![]() ![]() In these cases, we look for what are known as “natural experiments” - natural sources of random variation that mimic a randomized experiment.Ī good natural experiment used by Josh Angrist to measure the effect of military service on wages is the draft lottery imposed on U.S. A scientist would be hard pressed to justify a study that randomly forced people into the military. With all else equal, we can be confident that nothing other than their military service can drive differences in their wages. With a large enough sample, the distributions of all observable and unobservable characteristics across people assigned to treatment and control groups are the same, making the treatment itself the only remaining explanation for any differences in outcomes across the two groups. If we randomly assign some people to join the military, the group that joins (the treatment group) will have, on average, the same education and skills (and age, gender, temperament, attitudes, and so on) as the group that doesn’t join (the control group). We can do this by creating a control group. The challenge, then, is to control for these other factors while isolating the relationship we want to examine. So what looks at first like a causal relationship between military service and lower average wages might simply be a correlation induced by these other factors. And people with more education or skills choose not to enter the military (C causing both A and B). ![]() We can’t simply compare the wages of people that enter the military to those that don’t, because there are many other factors (C) that could be driving differences we might see in the raw numbers.įor instance, people with access to better-paying jobs are less likely to join the military in the first place (this is B causing A). Let’s say we want to know whether (A) joining the military (B) causes a person’s lifetime wage earnings to be lower. The key to making advertising pay is getting people to buy your goods (or donate to a political campaign or take a vaccine) who would not otherwise have done so. But unless the targeting is directed at customers who aren’t already prepped to buy the products, the conversion from click to cash will not generate any new revenue. Big brands pay consultants big bucks to “target” their ads at the people most likely to buy their products. Then I ask: “How much did those ads change your behavior?” Since they had all already signed up for the class long before seeing the ad, they all reply, “Not at all.” So, while the conversion rate on my ad is 100%, the lift from the ad - the amount of behavior change it provokes - is zero.Īlthough my example is a bit simplistic, it shows why the confusion of lift and conversion can create problems for measuring marketing ROI. I then ask them: “What’s the conversion rate on my ads?” They always correctly reply “100%” because 100% of the people who saw the ad “bought” or enrolled in the class. To explain the difference between the two to my students, I have them imagine that, on the first day of class, I stood at the door handing out leaflets advertising the class to every student who walked in. They back up the claim by pointing to the number of people who purchase a product after seeing the ad - typically referred to as the conversion rate. When marketing reps sell ad space to clients, they claim that ads will create or cause behavioral change - a phenomenon typically called lift. Because what’s getting in the way is not a lack of information - the problem Wanamaker faced - but rather a fundamental confusion between correlation and causation. It should be possible to answer this question, though. For all the data we have, it seems like companies still don’t have an answer to a question first posed by the famous 19th century retailer John Wanamaker: Which half of my company’s advertising budget is wasted? A similar analysis of Facebook ads threw up a number of 4,000%. ![]() A large-scale study of ads on eBay found that brand search ad effectiveness was overestimated by up to 4,100%. The effectiveness of digital ads is wildly oversold. ![]()
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