To “adapt” implies building specific resources and capabilities that previously did not exist. For example, a current client is trying to move from a hardware-sales focus to a “solution-provider”. They need to design solutions for distinct markets they serve (a solution = the hardware + control systems + ongoing support). Creating these solutions is a capability they currently do not have. Nor do they have the people (in sales, design, project mgmt) to start developing that capability.

We can specify not only the numbers of such people needed, but also the rate at which they can be hired or developed, the rate at which *they* can then build solutions, the numbers of potential customers those solutions can be sold to, the rate at which those potential customers can be developed, and thus how costs, sales and profits can grow over time – to get to a cash-flow forecast from this strategic initiative.

Contrast’s founder, Professor Werner Hoffman is working on building strategy dynamics into the company’s training programs, as well as Vienna University’s Masters courses in strategy to help build this important link from strategy to financial performance.

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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This goes back to the issue of “related diversification” – not essentially much different for mid-size firms than for large ones. Back in the 1960s, unrelated diversification was all the rage, spurred by the BCG matrix ideas, but it then became clear that ‘corporate’ added no value to such random business portfolios, and raiders broke them up, selling the bits to groups where they added value.

Same applies today – if any one of your businesses really has no connection with the others, then it is probably worth more to someone else with whom it *does* fit than it is to you. How to tell? Estimate its likely future cash flows in your hands, then ask if someone else could make those cash flows grow faster – for example, do they have sales channels that could build sales faster than you can, or do they have similar manufacturing plant that could be rationalised with yours? If yes – call them up and see what it might be worth!

If one of your businesses *is* related to others you own, though, check which resources and capabilities are common, and seek ways to strengthen them. It’s not exactly a “rule”, but shareable resources and capabilities are commonly at the back end of the business – R&D, IT, sourcing, production – with the front-end (marketing, sales, service) being harder to combine across different businesses. The challenge is to make this resource-sharing effective, without becoming cumbersome and holding back any of the business units involved in the sharing.

So, let’s stop debating this basic issue, and focus on working out, codifying, and explaining how to do strategy well, whatever the circumstances.

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 causes a tricky issue – on the one hand, equipment needs to be kept sufficiently young and well-maintained to perform well. On the other hand, we need to minimize the cash spent on maintaining equipment and on replacing especially poor items.

In the following figure, a utility company has increasingly unreliable units of equipment (e.g. pumps for a water supplier, power switches in an electricity distribution firm). There are 5 000 of these units, which are initially quite reliable with only 5 % failing in any year. To contain costs, spending on both maintenance and replacement is limited – $5m/year on replacing the worst of its units, sufficient to upgrade 100 units per year, and $5m/year on maintenance, enough to maintain each unit once every three years. Units deteriorate evermore quickly as the time between each maintenance event increases.

The attribute of concern here is the failure frequency per unit—strictly its “unreliability.” New units bring with them a low number of failures per year. Unreliable units that are replaced take with them their high failure rate. Since it is the worst units that are replaced, they take with them five times the average failure rate of the entire population of units. (This constant multiple is a big simplification!).

Current policy will lead to an ever escalating failure rate (dashed lines and light text for values at year 10). Costs of maintenance and replacement remain low, but costs are rising on fixing the growing number of failures. The problem only slows in its growth because the few units that are replaced are so unreliable that removing them nearly balances the high rate of new failures.

A strategy for improving equipment reliability.

We need to work out how much to spend on maintenance and replacement, and what this might do to future failure rates and total costs. In the strategy shown (solid lines and bold text), we increase maintenance spend sharply in year two, which increases total spending to about $39m/year, but at least the failure rate is not getting worse. Although this stops reliability getting worse, the high rate of 680failures per year is still too high, so we raise the rate of replacement from 100 to 400 units per year. This is costly, but cuts total failures to under 300/year, at which point, the replacement rate is cut back again. Costs more or less stabilize at $30.8m/year.

Until next time…

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Strategic implications of asset-decline

This situation focuses on the cost side of the firm’s P&L, rather than revenues — a big challenge in capital-intensive industries, where time-scales are long. Here, it is 5 years before higher maintenance spend is outweighed by lower cost of failures.

This makes it tempting to cut current costs to hit short-term targets and “investor expectations”, though investors will not be happy later with a business in bad shape and getting worse.

Customers may counter this temptation. You can tell when a hotel or restaurant chain is in trouble from the poor state of its outlets and go elsewhere, but such punishment is not always possible — we cannot usually choose another power grid or water network. So such industries are often regulated, with targets for prices and reliability. But regulators too can get it wrong—in one market, regulators approved investment rates sufficient only to replace water network assets every 250 years! .. and the US has seen the problems of power outages and bridge collapses caused by long-run underinvestment.

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

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“Projects” are certainly vital – a big part of Cisco’s past success came from its power as a ‘serial-acquirer’ of new technologies, each of which was a project. And a white-goods manufacturer recently specified a project process for entering each in a sequence of national markets.  But strategic management is not all about projects – the critical foundation is a robust set of policies for the repeated decisions that keep the plan on track, about pricing, product development, marketing, staffing and so on. Only if this is sound can we embark on the bigger steps that need project discipline.

[1] Executing your Strategy by Mark Morgan, Raymond Levitt and William Malek, Harvard Business School Press.

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.

Read more about the book on our website