Customer Sentiment Analysis – Why It’s Important For Business Growth

The best catalyst to boost your business growth efforts and increase your ROI is customer sentiment analysis. Whether you are a B2B company or a B2C, some of the best insights you can get to help you grow is by understanding the sentiment of your customers. 

It’s a given that companies must be data-driven to make sure that they are on track in pursuing their financial objectives and goals. Most companies emphasize data such as sales figures, total revenue, year-on-year growth, etc. but many overlook consumer sentiment insights derived from customer experience data.

AI-enabled customer experience (CX) analytics bridges this gap. They give you a data-backed roadmap to optimize marketing and sales efforts, better products and services, and generate more revenue. 

Understanding Customer Sentiment

Customer sentiment is how consumers feel about their experience with a brand or product. People express these opinions and feelings in social media forums, review websites, surveys, user-generated videos on YouTube, customer care emails, and other ways. 

Machine learning algorithms automate the analysis of this data using tasks such as natural language processing (NLP) and semantic clustering. These tasks help extract aspects, entities, and sentiments from the data and classify them as positive, negative, or neutral. They also combine the sentiment scores of various aspects and give an overall sentiment of a brand or product. 

Marketing departments use these detailed insights to understand market trends and aspects that influence buying decisions and purchase behavior. Through a study of this consumer sentiment data, they are able to make targeted decisions around brand awareness, product enhancement, buyer engagement, sales forecasts, and other important marketing tactics.

Why Customer Sentiment Analysis is Important

AI-driven customer sentiment analysis automatically detects how customers feel about a brand, product, and customer experience of a specific business. That’s why it’s an invaluable tool that can help businesses understand market behavior to revamp strategies and adapt to changing market demands. 

Here are some key ways in which customer sentiment analysis can help a business.

1. Improving products or services. Machine learning algorithms analyze sentiment in big data, whatever their source may be. Additionally, with video content analysis, ML platforms can analyze all data-types as well including videos and images. This is immensely helpful in social media marketing where you may want to do Twitch to know market sentiment around your product or brand. Or you may want to analyze YouTube videos for comments or perhaps Google reviews from customers. 

Analyzing this data gives you insights into your customers’ opinions and how they truly feel about your brand or specific aspects of your business or product.  You can leverage these insights to improve your products or services to align them with your customers’ needs and expectations just as American Airlines did when they announced they would create more legroom for passengers in all the aircrafts in their worldwide fleet. United Airlines soon followed suit, not wanting to be left behind in a competitive market. You can see how consumer sentiment is a driving force in influencing business decisions.

2. Optimizing marketing campaigns. Knowing your customers, their backgrounds, likes, dislikes, and what influences their behavior can be very useful in developing segmented marketing strategies. With the insights you get from customer sentiment analysis, you can get your products in front of the right people at the right time, using the right avenues. 

Having the right cultural contexts in your advertising, what offers and deals are most productive, which advertising type is most effective - tv ads, social media, banner ads, or print - you can extract this intelligence and more with a CX platform. Once you train the AI platform to your specific business and brand for customer sentiment analysis, the model keeps learning throughout its lifetime, thus becoming smarter and more intuitive.

3. Increasing market reach. Competitive market or not, you always need to provide your customers with the right customer experience. The problem is that you don’t always know whether you are as good as you perceive yourself to be, or why customers who rate you positively still purchase from your competitors. Consumer sentiment analysis gives you granular, aspect-based insights that can tell you exactly where you're performing well and where you need to pay attention to increase market engagement.

This is why many companies go beyond their typical corporate branding to build a greater persona in the market. Take, for example, Dove. The brand embarked on its journey to reach out to women for self-acceptance with its Real Beauty campaign after an extensive research and reassessment plan drawn by advertising agency, Ogilvy & Mather, and in the process became larger than life.

4. Monitoring brand reputation. Analyzing customer emotions allows you to identify sentiments around your brand. A sentiment analysis API gives you action-oriented insights so you know how exactly you can curtail a negative brand image, whatever the reason may be. You are also ahead of social media brand mentions with alerts and notifications for any spikes in mentions or keywords that may trigger an issue. You are able to identify any issues before they turn into disasters. 

On the positive side, you also get insights where you can identify other brands that are interested in you for an associated brand campaign, or social influencers who are apt to be your brand ambassadors as part of your influencer marketing strategy. You can even develop the right persona for your social media engagement like Wendy’s and Denny’s did and overcome barriers in customer demographics that are age or technology-driven.

5. Tracking sentiment over time. Another major benefit of customer sentiment analysis is that it allows you to track consumer sentiment over time. As a result, you’ll be able to track how the sentiment towards your brand changes over time and what effect any strategies you implement have on it. 

How is Customer Sentiment Analysis Done?

To understand how machine learning-based emotion mining works, let’s take a look under the hood and see how a sentiment analysis API processes data.

Step 1: Gathering Data

One of the most important steps in the sentiment analysis process is gathering data. This is because the quality of the results you’ll get depends on the quality of the data you use and how it’s been labeled or annotated. As such, it forms the foundation of a successful customer sentiment analysis effort.

When gathering data, you have two options:

• APIs. Firstly, you can use live APIs to upload data to the sentiment analysis API. This is usually done for social media content and videos when you. You can also use APIs for other sources like news content. There are several open-source repositories that allow you to do this such as Kaggle, Amazon, Yelp, etc.

• Manually. If there’s no API available, you can also upload the consumer sentiment data manually. This will, for example, be the case where you want to use data from your business’s CRM platform. In this case, you’ll need to compile the data as a .csv file which you’ll then upload onto the CX platform.

Step 2: Data Processing

Once you’ve gathered the data, the API will process the data. The way it does this depends on the type of data you use. For example, text data will require text analysis while video content requires additional steps to extract the consumer sentiment. 

To illustrate how this process works, let’s look at the steps the API will follow to process video data:

• Audio transcription. The API will transcribe the audio stream of the video content by using speech-to-text software. This ensures that all the data from the video content is extracted
• Caption overlay. If the video content contains any captions, the sentiment analysis API will extract these captions and analyze them for any entities, aspects, or themes that you might have identified
• Image overlay. Like overlays, the API will identify any images in the video content or text data by using optical character recognition
• Logo recognition. The sentiment analysis API will then analyze the video in order to identify any logos that appear in the video, no matter how small or inconspicuous they are. To do this, the API will break the video content down into a series of images which it will then analyze
• Text extraction. Finally, the sentiment analysis API will extract and analyze all text in the video. This includes comments on the videos. APIs like Repustate IQ can also recognize emojis and hashtags. 

Step 3: Data Analysis

The next step in the customer sentiment analysis process is data analysis. Like data processing, this step also relies on several subtasks:

• Model training. The first step of the data analysis process is training the model. To do this, it’s necessary to use a dataset that’s pre-processed and manually labeled. This labeled data will be used to compare the correctly classified data with the incorrectly classified data. In turn, this improves the custom model created for any brand or product
• Multilingual data. For customer sentiment analysis in multilingual data, the API will use an individually annotated dataset for each separate language. This is because the API won’t rely on translations which could distort information
• Custom tags. During this part of the process, custom tags will be created for aspects and themes appearing in the content. Once the model has been trained, the API will segregate text based on these custom tags
• Topic classification. For the next step, the API’s topic classifier will attach specific themes to the text
• Sentiment analysis. With every aspect and theme identified and isolated, the sentiment analysis API will analyze them for the sentiment. Here, sentiment scores will be awarded on a scale of between -1 to +1 and neutral sentiment will be expressed as zero. Based on these numbers, the API will calculate an aggregate score which indicates the overall sentiment customers have towards the brand

Step 4: Data Visualization

Once the data has been analyzed and the consumer sentiment extracted, the API will display the results on its sentiment analysis dashboard in the form of graphs and charts. You’ll then be able to get the insights you need from these as they show you granular, aspect-based results. 

The data will tell you where your business is performing well and where you should improve. This gives you the deeper insights you need to decide on the business strategies you’ll implement. 

Conclusion

Machine learning has changed the way businesses used to work by making operations more efficient, diligent, and proactive. If you want to have more sales conversions and generate more revenue, you must perform customer sentiment analysis for a deeper understanding of your customer’s experience with your brand. It will give you a holistic insight into consumer sentiment, based on which you can make better decisions on how to shape your offerings to grow your business and expand your market share. 

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