People perceive less risk when they believe that they can control it. For instance, the absolute odds of dying in a car accident each year in the United States is 1 in 56000. If we normalize the risk out demographically, if you’re between the ages of 16 and 28, and male, your odds of death are higher than those of any other demographic. It’s a leading killer in the United States. Yet, when we contrast that with deaths as a result of terrorism. Or deaths as a result of shark attacks. Or in airplane crashes — we get a very different degree of risk perception. People are far less likely to appreciate the real risks when they believe that they’re[…]
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If you roll a single fair die, what is the probability of rolling a 6? You count. There are 6 possible outcomes, a 1, 2, 3, 4, 5, or a 6. What’s the probability of a 6? 1 out of 6. What if you rolled a 1, then a 2, then a 1, then a 2, then a 1, and then you rolled a 2. What’s the probability of rolling a 6? Same thing…1 in 6. Is a 1 more likely? Nope. Each roll is independent, and the sequence of 1,2,1,2,1,2 is just a coincidence. Yet, from the time that we’re born, we’re trained to recognize patterns. It’s baked right into our brain. 3 million years of evolution. Consider how[…]
People tend to perceive more risk what they don’t see and don’t understand. Food irradiation (sterilizing food with radiation) is one of them. Dying in a plane crash is another (the physics of flight continue to elude many). New and unfamiliar technologies tend to cause a higher amount of risk perception – the first microwaves, cell phone technology, and limited exposure to environmental toxins. There’s an element here of people tend to fear the unknown. “The devil you know is better than the devil you don’t”. One key ways that an analyst can communicate risk is to make the unobservable, or the unknown…known. One method is to categorize all the risks into categories: What we know we knowWhat we know[…]
Risk communication is inherently wrapped up with risk perception, and then with a basic lack of understanding of uncertainty and probability. Yet – as web analytics practitioners and marketing scientists, we have to communicate risk all the time. What are the chances of this A/B test completely failing? What are the chances that we’re going to make a profit on this? What are the chances of losing the entire company if this strategy doesn’t go well? Typically, these questions are rooted around ‘chance’ or ‘odds’. Experts in health, safety, and policy have problems with risk communication too. What’s the real threat of Mad Cow to the population. What about a terror attack? What about the odds of your plane’s engine[…]
All 3, web analytics, business intelligence, and marketing measurement, fit together. Most businesses have disparate departments responsible for each though. The team that’s responsible for eMarketing (or eCommerce) typically has some form of web analytics software on their site. Web analytics traces its evolution from the IT space back in the early 1990’s. You had IT folk who were the first people who set up websites. Then marketing started to take it over. Then IT realized, through the magic of log file analysis, they could measure some of the effectiveness of websites. (This was back during the era of ‘hits’). Chaos ensued. Then the .bust. And now we have a number of options for web analytics – including Google Analytics,[…]
This is my first run at video blogging, and I think it’s alright for a first go at it. 🙂