As businesses digitally transform their solutions and processes, they’re also finding it necessary to transform how they interact with—and especially understand—their customers to be successful.
But it’s more than just offering simple customer surveys. You need to be actively involved in conversations about how your customer base feels about your brand. And sentiment analysis is the key to figuring that out.
Sentiment analysis, also known as opinion mining or emotion artificial intelligence, is a natural language processing (NLP) technique that determines whether a piece of content is positive, negative, or neutral. By analyzing text and statistics, a sentiment analysis tool can understand what customers are saying, how they’re saying it, and what they really mean—both from an individual’s and the public’s perspective.
Under the umbrella of text mining, sentiment analysis is routinely used to determine the voice of the customer in feedback materials and channels like reviews, surveys, web articles, and social media. As language evolves, it can become increasingly challenging to understand intent through these channels and defaulting to dictionary definitions may lead to inaccurate readings.
With an algorithm-based sentiment analysis tool adjusted to your customers’ voices, you’re able to reveal what your customers want and need from your product, service, or solution and how their opinions trend or change over time.
Some types of sentiment analysis are:
Traditionally, businesses relied on questionnaires and surveys to gauge customer opinion. For example, the Net Promoter Score (NPS) survey aggregated and assessed information needed to measure customers’ willingness to recommend a business. While valuable, it can severely lack the ability to provide deeper insights into customer experiences—such as when making purchases—across your digital channels.
But sentiment analysis can bridge that gap.
In monitoring, identifying, and extracting customers’ opinions and sentiments from text, sentiment analysis can help reveal the meaning behind each comment, social media like, idea, complaint, and query. And help you readily attend to your customers’ ever-evolving needs.
By analyzing the collected data, you’ll get a summary of each customer’s reaction, as well as any other additional feedback that could help shape the public perception of your product or business. When this data is placed on a positive, neutral, or negative sentiment spectrum, you’re able to see what drove the customer to make that statement—revealing the opinions that describe the customer’s sentiments and feelings towards a specific topic.
These opinions are then classified as direct (“This product is the best I’ve ever used!”) or comparative (“Product A integrated better with my org than Product B.”). While these are often easy to interpret, it’s important to also note that some may need further looking at. Classifications such as implicit (“The business knows what they need to do to improve this product.”) and explicit (“Feature A is easy to use.”), as well as word sequences that are positive yet contain a negative word, can be difficult to analyze and might require some manual review or adjustments to your sentiment models.
But once these key words and phrases on how others feel about you are discovered, they can help you plan your organization’s next move. But first, you need to understand how sentiment analysis works to benefit your business.
Sentiment analysis uses several technologies to distill all your customers’ words into a single, actionable item. The process of sentiment analysis follows these four steps:
By remembering descriptive words and phrases to assign them a sentiment weight, you and your team can build a sentiment library. Through manual scoring, your team decides how strong or weak each word should be, and the polarity of the corresponding phrase score, noting if it is positive, negative, or neutral. Multilingual sentiment analysis engines also must maintain unique libraries for every language they support through consistent scoring, new phrases, and the removal of irrelevant terms.
Sentiment analysis can distill these approaches into three different categories:
A mix of statistics, NLP, and machine-learning algorithms to identify sentiments. The system is trained to associate inputs with corresponding outputs, that is, customer text with polarity. Machines are classified with the input data and can adapt over time once trained. This can be tested with additional data to provide better predictions.
The most straightforward sentiment analysis uses dictionaries or lexicons to explore words and phrases and determine their associated sentiments. This type of approach works well with direct and explicit opinions. While this system is fast and easy to use, it rarely considers how words are combined in a sequence. Teams need to add rules for comparative opinions as this approach can’t readily understand implicit opinions.
Combining both rule-based and automated systems means you can gain the accuracy and precision you need to truly understand your customers. This is the most powerful system as it contains the emotional information gathered from lexicons, which can be adapted over time.
While social media only gives a glance at how people talk about your brand online, sentiment analysis provides immediate knowledge of how the public perceives both your brand and product. Many retweets on Twitter might seem positive, but if you notice the likes are drastically outweighed by the negative comments, you can conclude that’s a less-than-positive interaction.
Sentiment analysis can also enable your company to extract invaluable customer input from internal data sources. For example, by monitoring transcripts of customers’ online chats with service and support representatives, your company can be more quickly made aware of product quality, safety, and warranty issues. Other benefits of sentiment analysis include:
All these benefits offer your team a comprehensive view of what customers are thinking and how to respond accordingly. From these insights, you can guide internal teams like customer service to help enhance the user experience, or marketing and customer-facing teams to engage customer segments based on sentiment with targeted sales, marketing, and support efforts.
The best part is that sentiment analysis doesn’t work only for a single team. Every team can use this data to plan accordingly for everything from marketing campaigns to pricing strategies to order fulfilment or customer support. As different teams learn more about how customers feel about the product, brand, and business, they can use their knowledge to determine responses and optimize business operations. They may also reassess the goals of both the business and their customer, and define which actions to take to reach that goal.
Some examples of how teams use sentiment analysis include:
Having a tool that can understand complex human emotions is critical to receiving the feedback you need from your customer base. In the past, sentiment analysis required expertise in several technologies, but today, several software tools enable sentiment analysis with little to no knowledge.
Choosing a customer data platform (CDP) with an integrated, intelligent sentiment analysis tool should be a top priority for your business. To create successful omnichannel customer experiences, your team, as well as your organization, needs a CDP equipped with all the capabilities needed to generate holistic, real-time customer profiles. This includes a sentiment analysis tool that can contribute new insights for optimizing customer relationship management and other data you’ve collected.
Look for a CDP that uses NLP models to accurately and efficiently analyze customer opinions and emotions. Trained on a variety of data from public sources, the models should be able to generate customer sentiment scores and identify applicable business areas for targeted improvements.
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