Sentiment Analysis: Crucial to your Consumer-Centric Strategy

Cotsco. Amazon. Harley Davidson. What do these brands all have in common?

They are 100% focused on customer satisfaction. “Customer-centricity” is another buzzword that has made its way to both B2C and B2B industries. Investopedia defines being customer-centric (also called client-centric) as an approach to doing business that focuses on creating a positive experience for the customer by maximizing service and/or product offerings and building relationships.

Client-centric businesses ensure that the customer is at the center of a business's philosophy, operations, or ideas. Client-centric businesses believe that their clients are the primary reason that they exist, and they use every means at their disposal to keep the client satisfied.

Being client-focused is not a new concept but some businesses are taking customer service to a whole new level. Same-day delivery, free returns and 24/7 customer support are just the tip of the iceberg when it comes to customer experience. Truly customer-centric businesses spend a lot of time listening to their customers. From Twitter to Facebook to Amazon, consumers are constantly sharing their opinions on products and services.

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Reviews, tweets and forums contain a wealth of information that, when used correctly, can inform product choices, increase customer lifetime value and reduce churn. Carefully monitoring what your customers are saying can also prevent serious missteps in advertising or identify product failures early.

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What is Sentiment Analysis?

“Social listening” is the monitoring of your brand or product across various social media channels (Facebook, Twitter, Instagram, etc.) for direct mentions and/or customer feedback. There are many social listening tools online that range from free (Google Analytics) to several hundred dollars per month.

Sentiment analysis—or social media mining— is a method of text classification that combines natural language processing (NLP) and machine learning to identify and extract the sentiment or opinion from a text source. Rather than read through each product review or tweet to gain context, sentiment analysis employs an algorithm that combs through all of your social data and assigns a sentiment (positive, negative or neutral) to each text source. Reviewing this data over time can provide insightful trends, allowing businesses to more accurately guide strategy.

I recently completed a sentiment analysis for a popular non-alcoholic sparkling beverage using Amazon reviews. Findings are here.