Remember how people say having more knowledge helps you make better decisions? Turns out, it doesn’t. at least not in cases of ‘fashion products’ or those products whose sales are based on consumer preferences. According to an article published by Harvard Business Review, in certain cases, it helps when people make decisions or predictions based solely on context, than on historical data.
To figure out what role historical and contextual data play when humans make a decision about which products will fly and which will splutter and die, IE Business School’s Matthias Seifert and a team of colleagues carried out a research.
They tried to study predictions of which pop singles would enter the Top 100 chart. For this, the team rounded up about 23 senior managers at major record companies, who identified ‘predictor variables’ (of success). These included the marketing budget for each single; which other artiste was releasing his/her solo in that week; and whether the artiste in question was an established performer or a newbie.
They then gave 92 A&R (artists and repertoire) managers, lists of forthcoming singles, that included the predictor variables. Over a period of 12 long weeks, the managers filled out four online questionnaires, predicting the likely chart-entry positions of the singles they’d been assigned, using the predictor variables to make their forecasts — 210 in all. After the survey, the researchers then saw how each single performed.
After categorising each predictor variable as historical or contextual, the team then mathematically analysed the two and saw which worked best, and how they worked together. They found that when it comes to volatile demands, better judgements are made when people are given context, than historical data. In fact, in this case, historical data impaired the A&R manager’s ability to predict correctly.
So what did the team learn from this? That computers are better decision makers in predictable environments, when outputs depend on historical data. Humans, on the other hand, should be preferred when you need to make a decision in a volatile environment.
To read the article, click here