Market Research 3.0

In recent years, there has been lots of talk about incorporating Machine Learning and AI into market research. Back in 2015, I met someone at a firm who claimed to be able scale up market research survey results from a sample of 1,000 to samples as large as 100,000 using ML and AI.

Unfortunately that firm, Philometrics, was founded by Aleksandr Kogan – the person who wrote the app for Cambridge Analytica that scraped Facebook data using quizzes. Since then, the MR world has moved pretty slowly. I have a few theories but I will save those for later posts.

Back on topic, Knowledge Leaps got a head start on this six years ago when we filed our patent for technology that automatically analyzes survey data to draw out the story. We don’t eliminate human input, we just make sure computers and humans are put to their best respective uses.

We have incorporated that technology into a web-based platform: www.knowledgeleaps.com. We still think we are a little early to market but there might be enough early adopters out there now around which we can build a business. 

As well as reinventing market research, we will also reinvent the market research business model. Rather than charge a service fee for analysis, we only charge a subscription for using the platform.

Obviously you still have to pay for interviews to gather the data, but you get the idea. Our new tech-enabled service will dramatically reduce the time-to-insight and the cost-of-insight in market research. If you want to be a part of this revolution, then please get in touch: Doug@knowledgeleaps.com.

Patented Technology

The patent that has just been awarded to Knowledge Leaps is for our continuous learning technology.  Whether it is survey data, purchase data or website traffic / usage data., the technology we have developed will automatically search these complex data spaces. The data spaces covers the price-demand space for packaged goods, or the attitudinal space of market research surveys and other data where there could be complex interactions.  In each case, as more data is gathered – more people shopping, more people completing a survey, more people using an app or website – the application updates its predictions and builds a better understanding of the space.

In the use-case for the price-demand for packaged goods, the updated predictions then alter the recommendations about price changes that are made. This feedback loop allows the application to update its beliefs about how shoppers are reacting to prices and make improved recommendations based on this knowledge.

In the survey data use-case, the technology will create an alert when the data set becomes self-predicting. At this point capturing further data is unnecessary to understand the data set and carries an additional expense.

The majority of statistical tools enable analysts to identify the relationships in data. In the hands of a human, this is a brute-force approach and is prone to human biases and time-constraints. The Knowledge Leaps technology allows for more systematic and parallelized approach – avoiding human bias and reducing human effort.

The Power of Non-Linear Analysis

A lot of what an analyst does is perform linear analysis. These analyses are guaranteed to produce human-readable stories, even if they aren’t  insightful.

The world in which we live is not linear.  In the book, 17 Equations That Changed The World, the author, Ian Stewart,  selects only three equations that are linear, the rest are non-linear.

This shows the limitations of linear analysis in explaining the world around us. A lot of what we experience in life is non-linear, from the flight of a ball in the air (parabolic) to the growth of your savings (exponential).

What’s true of the physical world is also true of the human brain too.  One example is the way in which our brains use non-linear relationships to evaluate choices. One of the foundational tenets in the field of Behavioral Economics is Kahneman and Tversky’s Prospect Theory and Loss Aversion.

Loss aversion describes the non-linear relationship between the value associated with gain of an item versus the value associated with loss of the same item.  We would rather not lose something than find that same thing.

Whether we are conscious of this or not, our brains use it every day when we evaluate choices, protecting what we own is a greater driver of behavior than gaining new things,  it is one reason why the insurance market is so large.

A good analyst will over lay this non-linear understanding of the world when interpreting findings, however it would be useful if analytics software could allow for human-readable non-linear analytics (it’s what makes Support Vector Machines so powerful, yet so indecipherable).