Analytics for general managers

While good managers have always been required to be analytical, the level of analytical ability being asked of managers has been going up over the years, with the increase in availability of data.

Now, this post is once again based on that one single and familiar data point – my wife. In fact, if you want me to include more data in my posts, you should talk to me more.

Leaving that aside, my wife works as a mid-level manager for an extremely large global firm. She was recruited straight out of business school for a “MBA track” program. And from our discussions about her work in the first few months, one thing she did lots of was writing SQL queries. And she still spends a lot of her time writing queries and building Excel models.

This isn’t something she was trained for, or was tested on while being recruited. She did her MBA in a famously diverse global business school, the diversity of its student bodies implying the level of maths and quantitative methods being kept rather low. She was recruited as a “general manager”. Yet, in a famously data-driven company, she spends a considerable amount of time on quantitative stuff.

It wasn’t always like this. While analytical ability has what (in my opinion) set apart graduates of elite MBA programs from those of middling MBA programs, the level of quantitative ability expected out of MBAs (apart from maybe those in finance) wasn’t too high. You were expected to know to use spreadsheets. You were expected to know some rudimentary statistics- means and standard deviations and some basic hypothesis testing, maybe. And you were expected to be able to make managerial decisions based on numbers. That’s about it.

Over the years, though, as the corpus of data within (and outside) organisations has grown, and making decisions based on data has become fashionable (a brilliant thing as far as I’m concerned), the requirement from managers has grown as well. Now they are expected to do more with data, and aren’t always trained for that.

Some organisations have responded to this problem by supplying “data analysts” who are attached to mid level managers, so that the latter can outsource the analytical work to the former and spend most of their time on “managerial” stuff. The problem with this is twofold – it is hard to guarantee a good career path to this data analyst (which makes recruitment hard), and this introduces “friction” – the manager needs to tell the analyst what precise data and analysis she needs, and iterating on this can lead to a lot of time lost.

Moreover, as the size of the data has grown, the complexity of the analysis that can be done and the insights that can be produced has become greater as well. And in that sense, managers who have been able to adapt to the volume and complexity of data have a significant competitive advantage over their peers who are less comfortable with data.

So what does all this mean for general managers and their education? First, I would expect the smarter managers to know that data analysis ability is a competitive advantage, and so invest time in building that skill. Second, I know of some business schools that are making their MBA programs less quantitative, as their student body becomes more diverse and the recruitment body becomes less diverse (banks are recruiting far less nowadays). This is a bad move. In fact, business schools need to realise that a quantitative MBA program is more of a competitive advantage nowadays, and tune their programs accordingly, while not compromising on the diversity of the student intake.

Then, there is a generation of managers that got along quite well without getting its hands dirty with data. These managers will now get challenged by younger managers who are more conversant with data. It will be interesting to see how organisations deal with this dynamic.

Finally, organisations need to invest in training programs, to make sure that their general managers are comfortable with data, and analysis, and making use of internal and external data science resources. Interestingly enough (I promise I hadn’t thought of this when I started writing this post), my company offers precisely one such workshop. Get in touch if you’re interested!

The missing middle in data science

Over a year back, when I had just moved to London and was job-hunting, I was getting frustrated by the fact that potential employers didn’t recognise my combination of skills of wrangling data and analysing businesses. A few saw me purely as a business guy, and most saw me purely as a data guy, trying to slot me into machine learning roles I was thoroughly unsuited for.

Around this time, I happened to mention to my wife about this lack of fit, and she had then remarked that the reason companies either want pure business people or pure data people is that you can’t scale a business with people with a unique combination of skills. “There are possibly very few people with your combination of skills”, she had said, and hence companies had gotten around the problem by getting some very good business people and some very good data people, and hope that they can add value together.

More recently, I was talking to her about some of the problems that she was dealing with at work, and recognised one of them as being similar to what I had solved for a client a few years ago. I quickly took her through the fundamentals of K-means clustering, and showed her how to implement it in R (and in the process, taught her the basics of R). As it had with my client many years ago, clustering did its magic, and the results were literally there to see, the business problem solved. My wife, however, was unimpressed. “This requires too much analytical work on my part”, she said, adding that “If I have to do with this level of analytical work, I won’t have enough time to execute my managerial duties”.

This made me think about the (yet unanswered) question of who should be solving this kind of a problem – to take a business problem, recognise it can be solved using data, figuring out the right technique to apply to it, and then communicating the results in a way that the business can easily understand. And this was a one-time problem, not something you would need to solve repeatedly, and so without the requirement to set up a pipeline and data engineering and IT infrastructure around it.

I admit this is just one data point (my wife), but based on observations from elsewhere, managers are usually loathe to get their hands dirty with data, beyond perhaps doing some basic MS Excel work. Data science specialists, on the other hand, will find it hard to quickly get intuition for a one-time problem, get data in a “dirty” manner, and then apply the right technique to solving it, and communicate the results in a business-friendly manner. Moreover, data scientists are highly likely to be involved in regular repeatable activities, making it an organisational nightmare to “lease” them for such one-time efforts.

This is what I call as the “missing middle problem” in data science. Problems whose solutions will without doubt add value to the business, but which most businesses are unable to address because of a lack of adequate skillset in solving the issue; and whose one-time nature makes it difficult for businesses to dedicate permanent resources to solve.

I guess so far this post has all the makings of a sales pitch, so let me turn it into one – this is precisely the kind of problem that my company Bespoke Data Insights is geared to solving. We specialise in solving problems that lie at the cusp of business and data. We provide end-to-end quantitative solutions for typically one-time business problems.

We come in, understand your business needs, and use a hypothesis-driven approach to model the problem in data terms. We select methods that in our opinion are best suited for the precise problem, not hesitating to build our own models if necessary (hence the Bespoke in the name). And finally, we synthesise the analysis in the form of recommendations that any business person can easily digest and action on.

So – if you’re facing a business problem where you think data might help, but don’t know how to proceed; or if you are curious about all this talk about AI and ML and data science and all that, and want to include it in your business; or you want your business managers to figure out how to use the data ┬áteams better, hire us.

Vacation Shopping

This is yet another of those questions whose answer seems rather obvious to everyone, and to me in full hindsight, but which has taken me a long time to appreciate

For a long time I never understood why people shop during vacations, when both time and luggage space are precious commodities. With global trade, I reasoned that most clothes should be available at reasonably comparable prices worldwide, and barring some special needs (such as a certain kind of shoes, for example), there was no real need to shop on vacations.

The last day of our trip to Munich in June convinced me otherwise. That was the only day on the trip that the wife was free from work, and we could go out together before our afternoon flight. The only place we ended up going out to turned out to be a clothing store, where the wife freaked out shopping.

It didn’t make sense to me – she was shopping at a chain store which I was pretty certain that I had seen in London as well. So why did she shop while travelling? And she shopped far more than she does in a normal shopping trip in London.

In hindsight, the answer is rather simple – diversity. While the same stores might exist in various countries or cities, each is adapted to local tastes and prevailing fashions. And while everyone watches the same “runways” in Milan and Los Angeles, there is always a subtle difference in prevailing styles in different places. And clothes in the stores in the respective places are tailored (no pun intended) to these styles.

And it can happen that the local prevailing styles are not something that you particularly agree with. For example, for years together in Bangalore I struggled to find plain “non-faded” jeans – most people there seemed to demand faced or torn jeans, and stores responded to serve that demand (interestingly, jeans shopping in my last Bangalore trip was brilliantly simple, so I guess things have changed).

Similarly, the wife finds it hard to appreciate most dresses in the shops in London (and I appreciate why she doesn’t appreciate them – most of the dresses are a bit weird to put it mildly), and as a result hasn’t been able to shop as much in recent times. She had taken to claim that “they don’t seem to be making normal clothes any more”.

But the styles in London aren’t correlated with the styles in Munich (or elsewhere), with the result that in that one chain store in Munich, she found more nice dresses than she had in some 20 shopping trips over a year in London.

Fashion suffers from the “tyranny of the majority“. It makes eminent sense for retailers to only stock those styles and models that have a reasonably high demand (or be compensated for stocking low-demand items with a high enough margin – I have a chapter on this in my book). So if your styles don’t match with those of people around you, you are out of luck. ┬áBut when you travel, you have the chance to align yourself to another majority. And if that alignment happens, you’re in luck!

PS: On a separate note, I’m quite disappointed with the quality of clothes in London. Across brands, they seem to wear much faster than those bought in continental Europe or even in India.

Linearity of loyalty rewards

So I’ve taken to working a lot in cafes nowadays. This is driven by both demand and supply. On the one hand I’ve gotten so used to working for my current primary client from home that I’m unable to think about other work when I’m at home – so stepping away helps.

Also on the demand side is the fact that this summer has been incredibly hot in London – houses here are built to trap in the heat, and any temperature greater than 25 degrees can become intolerable indoors. And given that cafes are largely air-conditioned, that’s an additional reason to step away from home to work.

On the supply side, there are three excellent hipster cafes within 200 meters of my house. Yes, I live in a suburb, though my house is very close to the suburb’s “town centre”. And all all these cafes make brilliant coffee, and provide a really nice ambience to work.

So far I’ve discovered that two of these cafes offer loyalty cards, and given my usage, neither makes a compelling reason to be loyal enough. The “problem” (in terms of retaining my loyalty) is that the loyalty card at both these places offer “linear rewards”.

Harris+Hoole has an app, which offers me a free drink for every six drinks purchased. Electric Coffee has a physical card, which offers me a free drink for every ten drinks I purchase. Now, the rate of reward here (I’m writing this sitting in Electric) is lower, which suggests that I’m better off patronising Harris+Hoole, but some variety doesn’t hurt – also I’m queasy about ending up and parking in the same cafe more than once in a day.

Even when I was writing my book in Barcelona two years ago, I would never go to the same cafe more than once a day, alternating between Sandwichez, Desitjos and this bar whose name I could never figure out.

Ordinarily, if I were a low intensity user, one drink for every N drinks ($math 6 \le N \le 10 $) would have been a sufficient reason to be loyal. Given my rate of consumption, though, and the fact that I go to both these cafes rather often, the incremental benefit in staying loyal to one of these cafes is fairly low. I can peacefully alternate knowing that sooner or later the accumulated ticks on my card or app are going to provide their reward.

It wasn’t like this last year, when I was briefly working for a company in London. Being extremely strapped for time then, I hardly patronised the cafes near home, and so the fact that I had an Electric card meant that I stayed loyal to it for an extended period of time. At my higher level of usage, though, the card simply is not enough!

In other words, rewards to a loyalty program need to be super-linear in order to retain a customer beyond a point. The current linear design can help drive loyalty among irregular customers, but regulars get indifferent. Making the regulars really loyal will require a higher degree (no pun intended ) of rewards.

PS: Given the amount of real estate hours I occupy for every coffee I buy, I’m not sure these cafes have that much of an incentive in keeping me loyal. That said, I occasionally reward them by buying lunch/snacks or even a second coffee on some visits.

PS2: As a consumer, loyalty card versus app doesn’t make that much of a difference – one clutters the wallet while the other clutters the phone (I don’t like to have that many apps). A business, though, should prefer the app, since that will allow them to know customers better. But there’s a higher fixed cost involved in that!

 

A Dying Complex

During a walk through Jayanagar Fourth Block last evening, I happened to walk through the shopping complex. Now, this isn’t something I do normally – while my usual Jayanagar walking route goes along one side of the complex, I seldom cut across it.

As it happened, my wife had asked me to buy coffee powder from a specific shop (from where I’d last bought coffee powder twenty years ago), and the easiest way to get to it after I had remembered to buy coffee was to cut across the Shopping Complex.

And it was dead. In my childhood, I spent most evenings “putting beat” around Jayanagar 4th Block with my parents, and we would invariably go to the shopping complex. The complex was then full of respectable stores, including a HMV outlet, a fairly high end tailoring outlet (called Khanate) and the shop where I bought my first ten pairs of spectacles. It was then natural that a shopping trip to 4th block included a visit to the shopping complex.

Not any more, for the shopping complex is dying, if not dead already. The walls look the same, the shop structures are the same, but most respectable businesses seem to have made their exit from the shopping complex. In their place you have stores selling cheap footwear, cheap clothes, possibly counterfeit goods and suchlike. There aren’t too many “respectable shoppers” in the complex as well.

On the other hand, the area immediately around the now-dying shopping complex has emerged as a brilliant retail destination. You can find large-ish outlets of most major brands, a wide selection of restaurants and stalls, fresh vegetables, hardware stores and yes – shops selling coffee powder! Just that the shopping complex has pretty much died, and faded into insignificance.

Quickly walking through the shopping complex last evening (it didn’t appear that safe), I mulled over why it had died, while the surrounding area had flourished. I have one hypothesis.

Basically the shopping complex is owned by the government, and the rents in the complex didn’t rise along with the market. This meant that businesses that were not exactly flourishing (or sustainable) continued to do business in the complex (low rents meant businesses could afford to be there even when they weren’t doing well). This reduced footfalls, and reduced business for the relatively healthy businesses. Which again didn’t move out because they could still make the rent.

And so the shopping complex went through a downward spiral until the point when businesses that had chosen to remain got crowded out by less respectable ones, and figured it was time to move out even if the rent wasn’t much. And so you have some of the prime real estate in Jayanagar being squatted upon by sellers of cheap footwear and cheap clothes and electronics of suspect make.

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.