, case studies and mock projects. We also created staffing opportunities that would help our employees practice these newly learned skills. 

Overall, listening and acting on the voice of employees has helped us reduce the project time cycles, improved quality of deliverable and improved employee satisfaction. We plan to repeat this every quarter. Even large companies with hundreds of thousands of employees can adopt this solution. The new generation of text analytics solutions (such as our solution) can easily scale to mine the high volume of employee feedback.