This company gives private investors the opportunity to invest in pools of shares, rather than in individual companies. Investors buy units in one of the company’s funds on the advice of an investment broker, or invest indirectly through pensions and insurances. The funds range from safe, low-return investments, e.g. government bonds or corporate debt, to high-risk but potentially high-return funds investing, e.g. in high-tech sectors or emerging economies.

Emboldened by previously strong stock market conditions, this company like many others, grew substantially, adding more funds, investing in more equities across many industries and geographic regions. It also started marketing its wide variety of funds directly to the public, in the hope of capturing ordinary folk who were becoming investors for the first time through the increasing number of brokers with whom it was dealing. To support this expansion, they had hired many more fund managers—the clever people who choose where to invest the capital in each fund. To help them, the company had taken on many analysts to assess the likely performance of companies in which each fund’s capital was invested.

This expansionist strategy worked well during the years of strong stock market growth, but these conditions disguised a number of problems. Many of the new brokers were small, and the end-customers were investing only small amounts, but still generated a lot of transactions that had to be administered, increasing overhead costs. Many of the new funds were not performing well, so investors saw no increase, or even a decrease, in the value of their investments. The increasing diversity of the funds meant that neither the fund managers nor the analysts who advised them understood enough to make good decisions.

The company thus found itself with a poor “quality curve” for every one of its resources (figure 1); too small investors and brokers, a too-wide range of poor-performing funds, invested in too many stocks, managed by staff that included many underperforming people. These problems surfaced when stock market conditions collapsed following the bursting of the “dot-com” bubble, and led to a reversal of business growth and rapid loss of profitability.

Management reacted with a range of common steps – e.g. cutting its back-office staff (i.e. the people who actually made sure transactions were handled!), deferring IT investments that would reduce costs, cutting staff training, travel budgets, employment benefits, and so on. These did nothing to address the fundamental problem – that the “system” was accelerating its own demise—as every resource fell in quality, it undermined the ability of other parts of the system to perform. For example, the worse the performance of the smaller funds, the more investors deserted the company’s products, and the more brokers ceased recommending its funds to other investors. This self-reinforcing collapse had already been occurring, but had not been visible because it was hidden by the strong performance of the core business.

Figure 1: Some poor resource quality curves for the fund management company.

Fixing this situation went as follows (figure 2):

1. The poorly performing funds were identified and closed; that is, the product range was substantially rationalized. This was not entirely straightforward, since investors’ capital had to be switched into alternative funds.
2. This much narrower range of products would require fewer professionals, but to protect morale the plan included the transfer of some investment funds along with the associated staff to rival companies who were more successful in certain classes of investment.
3. Some remaining low-value clients had to be rationalized, some of whom departed with the cutting of the marginal products. For the rest, efforts were made to make them more worthwhile, for example consolidating several small investments into one, and simplifying service support.
4. The company stopped dealing with many smaller brokers who had in any case brought in mostly low-value clients.
5. With reduced support and transactional activity in the business, back-office costs could be reduced, but only after the business had been safely simplified.

Figure 2: The recovery path for the troubled fund management firm.

Further important side benefits of this rationalization soon appeared. Simply removing the complexity reduced the pressure on back-office administration, leading to more reliable service. This boosted the firm’s reputation with investors and brokers, and the product rationalization was turned into a further reputation advantage by explaining how closing down under-performing funds was actually in investors’ best interests.

After the crisis was averted, the team was able to use the architecture as a living control panel, on which they could track each month’s progress towards their better future, making adjustments if things worked out better or worse than expected. Like many firms today, they already employed a balanced scorecard system to track many of these factors — data that could be dropped straight onto the architecture.

Until next time…

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

Lessons for recovering from difficulties

Many features of this situation are to be found in firms struggling to escape financial troubles, whether caused by outside challenges or internal errors. Difficulties often originate in initiatives undertaken when things were going well. This heritage may offer the chance of strategic recovery, by thinking back to what the business looked like in better times.

First, as firms scramble to win growth opportunities, they rush to capture every possible new customer. In the process, poor-quality business is signed up, which only becomes apparent when market conditions deteriorate. (Customers are not the only resource that may get over-blown – see Starbucks’ over-growth of stores, featured in a recent presentation).

Unfortunately, it’s a tough decision to shut down customers at exactly the time when you seem to need every one you can get, which is why the decision must be linked to a coherent plan for improving other resources in parallel.

Whilst sales are booming, novel products and services can proliferate fast, and no one sees any need to check if the last great idea was successful. This can leave companies with slow-moving products, and rationalizing these provides the second opportunity.

With fewer, better-performing customers and products, less capacity may be needed, so that resource too can be rationalized.

Regrettably, the corollary of bringing business back to a sustainable core is a reduction in the staff needed to run a slimmer business. But by not acting, everyone is put at risk – and a strategically sound rebasing of the business is going to be substantially less troubling than the, unfortunately common, practice of indiscriminate and repetitive cuts that do little to fix the real problem.

This briefing summarises material from chapter 5 of Strategic Management Dynamics, pages 288-294.

Read more about the book on our website

At least this might keep business risk under control – right? Maybe not. If the business is squeezed so it only just about has enough people and revenue-spending  to keep afloat - what happens if market conditions worsen, competition hots up, or some misfortune arises? Our top-line numbers suffer, and we have no slack whatever to respond. If the CFO is still in the driving seat, she or he will likely urge more cost-cutting, making matters still worse.

I hadn’t spotted this before – Finance-led strategy probably gives us the worst of both worlds!

In the figure below, a charitable organization wishes to increase the income it receives from the regular giving of individuals. The total giving (at bottom right) is an attribute of the donors who support the organization. As for customer sales in commercial cases, there are three separate levers to improve this outcome — win more donors, keep more of those that we have, and get those donors to give more. Winning new donors is achieved by focusing the efforts of fundraisers on calling prospective donors. At the same time, however, existing donors must be called, both to ensure they continue supporting the organization and to increase the amount they give each quarter. This poses a difficult question about how best to allocate the efforts of those fundraisers (at top left).

The amount each donor gives each quarter is likely to be less than the maximum amount they could give, if sufficiently motivated by the good outcomes of the organization’s work. Each donor thus brings a potential for giving (see briefing 30), and a part of fundraisers’ efforts in calling existing donors is to win more of this potential. The organization therefore has a second issue on which to choose (middle left) —whether to seek new donors who may not be able to give much money but who are numerous and easily won, or seek richer individuals who can potentially give more money but who are few in number and harder to win.

In practice, some charitable groups, such as aid agencies, have a deliberate strategy of targeting even the smallest donations from very large numbers of donors. Others choose an almost exclusive strategy, targeting only very small numbers of the richest people – some charities supporting the arts choose this option, for example. We should add, of course, that there is much more activity around fundraising than this simple question of making calls, including advertizing, mail campaigns and public relations efforts.

The scenario in this figure plays out the organization’s fundraising strategy over five years, or 20 quarters. There are initially 80 000 donors, each giving $20/quarter, but with the potential to give $50. Total giving amounts to $1.6m/quarter, but could be $4m if those donors could be persuaded to give the maximum amount possible.

• For the first year, fundraisers’ time is split equally between calling potential new donors and calling existing donors, both to make sure of their continued support and to capture more of their potential giving. The winning of new donors and the capture of more giving from existing donors slightly exceeds the loss of giving from donors who leave, so total giving slowly increases.
• For the first year, fundraisers’ time is split equally between calling potential new donors and calling existing donors, both to make sure of their continued support and to capture more of their potential giving. The winning of new donors and the capture of more giving from existing donors slightly exceeds the loss of giving from donors who leave, so total giving slowly increases.
• In the third year, the organization moves its focus onto wealthier potential donors, targeting those who could potentially give $200/quarter. The success rate of these calls is much lower than when lower-value donors were being pursued, so fewer new donors are won each month. However, each person won brings with them more immediate giving, plus greater potential for more giving in future. Nevertheless, this rate of new giving is not sufficient to counter the loss of neglected donors. This situation continues through year four, with total donors declining, little change in actual giving, but strong growth in the potential giving that could be won from the new, more wealthy donors.

Note that this strategy could not be sustained indefinitely, since a point would arise at which most of each donor’s potential giving had been captured. Effort would then need to switch back onto acquiring new donors. A further general point to note from this example is that the organization’s choice regarding which category of new donors to pursue is closely analogous to the question commercial organizations face regarding “where to compete.” It may also be worthwhile to examine the “quality curve” of donors, stacking up the giving of each individual to see whether it is highly skewed towards a few large donors or more uniform. As in commercial cases, it is advisable to be conscious of this profile and to make deliberate choices about where on the curve to focus fundraising efforts.

Until next time…

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

Public service cases

Public services and government agencies also have reason be concerned about resource attributes and take the trouble to measure, model and manage them. In health-care and other services, for example, there is a ‘quality curve’ behind the demand on those services (see briefing 29). A small number of very sick people have very high demands for health-care, whilst others make virtually none. Indeed, a different kind of curve can be drawn showing how that demand varies through life, with an initial peak for new-borns, another small peak for women when children arrive, then very little demand until old age, when the demand rate escalates very sharply. Health policy clearly focuses on holding down or deferring this escalation in later life, but powerful forces are working in the opposite direction. Every year that an aging person can stay fit and active not only sustains their quality of life, but also keeps down the total cost of health provision.

The quality-curve also features in policing, where it is known that a small number of the most active criminals (equivalent to the ‘hottest water’ in the bath-tub, or to customers with the strongest demand for a product) drive the most crimes. Taking those few offenders out of the system has a disproportionate impact on the total crime-rate. In principle, this should make it possible to release police officers to focus on the next-worse group, and the next-worse and so on – although it is disappointing that such progress is generally eliminated by the emergence of new criminals.

Both cases, though, illustrate the benefits of ‘segmenting‘ policy to fit the challenges posed by distinct groups on the quality curve, in just the same way as it makes sense for business to choose distinct policies for different segments of customers.

This briefing summarises material from chapter 5 of Strategic Management Dynamics, pages 286-288.

Read more about the book on our website

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…

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

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.

Read more about the book on our website

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…

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

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.

Read more about the book on our website

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…

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

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.

Read more about the book on our website