What is Sentiment Analysis?

Sentiment analysis uses the wealth of data available from social media to provide vital information on brand perception over time or how a brand is viewed in relation to competitors. This text could be in the form of tweets, LinkedIn posts, Facebook comments or free text from a feedback form. By analysing this data with modern analytics techniques (such as Natural Language Processing) we can uncover much deeper insights than a simple positive or negative brand score.

Benefits

  • Establish the link between brand sentiment and revenue
  • Grow brand value by up to 30%
  • Pre-empt and prevent negative customer experiences

Inform Brand Personality and Positioning

Understand how your brand sits in the customer’s mind. Using machine-learning techniques combined with sentiment score, we can understand which adjectives are most associated with specific brands across the competitive landscape, and quantify which terms have the most positive or negative value.

This enables us to identify blue ocean terminology to inform brand personality and strategy and ensure that marketing campaigns are based on distinctive positioning. For example, we may be able to identify that within a product category there is already an established brand associated with ‘trust’ and ‘reliability’, but that no brand in this category is associated with being ‘elegant’, ‘efficient’ or ‘direct’ and furthermore, that these terms are associated with high positive sentiment.

This is important because brand distinctiveness is known to drive brand value. For example, recent research has shown that brands perceived as being highly disruptive and different have 28% higher brand value.

Monitor Brand Dynamics and Respond to Crises

Sentiment Analysis enables granular understanding of how brand perception is evolving at a daily, or even hourly level. This analysis could reveal which product launches generate positive or negative responses or understand the impact of a marketing campaign. Alternatively, a Brand Monitoring System could be setup to detect negative anomalies in brand perception and enable a rapid response to an emerging brand crisis on social media.

Improve the Customer Experience

By combining Sentiment Analysis with customer service data, we can uncover insights into how easy it is for customers to solve problems they are having or finding the information they need. These insights can then be embedded into the customer care process, where customer service response is prioritised based on a Sentiment Algorithm pre-emptively identifying particularly unhappy customers.

For example, certain interaction patterns such as repeated visits to certain pages or repeated clicks on the same button may be found to correlate with negative customer sentiment through social media analysis or analysis of customer feedback forms. By identifying and acting on these insights a business can ensure a smooth and hassle-free user experience for their customers.

The Lynchpin Approach

Sentiment Analysis needs to reflect the unique dynamics of your business, customer base and brand. Lynchpin offer a completely bespoke Sentiment Analysis service that delivers actionable recommendations that are relevant to your business

Download as PDF