A friend of yours asks for a recommendation on a fine dining restaurant. Knowing her, you suggest she visit an Italian restaurant which offers a fine dining experience, inside an Italian art gallery, just off the Pacific coastline. The perfect dining experience for an avid art lover like her. While some may say you know your friend extremely well, she may laugh at the comment saying that it took you several failed recommendations to understand that she likes Italian food, art galleries, and restaurants with a view. Since she is only a friend, there was no risk involved.
But, what if you were a food and restaurant recommendation engine and your livelihood depended on getting it right every time. Would your customer believe in giving you repeat business, if you continuously failed to gauge their tastes? You would probably get only a few chances before they would decide to visit another website.
Understanding customer bias and applying patterns of one market to another has become essential to maintain a leg up on competition, in today’s digital transformation era. Businesses in every field thrive on valuable customer feedback to serve their customers better and ensure repeat business. Up until very recently, customer feedback collected through various surveys, focus groups, and online portals were considered extremely valuable mediums in gazing into customer preferences and deducing their overall experience. But, this is no longer true.
Designing and conducting surveys that gather intelligent feedback, developing models such as structural equation modeling, variance decomposition methods, or even a simple regression analysis to understand the key drivers of superior customer experience are not always financially viable and are most often time consuming activities. Moreover, they frequently fail to provide an accurate picture of the customer experience due to various reasons such as sampling, bias, multi co-linearity, etc. eventually resulting in depiction of skewed insights.
Analyzing unstructured or semi-structured data on the other hand, through sentiment analysis, is a frugal option. It is also a better medium to understand and uncover the sentiment polarity behind the data as it provides a ‘relatively accurate’ impression on the product or service. Using variables such as context, tone, emotion, etc., you can identify if the customer’s attitude is positive, negative or neutral.
However, is a positive or negative reaction alone enough? Let us take an example. If the sales for your clothing line suddenly dipped, wouldn’t you want to know the key reason behind it? Did the customers take issue with the quality of the garments? Were the patterns and styles too bold or loud for the target customer in that geography? Were the prices above the average customers’ spending capacity? In such a case, a positive or negative reaction alone wouldn’t suffice. And without these answers, you would probably be compelled to close shop permanently.
So, where does sentiment analysis fall short? Sentiment analysis alone is simply tip-of-the-iceberg information. There is a treasure trove of information still waiting to be discovered and put to good use. This information is ripe for the picking, if we only know how to reach it.
Aspect-based sentiment analysis, as the name suggests, focuses on specific aspects, helping you achieve a level of granularity that is otherwise difficult to reach. So, answers to questions like, “Why did the sales suddenly drop?” can easily be addressed. Using the results from aspect-based sentiment analysis, companies can interpret customer reactions and swiftly act to improve customer experience, resolve customer issues, and change their market position. American eCommerce and Cloud Computing Company, Amazon, is a classic example of one such company that engages in successful aspect-based sentiment analysis.
The customer analytics platform provides end-to-end support across industries covering data management, reporting, insight generation, predictive modeling, analyzing text data with basic transactional inputs, and much more.
With volumes of unstructured data generated from multiple mediums, every second, companies need to wade through volumes of data and extract useful nuggets. This is extremely time consuming and tedious. Using an effective customer analytics platform, you can simplify the process. Don’t miss this rare opportunity to interact with the experts and transform your business.
About the author(s)
Dinanath (Dina) Kholkar is Vice President & Global Head, Analytics & Insights at Tata Consultancy Services (TCS). In this role, Dina guides some of the world’s best companies in their journeys to unlock the potential of their data through the power of analytics and artificial intelligence (AI) to uncover new business opportunities, drive business growth and increase revenue streams.
Dina advocates ‘data centricity’ as a strategic lever for business growth and transformation and believes that the need of the hour is ‘evangelization’ of data & analytics. His thought leadership in addition to his team’s expertise and collaborative working with customer organizations is empowering them to realize the power of their data in real-time decision making and ensuring success in their Business 4.0 transformation journeys.
With nearly 30 years of industry experience, Dina has held diverse leadership roles across the organization and amplified business value to customer organizations covering all major industries. He was responsible for building TCS’ data warehousing and data mining expertise and laying the foundation for the organization’s Business Intelligence practice. Dina has also led TCS’ Business Process Services (BPS) & Business Analytics units and served as the CEO & Managing Director of TCS eServe.
Dina is a member of the Board of Governors of his alma mater Veermata Jeejabai Technological Institute (VJTI), Mumbai and actively involved in the institute’s modernization journey.
Dina currently resides in Pune, India with his wife, son and parents. Outside of work, Dina currently serves as IEEE Pune Section Chair and provides leadership guidance in the areas of NextGen engineering education, agriculture and open data. He is a sports enthusiast who loves playing badminton and running long-distances. He loves to travel to explore new places and is a passionate photographer.