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4 Ways AI will Improve Research Tech in 2021 and Beyond

Research Tech (ResTech) offers a great avenue to apply AI and Machine Learning the modern context of doing business. As businesses across industries face an uncertain 2021, one thing remains clear: a constantly-shifting public health and economic landscape will affect everything from customers’ purchasing decisions, to their likes and dislikes about the products and services they use.

Companies looking for more effective ways to keep their finger on the pulse of their customers in 2021 must look beyond multiple-choice survey responses or numerical scores. Instead, they should focus on collecting open-ended survey responses to understand what their customers are saying in their own words—complete with slang, emojis, and misspellings—if they want to truly understand what their customers are thinking.

For decades, analyzing these open-ended responses has been a tedious process, with researchers reading and tagging each response to count concepts to quantify concerns and identify representative verbatims. Heading into 2021 and beyond, AI-powered applications will increasingly give researchers the ability to rigorously analyze the concepts and emotional content otherwise hidden in open-ended survey responses, offering an informed view of the nuanced thoughts and feelings driving customers’ actions.

Here are a few ways AI will improve research tech (restech) in 2021 and beyond:

Democratizing Tech Across Departments and Levels

Until recently, you had to be a data scientist or a technologist to figure out how to configure and apply AI technology for your research needs.

In 2021, I expect we’ll see more companies offering no-code AI-powered applications that allow people with minimal technical training to quickly surface, quantify, and visualize all of the concepts in the survey responses—even concepts researchers weren’t looking for and outliers that would otherwise have been missed.

As these easy-to-use applications become prevalent, we’ll see more instances of companies putting powerful research tools in the hands of analysts and business users, marketing teams, customer support specialists, and more.

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Eliminating the Need for Tagging Survey Responses

Most researchers have spent extraordinary amounts of time reading through unstructured survey responses and tagging each response that contains a concept of interest. With next-generation AI-powered text analytics, this tagging process will be completely automated, allowing researchers to focus on higher-value analysis and business recommendations.

Taking Sentiment Analysis to the Next Level

While sentiment analysis has been prevalent for well over a decade, the most common form of sentiment analysis involves evaluating whether a document’s sentiment is overall more positive than negative. This type of analysis is overly simplistic, as it fails to address nuanced comments such as customers explaining what they like and dislike about a product, or employee feedback about a company’s strengths and weaknesses.

With improvements in using AI to analyze sentiment, businesses across industries will be able to upload any text-based document, and quickly receive a nuanced analysis of the author’s sentiment regarding the topics they wrote about.

Decreasing Survey Questions and Increasing Response Rates

AI will allow researchers to have their cake and eat it, too.

Traditionally, fewer questions improve response rates, but more questions provide more feedback. In the coming years, researchers will increasingly use AI-powered text analytics to consolidate questions into a few open-ended responses and let the AI extract the rich and nuanced feedback contained within.

Remember, a single open-ended survey response often contains more information than a dozen multiple-choice answers. With AI making it possible to quickly and rigorously analyze open-ended survey responses, researchers in 2021 and beyond will rely more on the rich content provided in these ended verbatims than the multiple-choice or short-answer questions used in the past.

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