Does the retail industry need sentiment analysis?

Just one negative customer experience can make over a quarter of customers leave the brand for good; at the same time, catching those positive customer sentiments on the spot could be the driving force of customer Loyalty.

This is a small testament to why the capability to assess customer sentiments can be a powerful tool for retailers looking to gain an edge through data-driven intelligence. 

This is a process called Sentiment Analysis (SA), a technique that uses text analytics algorithms to classify the overall “sentiment” of the text content and helps brands classify customer emotions concerning their interaction with the retailers in terms of the product and/or the service they receive.

SA is not a newcomer in the business world, as it has been around for quite a few years already, however, its advancements in data harvesting technology, with top-tier data collection technologies like natural language processing, text mining, and data mining, are the ones that have empowered SA to fulfill its today’s promise, that is to tell companies what people think and how they act on their brands.

Sentiment Analysis in retail

Focusing our discussion on retail, the use of sentiment analysis can be extended across the 4 following areas of targeted improvement: 

• Operational

  SA can be used for operational improvements by analysis of service-related reviews to give a clear indication of the magnitude of the customer sentiment and the urgency that needs to be assigned to it.

• Product

  SA can provide very specific insights across product categories and sub-categories, often down to the level of individual features of the product. 

• Competition

  Enhance the brand’s competitive intelligence by gathering and analyzing information regarding a competitor’s performance, capabilities, and offerings.

• Brand reputation

  Assessing customer sentiments expressed through various channels, especially via social media to identify and address threats to the brand reputation, track the various brand influencers, and engage with customers.

SA as “Opinion Mining”

Another definition for SA can be an opinion mining process: the science of harnessing and analyzing consumer conversation to understand whether consumers feel “positive”, “negative” or “neutral” about a certain brand, product, or topic.

By listening to conversations being held online (such as on social media, blogs, forums, etc.), a retailer can understand consumer emotions and give them a connection that goes well beyond whether a product simply sells well or not.

Retailers can use SA to monitor their customers’ reactions and feedback to push content to understand what their customers care about and leverage that information to reposition their products, create new content or even provide new products and/or services.

In a technology sense, SA is a unique blend of machine learning and artificial intelligence, allowing companies to use digitally-based data tools to cull useful, actionable moves that steer social media consumers toward their products and services.

Within the larger data sets, there will be many trends operating independently, and only by using a strong multivariate analysis (like artificial intelligence or machine learning) will the trends become clear and actionable.

Future of Sentiment Analysis

The quest for Artificial General Intelligence (AGI) has resulted in several breakthroughs in the field of ML (Machine Learning). Using SA with these new techniques can turn it from a mere diagnostic analysis tool to an insightful capability. For example, an AI model can proactively monitor customer sentiments, thereby enabling the retailer to address quality issues faster than before.

Although SA will provide a certain degree of competitive advantage in any industry, early adoption could prove to be crucial in the retail industry, becoming an unmistakable ‘health indicator’ for retailers of all sizes.

The road to Sentiment Analysis’ new Era 

The “next-generation” SA is expected to explode in the next years, with the emergence of microservice APIs that will measure emotion in written content, but also voice and facial expressions.

But there is one strong consideration; as the backbone of sentiment analysis utilizes Big Data, retailers must make sure to have enough data available to gain actionable insights, so that they can leverage sentiment analysis to gather insights that would not be possible using traditional marketing methodologies.