Using Sentiment Analysis to Build Customer Loyalty

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Sentiment analysis is an essential feature required by companies who have invested in business intelligence solutions. It is one of the most advanced forms of AI that businesses can use to guide their strategic decisions. Also known as opinion mining, sentiment analysis is essential to improve customer relations. Sentiment analysis solutions process texts occurring in the form of tweets, comments, customer reviews, and complex documents to discover sentiments. Several leading organizations perform sentiment analysis to take critical business decisions related to product launches or business strategies.

Customer reviews have a significant impact on determining the fate of a company as they influence purchase decisions. Positive reviews are essential for a business to succeed and achieve sustainable growth. Sentiment analysis also enables businesses to improve their social reputation. In this blog post, we will discover how businesses can use it to effectively build customer loyalty.

How Sentiment Analysis Improves Customer RelationsArtificial intelligence systems enhance business performance by analyzing customer data to extract actionable insights. Customers now express their dissatisfaction with a product or service by posting reviews or commenting on social media platforms. These customer responses hold insights about their prior experience with a consumer brand. That being said, the manual analysis of these responses can be exhausting, especially when the customer base is large.

Researchers have started using artificial intelligence for sentiment analysis to overcome the challenge of analyzing a large volume of customer feedback. The advent of sentiment analysis has enabled AI-powered systems to effectively analyze customer interactions and extract hidden patterns. The systems are trained to understand human sentiments associated with written text using custom machine learning and NLP models.

Sentiment analysis mechanisms play a critical role in understanding consumer behavior and identifying their future needs based on past shopping experiences. They enable businesses to improve their products and services and fulfill customer expectations. The above flow chart illustrates how these systems identify the sentiments behind a customer review. A detailed illustration is shown in the next diagram below.

Understanding The Basics of Sentiment AnalysisAI-powered sentiment analysis systems work in the same way as humans understand sentiments occurring in written content. Each phrase or word represents a sentiment, which the system identifies by matching it with its sentiment library. A sentiment library is a collection of words, phrases, adjectives, and other grammatical components provided by human coders.

Besides, the system assigns a sentiment score to each entity for determining its true nature. For example, let’s assume that two customers rate a particular product as ‘bad’ and ‘awful’ with the same sentiment score of -0.5. The sentiment analysis tool will conclude that both words are equally negative.

In a sentiment analysis solution, every consumer response passes through a series of complex machine learning algorithms. The sentiments are extracted by analyzing hidden components in a multi-layered structure. The basic workflow of sentiment analysis is shown below.

The workflow may look simple, but the underlying technology is quite complex. Machine learning algorithms perform the following tasks at each level to provide accurate results.

• 1. Segmentation of text
• 2. Analyze components that would reveal hidden sentiments
• 3. Identify the positive/negative tone associated with the text

In this way, it extracts the true meaning behind a customer response.

The multilingual sentiment analysis systems maintain unique libraries for each supported language. Also, it requires regular updates for new phrases and the removal of irrelevant phrases. Under some conditions, small tweaks in sentiment score mechanisms are also necessary.

Closing ThoughtsUnderstanding customer behavior enables a company to improve its products and services with effective business strategies. It provides key inputs to deliver exceptional user experiences to retain your customers and build healthy relations. Several prominent brands use sentiment analysis systems to augment their upselling and cross-selling efforts with significant customer interactions.

Sentiment analysis bridges the gap between a brand and its customers by accurately analyzing every response. The same technology drives a number of AI-powered applications such as chatbots, recommendation engines, and several other business intelligence tools.

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