Sentiment Analysis: A Catalyst That Fuels Customer Centricity

There is a common belief that- “Expression says it all”. And we all know the importance of this statement in our daily life. But surprisingly, the statement has also influenced the fast-paced modern business fraternity and helped them successfully derive an intelligent method- known as sentiment analysis that can analyze and disclose effective insights from customer interactions.

Why is it important to analyze customer sentiments?

Nowadays, the success of an enterprise majorly depends on the opinion, emotion, and satisfaction of their customers. People generally recommend a product or service if they are pleased with it. Customer satisfaction fuels the reputation of a business. According to the latest research conducted by Nielsen, 83% of respondents say that they can confidently rely on the recommendations of their friends and family. However, based on a report by McKinsey & Company, more than 70% of customers tend to shift their inclination when they are not satisfied with the services. Enterprises that can nurture the opinion of customers or meet their expectations are amongst the A-listers in the business world. Sentiment Analysis (also known as opinion mining) is one of the key aspects for enhancing the capabilities of any business to systematically identify, extract, quantify and analyze sentiments of customers.

“Public sentiment is everything. With public sentiment, nothing can fail. Without it, nothing can succeed.”

~ Abraham Lincoln

How does sentiment analysis add value to the enterprises?

Let us consider a call center scenario where the agents get to deal with numerous customers. Each customer expresses their own views/feedbacks/issues etc. while being in a conversation. This voice data can be run through voice to text engine and the output received can be utilized for deriving sentiment analysis. These results can further help in understanding the scope of improvement in the process or in assessing the service levels rendered by the call center.

Apart from voice data as mentioned in the above call center scenario, the other input sources can be either in the form of the video signal; television; IVRS; text data; social media streams; search queries, and many others. Capturing real-time emotions is more in practice these days as it helps in understanding emotions immediately with probably a negligible delay. Real-time sentiment analysis can open scope of improvement for enterprises without any time slog.

How does Datametica help?

Datametica can help you with its strategic and exclusive way of capturing sentiment analysis with its unique frameworks and accelerators, which can further facilitate predictive analytics, rumor analytics, revenue optimization, sentiment polarity about products/services, and competitive analytics for enterprises so that they can make their business more customer-centric. Our Automatic Speech–To–Text Recognition and Conversion Framework ingests Audio/Video Signals and processes the audio files to text using Machine Learning Algorithms and Statistical Model via an ASR model. To draw the intent/sentiment our proprietary approach is a combination of machine learning, deep learning (deep learning libraries and framework), lexicon, and many others. With advanced techniques like silence detection, video to audio conversion, audio to text conversion, we use natural language processing to obtain specific words which can help in determining the sentiments. Additionally, the model is trained for improved accuracy up to 85%-90%.

Sentiment analysis has manifold advantages for a business. Enterprises can optimize their marketing strategies for an impressive foundation amongst their fraternity. 

Hence by performing sentiment analysis, an enterprise will be able to:

Improve customer service: Based on the sentiment analysis reports, emotion or polarity results, enterprises can improve and innovate their customer services.

Make customer segmentation: Identifying the top customers and capturing their sentiments for categorizing them into very high, high, medium, average purchasers, which can be considered for future references. This can help in segregating customers and work upon better retention plans for top customers.

Predict product performance: Based on identified polarity and text analysis, the top performing products can be categorized. This will help in identifying the products being used and to be used in future.

Improve campaign effectiveness: Monitoring the success of marketing campaigns in real-time is easy with sentiment analysis data. Based on the majority inclination of the customers towards a product or service a campaign’s effectiveness can be improvised.

Determine market strategy: The sentiment analysis data can help in determining number of customers added or removed after a campaign, website traffic after a campaign being run, understanding product feedback, and detect shifts in customer attitude. This can help in planning effective marketing strategies.

Do you have a customer-centric business model? We at Datametica can help you in analyzing the expressions and emotions of your customers. Are you ready to know what your prime stakeholders think about your business?