Financial Management: Introduction

Aris Catsambas
8 min readJul 9, 2020

Just as I have always liked the idea of writing — even when I’ve had nothing to write about — , I have always liked the idea of teaching. I’m not qualified to teach anything, but I’ve been hoping that if I do well in business, I may be invited to give guest lectures on strategy or finance. But there are two problems with this:

a) I do not have a grand, unifying theory of management or strategy to share, nor do I have much to say that has not already been said; and anyway

b) even if such a theory of business did exist, I do not know to what extent it would be teachable. I have been to business school, and I’ve been reading HBR and McKinsey Quarterly &c, yet the number of theories or ideas I remember is extremely low; the number of such theories I have seen implemented in practice is, unless I’m forgetting something, zero (people rarely bring up Porter’s 5 forces — and the few times I have heard it mentioned at work, it has not led to a change of course).

That’s not to say that I have nothing to contribute; it’s just that the things I can contribute are mostly tools and ways of analysing a business — the kind of thing that business schools generally consider low-brow, tactical, non-academic (and certainly not original). I doubt I would ever be invited to talk about such things at a university— but what I can do is write a series of blog posts about them (that said, I would love talking about these things to business school students — so if you work at a business school and are interested in hosting a guest lecturer, let me know!)

Obviously, this series is not going to be interesting to most people; but anyone working for a for-profit organisation might find some of the things I plan to share useful, and more importantly, immediately applicable. Indeed, with the exception of this introductory post, where I’ll start by laying out some general principles I’ve personally found useful at work, this series will explain how to do various kinds of analysis in such a way that anyone can do them the next day at work.

Equally obviously, I am not planning to share anything confidential, nor any of my current or previous employers’ (Google and P&G respectively) proprietary tools. For this reason, the wording or the examples in some of the posts in the series are going to be vaguer than I would like — but still (I hope) useful.

Finally, before I dive in, I want to note that all my past and current roles have been in finance. However, the finance function at P&G has a very broad remit, and the one at Google has a very vague remit; as a result, finance analysts in both companies can shape the role to suit their interests and strengths. A lot of the work I have done in my roles would fall under functions such as go-to-market, strategy, or sales operations in other companies. Hence, everything I share here can be used by managers and analysts working in a number of different corporate functions.

As mentioned earlier, the next few posts in this series will detail specific tools analysts can use to make business decisions. But before I get into that, I want to start by focusing on two principles to which (I think) all managers must adhere in order to run a successful business.

To demonstrate the first, consider the following LinkedIn profile descriptions, taken from three of my connections on the platform chosen at random. Before reading on, see whether you can spot what’s wrong with them, impressive though they are:

All three managers have strong CVs; all work for industry leaders. Yet there is something missing from all three snippets: the results they have achieved. They all describe what they do or what they worked on; none of them talk about how successful they were at it:

  • The trade marketing manager is responsible for their brand’s e-commerce channel; well, did e-commerce sales grow? How fast? How did that channel perform vs bricks-and-mortar, and (more importantly) vs competition? The same manager is also responsible for their brand’s P&L, yet there is no hint whatsoever on whether their brand has become more profitable over the three years they have been managing it.
  • Similarly, the investment manager talks about leading their department’s transformation, but there is no assessment of how successful this transformation was — what did it actually achieve? They also apparently design and implement key projects — to what end?
  • Finally, the financial analyst has delivered important work — including brand restages and geographical expansion — but again, there is no indication that the quality of their work was, in fact, high. For all we know, the brand restage could have been a disaster, and the geographical expansion cancelled a few months in.

You may think that this is because they were reluctant to share information that might be confidential on LinkedIn. I doubt that. First, you can always share results in a way that would be of no use to a competitor (or other third party). Second, I have seen the same mistake in many other settings. I have been in calibration meetings where people talked extensively about what their reportees did, or how they did it (“he built a new model using VBA — that’s ground-breaking!”), without any mention of the results they achieved. I have seen people earn awards for work whose results have not even come through yet; in one case, a colleague was lauded for running a best-in-class process for producing the following year’s business plan — a plan whose targets their team then missed by more than 10%, resulting in the worst performance in the organisation. In fact, I once checked the correlation between number of internal awards a business unit wins, and that unit’s revenue and profit performance; they two were strongly negatively correlated. And of course, regardless of how often I tell people I interview to follow the CAR model (Context — Action — Results), candidates rarely remember the R (I once interviewed someone who told me about how they were team leader for a university project competition; they did all the right things, they motivated their team, they allocated roles and responsibilities, they got it done in time; but when I asked, “and how did you fare in the competition?”, they told me they actually came second-to-last (kudos for honesty though)).

So, Principle 1: always, always focus on results. Find a way to quantify your impact — and if you cannot do that, then do at least provide some qualitative measure of performance (e.g. I have run Learning & Development programmes at both P&G and Google; though I have not been able to track whether people actually developed skills thanks to these programmes, I have at least tracked attendance and participant satisfaction). If you manage people, ask them to keep a record of the work they have done along with their results — and use that in rating and promotion discussions. In my experience, this gives you a huge advantage over other people managers who have not documented their reportees performance.

(This is all the more important if you are leading a large team. People are often dissatisfied with performance-management processes (such as those for rating or promoting people), but being focused on results helps, because it’s more transparent, and forces people to think about impact rather than presentation.)

All this said, a word of caution: focusing on results is important, but not sufficient. Think of poker or blackjack: sometimes, you are going to lose a hand even if you play perfectly and make all the right decisions. For an even more extreme example, consider Russian roulette: playing the game is very stupid even if you end up winning 5/6 times. So, the results alone don’t tell the full story — but unless you do focus on what was actually accomplished, you won’t have any feedback on the quality of your decisions.

The second principle is related to the first: it is not always easy to monitor results, certainly not in a timely and continuous manner; in addition, a high-level result such as revenue growth depends on many factors, so tracking it alone is not meaningful for making business decisions. It is therefore important to identify factors that have a causal relationship with the result you are interested in — ideally factors you can influence — , and track those.

Put like this, this principle is blindingly obvious. Yet you would be surprised how few people do it. I see three reasons people overlook this principle:

  1. They do not know what drives their business. In some cases, it is fairly easy to identify things that drive revenue — for example, when it comes to retail, it’s fair to say that share of shelf, or number of stores that stock your product, correlate with revenue. But, a) some times the relationship goes both ways (retailers aren’t stupid — they will give a higher share of shelf to popular or profitable products, and if there is consumer demand for a product, more retailers will want to stock it), which makes it hard to tell whether the factor you have identified really drives the result you care about, and b) there are some factors that are hard to track. For example, in a services B2B setting, sales effort correlates with revenue growth — but how do you measure sales effort? Number of client pitches? Pitch length? These are just proxies, not actual factors.
  2. They confuse input with output metrics. For example, many people track things like Share of Wallet. I agree that SoW is a very important metric — it’s always useful to know whether your business is growing because you are doing better than your competitors, or whether it is because you are benefiting from a growing market. But SoW is an outcome of other factors, not a direct cause of revenue growth.
  3. They have just never done it, and so it does not occur to them to start doing it — or, just as frequently, they track a metric, but don’t do anything about it. Tracking something is worthless if you do not have a plan for affecting it.

Hence, Principle 2: identify the factors driving your business that you can directly influence; track them, and set action plans for each of them.

And that’s it as far as the basics are concerned. In the next post, I will start discussing specific tools, always keeping these principles in mind.

Posts in the series:

  • Volume/Price/Mix: decomposing revenue growth into its constituent parts
  • Pricing: how to determine the optimal price to charge for a product, and various ways to set a price
  • Costs: identifying cost savings, the difference between margin and profit, and how to allocate costs
  • Introduction to modelling: some basic principles, Monte Carlo simulations, sensitivity analyses, and regression
  • Final thoughs: applicability across industries, automation, and big data.

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