Monday, November 26, 2012

How misleading "expected value" can be


The expression "expected value" can be highly misleading.

I was just writing some research results and used the expression "expected value" in relation to a discrete random walk of the form

$x[n+1] = \left\{ \begin{array}{ll}
   x[n] + 1 & \qquad \text{with prob. } 1/2 \\
  & \\
   x[n] -1 & \qquad \text{with prob. } 1/2
   \end{array}\right. $ .

This random walk is a martingale, so

$E\big[x[n+1]\big|x[n]\big] = x[n]$.

But from the above formula it's clear that it's never the case that $x[n+1] = x[n]$. Therefore, saying that $x[n+1]$'s expected value is $x[n]$ is misleading — in the sense that a large number of people may expect the event $x[n+1] = x[n]$ to occur rather frequently.

Mathematical language may share words with daily usage, but the meaning can be very different.

----

Added Nov 27: In the random walk above, for any odd $k$, $x[n+k] \neq x[n]$. On the other hand, here's an example of a martingale where $x[n+1] = x[n]$ happens with probability $p$, just for illustration:


$x[n+1] = \left\{ \begin{array}{ll}
   x[n] + 1 & \qquad \text{with prob. } (1-p)/2 \\

  & \\
   x[n]  & \qquad \text{with prob. } p \\

  & \\
   x[n] -1 & \qquad \text{with prob. } (1-p)/2
   \end{array}\right. $ .

(Someone asked if it was possible to have such a martingale, which makes me fear for the future of the world. Also, I'm clearly going for popular appeal in this blog...)

Wednesday, October 31, 2012

Why I'm somewhat apprehensive about Apple's reshuffle


Though I'm not as pessimistic about the Apple executive shuffle as the markets and Joy Of Tech, I'm apprehensive regarding the future of Apple's products.

Jony Ive is a great industrial designer, but Human-Computer Interaction is not Industrial Design. And some of the design decisions in recent hardware (meaning Ive's decisions) seem to ignore realities on the field. Take the latest iMac.

The new iMac doesn't have an optical drive; some pundits (and, I think, Phil Schiller on the Apple event) say that's a normal evolution. After all there aren't floppy disks on computers any longer and Apple was the first to drop them. And look how pretty the tapered edges of the iMac are.

Floppy disks existed as part of a computer-only ecosystem. CDs, DVDs, and BluRay Discs are part of a much larger ecosystem, which includes dedicated players and big screen TVs, production and distribution chains for content, and a back catalog and personal inventory for which downloads are not a complete alternative. (Some movies and music are not available as downloads and people already have large collections of DVDs and BluRay Discs.)

Using floppy disks as an example of change, implying that it is repeated with optical drives, shows a complete disregard of the larger ecosystem and willful ignorance of the difference between the earlier situation and the current situation.

For a laptop, the absence of an optical drive may be an acceptable trade-off for lower weight; for a desktop, particularly one that is a "home" desktop with a HD screen, the lack of a BluRay/DVD/CD drive is a questionable decision.

But look how pretty the tapered edges are, here in the uncluttered Apple Store retail shelves — oops, those computers will be in cluttered real world environments, where the necessary external drive (what, no BluRay drive yet, Apple?) will add even more clutter.

But, on the empty tables and antiseptic environments of "minimalist" designers' imagined world, that tapered edge is really important.

In the rest of the world, there are scores of people who like watching really old movies (available on DVD, not as downloads or streaming — except illegally), new movies in 1080p discs with lots of special features (i.e. BluRay discs that they can buy cheaply in big box stores), or their own movies (which they already own, and could rip — in violation of the DMCA — for future perusal, as long as they want piles of external hard drives); or maybe they want to rip some music that isn't available in download format, say CDs they bought in Europe that aren't available in the US yet.

So, using a decision that is not isomorphic at all (dropping the floppy disk) as a justification, Apple ignores a big chunk of the value proposition (consumption of media that is not available via digital download) on behalf of elegance. And, perhaps some extra iTunes sales — probably too small to make a difference on the margin.

What will this type of philosophy do to software? As Donald Norman wrote in this piece, there's nothing particularly good about fetishizing simplicity. Even now, many power users of Apple products spend a lot of time developing work-arounds for Apple's unnecessary rigid limitations.

Steve Jobs's second stint at Apple had the advantage of his having failed twice before (his first stint at Apple and NeXT), which tempered him and made him aware of the power of ecosystems (not just network effects). This is a powerful learning experience for an executive. Jony Ive hasn't failed in this manner.

Yet.

Monday, October 22, 2012

Can we stop talking about "manufacturing jobs"?


A lot of people worry about "manufacturing jobs," but the metric is seriously flawed.

Politicians and some financial analysts decry the decline of manufacturing jobs. There has been some decline, but the way these jobs are measured is inherently flawed, as it fails to take into account the change in managerial attitudes towards vertical integration.

Easy to see why with an example:

Ginormous Corp. makes widgets. In the 60s to mid-80s, as it went from being Bob's Homemade Widgets to Ginormous Corp., it added new facilities which had janitorial, accounting, cafeteria, legal, and other support services. All personnel in these support services counted as "manufacturing jobs."

In the mid-80s, Ginormous Corp. figured out (with a little help from Pain & Co and McQuincy & Co consultancies) that these support services were (a) not strategic and (b) internal monopolies. Part (a) meant that they could be outsourced and part (b) strongly suggested they should be outsourced. Let's say that Ginormous Corp. spun out these support services into wholly-owned subsidiaries, with no significant change in overall personnel.

So, all the personnel in janitorial, accounting, cafeteria, legal, and even some of the technical business support went from being in "manufacturing jobs" to being in "service jobs" without any change to what actually is produced and any actual job.

A metric that can change dramatically while the underlying system and processes don't change much is not a good foundation for decision-making. "Manufacturing jobs" is one such metric, as it depends on organizational decisions at least as much as on actual structural changes.

Metrics: useful only when well-understood.

Note: There are many reasons why focusing on manufacturing jobs over service jobs is a bad idea: Old Paul Krugman explains the most relevant, differential productivity increases, here.

Friday, October 19, 2012

Math in business courses: derivating + grokking


I used to start my Product Management class with a couple of business math problems like the following: let's say we use a given market research technique to measure the value of a product; call the product $i$ and the value $v(i)$. We know -- by choice of the technique -- that the probability that the customer will buy $i$ is given by

$\Pr(i) = \frac{\exp(v(i))}{1 + \exp(v(i))}$.

My question: is this an increasing or a decreasing function of the $v(i)$?

Typically this exercise divided students in three groups:

First, students who were afraid of math, were looking for easy credits, or otherwise unprepared for the work in the class. These math problems made sure students knew what they were getting into.

Second, students who could do the math, either by plug-and-chug (take derivative, check the sign) or by noticing that the formula may be written as

$\Pr(i) = \frac{1}{1 + \exp(-v(i))}$

and working the increasing/decreasing chain rule.

Third, students who had a quasi-intuitive understanding ("grok" in Heinlein's word) that probability of purchase must be an increasing function of value, otherwise these words are being misused.

Ideally we should be training business students to mix the skills of the last two groups: a fluency in basic mathematical thinking and grokking business implications.

- - - - - - -

Administrative note: Since I keep writing 4000+ word drafts for "important" posts that never see the light of blog (may see the light of Kindle single), I've decided to start posting these bite-sized thoughts.

Saturday, October 6, 2012

Thinking - What a novel idea


Or: it may look like brawn won the day, but it was really brains.

Yesterday I took some time off in the afternoon to watch the Blue Angels practice and the America's Cup multihull quarterfinals. Parking in the Marina/Crissy Field area was a mess and I ended up in one of the back roads in the Presidio. As I drove up, I saw a spot -- the last spot -- but, alas, there was a car in front of me. It drove into the spot, partly, then backed up and left.

I drove up to the spot and saw a block of cement with twisted metal bits in it, about three feet from the back end. I got out, grabbed the block, assessed its weight at about 100Kg, farmer-walked it to the berm, and got a parking spot.

Ok, so moving 100Kg or so doesn't make me the Hulk. What is my point, exactly?

There were at least two men in the car that gave up the space. They could have moved that block with ease. Instead they went in search of parking further into the Presidio; probably futile, if traffic was any indication. Why didn't they do what I did? Why didn't anyone before me (the parking areas well above the one I ended up in were already full as well)?

They didn't think of it.

Actually thinking is a precondition to problem-solving. Many problems I see are not the result of bad thinking but rather of the lack of thinking.

Monday, June 18, 2012

How I learned to make better presentations by paying attention to the performing arts

Cinema, especially documentaries, and the performing arts in general have a few useful lessons for presentations.

Of course, the main lesson regarding presentations is that preparation is key — just like with the performing arts. But this is a post about details rather than a rehashing of my big presentations post.


1. Have a script. A real script, like a movie script.

For blog post

In the past I used the Donald Norman approach: have an outline, annotated with some felicitous turns of phrase (those work better if you figure them out in advance) and important facts and figures. But now I find that having a script is a great tool, even if I tend to go off-script early and often in a presentation:

a) It forces me to plan everything in detail before the first rehearsal. Then I can determine what works and what doesn't and adapt the script. (Just like in a movie production.)

b) It makes obvious when there's over-use of certain expressions, unintended aliteration, tongue twisters, and pronunciation traps. Not to mention embarrassing unnoticed double entendres.

c) It creates a visual representation of the spoken word, which lets me see how long some chains of reasoning are.

d) It serves as a security blanket, a mental crutch, especially when I'm lecturing in a language different from the one I speak all day at the lecture location (say, speak portuguese in Lisbon, listen to Puccini arias in italian, read Le Monde in french, and then have to capstone discussion classes with english lecturettes).


2. Explicitly write treatments on script

By writing the treatments (slide, board, video, interactive, prop, handout, demonstration, discussion, etc) explicitly into the script I can identify potential problems:

a) If there's a block of more than 750 words (i.e. about ten minutes) that has no treatments, it had better be an interesting story. If I think that the story could use some attention-grabbing treatment, I have time to figure out what to do as I write the script.

b) If there's a diagram on a projected material that requires several builds, and is marked as such on the script, I identify that as an opportunity for a step-by-step construction on the board. That change comes at a cost of production values (upon seeing my drawings, arts teachers suggested I follow a career in text- or numbers-based fields); the benefit is the physicality of the writing and the motion of the speaker. (Note: draw on board, turn, then speak. Even with a microphone, speaking into the board is bad practice, as the speaker cannot gauge the audience's reaction.)

c) By having a cue to the upcoming treatment, I can compensate for lag in equipment response. For example, in the script above I want the video to start as I finish my sentence "money can't buy taste or style." Given my equipment lag, I need to click the remote when I say "money" so that the words in the video follow mine without noticeable pause. (The pause would distract the audience, and direct some of their attention to the technology instead of the content. Obviously I don't say "let's see a video about that" or some such.)

d) Explicit treatments also make it easy to check whether I have all the materials ready and to make sure that I don't forget anything in the mise en scène before I start. (This is a particularly useful reminder to set up demonstrations and double-check props before the event.)


3. There's only one "take" on stage – so practice, practice, practice

The first practice talk  is basically a check for design issues; many things that sound or appear adequate in one's mind ear or eye fail when said out loud, projected on a screen, or written on a board. After the first practice there's usually a lot of presentation materials rework to do. It goes without saying that failing to do this practice presentation means that the problems that would have been discovered during the practice will happen during the actual presentation – when it matters and in front of an audience.

A few iterations of this practice-analyze-rework (reminiscent of the draft-edit-redraft-edit- process for written word) should converge to a "gold master" talk. At this point practice will be for delivery issues rather than design issues: intonation, pronunciation, movement, posture, etc.

Full dress rehearsals, preferably in the presentation venue, are great tools to minimize surprises at the presentation time. I always try to get access to the venue ahead of time, preferably with the A/V people that will be there for the presentation.

If you feel ridiculous giving a full dress rehearsal talk to an empty room while the A/V people watch from their booth, just think how much more ridiculous it is to fail in front of an audience for lack of preparation.

(It goes without saying, but I said it before and will say it again, that practice is the last step in the preparation process before the presentation event; some presenters believe that practice can replace the rest of the preparation process, which is a grave error.)


4. Record and analyze presentations, even the practice ones

Given how cheap recording equipment is, there's no reason not to record presentations (except perhaps  contractual restrictions).

The main reason for recording is quality control and continuous improvement; a second reason is to capture any impromptu moments of brilliance or interesting issues raised during the Q&A.

Depending on various arrangements and the presenter's approach to sharing, these recordings can also be part of the public portfolio of the presenter.


5. The Ken Burns effect - it's not a spurious animation

I have railed against the profusion of unnecessary animations in presentation, so it's ironic that I'm advocating adding animation to static images. But there's a logic to it.

There are a few times when I have a few minutes worth of words that refer to or are supported by a photo. That photo is the projected material for those minutes, but I've started using very slow pans and zooms (the Ken Burns effect, after the PBS documentarian) to create a less boring experience.

My pragmatic guidelines for using the Ken Burns effect are:

a) Use sparingly: once, maybe twice in a presentation, and not in a row.

b) Very slow motion; the idea is to create a just-noticeable-difference so that there's something to keep the attention on the picture, but not enough to distract from what I'm saying.

c) The picture has to be high-resolution so that there's no pixelation.

d) In case of uncertainty, no effect. (Less is more.)

e) Since the photo supports the words I'm saying, and Keynote doesn't allow slide transitions in the middle of animations, the length of the effect has to be just short of the time it takes to say the words.


And a big difference from performing arts and documentaries: Every talk is new, even the canned ones.

Unless you're Evelyn Waugh, you don't want to give the same talk every time. Knowledge evolves, circumstances change, new examples appear in the media, and you learn new stuff from the question and answer period after a talk, or in the socializing period.

Having a script (and a master presentation a la Tom Peters) lets a speaker track the changes that a talk goes through over its lifecycle. It's an interesting exercise in itself, but also can give hints for how to adapt other "canned" talks one may have in one's portfolio.



Preparation, Practice, and Performance. Gee, it's like one of those management things where a complicated field is summarized by a few words that start with the same letter. But it's accurate.

—  —  —  —


Related posts:

Posts on presentations in my personal blog.

Posts on teaching in my personal blog.

Posts on presentations in this blog.

My 3500-word post on preparing presentations.

Saturday, May 19, 2012

Is Pete Fader right that Big Data doesn't imply big money?


He's right, in that Big Data doesn't necessarily lead to big money, but I think he exaggerates for pedagogical effect. Why he feels the need to do so is instructive, especially for Big Data acolytes.


Some days ago there was agitation in the Big Data sociosphere when an interview by Wharton marketing professor Peter Fader questioned the value of Big Data. In The Tech, Fader says
[The hype around Big Data] reminds me a lot of what was going on 15 years ago with CRM (customer relationship management). Back then, the idea was "Wow, we can start collecting all these different transactions and data, and then, boy, think of all the predictions we will be able to make." But ask anyone today what comes to mind when you say "CRM," and you'll hear "frustration," "disaster," "expensive," and "out of control." It turned out to be a great big IT wild-goose chase. And I'm afraid we're heading down the same road with Big Data. [Emphasis added.]
I think Pete's big point is correct, that Big Data by itself (to be understood as: including the computer science and the data analysis tools, not just the data -- hence the capitalization of "Big Data") is not sufficient for Big Money. I think that he's underestimating, for pedagogical effect, the role that Big Data with the application of appropriate business knowledge can have in changing the way we do marketing and the sources of value for customers (that is both the job of marketer and the foundations of business).

This is something I've blogged about before.

So, why make a point that seems fairly obvious (domain knowledge is important, not just data processing skills), and especially why make it so pointedly in a field that is full of strong personalities?


First, since a lot of people working in Big Data don't know technical marketing, they keep reinventing and rediscovering old techniques. Not only is this a duplication of work, it also ignores all knowledge of these techniques' limitations, which has been developed by marketers.

As an example of marketing knowledge that keeps being reinvented, Pete talks about the discovery of Recency-Frequency-Money in direct marketing,
The "R" part is the most interesting, because it wasn't obvious that recency, or the time of the last transaction, should even belong in the triumvirate of key measures, much less be first on the list.*    [...]
Some of those old models are really phenomenal, even today. Ask anyone in direct marketing about RFM, and they'll say, "Tell me something I don't know." But ask anyone in e-commerce, and they probably won't know what you're talking about. Or they will use a lot of Big Data and end up rediscovering the RFM wheel—and that wheel might not run quite as smoothly as the original one.

Second, some of the more famous applications of machine learning, for example the Netflix prize and computers beating humans at chess, in fact corroborate the importance of field-specific knowledge. (In other words, that which many Big Data advocates seem to believe is not important, at least as far as marketing is concerned.)

Deep Blue, the specialized chess-playing computer that defeated Kasparov, had large chess-specific pattern-matching and evaluation modules; and as for the Netflix prize, I think Isomorphismes's comment says all:
The winning BellKor/Pragmatic Chaos teams implemented ensemble methods with something like 112 techniques smushed together. You know how many of those the Netflix team implemented? Exactly two: RBM’s and SVD.    [...] 
Domain knowledge trumps statistical sophistication. This has always been the case in the recommendation engines I’ve done for clients. We spend most of our time trying to understand the space of your customers’ preferences — the cells, the topology, the metric, common-sense bounds, and so on.

Third, many people who don't know any technical marketing tools continuously disparage marketing (and its professionals), and some do so from positions of authority and leadership. That disparagement, repeated and amplified by me-too retweets and Quora upvotes, is what makes reasonable people feel the need for pointedly making their points.

Here are two paraphrased tweets by people in the Big Data sociosphere; I paraphrased them so that the authors cannot be identified with a simple search, because my objective is not to attack them but rather illustrate a more widespread attitude:
It's time marketing stopped being based on ZIP codes. (Tweeted by a principal in an analytics firm.)
Someone should write a paper on how what matters to marketing is behavior not demographics. (Tweeted by someone who writes good posts on other topics.)
To anyone who knows basic marketing, these tweets are like a kid telling a professional pianist that "we need to start playing piano with all fingers, not just the index fingers" and "it's possible to play things other than 'chopsticks' on the piano." (Both demographics and ZIP codes have been superseded by better targeting approaches many decades ago.)

These tweets reflect a sadly common attitude of Big Data people trained in computer science or statistics: that the field of marketing cannot possibly be serious, since it's not computer science or statistics. This attitude in turn extends to each of these fields: many computer scientists dismiss statistics as something irrelevant given enough data and many statisticians dismiss computer scientists as just programmers.

That's a pernicious attitude: that what has been known by others isn't worth of consideration, because we have a shiny new tool. That attitude needs deflating and that's what Pete's piece does.

-- -- -- --

* An explanation of the importance of recency is that it's a proxy  for "this client is still in a relationship with our firm." There's a paper by Schmittlein, Morrison, and Colombo, "Counting your customers," Management Science, v33n1 (1987), that develops a model of market activity using a two-state model:  the purchases are Poisson with unknown $\lambda$ in one of the states (active) and there's an unobserved probability of switching to the other state (inactive), which is absorbing and has no purchases. Under some reasonable assumptions, they show that recency increases the probability that the consumer is in the active state. BTW, I'm pretty sure that it was Pete Fader who told me about this paper, about ten years or so ago.