Eduardo A. Schilman, Ph.D
Ellsberg, D. (1961). Risk, ambiguity, and the Savage axioms. The quarterly journal of economics, 643-669.
Frisch, D., & Baron, J. (1988). Ambiguity and rationality. Journal of Behavioral Decision Making, 1(3), 149-157.
Tversky and Kahneman first described this phenomena as situations in which people make estimates by starting from an initial value which is then adjusted to yield the final answer. Adjustments to the starting point are typically insufficient, meaning that different starting points yield different estimates, which are biased towards the initial values. In their famous experiment they spun a wheel of fortune with numbers ranging from 0 to 100, that was actually rigged to stop on 10 or 65 only, and asked subjects if the percentage of African nations in the United Nations was greater than or less than that number (10 or 65). Then they instructed subjects to estimate the actual figure. Estimates were 25% and 45%, respectively, which are obviously related to the number spun on the wheel (the “anchor”), even though subjects could clearly see that the number had been generated by a purely chance process.
In a modern demonstration of anchoring, Dan Ariely, George Loewenstein and Drazen Prelec offered to sell various valuable consumer products (computer accessories, wine bottles, luxury chocolates, and books) to MBA students who were asked whether they would buy each good for a dollar figure equal to the last two digits of their social security number. After giving a yes/no response, subjects were asked to state the most they would pay (given an incentive that is known to produce truth saying in such experiments).
The anchoring Effect
When offering Gems, Wonderball Heroes suggests 6 different deals or bundles (I’ll be discussing in a future post the disadvantages of offering too many options). However, the game anchors the player/potential buyer to the 3rd alternative – 115 gems, 15% more for the same price. If you had to guess which deals were the most bought ones, what would you guess?
If you guessed 2 and 3 (55 gems with 10% more, and 115 gems with 15% more) you were right. Most players would see the anchor as a starting point for their inner-negotiations (I NEED LOTS OF GEMS vs. I need to spend less money on games…). If they felt the amount asked for 115 gems was too much, they adjusted downward, but mostly 1 tier down, to 55 gems. If the clever people at MoonActive wouldn’t outline and emphasize 115 gems as the “most popular” deal, they might have been seeing many more purchases on the lowest deal – 20 gems. So why not anchor 250 gems? Well, if they had only 3 bundles, I’d recommend anchoring 250 gems. It would make 115 gems be the most popular purchase indeed. Of course, overall price, the amount we are accustomed to spend on in-app purchases, and other factors might also be in play when people decide what to purchase.
Ariely, D., Loewenstein, G., & Prelec, D. (2003). “Coherent Arbitrariness”: Stable Demand Curves without Stable Preferences. Quarterly Journal of Economics, 118: 73-105.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. science, 185(4157), 1124-1131.
The results showed that those with high numbers were willing to pay more for the products. More than that, the valuations of subjects with the top 20% of social security numbers were typically greater by a factor of three. For example, subjects with social security numbers in the top 20% were willing to pay $56 on average for a cordless computer keyboard, compared with $16 on average for subjects with the bottom 20% of social security numbers. Evidently, these subjects did not have, or were unable to retrieve personal values for ordinary products, and the values they provided were anchored to their social security number which is essentially a random figure.
This, and many other anchoring studies seem to show that people lack preexisting subjective probability distributions over unknown quantities. Use of this knowledge can be advantageous. There’s a good reason why merchants at a Bazaar place ridiculously high price tags on very cheap merchandise. They are anchoring your price expectations to a very high number, so when you finish haggling with them and get a 60% discount, you feel quite proud, while actually paying 3-4 times the real value of the product.
Or take for example the well-known stock trading companies E*TRADE and Charles Schwab. Both offer an online trade transaction for a fee. If you had asked me 18 months ago what fee I would be willing to pay for an online trade transaction of stocks, I would had no idea. Literally, no idea. I later learned that E*TRADE’s offer is $9.99. So when I learned that Charles Schwab’s offer was $8.95, my reaction was yeahhh! That’s 10% cheaper! However, my reaction would have been different had I first learned about a third company’s rates, GetStocks, since the latter prices an online trade transaction at $7.50... Since anchoring often occurs on the first piece of information encountered (when other relevant data is lacking or the data is incomplete), GetStocks could do better by asking for a higher price. Of course, other considerations might set a company’s trading fees, but this serves as a good example.
As mentioned above, the anchoring effect comes into play particularly when we don't know what to expect, how to measure a price we are asked to pay, or understand the probabilities involved. You can make use of that to sway people's attention and minds towards a certain element in your site/product, but you can also use this knowledge to understand when you are being affected by it.