Latest Innovation in Online Market Research

The highly evolving landscape of technology in this era means that the field of market research has seen significant and innovative concepts. Technology has improvised traditional methods and given birth to next-generation tools for researchers. The deeper penetration of the internet has allowed companies to leverage online methods to process and analyze data in real-time, making for a more enduring and agile experience.

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 ethical webs, as the data is just a reflection or representation of the existing world. Therefore, it’s quite powerful for undertaking simulations that might seem impossible to conduct in real life. Moreover, the use of Synthetic data for research also reduces redundancies, complexities of data capture, cost savings, and a faster turnaround time.

AR and VR

What started as more of a recreative technology has now become a full-fledged research tool. Augmented reality and Virtual Reality are transforming how companies interact with customers while gaining useful insights. AR and VR are not just being used as mere marketing tools to engage users in an interactive and immersive manner, but also for a better understanding of their emotive traits.

Technologies such as heat maps, eye tracking, etc. help to detect finer and deeper layers of human emotion that are not captured even through in-person methods. Data captured likewise helps companies and researchers fine-tune their products to delight sensory receptors, driving satisfaction to whole new levels. Companies can simulate large, unreal scenarios or environments through AR/VR which saves huge investment costs and increases profits. AR/VR is great for new product testing and development, creating new experiences, or an excellent way to create hype for existing products and tap into new niches.

Sentiment analysis

Traditional research methods like surveys, often fail to gauge the right mood of the target audience, as participants tend to be shy or wary of sharing the correct information. That’s why researchers have now deployed sentiment analysis, which is a tool that autonomously monitors the user’s behavioral traits. For example, how does a customer sound when interacting with a support executive when registering a complaint, or their facial movements in an online focus group? Sentiment analysis uses data from voice, video, and on-screen trackers to accurately gauge the user’s mood and categorize them into indicators for real-time insights. Sentiment analysis involves machine learning models that are trained to capture the emotions of people accurately. Research firms also deploy a website tracking tool that tracks the mouse movements of the user while they navigate through the pages. This gathers data points like total time spent, time spent on particular products, distractions, etc.

IoT

The Internet of Things (IoT) is transforming how companies remotely collect important metrics from their products while in use by customers. The respective products use geolocation and internet connectivity to remotely send parametric data for advanced analytics regarding product performance, usage behaviour, defects, faults, etc. The best example of IoT-enabled market research is electric vehicles. This helps companies obtain real-time statistics, which further helps in improving products or identifying design deficiencies. The great thing is that these insights are not from a controlled environment but rather from real-world usage. IoT-based products not only enable companies to get valuable feedback but also act as a safety measure to alert their customers of some potential risks.

These innovations in online market research combine existing methods with next-gen technologies such as geofencing, AI, ML, AR, and VR to open up new vistas for companies to base their research on. They also reward customers with a more satisfactory, delightful, and fulfilling experience.