Can senior managers finally trust their management reports?

After years spent developing reliable data sources and reporting tools, management teams are now confronted with as fundamental a problem: decisions made on the basis of global data, mostly accurate, yet often grossly misleading when it does not reflect the behavior of underlying data points.

Let’s take a few concrete examples: First, a distributor decides to run a promotion with a 20% discount on all available products without any exception. Once the sale is over, the chief accountant reports to the Sales Director: “You have sold the same merchandise quantity as last years’ sale, yet the total average price has increased and so has our total revenue.” The Sales Director and other managers in the company are dubitative. How could average price increase when the price of all product items has decreased by 20%? Fortunately, the chief accountant can explain: Customers took advantage of the sale in order to buy overall higher priced products. So the global average price increased, despite the fact that all individual product prices were lowered.

Another example: Unions complain that a salary freeze has been in effect over the past three years. However, top management insists in good faith that the average salary has increased! Who is right? Everybody, as some of the manufacturing is now subcontracted in Asia. As a result, there are less factory workers on payroll and the proportion of white collar personnel has logically increased. So the company has gone toward a proportion of higher salaries, and mathematically the average overall company salary has increased, despite the fact that individual salaries haven’t.

Here is a third example: a company selling to a large retail chain has segmented its stores by regions. The company’s new Sales VP suggests that management time should focus on main regions, and that two smaller regions that contribute seemingly little to overall company revenue, should be left aside. But some among the sales force don’t agree: They put forward that one of the smaller regions, having indeed only a few stores, has the highest per store revenue of the whole chain. Plus those stores are fast growing. So what we have here is a small region on a global level, yet the best performing stores on a local level.

Those kinds of diverging perspectives between global and local, strategy and operations, are more the rule than the exception, contrarily to what one might believe. And these are not subjective perceptions but hard data. From that standpoint, we have introduced a new kind of metrics in order to measure disparities between global and local viewpoints: The “Intra share” (or simply “Intra”) measures the part of the local that behaves like the global; opposite, the “Extra share” (or simply “Extra”) measures the part of the local that doesn’t behave like the global. Thus the Extra share is a metric that enlightens potential differences between global and local behaviors. In reference to the above examples, the Extra share is each time equal to a 100%: A global average price increasing while all individual prices are decreasing; a global average salary increasing and individual salaries that do not; smallest revenue on the regional level with highest per store revenue. Thus three times in a row, we see global entities that behave entirely differently than their micro components.

We just went through a few basic examples. Yet, whatever the situation of an enterprise may be, our experience shows that Extra share is generally high. Without getting here into any detailed survey, let’ say that the Extra share is usually comprised between 30% and 60% with frequent picks of 70% and more. That really tells how different behaviors between global and local can be. How then do top managers run their company with management reports that often yield a very distorted view of operational realities?

At the level of a management team, such issues remain mostly unaddressed except in particular problem cases. Top managers rarely have the time to consider more granular or detailed data. Fortunately, managers at large know their business and their data (as in the example above regarding the store chain). It is human intervention on all enterprise levels, that makes up for present limitations of reporting tools.

Some believe, however, that such limitations are immaterial, and that they can be easily overcome with a needs analysis, in order to determine the most salient and transparent metrics to be used. Thus in the above retail chain example, one could introduce “per store revenue”, a metric that would provide a local view of the revenue mix, not only a global one per regions. But one needs to know ahead of time that per store revenue is relevant, which would not have been the case if stores were all similar in terms of revenue. Let’s take yet a third example: total company margin has increased 5%, however 70% of revenue comes from products whose margin has declined! In order to account for that remarkable phenomenon, due to mix variations across the product line, one can introduce a metric tracking each product contribution to global margin. But there too, one needs to know ahead of time the nature of the problem in order to devise a relevant explanatory metric.

Neither industry knowledge, nor the best needs analysis can aspire to uncover all data discrepancies, especially when performance may vary unexpectedly and when complexity is an issue: what to expect from a large corporate entity or even from a medium size business with thousands of customers and products to manage? Or from fast moving markets with many competing products? What should one do when it gets difficult to dwell into detailed data without loosing perspective? As the aphorism goes, “the devil is in the details” and may invalidate the best strategy, thought out from a global perspective.

Moreover, appropriate metrics change over time, given moving company environments and strategy reassessments. The “agile” enterprise needs “agile” metrics. Extra share (see above) allows pinpointing immediately divergences between global and local viewpoints, between strategy and operations… and may also be a basis to introduce, if needed, new explanatory metrics.

A first stage in order to structure that approach, would be to introduce the Extra share metric in company reports, allowing to know where to drill-down into detailed data. If Extra share is small, underlying data is rather homogeneous and global is a good approximation of local. However, when Extra share is large, underlying data are heterogeneous, and global is probably a poor approximation of local. There it is key to dig into details. A second stage would be to use complexity exploration tools, enabling to better manage unavoidable divergences of views between organizational levels. One more great undertaking for the future of Business Intelligence, to which EFFIS is already contributing with its working methodologies and proprietary software.

Strategy Implementation with Analytics

Management practices evolve overtime, as for instance the business environment changes and information technologies offer new possibilities. Yet, over the past 40 years since “Strategy” has been recognized as a practice of its own, one problem has remained relentlessly unsolved: How does one turn strategy into implementation?

Senior managers, scholars, consultants, all have offered solutions that may have been effective depending on a particular situation or leadership style, yet as of today, many top managers still wonder why it seems so difficult to execute on their plans, while operational teams feel their needs and priorities are not well understood by senior management. Disconnects often remain many despite best intentions on all sides.

One of the most widespread strategy tools today, remains the “matrix portfolio”, a bubble chart type usually segmented into four different quadrants. The chart is particularly information rich as it displays three metrics at the same time: In the example below, we can see “Margin%” vertically, “Margin% Change” horizontally, and “Revenue” being proportional to the size of each bubble. Bubbles beneath display product families for a distributor of ethnic food products. Green bubble families have above average and growing margin, red bubbles have below average and declining margin, while yellow bubbles are above average but declining.

Such matrices are well suited for financially oriented analysis and decision-making, for instance in the manner of managing a stock portfolio. However, often matrices are used to make decisions with operational impact, such as “that product family has been growing three points in margin% over last semester, let’s set a goal of an additional 2% for next semester”. Or “that red bubble is below average and in sharp decline, may be we should discard it altogether”. At a strategic level in a large company, product families will rather be product divisions, or business units and world regions.

Such key decisions, when they are mostly made on the basis of global data, may be misleading as they often don’t reflect the behavior of underlying entities. Henceforth rooted and pernicious problems in strategy implementation. Indeed, let’s now take a closer look at our “margin matrix”, at a detailed individual product level. We can see a very different picture than the original one on the global product family level.

All green product mini-bubbles for instance, belong to green product families located in the upper right quadrant; yet green individual products are scattered all over the four matrix quadrants. The same holds true for the other three quadrant colors and all global product families versus their individual constituents. The trouble here is that the scattering is not just innocuous. We are used to believe for example that if an entity is growing, most of its components must be growing too. In the above case study, an average of 40% of individual products does not belong to their original product family quadrant. So a green product family may be above average and growing in margin, yet 40% or more of its underlying products may be behaving otherwise, for instance declining. That is also true when weighting products by revenue: around 40% of total product revenue is earned outside of product family quadrants. That sets a very different picture than the usual global matrix portfolio. Thus, how can senior managers make well informed decisions, especially when they lack the time to take into account detailed data?

Indeed, our research at EFFIS has shown that discrepancies between global and detailed business matrices are usually comprised between 30% and 60% or more. In order to account for such substantial numbers, we have introduced a new type of indicators that measure disparities between global and local. The “Intra share” is the part of the local that behaves like the global: here it is individual products that remain inside the same quadrant as their global product family. Opposite, the “Extra share” is the part of the local that behaves differently from the global, meaning here products that belong to a matrix quadrant different from the quadrant of their parent product family. Thus the Extra share is a measure of disparities between global and local viewpoints. In the above example, average Extra share amounts to no less than 40%.

In order to account visually for Intra/Extra share, we add to each bubble within a given matrix, a crown that signals Intra share in green and Extra share in red (as displayed just below). When Extra share is low, underlying data is rather homogeneous and global aggregates portray local constituents fairly. When Extra share is high, underlying data is heterogeneous and global and local viewpoints may differ markedly; when that is the case, drilling-down into detailed data is usually preferable, as well as understanding how Extra share may be split between different quadrants. Thus a red bubble with a lot of Extra share may be worth salvaging after all, as a large portion of its constituents belongs to more valued quadrants. While the growth of a green bubble may be jeopardized if a large part of its constituents are nevertheless in decline.

In our experience, that kind of visually guided data exploration is especially useful when there is complexity in a business model: several thousand products and/or customers, possibly many competing products and/or market segments, conditions found in the consumer goods industry in particular, but not exclusively. At the corporate level, a large company always deals with at least some areas of complexity. And within that context often lies the temptation to reduce complexity. One approach is to limit heterogeneity and thereby Extra share, recurring to the kind of business segmentation that unfortunately often leads to organizational silos that impede transverse agility and empowerment.

Such senior management issues are often key when trying to implement strategy, yet they are hard to grasp with adequate metrics. Qualitative judgment may remain invaluable, yet Intra and Extra share offer a new type of measures that are welcome in a management world that has transformed with the growth of analytics and ubiquitous reporting tools. If implementing strategy remains challenging as an art, it may also become increasingly successful as a science.