By Christian FOURNIER, Retired Head of Finance Europe at Orange Business Services, Author (Globalisation - adapter l'organisation de son entreprise face à la mondialisation...)
Are your KPIs, Scoreboards and other metrics safe from the Simpson's paradox?
“Simpson’s paradox or Yule-Simpson effect is a paradox in probability and statistics, in which a trend appears in different groups of data but disappears or reverses when these groups are combined. It is sometimes given the descriptive title reversal paradox or amalgamation paradox”. (Extract from Wikipedia)
KPI, dashboard, other types of analytics,… that finance and FP&A use for communication may include or generate such paradox polluting the decision making process. Typically, those tools aggregate i.e. combine data in the perspective to give simpler and quicker ways to manage and communicate performance and take decisions. The general trend in management practices is to highly recommend limitation of indicators’ number, and as such to increase the aggregation level. It more or less focuses the attention of top and middle management depending on company culture and on rewards and recognition systems.
It is then highly possible for such aggregation to induce directly or indirectly “false” understanding or certitudes just like Simpson’s paradox would do. It should then be FP&A role to find and to fight such occurrence.
Still, we hardly hear or see mention of this concept during professional studies and life.
Practically, teaching and promotion of such tools includes few recommendations that would potentially exclude dropping in that trap but without formally researching or verifying whether it exists or not.
A sales director is looking at the ratio of commercial contact turned into sales in order to follow the performance of two teams and (among few other things) reward them on this basis.
Team 2 had better rewards periods after periods.
Frustrated Team 1 leader ask their FP&A to analyze in more details and feedback is
“Sorry, Simpson paradox!! Here is a better view of the relative performance! In fact, the two teams had the same performance over last 6 periods“
Of course, here I forced the figures to a ridiculously simplistic example to illustrate my point and make it easy to understand.
In real life, it is generally far more complex to identify such issue. My example is a reversal case (or nearly) which is more easy to spot. Still, in many cases it will not be so extreme, it will only hide (make disappear) the real evolution or performance.
The paradox appears thanks to few main causes:
Similar examples can be found with return on investments, any given type of expenses (say marketing) versus revenues, product lines revenue distribution or growth, etc.… (In fact, many of the widely known ratios or indicators may very well include or generate such effects if not properly customized and tested).
This brings few questions:
In my example, FP&A should have to make sure the KPI was not reduced (aggregated) to such a level and demonstrate that a more comprehensive set of figures needs to be considered, this should have been made during the set-up of the company systems and FP&A should have “tell the story” behind those figures all along the process in such a way that recognition should have been more in line with performance. A period to date indicator (or a weighted indicator) would have been more representative e.g.
In this example, it is highly intuitive, i.e. even without knowing about the paradox, a reasonably able person shall have made it happen. Still, reality can be far more complex and intuitive reasoning might not be sufficient to identify and adopt the right approach in each and every case, in particular, when the context is strongly oriented towards limiting the number of KPI, Indicators,… used by management. Who has not heard a manager saying “This is too much detail/figures, I just need to know (i.e. my compensation is only based on) these x indicators”.
Obviously, this brings into picture a different subject i.e. the reward and recognition based on KPI, scoreboard and other metrics. Still, is it really different, if the metrics can reveal to be paradoxical?!
Knowing (be conscious of) this paradox and systematically making sure to avoid its trap is then a key role for FP&A (i) when elaborating the systems and process that will be used (ii) when analyzing and commenting the ongoing results.
We may try to draw a list of a potential common case but it remains a high-level approach that cannot exempt from doing effectively the exercise.
Those applicable to revenues (but not only) may very well combine all together.
A very simple example would be a global company management concentrating on an overall revenue indicator mixing revenues from different geographic areas with product and markets in different maturity phase where customer profiles are evolving rapidly and where business is done in few pricing currencies but over a large number of legal entities (with different currencies then) and consolidating their results in its home currency. Such a global revenue indicator would mix so many effects and potentials evolutions that it may reveal paradoxical. Even more dangerous, as it may not have been a problem for few years i.e. whereas the potential of an issue was there but the reality of the markets was not challenging (activating) it.
We could bet in such a case that the information systems will “lose” the pricing currency within the consolidation process, potentially shortcut the entity currency to consolidation currency effect too; will aggregate the product into a few product lines historically driving the company development, will probably lose track of the customers profiles among few other “simplifications” justified by the necessity to keep things more effective. Furthermore, the FP&A people may be located in a limited number of shared service centers loosening the contact with local reality. The end result would be that the whole organization IS and processes, (probably culture) are cut from reality thanks to a basic misconception (lack of understanding/verification) during the process of defining and implementing tools.
FP&A peoples do not need to know all the mathematics about this paradox. They still need to understand the concept, recognize that it may “slip in” when defining their tools and processes, KPI, Scoreboard and other metrics and then make sure it is not the case.
By Steve Morlidge, Business Forecasting thought leader, author of "Future Ready: How to Master Business Forecasting" and "The Little Book of Beyond Budgeting"
In this article, Steve Morlidge, author of "Future Ready: How to Master Business Forecasting", argues that the quality of business forecasting – used to steer an organisation – is unacceptably poor. He goes on to present six simple principles that will help executives significantly improve the performance of their forecast processes. More reliable forecasts speed up decision making and so help make businesses more agile.
The recent economic crash has been badly damaged reputations as well as fortunes – no more so than those of economic ‘experts’ who have been roundly criticised for failing to forecast the catastrophe. However, failures in forecasting are not confined to large-scale economic systems. The forecasts used by business executives to steer their businesses have also proved highly fallible. ‘The financial crisis has obliterated corporate forecasts’ reports the CFO Magazine (Ryan, 2009); 70% of respondents to their recent survey said that they were unable to see more than one quarter ahead. However, the problem is not restricted to times of economic turmoil. Over the last four years, the 1300 companies quoted on the London Stock Exchange issued, on average, 400 profit warnings every year. On average, each one resulted in a loss of 10 to 20% of market capitalization (Bloom et al., 2009); some $200 million.
It is, therefore, no surprise that a survey of 540 senior executives recently conducted for KPMG (EIU, 2007) found that improving forecasting came at the top of the priority list for the next three years. ‘Ability to forecast results’ also comes at the top of the list of ‘Internal Concerns’ for CFO’s right across the globe (Karaian, 2009).
CFO’s are right to be concerned; business forecasting is riddled with bad practices. For example, most businesses for much of the year forecast no further than the financial year-end. As a result, there is little visibility of ‘the road ahead’. Forecasts are often too detailed and too late for managers to take action. Boardrooms resonate with acrimonious debate about what is the ‘right number’, yet many organisations have multiple forecast processes, each presenting competing views of the future, which are never reconciled. Obsession with accuracy may coexist with a culture where professionally prepared forecasts are arbitrarily adjusted on a routine basis. Leaders often give contradictory messages, such as ‘give me your best estimate of what you think will happen’ and ‘your forecast must come back to target’, leaving managers confused and disorientated. Manipulation of forecasts as part of a corporate political game is rife; numbers are frequently ‘sandbagged’ or ‘spun’ to create a favourable impression.
At the heart of the problems experienced with forecasting is a fundamental misconception: that forecasting is the same as prediction. The role of forecasting is to provide us with information about what might happen so that we can take action to avoid the forecast outcome if it is not what we want. If we do so, we invalidate the forecast. Forecasts are for helping you to steer to your destination; they do not prophesy your fate.
The failure to grasp the fundamental nature of forecasting is compounded by a second misconception. In my experience, managers either believe that forecasting is straightforward – ‘just common sense’ – or that it is extremely complicated - requiring the use of complex mathematics – and so best left to experts. The reality is that it is neither; it is a matter of properly understanding the nature of forecasting as an aid to decision making, and working in an organised and disciplined way to produce ‘good enough’ forecasts. Good tools and techniques may be necessary, but they are not sufficient. You need to know how to use them properly and create the kind of culture that encourages people to tell the truth.
Forecasting in business is a complex mess, but it need not be. I believe that there are six simple principles, which once mastered, will significantly improve the quality of forecasting in almost any organisation.
Business forecasting is like sailing at sea. It makes sense to plan before you start the journey, but the original plan is often soon out of date because of changes in the weather or tides. At this point, you need to forecast where you are headed, so that you can work out what corrective action is needed to get you to your destination.
The first thing that is clear from this example is that it is important to make a sharp distinction between a forecast (where you think you will be) and a target (where you would like to be). The second thing it helps us to understand is the role of forecasting: to support decision making. In order to do this well, a forecast needs the following qualities. It should be:
How far ahead do you need to forecast? The answer depends on how long it takes to enact a decision.
The captain of a super tanker needs to consistently forecast 3 miles ahead because that is how long it takes it to stop. A speedboat, on the other hand, may require very little forward visibility. In practice, this means that businesses need a rolling forecast horizon, based on the lead times associated with ‘steering actions’. A traditional year-end forecast is like overtaking on a blind bend – you have no idea of the possible outcome of your decision.
How frequently should you forecast? That depends on how quickly things change. More frequent forecasts are needed to safely navigate through the busy Singapore Straights than in the wide open seas of the South Pacific.
Any form of forecast requires a model; a set of assumptions about the way the world works. The model used in forecasting could be a statistical model; one that extrapolates into the future from the past. This approach can be effective, but often the future is not like the past. You might, therefore, choose to use a mathematical, or driver based, model; for example one that helps you to forecast the impact of volume on the cost base of the business. However, often the world is too complex, or the business too fast changing to make this approach workable. That is why forecasting in business often relies heavily on judgment; where the model is in the head of an expert or a larger number of people who ‘know what is going on’. This approach is not without problems. Human judgment can be flawed, and managers can feel under pressure to adjust forecasts to ‘avoid giving nasty surprises’ or ’sounding defeatist’. As a result, judgmental forecasts are particularly prone to bias.
The trick is to understand the range of methodologies available, choose the appropriate one, and take steps to mitigate its weaknesses. So, for example, a statistical or mathematical technique might be used to produce a baseline or ‘business as usual’ forecast and judgement to estimate the impact of the decision made to change the course of affairs.
The only guarantee that you can rely on a forecast to make decisions that affect the future is that those previous ones have proved to be reliable in the past. Yet, few businesses take the simple steps required to monitor their processes for evidence of bias, so that they can take action to eliminate it if detected. Most businesses fail to measure forecast quality at all.
Those businesses that do attempt to monitor forecast quality often measure the wrong things at the wrong time. A common mistake is to measure forecast errors at a point in time that is likely to be after the forecast has been acted upon. This is like blaming the navigator for having forecast a calamity that her forecast has helped avert. At its simplest, a series of four short-term errors with the same sign (positive or negative) is evidence of bias; any fewer is likely to be the result of chance.
The only thing that we know with absolute confidence about the future is that any forecast we make is likely to be wrong! Where there is the debate about the forecast, it should not focus on whether you have the right ‘single point forecast’, but how it might be wrong, why, and what to do about it. In particular, it is important to distinguish between ‘risk’ - random variation around a realistic single point forecast - and ‘uncertainty’ – resulting from a shift in the behaviour of a system that completely invalidates the forecast. Banks’ over-reliance on risk models that failed to take account of uncertainty was a major contributor to the recent economic collapse. Whatever form your ignorance of the future takes, it is important to develop the capability to spot and diagnose deviations from forecast quickly, and to create a ‘play book’ of potential actions to enable a swift and effective response.
Mastering forecasting is not an art, but neither is it complex science. It is mainly a matter of applying a modest amount of knowledge in a disciplined and organised fashion; as a process. A good process – like a good golf swing - will produce good results.
Building a good process involves doing the right things in the right order (cultivating a good technique), in the same way over and over again (grooving the swing). Those things that are responsible for bias (hooks and slices) should be designed out of the process (remodelling the swing), and the results of the process continuously monitored (the score) and minor flaws corrected as they become evident Again, like golf, temperament is as important as technique. Blaming people when the process is at fault is a sure way to encourage dishonest forecasting
Done well, forecasting will help a business respond swiftly and effectively to emerging reality and so gain a competitive edge. Done badly, management may be misled into making the wrong decisions. However, businesses have little option but to forecast, because without any kind of ability to anticipate, organizations can only react to those things that have already happened, which, by definition, they have no ability to influence. The tools and techniques that business need are already available, managers simply need to learn how to use them effectively.
By John Stretch, Management education in finance and banking
ECONOMISTS, ACCOUNTANTS AND FORECASTS
“The Bank of England’s chief economist has admitted his profession misjudged the impact of the Brexit vote. Blaming the failure of economic models to cope with “irrational behaviour” in the modern era, the economist said the profession needed to adapt to regain the trust of the public and politicians. The bank has come under intense criticism for predicting a dramatic slowdown in the UK’s fortunes in the event of a vote for Brexit, only for the economy to bounce back strongly and remain one of the best performing in the developed world.
Official figures have shown the economy was outstripped only by the US among the large economies last year after growth in the third quarter was upgraded to 0.6%. Forecasts by the Treasury, the International Monetary Fund and the Paris-based OECD, all pointed to a recession after the vote, based on assumptions of steeply declining consumer spending and business investment.
He blamed the profession’s reliance on models that were built for an age when consumers and businesses, and especially banks, “behaved rationally”. Answering critics of the bank’s gloomy November forecast for the economy, he admitted that the bank did not anticipate the resilience of consumer spending after Britain voted to leave the EU. “
Source: The Guardian, Thursday 5 January 2017. (See Note 1)
As we enter 2017, many companies are busy finalising their annual budget. But how do you budget in a world where the best economists cannot predict the future? Perhaps the only approach is to prepare a budget as best you can, live with the uncertainty and then adapt to it and respond very fast when the future eventually happens.
This may explain why organisations have converted their annual budget process to a dynamic system of rolling forecasts. Traditional budgeting uses annual budgets – one fixed plan for the whole year - as the basis for target setting, planning and control. By contrast, rolling forecasts are continuous updates based on the best information currently available. Regular forecasting helps to quantify, and by implication manage, the gap between the original approved budget and the forecast. We, accountants, believe that forecast accuracy can be improved through analysis, learning, judgement, detailed supporting data and good systems. We analyse data to understand internal and external trends and are able to model a range of forecast options. We make plans so we have plans to change.
Rolling forecasts, we are told, facilitate event-driven planning and responsive resource allocation. The conventional wisdom tells us that regular forecasts can help us to understand and manage risk.
But how? There are many types of risk. In 2017 our countries, our businesses and we as individuals will be challenged to deal with a spectrum of risks that range from the daily risks of living that we take for granted, to risks we don’t know about and can’t even imagine. A single estimate of sales, costs and profits is based on choices and probabilities which are but one view of a broad range of possible futures.
In spite of this, it is my experience that many senior managers still want to work with one number, a single view of the budget or forecast (which they hope is reasonably accurate).
In this world of uncertainty, finance departments must invest time and effort to understand and classify risk, make risk explicit, and find ways to estimate the reliability of the forecast.
“Large, long-lived, and successful organizations may be more prone to misclassifying risk and uncertainty. Years of success can lead to a culture where conventional wisdom rules and leaders do not want to be questioned. If a risk is not understood, classified and debated, and if data and analytics are not explicitly part of decision-making, the organization may falter.
Businesses need to create a culture of healthy dissent, fact-based analysis and decision-making to identify all potential issues, implications, and actions. Unchanging strategies and tactics work until they don’t, sometimes with disastrous outcomes.” (See Note 2)
The South African grocery retailer Pick ‘n Pay reported declining profit margins and returns for 4 years in a row between 2010 and 2013, before remedial action was taken.
Prior to the invention of the digital camera, and for some years thereafter, Kodak didn't foresee the risks to their traditional business and eventually filed for bankruptcy in 2010.
Accountants need to communicate the risks in a budget or forecast to their managers.
It is, therefore, critical that any budget and forecast should be supported by a detailed commentary on the risks implicit in the numbers, in four areas:
Forecast accuracy is unlikely to be consistent throughout the forecast. Thus a single point figure of the reliability of the forecast (“this forecast is 90% reliable”, for example), is misleading. An estimate of reliability should be provided for each phase or time period of the forecast.
Project managers describe a learning process known as the “cone of uncertainty”. At the beginning of a project, comparatively little is known about the project, and so forecasts are subject to large uncertainty. Uncertainty decreases over time. As more is learned about the project, the uncertainty tends to decrease, reaching zero at the end of the project.
Cone of uncertainty. Source: Wikipedia
Similarly, forecast uncertainty increases as we move further into the future. We are usually able to attach greater confidence to the forecast for the month or quarter immediately ahead, than for the month or quarter 12 months ahead.
The forecast period within the “cone of certainty” will influence the method of forecasting and the information which is used. Refer Appendix 1 for an industry-specific example.
The American politician Donald Rumsfeld was once asked: “Does the government of Iraq have weapons of mass destruction which it can supply to terrorist groups?”
He replied “There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. There are things we don't know we don't know. And if one looks throughout the history of our country and other free countries, it is the latter category that tends to be the difficult ones.”
Donald Rumsfeld 2002. Source: Wikipedia
(See Note 3)
“Known unknowns” refer to risks you are aware of, such as a major contract being cancelled. We try to understand these by identifying the upper and lower limits of each item, the probability of occurrence, and the past and likely future frequency.
Unknown unknowns are risks that come from situations not thought of, or have not been considered possible, or are so unlikely that they have never been considered worth forecasting. Mathematician Nassim Taleb uses the term “black swans” to describe these rare unplanned events with severe impact that, with hindsight, were predictable or explainable. Prime political examples in 2016 were Brexit and the Trump presidency. Black swans remind us that there are limits to knowledge when it is based only on observations and past experience. (See Note 4)
The “known unknowns” and how they have been dealt with in the forecast must be made explicit in the commentary.
Accountants and economists use trend data to try to uncover and convert uncertainty into known risk. We believe that “the trend is your friend.” (See Note 5). We use our understanding of trends to project the past into the future. For example, we can usually justify our assumptions about demographic shifts and certain new technologies.
Trends in technology, products, and customers, should be identified with an explanation of how they have been dealt with in the forecast.
Similarly, monetary trends in sales volumes and prices, raw material prices and other input costs should be highlighted.
Some trends such as technological change and new product sales may be exponential rather than linear and thus more difficult to forecast.
A single figure forecast reflects the preparer’s view of a likely outcome, based on choices from a multitude of alternatives. The forecast presentation should include a discussion of alternative outcomes and scenarios.
In scenario planning, we combine assumptions, trends, knowns and unknowns into a limited number of permutations that speculate on alternative views of the future.
We make assumptions about factors that will affect the nature of the future world in which the organisation operates. Examples are demographic shifts, new technologies, future interest rates, outcomes of political elections, rates of innovation, fads and fashions in markets.
These factors are analysed to understand how these major drivers for change will impact on key assumptions about the future. These are used to generate a wide range of scenarios which are then reduced to 2 or 3 most likely scenarios which are evaluated in detail.
In an uncertain and fast-changing world, line managers need to be made aware of the uncertainties and risk inherent in the financial forecasts provided to them. Uncertainty is difficult to manage but uncertainties can be converted into known risk as forecasting capabilities and data management improve. When risk is understood, it can be categorised, mitigated, managed, hedged or even avoided. Uncertainties require continual review to identify changing facts and circumstances that affect risk. In the future, winning organizations will have a greater ability to identify, understand and manage risk.
Note 1: A question of timing?
Answering critics of the bank’s gloomy November forecast for the economy, their Chief Economist admitted that the bank did not anticipate the resilience of consumer spending after Britain voted to leave the EU. But he said that he thought the bank was wrong about timing not about the fundamentals, and that the Bank of England still expected Brexit to harm growth.
Note 2: Reference
This quote is from an excellent article “Big Data: Blurring risk and uncertainty” by Bill Pieroni, chief executive officer of Accord, featured in LinkedIn Technology, August 22, 2013.
Note 3: The “unknown knowns”.
Beyond these three categories, there is a (psycho) logical fourth, the “unknown known”, where people are in denial about (or intentionally refuse to acknowledge) facts and issues they subconsciously know to be true. Rumsfeld thought that the main dangers in the confrontation with Iraq were the 'unknown unknowns', that is, the threats from Saddam which the United States suspected but could not prove. But often the main risks lie in the ‘unknown knowns’ - the moral values, beliefs, suppositions and practices people disavow and pretend not to know about. Did the US know there were no weapons of mass destruction in Iraq?
Note 4: Adapting to “black swans”
A colleague in the Reinsurance Industry suggests that many of these so-called once-in-a-lifetime, uncertain events actually occur with regular frequency and potentially knowable impact. Some Black Swan events occur every few years (earthquakes, tsunamis, stock exchange crashes) while others are separated by longer, yet regular, time intervals. The regularity of these events suggests that black swans are giving way to shades-of-grey swans. The Reinsurance Industry continuously monitors statistical data to identify, model, and mitigate black swan risks.
Note 5: The trend is your friend
“Extrapolating from the past is like driving down the road looking in the rear view mirror... fine until you come to a bend in the road” (Toby Wilson, Microsoft Financial Director, UK.)
Appendix 1: Suggested sources of information for rolling forecasts in different time periods
By Alexander Antonov, Head Finance B2B at Sandoz
"If all you have is a hammer, everything looks like a nail" - Abraham Maslow
P&L management focuses on the items included into P&L (profit and loss) report. It has become an increasingly popular technique used by the top managers around the world. P&L responsibility scope goes far beyond being simply responsible for profits or loss. Managers must not only understand what stands behind and drives each item of the report, but also need to take steps to improve P&L statements. In order for managers to be successful, they must be very confident in their ability to work with a variety of tools driving performance.
The proposed approach emerged as a synthesis of initiatives directed at P&L improvement during my work in FMCG and Pharmaceutical industries, both in country and global headquarters. Although most of the tools are already familiar to the business community, applying them in a systematic and holistic way is not a widespread practice, while that is unlocking great synergy potential. The proposed approach to P&L management allows identification and proactive handling of the challenges related to the company’s performance, well before they become a pain.
Going through P&L resembles a journey, and in the process of following the map (presented on the left side) you may gain deep insights into your business. The journey starts with studying the waters where you are going to sail. This is done with pre-P&L or market analysis, which gives you a reference point for all future steps. For example, a company growth of 10% looks spectacular in a stagnating market, but loses all its attractiveness in a situation of a market boom where it grows 20% a year. Models like PESTEL and Porter’s 5-forces provide a framework which helps determine market driving forces, locate them as if they were ocean currents, make them visible and use them to your advantage.
Evolution index is a convenient indicator to evaluate company’s performance dynamics over the years, as well as benchmark against the competition. It is instrumental for explaining changes in market share and company ranking (see the picture below).
Understanding the market position creates a basis for P&L analysis, which would provide answers to questions posed by in-market performance. “Stock in Trade” is a step where we measure end-to-end supply chain inventory, it is needed for closing the gap between external data and management reporting. Its tracking is not only an important control procedure, but also a tool to run the business. By saturating the distributor network with inventory before the high season, or before promotion campaigns, a company assures inventory availability for high consumer demand. Also, the company gains an advantage in the competition for limited customer shelf space. However, the ‘art’ is to keep the stock in balance, and prevent the temptation to increase it even when you have an internal or ‘political’ need to drive sales.
Considering the first KPI of managerial reporting, Gross Sales, it is instrumental to split it into two components: the volume of sold goods (see Resource Allocation part), and the price, which we will look into next. Taking Porter’s theory on Competitive advantage as a starting point (Porter, 1998), a company can consider price either to be defined by the market, and focus on cost leadership to secure its margins, or price as a variable that reflects product value. In the latter case, Porter argues that the company should choose a strategy focused on Differentiation, striving for adding value to consumers. It doesn’t mean that by choosing one strategy a company can forget about the other component, e.g. sticking to Differentiation, and neglecting the costs. It’s rather about being consistent whenever the company is making strategic choices. When pursuing Differentiation strategy, the company can’t compromise on product value, even when it is engaged in cost reduction initiatives.
Having committed to the strategy of Differentiation, the next challenge is to estimate product value for the customer, expressed in her willingness to pay, and measured by the price. Among widely used price-setting methods, value-based pricing is the most challenging, and potentially the most rewarding. The reward in this case can be manifold. It’s not only about allowing a company to set the price that most closely reflects customer’s willingness to pay, but also focusing its efforts on collecting relevant information, and understanding and increasing value of the product by promotional and product development activities. Although most of the insights on product value come from market research, it is worth to use a systematic way to structure this information in order to measure the product value. An example of such a tool is “ComStrat” from Simon-Kucher & Partners, which also helps figure out how to drive up value for each product, and set up a direction for product promotion. In the Matrix of Competitive Advantage (a view of ComStrat, see the picture below), factors affecting the product choice made by consumers are graded by their importance, and products of the company and its competitor are evaluated in terms of performance based on consumer preferences. The company can choose either to work on improving its performance on key factors (e.g., increase Visibility of its products in the picture below), or focus on communicating to consumers the importance of factors where it already has an edge (Variety in this example).
Having understood the product value, a company can further “weigh it” by comparing it with the price, which can be done in the second view of ComStrat called Value Map (see below). In the coordinate system “Relative price - Value”, a product can be located in three positions vs. competition:
Although position 3 looks ideal, it is not always the case, as the price is also an attribute of value. For example, Mercedes offered at the price of Lada not only would look suspicious, but also lose some of its image and perceived value. However, if carefully thought through and correctly communicated to consumers, the strategy where perceived product value is higher than the set price is a direct way to success. For example, Lee Iacocca [Iacocca Novak, 1986] gives an example of application of this strategy, when Ford launched its Mustang brand. According to marketing research, consumers perceived the car to be 1,000 dollars more expensive than its set price. The company resisted the temptation to increase price, and the car became a legendary success of the American car industry.
Next destination of our journey is Net Sales, and the course to it goes through waters known as Gross-to-Net (GTN). The landmark items here are discounts, bonuses and other payments, which a company offers to their direct customers, like distributors and retailers, in exchange for their services. Often, GTN system is quite complicated as it is typically built over many years in order to achieve different and sometimes even conflicting company objectives. This makes it not very transparent and difficult to comprehend for both customers and even people within the company. Therefore, setting a system for regular revision and control of GTN gives the company a lever to increase both Sales (via focusing discounts to enhance Sales drivers) and Profit (by eliminating low-value elements of GTN). The approach can be framed into the following 3 steps:
Having applied this system in its Ukrainian subsidiary, a large international pharmaceutics company managed to cut total GTN from 13% to 6%. This was mainly achieved by substituting 9% unconditional Sell-in discount by conditional performance driven bonuses. Moreover, such a project changed the mindset within the company, moving GTN from an "unconditional" and "determined by market" to a "choice" and "resource" area, which should be managed like all other resources.
Presentation of the three components of Sales increment (vs. previous period or budget) – price, volume and GTN – gives insights into what drives Net Sales in the period of analysis, while its dynamics versus competition allows to evaluate whether the selected strategy is right.
Marginality or Gross profit (our next short stop in the journey) is driven not only by Price and COGS, which are specific for each business, but also by forecasting accuracy. Forecasting is a tool of management control, which pushes business to learn about environmental changes before they become a pain. Increasing forecast accuracy not only provides tangible benefits like reduction in inventory levels and write-offs, but also intangible ones like improvement of relationships with production units and suppliers.
Considering the Functional costs, a company should focus on getting maximum results from effective allocation of its limited resources. This task is also known as productivity improvement. Typically the resources of an FMCG company consist of GTN, spend on advertisement and promotion (A&P), and Field Force (FF). An objective of the whole process is to distribute these resources in a meaningful way to achieve company’s goals. Usually the process of resource allocation is done in four stages, starting with setting of Strategy, then proceeding to Execution and Measuring the progress, and closing the loop with evaluating results, learning and making necessary adjustments (see the picture below). Although each stage is important, and a mistake at any point can lead to calamitous results, here we will focus on progress measurement as a part of P&L management.
Due to the complexity of this task, business usually sets up KPIs for measuring each resource separately. Improving outcome for utilization of each resource leads to the improvement in overall business efficiency. For Field Force, KPIs can be Weighted Distribution and Off-take (an average number of products sold by an outlet in a certain period).
The key to successful application here is to divide KPIs by territory and channel, and link them to incentive systems of the responsible managers. One of the most impressive business turn-arounds in my practice, when Evolution Index jumped from 94% to 111% in half a year, was done in Russian subsidiary of an international pharmaceutical company. A small number of very clear KPIs were set for the whole Sales organization, and management pursued their improvement by implementing a rigorous control procedure. As a result, a substantial positive change was achieved by performing simple small steps, made consistently and in the same direction.
To estimate required A&P investments, a company can consider changes in volume between historic trend (projection of market and company data from the past), and actual performance versus market, in order to estimate incremental Sales and Profit as a result of marketing activities or price changes. In this case volume data should be taken from an external source (e.g., Nielsen or IMS), while internal data can be used for a net price (incl. GTN) and A&P investments.
Although profit is a bottom line of P&L, and an ultimate goal of most businesses, a company often seeks a balance between profit maximization, Sales (and/or Market Share) growth, and liquidity (including Net Working Capital optimization). For example, for Differentiation strategy and value-based pricing, it is recommended not to capture the whole value surplus against competition in the price, but rather share it with consumers (Simon, 2015). In this case, the higher price captured by the company contributes to its profit, while residual value surplus helps the customer choose in favor of company products (hence driving Sales). In the example about Ford Mustang, described above, it would mean that the company had a chance to raise the price by $500, having estimated $1 000 price delta with consumer perceived value.
Profit, and especially Return on Sales (ROS) component, allows looking at the company performance from a somewhat unfamiliar side (as presented below), which helps make better strategic choices. This view presents Sales by product group on axis X, while ROS is shown on axis Y, which reveals contribution of each group of products to overall company profitability. Sales growth marker at the top of each bar adds insight into dynamics to the chart, and shows in what direction company profitability would move in the near future.
A regular journey along P&L, done in a systematic way like the one described above, offers managers new insights into the business. It also expands their arsenal with the new tools, and helps learn when and in what situation to use each tool. This ultimately allows the company to avoid icebergs, identify growth opportunities along the way and increases chances of success.
Iacocca, L., & Novak, W. J. (1986). Iacocca: An Autobiography. Bantam.
Porter, M. E. (1998). Competitive Advantage: Creating and Sustaining Superior Performance. Free Press.
Simon, H. (2015). Confessions of the Pricing Man. Springer.
A definition of quality is “a distinctive characteristic possessed by someone.” The work of FP&A practitioners focuses on thinking and learning about how financial activities – earning revenues, incurring costs, generating cash flows – affect organizations. The thinking and learning about this relationship require a mindset that creates results.
The mindset of FP&A practitioners comes from the qualities that individuals have. From my education and experience, FP&A practitioners should have a quality that establishes a foundation for doing their work. The quality that establishes the foundation for FP&A practitioners doing their work is curiosity.
A definition of curiosity is “the strong desire to know or learn something.” The strong desire to know something is a fundamental quality of a person who practices financial planning. The strong desire to learn something is a fundamental quality of a person who practices financial analysis.
Financial planning is thinking about how organizations will earn revenues, incur expenses, and generate cash flows. In order to stimulate thinking about revenues, expenses, and cash flow, an FP&A practitioner should know the purpose of the organization. Knowing the organization’s purpose establishes a foundation for preparing a financial plan. Building on the foundation is where an FP&A practitioner’s curiosity becomes evident. The evidence is provided through questions asked.
Financial analysis is learning about how organizations earn revenues, incur expenses, and generate cash flows. Learning about how organizations earn revenues, incur expenses, and generate cash flows is an excellent opportunity for FP&A practitioners to apply their curiosity. They can ask questions about issues like transactions recorded, relationships with stakeholders, and changes to strategic plans. They also can walk through facilities like stores to observe layouts as well as customer and employee behaviour. The limit to learning through financial analysis is the number of questions to ask.
A curious FP&A practitioner can help an organization, however, being curious is only a basic quality. An FP&A practitioner needs a quality that can keep the practitioner relevant in the value-added process. The quality that keeps an FP&A practitioner relevant is a commitment to continuous improvement.
A definition of continuous improvement is “an on-going effort to improve processes.” Financial planning and financial analysis are processes. Like any process, financial planning and financial analysis are not perfect. An FP&A practitioner needs to accept this fact because failure to do so results in failure to provide value. In order to provide value, an FP&A practitioner should strive to improve one’s work.
Improving one’s work in financial planning can come from education. Education in psychology can help FP&A practitioners manage biases when preparing budgets and forecasts. Education in mathematics can help FP&A practitioners formulate revenue, expense, and cash flow projections. Education in technology can help FP&A practitioners prepare and communicate financial plans. These examples have provided guidance for me and that is due to experience. Other FP&A practitioners may have faced experiences that lead them to improvement through other paths. That is fine as long as FP&A practitioners realize that value-added financial planning is an on-going effort to improve processes.
Improving one’s work in financial analysis, like financial planning, can come from education. My education in accounting plus my interests in psychology, mathematics, and technology provide a foundation for improving my analytical processes. I also find my desire to learn as a basis for my continuous improvement initiatives. Having a desire to learn leads me to consider different perspectives when performing financial analysis. Having different perspectives allows me to see activities within organizations in ways not easily seen within financial statements. Financial analysis is learning about how organizations function. Learning is not static, it is dynamic so an on-going effort to improve processes is most appropriate for financial analysis.
People who are or wish to be FP&A practitioners can describe their capabilities through their resumes. Resumes are not enough. FP&A practitioners need distinct characteristics that allow them to not only do what is expected but also go beyond expectations.
Remember…having an FP&A career starts with having FP&A qualities.
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