This year, Sarang Panchal moved into the chief position at MRSS India, the Indian division of Asia’s largest market research institute, Majestic MRSS. He brings to the position more than two decades of market research experience, from sales forecasting to advertising campaign assessment. Panchal also has focused on customer sentiment—the opinions and criticisms that consumers offer each day and are so incisive in illustrating the “wide spectrum of needs customers have.”
A veteran of Dun & Bradstreet and The Nielsen Company, Panchal spoke with Data Informed about the power of opinion in the Age of the Customer. Keen analysis of what people are saying about your company goes much deeper than mining Twitter or responding to Yelp posts. “The interesting thing about it is the sheer variety and volume of data that can be processed with amazing speed to gain insights in consumer behavior,” said Panchal. “This (data) is an immensely powerful tool that needs to be handled with extreme clarity—or else one runs the risk of being a boffin, going around in circles!”
Data Informed: How should marketers categorize the various avenues that customers can use to express their sentiments about a business?
Sarang Panchal: Clearly I see four avenues in the Indian context: social media, offline consumer forums, offline response to companies, and response via market research. Social media is arguably the most important avenue, and social media listening must be done to stay on top of things. It is the richest source of consumer sentiment. Companies would be well advised to capitalize on this “voluntary” source to win against their competition.
The scale of the discussion between customers and businesses is immense. There are so many social media posts, customer calls, blog articles, and tweets. How can businesses identify valuable data in the flow of words, praise, and complaints?
Panchal: A number of alternatives are available to the astute marketer by opinion mining through natural language processing (NLP) and text analytics. Software such as Melwater, Google Analytics, Tweetstats, and others are options. Through these, we analyze sentiments and categorize them as positive, neutral, or negative. Some categorize as angry, sad, and happy, while others use scaling systems by associating a number to positive /negative words from plus-10 to minus-10.
How to make praise actionable? Change is a constant in business, so how can businesses use praise to identify areas of improvement?
Panchal: Positive information received from customers is only a starting point. The marketer should strive to understand areas of delight and the areas that either simply do not matter or yet need working on. The role of conventional qualitative research cannot be undermined and, if sensitively and correctly done, can directly point us in the right direction. Personally, I like to use individual depth interviews as compared to focus groups.
Describe how predictive analytics can identify patterns in complaints.
Panchal: Complaints are an obvious measure of customer dissatisfaction, and it’s important to address them to prevent customers exiting. One needs to figure the pattern in complaints—are they about the product, delivery, service, or billing? Predictive analytics tools such as machine learning and text analytics can identify these patterns.
Can you offer some important advantages to analyzing customer sentiment?
Panchal: There are many benefits of sentiment analysis to business: Improving customer service, gaining an edge over competition, getting business intelligence, and identifying issues with products are some of salient ones that come to mind.
Joshua Whitney Allen has been writing for fifteen years. He has contributed articles on technology, human rights, politics, environmental affairs, and society to several publications throughout the United States.
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