If you work with customer feedback, you've undoubtedly seen a trend more and more firms use text analytics to analyze text input. What is text analytics, and why is it so important today?
Work for a major firm with many customers (or users). You'll almost certainly get a lot of customer feedback people will write about their experiences, complain about things that don't function and tell you about the things they like. The majority of businesses collect feedback in a standardized style, such as the Net Promoter Score. Other measures, such as Customer Effort Score or Customer Satisfaction, are used by certain businesses.
These standard metrics contain the quantity and free text input from your consumers that reveals why they like or dislike you or your product. Other types of feedback include pure text submitted directly to the firm via various means, comments on social media, reviews in app stores and online stores, etc.
Why should we use Text Analytics?
Scalable feedback. While receiving a few messages now and then is doable, receiving dozens or even hundreds of comments daily becomes a hardship. You can use text analytics to examine a large number of client comments in a scalable method.
Objective Results: When manually evaluating data, it's simple to make mistakes or create bias, not to mention the amount of time your team will have to spend going over each remark one by one. The technique of text analytics is objective, which means you'll obtain the same findings no matter who looks at your data. When it comes to making decisions that affect your business, this is critical.
Turn Unstructured Data into Insights: Consider text analytics as a tool to streamline the analysis of survey findings and make the most of the information you've gathered. It's almost like listening to your consumers openly discuss your items because you're looking at unstructured data.
This is where automated text analytics comes in, it can help filter out the most important themes discussed and provide the overall mood on each one.
If there is a lot of data, the classification may be highly detailed instead of persons, the categories can distinguish customer service employees from salespersons or break employee feedback into remarks on their conduct, knowledgeability, responsiveness, etc.
Text analytics may rapidly provide thousands of improvement suggestions if done correctly. According to text analytics, customers are complaining about your customer service response times, the information provided on the website, the attitude of your sales staff, the features of your most recent product release, and so on.
On the other hand, the management seems unconcerned with the 100 things that someone has complained about. They need to know what the top three areas of improvement are. Which should be taken care of first? Is it critical enough to justify the expense?
Basic text analytics is beneficial, but it is insufficient. The importance of things is not entirely shown by frequency and volume (what is being discussed). Even if you know the emotion (whether the feedback is favorable or negative), you have no idea how significant it is. In a nutshell, the findings are useless.
True leaders are those who, day after day, throughout the whole organization, will unlock the potential of unstructured data and transfer it into commercial value. To achieve this aim, a customer experience program underpinned by comprehensive text analytics technology is essential. This white paper is split into the following sections to give information on the significant aspects of such programs.
Listen to the Voice of Customers
Deliver actionable insights and personalized reporting
Interpret customer data to extract meaningful information
Measure and track the metrics and trends over time, to continuously improve the program
How to use Text Analytics?
To get information, software businesses use a variety of approaches. These are some of them.
The frequency of keywords may determine the most common themes, touchpoints, and difficulties among consumers.
A string of words can sometimes provide more information than a single phrase.
Sentiment analytics (also known as Opinion Mining) is a component that makes use of Natural Language Processing (NLP). It allows users to adjust the severity of feedback based on positive, negative, and neutral terms and the emotions associated with regularly used phrases. E.g., Users may detect the bad experience spectators may have had if a movie theatre gets a rating of 1.5 stars out of ten.
Filters and discovers feedback based on the contents of open comments after categorizing incoming feedback data. Search for terms like "cheap," "well fit," and "comfortable" on an e-commerce shopping website. You may be able to determine the proportion of customers who thought their clothing was inexpensive, fit wonderfully, and were comfortable to wear.
When considering feedback gathering or any other data and analytics activity inside the firm, bear in mind that the value of analytics is recognized only when the analytics influence decision-making and actions. As a result, simple text analytics is merely the first stage in the process of turning data into value. To migrate from data to value, you need many things:
Improved, more intelligent text analytics to better identify the most important factors for improving customer experience. The outcomes must be useful from a business standpoint and identify significant areas for development.
Fact-based decision-making that considers the determinants of customer experience and the financial cost and reward of suggested measures.
An organization that can respond to input.
And, most significantly, steps to enhance many aspects of the firm. Text analytics is actionable if it aids in optimal decision-making, and the findings of the analytics can be conveyed in a way that empowers the company to act.
Sharing the customer's voice across the business is critical, and actively inspiring your staff to actively deliver a better customer experience is a hurdle that must be overcome. Using text analytics to ensure that your customer management platform is user-friendly, straightforward, flexible, and scalable might differ between success and failure. Data can no longer be confined to the domain of specialists and researchers; it must instead be transformed into valuable and actionable insights for the entire enterprise.