The Future of Market Research: AI & Advanced Analytics

The Future of Market Research: AI & Advanced Analytics

Akin to every other operational product, market research is evolving with the help of AI. Thousands of AI tools have infiltrated the market in the past 2 years, yet some major transformations are yet to happen. 

Today’s blog is going to be an eye-opener for market researchers. We will talk about statistical assumptions, trends and patterns in our industry. Get ready, because the rules of this game are going to change! 


AI tools used in Market Research 

AI is transforming the market research landscape by offering powerful tools and techniques to gather insights and make data-driven decisions. Here are some prominent AI technologies used in market research:

  1. Natural Language Processing (NLP)

Analyzes text data from various sources like surveys, social media, and online reviews.

Identifies sentiment, emotions, and opinions expressed in the text. Enables automated analysis of open-ended survey responses and extracts key themes and insights.

Tools like Speak use NLP to convert audio and video interviews into actionable data sets.


  1. Machine Learning

Learns from vast amounts of data to identify patterns, trends, and relationships.

Used for tasks like

Customer segmentation: Grouping customers with similar characteristics and behaviour for targeted marketing.

Predictive modelling: Forecasting future market trends, customer behaviour, and demand.

Social media listening: Analyzing social media conversations to understand brand perception and customer sentiment.


  1. Chatbots and Virtual Assistants:

Conversationally engage with consumers, collecting data and providing personalized recommendations.

Conduct surveys, answer customer inquiries, and gather valuable insights from interactions.

Tools like Survey Sparrow utilise AI-powered chatbots to streamline data collection and improve engagement.

Also Read: What are Consumer Insights: Meaning, Examples and Scope


  1. Image and Video Analysis:

Extracts insights from visual data like facial expressions, body language, and objects present in images and videos. Used for analyzing emotions conveyed through facial expressions in video footage.

Market research for visual content: Understanding audience preferences for product packaging, advertising visuals, etc.


  1. Other notable AI applications

Data cleaning and analysis: Automating data cleaning tasks and identifying inconsistencies, saving researchers time and effort.

Survey optimisation: Recommending question types and improving survey design for better data quality.

Report generation: Automatically generating reports with key findings and insights from the collected data.

These AI technologies empower market researchers to gather deeper customer insights, make faster and more informed decisions, and gain a competitive edge in the market.


Advantages of implementing AI in market research 

  1. Enhanced Data Collection

AI automates tedious tasks like data entry, web scraping, and social media monitoring, freeing up human resources for more complex analyses and strategic thinking. AI tools can operate continuously, collecting data around the clock, even from dispersed sources, increasing the scope and timeliness of data gathering. 


  1. Improved data quality and accuracy 

AI can identify and remove inconsistencies, errors, and irrelevant data, improving the overall quality and reliability of datasets for analysis. AI can identify unusual patterns and potential issues in real-time data streams, allowing for faster response and proactive decision-making.


  1. Forecasting Market Trends 

By analyzing vast amounts of historical data and incorporating real-time information like social media trends, news sentiment, and competitor analysis, predictive analytics can identify early signs of emerging market trends. Predictive analytics can estimate the potential size and growth of new markets. This helps businesses decide on resource allocation, investment strategies, and product development plans.

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  1. Cost Effectiveness

By automating tasks and reducing reliance on manual processes, AI can significantly reduce market research costs. More accurate and insightful data leads to better decision-making, potentially leading to cost savings and improved return on investment (ROI).


  1. Deeper Data Analysis

AI, particularly Natural Language Processing (NLP), can analyze vast quantities of unstructured data like text, audio, and video. This unlocks valuable insights from sources like social media comments, customer reviews, and surveys. AI algorithms can identify complex patterns and trends in large datasets, revealing hidden connections and insights that might be missed by traditional methods.


AI models used by top market research companies

We have explored the potential and importance of AI in market research. But as we learn, some giants in the industry of market research are actively using AI power to analyse data sets! 


AI Models:

Large Language Models (LLMs): These powerful AI models, like GPT-3 (OpenAI) and Jurassic-1 Jumbo (AI21 Labs), can analyze vast amounts of text data and generate summaries, identify sentiment, and even create different creative text formats. Market research companies like Nielsen and Ipsos are exploring their potential for analyzing social media conversations, customer reviews, and survey responses.

Machine Learning Models: Various brands utilize custom-built machine learning models for specific tasks. For instance, Clarabridge utilizes sentiment analysis models to understand customer opinions expressed in text data, while Brandwatch uses anomaly detection models to identify unusual spikes in social media mentions.


AI Tools:

Survey & Data Collection Platforms: Platforms like Qualtrics and SurveyMonkey integrate AI features like chatbots for automated data collection, sentiment analysis for open-ended responses, and automated data cleaning.

Social Listening & Analytics Tools: Platforms like Brandwatch and Sprout Social use AI to analyze social media data, identify trends, and understand brand perceptions.

Data Visualization & Reporting Tools: Tools like Tableau and Power BI leverage AI to generate interactive dashboards and reports with insights and recommendations based on the analyzed data.

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Examples of Well-Known Market Research Brands and their AI Implementations

Nielsen: Utilizes AI and machine learning to analyze massive datasets from various sources, including purchase data, social media, and consumer panels, to provide insights on consumer behaviour and market trends.

Ipsos: Employs AI and natural language processing to analyze social media data, predict future trends, and understand consumer sentiment.

Kantar: Leverages AI to automate data collection and analysis, personalize surveys, and generate real-time insights from market research studies.

It's important to note that the specific AI models and tools used by different brands can vary depending on their specific needs and areas of expertise. The market for AI-powered market research tools is constantly evolving, with new solutions emerging regularly.


Final Word 

The global AI market is projected to reach $1.8 trillion by 2030, showcasing a significant increase and potential for broader adoption across various sectors, including market research. With these unbeatable advantages, AI becomes the inevitable future of market research. 

We hope this blog helps you to understand the strength and significance of AI. Consumers’ insights are valuable and processing them into actionable insights is a hefty task. Following the lead of top performers such as Nielsen, Kantar and Ipsos, many companies would soon start following the same model. 

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