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.
We rolled out our No Code Database feature today. Just plug in a data feed and add data to a customizable database with zero lines of code, and zero knowledge of the inner workings of databases. All this in under a minute.
Setting up a database in the cloud is confusing and complex for most people. Our technology puts the power of cloud-based databases at everyone’s finger tips. No need for the IT team’s intervention. No need to learn remote login protocols. No need to learn any code.
We have also added in some useful aggregation and summarization tools that let you extract data from databases straight into reports and charts. Again, no code required.
This is the valuing of your own labor rate that takes place after your third or fourth trip back from Ikea. You know you have saved some money buying a set of shelves that you need to assemble but part of the decision was made by assessing how much time it would take to put the item together vs how much you saved.
Ikea is in the business in devaluing our self-perceived labor rate so that they can charge the most for a flatpack item such that the discount achieved justifies the hours needed to assemble the item. (This is the same model used by meal-kit businesses, or at least it should be.)
For items that do not require assembly then the trade-off people have to make is their willingness to buy products that do not comply with the standards for that type of product bought everywhere else. For example, a desk lamp from Ikea requires no assembly so there is no time-cost to save. To justify the lower price there must be an investment from the customer. In this case it is the willingness to accept a non-standard shade fitting. The same is true of other non-assembly products sold at Ikea.
For those folks interested in alternative data, there could be a macro signal regarding wage earnings and wage growth buried in this data. Comparing the price of an Ikea product with a similar (assembled) non-Ikea product over time could be a useful economic indicator.
We have upgraded our online user guide. We have added pages on new functionality and pages on some use cases for the platform. It is a living document so it will change regularly over the next days, weeks and months as we add new content to it. Here is a link to the site. Enjoy!