The significant strides in Artificial Intelligence (AI) are reinventing the market research industry by addressing cost and time issues. As for the process and application, AI makes market research less labourious, faster, and more accurate. Machine Learning reduces the time to complete projects from weeks and months to hours and days. Algorithms make the job less cumbersome and more cost-effective.
What is Text Analytics, and what are its uses?
One of the newest trends and developments in market research is Text Analytics. Text analytics is a qualitative research method used to uncover the whole story behind the data so organisations can make better, more informed decisions. It refers to the automated process of extracting and translating information, insights, patterns, and trends from large volumes of unstructured text and data. This is done through text analytics software that uses Machine Learning and Natural Language Processing (NLP) algorithms to pull valuable information and meaning from unstructured text.
This text and data consist of open-ended feedback in text form, like emails, survey responses, product reviews, call center notes, and social media posts.
Can you imagine how tedious and time-consuming it would be to pull information and deep insights from such voluminous, unstructured text at scale?
Text analytics helps market researchers examine large amounts of information and data in real-time to track consumers’ sentiments and detect potential brand reputation issues before they become serious.
Text analytics also helps diagnose product issues and provide more profound insights like identifying patterns or trends. It aids in comprehending a negative spike in the customer experience, assists in collating and interpreting customer conversations from various online sources, and helps monitor an advertising campaign’s messaging and how it is being received.
Brands increasingly use text analytics to offer actionable insights that inform sound decision-making. It also enables organisations to examine vast amounts of data at scale, increase efficiencies and reduce time, labour, and costs.
According to Mordor, “The global text analytics market was valued at USD 5.46 billion in 2020 and is expected to reach USD 14.84 billion by 2026 at a CAGR of 17.35 percent.”
Companies use text analysis to help improve their customer, employee, product, user, and brand experience. Many cloud-based applications use text analysis for predictive studies, cybercrime, business intelligence (BI), and fraud management, to name a few.
The Difference Between Text Mining and Text Analysis
It is essential not to confuse text mining with text analysis as they are similar in process and methodologies but have very different applications.
Text mining uses statistical methodologies to extract quantifiable information from unstructured text, used for applications like fraud detection and screening of job applicants.
Text analysis has a more business and experience management focus that uses similar methodologies as text mining but uses the information to uncover trends, patterns, and sentiment to sweeten customer, product, brand, or employee experience.
So how does text analysis measure sentiment in the absence of language and tone?
Market research companies use Natural Language Processing (NLP) to analyze sentiment from the text so they can decode the emotion, feeling, or context behind blocks of plain text. NLP uses language processing algorithms to evaluate sentiment without any bias.
Brand and Market Research applications of Text Analytics
Text analytics is used in the field of Experience Management (XM), and it is widely used in the following four main areas:
Customer experience uses technology like Machine Learning to provide intelligence around the customer or user experience across all touchpoints. This allows brands to enhance the customer experience by making informed decisions based on the findings.
Text analysis provides feedback on the features that need improvement and those that need to be added in future updates. Product usage data and warranty information enable brands to invest in their customers’ most used and valued elements and features, reducing costs and boosting profits.
Text analytics collates data from multiple online sources to identify conversations around the brand. It is also used to analyze how effective marketing campaigns are and how the brand messaging resonates with the target audience—other data points like campaign reach, spending, and customer acquisition impact Return On Investment (ROI). It helps measure the overall brand experience.
Employee wellbeing and work-life balance issues have recently come to the forefront, and text analytics helps provide real-time reports and data around topics that concern employees. Employee attrition has always been a challenge for most organisations, and text analytics combines data around engagement scores to tackle employee attrition and boost employee retention and satisfaction.
Armed with good text analytics software and research methodology, brands can arm themselves with the ability to identify and monitor patterns and trends over time. Text analytics helps deliver insights to build a deeper understanding to win over target audiences.