Does your company work with a lot of documents or other text data? Do you find it difficult to make sense of all this information? If so, text analysis could be a valuable tool for you.
Text analysis is the process of analyzing text data to understand people’s opinions, preferences, and behavior. Also known as text mining, it’s a quick way to digest large datasets or documents and transform them into actionable insights. Companies can use text analysis for many different purposes, such as marketing, customer support, and product development.
A customer support team, for instance, could use text analysis on their review of customer service transcripts to look for patterns in customer complaints. This could help them identify problems with the product or service, and suggest ways to improve the customer experience.
If you’re considering using text analysis in your business, there are a few different ways to do it. Here are 7 text analysis examples that can help you get started.
When you think of "text," it's not just limited to articles or books. Text data comes from almost every interaction you have, whether it's a Tweet, support ticket, survey response, Slack message, Google search, or Facebook post. It's everywhere, and it's one of the most valuable data sources you have at your disposal.
Here are some benefits of text analysis.
Your customers are the lifeblood of your business, so it's important to understand them as best as you can. Text analysis can help you do just that by allowing you to analyze customer feedback, reviews, social media posts, support tickets, and more.
This understanding can help you make better product decisions, improve your marketing strategy, and provide better customer support.
For instance, consider PayPal's recent gaffe of threatening to fine users for misinformation. This could have been avoided if they had used text analysis on social media posts and customer support tickets to look for signs of customer discontent, potentially revealing that users were already wary of extra fees and loss of control.
PayPal isn't the only one who's made this mistake. Airline companies have also been known to make decisions that end up angering their customers (and often going viral because of it).
In 2017, United Airlines famously dragged a passenger off one of their flights, and more recently, they came under fire for banning emotional support animals. In both cases, a little text analysis could have helped them avoid the PR disasters.
Whether your business sees hundreds, thousands, or millions of customers, text analysis can give you valuable insights into their behavior.
Even Apple, with all of its resources, has seen underwhelming sales of the iPhone 14, with users hoping for a more innovative and ideally affordable product. Of course, Apple remains a behemoth in the tech industry, but this goes to show that even the biggest companies can't afford to rest on their laurels.
Innovation is key to staying ahead of the competition, and text analysis can help you identify areas where your product could be improved.
By analyzing customer feedback, reviews, and social media posts, you can get a better idea of what features your customers want and which ones they're not happy with. This information can then be used to inform your product roadmap and ensure that you're always working on the features that will have the biggest impact.
Beyond Meat is facing steep competition, and they've been forced to reduce their staff numbers by nearly a fifth. The plant-based meat company is up against some big names in the food industry, including Impossible Foods and Nestle.
Netflix, too, has had a rough go of it lately, with the likes of Disney+ and HBO Max gaining ground. The streaming wars are only going to heat up in the coming years, and Netflix will need to continue to innovate to stay ahead.
To stay competitive, it's important to keep a close eye on your rivals. Text analysis can help you do just that by allowing you to monitor their social media posts, press releases, and more.
This competitive analysis can give you insights into their product plans, marketing strategies, and even how they're interacting with their customers. Armed with this information, you can adjust your own plans accordingly and make sure that you're always one step ahead.
Wells Fargo, once a trusted name in banking, has been embroiled in scandal after scandal in recent years. To regain the trust of their customers, Wells Fargo has to spend over half a billion dollars on advertising a year.
That's nothing to say of the billionaire CEOs, who were once idols of the business world, now tarnished by (often false) accusations.
With misinformation spreading faster than ever, it's more important than ever to keep a close eye on your reputation. Text analysis can help you do that by monitoring social media, news articles, and customer reviews for mention of your company.
This information can be used to quickly address any negative sentiment and prevent it from spiraling out of control.
The so-called "retail apocalypse" has claimed many victims, but some companies have been able to weather the storm and even grow their sales. While the likes of JCPenney and Sears have filed for bankruptcy, companies like Domino's and Walmart have been able to adapt and thrive.
Beyond retail, companies in every industry are looking for ways to grow their sales. Text analysis can help you identify new opportunities by analyzing customer feedback, social media posts, and news articles.
For example, if you're a company that sells office supplies, you might use text analysis to monitor social media for mentions related to "back to school." This could be used to inform your marketing strategy and ensure that you're targeting the right audience at the right time.
With so much noise on the internet, it's hard to cut through the clutter and reach your target audience. Text analysis can help you understand what's working and what's not, so you can adjust your marketing strategy accordingly.
For instance, you could use text analysis to track the performance of your marketing campaigns, understand how customers interact with your brand online, and monitor social media conversations for insights.
Suppose you're running a social media campaign for a new product. You could use text analysis to compare the sentiment around this new product with that of both your old product and your competitor's products.
If you find that the sentiment around your product is negative, you could adjust your campaign accordingly. Maybe you need to change your messaging or target a different audience. By understanding how customers feel about your product, you can make your marketing more effective.
Repetitive tasks are the bane of every worker's existence, but text analysis can help you automate them.
For instance, suppose you work in customer support and have to respond to the same types of questions over and over again. You could use a text classifier to automatically categorize these questions and route them to the appropriate team members.
This would free up your time so you could focus on more complex issues, and it would help to ensure that customers are getting the support they need in a timely manner.
Similarly, an accounting team might use text analysis to automatically extract data from invoices, receipts, and other financial documents. This would automate a time-consuming and error-prone task, freeing up the team's time so they could focus on more important things.
Even if you're not in a customer-facing role, there's a good chance that text analysis can help you automate your work. For instance, if you're a research analyst, you could use text analysis to automatically collect and analyze data from news articles, social media posts, and other online sources.
There are various text analysis techniques that businesses can use to gain insights from customer surveys, Amazon reviews, and any other source of online reviews or unstructured text data. These are all examples of machine learning, and more specifically, natural language processing (NLP).
Text classification is a process of assigning tags to unstructured text data. This can be used to organize customer support queries into different priority levels, identify the language of the text, or extract which product feature is being discussed in a review.
Naive Bayes, support vector machines, and decision trees are all popular supervised learning algorithms that can be used for text classification. A Naive Bayes classifier is often used for text classification, as it is relatively simple and easy to train. However, it can be less accurate than some of the other methods.
Support vector machines are another popular choice, as they can be very accurate. However, they can be more difficult to train, and may require more data to achieve good results. Decision trees are a type of algorithm that is sometimes used for text classification. They are easy to understand and interpret, but may not be as accurate as some of the other methods.
Sentiment, or how people feel, runs the world. Social media has given a voice to the previously unspoken, and with it the ability for companies to monitor how people feel about their brand. No longer is it just large businesses with a marketing team that can access this valuable information – small businesses can too.
Poor sentiment leads to churn – customers leaving a product or service. In order to reduce churn, businesses need to understand how their customers feel and take steps to address it. This is called sentiment analysis. The categorization of customer sentiments in real-time is a powerful use case of gaining value from unstructured data, as businesses can quickly find negative brand mentions, negative reviews, or negative sentiment in any piece of text, and act accordingly.
Conversational analytics is similar to sentiment analysis, but rather than focusing on general opinion, it analyses specific conversations between customers and businesses. This can include transcripts of customer care calls, support tickets, chatbots, and reviews.
Understanding what customers are saying in their own words provides valuable insights that can be used to improve the customer experience.
Text extraction is a process of extracting specific datapoints from text. This could be numbers, percentages, keywords, or any other information that can be gleaned from the text.
This technique is often used to analyze customer reviews, as it can help identify which product features are being discussed most frequently. This information can then be used to make product optimizations.
The easiest way to implement text analysis in your business is to use a tool like Akkio. Akkio is a no-code AI platform that allows you to set up and build an ML model for textual analysis with minimal data in mere minutes.
Akkio offers a variety of features for text analytics, including text classification, sentiment analysis, and entity extraction. For example, you could use Akkio to set up a text classification model to automatically classify tweets by sentiment. This would allow you to quickly and easily analyze the sentiment of large volumes of text data.
Implementing text analysis in your business with Akkio is easy, fast, and requires no coding skills. This makes it an ideal solution for businesses of all sizes.
Text analysis is a powerful tool that can help businesses of all sizes gain insights into customer behavior, understand the competitive landscape, automate business processes, and grow sales.
There are many different text analysis techniques that businesses can use, depending on their needs. Some popular techniques include text classification, sentiment analysis, and conversational analytics. Traditionally, businesses would need to build teams of data scientists and business intelligence units to analyze text data. Now, anyone can gain value from data, without any data analysis or data science experience.
Akkio is a no-code AI platform that offers a variety of features for text analysis, including text classification, sentiment analysis, and entity extraction. Sign up for a free trial today to get started with text analysis in your business.