Voice of the Customer Sentiment Analysis
There is no better way to build and grow your business than by connecting with your customer base at a deeper level to understand how you can serve them better. AI-powered customer experience intelligence tools can help you achieve this goal, and more importantly, in an efficient and cost-effective manner. This article tells you how you can get actionable insights by using voice of the customer sentiment analysis to understand how customers feel about your brand, products, or service.
What is Customer Sentiment Analysis?
The meaning of customer sentiment analysis is to understand your customers’ feelings about your brand and its various aspects, preferably with a machine learning algorithm that uses natural language processing techniques for text and semantic analysis. With the help of voice of the customer (VoC) tools, you can gather and collate impressive amounts of relevant data that can be scrutinized for emotion mining. This voice of the customer sentiment analysis approach can be a guiding beacon for brands like you as you strategize brand awareness campaigns, customer loyalty programs, explore new customer demographics, brainstorm new product ideas, do competitive research, and more.
Why Do Businesses Need Customer Sentiment Analysis?
Businesses need sentiment analysis for customer insights to better their sales conversions, nurture clients relationships, acquire new customers, overhaul branding efforts, develop targeted advertising communications, and so much more. Let’s get to know them in detail.
1. Improve sales conversions
The most important thing about a business is that it has to be profitable. And what better way to make this happen than by improving efficiency and productivity- both of which can be achieved by using an artificial intelligence-based customer sentiment analysis platform. From knowing which customers are more likely to buy which products more, and which products can be apt for cross-selling, a brand can do much to increase sales conversions through sentiment analysis for customer insights.
2. Reputation management
Customer sentiment analysis helps companies to protect and manage their reputation proactively, especially given that social media listening cannot be ignored by any business. People air their opinions online, freely and without restraint, because platforms like Google reviews, Glassdoor, Amazon Product Reviews, Clutch, and numerous sites like these offer them a chance to share their experience, and some even reward them for it. Even if your company has its own review platform, it’s incredible the kind of customer insights you can get about your different store locations, pricing policies, customer service, after-sales service, exchange issues, and other facets of your business through a sentiment analysis API.
3. Personalize recommendations
Voice of the customer sentiment analysis provides rich information about things that certain sets of customers like and prefer. With the help of user account analysis, including their purchase history and customer service logs, brands can create personalized recommendations through newsletters and advertising. These tactics can be applied in both below-the-line and above-the-line marketing.
4. Stay ahead of the competition
The secret to staying relevant during changing times and being abreast of the competition is to constantly keep reinventing oneself. Whether it’s Madonna, whose music and persona is still relevant even after three decades, or legendary Danish company Lego, which began as a small, 10-person wooden toy company in 1936, the reason some brands are timeless is that they keep up with the times, acknowledge the pulse of the people, and know the importance of customer experience.
5. Product re-branding
Brands need to constantly assess themselves through the eyes of their customers. That’s exactly what BMW did when it realized that the market demographics were changing. Premium cars were always sold as a symbol of having arrived, which is why they were advertised aiming at affluent, middle-aged men, mostly in executive positions in companies. It was assumed that only this group of people would have enough purchasing power to afford expensive cars.
As economies evolved and socio-cultural diversity increased all around the world, BMW knew that if it didn’t rebrand itself, it would get drowned out by the noise of other competitors. They overhauled their entire marketing and advertising to cater to younger customers who were affluent by rebranding the BMW from a serious car to being fun. This is a live example of how voice of the customer sentiment analysis can turn around a brand’s future, if used and implemented correctly. Click here to explore other sentiment analysis use cases.
6. Enhance customer retention
Customer sentiment analysis can give data-backed actionable insights to develop schemes to retain existing customers as much as acquire new ones. Knowing what customers expect from a brand in terms of service, convenience, etc, can help a company overcome issues of growing customer dissatisfaction and foster a better relationship, just like this South African bank discovered.
7. Attract new customers
Brands can also get clever insights about what customers want from them in addition to their existing products and services, just like McDonald’s did. A viral TikTok video about their logo redesign lead to a whole new discussion on TikTok about onion rings that people started expecting from McDonald’s. Video content analysis showed that people had been expecting the fast-food chain to put onion rings on their menu for a long time. Whether the company changes its menu or not, it’s undeniable the kind of leverage you get when you have rich consumer insights like these at your fingertips.
How Is Customer Sentiment Analysis Done?
There are four main stages in customer sentiment analysis. They entail gathering, processing, and analyzing the data for sentiment. And finally, visualizing it on a sentiment analysis dashboard. Let’s look at these stages in detail.
Stage 1: Gathering the data
The most important thing to remember at this stage is that we have to choose the best set of data we can. The larger the data, the more granular and accurate the insights will be. For example, if you want to analyze video comments for sentiment, a video with 500 comments will give you better insights than one with just 30 or 40 comments. We can gather this data either through live APIs for social media such as those allowed by Instagram or Facebook, or we can scrape them manually and upload them onto a sentiment analysis platform as a .csv file.
Stage 2: Processing the data
Now that we have collected the data and ensured that it is relevant to our project, we will upload it to the emotion mining platform, which will now process it using different tasks such as the following -
Audio transcription - Turning podcasts and audio from a source video into text through speech-to-text software.
Collecting caption overlay - Read and extract captions if present in videos.
Capturing Images- Use optical character recognition to capture and extract images if any in the video or text data.
Logo recognition - Identify, capture, and extract logos in the video background like a poster on the wall or a logo on a mug. (This stage is vital for competitor brand analysis, especially in user-generated videos.)
Text analysis - All the text is extracted including emojis, hashtags, industry jargon, acronyms, social media code switches, etc. (This part is essential to parse false positives from sarcastic comments, for example.)
Stage 3: Analyzing the data
Customer sentiment analysis can involve multiple languages that the machine learning model might have to analyze. This means that the model needs to have speech taggers for each language it is analyzing or else the results could be quite inaccurate due to translations.
Apart from this, there need to be custom tags created for different aspects and themes in the data. In this way, the model will be able to extract and segregate the data. Sentiment scores will also be assigned to emotions with polarity. It is this polarity that will be calculated overall in a text and a sentiment score assigned to the comment. When combined, the aggregate score of the brand will be from a range of 0 to 100, with 100 being the highest positive score.
All these processes will be used to train the model. Finally, it will be tested against a validation dataset to check the accuracy of the insights. A good model will give the optimal accuracy within three interactions of training.
Stage 4: Visualizing the insights
Now that our machine learning model gives the most accurate results, we can analyze any customer sentiment analysis dataset and see the results on a visualization dashboard. You can see the sentiment analysis in a chart, as well as finer details like emotion-aspect co-occurrence that will tell you which emotions are the highest in relation to which aspects, and many findings like this.
Discover More: Best sentiment analysis tools