Allocations and Decisions

I frequently run into an issue with allocations.  Specifically, an issue with confusing accounting allocations for something that is useful in decision-making.  In accounting, allocations is:

the process of shifting overhead costs to cost objects, using a rational basis of allotment. Allocations are most commonly used to assign costs to produced goods, which then appear in the financial statements of a business in either the cost of goods sold or the inventory asset.

As an example, imagine this classic situation in engineering.  BigCo is developing a new product line.  My kid is 4 and obsessed with potty words, so we'll call it "Product Booger".  Product Booger will cost about $1,000 of RND spending to get ready for market, and once it goes to market, BigCo expects it will sell around 500 units at $20 each.  Each unit will cost $10 to manufacture.  Should BigCo do this project?

Here is the super-basic analysis

If BigCo has the $1000 to invest and doesn't have other investments with a better efficiency ratio, they should do this project.

Now imagine that a super-eager young person comes along with an idea for a product extension based on BigCo product Booger.  Product NoseGoblin is a derivative product that will cost $100 of additional RnD and should sell around 200 units at $16 each.  Each unit will cost $14 to manufacture.  It is safe to assume that NoseGoblin won't cannibalize any of Booger's sales or anything like that. Should BigCo do this project?

Here is this super-basic analysis:

So this NoseGoblin isn't as good as Booger, but it still has a positive return and recommends itself for investment if BigCo's alternatives don't have a better efficiency ratio than 1.0.

Then the accountants catch wind of NoseGoblin.  They decide that it needs to carry a portion of the shared investment from Booger.  Doing a classic revenue basis allocation--allocating the RND spending according to proportional revenue-- Nosegoblin picks up an extra allocation of $242 (at the same time, Booger looks $242 cheaper).  A few months later, a new financial analysis is performed and NoseGoblin's financials look like this:

It is now a negative financial return.  Kill it fast!  Super-eager person's heart is broken.

this what a GIS for "sad super eager person" finds for me

This is not a made up situation.  I have seen this happen.  It gets even crazier when BigCo decides that everybody needs to carry a share of overhead allocations in financial analyses, too.  Suddenly you see stuff like this:

Even though this project doesn't drive any additional SGnA, IT activities, shared technology development, new building construction, etc., GAAP rules require that these allocations get managed in the PNL so they tend to find their way into the financial analysis, too.  Load in things like depreciation, taxes (which are usually so convoluted that trying to estimate the real tax impact of a new product is fool-hardy) and you're ready to start shooting down loads of good projects that fail to meet a corporate hurdle that completely misses the point.

Accounting allocations being used for decision purposes leads to bad decisions.  When we do portfolio analyses, we keep them separate and link the spending through relationships and work hard to identify incremental spending.  Only spending that is truly incremental is relevant in the analysis.

A quick data visualization, fat with implications

I whipped this up last night.  I will refrain from comment, but please tell me what you think...

The parable in Tsar Saltan... "Gvidon's Squirrel"

I like to tell my 3 year old stories that are loosely based on Russian Folk Stories that I found in this book.  I take down the gore as much as possible and generally try to shift the jobs that people do to be a little more appropriate for modern times (and make the animals more fit for the desert, where we live).

On story I love to tell is based on "The Tale of Tsar Saltan".  In this story, a young man (Gvidon) ends up distant and unknown to his father (Tsar Saltan).  He helps out a swan, who turns out to be a magic princess and who falls in love with him and does him all sorts of favors.  Every time Gvidon sneaks back to his father's castle in the form of a fly, he comes back with a request for some new and crazy thing that he heard about there.  One example is this one:

 "After he flew back to the island, Gvidon told the swan the story he heard about the remarkable squirrel. Then the prince walked into his courtyard and, lo and behold, there was the singing squirrel, sitting under a fir tree, cracking golden nuts! The prince rejoiced at this and ordered that a crystal house be built for the little animal. He placed a guard there to stand watch and ordered a scribe to record every shell. Profit for the prince, honor for the squirrel!"

Basically, he found himself in possession of a magic squirrel/tree combination.  The tree produced golden nuts (each of which held a ruby inside) and the singing squirrel would go up and collect them, take them down and crack them and stack the riches up in piles.

The part that I love about this story is that Gvidon doesn't just take the riches, he then builds a completely non-value-added organization around those riches.  He establishes a completely superfluous crystal house for the squirrel, he organizes and accounting department to keep score and establishes a management structure that watches the whole thing happen.  

When I tell the story, I keep going.  The guards need management, so an executive structure is organized.  The accounting department eventually branches out into strategy and financial analysis and grows headcount.  Eventually there are organizations built up around selling the rubies and the gold on international markets.  The organization continues to get bigger and bigger and bigger until eventually the tree and the squirrel are at the bottom of the hierarchy, getting bossed around by MBAs and seasoned executives who have been in this business for years.  By the end, Gvidon's entire income is consumed by the organization.

How many times does this happen in the real world?  A start-up comes out with a great business concept and develops a product that is worth buying.  People value the services or the experience and the company starts to take off.  The need to scale and they take on investors, who want to track progress.  Before you know it, it's "Gvidon's Squirrel."

(if you're interested in reading the whole story, I found an online copy here, which is where I found that picture, too. 

If, when, and how often ... security flaws and fake news

When I was in middle school, my family moved from the sleepy rural town of Hurley, New York to the nearby city of Kingston.  While living in Hurley, my family had grown accustomed to the security standards of small communities where the nearest neighbors are quite a long distance from one another--that is to say no security standards.  My family almost never locked our doors, and my parents regularly left their keys in the car overnight.

Within a few weeks of moving to the comparatively populated Kingston, my father's car disappeared one morning.  He had left the car unlocked, with the keys in the ignition.  Some enterprising young man had noticed and taken advantage of the security hole.

A few weeks later we heard from the police at SUNY Albany.  Apparently, the guy had taken the car up to the campus and had been breaking into dorm rooms and storing stuff in the car (which he had also been living out of).  When the car came back, it was full of stolen stuff.  Amazingly, the SUNY police refused to deal with the stolen stuff because the car was originally stolen in Kingston and the Kingston PD refused to deal with the stolen stuff because the robberies happened on SUNY campus, so we were left with a whole bunch of stuff.  The guy who did all the stealing went to jail for a few months and my dad started taking his keys out of the ignition at night.

Amazingly, the day that the guy got out of jail, our car disappeared again!  Apparently, he had made himself a copy of the key.  That key was among his personal things when he was jailed and was among the personal things returned to him upon release.  He walked straight to our house, unlocked and started up the car and --creatively-- headed straight to SUNY Albany again and started robbing dorm rooms.  When the car came back the second time, Dad finally changed the locks.

I hope this is going somewhere...

When I was a teenager, I got involved with the early idea of online video games.  Back then, "online" meant dial-up, and specifically it meant direct dialing either a BBC or a friend's computer.  You would plug your phone line into the modem (external modem, of course) and then one of you would call the other one.  A few DOS commands later (actually a ton of inscrutably complex commands later) and you were DOOMing it up against one another.  In the '80s and early '90s, security considerations were a lot like Hurley... everything was unlocked and the keys were in the ignition.

Years later, at one of the first companies I started, I decided to host the email and web server for the company locally.  By "host locally" what I mean is that I got a static IP, stuck an old desktop in a closet and installed Apache.  Installing Apache was easy, but configuring security was complex and lengthy.  I thought back to the old BBC days and considered "what are the odds that somebody will discover this IP?"  The idea of learning about the security was interesting to me, though, and I put in a few days to educate myself, figure it all out, configure and install, etc. before activating the server.

What happened astonished me.  The first attack on the server came within a few seconds.  The next one just a few minutes after that.  By the end of the day, there had been dozens of intrusion attempts against this brand new server that was there to serve up a couple of lame web pages about a nobody video games company and to pass our completely uninteresting emails back and forth.  The pace of attacks didn't slow down, either.  It continued to increase as time went on.

At the time I started saying that security is serious business.  Security wasn't a question of IF you will get attacked or even WHEN, but HOW OFTEN will you get attacked?  This is probably more true now than ever before.

Over time, I have come to recognize that this equation is a function of network density.  The more dense a network of nodes are interconnected, the more interactions each node needs to expect.  These interactions can be anticipated, they can be serendipitous, or they can be hostile.  The more deeply and broadly we immerse ourselves as individuals into a more interconnected society, the more interactions we are exposed to.  The likelihood of "intrusion attempts" (hostile interactions) goes from "will it happen?" to "when will it happen?" to "how often will it happen?"

The next interesting question becomes "what will tomorrow's intrusion attempts look like?"  A decade ago everybody had at least a few Nigerian prince emails.  A few years ago, everybody was falling for phishing scams on Facebook.

This year was the year of fake news.  Possibly the most insidious of all hostile interactions, fake news plays on our human desire for an echo chamber.  Fake news spreads like a virus through the like-minded and hijacks our rational-thinking.  The Nigerian emails stole bank account numbers.  The phishing scams stole our identities.  Now fake news is stealing our ability to think.

The metaphor of the deer

Many moons ago (in college) I was driving on a small, country road in upstate New York ( somewhere around here, if I remember right ).  It was night and it was winter.  A dusting of snow was sprinkling down on the already frozen road, the sides of which were burmed with banks of tight-packed snow and ice with thick forest beyond.

This was college, and I was broke.  I was driving a Nissan Sentra Station Wagon, which was an absolute POS in case you're not familiar with it.

The POS in question

I was going around 60 and came over a hill to discover a deer hanging out in my lane, about half a mile away.  He was a big buck, facing to my left.  I seemed to have plenty of time to work out my options in my head, and it went like this:

a) Swerve around him to the left.  This would bring me into the empty oncoming lane, but I have always heard that a deer will jump forward if frightened, and that would mean he would jump right in front of my car.
b) Swerve around him to the right.  This would bring me right into the frozen snow bank.  Odds were really good that my junky little car would end up in the woods in the middle of nowhere.
c) Jump on my breaks.  The road was frozen and it seemed really likely that the bicycle tires on my car would lose traction and I would end up spinning out in the middle of the road.
d) Get off the gas, steer straight and hope for the best.

Now, it's worth mentioning that I was a truly terrible driver as a teenager.  These scenarios were not academic concepts to me, they were all experienced realities.  I had driven off the road several times (including a roll-over accident that crushed the roof of my car with a tree), I had lost control and entered into flat spins during bad weather, bad road conditions (and honestly sometimes under ideal conditions but bad choices).  I had wrecked 4 or 5 cars by this point IIRC.  I knew my way around crashing a car.

So, I did what they teach you to do in that circumstance.  Without any panicking, I took my foot off the gas and steered toward the right edge of the road.  I knew there wasn't room to get around the deer, but I was hoping he would jump out of the way.  In retrospect, I should have honked my horn, too.

About an hour later the local sheriff was there by the roadside, poking a buck with his foot and pronouncing "yup, he's dead alright.  Do you want to keep him?"

I spent the rest of the year driving a car with a completely smashed up front end, leaking Styrofoam from the bumper (did you know that cheap cars have Styrofoam bumpers??) and with a grill full of fur.

I tried a google image search for "furry bumper" and "fur in my bumper"... I don't recommend it.

There are lots of situations like this.  You can see them coming a mile away, but there isn't anything you can do about it.  In fact, anything you do is likely to make the situation worse.  When you look back at the outcome, it's easy to second-guess, but sometimes the right choice is to limit the downside--not to try to avoid it.

Bad stats -- "good enough"

I'm reading (or really listening to the audiobook during my commute) "Traffic: Why we drive the way we do" by Tom Vanderbilt.  It is a pretty good book and well researched and surprisingly interesting given the pretty dry nature of the material.

This morning, he was discussing what is a "safe road."  In particular, he was talking about small, curvy roads and whether they are made safer when the turns are labeled with reflective posts and recommended speed signs.

Proper signage is optional

Research has shown that the average speed on unlabeled roads is lower than on labeled roads.  The proposed reason is that the unlabeled road is observably dangerous, so drivers are more careful and drive more slowly.  The labeled road lulls drivers into a false sense of security and therefore they drive faster.  His conclusion is that the labeled roads are therefore more dangerous, as evidenced by the higher average speed.

Here's the thing though... average speed doesn't matter!

What matters is the occurrence of a driver entering a turn with a speed that exceeds the combination of his driving ability, the character of the turn (radius, traction, etc.) and the performance of his car.  If the "fly off the road threshold" (or put another way, "bad enough") isn't exceeded, the driver keeps right on going, no matter how close he might have gotten.  The conclusion drawn by the author is therefore baseless.

Further, if we imagine that the presence of the signage increases the average speed but decreases the standard deviation (or more specifically tightens up the distribution in one way or another), then we're probably less likely to hit the bad-enough threshold and have an accident, even though average speeds are higher.  This seems believable, since better information means that people don't have to guess how fast to go through the curve, and they should converge on something similar to the posted, recommended speed.

made with @Risk by Palisade

In the little simulation I did here, we have two distributions.  The blue represents the "no signs" scenario, where the driver has no idea what they're in for and tends to drive slower.  The red represents the good signs scenario, when everybody knows what's coming.  I set the "bad enough" threshold at 40 mph.  You can see that 0% of the good signs people crash, but 1.3% of the no signs people crash.

There is an analogous concept in marketing, called the "good enough" line.  The idea is that people have some threshold for purchase that depends on certain quality vectors.  In a car, for example, a driver might have a minimum requirement for horsepower, seating capacity, gas mileage, etc.  If your car doesn't meet that threshold, that person won't buy it.  If you exceed that threshold, they won't pay any more for the car and you don't monetize the value.  It's all or nothing.  This concept applies to lots of things.  While the car crashes when it exceeds the threshold, the customer buys something when their desire for the product exceeds the threshold.

So here's the problem.  Statistical process control (SPC) is all about removing variability.  Mass marketing and homogeneity of product offering is the way of the world.  What are the actual differences between different Android phones, for example?  As things move toward the red distribution, fewer and fewer outliers will exist in the tail, and fewer and fewer people will reach the purchase threshold for your product.

So the takeaway is that new products should desperately avoid the middle path.  If you're going to put out something new, put out something really new.  Expect most people to dislike it.  What you need is a few people who will like it, and you won't get there if you don't produce the wildly unpredictable product.

Growth and ungrowth - forcing the vote

Over the past 30 years the US economy has grown.

from http://data.worldbank.org/

At the same time, middle class incomes have stayed flat.

from: http://www.advisorperspectives.com/

I found a really great breakdown and analysis of earnings trends at this website.  It's interesting to note that the sources here are not political sources or think tanks.  This is the US census and a couple of investment advisers that have aggregated and charted the census data.

At the same time, there has been considerable growth in the income of middle class women, and some minorities.

http://www.bls.gov/opub/ted/2013/ted_20131104.htm

The majority of middle class men have not been realizing these gains.  Additionally, many recent college grads are not experiencing these gains.  Link to wikipedia on income

There is a significant body of research on happiness that points to the concept that an individual's sense of happiness is a function of how they feel they compare to their self-described set of peers.  When a person looks around at their friends they do a natural, precognitive comparison that establishes happiness on a sliding scale.  In the case of the economy, the rule is similar.  White men are still better off than most ethnographic groups in the US, but when they measure themselves based on growth, they feel left behind (they are left behind in growth).

Lastly, I'll leave this article on the slide of the American Middle class from the NYT.

Now I will make a logically unsubstantiated leap here and posit that a vast majority of Donald Trumps supports are accurately described by the above economic picture.  I believe that Trump's base is built on the frustrations of white men (and those who are close to them and care about them) who have legitimate economic grievances regarding their individual situation.

It is really a shame that Trump is their mouthpiece, however, because his root cause analysis is deeply flawed.  I don't think there are any credible economists out there who would blame illegal immigration for the trends I identified above.  Certainly there are no economists who would blame ISIS or gay rights or a woman's right to freedom from sexual assault or any of Trump's other defining positions.

The shame here is that he a) provides an unproductive distraction to the people who are getting screwed.  They're now getting screwed by him, too.  b) His angry rhetoric is producing a dangerous death spiral in the form of a reactionary populist echo chamber.  c) the result is that everybody who has been experiencing the gains in the economy assumes that those who are left behind are just racist crackpots.  The whole argument becomes illegitimate.

Ironically, the result is very similar to what you see with Black Lives Matter.  White men are not the subject of negative implicit bias and therefore are not systematically and consistently exposed to negative (and often lethal) encounters with the Police can't wrap their heads around the problem.  They look at the movement, find the worst in it, see some looters or rioters and decide the whole thing is bullshit.  Same story, different disenfranchised and marginalized set of experiences.

options in the wild: part II

At the very beginning, I did a straightforward ROI type of analysis.  The decision seemed very clear from that perspective.  There was one line that took a little more work to get to, but was half as long and had twice as many windows operating.  It should have moved twice as fast.  Easy choice!

I hadn't considered the uncertainty in the model.

The people in line "d" were long a call option on all of the windows to the right.  If any of the windows from "e" through "h" opened up, they could easily exercise that option by walking over and moving to the new line.  I hadn't thought of that until it happened!

Once window "e" opened up, I sat there considering moving to line "e", thinking they might open up "f" or "g" or "h" and I didn't want to miss that opportunity, even though it meant moving to the back of the line.  I decided I was pot-committed to "b" when the other option in the mix made itself known...

The crew of any plane that happened by was long a call on window "a" and "b"!  Since I was in line "b", I was effectively the counterparty and short a call on those windows.  When the crew turned up and took over the windows, they exercised their call and my line came to a stop.

This story is a pretty good representation of the value of using a real options decision framework for a couple of reasons.  The most obvious reason is the importance of involving uncertainty in your decision.  If nothing had changed, my first choice was unquestionably the best.

The second reason is the importance of having a dynamic decision framework that can adapt to new information.  It's easy to make a straight-forward ROI type decision when you ignore the changing world.  One line was served by two windows and was half as long!  That's an idiot-proof decision!  A few minutes later, the decision was more complex.  I was at the end of a line that was roughly the same length as the others, but didn't have the advantage of more windows possibly opening up.  A few minutes after that, I was at a stand-still.  A robust, options-based decision framework that encapsulated the upcoming decisions would have helped.

From an executive decision making perspective, this sort of decision tool is almost never available.  What can be available, however, is some intelligence about what the inflection points are.  If I had any idea about the chances of another window opening up, or what the chances of a crew showing up, I could have produced some heuristics about comparative lengths of each line and made dynamic adjustments to my strategy when the real world changed in front of me.

Options in the wild

A few weeks ago I was travelling on an international business trip.  On the way back I came to the top of an escalator to face the immigration / passport check area.  It looked like this:

(only the lines were a lot longer)

I just jumped into the line that was right in front of me and then sort of observed the lines.  The mental schematic I put together was this:

So I moved through the crowd to line "b" because it was the shortest one I could join (since I don't hold a EU passport).  Also, I realized that there were to windows serving that lane... both window "a" and window "b".  No wonder the line was shorter!

A minute later, the situation looked like this:

As the efficient, German airport had recognized a long line and opened window "e" up, and a mad dash had taken place as people near the end of the line for window "d" scrambled to be at the front of the new line.  My old location at the end of line for window "d" would have put me in pole position for the newly opened window!

Then, a minute later, this happened:

When the crew for some flight showed up and took over priority on window "a" and window "b".

Being a ridiculous person I thought "This is fantastic!  What a great example of real world options analysis!!  I can't wait to write this up!"

What are the options?  Take a few minutes to think about it.  In a few days, I'll publish my thoughts on it, but I would really like to hear what others say, too...

Visualizing your business model

The elements of the business model

If you haven't read my last post about the elements of the business model, do so now.  There's not much sense in reiterating what a prospect is, or how it fits with a product, etc.

Now, check out this picture:

The visual business roadmap

There is a great deal of information in this drawing.

Along the bottom you have the projects associated with your technology (product development) roadmap.  In the drawing I have here, you have two layers--real business can have many more, of course.  The bottom row represents core technology programs.  The layer above that represent product development projects.  Each product development project relies on one or more core technology program.   This is very typical.

Along the left side are market development projects.  These projects are intended to change the appetites and buying behaviors of the markets in general--whether or not you will capture the business produced.  For example, when Apple introduced the ipod the TAM for portable MP3 players exploded.

Along the right side are all sorts of projects.  In a real portfolio this is where most of the decisions really are.  These projects can be things like customer enablement programs, directed marketing programs, and anything else that will impact your expectations around your prospect.  That is to say, anything you think will impact the market share and/or pricing expectations for that product with that market.

Often a project will have impacts at multiple elements of the drawing.  

For example, adding a cool, new feature that has never been seen before may encourage people who would never have bought a product like yours to enter the market (TAM expansion), will help you increase your share of that newly expanded market (MSS expansion) at an increased price (ASP increase) but with the downside of an increased product cost (Cost increase).  In this case, you would be impacting markets, products and prospects all at once!!

modeling your business - projects, products, prospects, markets

What are the fundamental elements of modeling a business?

I often help people figure out how to model and value their business concepts.  A really common concern is "My business is different!"  That rallying call often serves as the number one roadblock people have when trying to build a business analysis.

The fact is this: No matter how different your business is, it's different in one of the same few ways.

All businesses expect to have customers.  All business hope to deliver something of value to their customers.  All businesses do some up-front work in order to have that valuable thing and to make sure their customers know about that valuable thing and to actually deliver the thing to them.  Whether the thing is physical (like a car, a microchip, a refrigerator, a satellite or a pencil) or a service (like a hair cut, consulting, vacation experience, healthcare), or something intangible like software doesn't matter.  The fundamental economics are the same.

If your business doesn't look like this--well you don't really have a business.

So let's take that "my business is different" issue another step.  How is your business not different?

Products

You have (or will have) something that you want to sell.  If it is a tangible, physical product, it probably has some sort of manufacturing cost associated with it.  You should get an idea of how much each unit you sell will cost to produce and deliver to customers.  This is the variable cost of manufacture, or "unit cost" for short.  Unit Costs will sometimes scale as a function of learning curves or economies of scale, and sometimes they'll just stay flat.  Sometimes (like software) this number will be zero.

Fixed Costs

In addition to the variable cost of manufacturing, you'll have overhead.  Maybe you need to build a factory in order to produce your product.  Maybe you need to build a brick and mortar store as a venue for providing haircuts.  Whatever it is, you will have some fixed costs.   This is often called PPE for Property, Plant and Equipment.  PPE will almost always scale with your expected volume, but with the critical distinction that you have to make your investment in PPE before your volume starts to realize.

Projects

Your business will probably require some spending up front that doesn't go into PPE.  Sometimes this is called RND (Research 'n' Development), sometimes it's called NRE (Non-Recurring Engineering), sometimes it's just called "Spending".  Whatever you call it, this is what you have to invest in time and people-hours and other expense items in order to get to the point where you're ready to start bringing your product to your customers.

Markets

You have customers who are (presumably) going to find your product valuable and pay you for it.  Smart companies undergo an exercise called "Market Segmentation", where they try to group their customers together into meaningful groups called markets.  Members of these markets have similar buying behaviors.  They typically value the same aspects of the products and are willing to pay similar amounts.  For purposes of business forecasting, you should take an "outside in" look at your markets and a great deal of your analytical work will be done just trying to size these markets.  How many potential customers are there in each market?  How many of them will make a buying decision in any specific quarter (or year)?

Prospects

To build a predictive business model you need to make forecasts about how well the products you're creating will be received by the markets you have characterized.  This is a "Prospect" -- the specific economic expectations for an individual product with an individual market.  You will spend most of your business analysis time on forecasting prospects.  When will this product hit this market?  How much market share can we hope to achieve?  How quickly do we think we will achieve our peak market share?  How long before we see that peak share get eroded away--either cannibalized by our own subsequent products or defeated by competitive ones?  What sort of prices will we be able to command for the products?  How will the price and the market share forecasts interrelate?

These are the fundamental elements of analysis for any business.  

Before folks should start thinking about how their business is different and how their model won't fit into a typical analysis, they should address these questions and frame the business through these lenses.  After these basics are sorted out, you should start worrying about things like competitive timing, synergies and roadmap effects, market saturation and disruptive effects, complex pricing structures and so on.  Normally those sorts of details won't move the needle on the big analysis.

This isn't to say that the more detailed analyses aren't necessary.  If you're in the middle of negotiating tiered pricing discounts with an important customer you should really figure out what those will mean to your revenue and NPV expectations.   Those things can wait until later.

Next time I'll put up some materials one the mechanics of using these essential entities to create an NPV.

Politics and Market Segmentation: Why is Trump/Sanders a surprise?

The current primary season had me thinking about market segmentation this morning.  In particular, I was thinking about how the US population is traditionally understood:

If we assume that people are basically normally distributed along this one-dimensional scale, you end up with a distribution that would look sort of like this:

Where each of those little blue dots represents some voters (the data is made up, with a random, normal distribution).  There are some way to the right and there are some way to the left, but most of them are clustered in the middle, with increasing density as you move toward the middle.  The two major parties basically jockey over where the dividing line is and try to establish their candidates to capture as much territory as possible while playing chicken on the issues they think they can slide by on.

The fundamental problem is that this market segmentation is based primarily on how American politics is organized and not on how American individuals actually think.  For example, let's assign some typically assumed attributes to each party:

Do these clusters make sense?  If I believe strongly in Christian values does that mean I have to be opposed to affordable health care?  If I think that affordable education is important, does that mean that I must also be opposed to gun control?  This is a market segmentation strategy that is "Inside Out" -- it is based on how the governing parties are organized.

Smart market segmentation is "Outside In" and starts with the criteria people use to make decisions and then organizes around that.

Instead of this one-dimensional approach to segmenting voters, let's consider some two-dimensional approaches.  For example, let's consider some classics, such as personal freedom v. national security or government size v. economic stability.

Or, let's make this very simple and assume the same old Left/Right continuum and add a single new dimension of "Anti-establishment".  In this case, I might argue that the population looks more like this (this data is completely made up):

The distribution along the X-axis is the same here as in the previous drawing.  What's different is that I now have that Y-axis to represent that person's opinion regarding the political establishment.  With a wild majority of people effectively sick of the status quo candidates and more interested in finding an anti-establishment candidate than a traditional left/right candidate.  This is why Sanders and Trump are popular.  Both of them represent a break from the traditional choice that voters are given and are a chance to vote on a different (more meaningful) decision vector.

It's easy to chalk up Trump supports as racists and Sanders supporters as crackpots -- and maybe there's some truth in each, but the reason for their popularity is that the two choices that are available fail to satisfy the buying public.

Common Pitfalls - part 2

Last time I wrote about common problems in business plans that are associated with market sizing, forecasting and especially about estimating market share.

Other common issues are associated with pricing and margin expectations, defining the target market, project risks and customer contact.

Pricing forecasts are too aggressive

We see two sides of this coin.  For established products, we rarely test the limits of pricing for product line extensions, roadmap refreshes and similar product introductions that have the possibility of increasing our asking prices.  In these cases our models will always hold pricing flat, and thereby underestimate the revenue potential.

On the flip side, for new products in currently unserved or underserved market spaces we regularly overestimate the likely selling prices.  A common method to estimate these prices is to do a value chain analysis, some sort of should-cost and required margin analysis and then to determine how much profit pool is left in the portion of the value chain that the product will displace.  This makes a lot of sense and gives a good feel for how much the product could sell for.  Then, when it's finally ready, sales has a volume quota to meet, leadership gets worried about failing to land anchor customers and everybody sort of loses their spines.  The result is that we ask a laughably low price and the customer readily agrees!  This is a real "Flaw of Averages" sort of problem -- you will never get a higher price from your customers than the one you ask.

Margins are unrealistic for a new product

This one happens when we do a lot of market benchmarking to try and estimate prices based on our expected production costs and the product margins reported by competitors who are playing in the same (or comparable) markets.  Benchmarking is a great place to get started, but eager analysts and business managers often overlook the fact that incumbent competitors have had a head start to work out their cost structures, to improve their manufacturing flows and to feel out the best price points.  Their margins probably have improved over time--our first few years will not be so fat.  Similarly, fat margins happen in low-competition environments so by our very entry, everybody will have to tighten their belts and margins all around will probably come down.

Serviceable Market is too broadly defined

Often we will do market surveys that start with a "Total Available Market" this is broadly defined and we draw our numbers from commercially available sources like IDC or Gartner, etc.  For example, if we're working on a part intended to be included in new home construction as a sort of OEM Smart Home product, we will buy research around new housing starts for the next 10 years or so.  Ignoring the fact that these reports are almost never stochastic (they almost never include estimates of uncertainty or ranges for their estimates), they also almost always describe a very broad potential market.  For this reason, we try to define a portion of the total market as a "Servicable" market.  This is the subset of that market that could hypothetically meet their needs with the product we're intending to develop.  Often this segmentation is just as broad as the Total Available segmentation and we overlook the nuances in purchasing decisions between members of that sampling.

Sticking with our smart home example, if the key features of this product are all associated with climate control (like Nest or something) then you should anticipate that it won't be very attractive to new home builders in mild climates.  You can also safely assume that builders below a certain price point (like KB) will have a much lower sell-in rate than custom builders or luxury builders (like Toll Brothers).  It's common that a business manager won't consider all this and will just say "let's just assume we can address 80% of the market".

This approach is not only guaranteed to misjudge the size of the market that we could really address, it also skips past one of the most important parts of building up a business plan; actually talking to customers.

Business Plan never learned about actual customer preferences

I almost forgot this one is on the list, and in retrospect I'm not sure how I could forget it.  This is the case where an entire business plan is built up around a perceived use for a product that we already have--it just takes some modification or rebranding--with a new group of customers that we have little or no experience with.  It can be surprising how quick people can be to extrapolate their own experiences into conjecture about a broad segment of the buying public.  Compounding this problem is that (typically) between our business plan and that hypothetical buying public is a constellation of partners, manufacturers, retailers, channels, and co-travelers, any of whom can throw up a "I don't get it" roadblock.  We should have a really good answer to that objection before we even start to get serious with the rest of these forecasts.

Project risks are ignored

For most of the analyses I see, the actual Project spending isn't material to the overall NPV of the project.  By this I mean that the amount of the NPV is de minimis when compared to the margin dollars involved.  For this reason, it's common for teams to ignore project risk when building up their forecasts.  The business development managers involved will pulse the project managers for a general idea of how long it should take to develop and how many heads will be required and some real primitive multiplication-type analysis will take place and the RnD budget for the project is roughed out.

This approach really leaves a lot to be desired.  For one thing, "a failure to plan is a plan to fail" and RND project management really needs have at least the major serial stages of development roughed out, with the parallel activities in each stage identified and some sort of risk register built up.  Even this basic analysis will often show that the back-of-the-envelope timeline proposed above will really underestimate how long the project will take.  Sometimes the project timeline balloons to as much as 2x when taken in this sort of risk-adjusted, expected value perspective.

Whether or not the spending itself becomes material, there are usually other projects waiting for those people to finish up and move on to the next thing.  Even more importantly, a product that hits the market months (or even years) after the expected launch date is very likely to experience a very different competitive environment than the original forecasts of market share and market pricing called for.  Competitors may have produced improvements to their products, new competitors or new substitute products may have appeared, in some cases the very ecosystem purpose for the product may have rendered it obsolete.

Common problems in business models - Market Share

Common Pitfalls when estimating Market Share

There are a few things that seem to pop up on a regular basis when a business analysis / business plan is being developed.  More often than not, these are the product of biases and short-circuited heuristics on the part of the analysts and business owners involved.

When I'm doing an analysis review (and especially when I'm doing a long day of these reviews) I start with this list and immediately look to make sure these problems don't exist:

Competitive environment is poorly understood

Most of our business decisions address a competitive environment in which we are the incumbent.  This sometimes trips up business managers who are looking to extend our business into adjacencies or into transformational arenas.  If the plan doesn't comprehend the current competitive situation as well as how that competitive environment is evolving, it's time to go back and break out Porter's Five Forces again.

Market share ramps too quickly

I occasionally see some really enthusiastic projections.  I mean, really enthusiastic.  For example, a brand new product, that serves a market we're not familiar with, customers we don't currently do business with, through channels we're unfamiliar with, but we'll ramp up our volume faster than the last iPhone.

Peak market share is too aggressive

I see this one a lot.  The logic seems to be "if we can't capture first place in the market, why would we even enter?"  This might be true, but it doesn't mean that we will capture first place in the market.  If the peak market share for a product introduction is not consistent with the competitive environment, it needs to be adjusted.  Similarly, if the peak market share makes volume assumptions that we can't physically produce with our current manufacturing facilities, supply chain and channel structure, it needs to be adjusted, too.

(more to come)

Important questions to ask when reviewing an analysis

Over the years I have had the opportunity to participate in many, many valuation exercises.  I have been asked to review or assist with many more.  In time, I have built up the following list of "review concepts" -- questions to ask and things to look out for when checking out somebody's work.  It's a good list to check my own work, too.

I'll just dump the list here, and maybe add some commentary in the comments.

Sources of Data

• Are all sources for inputs cited, with the name of the source and the date of collection?
• Is the data still recent?
• Do the numbers align with commonly accepted inputs from other sources, especially those that are "company dogma"?
• Is there good, historical research to support the forecasts?
• When estimating market sizes, are the market segments in the model defined specifically enough to be meaningful?

Time Horizon

• Does the model forecast the full spending horizon (or is there a spending hockey stick that occurs just after the model cuts off)
• Does the spending model comprehend all required spending (or just direct spending in this organization), common pitfalls I have seen in the past include:
• make sure you don't forget software spending!
• make sure you don't forget support spending!
• Does the model exclude terminal value (there is no excuse for terminal value these days)

Uncertainty

• Is there a "Tornado"?
• are the drivers of critical uncertainty at the top of the tornado well understood?
• Are all the "usual suspects" present in the tornado?  From my experience, these are:
• Market Share
• Price
• Completion Date
• Do all of the important inputs have ranges defined for their inputs?
• Is the Prospect start date (first volume) mechanically tied to the Project completion date (it should be!)

Interdependencies

• Are key dependencies on other programs or investments comprehended?
• Are major sources of risk external to the program identified?
• "Roadmap Risk" -- the risk that key ingredients from other partners will not be done on time or will be cancelled outright.
• "O/S Risk" -- the risk that the assumed operating system for the product will be irrelevant by the time it's done
• Competitive Risk -- the risk that a new competitor will appear, or that an existing competitor will change strategies
• Technical Risk -- the outright risk that we won't be able to complete the product at all.

Business model drivers (for new ideas)

• Is the value proposition for the product or service well understood?
• Do we understand what our customers want and how to reach them?
• Is the cost structure realistic?
• Are there regulatory issues we need to be aware of?
• Is the idea easily digestible by customers/management?