March 28, 2014 · 10:01 am

What Amateurs Can Teach Professionals

I saw something last evening that provides the inspiration for our Foodie Friday Fun this week.  If you’ve been reading the screed for any length of time you know that I’m a fan of Hell’s Kitchen.  The contestants are professional cooks (I hesitate to say “chef” since very few of them seem to have the qualities needed to be a team leader in the kitchen).  I believe all of them have been to culinary school but all do work in professional kitchens.  One would think that a work environment that’s filled with opportunities to do damage to one’s self would prompt a pro to make safety an intrinsic part of how they work.  As last night showed, not so much, which also prompted a business thought.

Photo: flickr user abdelazer

One of the cooks was using a mandoline to slice a potato.  As you can tell from the photo, a mandoline is a fabulous way to cut off the tip of a finger or two if you’re dumb enough to hold whatever is being sliced in your hand instead of using the guard/holder.  In a pinch you can hold the veggie against the blade with the palm of your hand pushing it down, but you never expose your finger tips to the blade just as you don’t dice with your fingers straight out.  Needless to say, the professional cook took a trip to urgent care to replace the piece of his finger.

Here is the business thought.  The cook has probably used this tool hundreds of times in just this way and without harm.  Most professionals do things over and over and at some point those things become second nature.  Unfortunately, that routine may incorporate bad habits. Amateur cooks like me have to think carefully when we use dangerous tools.  I’ll admit I think less when using a chef’s knife than when I use a mandoline, but I do pay attention in both cases since I don’t use either tool for hours at a time every day.

The same holds true with our business activities.  Reports become routine.  We do fill-in-the-blank analyses.  That’s when someone – the business! – gets badly hurt.  Business professionals need to learn from amateurs, or at least learn to approach the tasks they do daily with the same care as the person who rarely does those tasks.  Think to when you were given an assignment which involved something new.  You double and triple checked everything and were super careful.  That’s the amateur mindset.

And now it’s off to pull out my mandoline to remind me to be careful today.  Care to join me?

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March 27, 2014 · 10:25 am

Is Anyone Paying Attention?

A few things crossed my desk here at the world headquarters and they prompted today’s screed. There really isn’t much new in the data I’m about to share but when you put these things together it raises a question.

It’s no shock to anyone who is paying any attention to the way most of us consume media that there have been some pretty big changes.  The first bit of business comes from TDG’s Video Behavior in the Age of Quantum Video, an extensive analysis of US adult broadband users and their interaction with digital media:

Late Millennials (18-24 years of age) now spend more of their daily ‘TV time’ watching online sources than live broadcast/cable sources (33% versus 29% respectively). This is unique to the Late Millennial segment, as even Early Millennials (25-34s) spend significantly more daily ‘TV time’ viewing live broadcast/cable than online sources (30% and 23% respectively).

Of course, when you look at older folks (55+) those numbers are still overwhelmingly the “old” way of consuming – 61% to 4%.  However, as the older folks leave the consumption scene (a polite way of saying die off), these habits will become more pervasive.  These preferences for interacting with content via digital channels is having an effect on cable TV providers.  This from SNL Kagan:

Announced today, the U.S. multichannel segment posted its first full-year decline in subscriptions, according to SNL Kagan estimates for cable, DBS and telco offerings at the end of 2013. While seasonally driven quarterly declines have become routine for industry watchers, the annual dip illustrates longer-term downward pressure even as economic conditions gradually improve.

According to the tally for the trio of platforms, service providers collectively shed 251,000 in 2013, dipping to approximately 100 million combined subs. The industry added 40,000 video subscriptions in the fourth quarter, slightly weaker on a year-over-year basis and not enough to offset the broader downward momentum.

So the “prime” consumer marketers target – young adults who are forming their brand and consumption habits – are consuming via alternate channels and are cord cutting.  They’re also not particularly focused on the TV they are watching.  A new study from Millward Brown looked at multiscreen use while watching TV:

For US respondents:

  • 45% of daily smartphone time is spent simultaneously with TV
  • 37% of daily laptop time is spent with TV
  • 55% of daily tablet time is spent simultaneously with TV

What they also found was that 30% of simultaneous use is looking at related content (what they term meshing) and 70% of simultaneous use is looking at unrelated content (stacking).  TV is background noise.  So here is the thought.

I don’t think there is a “second screen”.  While younger people’s brains are wired a little differently with respect to multitasking, they seem to have decided that they’re going to program their own channels, access them through a different pipe, and then micro-program within the programs themselves by paying selective attention to the menu of choices they’ve created.   I realize there’s not much new in that but the pace at which it seems to be happening is new and will only accelerate as better broadband is available.

What do you think about all this?

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March 26, 2014 · 10:01 am

That Does Not Compute

One of the challenges any of us have in business is to predict the future.

English: Knuth's version of Euclid's algorithm...

. (Photo credit: Wikipedia)

The hardest part of my job – and maybe yours – is seeing over the horizon to help my clients get prepared for what is to come.  That might be a change in a market or it might be a change in technology.  No matter what it is, any of us who look ahead do so by gathering data.  In many cases that data is some measure of past behavior – how people bought from your website for example.  In many cases, those data points are put into some sort of algorithm which predicts what is to come.  Increasingly, many marketers and others use these models to drive their own business behaviors as the amount of data available grows exponentially.  While I’m not a believer that “big data means big problems,” a blind reliance on these algorithmic predictions can mean just that.

Let’s take one simple form of algorithm.  You probably see it every day.  it’s known as collaborative filtering and if you’re on Amazon or Netflix or any other site with a recommendation engine you’ve used it.  You may also have seen things offered to you as content on YouTube.  The algorithms use measures of your past behavior as well as of others like you (“people who bought XYZ also bought…”).  But what if you were buying a gift and the purchasing is not reflective or your tastes or interests at all?  What if someone else used your browser to search and purchase?  Cookies are browser-based – they have no way to tell if the activity is from one person or six.

Another problem.  Algorithms are built by people and those people are..well…human.  They might have confirmational bias operating as they refine the formula to eliminate noise – data that’s not germane to the prediction at hand.  The problem is that you don’t really know if it’s noise until it proves to be not significant.  Maybe it’s a new trend that your model misses altogether.

The thing to keep in mind is that modeling can only go so far.  It’s not very good  at predicting the unexpected.  It tends to ignore outliers.  As with all things, you need to ask questions, search for facts, and draw your own conclusions.  Yes, it’s impossible to make sense of all this data without algorithmically based analysis.  Just remember that while machines don’t make computational errors it was a human that gathered the data (or installed the code that does) and wrote the formula.  People often don’t compute.  Make sense?

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