Our frame of reference can be impacted by our own biases.
While there are lots of biases, let’s focus on two of them here.
a) Availability Bias: This occurs when a bias, or distortion, creeps into our objective view of reality because of information recently made available to us.
For eg. You tend to assume mustard is in trend when you see recently a lot of mustard on the streets or on the sites you browsed; or we tend to assume crime rate is going up because we see them in headlines more often while in fact, it probably is not. Availability bias is now more pronounced, as what you see on the internet is more and more filtered to your needs than an objective reality of the world. Eli Pariser, an Internet freedom evangelist calls this “Filter Bubble”.
b) Priming: In an experiment that became an instant classic, the psychologist John Bargh and his collaborators asked students at New York University- most aged eighteen to twenty-two- to assemble four-word sentences from a set of five words. For one group of students, half the scrambled sentences contained words associated with the elderly, such as Florida, forgetful, bald, gray or wrinkle. When they had completed the task, young participants were sent out to do another experiment in an office down the hall. The short walk was what the experiment was all about. The researchers unobtrusively measured the time it took people to get from one end of the corridor to the other. As Bargh had predicted, the young people who had fashioned a sentence from words with an elderly theme walked down the hallway significantly slower than the others.
We are all primed all the time and while we think we are autonomous, we are not.
The question now is, how do you stay objective, with these biases?
One clear way is informing your decisions with data rather than just intuition, that also with data outside of the Filter Bubble.
This is exactly the reason Stylumia exists, to enable fashion brands and retailers with
a) Global Fashion Intelligence through one of its kind Consumer Demand-Sense (First Principle thinking)
b) Visual Business Intelligence for their omnichannel data using Computer Vision (Right Frame of Reference)
c) Predict Demand of unseen new products with proprietary ML models using market and own data (Right Frame of Reference)
d) Predict the optimal distribution of products to Stores using store taste and product relevance (Right Frame of Reference)
We also enable you to test assumptions as a part of our engagement.
A visual summary of all our solutions is below.