The power of 'other' - paving the paths your customers prefer

In Universal Principles of Design, a desire line (or design path) is a trace of use or wear that indicates "preferred methods of interaction with an object or environment". On the face of it, a very esoteric concept but in practice it is quite simple.

Many years ago, I read of a case where a new college campus was built and there was a central grassed area. Instead of laying down cement paths, the area was left for a year without marked paths. At the end of that time, paths had been worn by pedestrians taking their most preferred paths across the grass. After that it was a simple matter to lay paths over those already worn by the pedestrians. These paths, of course, perfectly matched the needs of the pedestrians.

However, the principle is not limited to physical settings. A recent example I am aware of was where a new web-based system was put in place for customers to submit a form on-line. In designing the form, the company only wanted the customers to choose a reason for submitting the form from a limited number of options preferred by the company. In order to force this, there was no 'Other' option. This was done in the belief that the customers would just select from the available options. Instead about 30% of customers couldn't find the option they wanted and so just chose a random option and then put their real reason in the text box provided. In effect, they worked around the constraints imposed by the system and in the process made it more difficult for the company to analyse the reasons why customers were submitting the form (which had flow on effects for training and work allocation.)

A better alternative would have been to provide a limited range of the most common reasons expected but also to provide 'Other' as an option. This would have allowed the 'Other' options to be analysed to see if they yielded a further set of explicit options to add. In effect, adding the 'Other' option would have allowed the customers to wear their own 'desire path' which could have then been 'paved over' by providing the additional options they desired.

Too often our preconceptions about customers blind us to what they really want. The power of 'Other' is that it gives your customers the opportunity to tell you!

As Tim Halbur puts it:

...the human element is going to find its own way.... The people who disobey the beautiful logic of smart growth and urban design are trying to tell us something, and we need to watch and listen. We need to go back to the places we create and see how they work in real life. We need to plan for opening day, but make sure we’re also there a month, a year, five years later to adapt and refine based on how people actually use the built environment. The desire paths are there for the finding, if your eyes are open

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If we let our customers wear their own preferred paths and we then build over them, they are happier and we end up with more efficient systems.

It may not be what you think

Here is a story from Raymond Smullyan's book "This Book Needs No Title":

Once upon a time there was a man. This man had a dog. This dog had fleas. The fleas infected the entire household. So the man had to get rid of them. At first he tried to get rid of them individually using a fly swatter. This proved highly inefficient. Then he tried a flea swatter. This was also inefficient. Then he suddenly recalled: "There is such a thing as science. Science is efficient. With the modern American equivalent, I should have no trouble at all!" So he purchased a can of toxic material guaranteed to "kill all fleas," and he sprayed the entire house. Sure enough, after three days all the fleas were dead. So he joyously exclaimed, "This flea spray is marvellous! This flea spray is efficient!" 

But the man was wrong. The flea spray was totally inefficient. What really happened was this: Although the spray was inefficient, it was highly odiferous. Hence he had to open all the windows and doors to ventilate. As a result, all the cold air came in, and the poor fleas caught cold and died.
 

Another story, this time from my own experience:

A manager is worried about the backlog of work that is piling up. An employee looks back over the previous three years, does some analysis which shows that there is a regular pattern of workload every year and that the current year matches that pattern. They show this to the manager. The manager still pushes staff to get more done even though it is a proven fact that the workload will drop without any additional effort. If the backlog reduces, does the manager think:

a. The backlog has dropped because I pushed everyone to work harder
b. The backlog dropped because it always drops at this time of year

A third story:

Many years ago when I was studying epidemiology, we were given a hypothetical study to analyse in which test subjects who were suffering from a particular illness were put on a diet where they had to eat 200gms of chocolate a day. When I did my analysis I raised the following point: whatever was to happen from such a study, the result would not necessarily be because they ingested the chocolate. The result could equally have been what they had stopped eating as a result of having to eat the chocolate. Without knowing what their eating habits were prior to the study you can't determine what if anything was eliminated from their diet that could have caused the improvement in their health.

These stories illustrate three points:

• Sometimes an improvement doesn't come from an action you deliberately took, but is due to an unnoticed side-effect.
• Sometimes an improvement would have happened even if you had done nothing.
• Sometimes it isn't what you have started doing but what you have stopped doing that has resulted in an improvement.

Managers often think that they have to DO something to improve a situation. But sometimes things will improve if they simply let the situation be or STOP doing something that is causing the problem.

 

Should you trust the experts?

With the volatility in world economics over the past few years, it is worth asking whether 'experts' can be trusted to provide any worthwhile guidance to protecting your financial future.

My personal view is that economics as a 'science' has about as much validity as astrology, especially given the demonstrably false assumptions on which most contemporary economic thinking is based. In The Sages, Charles Morris points to a 2008 survey by the Wall Street Journal which ranked 51 economic forecasters and found that of 102 separate forecasts, 101 were wrong and in the same direction. And since then economists have made repeated predictions which have failed to materialise.

So what do we do?

My own approach is to watch the news and to look at world events and draw my own conclusions about what is likely to happen. I watch a variety of different news programs from different countries in order to try and get a balanced view. Based on what I could see happening back in May ( the Eurozone crisis (particularly the uncertainties surrounding the Greek election), the continued debt problems and dysfunctional political conflicts in the USA), I switched all of my investments into Australian Fixed Interest investments, on the assumption that all of these changes would lead to decreased investor confidence and increased volatility in the share market. Sure enough the stock market fell. The Australian economy is still strong without the level of sovereign debt of Europe, the USA and Japan and is riding on the back of a mining boom that is expected to last at least into next year (though the boom could bust if the Chinese economy significantly slows.)

Looking ahead, the issues with the US debt ceiling are likely to rear their ugly heads again in September or October 2012 when US government spending bumps against the debt ceiling agreed last year and the Obama administration will need to seek a further increase. Given the coming US election it seems to me that this will be just as bloody as last years negotiations, if not worse and none of the options available to the US government ( increase debt, default on debt, print more money, adopt a more sensible taxation regime) is likely to bode well for the world economy. In Europe, the last debate in the German Parliament regarding the Spanish bailout made in clear that Germany is losing patience with bailing out the rest of Europe's economic mismanagement, which foreshadows future problems in the Eurozone. Based on all of this, my best judgement for my personal finances has been to keep my money in low risk, capital preserving investments with moderate yields.

I'm not saying that everyone (or even anyone) should follow my example. My background is in mathematics and statistics, not finance. However, I think it is open to anyone to look at the news and judge for themselves what is likely to happen in the world economy and invest accordingly.

I did try read a few different books which purported to provide good advice on defensive investing. However, as is usually the case with economic experts, their advice was contradictory, some predicting inflation in the US economy, while other predicting deflation. I investigated investing in gold bullion, however after looking at annual average gold prices since 1979, I've come to the conclusion that gold is likely to be the next bubble to burst. I base this on the fact that from 1979 to 2004, the average gold price was relatively flat, oscillating around $500 an ounce, but since 2004 it has tripled in price, showing the typical pattern of a bubble with greed and fear driving up the price beyond any reasonable value. Some of the books I have read claim that gold could rise to $10000 an ounce. But my gut feeling is that this is just another example of irrational exuberance. So if I invest in gold at all, I intend waiting till it drops back to less than $600 an ounce.

Investment experts will tell you that the stock market always rises in the long term. However, I believe that we have reached a point in history where the economic balance is shifting towards emerging economies and that the past record of the Western stock markets cannot be a reasonable guide to the future. You can't just look at graphs of stock markets over the past 100 years and extrapolate the upward trend; you need to also consider the changing realities that impact on the values of companies traded in these stock markets, realities which do not necessarily bode well for Europe and the USA.

So in a world of uncertainty and volatility, both economic and political, I think the prudent thing is to protect and preserve what you have, batten down the hatches and whether the storm. Once the fallout of the US election and the Eurozone crisis have settled, then it will be time to reassess the best way to invest.

As I said, I am not an investment expert and what I have discussed is purely my approach to protecting my own finances. Listening to experts who get it badly wrong more often than not is not an option. So it is up to each person to use their own judgement and assess what is best for them.

B.O.W. (Based on what?)

At a meeting of a number of related organisations a question came up about what effect a recent change in legislation would have on the number of people wanting to contest matters in court.

• one group believed that it would make no difference because, based on their experience, most people didn't realise that the law hadn't always been as under the changed legislation.
• one group didn't care since it had no impact on them.
• one group stated that they hadn't collected any figures that would lead them to a reasonable estimate

And then the CEO of a fourth group spoke up and said "Another 20,000 cases a year".

The question you would need to ask is "Based on what?". While any of the other three organisations could be considered to have taken a somewhat reasonable position, conjuring a figure out of thin air doesn't seem very reasonable. What makes it even less reasonable was that the types of cases that were likely to be affected by the change in the law only accounted for about 10% of those going to court and the total going to court numbered less than 20,000 per year in total. So the CEO of the fourth organisation was surmising a ten-fold increase in the number of those kinds of cases going to court, from 2,000 to 20,000.

When you see things like that happening, you have to wonder how they came up with the figure and why they felt obliged to come up with any figure at all, especially in an area in which they were clearly unfamiliar.

In some organisation you see this happening all the time. A CEO tells employees that business is going to double in the next 2-3 years, based on a naive confidence that a business that they supply is going to meet its growth targets even though the other business has consistently failed to meet its targets in the past and even though there is likely to be a change in owners within a year.

There are times when you need to articulate your assumptions and then make a realistic estimate as to how likely it is that those assumptions hold true. Once you spell them out in black-and-white then it becomes clearer whether what you suppose to be the case is based on anything at all, whether it is based on shaky assumptions or wishful thinking. And when assumptions are surfaced, you can provide the opportunity for other people to question them and possibly gain new information of which you were previously unaware. This in turn can shape a re-estimate of how things are likely to turn out.

However, if you fail to bring assumptions into the light of day where they can be questioned, if you fail to ask yourself "Based on what?" then you are setting yourself and your organisation up for failure. You may invest resources where they are not needed and raise the costs of your business without any corresponding gain. And you may lose the confidence of employees as to whether you have sound judgement, if you repeatedly make claims which fail to materialise.

A similar question is: What makes you think that? I find this question useful when a colleague makes a judgement about someone else in my organisation, especially when the judgement surprises me. I want to know if there is something useful my colleague knows that I don't. What I tend to find is a mixture of fact and interpretation and when I make a few tentative interpretations of my own I begin to draw out the information on which their judgment is based, whether it comes from a credible source and whether the judgment is reasonable or whether there may be a more charitable interpretation. In some cases, I can add things that I know that shed light on the situation, so that we both emerge with greater clarity.

The lesson here is that judgments don't exist in a vacuum. They are underpinned by:

• biases,
• blindspots
• assumptions,
• interpretations 
• limited knowledge
• ignorance
• failure to effectively use the knowledge you do have
• believing something which is in fact not true
• not adequately weighting the reliability of different information sources
• believing a situation is stable but which is actually in a state of flux (or vice versa)
• overconfidence in your own infallibility.

When you are surprised that something didn't pan out the way you expected it is a signal to re-evaluate your assumptions and to learn how you went wrong. The situation may have changed in a way that could not have been predicted. But equally, it could have changed in a way that was foreseeable given the facts you had at your disposal. It is an opportunity to learn about your particular weaknesses in judgement so that you can correct them or so at least in future you can ask yourself: "Am I making the same mistake again? What am I missing?".

At least then a momentary failure can sow the seeds of better judgments in the future.

How favoritism undermines businesses

If you run your own business then it is in your interests to get the best person for the job and to promote people based on the value they add to your business. However, if you choose to do otherwise, such as hiring and promoting your friends or relatives or people you like, then ultimately that is your choice: it's your money and if you lose out as a result of your decision then that is your prerogative.

However, if you are a manager in a government organisation or a publicly traded company, then basing decisions on factors other than what the person adds to the business effectively means that you are stealing from the "owners" (i.e. taxpayers or shareholders). In effect, you would be deliberately creating a sub-optimal outcome for reasons that have nothing to do with the success of the business, aiming instead to benefit your "favourites".

And this effect doesn't just stop at sub-par work being performed by your favorites. Let's look at some of these consequences.

Firstly, you undermine confidence in the competence of those so favoured. Generally, the feeling among employees who see what is happening is that if the favourites were all that good then they would be able to compete on their own merits. So the fact that they had to be given the opportunity rather than earn it suggests that they are less competent than others who might have competed for the opportunity. It also suggests that the person playing favourites is aware of this and has deliberately short-circuited any competitive process for that very reason. In some ways being the favorite is a double-edged sword: on the one hand you are being given the benefit of an opportunity, but on the other hand, even if you would have won the opportunity on merit in a competitive process, your reputation as competent in your own right is being undermined. And that can have consequences later on if your "protector" leaves the business or is moved elsewhere within the business.

Secondly, the person loses the confidence of the workers. If they are prepared to act with so little integrity in this matter then what else may they be doing? Can they be trusted? Who may they be undermining behind the scenes without that persons knowledge? Where there is a lack of transparency, workers may fill in the blanks themselves and draw their own conclusions, tinged with a justifiable paranoia.

Thirdly, such favoritism demotivates other workers: if promotion is based on being the boss's favourite then what is the point in doing a good job? Or, they may continue to do a good job just so that they get a good recommendation when they apply for jobs in other, fairer organisations.

Fourthly, it undermines co-operation within the workplace. You can end up with an environment were people do the least they can do without getting fired and where change is a struggle because disaffected workers withdraw their participation in change measures. Where rewards are not based on merit, passive resistance becomes the strategy of choice.

Finally, you fail to recognise and fully deploy the skills and knowledge of other employees who may have a greater claim to the opportunities on offer.

In summary, if you are a manager working in a business you don't own, then by playing favourites you are not only failing to act with fairness or integrity but aren't even earning your own salary since you are sowing the seeds of problems and dissension within the business instead of moving it optimally in the direction of its objectives.

I've painted a pretty grim picture. But unfortunately it is a reality in many organisations today when managers get it into their heads that they are in charge of their own little fiefdoms and lose sight of why they were hired in the first place.

Three lessons from spaghetti

Sometimes an everyday word becomes a rich metaphor that can teach us a lesson by the visual analogy that it embodies.

Take, for example, the following three lessons we can learn from spaghetti.

Firstly, consider spaghetti communication, that is communication where the source (sauce?) of the communication may be unclear, where different strands may overlap, wrap around each other and double back and become entangled in such confusion that it may be impossible to straighten out the message (somehwat like that sentence!). There may be plenty of color and flavor but not a lot of meat, so you end up being dissatisfied and needing further communication to clear up the confusion, which may then just result in more confusion. In this case, the lesson is to avoid spaghetti to begin with and instead think through the communication clearly and then communicate through a single channel.

Then there is spaghetti code, computer code that has a complex and tangled control structure. This makes it difficult to follow, increases the risk of unforeseen and undesireable side effects and makes it hard to update. However this isn't limited to computer code. We see it in legislation where the use of complex definitions and cross-referencing to other parts of the same legislation may make it difficult to understand and even at times self-contradictory. And it may also occur in the standard procedures used in our businesses where they may have multiple levels of approvals or complex loops through different work units before being resolved. By eliminating this spaghetti we may be able to bring clarity and efficiency to our processes and eliminate waste.

On a more positive note is the lesson we learn from spaghetti sauce is this video presentation by Malcolm Gladwell:



In an earlier post, I mentioned communication differences between different generations (Boomer vs Gen X vs Gen Y) and to a degree this parallels the spaghetti sauce analogy: some people may like their communication to be "extra chunky" (watch the video) whereas others may want it to be smooth, some want it to be thorough and detailed while others may want us to cut to the chase. The lesson here is that instead of pursuing a 'one size fits all' approach, we instead tailor our approach to what best suits our customers.

One food, three lessons. But common to all three is the need for clarity: clarity of intent, clarity of process, clarity in satisfying customer needs.

7 or 6?

Consider the Englsih sentence:
      "This sentence contains at least seven words"
If we wanted to translate this into German then we could say:
       "Dieser Satz enthält mindestens sieben Wörter"
but now a statement that was true in English has become false in German.
Or we could translate it as:
       "Dieser Satz enthält mindestens sechs Wörter" (i.e. "This sentence contains at least six words")
which preserves the truth of the statement but only by translating 7 as 6.

So which is correct? The first translation is literally accurate but at the expense of truth. The second is true but at the expense of accuracy. Which you would use would depend on your purpose.

Sometimes something similar happens with best practice. An attempt may be made to transfer the practice of another organisation point by point to your organisation, based on the idea that we only know it will work in its original form - we don't know what will happen if we modify it. However, the same process that works well in one organisation may fail in another because in the act of "translating" it to a different set of circumstances we have failed to preserve its "truth". So we need to modify it to preserve its truth within the context of our organisation.

Something similar can happen when we attempt to communicate. We can attempt to communicate a consistent message to all of our customers (i.e. use the same form of words) or we can attempt to communicate in a way that ensures each customer receives the same message (i.e. where different words are used to cater for different customer characteristics.)

For instance, in my home state of New South Wales, for people over the age of 18 we have the following breakup by generation:

• 12% Depression/WW2 Generation
• 27% Baby Boomers
• 38% Gen X
• 23% Gen Y

While it may be over-simplifying a little ( different people witin the same generation may respond differently), if we send the same letter to different people, what Boomers may see as professional, Gen Yers may see as patronizing, pompous, pretentious or officious. Alternatively, we could express the same message in different ways targeted to different generations in order to get a similar response from each group.

So we have a choice: we can send a message that is literally consistent or we can send tailored messages that obtain a consistent outcome.

In all of the examples, the difference is between an absolutist perspective in which literal accuracy is a key value and a relativist perspective where context needs to be taken into account. In general, the more nuanced contextual approach will be more effective.

So when you are faced with "translation"-type issues, it may pay to consider wat you want to happen and whether contextual modification is important - sometimes translating "7" as "6" is necessary to preserve the effective essence of a process or communication.

What an average doesn't tell you

Recently, I had a hunch that the average age of males was lower than the average age of females. My logic was that women tend to live longer than men and that slightly more male babies are born than female babies. I wondered if my reasoning was correct so I downloaded the demographic statistics for NSW from the Australian Bureau of Statistics and I found the following:

• There were 4.3% more males than females under 31 years of age
• There were equal numbers of males and females aged 31 years of age
• There were 6.3% more females than males over 31 years of age
• 41.2% of the population were less than 31 years old while 57.4% were more than 31 years old

When I crunched the figures I found that the average male is 1 year 8 months younger than the average female. So my hunch was correct. But the question is: what does this tell us about any given person?

The answer is: nothing!

Averages are about populations not about individuals. So if you were to see a statistic such as "Males are on average 1 year 8 months younger than females", you aren't being given an answer so much as a provocation to ask "Why would this be the case? What does it mean?"

When you are surprised at an average then it may help you to surface your assumptions about whatever it was that was being measured.

For instance, if I were to ask you what the average height for a human being was, the chances are that you would say something like 5'2" or thereabouts. So it would surprise you if I were to tell you that the true figure is under 5'. I will explain this claim in a moment, but first would it surprise you to learn that short people often have lower literacy levels than taller people? Yes?

Well interestingly the reason for both of these statistics is the same: children are human beings too!

Most children are under 5' tall and most childen have lower literacy levels than adults and these two facts result in the given statistics. So if you were surprised at the claims I made, it would be because you assumed that we weren't counting children in the averages. Even if we weren't aware of it, we implicitly assumed that we were talking about adult humans.

This can be true of almost any average. The question we always need to ask is: what is the true population that is being averaged? What assumptions are we making about this population? Are there groups we are excluding which we should be including? Does it make sense to use a single average when you are dealing with a 'mixed' population (e.g. adults vs children, males vs females). Does comparing separately calculated averages lead us to ask new questions about the reasons for any differences? Are the reasons obvious or do we need to dig deeper?

The other question is whether we are comparing the right averages.

In Australia, a serious issue is that average Aboriginal life expectancy is significantly lower than that of other Australians. However is this the right comparison? If we were to segment the two populations by socio-economic level would we find that the issue isn't race but poverty i.e. that people in lower socio-economic groups have much lower life expectancies than those in higher socio-economic groups? In other words, the underlying reason may not be racial but economic. Whatever the answer may be, it determines what sort of strategy you use to approach the issue: do we directly target health services to Aboriginal people or do we adopt a broader strategy of reducing poverty across the board?

I don't know what the answer is, but I raise this issue as just one example where the populations you choose for comparing averages can make a significant difference to what strategy you use to investigate the causes of any differences and the consequential strategies you adopt to deal with such causes.

Averages are one of the simplest statistics to calculate and this is one of the reasons that they are so frequently used. However they may conceal differences and assumptions that need to be surfaced if you truly want to understand what is actually going on.

Do they really care?

A key question that you really need to answer is:

What do our customers value?

On the answer to that question hangs where you need to focus your improvement efforts.

If you continually try to refine aspects of your business that are of no interest to your customer then you may be throwing money away unnecessarily.

For example, in your written communications with your customers you may spend a lot of time and effort in choosing just the right word, in ensuring that the writing is polished and professional. Yet your customer may barely notice it. If the customers you deal with have issues with literacy or with the language (such as English) of your communications then you may need to simplify things to the most basic level, possibly using colloquialisms instead of jargon, instead of trying to make it sound like something they might read in a magazine. Even if it contains some spelling errors this could work in your favor since at least it wouldn't appear to be some standard response that is sent to all your customers.

This isn't to say that you shouldn't have a professional image. But you should focus your efforts in ways that give greater weight to substance than image.

 I remember dealing with an employment service once who were very professional in their appearance and in their customer liaison, but were completely useless in almost all other respects. They were continually late with their billing, it was wrong more often than not and they stuffed around the people that they had contracted with us. A year after we ceased dealing with them we were still getting correspondence from them about their billing which they had still not straightened out. They had a veneer of professionalism but nothing beneath to back it up and needless to say they were incredibly frustrating to deal with.

Customers will forgive a bit of roughness around the edges if you deliver on the things they really value. What they won't forgive is failure to deliver, regardless of how polished and professional your image.

The message here is to put your resources where they matter and that means actually taking the time to understand things from your customers perspective. Otherwise, while you are patting yourself on the back about the quality of your communication, your customers may be seething about the poor quality of your service.

Compared to what?

The judgements that we make can at times depend on the subtlety of the measures we use.

Consider an insurance company where the employees have some latitude to waive the excess on a policy if the person making the claim has mitigating circumstances. Each person may make decisions on a variety of different policies (home. contents. car, boat, accident, personal liability etc.)

Now suppose you wanted to judge whether any of your employees were being overly generous in waiving excesses.

One measure you might use is to simply look at the proportion of claims that a person processed during some time period for which they waived the excess. You might compare this to the total proportion waived over all claims processed by all employees and target those employees who deviated too much from this average. However this is a very crude measure since it fails to take into account the types of claims each individual is processing.

A more refined measure might be to take the average proportion waived by all employees for each individual claim type and then calculate what each individuals waive rate would have been had they waived at these rates for the claims they actually processed. This is a fairer measure since it takes into account that the waive rate might differ between different kinds of claims. And again, you might target those employees for whom the deviation between actual and expected waive rates was too great. However, if any of your employees is just processing a single type of claim then they may be making the largest contribution to the waive rate for that claim type so you would effectively be comparing that person against themselves with little chance of a major deviation.

Both of these methods compare what an individual is doing against what everyone else is doing so it is a comparison against a norm of behavior rather than against any objective measure of what should be happening. For all you know, most employees might be being less generous than you might like, so the person who appears to be being more generous might actually be doing what you want. However, the attraction of these methods is that they are simple to implement.

The more important question however is what is the intended outcome of this policy and whether what your employees are doing is achieving this outcome. Every time an employee waives the excess it is a cost to your company, so presumably you want an outcome that will compensate for this cost. Otherwise, you would just be throwing money out the window. You would need to establish some metrics against which to measure the success of your policy (e.g. customer loyalty as measured by policy cancellations, taking out additional policies etc).

In the case of increased customer loyalty, you might gain an increase in policies due to word of mouth or conversely be able to reduce your advertising costs. Whatever measure you use, you should be able to measure the costs of the policy to your company versus the benefits gained and this in turn might give you a clearer indication as to how generous your employees should be. This would take a lot more gathering and analysis of data but would allow a more targeted approach. You might find that waiving the excess for one type of claim might result in no benefit at all. Or that placing conditions (e.g. 10 years without a claim) around a policy type in order to realise any benefits.

The major differences between this approach and the approach based on averages are:

• In this approach the policy has a clear purpose and you are more interested in whether the total effect of employees actions is achieving that purpose. In the 'average based' approach, you are interested only in how employees compare with each other (whether or not this benefits the business)
• This approach provides information that enables you to evaluate and refine the policy itself. The 'average based' approach allows you to change the behavior of individuals but such change might actually adversely affect the business.

In other words, one approach is looking at te big picture of the businesses actions and its customers reactions, whereas the other is treating the business as if the customers don't matter at all. It seems like a no brainer as to which approach to use but all too often managers employ the easier option rather than investing time and effort in analysis and refinement or more importantly on thinking through what a policy is intended to achieve.

The best get worse, the worst get better

Only the mediocre are always at their best
~ Jean Girandoux, French diplomat, dramatist, & novelist (1882 - 1944)

Suppose you get a group of people and give each person 6 coins to toss. And suppose that you class heads as a good result and tails as a bad result. As everyone starts tossing their coins, someone soon gets 6 heads and you praise them for getting such a good result and hold them up as an example to everyone else. But on their next throw they are back to throwing the same number of heads as everyone else. Conversely, someone gets 6 tails and you criticize them for getting such a bad result and hold them up as an example to avoid. And on their next throw, they are back to throwing the same number of head as everyone else.

What might you learn from this?

• If you praise someone after good performance then they will just slack off
• If you criticize someone after bad performance then they will improve

Well, you might learn these lessons, but given an obviously random process, it would be stupid to draw such conclusions wouldn't it?

Yet every day, we draw such conclusions about the performance of those who work in our organisations. The fact is that everyone varies from day to day in their performance for reasons that ave nothing to do with competence or motivation. One day someone achieves a personal best and we enthusiastically praise them, only to see their performance drop. Or someone 'achieves' a 'personal worst' and we have a 'quiet chat' with them and they improve. On a day-to-day basis each persons performance varies around their average performance and the greater the variance from their average on any particular day, the less likely that it will be repeated the next day.

In terms of the coin example, a person has only a 1.6% probability of throwing 6 heads, so there is a 98.4% chance that they will achieve a less extreme result on their next throw, and similarly for 6 tails. The technical term for this in statistics is 'regression to the mean'.

For example, in 1940, McNemar published a study in which he had measured the IQs of children in an orphanage on two occasions a year apart. He found that those who had scored highest the first time did not perform quite as well the second time around. Conversely those who did not perform so well the first time round, did better on the 2nd occasion. And this was entirely due to regression to the mean.

In the workplace, a similar result can occur. We check a group pf people's work and then look at those who performed worst. We take these people aside and coach them, and the next time we check their work, they are doing better. But is this due to the training or due to regression to the mean? Was their poor performance atypical of their average performance? Who knows? Sometimes what we think is happening isn't happening at all.

We could do an experiment and divide the group in two and only coach half of the poor performers and then see if both groups improve. And we might then find that our training was a waste of time and resources. But most organisations are loathe to experiment and as a result they deprive themselves of the opportunity to learn what works and what doesn't.

This doesn't mean that we ignore poor performance. It does mean that to be fair to the people who work in our organisations, we need to track performance over time and not just focus on single points of extremely good or extremely bad performance.

In any group of people if we do a measurement at a single point in time, there will always be someone who performs best and someone who performs worst. We need to be careful that we don't give undue weight to what may turn out to be a regression effect. This is both fairer to individual workers and better for the performance of the organisation as a whole.

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The example of IQ measurement in an orphanage is from A Primer on Regression Artifacts (Donald Campbell & David Kenny)

For an actual example of regression to the mean in performance among pilots in the US Air Force see:

Regression to the Mean in Flight Tests (Reid Dorsey-Palmateer and Gary Smith)

An experiment you can do yourself:

Generate 20 random numbers each in two columns of a spreadsheet and sort the columns from lowest to highest in the first column, ensuring that each number in the first column remains with its partner in the second column. Then in general, you will find that the partner of the lowest number in the first column is not the lowest in the second column and similarly for the partner of the highest number in the first column. If we imagine these numbers to be scores on people's work at two different points in time, the worst performer has improved their relative ranking, while the best performer has worsened their relative ranking. Yet clearly the data was generated randomly.

The downward flow of mediocrity

Some years ago a study was done which found significant differences between effective managers and successful managers. Successful managers were defined as those who gained rapid promotion relative to their length of time with the company, whereas effective managers were those who actually did the work to make the company work.

Whereas successful managers spent most of their time on networking and politics, effective managers spent more time communicating with the people who reported to them, in planning and in getting things done. The sad thing is that the successful managers were promoted more rapidly than the effective ones.

One of the many dangers that organisations experience is when those who lead them are mediocre in their performance. Because of their mediocrity, they do not deal with the mediocre performance of those who report to them, whether this is because they do not recognise that anything is wrong or because it is too much trouble to deal with. And as a result they find themselves surrounded by mediocrity. And the mediocrity continues to flow ever downwards. Such managers find the mediocrity of those around them comfortable since there is no risk of anyone performing better than themselves and it allows them to hide their lack of performance.

The one thing they fear is someone who reports to them who is actually effective. An effective and conscientious manager who is surrounded by apathy and incompetence, may end up trying to resolve issues outside of their area of responsibility and as a result generate hostility from the less competent managers, And they may be seen as threatening by their own manager since they ask the awkward questions that such a manager would rather not answer, and because they know where all the skeletons of things which have gone wrong are buried.

So paradoxically, the manager who actually keeps the organisation afloat may be the very manager who is resented the most, all the more so because they cannot be eliminated because their contribution is also the only thing that keeps the mediocrity of the more senior manager hidden.

I know of organisations where senior managers jump to making decisions without any data gathering or analysis, without considering the implications and consequences. And when a manager tries to put the brakes on and raises issues with what they are doing, they are seen as being obstructive.

Not a pretty picture, but this dynamic explains a lot of what has happened recently in the finance and airline industries, as well as the financial problems in the Eurozone: when people who are successful because of networking and politicking rise to positions of power for which they are ill-equipped, the results can be catastrophic.

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Further reading:
Successful vs Effective Real Managers Fred Luthans

Real Managers Luthans, Yodgetts & Rosenktrantz

Failure to Learn

A few months ago my mother was admitted to hospital with pneumonia and while she was in hospital two incidents occurred which highlight how standard treatments can block learning.

In the first incident, over several days while intensive antibiotics were being administered, my mother became delirious and paranoid. I did a little bit of research on the Web and found that certain antibiotics can cause such reactions (in fact several classes of antibiotics can do so). Now, to me, the logical response to this should have been to change the antibiotic. Instead her doctors added an anti-psychotic drug and a sedative. In other words, they added two more drugs to deal with the effects of the first one.

In the second incident, she was having problems with maintaining blood oxygenation due to chronic heart failure. I did some further research on the Web (one of my degrees is in epidemiology, by the way) and found that the herbal supplement hawthorne has been proven to be beneficial - in fact I found a meta-analysis of 14 different research studies published in reputable medical journals that confirmed the benefit of hawthorne and the absence of side effects. I brought this to the attention of her doctor but he was unwilling for her to try this supplement. I presume this is because it is not a standard treatment in Australia (yet it is an accepted treatment in Germany).

In both of these cases, following a standard treatment regimen meant that there was no possibility of learning, in the first case failing to learn that a serious side effect of a drug could possibly be eliminated by switching the drug rather than adding additional drugs with potential side effects of their own. And in the second case, failing to learn that an accepted treatment in another Western country could conceivably be beneficial to patients in Australia.

The nett result of this is that patients in this country receive less than the optimal treatment. There is no telling how many deaths or complications occur annually as a result.

However, this isn't limited to medicine. About 18 months ago, I had an abscess in a tooth. My dentist said that I needed to have root canal to treat it, which ended up costing me around $800. Presumably this is the standard treatment. A year later the same tooth became infected again, but instead of going to the dentist, I took a course of antibiotics and the infection cleared up with the tooth remaining sound. Had I gone back to the dentist I imagine they would have said that it needed to be extracted.

But here's the thing: suppose that I had been prescribed a course of antibiotics the first time around instead of the root canal. And suppose the infection had cleared. Then this would have saved undermining the integrity of the tooth and possibly the second infection. Is it likely that dentists will ever learn this? My guess is no: firstly because an expensive treatment provides no incentive to try a much much less expensive treatment and secondly because if they only ever use the technique they were taught they have no opportunity to observe the effect of alternatives.

So what do these personal examples tell us about continuous improvement?

Firstly, that where there is an accepted way of doing things that 'works' or appears to work, it may be difficult to get someone to try something that could work better, especially where there is an incentive to continue with the current practice.

Secondly,  people (including experts) are more likely to deal with symptoms than go back to root causes in solving problems. As a result, they add a further layer of complication which may obscure what is really happening.

Thirdly, the ready availability of quality information on the Web is no guarantee that it will be used. Natural human inertia will serve to keep things going the way they have always gone.

Fourthly, expertise and specialisation may blind someone to better possibilities in dealing with problems. What is standard in a profession may block learning of better ways of doing things.

Finally, there can be cultural barriers to improving methods, techniques and treatments. (In Anglophone countries, for example, herbal and traditional medicine are not held in high esteem, so effectively doctors discount the benefits of Chinese and Ayurvedic medicine, despite thousands of years of proven benefits.)

Multiple causes, multiple consequences - Part 2

"Is there any other point to which you would wish to draw my attention?"
"To the curious incident of the dog in the night-time."
"The dog did nothing in the night-time."
"That was the curious incident," remarked Sherlock Holmes.

~ From "Silver Blaze" in "Memoirs of Sherlock Holmes" by Sir Arthur Conan Doyle


Noticing what didn't happen can be both difficult and important. Difficult because things that don't happen don't register on our senses, important because what is absent may provide important clues to what we need to do to either improve or conversely avoid disaster.

Consider the following examples:

Example 1: Edward Jenner's observation that milkmaids did not generally get small pox led to his discovery of vaccination ( from the Latin word for "cow"). By looking at people who dd not get the disease he derived a way of preventing it.

Example 2: During World War II, the patterns of bullet holes in returning aircraft were being studied to determine where they should be reinforced. Statistician Abraham Wald however had the insight that the bullet holes in surviving aircraft were clearly non-fatal and that it was the areas without bullet holes which were more likely to need reinforcing and this was confirmed from studying wreckages of planes that had been shot down. (Note: This is an  over-simplification: for Wald's original work see link below)

Similarly by noticing what information is missing we may prevent ourselves from making bad attributions in relation to causality.

Consider the following example:

Suppose in one group people follow strategy A which leads to major failure 90% of the time and outstanding success 10% of the time. And suppose that in a second group, they follow strategy B that rarely leads to major failure but which generally leads to moderate success. If the only information available to us was that strategy A leads to outstanding success then we might falsely conclude that strategy A is better than strategy B.

Jerker Denrell has published numerous articles on precisely this issue, how only studying successful organisations and individuals provides a misleading picture of the types of strategies that lead to success. For example in Predictng the Next Big Thing he argues that making an accurate prediction about an extreme event may in fact be an indication of poor judgement. It may simply be an indication that the person makes extreme judgements in general and that this time they got lucky. But unless we look at the full picture, we may conclude that such a person is some kind of genius.

So, we need to ask ourselves:

• What am I not seeing that I should be seeing?
• What is this person/organisation's track record like (i.e. not just their personal best)?
• Is this attribute or characteristic common to failures as well as successes?
• What isn't happening in this situation?
• What didn't happen that was critical?
• What information am I missing that is necessary to make a valid judgement?
• What information do I need to collect and analyse to see what is really going on?

This last question is extremely important.

There are organisations where none of the following are documented: why a decision was made, what information and analysis it was based on, and what were the outcomes and consequences. As a result there is virtually no capacity within such an organisation to learn from errors or refine decision-making nor is there any accountability, a recipe for mediocrity.

The way to avoid this is to be vigilant in documenting what was done (and thus what may in retrospect be seen to have been overlooked) and to look for not just what happened but what didn't happen. It is the missing piece of the jigsaw that is needed to show the full picture.

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"Psychology and Nothing" Eliot Hearst ( American Scientist Volume 79) is an interesting article which discusses the perceptual and cognitive difficulties of seeing what isn't there (Unfortunately, I haven't been able to locate a free version of this paper on the Web)

Failure is a Key to Understanding Success (Standford GSB News, January 2004)

The Weirdest People in the World (Heinrich, Heine and Norenzayan): Posits that most psychology research is based on a very narrow sample, people who are from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies and that as a result the findings may not be as generaliseable to human beings in general as usually thought.).

A Method of Estimating Plane Vulnerability Based on Damage of Survivors (Abraham Wald)
Abraham Wald's Work on Aircraft Survivability (Mangel and Samaniego)

Multiple causes, multiple consequences - Part 1

“What is Fate?” Mulla Nasrudin was once asked by a scholar.

“An endless succession of intertwined events, each influencing the other”, he replied.

The scholar raised a sceptical eyebrow: “I can’t accept that. I believe in cause and effect”.

“Very well”, said the Mulla and drew his attention to a procession of people, leading a man to be hanged. “Is that man going to die because someone gave him the money that let him buy the knife he used for murder, or because someone saw him do it, or because nobody stopped him?”

~Idries Shah

As the above story shows causality isn't a simple concept. There may be factors that contributed to the particular event but which could equally have resulted in no serious consequences. There may have been inhibiting factors that ceased to act. There may have been a confluence of factors that each had little or no impact but which together were disastrous. A situation may have been in a delicate balance that was tipped by something of comparatively minor importance (the straw that broke the camel's back, the butterfly effect)

Sometimes responsibility can be muddied by questions about causality:

Three people Alfred, Bob and Charlie were crossing a desert and they stopped at an oasis when night fell.. Alfred hated Charlie and decided to kill him, so in the middle of the night wile the others slept, he got up and poisoned the water in Charlie's canteen. Bob also wanted to kill Charlie and, not knowing that Charlie's canteen had been already poisoned, and got up in the early hours of the morning while the others slept and made a hole in Charlie's canteen, so that the water slowly leaked out. The next morning the three went their separate ways and a few days later Charlie died of thirst. Who was the murderer - Alfred or Bob? Or to put it another way: who caused Charlie's death?

In a case like this, it is clear that if neither had acted then Charlie may still have been alive. Yet neither individually caused his death. He didn't die of poisoning so Alfred is not individually responsible, yet if Bob had not acted Charlie would have died anyway as a result of Alfred's actions.

This kind of thing happens all the time in organisations. One person fails to act to prevent the problem or to put controls in place that would have identified it early enough to ameliorate it, another person acts in a way that would have been harmful to the organisation, except that a third person's action neutralised their contribution without preventing the final disastrous outcome. So who is responsible? In the end what purpose is served by assigning blame? Perhaps it is better to work out what factors facilitated and inhibited the final outcome and how the balance changed so that that outcome occurred.

There are a lot of questions we can ask ourselves to try and determine what happened:

• Why did this problem occur?
• Why did it happen now?
• What previously prevented it from happening?
• What occurred that hadn't previously occurred?
• What didn't happen that would usually have happened? (e.g. maybe a person was absent whose actions would normally have prevented the problem)
• Was there a catalyst (i.e. something that either promoted or inhibited what occurred while not being directly involved)
• What near misses previously occurred?
• What did we do about them, if anything?
• How much of this was wishful thinking? For instance, did we put controls in place when we really had no idea of what the causes were in the hope that doing something was better than doing nothing?

But essentially, we need to move away from a simplistic:

A caused H

towards a more nuanced understanding of causality:

{A,B and C} plus {the presence of D} plus {the absence of E,F and G} caused H.

It is only when we can understand a situation in this way that we can effectively deal with it and deal with its deepest roots rather than its superficial symptoms.

One of the most difficult aspects of this is recognising what didn't happen. This will be the subject of Part 2 of this article.

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• A film worth watching to see how complex and difficult to trace causality can be is the French film Happenstance where the circumstances under which two of the protagonists meet at the end of the film are the result of a complex chain of random events.
• There are many many episodes of the TV series Seinfeld where a final outcome occurs as a result of a convergence of unrelated events in different characters lives. (e.g the episode where Kramer hitting golf balls out to sea results in George's deception about being a marine biologist being exposed.)
• Also of interest: "Accidents at sea: Multiple Causes and Impossible Consequences" 

Some Lessons from Simpson's Paradox

Simpson's Paradox is a statistical paradox which at first seems counter-intuitive. Basically, under certain conditions, Person A might achieve a better result than Person B on two different tasks, but when the results are added Person B may achieve a better overall result. This is a result that has been found in fields as diverse as medical research, anti-discrimination research and baseball batting averages.

For a numerical example consider the following:

Person A:   Task 1: 64/80 (i.e. 80%)   Task 2: 19/20 (95%)   Overall score: 83/100
Person B:   Task 1: 14/20 (i.e. 70%)   Task 2: 72/80 (90%)   Overall score: 86/100

So although Person A performs better on both tasks, Person B is the overall winner.

So how is this possible? If you think about it, the answer is simple: Person B invested most of their efforts in the task that they were best at, whereas Person A invested most of their efforts in the task they were worst at. It illustrates the old saying that what you lose on the swings, you win on the roundabouts.

There are two lessons we can learn from this that can be applied in business.

Firstly, when comparing the performance of two people, it may not be enough to simply consider their aggregate score. If for example, Task 1 is far more important to your business than Task 2, then the score on Task 1 should be treated as of greater significance than the aggregate result. Similarly, a worker may appear to have a better overall performance (e.g. make fewer mistakes) simply because they are tackling less of the harder work.

Secondly, if you want to compete effectively against someone who is better than you, then the best way is to focus most of your efforts in your strongest area. Trying to be an all rounder may yield a lower aggregate performance than playing to your strengths

Note that the paradox is only likely to emerge if the samples are of different sizes for each dimension. If we change the above example so that each person has the same sample size within each task, we get the following:

Person A:   Task 1: 64/80 (i.e. 80%)   Task 2: 19/20 (95%)   Overall score: 83/100
Person B:   Task 1: 56/80 (i.e. 70%)   Task 2: 18/20 (90%)   Overall score: 76/100

So now Person A is the overall winner. Even though the proportions are identical between the two examples, a simple change in sample size resulted in a different aggregate outcome.(In this case, both people are investing most of their efforts in their weaker area)

The overall lesson here is that when comparing performance on multiple dimensions, it may pay to drill down to individual components of that performance rather than simply looking at an aggregate figure, because this may give you a clearer picture of what a person is strong in, or conversely, where they are investing their efforts.