Are your Market research processes manually intensive and they take up all of your time? Are you spending 60 percent of your resources on market research? Well, the good and the bad news is you are not alone. In fact, more than 90 percent of market researchers out there face these problems. Not anymore!
Karna AI is here to show you how AI can automate your Market Research efforts making them quicker and resource intensive without compromising on the accuracy.
Karna AI has curated some of the most out of the box solutions to completely change the market research domain. In the previous post, we discussed two of the most innovative AI-based market research solutions that are out there (SmartGaze and SmartReader) but these are not the only guns in The Karna Arsenal.
I will dive into how Karna’s AI solutions that are transforming the current Retail Audit processes. We have reduced the turnaround time drastically using our tool ShelfWatch.
We will also take a look at Perceptron, a solution that has successfully automated the age-old process of Observational Research.
ShelfWatch: Automating Retail Marketing Audits
A retail store shelf is the first point of interaction between your consumer and your product. Purchase decisions are made while browsing through the shelves of a market place, according to the current research studies.
As a result, it is imperative to have a market research process that is dedicated to monitoring brand performance on retail shelves. Click on the image below to see how ShelfWatch AI can detect your SKUs (Stock Keeping Units) & POSMs (Point of Sale Materials) on the shelf.
When it comes to retail auditing we must understand that a consumer buys what a consumer sees. People buy with emotions and justify it with logic, so it is of prime importance to attract what we call the monkey brain with attractive visual merchandising.
Some of the highest selling products stand out in terms of eye-catching product packaging. For example, let’s consider BIRA’s meteoric rise to be the market leaders leaving behind international brands. Their packaging, the eye-catchy colors, and creative design, it makes you want to buy it right off the shelf.
Or in other words BIRA appeases your monkey brain better than its competitors.
Challenges with traditional retail auditing and ShelfWatch, the perfect solution
When it comes to retail auditing we must take into account visibility, shelf space, merchandising attractiveness
Acquiring prime shelf positions is an Investment rather than a Cost. But if you don’t have any clue as to how this money is being spent it is not long before it becomes a Cost and a heavy one at that. Even when you employ all the resources to carry out this market research process, the results are still vulnerable to human-induced errors.
So how do a retail company get to know if the money is being spent correctly, without much
At this point you must be asking is there a better solution. Fortunately, Karna AI has leveraged Machine Learning, and advanced computer vision to create the perfect solution to all your retail auditing woes- ShelfWatch. This solution is completely automated and produces in-depth results upon extensive analysis.
ShelfWatch helps the field agents by giving them the flexibility to take all possible pictures in any orientation, lighting or positioning. Such flexibility is allowed because shelf watch is not dependent on standard uniform images to give accurate output. Using state-of-the-art AI algorithms, ShelfWatch is able to analyze even the most distorted images because it uses packaging recognition technology.
How does ShelfWatch work?
ShelfWatch is not an off-site solution because of its complexity. All we require is the images of the various retail shelves displaying your product. The technology behind the solution analyzes these images and identifies your product. The solution also undertakes an extensive competitor analysis. Quantifying the results that can produce concrete insights. Our team of highly qualified analysts crunches these results and figure out exactly what works and what doesn’t. Click here to schedule a free demo for ShelfWatch.
Industries ShlefWatch has helped succeed
The beauty of ShelfWatch is in its diversified usability. ShelfWatch has been used to great results by one of a major tobacco giant. This solution has transformed retail auditing for packaged products by analyzing their shelf presence. Using the insights generated by ShelfWatch brands can pinpoint the exact causes of decline or growth in their sales.
Perceptron: Automating the Market R
esearch process of Observational Research
Before we try to understand how Perceptron has impacted Observational Research, let us build an understanding of exactly what is this market research process. Observational Research is perhaps the oldest play from a market researcher’s manual. It is a qualitative research method and as the name suggests it involves observing consumers while they are interacting with a product.
Observational Research has historically proven to be very successful due to a variety of reasons. This market research process successfully overcomes the problem of recall bias since the consumers are not required to recount their experiences instead a researcher generates insights based on real-time observations. The method is quite versatile in terms of conditions of observation, for example, the researcher can move closer to the subjects for a better vantage point.
How has Perceptron overcome the traditional Observational Research?
Perhaps the greatest challenge with this age-old market research process is that it is very time consuming and highly prone to human errors on the part of the observer. A high degree of patience is required to observe the consumers without a lapse in concentration for carrying out observational research successfully.
When we consider the minute observations, the task becomes all the more cumbersome and prone to human-induced errors. For example, the time taken to smoke a particular cigarette can be observed by a human but consider quantitatively observing the number of times he takes a drag, the duration of each drag or the time interval between two consecutive drags. These minute data points cannot be mapped without observing the subject very closely and close direct observation can cause the subject to display idealistic behavior.
Perceptron essentially acts as an AI assistant for observational research. The tool matches and in many cases beats the accuracy of the rigorous manual observations. Perceptron is very scalable and time intensive. It produces data that can be analyzed to generate very targeted insights. The AI-based technology behind Perceptron analyzes the smallest and most redundant data points and is not vulnerable to human errors and biases.
Click here to schedule a free demo of Perceptron.
How does Perceptron work ?
Perceptron is a rising star in the observational market research domain. This solution is very customizable, each problem set has a new and distinct approach with a completely different set of data points. In observational research more the number of data points deeper the analysis. Depending on the number of data points and the number of observational subjects, we create a timeline for our clients. Once our technology produces the numbers from the observational research, a team of highly competent analysts crunches these number to produce various insights. We promise to deliver better or at the very least match the results that a team of human inspectors can achieve in
Industries our solution has helped
Perceptron is a true industry agnostic solution. This solution has revolutionized the market research process of observational research across many different industries including grooming, cosmetics, retail goods, packaged products, perishable consumables and many more. In one of our previous post, we discuss how Perceptron helped a popular men’s beard trimmer carry out in-depth observational research producing quantitative data-backed insights.
We hope you liked the article. Want to transform your market research efforts? Click here to schedule a free demo.