Pricing strategy has a well-established place in the library of management practices. Setting the right price, or even better, set of prices, is an important way for a business to maximize its revenue and/or profit. What gets less attention is the consumer psychology of price setting, i.e., the “how, when, where, and in what form” of pricing. We can decide that $1000 is the right price for a product, but many important pricing issues remain. Should it be paid as a lump sum, or installments? Should it be paid up-front, or at the conclusion of a contract? Should we sell the product individually, or bundle multiple products under the umbrella of a single price? The answers to each of these questions can have a significant impact on a customer’s perception of price, and therefore on his or her perceived value of our product and likelihood to purchase.

Harvard Business School Working Knowledge interviews professor John Gourville about his research into these psychological aspects of price setting. Here’s an excerpt:

Q: You talk about sunk cost, the idea that people will use a product or service more right after they pay for it. How can companies make this work for them?

A: Sunk costs are a curious bit of psychology. Economists say that attending to sunk costs is not rational—when considering whether to go to a play or attend a football game, the amount of money you spent on the tickets should be irrelevant to the decision to go. The only things that should matter are the costs not yet incurred, such as cost and hassle of driving, and the benefits to be consumed, such as the enjoyment of the game. At the same time, almost everyone pays attention to sunk costs. We go to plays or concerts that, in retrospect, we’d rather not go to simply because we have a $50 ticket in the pocket.

Similarly, one of my colleagues describes a person who pays $300 to join a tennis club, only to come down with tennis elbow. Nevertheless, he continues to play in spite of the pain, reasoning, “I don’t want to let my $300 go to waste.” And some people even count on their own irrationality and buy season tickets to a play or symphony series, knowing that it will force them to get out of the house.

Is any of this rational? No. Is it a good thing? That’s tougher to say. If people are happier to attend to sunk costs than to let the money spent on an item “go to waste,” perhaps it’s a good thing. Can it be used by companies to influence consumers’ behavior? Absolutely.

Take your average health club. It faces two tasks: getting people to join and getting people to renew. (The churn at health clubs can exceed 50 percent.) Knowing that members are going to be more likely to renew in year two if they feel that they have “gotten their money’s worth” in year one, a club should look to encourage attendance. One way to do this is through the timing of payments. Many clubs demand payment in full at the start of a year-long membership. The result is that people work out a lot in the first month or two, while that payment is still fresh in their minds, but gradually stop going as the payment fades from memory. In this case, the sunk cost effect weakens the further one is from that initial payment. Now consider the member who makes payments monthly. For him or her, the cost of membership will always be vivid and they will feel obliged to work out on an ongoing basis. At the end of the year, who is more likely to renew? Clearly, the person who worked out regularly will have a higher likelihood of renewing his or her membership.

The same concept can be applied to health care. In its current form, most of us pay for blanket health care coverage that entitles us to a number of periodic services such as checkups, shots, mammograms, etc. The problem is that the costs of these benefits are not particularly clear. I don’t know what that annual checkup is costing me, so I don’t perceive it as a cost. If health care providers could make the costs of these procedures more salient—perhaps by sending me periodic reminders that these procedures are costing me, say, $50 each regardless of whether I use them—they would be able to tap into the sunk cost effect. Patients would be more likely to say, “I’m paying for it, I shouldn’t let it go to waste.”

Read more: Use the Psychology of Pricing To Keep Customers Returning

If you use email with any frequency, you have probably by now received stock-related spam. Typical emails tout the astronomical profit potential of investing in a penny stock before its coming surge. Here’s an example from my own inbox, which I received on August 18th.

Do these emails have any real effect on the market? New research claims they do. Laura Frieder and Jonathan Zittrain compared a database of collected stock spam against historical market activity to examine the effects of spam on both market volume and price. They found that stock spam does make a significant impact on the market. From their abstract:

Based on a large sample of touted stocks listed on the Pink Sheets quotation system, we find that stocks experience a significantly positive return on days when they are heavily touted via spam, and on the day preceding such touting. Volume of trading also responds positively and significantly to heavy touting. Indeed, on a day when no tout has been detected in our database, the likelihood of a touted stock being the most actively traded stock that day is only 6%. On the other hand, on days when there is touting activity, the probability of a touted stock being the single most actively traded stock is 81%. Returns in the days following touting are significantly negative. The evidence accords with a hypothesis that spammers “buy low and spam high,” purchasing penny stocks with comparatively low liquidity, then touting them - perhaps immediately after an independently occurring upward tick in price, or after having caused the uptick themselves by engaging in preparatory purchasing - in order to increase or maintain trading activity and price enough to unload their positions at a profit. Selling by the spammer then results in negative returns following touting. Investors who respond to touting are losing, on average, 5.25% in the two day period following touting. For the quintile of stocks in our sample that are touted most heavily, this 2-day loss approaches 8%. These estimates are conservative, as they do not account for transaction costs.

For a nontechnical review of the paper, see Spammers Make a Sound Investment in Stocks. The original paper is here: Spam Works: Evidence from Stock Touts and Corresponding Market Activity.

It’s important to note that timing is everything when it comes to profit or loss from temporary market manipulations like these. The Spam Stock Tracker is a mock portfolio of penny stocks touted in spam received by the author. As of today, his portfolio has lost over $47,000 (on paper), based on an investment of $70,987.

Via kottke

UPDATE: Roger Ehrenberg at Information Arbitrage has an insightful post on the same topic: Stock Spamming for Profit - A Sucker Born Every Day

The latest Mauboussin on Strategy paper from Legg Mason summarizes what we know and don’t know about assessing risk in complex environments. Mauboussin starts by reiterating Frank Knight’s crucial distinction between risk and uncertainty. Knight defined risk as a situation where we know the underlying probability distribution, but don’t know the outcome. Uncertainty, on the other hand, is a situation where not only the outcome, but even the underlying probability distribution itself is unknown to us. As Nassim Taleb argues persuasively in his book Fooled by Randomness, many people today are modeling uncertain situations using the statistical tools built for risk, which can and has lead to catastrophic errors.

Mauboussin then makes the distinction between endogenous risk, which emerges from a complex system itself, and exogenous risk, which is forced on a system from outside. System dynamics that emerge endogenously, without being precipitated by any outside event, are behind many of the interesting phenomena we see in the markets and society at large today. While endogenous risk is just beginning to be understood, it’s a critical area of research. We’ve looked at some examples of endogenous system dynamics earlier this week with Exploiting the Herd, A Case Study and Exploiting the Herd: Case Study Two. Mauboussin presents some of the common frameworks used to understand endogenous risk:

The first framework is the wisdom of crowds, which writer Jim Surowiecki laid out well a couple of years ago in his book of the same title. The basic idea is simple and somewhat counterintuitive: if you get a diverse group of people together to solve a problem, the group’s answer will typically be better than that of any individual, even an expert. The wisdom of crowds is a more common way of describing a type of complex adaptive system–the heart of the Santa Fe Institute’s work–and is an apt description of the stock market.

The key is that the crowd is only wise under certain conditions. You need agent diversity, an aggregation mechanism, and some sort of incentives. When one or more of these conditions is violated, all bets are off. In human systems, diversity is the most likely condition to be violated. When you take away diversity, the complex system can become fragile and in some cases will lead to large-scale changes. Booms and crashes are good examples of diversity breakdowns in markets. Fads and fashions also illustrate the concept. And that leads to the second framework: diffusion theory.

Technologies, ideas, and illnesses tend to diffuse following an S-curve pattern. So, for example, a new technology will start with only a few adopters, and will grow at a relatively slow rate early on. The rate then accelerates, and the technology takes off. This field has been studied in detail, and is of prime interest to epidemiologists and technologists, just to name two groups. The key point is the growth rate is not stable: it’s low to start, rises, and then slows down again. Also important is that most technologies or ideas don’t diffuse–they simply sputter out.

The final framework is network theory, or how the individual nodes in a network are connected. Network theory bears on a wide variety of phenomena, including your network of friends, transmitters on the power grid, or the spread of disease. In recent years, scientists have made major advances in understanding the nature of networks. We now know that the structure of the network is important in understanding how things get transmitted over the network.

There are two features of these frameworks worth emphasizing. First, they are non-linear. For example, in the case of the wisdom of crowds you can reduce diversity, reduce diversity, and nothing happens. Then you reduce it a bit more and the system reacts violently–the proverbial straw that broke the camel’s back. Many of you know this idea as the tipping point.

That leads to the second feature: lack of proportionality. The size of the perturbation and the outcome are not always linked. Sometimes small perturbations lead to large outcomes, and vice versa. When you combine a lack of linearity with a lack of proportionality, it’s not hard to see that predictions are difficult and cause and effect thinking is often futile.

Read more: Mauboussin on Strategy: Interdisciplinary Perspectives on Risk

Previously:

• Michael Mauboussin: How Do You Compare?
• More Than You Know
• Mauboussin on Discounted Cash Flow Models
• Mauboussin on Strategy: Size Matters
• All posts tagged Michael Mauboussin

Columbia professor Ray Fisman was interviewed by Hermes magazine about his research on speed dating. Some interesting observations relavent to behavioral decision making:

3. Does your study measure how well what people say they look for matches with what they actually look for?

Most of what I do is work on corruption in poor countries. If I want to know how much someone is paying in bribes, I’m not going to ask them, “How much did you pay in bribes last year?” I’m going to say, “The guy down the street from you, who looks pretty much like you, how much did he pay?” Similarly, in the speed-dating study we ask people, “What do you care about?” We also ask them, “The average man, what do you think he cares about?” But then we actually see how they behave in the game. And, not at all surprisingly, what they say the average man cares about lines up much more closely with what they actually reveal through their actions than what they claimed they cared about beforehand. In particular, everyone — both men and women — says they care less about physical attractiveness than the average.

4. Do you think speed-dating is more efficient than traditional search methods?

In some sense, it’s efficient: there are all these slice studies on how 10 seconds’ worth of observation is as predictive of your experience with a professor as a semester’s worth, and they’ve reduced it to 2 seconds and that’s just as good; and they’ve reduced it to just a photo and that’s pretty good, too. So you learn a lot in four minutes, perhaps as much in four minutes as you do in a much longer superficial interaction like, say, a date. So, this does meaningfully provide you with 20 rapid-fire dates, to the extent that we form as much of an impression in 4 minutes, or 10 seconds, as we do in 4 hours. The thing that’s left out of this neat decomposition of people into attributes, though, is actually learning to love someone. And that’s what I think is kind of missing. Focusing on people as a bundle of attributes almost makes people think about this decision in the wrong frame of mind.

5. Do you think people become unwilling to commit because of all the choices dating services enable?

Yes. And the way that you can make these choices — just the very fact that it’s set up in this way — distorts the way people choose. There was an article in the New York Times on a backlash against Internet dating, and I wonder to what degree that’s at least partly as a result of these sorts of realizations.

6. The results of your speed-dating studies, particularly with regard to intelligence and physical appearance, seem to reinforce gender stereotypes. Why do you think this is?

Well, they are stereotypes for a reason. However, it’s not as simple as, “I avoid all women who are ambitious or intelligent.” It’s about, “Intelligence and ambition is OK until it supersedes my own.” It’s also worth mentioning that these are average effects — there are surely men who do not have this property. I like to think I’m one of them: my significant other is definitely a lot smarter than I am. When her grandmother heard about me, she said, “I told your mother this, and now I’m going to tell you: never let a man think you’re smarter than he is. Men don’t like that.” Everyone laughed and thought this was so anachronistic, but it shows up in our data. Grandma’s views on dating aren’t so dated after all!

Read more: Dating Data: Economic Theory and the Search for a Mate

Earlier this week I wrote about James Montier, global equity strategist for Dresdner Kleinwort, and his contention that purely quantitative models outperform independent human judgment for a wide array of decision problems across many fields of expertise. You can read his whole article here: Painting By Numbers: An Ode To Quant. He gives examples of quantitative models outperforming experts in medical diagnosis, university admissions, predicting criminal recidivism, and even judging the quality of wines. When there is so much evidence of quantitative models outperforming exports, why are the former used relatively rarely?

The most likely answer is overconfidence. We all think that we know better than simple models. My own confession at the start of this note is a prime example of such hubris. The key to the quant model’s performance is that it has a known error rate, whereas our error rates are unknown.

And furthermore:

Grove and Meehl suggest many possible reasons for ignoring the evidence presented in this note; two in particular stand out as relevant to the discussion here. Firstly, the fear of technological unemployment. This is obviously an example of a self serving bias. If, say, 18 out of every 20 analysts and fund managers could be replaced by a computer, the results are unlikely to be welcomed by the industry at large.

Secondly, the industry has a large dose of inertia contained within it. It is pretty inconceivable for a large fund management house to turn around and say they are scrapping most of the processes they had used for the last 20 years, in order to implement a quant model instead.

Another consideration may be the ease of selling. We find it ‘easy’ to understand the idea of analysts searching for value, and fund managers rooting out hidden opportunities. However, selling a quant model will be much harder. The term ‘black box’ will be bandied around in a highly pejorative way. Consultants may question why they are employing you at all, if ‘all’ you do is turn up and run the model and then walk away again.

It is for reasons like these that quant investing is likely to remain a fringe activity, no matter how successful it may be.

Read more:  Painting By Numbers: An Ode To Quant

Earlier:

• James Montier: Quantitative Strategies Rule
• All posts tagged James Montier