Why Sentiment Analysis plays a key role in strategy formulation

By March 2020, Covid-19 pandemic left the world stunned and the economy decelerated as governments were compelled to announce lockdowns to curtail the spread of the pandemic. A few months later, realizing the need to balance between health and economy, businesses are reopening with preventive and precautionary measures to resume production without risking employee health.

While there can be no denying the impact of the virus on lives and livelihood, it is also a time when operationally intensive sectors felt the need to and accelerated automation and digitization, according to a McKinsey report. While this will require upskilling and reskilling of the workforce on the one hand, businesses will need to reshape their sales function, promoting collaboration between retailers and manufacturers.

With the pandemic compelling businesses to review their cost structures and reduce their resources, business leaders need to have a finger on the pulse of the market in the following aspects:

  • Strong understanding of their customers’ wants and needs
  • Brand perception in the current market
  • And an inherent understanding of your own organization’s strengths and weaknesses

Needless to add, decision-makers rely on automation and digital tools to develop this capability of listening and conducting sentiment analysis of these factors. The right automated process will lead to a more customer-centric approach to product development, service enhancement, sales, marketing and pricing, among others.

Listening to the Chatter

There was a time when businesses only rarely heard their customers’ views on their products and services. Most of the time, they would hear only complaints and dissatisfaction. Rarely, a superlative experience may prompt a customer to share their appreciation in a public forum. But otherwise, the rate of repeat customers is probably the primary indication of customer satisfaction.

From such a drought of feedback, today businesses are inundated with myriad reviews about their own business or those of competitors. Sometimes, knowing how a related business is faring can also become important. With reviews flowing in from multiple sources (and in a variety of formats), separating the grain from the chaff can be daunting and humanly impossible.

Digitization technologies such as Big Data and Advanced Data Analytics using artificial intelligence, machine learning, deep learning and natural language processing enable businesses to extract meaningful insights. This can help devise meaningful strategies that serve customers better, improve customer delight and thereby plan for overall business growth.

Also Read: Text Analytics of Social Media Comments Using Sentiment Analysis


Fine Tuning Text Analytics

Listening to the chatter on social media, or sentiment analysis solution involves extracting data from sources such as tweets, reviews on different sites, comments, Facebook, pdf documents, research reports, data sheets and so on. It is then categorized as positive or negative and can be done at three levels:

  1. A small sample of words with their meanings and classification is submitted to the neural network for creating a model and training the system. Based on this, the model can classify sentences i.e. text classification solution as positive or negative feedback.
  2. In the second level, the words are paired to their root and then classified following the above method. This enables classifying reviews with mixed sentiments accurately. For instance, in the same review, there may be a positive comment about the product but a negative comment about the service. Both these are classified to provide a deeper insight into the sentiments expressed in the review.
  3. In the third level, entities are classified and the sentiment analyzed. This works best for isolating reviews related to a particular entity like a personality or a corporate brand in a generic site such as Twitter or an e-commerce portal and extracting the views expressed

Customer feedback is critical to understand what works and what doesn’t. It helps the business identify its areas of strengths and weaknesses. It can help in building on the strengths while correcting the weaknesses.

Analytics can also tell businesses which product is most talked about and which is not. This can help in creating focused promotions to improve the sales of the less popular product or rectifying errors that have made it less popular while enhancing the reach of popular products to a wider audience.

Text analytics can also help understand which geographies to focus on, where to have offers that would improve sales and how to upsell or cross-sell products.

teX.ai for Deeper Insights

Our text analytics tool enables sentiment analysis at all three levels. The AI team with more than seven years of experience in this field has worked on sentiment analysis for different industries, enabling them to understand their customers better and design strategies that are more effective.

Some of the use cases where sentiment analysis can be used effectively include:

  • E-commerce sites to understand the performance of their product mix
  • Retailers to understand which of their stores have low footfalls and how to improve it
  • Manufacturers, banking and financial institutions to understand what their customers are saying about their products and do SWOT analysis

These are just a few examples of how sentiment analysis has helped businesses across industries.

Give us a shout, if you would like to talk to our NLP experts in healthcare and life sciences. Visit https://www.tex-ai.com/ for more details.

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