To Waterboard a Metaphor

July 29, 2011

It’s fun to use a credit card because you get your dinner or haircut or iPod now, but your bill doesn’t come until later.  Credit cards are accepted almost everywhere.  You can put most of your monthly expenses like utility bills on them and even make arrangements to be billed automatically.  But the best part is that you can even choose not to pay your bill in full!  Sure, it costs something to carry a balance on your credit card, but it’s so much less fun to pay bills than it is to buy things.

The best way to maximize your ratio of fun to not-fun is therefore to pay the minimum on your credit card every month.  Eventually, though, you hit your credit limit and the fun threatens to stop.  At that point, there are a few options.  One option is just to keep using another credit card, but let’s assume that’s not possible.  The next option is to pay down some of the balance (or go back to making purchases in cash), but that isn’t fun, because we’ve already established that it isn’t fun to pay bills.  Another option is not to pay the bill at all, but that would be even less fun – not only would you be cut off anyway from buying more things, you’d also start getting visits at home from guys with thick necks and questionable taste in jewelry… 

But my Readers are clever enough to have spotted the most fun option: request a credit limit increase!

The credit card company might balk a little bit at how much debt you have already accumulated, not to mention your other debt (like your huge mortgage!). But, frankly, your payment history is pristine, and the credit card company likes to earn its little bit of interest and transaction fees every time you make a charge.  After all, the credit card company needs to put its money to work somewhere.  Some shareholders have trouble understanding why the company keeps lending to customers like you.  They wonder, shouldn’t the company be supporting some local small businesses instead?  The company’s decision definitely would be likely to weigh on its stock price.  Fortunately, though, you happen to know that the company actually thinks it’s a competitive advantage to have a cheap stock price.  So the company hasn’t given you any indication that they’re going to deny a request from you anytime soon.

As long as the credit card company keeps increasing your credit limit, you can have basically as much fun as you want.  That is, unless some JERK comes along and hides your cell phone and locks you in your apartment to try to prevent you from actually calling the credit card company to request that increase.  Not fun!

That jerk would probably think he’s being pretty clever, since you’d have to finally start paying more of your bills every month in order not to run out of cash.  That would totally not be fun for you, but the worst part would be watching his smug reaction as you scrambled to keep the electricity on, while trying not to disappoint your hairstylist and the others who have enjoyed the benefit of your spending.  You just know he’s going to call up your hairstylist at the end of the month and have a good chat with her about how disappointed in you they both are.

But, wait a minute.  If my Readers were in this position, they would have spotted something fishy about the whole scenario long ago.  How on Earth did that jerk know where you live, never mind where you keep your phone? 

The most likely explanation is that he’s your embittered ex who is, for some reason, still a co-signer on the credit card. 

It’s not like that jerk hasn’t also been having a grand old time running up the charges every month!  In fact, half the charges that you have to pay for every month are for things like his multiple gym memberships and church dues and all those other bills that he put on auto-pay years and years ago.  Sure, it’s not fun for YOU to be paying HIS bills, but you’d always had a kind of understanding that everyone would have more fun if the credit limit just kept on being increased whenever necessary.  Who does he think he is, trying to stop your fun unilaterally?

Nevertheless, suppose you found yourself locked in the apartment just as the monthly bill was coming due.  You and the jerk could have a good shouting match; but although that might be fun for you, the neighbors would probably be scandalized by the whole tacky spectacle.  (You probably will do it anyway, though.)  You are NOT going to give him the satisfaction of begging for the phone back; that’s even less fun than not being able to buy things anymore.

So, you have two options.  The first option is to climb out the fire escape and call your buddy Ben, who seems to be able to create cash out of thin air.  (You’re always surprised that he doesn’t seem to be having any fun, with that kind of magical power.)  He’s a bit of an eccentric fellow with an artistic bent, so you know you can count on him to buy whatever doodle you put on paper for him.  You can then run that cash over to the credit card company in the nick of time.  It’s not fun to pay a bill, but it is fun to make doodles; so it’s sort of a wash.  And then you get to give a big raspberry to the jerk until the next time either of you needs to use the credit card.

The other option is to hold your breath until the jerk gives you the phone or you pass out.  You may risk some permanent brain damage that way… but, hey, some people get off on that sort of thing.

Addendum: Karl Smith has something serious to say about this metaphor.  For what it’s worth, I agree with his conclusion.


Paper or Plastic

April 19, 2011

Rationalization is a fact of life.  We’re complex, our world is complex, and we often have to balance competing desires.  It shouldn’t be surprising that some of our decisions or patterns of behavior conflict with our stated beliefs and values.  One may think of oneself as “concerned about the environment” but have energy-intensive habits (or at least not be perfect about turning off the lights when one leaves home).  One may think of oneself as “in favor of small government” but like receiving certain benefits.  One may think of Wal-Mart as the root of all evil but shop there anyway.  Cognitive dissonance is uncomfortable, so one has to engage in some sort of rationalization (consciously or not) for the behavior in light of the stated beliefs.  I think it might be revealing to try for a day to take note of all my actions or inactions that cause me to rationalize, but even then I’m sure I would miss some.  I think it’s imperative, though, to look for such patterns over time and come to terms with them.

I’m brought to this discussion via Felix Salmon (thanks to YA) who calls attention to a really delightful takedown of JPMorgan Chase CEO Jamie Dimon, by Illinois Senator Dick Durbin, on the subject of debit interchange.  (Here’s one example of cognitive dissonance: I like to believe I’m a nice person, but I always enjoy a snarky slam when it comes from a place of righteousness – like a priceless Frank Bruni critique of an awful restaurant.)

It really shouldn’t be controversial to assert that interchange (on debit and credit transactions) is too damn high.  There’s a religious debate to be had over whether (and how) government should intervene in oligopolies that are superficially competitive but, really, collectively extract huge economic rents. 

I’m more interested in how people (including myself) rationalize their usage of debit and credit cards as payment mechanisms in light of what we know about interchange fees.  Put simply: it cost merchants something to accept card payments.  This cost can be relatively small, in the case of a PIN-based debit transaction; or it can be several percentage points, in the case of an American Express transaction.  The merchant receives some benefit from this arrangement, such as the volume that results from people’s tendency to spend more freely on plastic, as well as the security of not having to handle as much cash on premises.  The customer receives some benefit too, which is generally proportional to the fee charged to the merchant.  A PIN-based debit transaction only gets me the benefit of not having to carry cash for my purchases.  An AmEx transaction gets me that benefit as well as loyalty points, an interest-free grace period before I actually have to pay my bill, and whatever cachet AmEx seems to think it confers.

Here’s what should cause dissonance, though: credit cards, particularly when used at low-margin businesses, generally transfer wealth from less-affluent customers to more-affluent customers (and large financial institutions).  Progressives hate this concept in the general case, but seem to accept it as a consequence of their (our) payment choices.

When I use my AmEx at a fancy restaurant, I think it’s a clear win for everyone.  It’s hard to guess what my spending would look like in a world without plastic, but it’s almost certainly true that I would spend less freely and less often.  So the restaurant probably ends up incrementally better off, which can be possible even at a lower margin if there’s an offsetting increase in sales volume.  As such, they would not necessarily need to raise prices. 

But the dynamics are different when I use my AmEx at a neighborhood grocery store.  Grocery retailing is generally a competitive, commoditized business.  Operating margins tend to be in the single digits.  My basket of groceries at D’Agostino’s (or Stop & Shop, etc.) is probably about as profitable as a pensioner’s or middle-class family’s.  There are plenty of grocery stores within walking distance, so no store can be too much of an outlier on price.  When AmEx takes a few percentage points off the top, it can be very meaningful to the grocer’s overall economics.  That lost margin needs to be made up somehow.  It’s not volume; I’m not likely to buy more eggs just because I have plastic instead of cash.  To some extent, the grocer can push back on its suppliers; but the largest of these, too, are low-margin, competitive businesses.  To some extent, the grocer can try to shift me to higher-margin products, like store-brands or fancy organic foods; but I’ll buy what I want to buy.  The other lever is simply to charge higher prices across the board.  Implicitly, this means that everyone pays higher prices for benefits that accrue only to credit card users and the financial institutions behind them.  This is what I mean by wealth transfer.

A progressive person should prefer to pay cash, or use PIN-based debit transactions, in order to minimize the cost to merchants (and, by extension, the costs that are passed on to other consumers).  But the prisoner’s dilemma gives him or her an easy out for rationalization: someone else is going to use a credit card, which means there is going to be a mark-up on prices anyway – so I might as well get my points too!

Maybe it doesn’t occur to people that it costs merchants something to process a card transaction.  But I think the more likely explanation is that card usage creates externalities, and people (including myself) generally have an easy time rationalizing decisions when the harm is vague and diffuse, but the benefit is clear and personal.  (Wonder why we have a $14 trillion national debt?)

Bonus? You Just Met Us! (Part 3)

January 25, 2011

Certain professional roles allow individuals to directly and measurably contribute to the success of a commercial enterprise and, in so doing, to generate profits (and losses) on a vastly greater scale than is possible in the roles in which the majority of people work.  As a consequence, these individuals are often in a position to attract outsized (and at times, outlandish) compensation for their effort.  It can be tempting to attribute this earning power to unusual individual skill, education, work ethic, or the like, but these attributes are rarely sufficient and may not even be necessary (certainly not on Wall Street, at least).

On some level, it’s just a matter of being in the right seat at the right time.  An investment banker’s career is heavily influenced by the robustness of the sectors in which she specializes.  An institutional salesman or relationship manager may print money in buoyant markets, but may find himself out of business if a few key clients defect.  A trader who generates profits is progressively given more capital and more opportunity (in dollar terms) to generate future profits, and to capture a share of that increasing pie for himself.  Wall Street is not alone in this regard: how important is the “big break” in a performer’s career, or the opportunity to appear in front of the right talent scouts (and avoid injury) in an athlete’s?  It is extremely competitive to even have a chance at ending up in that right seat at that right time, and it is not guaranteed by any amount of skill or hard work.  I believe this dynamic contributes to the difficulty “Wall Street” and “Main Street” have in understanding each other when it comes to compensation (with continued apologies for the oversimplification and caricature).  Main Street looks at how much those people in those chairs make, and thinks it’s indecent.  Wall Street looks at how bitter the struggle is to even get in those chairs, and thinks it’s justified.

One might think that investors and paymasters would attempt to diagnose to what extent skill, luck, momentum, and ‘beta’ (i.e., general market direction), among other possible factors, contribute to the success of a line of business or to the returns on an investment.  In reality, they do make an attempt, but this is a pretty hard exercise, and it’s a more pleasant and convenient practice for all parties to attribute as much as possible to ‘skill’ – this makes employers look sophisticated and makes their investors happy to believe their capital is in the best hands.  Nassim Taleb is one of several scholars who have explored this topic in illuminating detail (Fooled By Randomness is more on point than The Black Swan for my discussion here).

Is it ‘fair’ that equally skilled, knowledgeable, and hard-working traders can have vastly discrepant career trajectories just as a result of small differences in their initial conditions (e.g., how much opportunity were they initially given to take risk, and how did those trades go over the time-horizon of their manager’s patience)?  On an intuitive level, probably not; but nobody really cares.  Within the Wall Street bubble, there’s the gambler’s mentality that imagines beating the odds and hitting it big, so there are few tears shed for those who weren’t so fortunate.  Outside of Wall Street, people don’t even understand why these people should be making so much money in the first place, so the search for gradations of fairness seems pretty silly.

That all being said, attempting to control for randomness in promotion and compensation seems like a generally good practice for any professional organization, but it doesn’t really tell us how to balance this with the organization’s need to “attract and retain top talent” (howsoever defined).  In my view, the conceptual problem has nothing to do with attracting and retaining top talent, but everything to do with the problem of running an organization where everyone wants to believe that he or she is top talent.  In organizations (not to mention entire industries) with such an orientation, pay expectations will always be anchored to the very top of the spectrum – and as long as the cost of meeting those expectations can be passed through to clients and investors, it will be possible to do so.  What does this mean, in practical terms, for ‘reforming’ Wall Street compensation?  I’ll return to this question some other time.

Bonus? You Just Met Us! (Part 2)

January 20, 2011

Wal-Mart, AT&T, and Goldman Sachs are giants in their respective industries and routinely rank among the world’s most profitable companies.  In the last four quarters for which information is available as of the date of this post, Wal-Mart earned net income of $15.1B, AT&T earned $12.8B, and Goldman Sachs earned $10.3B.  Each of these numbers is staggering, but consider another few morsels of data.  Wal-Mart reports having 2,100,000 full-time employees.  AT&T, 267,720; Goldman Sachs, 38,900.  If you’re looking for a quick answer as to why employees of Goldman Sachs so well-paid, whereas Wal-Mart is often accused of squeezing its ‘associates’ at every turn – divide the figures above.  On a per-employee basis, Wal-Mart earns roughly $7,200; AT&T, $48,000; and Goldman Sachs, over $260,000.  And these profits are calculated even after taking into account the cost of compensating employees!  The reality is that the average employee of Goldman Sachs generates more profits for shareholders than does an employee of Wal-Mart by a factor of 35.  Forgetting about questions of justice and merit for a moment: should it be so surprising that the pay of an average employee of Goldman Sachs is many multiples greater than that of an employee of Wal-Mart?

My former colleagues at McKinsey argue that profit per employee is a meaningful measure of corporate performance; in particular, of how effectively firms in knowledge-intensive sectors leverage their intellectual capital.  (Didn’t that sound so consultant-esque? Full disclosure: I contributed to the research cited in the link.)  My choice of companies for the purpose of this comparison is meant to make a similar point.

  • Wal-Mart’s business model is highly labor and capital-intensive; it’s a logistical masterpiece that sells many goods, on impossibly thin margins, at prices competitors can’t match.  The competitive advantages of Wal-Mart include its size, lean-ness, and omnipresence.  Only a small percentage of employees are truly critical to this equation.
  • AT&T generates its income through a combination of labor- and capital-intensive, traditional telecom business lines; and higher-margin business lines (such as wireless), which include some deliciously rich opportunities to generate almost free money from roaming charges and ringtone downloads.  Certain populations of employees are largely fungible in terms of the overall business plan of AT&T, e.g., those in customer service; others, perhaps highly skilled engineers who come up with the grand designs for the next wave of wireless infrastructure, can directly influence huge successes or failures of the enterprise.
  • Goldman Sachs… well, most people probably have no idea how Goldman Sachs makes money, beyond the suspicion that it must be nefarious.  Truthfully, many of Goldman Sachs’ employees are not as critical to the enterprise as they might like to believe, but theirs is also a lean organization in which the costs of unwanted staff turnover can be significant.  Almost any business function, from big-ticket deal-making to trade reconciliation, carries meaningful economic consequences for success or failure.  It is up to the firm’s employees to create ways to profit from the firm’s financial capital and other intangible assets (e.g., intellectual capital and privileged relationships).

To take the argument to an extreme: why do certain hedge fund founders earn billions of dollars?  Because hedge fund management companies are contractually entitled to a percentage of the dollar profits that investors realize; and hedge fund founders typically own the lion’s share of the economic interests in the management company.  This is a thoroughly ridiculous arrangement that the market seems to tolerate; I will come back to this another time.  But, indulging for a moment: it is not unreasonable to assume that a hedge fund management company may run $5B with a staff of, say, 100.  In a year where their investments are up 20%, they will generate $1B in gross profits, of which investors might receive $800M and the company receives $200M.  In other words, the average employee ‘earns’ the company two million dollars, roughly a factor of 8 greater than our Goldman Sachs superstars, and over 275x the employees of Wal-Mart.  And people wonder why Goldman Sachs gets jealous about hedge fund compensation!

Defenders of Wall Street pay practices often make tone-deaf arguments about how hard their professionals work, and how the pay is necessary to “retain top talent.”  Many people toil in unglamorous roles and if ‘working hard’ were a meaningful criterion for determining pay, there would be many more millionaires coming out of slaughterhouses and classrooms.

A more intellectually honest argument (which I am going to make in a tone-deaf way, just to be clear) is that Wall Street is one of the few sectors of the economy in which a significant proportion of workers are not individually irrelevant to the success of the enterprise.  Wal-Mart may have some extraordinarily competent and diligent minimum-wage staffers in its stores, but the consequences of their exceptional performance is virtually invisible in the results of the enterprise.  However, the departure of just one senior investment banker and her small team could cost Goldman Sachs millions of dollars in annual revenue.  When the performance of a specific individual is so closely tied to the economic results of the enterprise (particularly if it is tied in a measurable way) it is obvious that such an individual will be positioned to capture a significant share of the value they create for shareholders.

This observation holds across other sectors with professionals who are generally paid well, and where some individuals receive shockingly high compensation: law, technology, medicine, media, sports, etc.  It is tempting to draw the wrong conclusion that these salaries are a justified consequence of individuals’ effort and educational attainment (e.g., in law or medicine) or of individuals’ innate talents (e.g., among celebrities and star athletes).  Those may matter, but they are nothing without a context in which they belong to someone who has measurable individual influence over the success of a commercial enterprise.  Many talented athletes will never play professionally.  Many exceptionally smart and highly-educated people will not have (or seek) the opportunity to become a partner at a white-shoe law firm.

A much better question to ask with respect to compensation, particularly on Wall Street, is: why are certain roles (and the people who fill them) so critical to the success of the enterprise?  The head of a major trading desk is in a unique position among human worker bees: he or she can make or lose millions (in some cases, billions!) of dollars.  Billions of other humans will never have that chance – but of course, at least three billion people out there would be better than average at it.  To put a less facetious point on it: why is there rarely an attempt to normalize for the conditions that enable certain individuals (by virtue of their institutional roles as well as their innate abilities) to exert significant individual influence over the results of the enterprise?  I will explore this question next time.

Some house-keeping notes on the above:

  • Any company data cited above were pulled from Yahoo! Finance on 1/20/2011 and pertain to the most recently available SEC filings as of the date of this post
  • One can quibble over how to appropriately measure profitability; I choose GAAP net income here because it’s consistent, but I would argue that the choice is immaterial to my argument
  • AT&T reported an $8.3B increase to its Q3 2010 net income as the result of a one-time tax settlement with the IRS, which I subtract from the reported GAAP net income to arrive at the figure I report above; again, I claim this is immaterial to my argument

This Post Has Been Brought to You by the Letters ‘I’ and ‘P’

January 15, 2011

I’ve been tracking an amusing phenomenon during my commute, in which I walk through Times Square and occasionally pass near Rockefeller Center.  A few months ago, I first noticed someone dressed in a SpongeBob mascot suit, standing outside the M&M World store on Broadway and West 48th Street.  This didn’t strike me as particularly odd – it would be a good place to encounter children, and Viacom (the owner of the Nickelodeon channel) has its corporate offices in the vicinity, so perhaps a marketing executive put two and two together one day.

Not long after that, SpongeBob was joined by a mascot generally resembling Minnie Mouse, although some of the details of the costume were not quite right (it says something about the power of familiar childhood characters that it’s possible to intuitively detect a knock-off).  Over the following weeks, particularly as Christmas and New Year’s approached, more and more costumed characters appeared up and down Broadway, and over on Sixth Avenue: Minnie was joined by Mickey, Elmo, Batman, and Cookie Monster (or Grover? It was kind of a fuzzy, blue mess).  My favorite was a short fellow in a greenish-looking mask that seemed like it came off the shelf at Duane Reade, who was carrying a stuffed donkey and wearing a “Skechers” backpack, which I assumed was a creative attempt to conjure up some semantic relationship with the character he portrayed.

The proliferation of costumed characters with no real rationale for being on the street – not to mention the fairly obvious poor quality of the costumes themselves – raised my suspicion that they were not likely to have been sponsored by the city, or authorized by the copyright owners of the characters’ likenesses.  Times Square is not a theme park, despite its investment in becoming family-friendly, and even an institution like the Naked Cowboy emerged from the grassroots.  Yet the influx of competition seems to suggest that there is some custom to be had from posing with tourists who, despite their legendary gullibility, should have some idea that they’re essentially buying a $5 “Kate Spode” [sic] bag for their children when they give a tip to pseudo-Elmo.

I’ve been reflecting on what exactly is being ‘bought’ in these interactions, and why anyone is ‘buying’ them.  Presumably kids are excited to see a beloved character and are able to twist their caretakers’ arms into taking a quick photo, for which a modest tip seems appropriate.  Should it matter that it isn’t the ‘real’ Mickey Mouse, considering how slippery the question is when applied to a two-dimensional fictional character?  On its face, probably not.  It makes the kid happy and is relatively inexpensive in the grand scheme of things.  It pre-empts the uncomfortable discussion that might follow the adult’s remark of, “oh, that’s just an underemployed worker wearing a smiling rodent suit.”  Perhaps it even will provide a valuable teaching moment about fiction and reality, later in life.  If the child is happy to see the knock-off character, would he or she be incrementally happier if the actor were wearing a fully-authorized costume built to exacting specifications?  And presumably it helps in this line of work to be friendly to tourists and families, for which people generally are willing to pay a bit extra in other circumstances (e.g., restaurants, customer service).

On the matter of copyright, it seems clear that some sort of violation has occurred, although it’s difficult to assess the harm.  The copyright owners weren’t deploying their assets for the purpose of getting tips, and perhaps it’s incrementally beneficial to Viacom that children are reminded of their love for SpongeBob in the heart of one of the world’s great hubs of shopping.  And where does responsibility for the violation attach?  The manufacturer of the unlicensed costume?  The worker who is wearing it?  The tourist who snapped a photo instead of paying to go to Disney World?  In a sense, all are complicit, but none are entirely responsible.

My preliminary sense is that the externalities are pretty modest, but I haven’t thought much about it.  It’s annoying that these activities take up precious sidewalk space, but tourists will always find ways to interfere with pedestrian traffic.  It’s generally good when people find work and even better when it doesn’t displace others.

And of course I remain impressed by the irrepressible capitalist instinct here in fair Gotham.

On Knowing What I Want (to buy), Part 3

January 14, 2011

As I’ve reflected on the wealth of data available to certain retailers and advertisers, I’ve generally assumed that it was beneficial to have a granular knowledge of customers, particularly with respect to their purchases, habits, demographics, psychographics, social relationships, and the like.  I tend to have some faith in the power of predictive modeling, so with a large enough data set one ought to be able to have some confidence in predicting that, if a customer with certain characteristics buys X, she might also be interested in buying Y as well.  The cost of making a ‘pitch’ for Y is virtually zero for an online retailer – a bit of screen real estate, perhaps a bit of nuisance for the customer, plus some coding and maintenance that theoretically amortizes over hundreds of thousands of customer interactions.  It seems, then, that it shouldn’t be hard to come up with a business case for this strategy if one has any confidence in the forecasts one generates.

The humble grocery store, in my view, has a phenomenal implementation of this concept.  I’ll assert that most people shop at no more than a couple of grocery stores, and so each grocery store gets a fairly comprehensive view of a person’s typical grocery list thanks to those ever-present loyalty cards.  At the point of sale, the store can deliver coupons that respond directly to the items in the customer’s basket.  Suppose the customer just bought a bottle of Heinz ketchup.  Maybe he will get a coupon for fifty cents off his next bottle, to keep him in the family?  Maybe he will get a coupon for fifty cents off his next bottle of Hunt’s ketchup, to encourage him to switch?  Or perhaps the coupon will be for hamburgers or hot dogs, which go nicely with ketchup?  Essentially, the grocery store could probably deliver meaningful coupons (i.e., cross-sales) without any historical information about the consumer – but perhaps building this store of knowledge over time allows them to refine their methods.  I don’t know if this actually happens, but I wouldn’t be surprised if customers who frequently purchase expensive brands are pitched different items than those who frequently purchase store brands.

However, I think this trick is harder for an internet retailer to pull off.  Since I will be projecting from my own habits, I should describe some of them for context.  I rarely window-shop online, although I will comparison-shop if I’m already pretty confident that I want to buy, say, a new laptop.  I’m not particularly loyal to specific retailers, although for particular categories of ‘need’ my defaults tend to be pretty stable (e.g., for books and, increasingly, digital music that’s remarkably cheaper than iTunes; for certain essentials that are hard to find in my neighborhood; for well-priced vitamins and supplements).  So no single retailer has anything close to a comprehensive view of my shopping habits (in contrast, say, to my grocery store).

If my habits are close to those of a typical shopper at an internet retailer, it is easy to see that they will struggle to deliver worthwhile recommendations.  My experience with Amazon, for example, bears this out.  My purchase history of late has largely consisted of video RPGs for the Playstation 3, and non-fiction generally pertaining to business and finance.  Amazon doesn’t know much about my preferences outside of these domains, so it will tend to have the most confidence at recommending me (a) other video RPGs, although bizarrely it has not yet ‘learned’ which systems I own; or (b) other non-fiction generally pertaining to business and finance.  Fundamentally, this is no more sophisticated than organizing products by genre in a bricks-and-mortar store.  What is more, Amazon is likely to conclude that I’m most likely to buy other popular items in those genres, which will lead them to recommend products that I’m already likely to have, or likely to know about, thereby reducing the extent to which their granular insight about me is incrementally useful.

Similarly, if Amazon were to look for correlations (e.g., customers who bought X usually bought Y at the same time), its most likely conclusions would also be (and have been, in my experience) fairly trivial – for example, a strategy guide for the video game in my cart, or a set of practice problems in the field where I’ve just bought a textbook.  Of course there can be value to presenting cross-sell options at the right time, but it does not take proprietary insight to deploy the strategy I’ve described.  Unless, for example, it’s more efficiently scalable to apply an algorithmic approach rather than identifying a priori which items are ‘accessories’ for other items (I am skeptical of this, though).

A question I’m not considering here is whether attempting to cross-sell, as an internet retailer, may actually be detrimental, thanks to the paradox of choice (although, see the link and comments at one of my favorite blogs for a more rigorous and skeptical discussion), which posits (in brief) that consumers can actually become overwhelmed and less likely to make any decision (i.e., purchase) whatsoever as more options are presented to him.  If Amazon were most likely to recommend other finance textbooks as I’m considering the purchase of one particular title, could that actually lead me to buy none at all?  I’m not sure about this, but I’m sure retailers can fairly easily test and optimize the number and type of recommendations to make.

As a consumer, I’d find it amusing if an Amazon were able to recommend me music or clothing on the basis of my taste in video games, for example; but I suspect they would struggle to make a good recommendation, and I’d assume their most successful recommendations would again be products that are likely to already be popular, which reduces the proprietary ‘edge’ of such a strategy (relative to, say, naively recommending the most popular product in a vertical).

I still believe fundamentally that granular consumer data ought to be useful for generating non-trivial cross-sales, but I’ve become less confident in its potential the more I’ve thought about it.

The Driven Snow

December 30, 2010

New York City received a lot of snowfall several days ago, greatly disrupting transportation within, into, and out of the city.  Having lived through several blizzards and Nor’easters, and even one epic blackout, I’m generally pleased and impressed by how effectively the city is able to cope with nature’s inconveniences.  Emergency personnel seem to clear streets and manage dangerous intersections pretty promptly, and ‘ordinary’ New Yorkers tend to show their better natures by helping taxis un-stick themselves from snow drifts, among other small acts of generosity.

The general consensus about this week’s blizzard, however, is that the city’s response was woefully inadequate.  Streets were too slow to be cleared, if they were cleared at all.  Subway and bus lines were disrupted to an unacceptable degree.  Critical services, such as emergency medical care, were strained beyond their contingency plans.  It certainly did not help that the ‘snowpocalypse’ arrived right after Christmas, when it’s difficult to accomplish anything on even a perfect day.  But, even so, there seem to have been some failures in judgment and execution that are worth reviewing and learning from. One ought to ask questions like: How could we have made a better decision with the information we had available?  Should we have had additional information and, if so, can we reliably obtain it in the future?  Did we experience poor execution because of known weaknesses (e.g., we have had to cut staff) or unknown weaknesses (e.g., hypothetically, we discover in hindsight that our weather forecasting models are really accurate and precise at estimating snowfall between 1″ and 3″, but not between 6″ and 18″, which obscured our understanding of the likely severity)?

I tend to think that firing visible (and potentially otherwise-highly-competent) people is the obvious political answer but not obviously the best answer.  It is also easy to take the position that “they should have done something” and propose a set of measures that perhaps could have mitigated the current problem but would not necessarily make good standing policy; the common and not-unfair criticism in finance is that regulators are always preventing the last crisis (or generals fighting the last war, or politicians winning the last election, etc.)

A more interesting set of questions, in my view, concerns how we prepare for and respond to ‘tail risks’ in a world of constraints.  In this instance, I’ll focus on tail risks whose nature and magnitude can be predicted with some reasonable confidence – e.g., we know that in a given winter, there’s about an x-y% of getting more than 12″ of snowfall in a single day.  One question to ask in such cases is whether these phenomena are analogous to insurance problems.

Insurance emerges in response to phenomena of reasonably predictable frequency and severity.  One can self-insure against such phenomena (i.e., do nothing, or perhaps set aside some ‘reserves’ via savings) or one can pay a premium to the expected cost of the occurrence in order to reduce volatility (assuming the insurance is well priced, although I don’t think that assumption matters for what follows).  If I told you that your flat-screen television, which just cost you $1000, has an 0.1% chance of having a fatal flaw that will render it inoperable and worthless (and happens not to be covered by a manufacturer’s warranty or retailer’s return policy… bear with me here) you might consider that your expected cost from the design flaw is $1 (for this simple example, the probability of incurring the loss multiplied by the severity of the loss given that it has occurred).  If someone offered a warranty against this flaw for any price less than $1, you’d be a fool not to take it.  More likely, it would perhaps cost you $3 for such a warranty, so that the insurance company would expect to earn a profit over a large enough sample of identical and independent risks.  Depending on your tolerance for volatility, among other considerations, you may be willing to ‘lose’ $3 in the 99.9% of scenarios where the flaw does not materialize in order to be protected from losing the full $1000 in that unfortunate tail event.  There is clearly some price at which the divergence between the expected cost to the insured, and the actual price of the insurance, becomes too great to justify buying the insurance.  You wouldn’t be able to judge the wisdom of an insurance purchase on the basis of the outcome; it does not become a good decision to have spent $100 on it just because your television breaks.

When one criticizes the city for not having had enough personnel or machinery to plow the streets promptly, was this an example of a self-insurance bet that didn’t work out?  Clearly it costs something to have those resources on a permanent basis.  It may also be that, in most scenarios, those incremental resources don’t add sufficient value relative to other competing demands for dollars across the city (or, for a purist, relative to however one conceives of the ‘cost of capital’ for the city).  Let’s call the cost of those resources our insurance premium; we could debate how to calculate that cost but we’ll save that for another time.

Certainly our ‘insurance resources’ would have mitigated some of the downside of the snowpocalypse scenario.  There are local examples where it’s easy to quantify the economic impact of the blizzard (e.g., the person who couldn’t get to work) but it’s difficult to imagine that one would have even the right order of magnitude on an attempt to aggregate these and their interactions into some sense of the impact on the city (though some brave souls in need of a citation will try).  It’s even harder to attempt a serious analysis of the marginal impact of, say, plowing the streets 10% more efficiently, or whatever would have been the operational consequences of having our insurance resources at the ready.  A lot of good this conceptual framework is, then.

But we could, for example, ask ourselves what we would need to believe about their marginal impact in order to think the insurance premium was worth paying.  We should have a pretty good basis for predicting the likelihood of one, two, three, etc. blizzards in any given calendar year.  (Assume for now a binary world of blizzards and not-blizzards; it’s conceptually easy to introduce gradations of storm severity.)  We should also have a good estimate of the cost of our insurance premium: some number of fully-loaded workers, some number of machines and their maintenance, some number of managers and technicians, etc.  If hypothetically we estimate our insurance premium cost is $5 million annually, and the risk of a blizzard is 0.5% annually, we should feel pretty comfortable with that cost if we think we’d save the city from about $1 billion or more in damages in the event of a blizzard.

Imagine if instead of hiring $5 million worth of insurance resources, the city bought an insurance policy that paid out $1 billion to NYC in any year where there was a blizzard.  That type of risk could probably be priced pretty efficiently by a major insurance company and its reinsurers; say, a Berkshire Hathaway.  The cost of that insurance relative to the cost of our insurance resources seems like a meaningful indicator about the efficiency of retaining those resources.

Ah, but the “damage” to NYC isn’t borne entirely by the city treasury; non-salaried laborers and retail establishments, to name a few examples, suffer directly as well.  Let’s say there are 8.5 million people in NYC.  What if the city bought an insurance policy that paid about $117 to every man, woman, and child in NYC (about $1 billion in total) in any year where there was a blizzard – call it compensation for lost wages, or for strain from shoveling, or from general inconvenience.  What if we levied a $0.59 blizzard preparedness tax on every man, woman, and child in NYC to finance the purchase of that insurance policy (assuming it cost the same $5 million as our insurance resources)?  Obviously all these numbers are made up, but perhaps there are some levels where we’d frankly rather have a cash rebate than incrementally quicker-plowed streets?

In any event, I hope that some lessons are learned from this blizzard and that the response is not a reflexive addition of capacity to clear the last snowstorm.