AI and ML
Artificial intelligence(AI) and Machine Learning(ML) have revolutionized the way we conduct our research. The tools that AI and ML provide have opened a plethora of possibilities for market researchers. The real power of AI is its ability to harvest large and complex data sets quickly, and in real-time and produce meaningful results. Its repeatability, scalability, and flexibility are a blessing for organizations trying to capture the most relevant insights in an ever-competitive market.
The latest avenues for the deployment of AI and ML are-
Automation
Online mundane and repetitive tasks, be it query form fillup, feedback surveys, or responding to standard emails, are great scenarios for harnessing AI. This has helped companies invest their human resources in more fruitful endeavours and gain insights quickly to make better business decisions. AI models can quickly mine data from social media, emails, chats etc. and transform them into actionable data.
Predictive Modeling
‘Predictive Modeling’ is a recent innovation in online market research where mathematical models are being used to predict outcomes from historical and current data sets. It uses a form of data mining to process data and detect patterns, which are fed into an ML algorithm to forecast future trends pretty accurately. Like, what products the customers are most likely to purchase in the new month? Predictive modeling is a precursor to the research tool we call ‘Predictive Analytics’. It is a great tool for online research and helps companies forecast buying behaviour and sales, potential risks, etc.
It enables the generation of insights often missed by other research tools. Here’s the 7-step process for predictive modeling:
- Defining objective
- Data gathering
- Data Preparation
- Hypothesis testing with the data
- Building the model
- Model deployment
- Activate, evaluate, and evolve the model
Synthetic Data
With the advent of online data collection for research purposes, a major concern globally is data privacy along with bias, underrepresentation, etc. This is where synthetic data steps in. It mimics real-world data, capturing its complexities, randomness, traits, and patterns while still being hypothetical, thus allaying privacy and breach-related concerns.
Synthetic Data is created using machine learning algorithms that are fed with a variety of data sets. Think of synthetic data as a vast sandbox of imaginary data closely built on real data. This allows researchers to test their models without prying into anyone’s personal lives. This works as a testbed to gain new insights, augment existing data, and develop new hypotheses without being directly related to or connected to real-world data.
It also helps companies avoid getting tangled in legal or ......Read More
