Lessons from Shoe Dog

I first came across Shoe Dog, Nike founder Phil Knight’s memoir, from this post on Tren Griffin’s blog. Soon enough, I saw the book pop up multiple times on my GoodReads, and when I got the Kindle sample last week, I noticed that all my friends on GoodReads had given it a five star rating.

So while I normally don’t read autobiographies (the only other one that I remember liking is Andrea Pirlo’s), the recommendations made this one hard to resist. And it was a brilliant read. Finished the book in two days.

It’s a story very well told, written in an engaging style that makes you sometimes wonder if it is a work of fiction. Knight has eschewed the boring details and focussed on the interesting, and impactful, stuff, and the book is full of stories about the early days of the firm (it basically “ends” with Nike going public).

What struck me about Nike’s story is the number of times it nearly went under (which is what possibly makes it such a great story). In the light of those challenges (lawsuits, supply issues, constant working capital troubles, leverage), it is a wonder that the company survived long enough to thrive! In that sense, while it makes sense to draw business lessons from Nike’s story, I sometimes wonder if it’s simply a case of survivorship bias.

For starters, for nearly twenty years after founding, Nike remained a closely held private company, with little outside investment. While the company was mostly profitable (except on one occasion when it broke up with a supplier), it was forever cash poor. Well into its fifteenth year of operations, Knight talks about the company “not having money” for stuff like advertising (for example).

Instead, the company relied on heavy leverage, borrowing as much as it could from any bank that would deal with it (which was basically all banks in Oregon – in the 1960s and 70s, there was no inter-state banking in the US). Several times, the company came close to running out of money, when banks refused to extend its credit. But then it survived.

Griffin’s review of the book shows all this as “learnings” – innovative sources of financing, high growth, dealing with crises, but to me it looks like a lot of bad financial management. Too little equity for too long, an obsession for control (finally Nike went public only when Knight figured he could issue dual class stock), high leverage and all that.

The other thing that struck me about Nike is that even in the late 70s, when the company was 15 years old, it seemed like a bunch of buddies of Knight running it – there wasn’t that much of professional management around, and this could again be attributed to the business being continuously short on cash. I guess times are different now, and equity financing is more available, and firms can start hiring professional managers early, but a 15 year old company being seemingly run in a chaotic manner seemed odd.

Finally, back in business school, we were told that when applying to companies such as Nike or Adidas, we should highlight whatever sporting achievements we might have on our CVs. That struck me as odd – what impact could having played cricket for my hostel wing possibly have on how I could sell shoes?

Reading the book, though, it seems like a culture issue. In several places in the book Knight talks about the firm being driven by a “passion for sport”, with the early employees all being sportsmen. Culture permeates, and you hire more people like you. There is this vague sense of brotherhood, among people who have played competitive sport, and that’s hard to permeate for non sportspersons. And the culture goes on. Whether this lack of diversity is good for the company is another matter!

The Derick Parry management paradigm

Before you ask, Derick Parry was a West Indian cricketer. He finished his international playing career before I was born, partly because he bowled spin at a time when the West Indies usually played four fearsome fast bowlers, and partly because he went on rebel tours to South Africa.

That, however, doesn’t mean that I never watched him play – there was a “masters” series sometime in the mid 1990s when he played as part of the ‘West Indies masters” team. I don’t even remember who they were playing, or where (such series aren’t archived well, so I can’t find the score card either).

All I remember is that Parry was batting along with Larry Gomes, and the West Indies Masters were chasing a modest target. Parry is relevant to our discussion because of the commentator’s (don’t remember who – it was an Indian guy) repeated descriptions of how he should play.

“Parry should not bother about runs”, the commentator kept saying. “He should simply use his long reach and smother the spin and hold one end up. It is Gomes who should do the scoring”. And incredibly, that’s how West Indies Masters got to the target.

So the Derick Parry management paradigm consists of eschewing all the “interesting” or “good” or “impactful” work (“scoring”, basically. no pun intended), and simply being focussed on holding one end up, or providing support. It wasn’t that Parry couldn’t score – he had at Test batting average of 22, but on that day the commentator wanted him to simply hold one end up and let the more accomplished batsman do the scoring.

I’ve seen this happen at various levels, but this usually happens at the intra-company level. There will be one team which will explicitly not work on the more interesting part of the problem, and instead simply “provide support” to another team that works on this stuff. In a lot of cases it is not that the “supporting team” doesn’t have the ability or skills to execute the task end-to-end. It just so happens that they are a part of the organisation which is “not supposed to do the scoring”. Most often, this kind of a relationship is seen in companies with offshore units – the offshore unit sticks to providing support to the onshore unit, which does the “scoring”.

In some cases, the Derick Parry school goes to inter-company deals as well, and in such cases it is usually done so as to win the business. Basically if you are trying to win an outsourcing contract, you don’t want to be seen doing something that the client considers to be “core business”. And so even if you’re fully capable of doing that, you suppress that part of your offering and only provide support. The plan in some cases is to do a Mustafa’s camel, but in most cases that doesn’t succeed.

I’m not offering any comment on whether the Derick Parry strategy of management is good or not. All I’m doing here is to attach this oft-used strategy to a name, one that is mostly forgotten.

Waiting for Kumaraswamy’s Tiger

Finally, last week Softbank announced that it has closed its $9.3 Billion investment in Uber. Since the deal was in the making for a long time, the deal itself is not news. What is news is what Softbank’s Rajeev Misra told Uber – to “focus on its core markets in US, Europe and Latin America”.

One way of reading this message is to see it as “keep off from competing with our other investments in Didi, Grab and Ola“. If Uber takes Misra’s words seriously (they better do, since Softbank is now probably Uber’s second biggest shareholder, after Travis Kalanick), it is likely that they’ll go less aggressive in Asian markets, including India. This is not going to be good for customers (both drivers and passengers) of taxi marketplaces in India.

Until 2014, the Indian market had three vibrant cab marketplaces – Uber, Ola and TaxiForSure. Then in early 2015, TaxiForSure was unable to raise further funding and sold itself to Ola, turning the market into a duopoly. Back then I’d written about why it was a bad deal for Indian customers, and hoped that another company would take TaxiForSure’s place.

Three years later, that has not come to be and the Indian market continues to be a duopoly. When I visited Bangalore in December, I noticed service levels in both Uber and Ola being significantly inferior to what I’d seen a year earlier when I was living there. Now, if Uber were to cede ground to Ola in India (as Softbank implicitly wishes), things will get further worse.

Back in 2015, when TaxiForSure was shutting down, I had assumed that another corporate entity, perhaps Meru (which runs call taxis) would take its place. And for a really long time now there have been rumours of Reliance entering into the cab marketplace business. Neither has come to be.

So this time my hopes have moved from corporates to politicians. The word on the street in Bangalore when I visited in December was that former Karnataka Chief Minister HD Kumaraswamy had partnered with cab driver associations to start a new cab marketplace, supposedly called “Tygr” (sic). The point of this marketplace, I was informed during my book launch event in Bangalore in December, was that it was going to be a “driver oriented app”.

This marketplace, too, has been coming for a long time now, but with the Softbank deal, it can’t come sooner. Yes, it is likely that it will not be a great app (if it is “too driver oriented”, it won’t get passengers and the drivers will also subsequently disappear), but at least it will bring in a sense of competition into the market and keep Ola honest. And hopefully there will also similar competition in other cities in India, though it is unlikely that it will be Kumaraswamy who will disrupt those markets.

A lot is made of the fact that investors like Warren Buffett own stocks in all major airlines in the US. Now, Softbank seems to be occupying that space in the cab marketplace market. It can’t be good either for drivers or passengers.

Patanjali going online

Mint has a piece on Baba Ramdev-led FMCG company Patanjali going online to further its sales.

Some may have seen the irony in Patanjali Ayurved Ltd tying up with foreign-owned/funded e-commerce companies, even as it swears to end the reign of foreign-owned consumer brands in the market.

Patanjali is only being pragmatic in doing what’s good for its own business, of being available where the consumers are. Its decision is one more pointer to the growing importance of e-commerce as a distribution channel for packaged consumer goods.

I have an entire chapter in my book dedicated to this – about the internet has revolutionised distribution and retail. In that I talk about Dollar Shave Club, pickle sellers from Sringeri and mobile manufacturers such as Xiaomi who have pioneered the “flash sale” concept. In another part of the book, I’ve written about how Amazon has revolutionised bookselling, first by selling online and then by pioneering e-books.

Whenever a new consumer goods company wants to set up shop, one of the hardest tasks is in establishing a distribution network. Conventional distribution networks are typically several layers deep, and in order to get to the customer, each layer of the distribution network needs to be adequately compensated.

Apart from the monetary cost, there is also the transaction cost of convincing each layer that it is worthwhile carrying the new seller’s goods. The other factor to be considered is that distributors at various levels are in a sense loyal to incumbent sellers (since they are responsible for a large portion of the current business), making it harder for new seller to break through.

The advantage with online retailers is that they compress the supply chain, with one entity replacing a whole network of distributors. This may not necessarily be cost-effective from the money perspective, since the online retailers will seek to capture all the value that all the layers of the current distribution chain are capturing. However, in terms of transaction costs it is significantly easier since there is only one layer to get past, and online retailers seldom have loyalty or exclusive relationships.

In fact, the size and bargaining power of online retailers (vis-a-vis offline distributors) means that if there is an exclusive relationship, it is the retailer who holds the exclusive rights and not the seller.

In Patanjali’s case, they have already established a wide offline network with exclusive stores and partnerships, but my sense is that they seem to be hitting the limits of distribution. Thanks to Baba Ramdev’s popularity as a yoga guru, Patanjali enjoys strong brand recall, and it appears as if their distribution is unable to keep pace with their brand.

From this perspective, going online (through Amazon/Flipkart) is a rational strategy for them since with one deal they get significantly higher distribution power. Moreover, being a new brand, they don’t have legacy distributors who might get pissed off if they go online (this is a problem that the Unilevers of the world face).

So it is indeed a pragmatic decision by Patanjali to take the online route. And after all, in the end, sheer commerce can trump nationalist tendencies and xenophobia.

Machine learning and degrees of freedom

For starters, machine learning is not magic. It might appear like magic when you see Google Photos automatically tagging all your family members correctly, down to the day of their birth. It might appear so when Siri or Alexa give a perfect response to your request. And the way AlphaZero plays chess is almost human!

But no, machine learning is not magic. I’d made a detailed argument about that in the second edition of my newsletter (subscribe if you haven’t already!).

One way to think of it is that the output of a machine learning model (which could be anything from “does this picture contain a cat?” to “is the speaker speaking in English?”) is the result of a mathematical formula, whose parameters are unknown at the beginning of the exercise.

As the system gets “trained” (of late I’ve avoided using the word “training” in the context of machine learning, preferring to use “calibration” instead. But anyway…), the hitherto unknown parameters of the formula get adjusted in a manner that the formula output matches the given data. Once the system has “seen” enough data, we have a model, which can then be applied on unknown data (I’m completely simplifying it here).

The genius in machine learning comes in setting up mathematical formulae in a way that given input-output pairs of data can be used to adjust the parameters of the formulae. The genius in deep learning, which has been the rage this decade, for example, comes from a 30-year old mathematical breakthrough called “back propagation”. The reason it took until a few years back for it to become a “thing” has to do with data availability, and compute power (check this terrific piece in the MIT Tech Review about deep learning).

Within machine learning, the degree of complexity of a model can vary significantly. In an ordinary univariate least squares regression, for example, there are only two parameters the system can play with (slope and intercept of the regression line). Even a simple “shallow” neural network, on the other hand, has thousands of parameters.

Because a regression has so few parameters, the kind of patterns that the system can detect is rather limited (whatever you do, the system can only draw a line. Nothing more!). Thus, regression is applied only when you know that the relationship that exists is simple (and linear), or when you are trying to force-fit a linear model.

The upside of simple models such as regression is that because there are so few parameters to be adjusted, you need relatively few data points in order to adjust them to the required degree of accuracy.

As models get more and more complicated, the number of parameters increases, thus increasing the complexity of patterns that can be detected by the system. Close to one extreme, you have systems that see lots of current pictures of you and then identify you in your baby pictures.

Such complicated patterns can be identified because the system parameters have lots of degrees of freedom. The downside, of course, is that because the parameters start off having so much freedom, it takes that much more data to “tie them down”. The reason Google Photos can tag you in your baby pictures is partly down to the quantum of image data that Google has, which does an effective job of tying down the parameters. Google Translate similarly uses large repositories of multi-lingual text in order to “learn languages”.

Like most other things in life, machine learning also involves a tradeoff. It is possible for systems to identify complex patterns, but for that you need to start off with lots of “degrees of freedom”, and then use lots of data to tie down the variables. If your data is small, then you can only afford a small number of parameters, and that limits the complexity of patterns that can be detected.

One way around this, of course, is to use your own human intelligence as a pre-processing step in order to set up parameters in a way that they can be effectively tuned by data. Gopi had a nice post recently on “neat learning versus deep learning“, which is relevant in this context.

Finally, there is the issue of spurious correlations. Because machine learning systems are basically mathematical formulae designed to learn patterns from data, spurious correlations in the input dataset can lead to the system learning random things, which can hamper its predictive power.

Data sets, especially ones that have lots of dimensions, can display correlations that appear at random, but if the input dataset shows enough of these correlations, the system will “learn” them as a pattern, and try to use them in predictions. And the more complicated your model gets, the harder it is to know what it is doing, and thus the harder it is to identify these spurious correlations!

And the thing with having too many “free parameters” (lots of degrees of freedom but without enough data to tie down the parameters) is that these free parameters are especially susceptible to learning the spurious correlations – for they have no other job.

Thinking about it, after all, machine learning systems are not human!

Profit and politics

Earlier today I came across this article about data scientists on LinkedIn that I agreed with so much that I started wondering if it was simply a case of confirmation bias.

A few sentences (possibly taken out of context) from there that I agree with:

  • Many large companies have fallen into the trap that you need a PhD to do data science, you don’t.
  • There are some smart people who know a lot about a very narrow field, but data science is a very broad discipline. When these PhD’s are put in charge, they quickly find they are out of their league.
  • Often companies put a strong technical person in charge when they really need a strong business person in charge.
  •  I always found the academic world more political than the corporate world and when your drive is profits and customer satisfaction, that academic mindset is more of a liability than an asset.

Back to the topic, which is the last of these sentences. This is something I’ve intended to write for 5-6 years now, since the time I started off as an independent management consultant.

During the early days I took on assignments from both for-profit and not-for-profit organisations, and soon it was very clear that I enjoyed working with for-profit organisations a lot more. It wasn’t about money – I was fairly careful in my negotiations to never underprice myself. It was more to do with processes, and interactions.

The thing in for-profit companies is that objectives are clear. While not everyone in the company has an incentive to increase the bottom-line, it is not hard to understand what they want based on what they do.

For example, in most cases a sales manager optimises for maximum sales. Financial controllers want to keep a check on costs. And so on. So as part of a consulting assignment, it’s rather easy to know who wants what, and how you should pitch your solution to different people in order to get buy-in.

With a not-for-profit it’s not that clear. While each person may have their own metrics and objectives, because the company is not for profit, these objectives and metrics need not be everything they’re optimising for.

Moreover, in the not for profit world, the lack of money or profit as an objective means you cannot differentiate yourself with efficiency or quantity. Take the example of an organisation which, for whatever reason, gets to advice a ministry on a particular subject, and does so without a fee or only for a nominal fee.

How can a competitor who possibly has a better solution to the same problem “displace” the original organisation? In the business world, this can be done by showing superior metrics and efficiency and offering to do the job at a lower cost and stuff like that. In the not-for-profit setup, you can’t differentiate on things like cost or efficiency, so the only thing you can do is to somehow provide your services in parallel and hope that the client gets it.

And then there is access. If you’re a not-for-profit consultant who has a juicy project, it is in your interest to become a gatekeeper and prevent other potential consultants from getting the same kind of access you have – for you never know if someone else who might get access through you might end up elbowing you out.

Bond Market Liquidity and Selection Bias

I’ve long been a fan of Matt Levine’s excellent Money Stuff newsletter. I’ve mentioned this newsletter here several times in the past, and on one such occasion, I got a link back.

One of my favourite sections in Levine’s newsletter is called “people are worried about bond market liquidity”. One reason I got interested in it was that I was writing a book on Liquidity (speaking of which, there’s a formal launch function in Bangalore on the 15th). More importantly, it was rather entertainingly written, and informative as well.

I appreciated the section so much that I ended up calling one of the sections of one of the chapters of my book “people are worried about bond market liquidity”. 

In any case, the Levine has outdone himself several times over in his latest instalment of worries about bond market liquidity. This one is from Friday’s newsletter. I strongly encourage you to read fully the section on people being worried about bond market liquidity.

To summarise, the basic idea is that while people are generally worried about bond market liquidity, a lot of studies about such liquidity by academics and regulators have concluded that bond market liquidity is just fine. This is based on the finding that the bid-ask spread (gap between prices at which a dealer is willing to buy or sell a security) still remains tight, and so liquidity is just fine.

But the problem is that, as Levine beautifully describes the idea, there is a strong case of selection bias. While the bid-ask spread has indeed narrowed, what this data point misses out is that many trades that could have otherwise happened are not happening, and so the data comes from a very biased sample.

Levine does a much better job of describing this than me, but there are two ways in which a banker can facilitate bond trading – by either taking possession of the bonds (in other words, being a “market maker” (PS: I have a chapter on this in my book) ), or by simply helping find a counterparty to the trade, thus acting like a broker (I have a chapter on brokers as well in my book).

A new paper by economists at the Federal Reserve Board confirms that the general finding that bond market liquidity is okay is affected by selection bias. The authors find that spreads are tighter (and sometimes negative) when bankers are playing the role of brokers than when they are playing the role of market makers.

In the very first chapter of my book (dealing with football transfer markets), I had mentioned that the bid-ask spread of a market is a good indicator of market liquidity. That the higher the bid-ask spread, the less liquid a market.

Later on in the book, I’d also mentioned that the money that an intermediary can make is again a function of how inherent the market is.

This story about bond market liquidity puts both these assertions into question. Bond markets see tight bid-ask spreads and bankers make little or no money (as the paper linked to above says, spreads are frequently negative). Based on my book, both of these should indicate that the market is quite liquid.

However, it turns out that both the bid-ask spread and fees made by intermediaries are biased estimates, since they don’t take into account the trades that were not done.

With bankers cutting down on market making activity (see Levine’s post or the paper for more details), there is many a time when a customer will not be able to trade at all since the bankers are unable to find them a counterparty (in the pre Volcker Rule days, bankers would’ve simply stepped in themselves and taken the other side of the trade). In such cases, the effective bid-ask spread is infinity, since the market has disappeared.

Technically this needs to be included while calculating the overall bid-ask spread. How this can actually be achieve is yet another question!