Blog: AI-based Market Research: 10 ways to boost Consumer Insight
AI is perhaps the greatest success story of our generation. In the last few years, AI has made a giant leap from academic research to a household name. This astronomical change can be credited in part to the fantastic yarn spun around AI by Hollywood and the development of AI-based industry-changing technology. In a recent post, we discussed the in-depth applications of AI that has revolutionized the retail industry. This article aims to shed light on the dimensions of AI-driven transformation that has helped next-gen market researchers succeed.
Industries touched by AI
Artificial Intelligence is a science that is truly industry agnostic. AI has improved industries that are completely unrelated to each other.
- Agriculture: Smart farmers use computer vision and cutting edge AI techniques to automatically identify crops that are ripe and ready to be harvested. Hence, improving their overall efficiency.
- Academia: Educators today have incorporated AI-based innovation to grade homework, understand and act on student sentiment, decide the subject matter.
- Mining: AI has been used to identify large ore deposits for sustainable and profitable mining practices.
- Retail: The retail industry has been completely revamped with the aid of AI-based innovation. The gradual shift of the retail industry to personalization can be credited to the retail technology based on AI techniques.
- Market Research: AI has affected almost all the dimensions of market research. A survey was undertaken by Qualtrics which indicated that 96 percent of market researchers feel AI is the next frontier that the industry will achieve in the coming decade.
AI and Market Research
Almost 80 percent of Market researchers feel AI will harbor a positive impact on the current methods of market research. Let us look at the reasons for this overwhelming optimism.
Let us take a closer look at the basics of market research and how AI is helpful in it, before diving into the magnificent world of AI-based market research.
Market researchers define their success based on 3 Es- Efficiency, Effectiveness, and Enhanced Results. AI has proven it’s worth by delivering on all these parameters.
Efficiency: AI-based market research is much more efficient than traditional market research. AI-based market research tools can deliver results in near real time, select the right target audience, and automatically classify and mine text for key insights, all in a matter of hours or at most a day.
Effectiveness: Research has established that there is a direct connection between survey design and the quality of data. AI-based market research has opened doors to interactive surveys. Such surveys change on the basis of the respondent’s answers. Such dynamic surveys are way more effective than traditional static surveys.
Enhancement: AI-based market research is an enhancement on the traditional methods. Real time results have made insight discovery faster than ever before. AI generated results are more accurate and free of any human induced errors or biases.
AI-based Market Research real life use cases
#1 OPEN-ENDED TEXT ANALYSIS
Feedback analysis is at the very core of market research. A high percentage of users tend to leave feedback remarking on their experience and scope of improvement. Popular metrics are improved by pairing them with open-ended questions. Research has repeatedly shown that open-ended feedback are more detailed and can be very effective in understanding the user -mindset. Improvement in speech recognition technology has saved everyone from the pain of typing long and tiring feedback. This in turn has increased the number of responses unstructured questions generate.
Acquiring open-ended feedback is important but a feedback is only as good as its analysis. This is the problem Natural Language Processing aims to solve. AI-based market research has contributed, and in many ways transformed, open-ended feedback analysis. AI and NLP techniques have been used to mine sentiment, meaning, and emotions from text.
Using NLP and Machine Learning techniques, ParallelDots has created the ultimate AI-based market research tool to assist with open-ended text analysis called SmartReader. This tool can also identify themes that your data revolves around. The tool learns from your data and in a way is tailor-made to suit your needs and produce highly accurate results. This D-I-Y AI-based SaaS tool works its magic directly on your Excel Spreadsheet, making it very easy to use.
#2 FOLLOW UP THE FOLLOW UPS
AI-based market research has enabled organizations to streamline their customer support efforts. AI-powered chatbots have the capability to find the patterns of consumer interaction and use it to keep them engaged.
Chatbots can also be trained to carry out a whole host of applications, all adding to the overall user experience. AI-based market research has made insight collection conversational.
AI-based market research tools can easily map-out and access entire chain of conversations much faster than the traditional methods of market research. .
#3 TARGET THE BULL’S EYE
AI-based market research tools analyze the behavior of past clientele based on your CRM data to generate an ideal customer profile (ICP). AI can also help you scan a large number of interacting audiences and identify the people who match your ICP.
AI has helped market researchers devise separate strategies to target different types of audiences. This audience is automatically identified by Machine Learning from a huge pool of potentials. This practice makes marketing and lead generation efforts much more effective.
#4 MAKE OBSERVATIONAL RESEARCH EFFICIENT
Observational research has the potential to be one of the most efficient market research strategies. Market research is all about understanding the consumer’s mindset when they decide to buy a product. Observational research has the potential to create a road map explaining how the consumer interacts with a product.
Observational research has great potential but is wrought with logistical disadvantages. Carrying out extensive observational research can be expensive and extremely time taking. Observational research is based on data points identified while interacting with the product. For example, while conducting observational research on a cigarette, the data points can be the number of puffs, the duration between consecutive puffs, etc. The efficiency of observational research depends upon the number of data points taken into consideration.
Karna-AI has made a brilliant addition, called Perceptron, to the current AI-based market research by completely automating the process of observational research. Perceptron is capable of identifying thousands of very minute data points without making any errors.
#5 IMPROVE EYE-TRACKING CODING METHODOLOGY
One of the most innovative AI-based market research methods is to track a buyer’s eye movements to analyze what catches their attention. After all, what a customer sees is what a customer buys.
Traditionally, Eye Tracking is considered a difficult process requiring a lot of resources. Now, with the assistance of AI and computer vision eye tracking coding has become easier than ever.
Karna-AI has devised a state-of-the-art eye tracking coding technology called SmartGaze. SmartGaze completely eliminates the need for cumbersome manual coding methodology. This technology generates a gaze map for offline (in-store) as well as online retailers.
#6 CONVERTING OPERATIONAL DATA INTO EXPERIENCE DATA
Large enterprises generate a lot of data on everyday basis. Most of this data does not get used, this data is termed as operational data. Operational data is not used to devise an organization’s strategic operations. Market researchers try to identify data that can be crunched to generate strategic insights. This type of data is called experience data.
AI-based market research techniques have enabled value makers to make intelligent use of the data they already possess. Operational data can become experience data as time passes. AI algorithms are trained to comb through a company’s stored data to identify experience data. This can help in effective strategy building.
#7 REMOVE THE HUMAN INDUCED ERRORS AND PREJUDICES
AI aims to replicate human intelligence and decision-making abilities. However, humans are also prone to committing errors borne out of fatigue and prejudices. Such errors can cause analysis to go completely askew leading to a flawed strategy.
AI-based market research techniques help in removing such errors and biases to a very high degree. This helps organizations craft a sound strategy that can lead to stable and sustainable growth.
#8 HOLD ONTO YOUR CUSTOMERS
A customer success team is vital to an organization’s well being. The main focus of such a team is to keep the customers from dropping off by maintaining a steady conversation with them. An internal insights team often works with the customer success team to help them identify customers that are likely to discontinue.
AI-based Market Research tools can bolster community maintenance efforts. It does this using predictive modeling, by crunching login data, bounce rate, average time spent, etc. This way AI can essentially identify customers who are likely to drop-off and the customer success team can retarget them with personalised offers.
#9 AI CAN HELP WITH DEMAND FORECASTING AND PRICING STRATEGY
AI-based market research techniques use predictive modeling and analyze sales data to forecast demand. Such data can help market researchers devise an effective inventory management strategy to avoid overstocking and understocking.
AI-based market research has also helped organizations in building efficient pricing strategy. AI can identify KVI (key-value items), these items are essential to driving a customer’s perception of an organization’s prices.
#10 AI-BASED MARKET RESEARCH CAN HELP WITH MANAGING AN ORGANIZATION’S RETAIL PRESENCE
An organization with national or international distribution follows a fixed and uniform shelf structuring across all the retail stores. This structure is called the Planogram. Organizations spend a fortune to see if the distributors and retailers are stacking the shelves in a planogram complaint manner.
AI can be trained to check if the retailers are following the planogram strategy devised by the organization. AI tech used to monitor the shelf presence of a brand provides accurate and efficient results in terms of logistical and time resource investment. Karna-AI product — ShelfWatch — is capable of in-depth retail shelf analysis.
Do you want to be innovative in your market research strategy?, book your free demo of Karna AI’s products.