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.

Source: https://theprauthority.com/

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

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.

2.     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.

3.     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.

4.     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.

5. 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.

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. 

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

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.

 

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.

2.   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]

Source: https://braindispenser.wordpress.com/2011/02/20/how-to-use-bdi-and-cdi-for-planning-updated/

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.

 

 

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 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.

 

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.

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

 

 

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.