The Crucial Source of Information: People

A couple of days ago we received an enquiry from an executive: his impression was that a competitor was executing the strategy of systematically targeting countries, one at a time, directing a disproportionate level of marketing resource at that country until the firm achieved the No. 1 position in that country, then moving on to the next country.

The shift to the next country necessarily involved the reduction of resource aimed at the first country, so that the resource could be transferred to the next. Presumably the customers and channel partners in the country either wouldn't notice the reduction or, if they did, had become so loyal to the vendor that they wouldn't bother to switch to another vendor.

Was the executive's impression correct, he asked? And if so, how could we counter the strategy?

Led by our director, our initial position was the null hypothesis: the vendor wasn't targetting countries one at a time, but instead behaving consistently across all countries, and over time. Indeed it's very difficult to find evidence that suggests the contrary:

• We forwarded the question to our worldwide counterparts, who are normally assumed to be wise in all matters. They took some time and some pushing to respond. When they replied, they knew of no serial targeting strategy in Europe by the vendor.
• There was no secondary research, no single report, that focussed on the vendor's country-by-country strategy or gave any impression of the timing of such a strategy.
• There was nothing that we could look for in our own financial records that could possibly demonstrate a single vendor exercising a country-by-country strategy. If our results go up or down, it is usually due to a load of market effects and the actions of multiple competitors. To show the movements of a ghost vendor through our financial records seemed impossible.
• We found several news clippings from 2007 that indicated that resellers liked this vendor for its consistency.
• We didn't approach any employee who had previously worked for this vendor, because to do so would be unethical.

A first draft of a 'No Evidence' presentation was forwarded to the original requesting executive. A second version, polishing the English and improving the flow of the argument, but basically maintaining the 'no evidence' position, was produced.

Then suddenly, a contrarian view came through. Our European channels specialist, who had been copied on the request, not by us but by one of our respondents, said he had spoken to a respected external analyst, who said the vendor did, at times, employ a targeted, country-by-country approach.

So much for serendipity. My personal view is that the exernal analyst is so experienced and respected that we should take his word for it. Besides, he has direct contact with the vendor itself.

So the moral of this story is that much of the time in market intelligence, you'll be relying on the judgements of people. Numbers have their place, but too often they are historical—too historical to act upon. The more accurate they are, the older they are likely to be. If you try to work it out all on your own, you're almost bound to miss some important points. So ask for input, almost always.

Rewarding good market selection

I'll be brief tonight, as it's been a long day and I haven't had a chance to think deep thoughts.

I spent the day in the City of London, as guest of one of our investment advisor firms. They were educating us about interest rate swaps and inflation swaps—financial tools that UK pension funds are increasingly employing.

But perhaps the most intriguing nugget emerged from a discussion in the morning: our equity fund managers don't get directly rewarded for good performance—e.g. a financial incentive for exceeding the benchmark for that asset class. The reward they get is the continuation of their contract with our pension fund. But, unless the manager's performance is absolutely appalling, it tends to take at least three years to collect enough data to indicate that the manager should be sacked.

I guess this shows what a random process stock-picking is assumed to be. So, nothing like market selection in the world of business strategy, then ...

The Unmeasurables

Pick a typical action-oriented decision:

1. Whom will you choose as your next partner?
2. Whom will you vote for?
3. Which CD will you buy next?

How are you likely to make that decision? My guess is that no-one would make any of these decisions on entirely numerical criteria. You might have included some numerical factors—e.g. 'height greater than 150cm' or 'jazz band with at least two lead instruments'—but I would imagine that you would also include at least one personal judgement criterion, such as 'personality that I would rate at least 8 out of 10' or 'any new CD by an artists whose previous albums I have been at least 70% satisfied with'.

If you're committing your future or your money to a decision, you wouldn't leave it to entirely numerical criteria. You might let Amazon recommend a CD purchase, but you wouldn't let a computer make your purchase decisions without consulting you first.

This refusal to make personal decisions on entirely numerical criteria makes me highly suspicious of any business decision-making tool which uses only numerical criteria. Would you buy or divest a business on purely numerical criteria? I hope not—there would presumably be many qualitative factors, such as your judgement about the degree of fit with your existing business and the calibre of its management.

But in my job, I see an increasing number of tools being proposed for internal usage which aim to produce a composite number out of a series of purely numerical factors. Some of these tools generate a number, and others produce a 'traffic light' colouration of cells in which the decision to go for red, amber or green is based on a number. Other tools promise to create a 'heatmap', whatever that is. Many of them lhave a passing resemblance to a half-hearted GE multi-factor Matrix.

What they all share is the implicit message that if you can't measure it, it shouldn't be a consideration in the invest/divest decision. They don't trust the client management to supply qualitative judgements on unmeasurable factors (such as 'quality of indirect channels') because they fear that these managers will adjust their input to get the result they require.

And yet proper use of the GE multi-factor matrix should always involve some input criteria which require qualitative judgement. People seem to think they can include what they like in these tools, without realising that their proper construction has some intellectual basis. For instance, the 'Boston Box' arose out of the Experience Curve, though I can't recall the precise connection at the moment. It must be time to finish.

Tomorrow I'll discuss these matrices further, or something similar, or perhaps something quite different.

Make sure you ask the question very clearly.

Q1. What is the minimum number of people you need in a room before it is more likely than not that two of them share a birthday?

The answer is 23. Bypass the next paragraph if you either are not interested in how the answer is calculated or already know the solution.

Without substantially altering the answer, we'll assume the year is 365 days long. We'll focus on the chance of everyone in the room having a different birthday.

• The first person enters the room. The second person has 364 out of 365 chances of having a different birthday to the first person.
  
• The third person has 363 out of 365 chances of having a different birthday to the other two.
  
• And so it goes on. The 23rd person has 343 chances of out of 365 of having a different birthday.
  
• Multiply all these probabilities together to get the probability of all the events happening. You can do it on your calculator.
  
• After 22 people have entered the room, the probability of them all having different birthdays is still greater than 0.5. But multiply that number by 343/365 when the 23rd person enters the room, and now the probability is less than 0.5, so it more likely than not that two of the people in the room share a birthday.
  

Q2. What is the minimum number of people you need in a room before it is more likely than not that one of them shares a birthday with you?

The answer is about 254. Each person who enters the room has 364 chances out of 365 of not having the same birthday as yours. If x is the number of people in the room needed, then we have to solve the equation (364/365)^(x-1) = 0.5.

What intrigues me is that the questions are very similar, yet the answers are very different: 23 versus 254. Indeed the questions are so similar that many people might not detect the nuance. If we put the questions through an automated translator (e.g. Babelfish), and translate them into, let's say, Spanish and back again, we get:

Q1. Whoever is the most minimum number of persons that you need in a quarter before you are more likely that no those two from her part in the birthday?

Q2. Whoever is the most minimum number of persons that you need in a quarter before you are more likely that no that one of the parts in the birthday with you?

Both sentences no longer make sense, although they still differ. Will anyone understand the questions? It's highly unlikely that anyone will come up with 23 and 254.

The moral of the story

I'm not trying to make a point about not giving numerical tasks to foreigners. The key issue is about clarification of the question. English can be a very compact language—small differences in wording yield very different answers. Mechanical translators have a long way to go before they can understand these nuances. (And I have to admit that it is mathematically interesting that the two answers are so different.)

Is the whole world going downhill, or is it just me?

One of the key skills of an analyst, when noticing some important ups or downs in the results of a competitor, is trying to decide whether these anomalies pertain just to that vendor, to a particular pocket or two in the market, or to the entire market.

Take the current economic uncertainty. The key question everyone wants answered is: how is it affecting IT spending?

The analyst firms such as Gartner and IDC conduct market research to find out about buying intentions, and these certainly give some indication of what's going on, but most of the time it's inconclusive. If the result is that anywhere between 20% and 45% say they are increasing their spending, 20-45% say they are decreasing their spending, and 20-40% say "No change in plans"—which is typically what you get from these surveys—then it's very difficult to claim that a new trend is afoot.

Another source of IT spending data comes from government statistics and Gartner/IDC trackers, but these tend to arrive rather late on the scene.

No, if you want hard data on the market, the earliest reliable material tends to come from the quarterly vendor financial results.

Last night, Dell published its results. Profits were down 17%, but revenue was up 11%. Sadly Dell didn't publish a constant-currency growth figure, so that will have to be estimated. However Dell did say that the earnings squeeze was a result of technology spending slowdowns and its expansion into Europe and Asia.

But is Dell telling the truth? Some watchers clearly think so: the chief investment officer at Solaris Asset Management, said: "[Dell is] saying lower IT spending is spreading. That is evidence of a global slowdown in IT spending. This certainly isn't good news for tech overall."

Personally, I believe the guy is being too hasty in jumping to this conclusion. We need to see several more sets of results from other vendors before we can confirm the conclusion. There has been a tendency in previous IT recessions for customers to stick with trusted brands. But is Dell trusted today? Dell is still in recovery, so there are a number of factors influencing its financial results.

So it may be just Dell. We need to see the results of many more vendors. In this instance, you cannot see the world in a grain of sand.

Averages are so simple, aren't they?

As I drove down the M3 today, I noticed a speed limit sign saying '50', closely followed by another sign saying 'Average Speed Check'. But are averages—or more precisely, the correct methods for calculating them—widely understood?

For example, if I drive for twelve miles at 40 mph, then another twelve miles at 60 mph, will I be breaking the average speed limit? The answer is No, because my average speed is 48 mph. {The formula for average speed is total distance divided by total time. Total distance is 24 miles; total time is 0.3 hours (for the first bit) plus 0.2 hours (for the second bit), i.e. 0.5 hours.}

But how many people can do the calculation in their head while driving?

Not that I would advocate Jeremy Clarkson's idea for defying average speed limits—i.e. bomb up the motorway at a crazy speed, then make up the total time by having a meal at a service station.

Another area where averages aren't quite what they might seem is in currency conversion. If someone asks you what was the average dollar:euro rate in 2007, you need to be aware that one isn't the precise inverse of the other. Check it out on oanda.com:

• The average rate from dollars into euros in 2007 was 0.73082
  
• But the average rate from euros into dollars in 2007 was 1.37074, the inverse of which is 0.72953.
  

The difference isn't due to rounding error. You can demonstrate it to yourself with a simple example—suppose for the first six months of the year, the euro was worth two dollars, and that for the second six months, the euro was worth one dollar:

• Thus if I trade one euro in the first six months, I get two dollars in exchange; and then I get a single dollar for my second euro in the second six months. So I get a total of three dollars for my two euros: average rate is €1=$1.5.
  
• But if I go the other way, and trade one dollar in the first six months, I get €0.5 in exchange. for my second dollar, traded in the second six months, I get €1. Thus $2=€1.5, i.e. €1=$1.333.
  

So the average rate depends on how much you exchange. It's an important point for the intelligence analyst calculating revenues overseas. And the difference between the two averages, however small it may seem, enables the wily trader to pull a fast one over the unsuspecting customer.

How accurate is that estimate?

(All the examples quoted in this article are over five years old—to minimise the risks to confidentiality.)

Internally we publish many numbers—for example, forecasts of market sizes and estimates of competitor revenues. But in most cases, we don't say how accurate those numbers are likely to be. Arguably we should publish, with each estimate of a number, an upper and lower bound, which define an interval highly likely to contain the true value.

Here is a sample of the maximum errors observed during 2002-2003:

Market Forecast for the year (made just before its start)

• Product Category (e.g. 'External Disk')
  
  
  
  • At the Country level: +/- 27%
    
  • At the EMEA level: +/- 25%
  
  
  
  
• Product Sub-category (e.g. a Server price-band)
  
  
  
  • At the Country level: +/- 34%
    
  • At the EMEA level: +/- 31%

Instances:

• For the year 2002, our forecast in 2002 was that 'External DASD' in the UK would reach $1,226m. But this 2002 estimate was corrected to $895m in 2003.
  
• For the year 2002, our 2002 forecast was that 'External Disk' in EMEA would reach $7,419m. But this 2002 estimate was corrected to $5,551m in 2003.
  
• For the year 2002, the 2002 forecast of 'Servers $25K to $100K' in the UK was $305m. But this 2002 estimate was corrected to $410m in 2003.
  
• For the year 2002, our 2002 forecast of 'Thin Servers' in EMEA was $319m. But this estimate was corrected to $219m in 2003.

A vendor's revenues for a quarter in the recent past

• Product Category (e.g. 'Servers')
  
  
  
  • At the Country level: +/- 47%

Instance:

• For 2Q02, one analyst firm estimated that IBM's server revenues in the UK were $93m. But according to internal accounts, our revenues were $63m.

It's worth commending the company for changing its numbers when they are known to be incorrect. One analyst firm, on the other hand, has historically not changed figures published in its server tracker when shown to be wrong. Instead it will adjust future figures in order to make the running total correct. So if any of the 2Q08 numbers turn out to be wrong, for example, it will adjust the 3Q08 figures to cancel out the error.

I suppose this approach has the advantage that, with each new release, you only need add the new column of data to your spreadsheet—i.e. you never need worry about previous quarters changing—but it does make you worry about the true accuracy of any individual quarter's estimates.

Another acquisition pairing not predicted: Infosys to buy Axon

There's a certain amount of embarrassment that comes from not forecasting that HP might acquire EDS—this year's mega-deal in the IT industry. I feel a small twinge of personal embarrassment at not predicting yesterday's news story: that Infosys would buy Axon.

I'd been watching Axon for the past nine years—since they went public—perhaps because they're based at Egham, just a few miles down the road from our Bedfont office. Equally, I knew that Infosys, a major Indian outsourcing services provider, was flush with cash, and was looking for an acquisition in northern Europe in the $300-400m zone. It just never occurred to me to connect the two together.

For what it's worth, here are the notes I've gleaned from the Web today and from my archive of clippings:

Infosys has announced its intention to acquire SAP services specialist Axon for about $750m, a 19% premium over the share price. The board of Axon is unanimously recommending the deal.

The acquisition will create the largest SAP services provider of the major Indian offshore players, and should place Infosys somewhere inside of the top 10 suppliers of SAP consulting services in the USA. Key clients include Xerox, BP, Orange, TXU, Transport for London, as well as major sub-contracting engagements in the USA, such as the Home Depot.

Despite the economic slowdown, SAP-related services remains a high-growth area. Infosys claims their SAP practice is growing 65%+ CAGR; other Indians are seeing similar growth rates. It’s not new applications that are driving growth. Instead the market is being driven by consolidation of multiple SAP platforms, upgrades to SAP’s latest releases—customers get penalised by higher maintenance charges if they stay on old versions—and the relocation of application management offshore. In summary, it’s all about cost reduction. The Indians that are seeing their SAP practices grow fastest say it's because they have the lowest delivery cost.

Axon brings a raft of clients to Infosys where it needs them most: in Europe. Infosys gets about 27% of sales from Europe, which should pan out to around £600-700m this year. The extra £120m+ of EMEA revenues that Axon brings will come in very handy, albeit at around half of Infosys’ margins.

”Our stated policy is to move up the value chain, move into the consultancy space,” said the CFO of Infosys.

The chief executive of Infosys said yesterday: “A lot of Indian companies are looking at Western Europe. This is our first acquisition there, and right now our focus is to make sure that this process goes through before we talk about where we go next.”

Axon Background

Axon was set up by Mark Hunter in 1994 after he left SAP to create a team of ERP services specialists.

Back in 1999, describing itself as a 'UK SAP implementer', Axon was available to provide statements for the press on the state of the R/3 market.

In 2000, calling itself a 'leading UK ERP integrator', Axon was happy to put the boot into the troubled Baan with a comment or two about its general unsuitability for most customers.

In 2002, Axon acquired an Australian consultancy which contributed £7m to its annual revenues of £43m.

Early in 2003, Axon was announcing an 87% downturn in net profits the previous year, due to the SAP market slowdown.

In 2006, Axon acquired three vertically focussed SAP consultancies in the USA, which contributed £32m to the company's total revenues of £138m. But it also disposed of its Middle East operation. Axon had just £6m in cash—which could have put its survival at risk if it expanded too quickly—so it arranged a £40m loan facility with its bank. Ovum, now describing Axon as 'the most spectacular beneficiary of the current revival in demand for SAP services', outlined the secret of Axon's strategy:

1. Qualify deals early,
   
2. Stick to a handful of verticals (particularly local government in the UK),
   
3. Concentrate sales effort on a handful of big contracts,
   
4. Ride on the back of a large outsourcer (especially Capita) that needs a C&SI partner to win deals. (This may give a clue as to Infosys's interest in Axon.)
   

Early in 2007, Ovum was lauding Axon's offshore investments in Malaysia, where it had 300 staff. Axon split the roles of CEO and chairman. Its stated goal was to become one of the biggest SAP practices in the world. Founder and executive chairman Mark Hunter said he believed Axon now had 1% of the global SAP services market.

In the second half of 2007, Ovum was describing Axon with words of praise that it normally reserves for Accenture and Oracle: 'Axon's model continues to work wonders ... Axon has become the poster child for the focussed buy-and-build strategy'. The UK public sector had been particularly good for Axon, with a large programme at Birmingham City Council. Axon bought another firm in the Far East, this one with 150 SAP consultants in China, Singapore and Kuala Lumpur. Its CEO described Axon Group as 'the largest consulting firm in the world focussed on the $24bn SAP services market'. But chairman Mark Hunter left the firm, and according to analyst Richard Holway, this made it inevitable that Axon would get swallowed up in an acquisition sooner or later.

In January, Axon shares halved in price for no clear reason. In March, Axon reported revenues of £205m, organic growth of 29%, and operating profit margin of 17%. It admitted that there was little room for growth in the UK; hence its focus on the USA and what it calls the DACH region of Europe: Germany, Austria and Switzerland.

So how did I miss this one? Was it simply the mismatch between Infosys's supposed target price range and the eventual price bid for Axon? Or is it that there are just too many possible pairings of cash-rich acquirers and potential targets?

Sources:

• http://www.feedingthesapecosystem.com/2008/08/infosys-to-acquire-axon-potentially-top.html
  
• http://hotviews.blogspot.com/2008/08/infosys-to-acquire-axon-for-407m600p.html
  
• http://www.axonglobal.com/pages/about_us/news/infosysbid.asp
  
• http://www.ft.com/cms/s/0/7798b574-730f-11dd-983b-0000779fd18c.html
  
• http://business.timesonline.co.uk/tol/business/industry_sectors/technology/article4606620.ece
  

(Bank Holiday)

It's a public holiday here in the UK, so readers will be spared an entry today from this blogger.

Of Actual Currencies and Plan Rates

Yesterday I voiced some frustration at the infinity of questions that can be asked of a Market Intelligence professional, and suggested one way to filter out the questions that should be discarded: "Will you take the same decision whatever the answer?". I gave the example of asking for charts to be done in both Actual Dollars and Plan Dollars, and suggested these requests create extra work for no discernible additional value.

Today I'll try to attack the question of when to use Actual Dollars and when to use Plan Dollars.

For at least the past four years, the exchange rate between the pound and the plan dollar has been £1 = $1.55. This isn't an exchange rate that anyone outside IBM would recognise, but a constant currency rate used inside an international company has two immense advantages:

• Plan rates are better for sales target-setting. Sales representatives (SRs) and their managers shouldn't have to worry about fluctuating exchange rates when deciding how they are going to achieve their annual targets, because exchange rates are beyond their control. Customers in the UK pay IBM for its products in pounds, not dollars. So if SRs targets are set in dollars, it is far better if they are in plan dollars, not actual dollars.


• Plan rates enable fairer year-on-year comparisons. If you want to measure success from one year to the next, then it's far better to measure sales growth of a sales representative, division or subsidiary in constant currency than at actual currency exchange rates. The reason is the same: exchange rate movements are beyond their control, so they should be punished or rewarded for these movements.

There is little point in requiring people to carry two parallel sets of figures around in their heads—both plan dollar targets and actual dollar targets—so in order to calculate their market share, they will want to see the market size calculated in plan dollars.

However, the rest of the world operates in actual dollars:

• American IT companies publish their results in actual dollars.
  
• The values of publicly announced contracts are stated in actual dollars / euros / pounds.
  
• IDC, Gartner, Ovum publish their estimates of competitor revenues in each IT market in actual dollars.
  
• If you state plan dollar numbers to anyone outside of IBM, they won't recognise any of them.

A company which translated all of this data back into their own internal constant currency could justifiably be described as too inward-looking. Every time that you saw an externally produced estimate of the size of an IT market, you would have to translate it into plan dollars in order to check whether it looked reasonable.

So at some point, it's best to talk in actual dollars, to avoid becoming too unworldly. Our principle tends to be as follows:

• Market size charts tend to be given at Plan Rate.
  
• Charts of competitor revenues are given at Actual Dollar Rates.

There is, as you might expect, a conflict when we try to apply competitor revenues to a particular market.

And I haven't even tried to address the topic of the best measure to use when there is high price inflation...

Don't ask the question if you'll do the same whatever the answer.

schnauze /ˈʃnaʊz/ vb (tr). to spend time seeking out bits of information for which one has no practical use, and in so doing, waste the time of others.

Attached below is a picture of Zelda, our six-year-old Miniature Schnauzer. We love her dearly, but when on lead, she has a frustrating habit of holding up our walks with almost every step, to sniff around at another piece of pavement, wall or tree. Clearly she is seeking scraps of information—probably about who else has been to the same spot recently—but why does she need to know? It can't be an interest in other dogs, because she's generally not keen on other dogs.

Strangely, she tends not to sniff around as much when she is off lead, so perhaps she does it just to annoy me. So in honour of Zelda, I offer up the word schnauze, which I defined as above. It's not original—a quick Google reveals that several other Schnauzer owners are using the term in an eerily similar way. And the verb may well mean something in its original German.

Staff departments in large companies can lapse into a degree of schnauzing if they veer too far away from the company's core activities, which I summarize as developing stuff, making stuff, selling it, and servicing it. It's very easy to ask questions, but if you have no practical use for the answer—or if your actions will be the same, whatever the answer—then you shouldn't have asked the question in the first place.

It's easy to schnauze a market intelligence department. There are so many different ways of looking at the IT market that, despite us feeling we may well have the best grasp of what's going on of any observer of the IT industry, for most questions, we don't have the answer instantly ready to hand—some research is necessary. For example, market intelligence has to measure many things in at least two currencies: not just the actual US dollar that most of the industry has agreed upon as the standard rate of measure for financial activity, but also the plan dollar that is used internally to help each country's operation measure its sales success. So if market intelligence produces a graph in actual dollars, why not then ask them how the graph would look in plan dollars?

Tomorrow I'll give my views as to when is the right situation to use actual dollars and when to use plan dollars. Hopefully someone will reply, and I'll learn something, and we can begin to distinguish the action-oriented questions from the schnauzing.

Where do IT vendors want to be next quarter? (Part 2 of 2)

Yesterday I introduced the Growth Forays tool that we sometimes use to plot growth strategies on a single chart. Today I give a few recent examples:



Note that there is no precision to the plotting of points. It's down to judgement, and don't spend too much time worrying about precisely where to place each company strategy. To avoid overcrowding, I try to keep the label for each point down to 2-3 words—a fuller description of the activity is given in the notes.

Here are the notes for the chart above:

Acquire:

HP-EDS: HP is acquiring EDS—both have strong SAP competencies. Clients want 'one throat to choke‘.

Oracle: Oracle is acquiring large and small ISVs in order to maintain high (admittedly inorganic) growth rates, and because it has the cash.

HP: SaaS vendor: HP is expected to make an acquisition in the SaaS or Cloud Computing space.

Dell: Solutions: Dell acquiring MessageOne and other small, niche firms to build itself into a solution provider.

Infosys: N. Europe vendor? Infosys is strongly tipped to make a consulting acquisition in northern Europe this year valued at $300-400m. Its reasons: the US slowdown means the firm cannot rely on organic growth to achieve its stated targets; and Infosys has the largest cash reserves of any Indian vendor.

Expand geographically:

TCS: Morocco: Build sales centres on-shore or near-shore to target countries. For example:
TCS expanded into Morocco for its French (and Spanish) language skills as a means of targeting France and Spain, and TCS is building centres in the Middle East to target markets in the Middle East.

EMC: Disk #1: Target any country where they aren't No. 1 in their field.

Accenture: US skills: Transplant experience grown in one geography to another geography—e.g. Accenture is expected to bring its US-cultivated skills in lean Six Sigma and Command-and-Control systems to Europe.

Wipro: NEIOT: Expand in familiar or similar markets, rather than targeting culturally different markets—for instance, Wipro is expected to step up a gear in northern and eastern Europe before it goes after southern Europe. And there are similarities between much of SW IOT and Latin America. Wipro is relatively small in Latin America, so is unlikely to go after southern Europe while there remains so much potential in the more familiar northern Europe.

Sage: Diversity: Emphasise (to investors and customers) the protection offered by the diversity of their portfolio, particularly in the BRIC countries—for example, Sage and IBM.

Accenture: Grow Europe: Focus sales resource systematically on specific cities—as Accenture has done with its Grow Americas and Grow Europe campaigns.

Sales resource actions:

Dell: Indirect: Increase use of the indirect channel. For example, DSG has agreed to sell Dell laptops and desktops at its PC World stores in much of Europe, including Spain, Greece and Italy.

HP, FSC, Cisco: Channel loyalty: Focus more channel resources on those partners who show greater commitment.

HP: Unix specialists: Switch sales responsibility from generalists to specialists—as HP has successfully done with its Unix servers.

HP: Cross-sell: Focus on cross-selling and 'attach' rates—for example, HP is pushing a integrated approach, designed to sell products from all three business units—servers, storage, PCs and printers.

Concentrate on customer needs:

Accenture: Client Value: Focus intensely on the growth and value the firm can bring to each client—as Accenture does in its planning process.

HP: Virtualisation: HP is capitalising on a strong market for virtualisation in the data centre.

EDS: Cust Sat and Capgemini: Intimacy: Get closer to big customers and potential customers—as Capgemini is doing with its client intimacy strategy, and EDS did last year by soliciting client feedback on its services capabilities in Outsourcing. (EDS discovered this approach improved its win rate on new contracts.) Fujitsu Services has an open book accounting relationship with some clients to share financial information about the cost of servicing their contacts.

Accenture: Datacentres: Data centre consolidation is a popular theme, and Accenture is growing its Systems Integration services there.

Dell: Simplify IT: And Dell is pushing a Simplify IT message, for which capability Dell has made seven acquisitions.

Improve reliability of revenue streams:

Some vendors shift towards reliable and predictable sources of future revenue…

SAP: Withdraw budget support: Raise prices in subtle ways—for example, SAP is withdrawing its entry-level support options.

Dell: services: Re-position the firm as a supplier of higher-value annuity offerings—as Dell is trying to do by changing perceptions from box-shifter to solution provider.

Accenture: Public Sector: Target the Public Sector as a robust source of spending less affected by economic conditions than most sector. Despite its troubled reputation in the Public Sector as a result of withdrawing from the UK Health Service mega-deal, Accenture is trying to re-establish itself in that sector.

CA: support: Look to the annuity revenues provided by services contracts and software rental/support—for example, Sage and Computer Associates.

Make a strategic commitment:

Accenture: BI?: Declare a key investment area each year—a broad technological area where the firm needs to spend in order to achieve at least parity with the industry leader. HP has also identified BI / Data Warehousing as a major growth area.

Focus on existing customers and strengths:

Narrowing their client focus – i.e. avoiding being spread too thinly – may create disproportionate dividends in their chosen niches.

Some firms are narrowing their sales focus to a specific set of customers—as Ericsson has done by selling off its PBX business which sold to a full range of verticals. It now sells only to telcos and service providers, which means its SRs can be more specialised.

Atos: niches: For firms already in trouble, launch a strategic recovery programme—for example, Atos Origin with its '3o3' transformation plan.

EDS: installed base: EDS tends to be much better at selling to its installed base than in acquiring new customers in new locations.

Where do you want to be next quarter?

One tool we find useful in the analysis of competitive strategies is the Growth Forays chart.



The pentagon at the centre of the graphic symbolises the vendor and its current market today. This represents its home, its place of safety. Each vendor has several dimensions of growth opportunity available to it, which we represent by axes on this chart. The further a vendor moves outward along an axis, the further it gets from its comfort zone.

These five axes are intended to be orthogonal, but can only be represented on a chart in two dimensions.

The seven principal categories of strategy, which our competitors are using to grow revenues, form six ‘petals’ and one centre of this ‘flower’:



Struggling vendors tend to be defensive—focussing on their installed base—but industry leaders typically use multiple strategies to expand their scope.

Tomorrow I'll give a few current examples, and try to place them on the chart.

P.S. Prior to writing this blog today, I hadn't got around to suggesting a name for the chart. We just use it without worrying about that. But if we are to use a single, simple analogy to explain the chart, it probably doesn't help to mention 'forays' in one breath and 'petals' in the next. Any suggestions would be much appreciated.

It may be that the usefulness of the chart is purely presentational. But it helps management to see, on a single page, the multiple growth strategies that a competitor is employing, and get some sense of the risk arising from exposure to multiple dangers.

The UK stands out in Europe for its willingness to outsource.

The four connected graphs below may help to show how outsourcing, and the willingness to source to Indian providers, are more prevalent in the UK than in the other European 'majors'. (My apologies for the text which is at times sideways or upside-down.) All data relates to 2007. IT market numbers come from IDC Trackers, and GDP comes from the CIA Factbook.



The top-right graph shows GDP against server market for the big four nations of Europe: Germany, the UK, France and Italy. As you might expect, the size of the server market is roughly proportional to GDP—the greater the income of a country, the more it spends on computers.

Move across to the top-left graph and we have a plot of server market against data centre outsourcing market. For the three continental countries, there is a roughly straight-line relationship between spending on servers and spending on data centre outsourcing: the more you spend on servers as a nation, the more you also spend on outsourcing. But the UK is clearly off that straight line—it outsources data centres disproportionately more, given its server spending. Indeed, although Germany is the bigger country in terms of both GDP and server market, the UK has the bigger outsourcing market.

Down to the bottom-left graph, which shows a plot of the applications management market against the data centre outsourcing market. These are both sub-segments of the managed services / outsourcing market, so you would expect the linear relationship that the graph broadly shows—although France does proportionately more application management, given its levels of data centre outsourcing, than Germany.

And now across to the most interesting graph, which shows the proportion of each country's applications management market which goes to the top three India vendors: Infosys, TCS and Wipro. Whereas they account for less than 3% of the market in France, Germany and Italy, in UK they take almost 10% of the market.

Another reason to avoid those slow-growth markets

A couple of weeks back, I was asked to produce a list of the top three vendors in each of the major markets in both Germany and UK. It wasn't part of the request, but as a by-product, I produced the graph below, plotting the growth of each of these markets between 2006 and 2007 against the total market share held by the top three vendors in each market.


(Very broadly speaking, 'GBS' consists of consulting and systems integration, and 'GTS' consists of outsourcing and other IT services. Source: IDC Tracker)

There's a strong diagonal trend from top left to bottom right—in a nutshell, the more concentrated a market, the slower the growth.

This shouldn't be a complete surprise: the less growth there is in a market, the less incentive there is to stay in it if you're an also-ran, and the lower the attraction of entering it if you don't already play there. There's a GE dictum that if you're not in the top two in a particular market, you should aim to get out in the long term.

The graph also provides some confirmation for the long-term product life-cycle, in that the older product families—the IT industry began with servers and storage—tend to experience slower growth than the newer families such as IT consulting and services.

How to predict a company's revenues without knowing anything about its business (Part 2 of 2)

In yesterday's post, I suggested one mechanical way of estimating the next quarter's revenues of a firm, based on the straight-line extrapolation of its four-quarter moving average. Today, I'll take it one stage further, investigating whether looking at second-order extrapolation buys us much. And then I'll take a different approach by leaving it entirely to the computer to find the set of coefficients which create the best fit for a linear formula for next quarter's revenue in terms of the previous six quarters.

Second-order Predictor

Here we assume that the rate of change represented by the next quarter's moving average is the straight line extension of the rate of change from the two previous quarters:



The definition of D_n is the gradient from A_n-1 to A_n:


The assumption of straight-line growth of the rate of change of A_n gives us:


Rearranging the equation:


So, by substitution:


And by further substitution:

Assessing the Accuracy of the Predictor

When tested out on the same set of vnedor financial data as before (i.e. from 2003 to 2007), the average error generated by this formula improved a little on the previous formula: 6.6%, across 616 readings.

And creating a new predictor which is the simple average of our two previous predictors merely results in an error rate which is the average of the other two: 6.7%.

Using Excel Solver to derive the optimised linear formula

We can use the Solver add-on within Excel to calculate the linear coefficients which minimise the average error.

This has enabled us to get the average error down from 6.8% (first-order approximation) to 6.6% (second-order) and now down to 4.1% (''Solver''-optimised).

Potential for Improvement

One obvious improvement that could be made, particularly in this era of IT industry consolidation, is to allow for acquisitions. Our current model does not use any information about, for example, the size of the companies that Oracle is acquiring, to generate the forecast on a pure time series basis.

But when one firm acquires another, it makes sense for the analyst to add the revenues of the two firms together. (No company destroys that much value within a quarter!) Our revised time-series model should do the same.

How to predict a company's revenues without knowing anything about its business (Part 1 of 2)

Next Tuesday, HP announces its third quarter results. The following week, Dell releases its second quarter results. What revenues will the two companies announce? Pundits in Wall Street and beyond, many of whom spend a lot of time tracking these companies, will make their forecasts. First Call will produce a consensus of the estimates of the more respected analysts.

But here's a mechanical way to produce a forecast in which you need to know nothing about what the company does, its markets or its competitors. All you need to know are the company's revenue figures for the past six quarters. This is Time Series forecasting.

Introduction

Four-quarter moving averages tend to smooth out the irregularities in a company's quarterly performance. The graph below shows IBM's worldwide revenues in blue, and the four-quarter moving average of the revenues in red.




Note also that the moving average graph starts after the actual revenue graph—three quarters later, in fact. This is because the moving average is the average of the quarter in question and the previous three quarters.

Four-quarter moving averages look tantalisingly predictable. If we can predict the next quarter's value for the ''moving average'', then from that figure, we can derive a prediction for next quarter's ''revenue''. There is likely to be a degree of error, and if we repeat the process by predicting the following quarter by applying the formula to a set of numbers, one of which is a forecast—not an actual—we will compound the error and increase the inaccuracy. But most of the time, people only ask us to predict next quarter's revenues, and if we could do that with sufficient accuracy, we could make ourselves rich.

The Maths

If T_n is the Four-Quarter Moving Total in quarter n, then

where R_n is the revenue in quarter n.

And if A_n is the Four-Quarter Moving Average in quarter n, then simply

First-order Predictor

Assuming the next quarter's moving average is simply the continuation of the straight line from the previous quarter to the current quarter, then:

where P_n+1 is the predicted value of A_n+1.

So, rearranging the equation and substituting:

Assessing the Accuracy of the First-Order Predictor

When tested out on 660 quarterly revenue figures of IT vendors, going back to 2003, the average error generated by this formula was 6.8%.

Well, it's not the end of the world, but 6.8% is bigger than the annual growth many of these firms are achieving!

Tomorrow I'll describe a second-order predictor, whose accuracy is only a little better, and I'll demonstrate the accuracy of the formula that the Solver function within Microsoft Excel produces, when required to produce coefficients for a linear formula.

Hello, world.

This blog is an experiment for me. My intention is to create one entry per day, on either a market or competitor intelligence topic. But for how long can I maintain that discipline?

Reader feedback will be vital to my sticking to that commitment. Where it goes will be down to you, whoever you are.

Let's get started tomorrow.