Post Pandemic Trust Issues


No doubt we’ve learned a lot over these four months or so. Maybe one of the larger lessons was the inherent fragility of our supply chains. The two sides of the seemingly same coin, globalization and just-in-time operations both showed weaknesses, albeit in a time of extraordinary circumstances. I think there’s a lot of companies walking away from the pandemic with a less than completely trustworthy feeling about both.

But how will these lessons be acted upon going forward? The easy answer is companies cannot rely on getting parts, materials and products from around the globe as they thought they could. For some, that will mean diversifying supply chains, but I’d think it also means most will start looking for ways to buffer their supply chain, as well. That means keeping certain amounts of stock on-hand or at least not on the other side of an ocean.

This cashing of materials may be a bit problematic for many, as we’ve had decades (at least in the USA) of living under lean manufacturing and lean supply chain doctrine that all but removed the notion of on-site inventory. So much so in fact, that many production and retail facilities were purposefully designed with the smallest amount of storage necessary to continue operations. And it’s not just the end users, the bulk of the supply chain has made similar adjustments, from suppliers to retailers.

It will be interesting to watch how painful the shortages of the last quarter or so are compared to the motivation to keep materials on hand in case similar events happen again. Will this drive firms to create inventory space in an effort to smooth any sort of speed-bumps or will they lean on suppliers to carry a larger amount of stock for them?


These questions might boil down to who will have to set aside more warehousing space. This will be answered by the power dynamics of supplier/buyer relationships. Will stores reduce their sales floor to increase storage? Or will it be vendors? Perhaps something even more left field could develop like docks and cross docks also becoming buffers or a new middleman industry of short term storage.

What I do predict is we’ll all have to dust off that supply chain book with the chapter about calculating safety stock – a chapter we all glossed over as we flipped as quickly as we could to get to lean operations.

Covid-19 and Habit Forming


There’s a number of viewpoints about what the world will look like after our focus on Coronavirus recedes. “Things will return to normal,” or “things will never be the same.” These are the two endpoints of the spectrum. I’m going to propose that it’ll be much closer to the latter than the former. Why? Because of the structure of human habits.

The core of my argument is it takes a person typically between one and three months to either effectively stop, modify or start a habit. Most of us have been in lock down for at least a month with some closer to two. This means that some of us certainly had the time to get used to doing things differently. 

While I don’t expect a completely different world than what was going on in 2019, I think there’ll be a summation of small changes that will end up having a great impact on the world going forward. I also don’t expect everyone to continue behavior that they’ve gotten used to in lock-down. 

There certainly will be a large amount of snap-back to pre-2020 behavior for many, but it doesn’t take a population-wide change to make an impact on how our world will look and operate. Take, for instance, the possibility of just 10% of the US population deciding to cook at home for one more night of the week instead of going out to restaurants, what does that look like?

It looks like nearly 21 million adults staying in one more night a week. Let’s say we average the cost of that person’s dinner at about $50. With a population of about 660,000 restaurants in the USA (2018), the reduction in revenue for each restaurant would be something like a little more than $6,000 a month and a yearly dip of about $75,000. That number is easily one worker’s wages – and in the restaurant industry, I’d guess it might be at or more than two, actually. That would mean there would be 1.2 million people permanently unemployed – all because some of us modified just one habit we’ve had pre-Covid-19. And that’s only one potential habit that could be changed or modified by a relatively small amount of the US population.

There are doubtless many more small changes that will have just as far reaching effects going forward. The above is just one example. With a large proportion of the US population having to change virtually every aspect of how they’ve done things – and for a time period similar to what many believe takes to form habits – even the small changes can stack up to change the economic complexion of the country going forward. Enough of these small changes could lead to the world being noticeably different than what we knew before. 

Apple buys 5G Modem Business from Intel – the other side of the story

Sad Punch Bowl
Yep, completely unrelated but it does look like a cool place to go to.

As was reported with great fanfare by Forbes, Apple has purchased the 5G modem business from Intel. The article focuses on how this new functionality gives Apple a sort of leg up as it now has even more verticality in its supply chain. This should give the company a bit more ability to control for component prices as it now has captive capacity that it can completely earmark for itself as well as to use as a tool against Qualcomm pricing. 

There’s another story here that I think people are not picking up. There could be an alternate story that goes like this, “Intel exits from 5G modem industry.”

Why is that interesting? First, Intel usually does a pretty good job of exiting industries and product lines that look like they’re becoming commodity. Of course there’s been some bungles in the past but generally they have a good read on such things. 

The interesting aspect is what made Intel sell? The easy answer was that Apple way overpaid in Intel’s eyes. Intel could have done the calculus and came to the reasoning that the price offered by Apple would constitute a better return than what they’re anticipating in the market for the next few years. Apple could have desired the division much more than Intel as it would give them the aforementioned control that they like with suppliers. The extra cost may be worth it for them. 

Another aspect is that Intel may not have had the opportunity to control the intellectual property properly enough to ensure longevity in the market – or more to the point, control prices in the market. It’s known that at least Qualcomm, Broadcom and Xilinix have competitive products. That’s some big players who could also put pressure on prices. Best to focus on markets Intel can control rather than just participate.  

Perhaps too, that Intel may not have been the winner in the opening rounds of who wins the OEMs. All of the largest players in this case have chosen a 5G chip dance partner leaving Intel at the punch bowl. If this is the case – and all this is postulation on my part – then 5G is decided and will be coming to everything much faster than people are letting on. 

Filter Bubbles vs The Daily Randomizer

I guess we’re all in our own little bubbles. The abilities of technology nowadays allows us to craft, select and deselect what and from where we receive our information about the world. Of course this may be a bit dangerous. It can create little echo-chambers of our own thinking where the only new ideas we already like are siphoned in.  If that’s not bad enough, it also hardens the ‘us-vs-them’ mentality in an already divisive time.

Wouldn’t it be interesting if we could see how the rest of our world thinks about things? If we could, would it surprise us if we were to glimpse into those other guy’s bubbles and find out they’re not so crazy? Maybe even find out on just a few points we might have a commonality of opinion?

Thinking along those lines, I thought it to be rather interesting to reach into a number of different news sites to grab stories, but present them in as impartial environment as possible – no logos, no taglines, no talking heads or personalities – just batches of news stories. That’s what I’ve done with my Daily Randomizer site.

The site culls batches of stories from a variety of sources (and as balanced as I could with the technology I’m using for this.) It presents five different sections: Top News, Business, Politics, Entertainment and Sports. The hook here is that you can see the news article titles but unless you click on the link, you’ll never know where they’re from! It’s random, and it refreshes every 30 seconds or so.

In my little part to help build bridges between everyone in this crazy world I ask you to give the site a try, keep an open mind and check out the site.

Jeff Foxworthy, Machine Learning and Changing Your Mind

Foxworthy Hal

Right now, I should probably be writing that promised article on what makes a good advertisement, but instead let’s talk about the magic of Machine Learning and people.

We’ve all heard the hip, new cool thing is machine learning. It’s PyTorch or Keras or whatever other flavor that’s out there. I got taken up with it, too. Heck, I even have a certificate around here that says I survived learning how to use Tensorflow. All this means is that I’ve peered behind the curtain a bit to get a glimpse of the little man at the controls.

It is a wonderful technology, to be sure. It allows a great many conveniences that will or have already lead to some pretty amazing insights. Because what it does is essentially multivariate regression analysis – but at a massive scale. That means it basically line-fits large sets of data so that it can determine the statistical probability of similarity to other things – not just the one or two variables Excel can do.

I’m sure right now you’re thinking that was an un-welcomed digression that’s left an accounting term-like mental malaise in its wake. What’s the point? Well the point is this: While the tech is good at boiling down complex data sets to provide you with, say movie choices that you may like based on what you’ve already liked, I don’t think anyone has factored in people changing their tastes from time to time – or the fact that while there may be a large number of people looking at things, there’s only a small amount of things they can look at.

Examples could found in a variety of places, perhaps the most memorable for all of us digitally plugged in would be either online dating, shopping recommendations from online retailers or streaming services like Netflix. By now, I’m betting you have had an experience with at least one of these. That means we’ve all experienced the pain of only getting exactly the things we’ve liked in the past. The trouble is what I liked to watch five years ago may not be what I want to watch now. Or, it has finally dawned on me that the type of person I was into back then is not the best choice going forward, but technology has doomed me to stay in the same dating pool.

When will these machine learning things figure that out? I think we all feel like they never will.

Why? Because the very large data sets that give machine learning tech their hyper-focused funneling power is the very same massive data set that truly has to turn over before our new tastes are included – and that’s only if the programmers of these systems have put in place code that allows for this to happen.



If you look at the machine learning math (and I won’t open that Pandora’s Box), it’s easy to see  watching only one Tom Segura stand up special has no chance of offsetting those ten Jeff Foxworthy shows your uncle watched on your account. Of course, that’s IF they built the software to recheck your preferences from time to time.

To really simple down the math (and yes the math is much more complex than this, Mr. Data Scientist, but it’s an analogy for everyone else), the effects are similar to every time Foxworthy spends 30min explaining what a redneck is on your Netflix account the software multiplies the Foxworthy counter by ten. The same would happen for watching Tom.  So in the easy math world it looks like this:

One viewing of Jeff: Jeff * 10 = 10Jeff

One viewing of Tom: Tom * 10 = 10Tom

Things get bad when that scales – as in your uncle just watched 10 Foxworthy specials on your Netflix account:

10 viewings of Jeff = 10Jeff^10 =10000000000Jeff

Compare this to one 10Tom and one viewing of something else isn’t going to change what the machine learning system is thinking you like, but what more important is that there isn’t 10 Tom Segura specials, there’s only like five.

Five Tom specials = 10Tom^5 = 100000Tom

10000000000 > 100000

The exponential math here means that there’s really no way watching less than the same amount of something else is going to offset the binge of a previous show, genre, actor or however else they’ve segmented their content.

Sorry, you’re doomed to finding out (over and over again) refrigerators on your porch probably makes you a redneck -unless Segura gets off his ass and makes five more specials.

Of course we’re all really in for it if they’ve also added in counting the frequency between views of the same materials. If that’s the case, binge watching is particularly dangerous.

All of this content railroading is compounded by these services continuing to only show you selections that fit with what you liked previously. There seems not to be any sort of random content display to break you out of your filter bubble. This makes you have to independently locate different selections – which is hard because how would you search for something when you don’t know what it is?

I’m hoping that eventually the powers that be will figure this out, because, while I really like Korean action movies (and you should check them out, too), I’d like to have the opportunity to see Thor: Ragnarok without having to spell all that out with the ridiculous Roku remote every time.


If Electric Bikes are its Future, is the Harley Davidson Brand a Help or a Hindrance?


Initially, I started writing this article from a perspective of the decision making that would go into how Harley would brand their new electric vehicle lines. The longer I worked on it, the more I realized that the real question is does it even matter?

From a product standpoint, going from the current Harley bikes and accessories to electric motorcycles and e-bikes is truly a massive departure from its core. Sure, both have two wheels, stalk the roads and require helmets (or should), but beyond that, they’re two completely different beasts.

The current Harley offering is more of a lifestyle built around an amazingly large, expensive machine that fights tooth and nail to stay rooted in the past – and certainly not the pinnacle of efficiency, either. The electric bike world is, for the most part, exactly opposite.

harley riders

So too are the target demographics for each product group. For the current rider, it’s the sigil of open-road freedom and luxury that only a large amount of disposable income can easily afford. It’s also something of a recreational item with only the thinnest of connections to utility.

Conversely, what today’s electric bike market looks like is based in the desire for cost-effective and convenient [city] transportation. It becomes even more true when Harley starts looking outside of its core US market. Further, those that will be in the market for e-bikes and electric motorcycles will be of younger generations that have proven they don’t need a vehicle to help support their idealized selves – or one to connect them to new people and experiences. They have the mobile devices and social media for that.

So for all the effort Harley has put into building this current incarnation of the brand, it would appear to completely stand in the way of how they need to be perceived for the proposed pivot into these new markets. I’d further posit that it’s so far apart that you couldn’t even marginally connect the current brand to a new one like, ‘YYY …by Harley Davidson.’ Might as well try saying, “The Future, by Old-timey.” in that ‘Kitchen of the Future’ voice from the ‘50s.

An electric motorcycle-focused brand from the firm would have to be completely different than what they have today to the point that it would need to be essentially a completely different company. The question becomes: does Harley have the gumption to do it well?

The Calculus of Ordering Stuff Through Alexa.
Amazing! An image that exactly fits with my article. Thanks GeekWire!

It’s recently come out that the vast ownership of Alexas pretty much don’t use them for shopping, even though this was Amazon’s primary reason for launching the product. Having watched and read a number of people postulating that this voice-driven platform will be the next far reaching commerce platform, I have to say that I’ve never really seen how Alexa-like devices were going to command so much sales.

Thinking about it more indepth, I think the main reason for not seeing sales through the platform may be the calculus people do between the desire for specific products versus the speed of receiving said product.  The crux of the formula is if I can’t have something right now, then I want to have more command over what I’m getting, but if I can get it right now, I’m far less picky.


Imagine this setting: you want pizza. Right now. And perhaps some sort of pizza is available essentially immediately. Who knows, maybe you’re jonesing for a slice and you’re near the food court at the mall (if people still eat at the food court at the mall?) The desire to have exactly a specific brand and style of pizza may not be as strong as being able to just get some pizza right now.

The result would be completely different if you were at home and had to either travel to get some pizza or wait about the same amount of time to have pizza delivered. In this instance, I’d bet you’d consider much more closely your options. The nearly all-bread pizza at the mall would probably not make it to the recall set.


Applying this thinking to summoning Alexa to buy whatever pizza product Amazon desires to send you ends up within the aforementioned ‘formula’ – because any option Amazon has is well outside of instant gratification. Regardless of the ease of absentmindedly bellowing for pizza, you’re still waiting for delivery by conventional means – even though it may be through a relatively quick Ubereats or Dominos. If you have that time available, then you’ll want to be more specific than ‘Alexa, order me (any) pizza.’ That’s the rub of buying through the device. Factoring the probability of getting exactly what you want against how long you’d wait doesn’t seem like a good bet.

Sure, there are some purchases that could work well with the platform. Playing or buying digital media is a good bet. Even buying apps would be. On the other hand, tangible items (like the bulk of what’s available on Amazon)  we’ll have to wait for. Waiting builds expectations – as well as the magnitude of disappointment when Amazon guesses it wrong. Maybe this is the reason we’re seeing Alexas with screens coming out and delivery drones. Amazon has to either fix the specificity issue or it has to solve the instant gratification problem to really make buying on Alexa work for the masses.

Looking at What Makes a Good Ad


In today’s age of digital metrics and multi-channel engagement, the conventional ad is sometimes thought of as an ineffective dinosaur. This thinking comes from the experience of running ineffective ads and seeing nothing tractable from the effort -especially in the face of digital offerings that can at least send back some sort of metrics. Having worked with a number of clients to build advertisements over the years, I’ve learned that making an effective ad is something that’s a bit of a mystery for many.

To shed some light on effective ad development, I’m going to be doing a series of posts that speak to the more important aspects of ad design by using actual advertisements as examples.

Some examples will be shown for the good things, some will be for the not so good things. I’m going to try and mix up the kinds of example ads and the target markets they aim at. At the end of the day, it’s not to chastise the bad or the lost. Regardless of the sorts of products or consumers aimed at, the breakdown of these ads should help future ad creators in making their work a bit more impactful – and for the right reasons.  The goal is always informative.

To summarize the core tenets of what makes a good ad, I’ve put together an entry-level list of the most important aspects. I’ll go into detail about these aspects in successive posts.

What makes a good ad:

  • It speaks the target audience’s language
  • It’s placed where the target audience will be
  • It has asks a specific action of the viewer
  • It has one specific and focused message
  • It’s designed to impart that message within a blink of an eye
  • It’s built to impart and support the brand positioning of the firm

While this series is designed to be primarily aimed at conventional advertising, by no means does it disqualify the salient points from being completely true and useful for the digital/social world. In fact, regardless of today’s abilities in A/B testing, availability of analytics or even healthy slatherings of machine learning or other buzz-friendly technologies, the core tenets here will still have much more impact and utility to crafting an ad that gets the customer results desired.

The End of “Request Quote”


The B2B industry has long relied on the phrase “request a quote” for the opening of a business sale. As far back as when people wanted to buy things from people not right next door, the phrase has been in place. It was in place when there was just phone books, magazines and even personal printed versions of the big green Thomas Register – and it was there when the Thomas Register and the rest of us went online.

For a while the phrase, and then the button that leads to a form (that we all were confident would never be responded to), was working quite well. But technology eventually catches up to everyone and B2B is no different. Most suppliers and manufacturers have seen the benefit of merely having a presence on the internet. And they should, as studies have shown that even the B2B world sees 93.7% of purchases start with search. So what’s my issue with putting up a quote form rather than a cart?

The issue is that the B2B shopper is also a B2C shopper. If we all dig down, we all know it to be true. The B2B shopper (us) has had something like two decades of point-click-buy experience. It’s no longer a wish or desire, it’s a necessity for consumer-focused firms to be able to close that sale with as little friction as possible. With this in mind, I would posit that it’s almost impossible to close B2B stock product sales by hoping the customer picks up the phone instead of navigating to a site with a buy button.


The question becomes, “So why isn’t every B2B supplier ditching the quote button for the buy button – especially with stock components?”

One of the most lauded reasons is that the capability to quote parts rather than just price them is that it gives a black box aspect to the sale. This layer of obfuscation helps firms adjust prices to the scale, complexity – or even the sort of customer that’s inquiring. Small run? Well it costs virtually the same to service a small order than it does to do a big run. Therefore the short run costs more because the time could be spent on large orders. High complexity? It takes more brain power. The price goes up. A customer who has deep pockets or a short time frame? Price goes up. That flexibility in pricing is hard to give up, for sure.

Another is the perception of insurmountable complexity in setting up an online ordering system. The fear is that it requires a room full of people and a stack of servers humming along in the basement, killing the AC and sucking the life out of the bottom line.In reality, the barriers preventing companies from opening up ecommerce operations is the lowest it’s been ever. Personally, I have been able to go from bare web server to storefront in under two days. Granted, it’s no Amazon or McMaster Carr but if I can do it, anyone can.

Perhaps the scariest reason of them all is,”we’ve always done it this way.” Sadly, I’ve heard this more than I should, especially in the B2B world.

What’s the cost of all this fear? Lost sales. The B2B purchasing manager who has gotten used to buying all manner of products for themselves and others with a finger touch (and probably on their phone) knows that there’s a firm out there that has a site which will tell them the price. Of course, that site is also the one that has a ‘buy’ button so they can just get it now, not wait a few days to have a quote compiled, received, compared, agreements signed and so forth for an off-the-shelf-part.

Don’t believe me? Studies have shown 57% of the buying process is done prior to engaging with Sales – that’s before requesting a quote. That’s probably because 80% of companies implementing B2B e-commerce believe that their customer expectations have changed due to B2C practices.

Maybe the scariest fear should be the feeling of prospective buyers skipping over a quote site for the vendor where they can simply press ‘buy.’ What’s even scarier is that buyer will never find out a part is cheaper, faster to ship or better in some regard, the sale was passed over because there wasn’t a ‘buy’ button.

A different take on GoPro’s Woes

A harrowing story caught on a GoPro – Check it out.

I read an article the other day on TechCrunch that I’d summarize as “Forces are holding Silicon Valley back from creating viable hardware companies.” One of the reasons that sticks out in my mind most from the article is that the largess of the incumbent players in the consumer hardware market make it extremely difficult to enter said market. The article used the recent stock gyrations of GoPro and by extension, the talk that the firm may be up for sale as proof of such a hurdle in the market.

I don’t know if GoPro is up for sale and I don’t know if they should sell. Heck, I don’t even know if you should buy or sell the stock – use your best judgement, not mine. However, I was interested if their financial data would show some sort of barrier to entry issues that has caused financial issues with the firm. For this exercise, I took a look at the quarterly income statements and balance sheet data (Courtesy of AmigoBulls), as well as the quarterly stock price for the firm (From Yahoo Finance) for the term of March 2013 to August 2017. The firm’s statements would indicate it went public in somewhere around June 2014 with an initial price of $24 a share, a little while after it officially adopted the name GoPro. The financial statements also contain data prior to the IPO that I assume is part of the regulatory filings necessary before the initial sale.

When you have a look at GoPro’s numbers it appears as though it’s a company that’s doing not terrible. Below are the Net Sales, CoGS, and Net Income.

sales cogs chart

Through January 2016, the firm seems to be growing quite well, with the rather predictable Christmas bumps in the numbers.  There’s no spectacular growth here so it’s probably moving to maturity in the market with the current products it has.

The sales to cost of good sold remains more or less congruent through the period. That would indicate there hasn’t been any shenanigans with suppliers or absurd price pressure from ones with larger economies of scale. I’d assume that after January 2016, the low price competitors have entered the market and had begun swipe away the low hanging fruit, hence the drop in net income. By the next year, it would seem GoPro had puzzled out some responses to the invaders.

retained earnings chart

Looking at the Balance sheet, it would appear that these numbers would support a re-targeting of the firm by investing in areas where GoPro could best compete. This could be reflective of the drop in retained earnings and the increase in assets and liabilities. I’d assume the retained earnings were re-invested rather than being returned to shareholders during this period.

Going back to the income statement, it would show that R&D pulsed at the time sales dropped and had leveled off in the second quarter of 2017 where I’d assume those development efforts moved to production and instigated the need for capital spending support, thus the uptick in assets and liabilities at the end of the previous graph.

While I’ve dug out nothing more to go on – call it looking for Occam’s Razor…or laziness – this would indicate to me a rather stable firm with a management group that’s on their game at some level. But more importantly, these findings fail to indicate any tremendous pressure from a large established consumer electronics firm bent on crushing outsiders.

So where’s this insurmountable barrier for Silicon Valley hardware startups?

I think the real culprit can be found when looking at the movement of the stock price. Here’s the quarterly stock price for the firm for the same time periods above:

stock price chart

When you put the stock price up against the previous income statement graph, the issue becomes clear. In the beginning, the stock price seems to be independent of the movements of the underlying company metrics but falls more inline around January 2016.

Here’s my favorite graph so far.

EPS chart

What you see here is the average trailing PE for consumer electronics (and office) products as offered by Sterns in red. It’s the PE for the entire industry. The BLUE line is when you take the first quarter share price of the newly public company and use it to calculate the three quarters BEFORE launch from the provided EPS. After launch the blue line is calculated by using the real EPS divided into the correlated share price. The smaller spike before January 2015 is the IPO issuance.  

My takeaway is it’s not that Silicon Valley cannot produce a competitive hardware company, it’s that they can’t price a hardware company for IPO adequately at best, and perhaps they don’t really understand the investment requirements that physical goods companies require at worst. What’s worse is investors to this day use that hype induced IPO spike as a barometer of the company’s real worth going forward – (a tribute to the power of behavioral finance and perhaps bad VC profit harvesting practice) damning the firm to under-performance perceptions going forward.

Further, GoPro may be a fine company at the scale it is now – and perhaps this is the scale it always should have been considered at. Pumping its stock price to the stratosphere served only to hurt the firm in the long run, which is why what seems to be a perfectly fine company has to fend off rumors of a potential sale rather than incrementally building itself – which is what most solid companies do that are not founded in Silicon Valley.

In the end, GoPro, TechCrunch’s pinata, may actually be priced pretty closely to fair right now, too bad it wasn’t in the beginning…but your metrics may vary.