Customer feedback is not always as straightforward as you expect. Not all customers care to fill in your dedicated feedback surveys or answer your feedback phone calls. In many cases, you need to gather insights from conversations that are not considered direct feedback. Good news! There’s a way to analyze all customer conversations and derive valuable insights, with sentiment analysis being one of the most significant benefits of customer analysis.
Customer sentiment analysis helps you analyze a large data set of conversations and find out customer sentiments that would otherwise be concealed and missed.
Let’s see what sentiment analysis is and what are the benefits of comprehensive sentiment analysis.
What is Sentiment Analysis?
Sentiment analysis or opinion mining is a type of machine analysis done on a piece of text to determine its sentiments, that is whether it is positive, negative, neutral, or if any other insights could be derived.
Tools for sentiment analysis are complicated tools that use natural language processing (NLP), artificial intelligence (AI), and statistical analysis to perform sentiment analysis on texts and offer insights into them.
Sentiment analysis is widely used in business intelligence and there are many various benefits of sentiment analysis. Companies use sentiment analysis tools to unravel the sentiments in their customer conversations such as social media posts, surveys, reviews, call logs, chatbot/customer service conversations, etc.
It’s important to note that a large data set for a comprehensive sentiment analysis could provide better insights because sentiment analysis tools become more accurate over time as they gain more data.
If you’re wondering how you can use sentiment analysis for your business, here are top 5 benefits of sentiment analysis.
Benefits of Sentiment Analysis
Improving brand sentiment
Brand sentiment analysis is a way of determining the general attitude toward your brand, product, or service.
Companies can use sentiment analysis to track their reputation metrics such as sentiment score, net promoter score, customer satisfaction score, etc.
It can assist you in understanding the most relevant and impactful feedback from your audience, which you can use to create a feedback loop. This loop can also be used for improving customer satisfaction, customer acquisition and product development.
Improving factors such as prompt issue redressal and timely resolution can go a long way in improving brand image. You can scale these features with the help of AI sentiment analysis tools, since AI tools can comb through a lot of data in minutes. This can be especially helpful in crisis situations, where the need to address issues quickly is paramount.
Example of improving brand sentiment
Let’s see this with an example of Starbucks, the multi-billion coffee chain. They get an average of 10 tweets per second. And let’s say that they need to track consumer insights about a new flavor they have just launched.
How long do you think a human needs to read through those tweets and generate product-specific insights? Let’s not forget that in addition to tweets about the new flavor, there would also be other tweets, such as customer complaints, news articles, etc. This means that a human operator must sift through a mound of text to access relevant details.
However, processing such a large amount of data is easy for AI. AI algorithms can easily classify and catalog specific mentions of their new flavor and run sentiment analysis on them. They can also use this information to generate product-specific consumer insights and trends.
This is probably the reason why Starbucks’ social image is mostly positive. Both male and female demographics reasonably like the brand, and the overall trend leans towards the positive side.
Improving customer service
Almost 80% of customers stop business with a brand if they receive poor customer service. In contrast, companies that provide exceptional customer service record more revenue and get more referrals, leading to continued business. Improving customer service is a sureshot way to increase a business’s ROI.
Since enhancing customer service is of such importance to brands, many companies have now started using sentiment analysis to understand what their customers are saying about them. This is especially helpful in a B2C scenario where you have a lot of customers to manage.
Sentiment analysis allows you to engage in a proactive manner. You can use it to capture and process the voice of the customer (VoC). This information can also be used to identify and address specific pain points, which can improve customer satisfaction and retention.
A customer-first mindset would help your brand become more empathetic to customer needs and strengthen your reputation. Let’s see this with an example.
To understand its customers, Airbnb uses an AI-based sentiment model to complement its Net Promoter Score (NPS),
Since NPS is oftentimes slow and doesn’t tell the full story, Airbnb uses NPS combined with sentiment analysis to generate real-time customer insights from their data. Furthermore, it allows them to reach a wider customer base than feedback forms.
Lastly, it also helps them perform extensive A/B testing with their products, which can be useful for long-term business goals.
Refining marketing strategy
As mentioned above, sentiment analysis/opinion mining can be used to understand your audience and target them with the right message. If your marketers understand your audience, they can develop better content and adjust their copy to best fit their audience.
Through sentiment analysis, you can segment your audience and tailor your message for specific segments. This enables marketers to optimize existing content to better target their customers and improve the effectiveness of their campaign.
Marketers can use sentiment analysis to inform marketing strategies and create customized user journeys. You can also use it to identify customer trends.
What’s more, you can take advantage of sentiment analysis to analyze VoC. Sentiment analysis can be used to answer product-specific questions such as
- Did the users like a specific product/feature?
- Do they like the product more than its competitors?
- Are there any enhancements/improvements required in the next release?
An amazing example of this is Pepsi. They have often mentioned that they use sentiment analysis to track brand mentions and understand public perception. This allows them to generate timely consumer insights and identify industry-specific trends.
Pepsi uses this information to segment its audience and engage customers through relevant content. This enables them to create a strong social media presence, improve their social media reach, and make better marketing decisions. They can also use this information to identify cultural and regional preferences, which can help identify emerging markets.
You can use sentiment analysis to track audience sentiments and understand their thoughts about a specific product or service. This allows you to track product adoption and see if your new product is being well received in the market or not. The same methodology can also be used to understand feature adoption.
Sentiment analysis enables marketers to identify market trends, which is useful for generating new product ideas. What’s more, you can use it to analyze individual product objectives.
For example, you can create a survey to ask your customers if they like your product pricing tiers. You can analyze their answers via sentiment analysis to identify if they exhibit dissatisfaction with your current pricing model. If the answer’s yes, you can look into changing your price tiers as per market requirements.
Moreover, you can use sentiment analysis to identify bugs and issues with your product. Specific feedback can be used to create support tickets and release fixes.
Example of sentiment analysis for product development
Google uses sentiment analysis to analyze customer mentions. They use this information to find out what customers have to say about their products and services.
For example, their Chrome development team constantly checks direct and indirect user feedback and runs them through sentiment analysis algorithms.
Moreover, they analyze specific keywords, such as mentions of new features, scalability and security issues, UI considerations, etc. It is always important to keep tabs on what language and words your audiences are using. This is great input for content development and in combination with rank tracking software to find the “voice of the customer” and what they are searching for. They also track product recommendations and inclinations to specific browser elements/extensions.
This allows the company to document its product’s strong and weak points and identify which features its users like. It also helps them recognize areas of future research and development.
You can run sentiment analysis on your customer’s social media mentions to generate competitor insights. For many businesses, it’s a legitimate way to benchmark their product against their competitors and compare their offerings.
Understanding which competitors’ features fail or succeed can help you create a development strategy. You can check which product features of your competitors their users like. This can help you identify which features to add to your own product.
From a marketing perspective, you can also use this analysis to compare your product’s strengths and weaknesses against your competitors. Their pricing strategies can be used to modify your own strategies, and gaps in their content can be used to identify opportunities for your website. You can add this information to your landing pages to improve conversions.
Doing this process over a period of time can have a long-term impact on your ROI. It also saves a lot of time and effort since you don’t have to run A/B tests with those sceneries.
From a customer service perspective, you can use sentiment analysis to analyze how your competitors engage with their customers. Later, you can check if their engagement has a net positive and negative impact on their brand image. If the impact is positive, you can include those customer service elements in your outreach strategy.
Streaming platforms competitive case study
Let’s see a case study between the biggest streaming platforms, Netflix, Disney+, and HBO Max. Using a sentiment analysis tool to check user sentiments for these platforms, you’ll find that Netflix has the most social media mentions, followed by HBO Max and Apple TV. Also, Netflix has the highest number of positive mentions by percentage, followed by HBO Max and Disney+.
If you delve deeper into the data, you’ll be able to see the reason why Netflix is so popular. Firstly, it’s one of the oldest streaming platforms in the market. They also aim to be a customer-centric brand. For instance, Netflix prompts its consumers to downgrade their plans for services they don’t need.
Of course, predicting which company will remain the most irrelevant in the future would be impossible. Even though Disney+ and HBO Max are much newer than Netflix, they have now developed their niche following due to original content.
A great part of customer sentiment is not manifested through direct customer feedback. Customer sentiment analysis is an automated process where customer sentiment is extracted out of a large data set of customer conversations. So in a sense, true customer-centricity could not be possible without using sentiment analysis.
In this article we talked about 5 key ways you could benefit from customer sentiment analysis. These benefits of sentiment analysis include:
- Improving brand sentiments or how your audience views your overall brand;
- Improving customer service or how you could identify and respond to customer needs and complaints;
- Refining marketing strategy or how you could craft your messaging and target the right audience;
- Product development, that is how to identify areas of your product that could improve;
- Competitor analysis by analyzing your competitors’ audiences and trying to derive insights from them.
Want to know more about sentiment analysis? Book a demo with one of our media monitoring experts.
Mostafa Dastras has written for some companies such as HubSpot, WordStream, SmartInsights, LeadPages and MarketingProfs. Over the past years his clients have primarily relied on him for increasing organic traffic and generating leads through outreach campaigns. Visit his blog, LiveaBusinessLife, or connect with him on LinkedIn.