One of the most time-intensive tasks in research is analyzing large amounts of open-ended data typed by research participants. If you’ve never done it, trust me on this one.
Text analytics helps users better understand the results of open-ended text typed by survey participants, customers, or general consumers. Examples could include survey questions that have a text box for participants to type their answers, social media posts, and online reviews or ratings.
The way researchers have traditionally analyzed open-ended text is to gather the data in a spreadsheet, read each piece of text, and assign codes as needed. This is called coding in market research.
Disadvantages of Coding
For a survey with 1000 responses, coding one open-ended question can take between 1 to 4 hours. The time required depends on the length of the text typed by respondents (i.e., longer responses means more time is needed to analyze and assign a code to each piece of text).
Learn more about the pros and cons of coding in market research.
Advancements in Text Analysis
Rather than coding, major advancements in text analysis have simplified this process. *I’m cheering right now! Hooray for saving time and better analysis*
Learn more about what text analytics is, how it works, and a few examples of how it’s used below.
What is text analytics?
Text analysis software is a major time saver to market researchers. There are several software platforms available and each is somewhat similar. Most text analysis software assigns codes to open-ended text in a matter of seconds or minutes rather than hours. Again, note the huge time savings.
In addition to reducing the time needed to code open-ended responses or text, text analysis software can typically analyze other data collected. This adds a deeper level of analysis than just simply coding the results.
So text analytics not only saves time, but it also gives users access to advanced analytics to understand underlying motivation/attitudes, strengths, weaknesses, and more.
How does text analysis work?
If you are familiar with other analysis software used in market research, most text analysis platforms are similar and intuitive.
Typically, the process includes creating a spreadsheet in Excel and uploading it to the platform. Common fields included in the spreadsheet are ratings, demographics, and the open-ended text fields.
Each row in the Excel spreadsheet should represent a single respondent. Here’s a quick example:
If there were 1,000 ratings, you can see how reading each response and assigning codes would be time-consuming. Luckily, text analysis software is making this process easier.
Once the data is uploaded, the text analysis software assigns topics and sentiment to each row of data. This means the software is picking up topics in addition to underlying emotions about the topics.
For example, in addition to knowing what comments are about staff, it will be able to tell how many comments about staff are positive, neutral, or negative.
Again, all of this happens in a matter of seconds or minutes.
Why is text analytics important?
In addition to the sentiment of a topic, some text analysis software is also able to show how often other topics are mentioned.
For example, it could say of the 70% who mentioned staff, 40% mentioned the greeter, 30% mentioned the bartender, and 10% mentioned the manager.
From here, we can dig even deeper to find those who mentioned the greeter had a positive sentiment, those who mentioned the bartender had a neutral sentiment, and those who mentioned the chef had a negative sentiment. This level of depth leads to more impactful data for strategic decision-making.
Going even further, some text analysis platforms can use advanced analytics to see how ratings and other data uploaded effects satisfaction. It can run matrixes, identify which variables are correlated and which are not, and more.
What are examples of how text analytics are used?
Text analytics can be really helpful to understand survey results, social media posts/interactions, and online reviews. Social media and online reviews are two examples of how organizations can leverage the data they already have!
Many organizations know their rating on Google, Facebook, Yelp, etc. and likely skim through the reviews, but they never truly analyze this data. Text analytics brings this data to life.
Text analytics is a cost-effective way to enhance decision making and get a leg-up against the competition.
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