The most visible example to the general public of this principle is when opening a new retail store in an additional town gives access to consumers who we could not previously reach. We constantly hear of new outlets opening for Starbucks, Wal-Mart, IKEA, etc., and many of us change our shopping habits when an appealing new store appears in our neighborhood. Behind this expansion lie sophisticated procedures for assessing the likely customer-base, sales and profitability of each new unit.

But gaining access to potential customers is not the same as actually winning those customers. Having opened a new store, we must of course offer products and services that its target consumers want at prices that offer good value. Then there is the question of how far to push that expansion. Early in the life of a retail chain, every new store can be opened in a locality that is new for that chain, and where there are large numbers of potential consumers. As expansion continues, however, two things change:

• locations for new stores reach only smaller numbers of potential consumers
• each new store’s catchment area increasingly overlaps with existing units, so much of its sales only arise by taking sales from neighbors.

In the figure below, an initially successful retailer expands its network of stores over a 10-year period. With its first stores able to reach 20 000 consumers who each spend $500/year on its goods, management believes there is potential for over 200 stores reaching up to five million consumers. For the first five years, plans go well, but as expansion passes the target number of stores, consumer numbers and sales fall short of expectations. Nevertheless, with each new store seeming to win enough consumers to be worthwhile, and sales continuing to grow, the company presses on with expansion.

New retail stores bring access to ever-fewer new potential customers

Unfortunately, hidden beneath the reasonable top-line indicators there is a sharp fall in the true number of new consumers won with each new opening, and new stores increasingly succeed only by taking sales from others. Furthermore, the later consumers turn out to spend less with the stores than those captured from around the initial locations. The costs of operating these later stores is not covered by the incremental revenues and gross profit from the sharply reducing rate of new consumers, and profits go into decline.

The generic structure this example illustrates consists of:

• The in-flow of new stores (the primary resource) brings with it an attribute co-flow or new potential customers (the secondary resource)
• That growing potential of the attribute resource is then converted into an active resource, in this case consumers who really use the stores
• The secondary resource (customers) drives revenue, and costs are incurred
  [a] adding the primary resource (stores)
  [b] operating that resource (running the stores) and
  [c] converting potential customers into active ones.

Had this company cut its expansion rate when the additional numbers of consumers with each new store dropped sharply (e.g. as shown in grey text about half way through its expansion in year six), it would have attracted most of the potential market, kept profits at $32m/year, and only had to invest $240million of capital rather than the $385million it eventually spent.

You (or your students!) can explore this strategic management of market saturation in the Beefeater Restaurants business game. In addition to this issue, the game includes the impact of product development on expanding market potential, the pressure to accelerate progress when a competitor is pursuing the same opportunity, and the need to satisfy investors’ requirements in order to attract the capital to continue expansion.

This is not to say that management should give up at the first sign of having “used up” their business opportunity. It is often possible to find ways to serve smaller markets profitably. Second, many retail chains have expanded the potential market, and thus lifted the ceiling on viable growth by extending the range of products and services offered in the same space. Many have also developed slimmed-down units, offering limited product-ranges and incurring much reduced operating costs precisely to enable them to reach smaller local markets.

These principles can readily be adopted with suitable adjustment by businesses in other sectors and applied to other kinds of resource. We have featured the low-fare airline Ryanair in early briefings – a company that has relentlessly opened large numbers of new airports and routes over many years. Each new airport gives access to new potential travelers, and additional routes are one important means of developing those people into active customers. This issue is explored in another business game – the LoFare Airline microworld

Product-range extension can also give access to additional potential customers, but again can be over-done. In one country market, a confectionery company sold over 30 distinct products, but still had lower sales than Mars, who offered only 18. The competitor kept adding new products in the hope of winning new consumers, but each new line took more sales from its own range than from Mars.

Until next time…

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Real-world examples

Even world-leading retailers fall into this trap. Starbucks, for example, had to close over 500 stores during 2008, and more in 2009 because of inadequate sales, at a very high cost and having spent hundreds of millions of wasted dollars to open them in the first place, just a few years earlier (see my video presentation ” Lessons from the crisis ” (Note: Duration is 72 mins)

This strategic error can lead to further difficulties, such as damage to the firm’s reputation in the market as it is seen to operate second-rate units, diversion of management attention onto solving the problem they themselves created, damage to investor confidence, and poor morale amongst middle – and front-line management. It is worth recalling that McDonalds’ too once got into this difficulty. In its 2002Annual Report letter to shareholders, the new Chairman stated that the business was “in transition from a company that emphasizes adding restaurants to customers to one that emphasizes adding customers to restaurants.” The company also cut its target annual profit growth rate. Result? – a subsequent increase in profit growth and a strong recovery in its stock price.

This briefing summarises material from chapter 5 of Strategic Management Dynamics, pages 274-280.

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The upper part of the following figure shows an example, constructed by showing first the revenue from a company’s largest customer, then adding the revenue from the second largest, the third largest, and so on. At the far right is the very small revenue contributed by the smallest customer.

The size profile of a customer base can vary widely, from a quite balanced distribution, where customers differ little in size, to highly skewed, where the few largest customers dominate the company’s revenue, and a long “tailof small customers contribute very little. Such differences can reflect either features of the market or deliberate policy. An insurance company, for example, developed a product aimed at households in the second quartile of income (i.e. 25 % of households earn more, and 50 % earn less). The income distribution within this band is not especially wide, so the largest customers’ policies were not many times larger than the smallest. Conversely, a skewed distribution could result if, for example, management decided to move from a history of serving smaller customers by chasing a few large deals.

A strategy for improving equipment reliability.

Most companies would benefit from knowing the shape of this curve, and from an explicit policy for how they wish it to develop, covering the three classes of change highlighted earlier — adding (or losing) larger customers, losing (or adding) smaller customers, and seeking to grow existing customers. It is useful to develop equivalent quality curves for other resources too, such as the productivity of individual employees in a team, market-reach of distributors, numbers of customers attracted by separate products in a product range, and reliability of individual items of equipment [see briefing 28].

Similar pictures can be helpful in voluntary organizations and the public sector, for example the varying contribution of funds from donors to charitable organizations or political parties, the rates of illegal activity committed by criminals, and so on.

Before deciding how to drive change in the quality profile of a customer base, it is important to note that customer size is not the same as customer value, so we need to distinguish customers’ contribution to sales volume or revenue from their contribution to profitability.

As you can see in the lower part of the figure, this ordering of customers by profitability may not match the size-order for various reasons:

• larger customers may press for lower prices, so although their size is large, the profit margin on their purchases is low
• larger customers may expect higher levels of service, which incurs more cost and again depresses profitability
• larger customers can be more complex to serve, again raising cost and reducing profitability

But BEWARE! It is vital to deal properly with overheads in this analysis or you can end up making things worse rather than better. Closing customers between “D” and “B” above will not remove the need for these kinds of cost, so it is vital to know by how much total costs will actually be cut if such a rationalization is to be carried out.

Until next time…

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Don’t accept the quality curve as given

It is tempting to interpret the profit quality curve as saying simply “close down unprofitable customers to the right of D and profits will jump from C to E” However, in addition to complications caused by cost allocation, there are several further reasons to investigate before taking such simple actions.

It may be possible to develop loss-making customers so they move over to the left of D. Banks, for example, accept young adults as unprofitable customers in the knowledge that they will become profitable as they mature and enjoy rising incomes.

Customers may be linked. That bank may incurr losses in serving that same young customer, but it would be careless to close their account and risk upsetting their millionaire parents!

Finally, it is often possible to challenge fundamentally why the curve is the shape it is. Simplified and cheaper ways to serve smaller customers could enable viable profits to be made that would not be possible with the full-service business model. This effectively skews the right-hand end of the profit upwards. Such differential service models are common in many industries, from telecoms and IT support to industrial equipment supply — indeed customers may be divided into several bands, rather than just large versus small.

This briefing summarises material from chapter 5 of Strategic Management Dynamics, pages 264-267.

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This first figure shows two scenarios for what could happen to staff numbers and skill levels. In both cases, hiring is enough to both replace staff who leave and to increase total numbers as fast as needed. In the first case (dashed lines) training of 7.5 days per month would be enough to sustain skill levels for the initial 80 staff, but is not enough to provide the additional skills needed by the fast arrival of unskilled recruits. Average skills therefore fall (right-most chart). To sustain average skill levels (solid lines) training inputs must actually rise at a faster rate than staff numbers, because of the disproportionate impact of the many new recruits.

What difference does this make to service performance? Well, in this next figure, the business starts with a stock of 200 000 customers, who on average make one call per month to the call center. The 80 staff are enough to answer all incoming calls, and their skill level is high enough to ensure that virtually no customer calls are badly handled. About 5000 new customers are being won each month, which increases the rate of calls received. The call center hires an additional two staff per month, which raises capacity in line with growth of customers and call volume – so there continues to be enough staff to pick up the phone for every caller.

In the first case (dashed lines) even the small number of additional new staff progressively dilutes the center’s skill level. Although all calls continue to be answered, an increasing proportion of customers’ enquiries are not dealt with properly. Even assuming that only a fraction of disappointed customers leave each month (20 % in this illustration) the loss rate rises until, by month 24, the business is losing as many customers as it wins. This is in spite of the fact that it has plenty of capacity to actually answer calls: 320,000/month versus a call rate of 270,000. If this example is carried forward beyond month 24, the loss rate continues to escalate, leading to a real decline in customers.

In the second case (solid lines, and bold text figures for the month 24 situation), the center manager raises the training time at a rate sufficient to ensure that the additional new staff are also trained. Average skill levels are maintained, customer enquiries are handled well, and virtually no customers are lost – at least not because of poor service by the call center!

Until next time…

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Skills and Competencies

This numerical approach to estimating how skills develop may seem rather mechanistic for an issue that is somewhat intangible and hard to define. However, specifying skill requirements and auditing organizations’ skills or competencies is now a common practice, and many consultancy organizations offer support for such assessments.Furthermore, such audits are not limited to the routine task skills of operating-level staff, but are also applied to middle and senior management roles. A typical management competency matrix would list the competencies required – for example, commercial understanding, leadership, communications, delivering performance – and individuals’ competency on each measure would be assessed according to specified levels on each measure. Summed across a whole population of staff at different levels, this assessment can indicate the overall health of the management group and highlight opportunities for their development.

It is important not to take this approach too far, of course. There is more to management capability than a list of criteria, and it can be important to leverage the widely differing strengths of individuals, rather than insist everyone fills in weaknesses. Nevertheless, such information can be valuable if used well.

This briefing summarises material from chapter 5 of Strategic Management Dynamics, pages 254-256.

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Table: Adding larger customers to the manufacturing firm’s customer base

Table 1: Examples of attributes for tangible resources

Start of month	1	2	3	…	22	23	24	end
Customers	240	245	250		345	350	355	360
New customers per month	5	5	5		5	5	5	5
New sales per new customer units per month	105	105	105		105	105	105	105
Increase in total sales rate	525	525	525		525	525	525	525
Total sales units per month	8,400	8,925	9,450		19,425	19,950	20,475	21,000
Average sales per customer	35.00	36.43	37.80		56.30	57.00	57.68	58.33

This change in customer quality can be displayed as a time chart (below). As in earlier chapters, the causal arrows reflect specific, explicit relationships:

New sales each month = new customers per month * new sales per new customer

Average sales per customer = total sales per month divided by customers

Depicting changing customer quality as larger customers are won.

The attribute Total sales per month is shown as a resource, in parallel with the Customers resource. This is because, like customers, it accumulates and would deplete if customers were lost. In other words, sales per month is something that “flows with” customers, so this relationship is referred to as a “co-flow” structure. There are two potentially confusing features of this structure to clarify.

• The attribute of Total sales: units per month is increased by the arrival of new sales from newly won customers. The units of this increase are therefore “units per month, per month,” so the phrase New sales each month: units per month is used to avoid confusion.
• Why is the attribute resource given as total sales? If we want to track the average customer quality, why does the lower resource not contain average sales per customer? The reason is that the math works out much more easily if the lower resource contains the total sales, rather than the average per customer.

Extending the bathtub analogy introduced in earlier briefings may help clarify why this works. Imagine that the resource itself is the amount of water in a bath. The quality of interest is the temperature of that water. If you want a warmer bath, you can add hot water. How much warmer it gets depends on how much heat the added water brings with it. The initial temperature is the initial quantity of heat divided by the amount of water that shares it, and the final temperature is the new quantity of heat divided amongst the new, larger quantity of water. Unlike our manufacturing firm, however, the water does not stay divided into two separate amounts, one of which is hot and the other cool, but gets mixed up so that the whole bathtub reaches a similar temperature.

Two options to improve the customer-base

Adding larger customers is just one way to increase the average size of the customer base. An alternative is to remove smaller customers. Consider what might be done if the company’s initial 240 customers who are buying an average of 35 units per month actually consisted of 120 buying 50/month, and another 120 buying 20/month. The company could allow the smaller customers to be lost, leaving itself with a customer base increasingly dominated by larger customers. If five smaller customers were to go each month, the company would end up after 24 months with only the 120 customers buying 50 units/month.

Although the company improves its average customer quality by this mechanism, it does of course suffer a fall in total sales. Consequently, it could end up making lower profits in spite of the better quality of its customers, depending on how much it can reduce costs as customer numbers fall.

Until next time…

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Doing it right: attributes may “break the rules,” on defining resources

Surely the additional resource in this figure is breaking the rules that were so carefully laid down in earlier briefings about what is or is not a resource? We insisted then that variables such as sales, revenue, costs and profits are not resources. Since their units are “XXX per time period” they are instead flows of orders or of money. That rule remains important, but the attribute resource in this figure is being used only to describe the quality of the customers, not as a resource in its own right.

We can (and should!) still calculate Total sales from Customers multiplied by average sales per customer and the resulting chart of Total sales would be exactly the same as the lower chart shown here. Sorry if this seems pedantic, but it’s right – and important.

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• Performance at any time of any organization depends on the levels of resources in place at that time
• Resources fill up and drain away over time – mechanisms that therefore cause performance to change over time
• The rates at which each resource fills and drains depends on existing levels of resources, which may include actual and potential levels of that resource itself.

[Briefing 16 already mentioned that managerial decisions and external factors also feature in this system.]

This is an example of ‘deep’ theory – a principle that applies very widely – rather than mid-range theory that only applies to a narrow set of circumstances. It describes how a vast variety of systems work, not just businesses and other organizations, nor just social systems, but whole classes of environmental, biological, physical and other systems.

So now we need to look in more detail as how resource flows at any moment depend on already existing levels of relevant resources. Simple cases are pretty obvious – more sales people means we can win new customers faster, more customers drive sales revenue that means we accumulate cash faster, more R&D facilities mean we can launch new products quicker and so on. The special case here concerns cash, which can mostly only drive growth in other resources if it is itself consumed. Here’s a specific example from the Ryanair case we have looked at before, where larger numbers of routes [the company’s product range] have given more choice of places to fly to, and hence won new customers faster than would otherwise have been the case.

Resource loss rates too depend on existing resource levels. Too few service staff lead to customers being lost more quickly, too little production capacity leads to product shortages that also drive customer losses, too many customers place pressure on sales and service staff that may cause them to leave more quickly. Here’s an example of this phenomenon for Ryanair.

It is pretty easy to see how several such mechanisms can work together to determine how the level of just a single resource changes over time, as this example of Ryanair’s customer-base shows.

Just note how our relentless pursuit of ‘what causes what’ works here, and in general. We have to explain each flow-rate [e.g. customer losses] and if you trace back along the chain of links leading to that flow-rate, you always end up at existing resource levels [aircraft, staff], at people’s decisions or at factors outside the system. It is the first of these that leads to feedback, i.e. interdependence, amongst the resources of a business, which help drive growth or decline.

You will see, by the way, that tracing these links can bring you back to the current level of the same resource itself – the customer loss-rate depends on existing customer numbers – an issue we will say more about next time.

Until next time…

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Take care with ‘feedback loops’

You may have come across the idea of feedback before, perhaps from Peter Senge’s book ‘The Fifth Discipline’. This describes the two forms of feedback and goes on to lay out a variety of common feedback structures that explain characteristic behaviours, such as ‘limits to growth’, ‘boom and bust’ or ‘tragedy of the commons’.

In ‘reinforcing’ feedback, an increase [or decrease] in factor A causes and increase [decrease] in factor B, which then creates a further increase [decrease] in factor A again. This leads to self-reinforcing change, commonly known as virtuous [or vicious] circles.

In ‘balancing’ feedback, an increase or decrease in factor A causes and increase or decrease in factor B, but that then creates a decrease [increase] in factor A, reversing the original change. This type of feedback gets its name because any deviation from the starting position corrects itself and brings it back into balance. In this case, it is staff capacity that imposes this constraint on customer numbers.

Problem is, it only works in one direction – if customer numbers grow too much, they get pulled down again. The opposite doesn’t work because there is nothing in the loop itself to push customer numbers back up again if they fall. So if customer losses start for some other reason, such as a price change, they can just keep going, dragging the business down to nothing.

An equivalent problem arises with reinforcing feedback, the most commonly understood example of which is word-of-mouth between existing and new customers. The mechanism may well drive growth in demand, but the same feedback loop cannot drive decline, since its only flow-rate is ‘customers won per month’.

This briefing summarises discussion from chapter 4 of Strategic Management Dynamics,
pages 189-192

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]]> http://kimwarren.com/strategy/briefings-18-resources-drive-each-others-growth-or-limit-it/feed/ 0 http://kimwarren.com/strategy/breifings-17-the-problems-with-correlation/ http://kimwarren.com/strategy/breifings-17-the-problems-with-correlation/#comments Tue, 19 Apr 2011 09:00:40 +0000 Kim Warren http://www.kimwarren.com/?p=1786 A devastating implication arises from this interdependency between resources from the previous briefing. Here is a simple story…
A company’s profits come from its revenues minus its costs, and its revenues reflect the number of customers. Amongst its costs, marketing spend mostly acts to win customers, rather than increase their purchase rate

• The company raises marketing spend in order to grow sales and profits
• But first, its profits fall because of the higher marketing cost
• However, the marketing spend does win a few more customers, but the small growth in sales is not enough to pay for the continuing higher marketing spend.
• The following month, customer numbers and sales rise again, but still not enough to pay for the higher spend.

This could continue for many months, during the whole of which time the higher marketing spend seems to have caused lower profits.

• The head of finance loses patience and pulls marketing spend back to a lower rate even than at the start.
• The saving leads to an immediate jump in profits – to a higher rate than at first, due to the new customers who have been won in recent months.
• With lower marketing spend customers are won slower than they are won, so sales fall.
• Profits start to decline from month to month, but are still higher during the period of high marketing spend.

Lower marketing spend seems to have caused increased profits.

The data here is longitudinal, i.e. the same few observations repeated over many periods. But it could also be ‘cross-sectional’ – the same factors observed at the same time across many firms in the same market, each with its own history (on the lower chart). If you used normal regression methods to see if ‘marketing drives profits’, you could easily ‘prove’ a negative correlation. Positive correlation, or none, is also quite possible, but all possible findings are meaningless.

Problem is, the accumulating stock of customers destroys any direct link from marketing to sales and profits.

Today’s results reflect the whole history of marketing spend.
Simple rule: Don’t trust regression results when there may be accumulating stocks between the outcome you want to explain and the factor[s] you think are driving it.

… and here we only have one such item. Think what a mess you could get when several accumulating items are at work, for example when R&D spend grows new technology, which leads to more new products, which grows customer numbers…! No wonder we can’t work out what drives business performance by studying large samples of companies.

Until next time…

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What ‘performance’ do we want?

We have largely taken it for granted up to now that what we want – at least in business cases – is growth in profits over time. Curiously, that’s not how academic research into business strategy generally views the question. Instead, this research tends to focus on levels of profitability – return on sales or invested capital, for example.

The reason for this is the micro-economic foundations of strategy research, where researchers want to know why some firms are more profitable than others. But investors value cash flows, specifically the present value of the cash flows they expect to receive in future periods. They don’t care, then, if firm A is less profitable than firm B, provided that A’s cash flows are growing faster. In extreme cases, investors accept very low, or even negative profitability for many years – still giving the firm’s shares a positive and growing value – because they expect to see rising cash flows in later years.

To see how important this distinction is, consider Amazon.com who made losses right up to 2002, and continued to make very low returns on sales and invested capital for some years. On the first basis, researchers would be wanting to explain why Amazon had been so persistently unsuccessful, whilst investors were happy to enjoy its real-world success!

This briefing summarises discussion from chapter 3 of Strategic Management Dynamics,
pages 177-179

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]]> http://kimwarren.com/strategy/breifings-17-the-problems-with-correlation/feed/ 0 http://kimwarren.com/strategy/briefings-16-what-drives-gains-and-losses-of-resources/ http://kimwarren.com/strategy/briefings-16-what-drives-gains-and-losses-of-resources/#comments Thu, 14 Apr 2011 11:34:33 +0000 Kim Warren http://www.kimwarren.com/?p=1775 I hope we have now made a pretty solid case for the first two parts of the core strategy dynamics framework:

• Performance at any time of any organization depends on the levels of resources in place at that time
• Resources accumulate and deplete over time – mechanisms that therefore cause performance to change over time

We got to these principles by repeating relentlessly the question ‘What causes what?’ So the next logical question is… ‘What causes the rate at which resources are won and lost?’
Answering this question will bring us shortly to the final part of the theory and thus complete the core ‘strategic architecture’ for a business or other organization – a rigorous, quantifiable, diagrammatic representation of the structures causing performance to change over time.

So – what does cause the rate at which resources are won and lost? Just three clear kinds of factor are involved:

• Certain management decisions. We decide, for example, how many people to hire, how much capacity to add, and how many products to launch from period to period.
• External factors. Competitive issues are the obvious category here, such as price levels or product improvements which may steal our customers. More general market factors are also influential, though. Increasing disposable income for consumers, for example, may cause them to become customers for goods and services that they previously could not afford.
• Existing levels of resources. This turns out to be the third and final piece of the theory we have been building, and we will have much more to say about this in the next few briefings.

Let’s just look at the first of these items in a bit more detail.

How Management Decisions drive resource flows

It would be neat if all resource flows could be determined directly by management, i.e. we get exactly what we want, the minute we ask for it. Want 100 more customers next week? – so send an email to make it so! Unfortunately, of course, that isn’t possible. It is possible, however, for some kinds of resources. For example, a transportation firm can generally decide directly how many additional vehicles to add to its fleet. Unless there was suddenly a worldwide shortage of the particular vehicle type required, that decision will simply happen. Similarly, many companies can decide how many products to discontinue, or how much capacity to shut down.

Others’ decisions may be wished-for, but be moderated by other factors. You may want to hire 50 people each month, and set your human resource department the task of getting these people, but though you may interview 200 people, and offer 50 jobs, you can’t be exactly sure whether 50 people will accept, or 30 or 80.

Other decisions have much more unpredictable impacts on resource flows, the most substantial example being customer acquisition. As indicated above, while you may wish to add 50 customers in any year, that rate is ultimately in their gift, not yours, and is strongly moderated by factors outside your direct control. Marketing spend may build awareness and understanding among possible customers, a price cut may increase their perceptions of value, and sales effort may be sized to target a known number of possibilities. However, whether these factors will bring in 50, 10 or 100 new customers—or none at all—is rarely certain.

The figure below shows this spectrum of influence that management has over the rate at which resources are won – from almost total, immediate control, to indirect and uncertain influence.

In the next few briefings we will go on to look at how existing resources help or undermine efforts to develop other resources into the future. We will return to the impact of external factors – especially competitors – later.

Until next time…

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When you ‘step on the gas’ and nothing happens…

You may find that the degree of control you have over the capture and retention of key resources changes, sometimes suddenly and surprisingly.

Law firms, for example, depend on hiring each year enough newly qualifying lawyers with the skills they need. Normally, well-established firms can be reasonably confident of getting these hires, but that was not the case on one occasion for one particular firm.

This firm regularly received about 300 applicants each year, to whom it would offer 100 positions, and expect to hire 50 people. It was surprised one year, though, when applications fell to only 80 and it managed to hire only a small fraction of the staff it needed. After worrying for some time about how this disaster had occurred, the leadership was mortified to learn that they had brought this problem on themselves. In the year in question, the firm had merged with another and changed its name. Whilst it had taken great trouble to publicize and promote this change amongst its clients and potential clients, it had neglected to communicate to the ‘market for talent’ – so the young graduating lawyers simply did not recognize the new firm’s name among the list of recruiters!

This briefing summarises discussion from chapter 3 of Strategic Management Dynamics,
pages 142-145

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]]> http://kimwarren.com/strategy/briefings-16-what-drives-gains-and-losses-of-resources/feed/ 0 http://kimwarren.com/strategy/briefings-15-insights-from-asking-simple-resource-flow-questions/ http://kimwarren.com/strategy/briefings-15-insights-from-asking-simple-resource-flow-questions/#comments Tue, 22 Mar 2011 09:00:06 +0000 Kim Warren http://www.kimwarren.com/?p=1755 After all the years I have been using strategy dynamics, I still find myself shocked at the power of this most basic of questions. Time after time, executives get completely new insights from asking about just three numbers…

• How much have we got?
  
• How quickly are we winning more?
  
• How quickly are we losing?

… whether it’s customers, staff, or some other critical resource.

Take the case of a branded pain-relief product in the mature US market. Despite some growth in people using this brand, market share was declining because their average purchase rate was falling. Close examination of consumer behavior discovered that, far from the stability that the low market growth rate implied, buyers of pain-relief products were actually churning quite quickly, with nearly 13% switching their preferred product each year. The consumer churn for this specific brand was comparable to these rates for the market as a whole.

Consequently, although net change in consumer numbers and sales was very small, the scope for improving the situation was considerable. Rather than trying still harder to capture sales from the market leader, attention was shifted to retaining specific consumer groups who were leaving most rapidly. The falling average purchase rate per consumer was explained by the fact that consumers being won were lower volume buyers than those being lost.

Now I have used this example with students and executives for a few years, and start by explaining that the product is in slow decline and asking what information might be needed to solve the problem. I can fill whole boards with answers to this question – people would like to know, for example, how much marketing money is being spent, competitive price levels, sales force numbers, product availability in stores, and so on and so on. All reasonable enough questions of course, but unless you ask these questions, none of the other answers will be much use.

“But that’s obvious!” everyone says – yes, it is, but if you don’t ask them, you won’t know what the numbers are telling you, and you will never know what to do.

People issues are another common area where these simple questions are powerful, but don’t get asked. One of the largest US banks had an objective to increase the representation of people from minority groups amongst its senior executives. It made a public commitment to increase the fraction from 6% to 20% over 5 years. The bank had about 1000 senior staff in total, so 60 were from the identified minority groups. To aid in this goal, they had a program to identify and support junior staff with the potential for senior positions.

So what flow rates are involved here? Turnover of senior, minority staff already in place was running at about 25%, so they needed to promote 15 new people each year just to stand still. Getting to 200 in 5 years means they would have to promote 43 each year (140/5 + 15), and even that requires a steady fall in the fraction who leave each year from 25% to 7.5%. There was simply no way to boost the flow of senior-potential staff to those kinds of rates within the time-scale needed to hit the target. So here we have a perfectly laudable objective that simply can’t be met.

Until next time…

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Big shifts can sometimes be made to happen

Here’s an example in the public sector.

In August 1996, Bill Clinton signed a law requiring states to encourage welfare recipients into work—a policy that came to be known as workfare. This policy included limiting families to just five years of federal money, together with incentives for states to shrink their welfare caseloads. This step change in policy triggered some significant changes in states’ approaches to welfare, including the requirement for applicants to try job searches, assistance in finding child care, and subsidized travel to work.

While the policy remains controversial and problems of poverty remain, the next decade saw welfare caseloads fall by 60 % from 5 million to 2 million families. An important objective for many states was to achieve long-term, quality employment for claimants, with the intent of helping people into careers, not just low-grade temporary jobs. New York State, for example, was able to see employment among single mothers increase from 40 % to over 60 % in just five years.

The great work that helped on this was done by Professors George Richardson, David Anderson, Aldo Zagonel-Santos and others at SUNY-Albany.

This briefing summarises discussion from chapter 3 of Strategic Management Dynamics,
pages 131-134

Read more about the book on our website

]]> http://kimwarren.com/strategy/briefings-15-insights-from-asking-simple-resource-flow-questions/feed/ 0 http://kimwarren.com/strategy/briefings-14-a-spreadsheet-view-of-resource-accumulation/ http://kimwarren.com/strategy/briefings-14-a-spreadsheet-view-of-resource-accumulation/#comments Tue, 08 Mar 2011 09:00:22 +0000 Kim Warren http://www.kimwarren.com/?p=1732 All this talk about how accumulation is different than the usual way we think of causal relationships may have got you worried… Is this some mysterious process that needs fancy methods to work?

Although the time-charts and diagram structures we use may see an unfamiliar way of looking at business performance, they simply re-present what could equally be shown in a spreadsheet.

Take this figure from the last briefing, where a constant rate of customer gains interacts with a rising loss rate to cause growth and decline in the company’s total number of customers, and hence in its sales.

• In month 1, we add 20 customers and lose 12, so end the month with
  100 + 20 – 12 = 108.
  
• In month 2, we add 20 customers and lose 14, so end the month with
  108 + 20 – 14 = 114
  
• … and so on.

The spreadsheet also adds an extra line for average sales last month. As discussed before, when factors change over time, periodic calculations become increasingly accurate as the time between those snapshots shortens. In this example, unless sales rates change a lot from week to week, monthly periods are accurate enough for estimating future sales patterns.

A few things to note:

• This is no different than what we do to track cash-flow and cash
  levels from period to period – so if we ‘account’ for cash like this,
  why would we not account for customers, staff and other valuable
  resources in the same way?
  
• Whilst you can do this with a spreadsheet, there’s a big jump in
  understanding from the visual presentation of the time-paths.
  
• The resource flow rates — new customers and customers lost
  per month — are telling us the trajectory on which the business is
  heading at the start of each period.

In this case, the initial customer win rate of 20/month and losses of 12/month mean that the customer base is heading upwards by a net +8 per month. By month 4, customer losses match the win rate, so there is no net change in the customer base. By month 12, customer losses of 36/month are way faster than the win rate of 20, so into the next month (month 13), the net change will be –16/month.

Here, winning 20 customers and losing 12 is not the same as winning 100 and losing 92! And this distinction is important in relation to other resources too. For example, for many years it was taken as normal for staff in the fast food sector to turn over quickly, with the average person staying for just a few months of temporary employment. Work methods and training were designed to get new staff productive quickly. But some aspects of staff performance, such as good customer service, come with experience. Consequently, Starbucks enjoyed for years a big advantage with a staff employment system that succeeded in retaining staff for much longer periods—typically well over 12 months. Not only were hiring and training costs reduced, but customers got a better experience too.

So going back to the key point of this briefing – accumulating resources are perfectly easy to model in a spreadsheet if you really want to. However, it’s not so easy to set up the clear graphical relationships that packages like mystrategy™ offer. [ This is the mapping and modeling software used to create all the diagrams in these briefings and the book – see www.strategydynamics.com/mystrategy]. Using such software packages has another big advantage over spreadsheets – because you can only set up equations using variables that are physically linked, it’s much harder to make structural errors in your models.

A friend of mine uses this facility to audit clients’ spreadsheet models – and alarmingly finds important errors in most cases! The variables are also named, so your equations look like “sales = customers * sales per customer” not “D13 = A$12 * C13”.

Surely not so simple!

A response I get quite early on to the basic idea that resources accumulate is ‘Yes, but there’s more than just winning and losing customers – some are bigger or more profitable, and others are smaller.’

True, and we certainly have to deal with this, but let’s not try to run before we can walk. It will be some time before we can get into how to deal with a complete profile of a customer-base, but we can already work on customer-differences with the simple concept of ‘segmentation’, i.e. splitting them into groups.

To take a simple example, you may want to differentiate between a small but important group of large customers and a more numerous group of small ones. This is dealt with simply by ‘photo-copying’ the figure in this briefing – one copy for the large ones and a second copy for the small ones – and adding together the resulting ‘total sales to large customers’ and ‘total sales to small customers’ to produce ‘total sales to all customers’.

The spreadsheet equivalent would be to have separate worksheets for large and small customers – the analogy of a ‘photo-copy’ still works – and a front-page worksheet where the total is calculated. This is pretty trivial if all you are doing is tracking the customer flows and resulting sales for two groups as we are in this example, but could clearly get more complex, and therefore vulnerable to mistakes, if multiple groups are involved with more extensive information for each.

For an example, see the sidebar…

Until next time…

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Not all customers [or staff] are equal

Segmentation can get rather sophisticated and complex.

For example, in my previous experience in the restaurant industry, we carved up the market very successfully by distinguishing
[a] the different groups of consumers who eat out – younger couples, older couples, single-sex groups, families with children etc. – from
[b] the various needs that a particular eating out occasion was fulfilling – celebration, ‘refuelling’, intimate meal and so on.

The result was a rather extensive matrix with 40+ significant combinations of groups-v-needs contributing to our total sales. For each ‘cell’ in this matrix, we needed to track the flows of people won and lost, the frequency of their eating-out occasions, the average spend per occasion, and so on. Handling all this in a single spreadsheet with no errors requires quite some expertise!

It is beyond the purpose of these briefings to get into exactly how these kinds of situations are actually modeled in dynamics software. But it is worth mentioning that they enable this kind of complexity to be handled rather easily. You simply set up the causal structure one time, including the customer-resource and its flows, then tell the software that the structure is replicated for the various segments and add the relevant data for each.
[You need a more powerful software than mystrategy for this purpose, however.]

This briefing summarises discussion from chapter 3 of Strategic Management Dynamics,
pages 131-134

Read more about the book on our website

]]> http://kimwarren.com/strategy/briefings-14-a-spreadsheet-view-of-resource-accumulation/feed/ 0 http://kimwarren.com/strategy/briefings-13-doing-it-right-with-accumulating-resources/ http://kimwarren.com/strategy/briefings-13-doing-it-right-with-accumulating-resources/#comments Tue, 22 Feb 2011 09:02:24 +0000 Kim Warren http://www.kimwarren.com/?p=1715 There is one last thing to do before going on to the implications and uses of resource-accumulation. Sorry – but it’s just got to be done right! If you get this wrong, everything else you try to do with strategy dynamics will be messed up.

Defining and measuring resources and their flows

We were careful in earlier briefings to make sure the causal connections were arithmetically accurate, for example, that quantity sold per month was equal to customers multiplied by quantity per customer per month. It is just as important to define the units of resources and flows accurately. This is simple, though:

Whatever the units by which a resource is measured, the inflows and outflows are always measured in the same units per time period.

There is never any exception to this rule. The table below lists the major types of resource, the units by which they are measured, typical inflows and outflows for each, and the units for measuring those flows. The only judgment to be made about the correct units for resource flow rates concerns the choice of time period – should it be people per week, per month, or per year? I previously explained that time periods should be short enough for change during any period to be relatively small, compared with the overall time-scale of the situation you are looking at. If, for example, you are looking at how profits have been changing from quarter to quarter, then customer gains and losses can be measured in customers per quarter. If you are in a faster moving business and want to understand why sales rates change substantially from week to week, then you need information on weekly sales, and your measure of customer flows should be customers won per week and customers lost per week.

The last item in the table shows a small complication that comes up in some cases – when a resource itself includes time. Production capacity for, say, a cement or steel producer is measured in “tons/day.” If we change capacity by adding new equipment or closing a plant, the result is an inflow or outflow of a certain number of “tons/day this year.” This is a one-off inflow, but continuous changes in flow rates may also occur. The production rate for an oil field is measured in “barrels per day”, but as a field is drained, its production rate typically falls. This decline would therefore be measured in “barrels per day, per year.”

Depicting resources, flows and the factors that drive them

To continue in our quest for an accurate causal explanation of performance, we must maintain the discipline of depicting correctly “what causes what,” just as we did with the causal connections in earlier briefings. Since the current quantity of a resource is “caused by” whatever we had at the start of a period, plus what was added, minus what was lost, it cannot be caused by anything else. The first figure below must therefore be wrong—the marketing spend of $5 000 per month cannot explain the number of 1 015 customers at the end of the month. It is missing all three of the numbers needed to work out that quantity (the number at the start of the month, and the numbers gained and lost), and the causal link from marketing spend to customers is meaningless.

The second figure shows the correct causal structure. The “stock and flow” structure includes all the values needed to explain the end-of-month number of customers. It also shows the causal link that “marketing spend of $5 000 per month has won 20 customers during the month.”

(If you ever look at simulation models of how an organization is performing over time, you might see links like the ‘illegal’ one in the first figure. Don’t be misled, though, the simulation needs to know the start-value for the resource, and those links only set that value (1000 customers in this case). All values for later time periods are calculated from the in- and outflows.)

It’s just hard!

It’s pretty obvious that if we have 100 customers now and win five during this month, then we will have 105 next month. Obvious, yes, but it turns out the human mind simply cannot take this simple notion and work out how resource levels will change over time if the flow-rate varies. Believe it or not, most Masters students at top-level technical universities cannot look at a picture like this with the time-chart for ‘total customers missing and sketch in accurately what will happen to that stock.

They aren’t dumb – it just seems that the ability to do this had no evolutionary value. If our cave-dwelling ancestors had no food, they went out and got some – they didn’t bother estimating the rate of depletion and how many days they could lounge around before getting hungry. Later, when humanity could store food, this became a useful skill (indeed this need triggered the first use of ‘accounting’ – for stocks of grain!)

This estimation becomes particularly difficult when, as is common, more than one resource flow is involved. Customers may be lost as well as won, so this overall win rate of five customers per month could easily be the net result of winning 10 and losing 5 each month, winning 20 and losing 15, or even winning 100 and losing 95. These alternative situations are not equivalent! A company experiencing the last of these cases will have some serious problems:

• it requires effort and cash to win customers, so a high rate of customer churn will be costly
• the large number of customers being lost will likely damage the company’s reputation
• if there is a finite number of potential customers, the company risks running out of customers

So – it is worth reemphasizing an important implication of this structure:

It is vital to know, separately, resource in-flows and out-flows.

Sorry if you feel I’ve beaten you up rather a lot in this briefing. Believe me – if you take the time to really work at these principles until you have them nailed, you will thank me for it later!

Until next time…

If you would like to receive the series from the beginning in your email inbox, please register on on our website and subscribe to Briefings in “My Account”

Does this matter?

It does if the confusion makes us mistake the consequences of our decisions.

• If you are worried about the level of debt on your credit card, for example, and you do not appreciate the distinction between levels and rates, you might imagine that cutting your “level” of spending would reduce your debt. But up to a certain point it won’t—reducing the rate of spending would merely slow the rate at which your debt is rising!
• A company concerned with falling sales revenue might mistakenly think that reducing the “level” of customer churn would lead to increasing sales. But customer churn is the rate of customer losses (units being customers per month), and sales revenue will continue falling until that rate is less than the rate of customer acquisition.
• The confusion also matters on bigger issues. Governments are to varying degrees committed to reducing the “level” of greenhouse gas emissions, in the belief that this will “tackle” global warming. It won’t. Greenhouse gas emissions are a rate (units are billions of tons per year) that is adding to the level of those gases in the atmosphere. As long as the emission rate exceeds the absorption rate of the planet’s biosphere, any reduction in emissions is merely slowing the rate at which that level is rising. If you are filling a bathtub with a fire hose, turning the hose down by a few percent is not going to stop your house from flooding!

This has important implications for how management decides on objectives. If you fear that your credit card debt is too high, the only appropriate objective is for your spending rate to be cut to less than your repayments, minus interest charges. The company with falling revenue must aim for the rate of customer churn to be less than its rate of customer acquisition. And if governments believe high levels of greenhouse gases to be dangerous, then the only appropriate aim is for the rate of emissions to fall below the planet’s absorption rate, which is a very large cut indeed.

This briefing summarises discussion from chapter 3 of Strategic Management Dynamics, pages 127-130

Read more about the book on our website

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