[following the previous posts: How to mess-up prices, based on costs: the absurdity of cost-plus pricing (1-2/3)]

In the previous posts, I emphasized the “centrality” of pricing in the strategic decisions aimed at improving firm’s profitability, criticized the “intrusion” of management accounting in the discussion about ways of determining price levels, and pointed out the obvious fact that “the market doesn’t give a damn about our costs!”.

Now, I will try to show, with a simple spreadsheet model, how and why the cost-plus pricing method cannot work.

The cost-plus pricing method is intrinsically illogic and incongruent.

I bet that you too have frequently listened to the following “famous” sentence: “My product’s price should cover all my costs and allow me a profit margin”.

Let us see how, according to this conviction, the price level is determined.

In figure 3, I identified in bold italics on yellow cells the “objective” data, and on blue-shaded cells the target values, the rest being calculated by the model:

-  the unit variable cost (cell C4) is obviously a fact (we have the invoices showing it!)

-  in order to attribute (however, in a totally arbitrary way!) to the unit of product a fixed cost “responsibility” (cell C5) we should take the total fixed costs (another fact, cell F3, unless we messed-up the accountant’s figures!) and divide them by the quantities that we expect to sell (cell F4) or, even worse, by the quantities that we plan to produce!

-  we therefore sum-up these two unit costs and obtain the so-called “unit full cost” (cell C6)

-  if we want a margin of 70% on price (cell D7), we calculate the price (cell C8) dividing the unit full cost by [1 minus the margin].

If you reproduce this little model on a spreadsheet, position yourself on cell C8 and click three times straight on the icon “Trace Precedents” of the “Auditing” toolbar (or similar tool on spreadsheet versions different from mine), you will see the blue arrows highlighted in the figure, from which we can easily see that, to sum up, you made the price depend on quantities, i.e. exactly the contrary of what happens in the market place!

But let us see why with this approach, in addition to being “against nature” from a conceptual standpoint, we incur the risk of making wrong decisions:

-  what would you do if, during the year, you realized that your sales expectations (30,000 units) were pessimistic, and that, at a price of 200, you could practically sell twice as much?

-  unless you are totally dumb, the least you would do would be to maintain the price (provided that you can produce 60,000 units), or even increase it

-  this behavior, perfectly reasonable (in fact, it would be adopted even by those who had initially used the cost-plus), evidently proves the total uselessness of the method described in the figure: if you entered in cell F4 of the model the new projection (60,000), the “suggested” price would actually collapse to 167!

Vice versa, obviously, if you realized that, at a price of 200, you could not sell more than 50% of the projected quantities: the model would “suggest” raising the price by 33%! Can you imagine what would happen to your sales?

Therefore, entering the fixed costs in the calculation of price (evidently artificial exercise, without any real correspondence) does not solve the problem, on the contrary: we can be sure that, apart from a stroke of luck, with this method we would lose either sales or margins, and then it would not be easy to adjust prices (especially upward) in a relatively short time and without displeasing our clients.

In fact, we should remember that the unit fixed cost does not exist in nature, but is just the result of a pure accounting calculation: we therefore do not see the rationale of using this parameter for making decisions which have, instead, a real impact on market’s behavior.

However, at this point, you could be tempted to ask the “famous” question that often shows up in seminars on pricing:

“… in principle, all the above could make sense, but sooner or later I need to cover my fixed costs, isn’t that right?”

Of course we need to cover the fixed costs! But this task must be accomplished by the total contribution [i.e., (price – unit variable cost) x units sold], not by the price, which is precisely the factor that has an impact on sales, via the variable “slice of the pie”, given a certain size of the “pie” (see the previous post).

If we are not able to conceive (and implement!) strategic choices (concretely translated into variable costs, fixed costs, and prices) able to produce a total contribution well above the fixed costs, there is something wrong: choice of the segment? disproportionate variable costs in relation to the likely value perceived by the market? incoherent price? exorbitant or not adequately targeted fixed costs? investments below critical mass? etc.

If these are the issues, a change in price via a reallocation of costs will certainly not solve the problem: on the contrary, it is almost sure that it will make it worse!

Rather, after having said what we shouldn’t do, it could be interesting to discuss the right approach to pricing … but this is another story …

(the end)

[following the previous post: How to mess-up prices, based on costs: the absurdity of cost-plus pricing (1/3)]

In the previous post I emphasized the “centrality” of pricing in the strategic decisions aimed at improving firm’s profitability, given a certain market and competitive context, and criticized the “intrusion” of management accounting in the discussion about ways of determining price levels: particularly, some accountants’ insistence on suggesting the cost-plus pricing approach, which has a pernicious impact on management’s behavior.

Now, I will further support the reasoning behind this critique, postponing to the last post a concrete “demonstration” of the absurdity of these accountants’ suggestion.

Our client couldn’t care less about our costs!

Did we ever decide a purchase after having assessed the costs (in particular, the fixed costs) incurred by the supplier?

… and, therefore, why should our client adopt that approach in relation to our offer?

Other things equal (or ceteris paribus, as my grandfather used to say), given a certain product/market combination or strategic business unit, a specific competitive context (i.e. a certain competitive profile of our company in terms of perceived value), and within a well defined price range, the market’s willingness to buy from our company (i.e. to award us a “slice” of the overall market “pie”) is directly related to our price level.

This type of relationship is normally summarized by the famous “demand curve” (in this case, our firm’s demand curve) described below.

By the way, and for the sake of simplicity, we do not consider the potentially positive impact of higher price levels on the perception of value (see figure 1 in the previous post), since this could modify the analyzed context.

As you can see in “Pricing – Fig. 2”, I avoided the economists’ approach (prices on the Y axis, and quantities on the X axis), for several good reasons:

-  we are not talking about commodities, but about our company’s sales in a given competitive context

-  it is obvious that, given that context and other things equal, the size of our “slice” depends on price (dotted arrow in the figure), and not vice versa!

-  the quantities sold depend, in turn, on our ability to gain a “slice” of the overall “pie”, given its size: these dimensions (the pie and the slice) are the true variables that, in a real life context, directly affect quantities, while price has just an indirect impact (precisely, via the “slice”).

You can also notice the little “wall” that I tried to design above the curve: it just means that the demand curve is an objective and very real thing, against which we incur the risk of hitting our head, if we are not able to see it, or at least to feel it. There’s no help for it:

-  the market “sees” our price (at a given level of perceived value, and unless we are talking about “status symbol” products or the price is by itself an indicator of value): if we increase it, our “slice” shrinks, if we lower it, our “slice” grows

-  in any case, the market doesn’t give a damn about our costs!

(to be continued)

I have been teaching strategy and strategic marketing for more than, alas!, thirty years, in the context of management education programs addressed to SMEs’ owners and managers, public officers, young graduates, and more …

Very frequently, during the same programs, I run into the intrusion, in the finance and management accounting sessions, of teachers (coming from the academic world or the professions = reputed CPAs!) who pontificate “explaining” to the audience how-to-set-prices.

Too bad that, frequently, their suggested method refers to the regrettable and unfortunately widespread practice of setting prices based on three parameters: unit variable cost (I’ll let it pass this time), unit fixed cost (and this shouldn’t pass …) and desired margin (another nonsense!).

How is it possible that “otherwise” intelligent people (these teachers, and all the entrepreneurs and managers who adopt the cost-plus pricing) could not see the absurdity and riskiness of that approach?

I therefore think that an attempt to dismantle the cost-plus (il)logic could be useful, drawing your attention to few aspects that seem obvious to me, but apparently are totally ignored by an incredible number of managers and a significant share of management accounting teachers (especially in Italy!) who don’t understand a word of strategy. 

The decision about the price level has a central role in marketing strategy … what does management accounting have to do with it?

I guess it should be evident to everybody that the buyer of any good or service makes his/her choices (among the available alternatives, and in the context of specific needs) based on a more or less conscious comparison of the ratios between the perceived value of the various offers and the economic-financial requests of the suppliers (prices and credit terms).

From the perspective of a supplier who wants to create wealth for his/her company through the satisfaction of buyers’ needs (and if s/he is not able to satisfy these needs s/he will not sell anything, to the competitors’ advantage), the relationships among the relevant factors can be summarized as shown in figure 1.

It is clear that this conceptual model, that I challenge anybody to dispute, identifies real and “physiological” cause-effect relationships, and not purely theoretical assumptions or simple conventions. Beyond the obvious relationship between margins, revenues, and costs, and that equally obvious between volumes, price, and revenues:

-  the volumes presuppose the existence of a market demand (no matter how generated) and the company’s ability to gain a share of it

-  the acquisition of a demand’s share presupposes that the ratio between the value perceived by the market and the price paid is satisfactory and “competitive”, compared to the available alternatives

-  the price level is inversely related, other things equal, to the willingness to buy, but it is often directly related – at the same time – to the perceived value

-  the value perceived by the market depends, to a large extent, on the investments (broadly speaking), and therefore on the costs incurred by the company to produce it

-  the same company’s investments can contribute to the market’s expansion (for example, acquiring new consumers to the industry via communication campaigns), but the logic of acquiring a “slice” of the available “pie”, to the competitors’ detriment, remains applicable  (no matter how the pie was generated).

Can we possibly see any “physiological” and cause-effect relationship between costs (or investments) and price?

Certainly not: if costs have an impact on price, it is just because somebody decided – as we will see, making a blunder – in that way.

Nobody can deny that price is a significant variable for traditional management accounting, but the choice of the price level is evidently a matter of strategy, it directly determines the type and level of the firm’s market position, and falls within the competence of the marketer or the company’s owner, certainly not of comptrollers or CPAs!

We should therefore ask ourselves on which ground these guys pretend, and presume, to invade a field that is outside their competencies.

(to be continued)

In addition to the issue of the “Value Curve”, addressed in the previous posts, I think I should try to interpret and clarify another related and important aspect discussed in the Blue Ocean Strategy book.

Obviously, the reasoning behind any representation of the strategic moves adopted by companies, both with the “Value Curve” and the “Strateco Dashboard” approaches, implicitly assumes that these moves are appropriate for a given and specific market context, and that this context is “relatively” homogeneous: otherwise, the strategy would be either too generic or too focused on selected characteristics of the market, at the expense of others.

Since the birth of the marketing discipline, many decades ago, we have been told that product/market segmentation, i.e. the distinction among different groups of users moved by different needs and feelings in relation to specific product or service characteristics, is critical for addressing these groups with appropriate and, at least in principle, different strategies.

However, one of the most provocative points made by the authors of the book (in addition to that about the uselessness of numbers!) is that of dismissing, at least apparently, the need for accurate segmentation:

·        As companies compete to embrace customer preferences through finer segmentation, they often risk creating too-small target markets.

·        To reach beyond existing demand, think .... commonalities before differences; and desegmentation before pursuing finer segmentation.

·        Avoid hypersegmentation.

My feeling is that an unsophisticated reader of these statements could get the message that a serious product/market segmentation is neither useful nor fashionable anymore.

That is why I think that an attempt to clarify the issue is de rigueur, also, at least partially, to the benefit of the authors:

·            First of all, based on my understanding, they do not say that segmentation is useless:

o          saying that a “finer segmentation” could be risky, and that “hypersegmentation” should be avoided, implicitly means that segmentation should stop at a more aggregate level and, therefore, that it should be preliminarily adopted

o          this is confirmed by their suggestion of “desegmenting”, which assumes that a segmentation had to be preliminarily performed: as a matter of fact, I do not see how we could identify “commonalities” across segments if we do not identify these segments in the first place.

·           Secondly, I think that, once forever, we should try to get rid of a major semantic and operationally misleading confusion, which continuously surfaces in the marketing literature (that regularly refers to “segmentation strategies”, a typical contradiction in terms), including the Blue Ocean authors’ book:

o          “Segmentation” is a totally different exercise than “strategy" or "strategy differentiation”: the latter presupposes the former, but does not necessarily follow the former.

o          Indeed, nothing prevents from adopting a unique and homogeneous strategy after having accurately segmented: we will likely adopt the strategy that, overall, satisfies the needs of the most promising segments, and this could be the case of most SMEs which do not have enough resources for differentiating their strategies significantly, but are not so stupid to forget that customers are not all the same.

o         The problem is that people, including gurus!, confound “segmentation” (= discrimination, distinction), which is a purely “cognitive” and analytical activity (nothing to do with strategy: we do not "decide" anything, but we try to "understand" which are the most relevant and differentiating characteristics of the market.. unless you want to maintain that trying to be intelligent is a strategic decision!) with “strategic differentiation” (= selective and differentiated resource allocation), which is the real strategic and decision-making activity.

o         Therefore, there is nothing wrong with “hypersegmentation”, which should not be interpreted in the wrong sense of "hyperdifferentiation of strategy" implicitly attributed by the above sentences: on the contrary, it is a good way of analyzing the different facets of the market, also in order to identify which could be the most relevant and recurring “commonalities” and the most promising strategic compromises, in response to our in-depth analysis.

[following the previous posts: Blue Ocean revisited (1-4/5)]

Last time, in the context of a suggested new set of guidelines and analytical tools for developing and assessing strategic decisions, in any possible competitive or uncompetitive (“pure” Blue Ocean) sectors, I presented the first component of a “Strategic and economic control dashboard” (“Strateco Dashboard”^® for short): a numerical and graphical “Investment curve” framework, that allows the assessment of “actual” investment levels in “actual” tools, i.e. a precise description of strategic resource allocation choices.

This time, I will conclude my “revisiting” of the Blue Ocean approach (that, however, would deserve additional corrective comments in areas other than the value curve!) with a partial presentation of the second, third, and fourth possible components.

2.    The estimated competitive profile in terms of value and the “value/investment compass^®” 

Based on the relative importance of the various resources or tools for the individual components of value, presented in figure BOS-3 (price excluded), the investment levels in each tool, and the relative importance of the components of value for the market, we can calculate a weighted average index of perceived competitive profile in terms of value (“pcpV”^® for short), always on a 1-10 scale.

Considering these aspects, we can therefore design what I call “value/investment compass”, from which we can assess the degree of coherence between:

·       the importance presumably assigned by the market to the components of value (X axis in figure BOS-4): in this case we assume that brand image and quality have the same importance (i.e. 40% each) and that service is relatively less important (20%)

·       … and the related company’s investments in the resources that have a direct impact on these components (Y axis in the figure: average investments, coming from the “investment curve” described in the previous post, and weighted in accordance with the estimates presented in figure BOS-3).

For example, always referring to company B and its investment levels of 7.0, 10.0, and 8.2., respectively, in raw materials, R&D, and production (figure BOS-2), and assuming a relative impact on quality of these resources of 40, 30, and 30% respectively (figure BOS-3), the overall performance on quality of company B is measurable with an index of 8.3 (figure BOS-4).

The same reasoning applies to the other components of value (brand image and service).

In practice, company B’s actual performance in terms of value (thanks to the various investments or expenses) is respectively equal to 8.3, 6.1 and 6.0 on quality, image, and service, which generates an overall "actual" value index (“pcpVa”) of 7.0 (weighted, depending on the relative importance of these components for the market).

The existence of competitors for the calculation of this “pcpV” is totally irrelevant: if, however, competitors do exist, their performance can be estimated exactly in the same way, as depicted in figure BOS-4.

The dotted lines in the figure identify a sort of “ideal” investments, had the companies allocated the sum of the indices representing their actual investments exactly in proportion of the estimated importance assigned by the market to the components of value (with the same reasoning, we can calculate an "ideal" overall value: "pcpVi"): in the case represented in the figure, we can see that company B, compared to company C, invested in a less coherent and selective way.

However, we can also see that the investment levels of company B are higher than those of company C, and this produces a better “pcpVa”, probably at the expense of some inefficiencies from a strictly economic standpoint: as a matter of fact, we should also consider a number of other factors (briefly summarized below), which, however, are not essential for the point I want to make.

In any case, it seems to me that presenting the companies’ strategies with this “compasses”, is more exhaustive and meaningful than adopting the “value curves”, since  the “compass” identifies and distinguishes both the relative importance of the components of value for the market, and the investment levels in the resources which manage these components.

Simply stated, it is a way of keeping under control, in conjunction with the other components of the “Strateco Dashboard”, the presumably “right” course of action, in any possible ocean, sea, lake, or pond, and in waters of any possible colors (blue, light-blue, reddish, red, etc.).

Always based on these analyses, we can also estimate, for each competitor and each component of value, the difference between “actual” and “ideal” investments: in figure BOS-5 we can see that company B was rather coherent in terms of “quality”, but much less consistent in terms of “service”.

3.    The value/price ratio and the demand curve.

Starting from the value indices described above and the range of variability of prices,  we can therefore estimate the value/price ratios (which, in practice, represent indices of competitiveness) and the related demand curve, obviously depending on estimates about its relative elasticity to price vs. value. In the presence of competitors, the related value/price ratios could provide a rough estimate of their likely market shares.

4.    The impact on the profit & loss statement.

Finally, based on the above estimates, the various investment or expense items (actual amounts, not indices anymore), enter a profit and loss statement, in which revenues are made of the projected demand size at different price levels, and, where applicable, of the estimated market shares. It is therefore easy to assess, with simple sensitivity analyses, which could be the economic results of alternative strategic assumptions, and, hence, their attractiveness and feasibility. That is not all: thanks to the analytical tools made available by the electronic spreadsheet, we can identify investment strategies that optimize, alternatively, the pcpV, the value/price ratio (hence, where applicable, the market share), the revenues or the contribution, other things equal.

I will not go further in-depth on these last two points, since they are straightforward, based on traditional economic approaches, which could be complemented, especially in the case of new market spaces, by some qualitative approaches suggested in the BOS text.

Furthermore, I will not consider here other important aspects in the design of a strategy, such as the impact of what I call “indirect tools” (information, control, and reward systems, etc.), nor I will consider the impact of professional profiles on the effectiveness and efficiency of the investments in both the direct and indirect tools, since this goes beyond the purpose of these posts, mainly aimed at systematizing and integrating some of the most stimulating insights proposed by BOS.

(the end: however, a post-scriptum will probably follow…)

(download)

Click here to download:

Blue_Ocean_revisited_55.zip (99 KB)

[following the previous posts: Blue Ocean revisited (1-3/5)]

In the first three posts, I introduced the main logic of the original BOS approach, with particular reference to the “value curve” framework, and then discussed the major weaknesses of that framework. Here, and in the coming last post, I will propose selected excerpts of a methodology for overcoming these weaknesses with more rigorous and comprehensive analytical tools.

An integrative approach

In my view, the potential strategic opportunities faced by any company should be analyzed more rigorously and systematically, not just in the most frequent cases in which a competitive context already exists, but also when new markets are created (needs previously unmet by any industry) or new demand spaces  in the context of existing sectors are identified.

1.    Firstly, it is important to explicitly distinguish:

·        on the one hand, the choice criteria that we expect will (or could) be adopted by the market, i.e. the components of the perceived or perceivable value; for example, and simplifying:

a.    brand image

b.    product quality (in terms of functional performance)

c.    product accessibility (in terms of availability and traceableness)

·        on the other, the resources or tools in which the company should invest in order to satisfy these criteria; for example, and simplifying:

a.    R&D, raw materials, components, and manufacturing processes for managing quality

b.    again, raw materials and components (“visible” by the market in many industries), type of distribution channels, advertising and promotion for managing brand image

c.    distribution channels and sales force, credit terms, logistics, and, at least in part, again advertising and promotion for managing product accessibility.

2.    Secondly, both for the choice criteria and the tools, it is important to estimate their relative importance: of the former for the market, of the latter for the components of value that they are supposed to manage (for the sake of simplicity, we assume that both the criteria and the tools are independent from each other: a consideration of their interrelationships would require a more sophisticated model which, however, would not add significantly to the conclusions).

3.    Furthermore, for each type of resource or tool, and for the price, it is equally important to estimate, based on our own experience, intuition, and judgment, especially in the absence of an in-depth knowledge of the industry (in case of radical innovations),  a “reasonable” range of variability between minimum and maximum investment, expense, or price levels.

4.    Finally, it will be important to develop equally reasonable estimates about the demand size, at a given value level, depending on extreme and intermediate price levels: i.e. assuming, although tentatively, possible “demand curves”.

The need for appropriate quantification

Overall, it will be necessary to “quantify” in some way the estimates, in order to be able to control the performance of the suggested strategies.

As a matter of fact, contrary to the purely qualitative approach (“high” vs. “low”) suggested in the Blue Ocean book, and the insistence on avoiding quantification (“focus on the big picture, not the numbers”), I am convinced that it is impossible to manage what cannot be measured, and I totally share H.J. Harrington’s opinion (http://www.hjharrington.com/): “If we do not measure we cannot control, if we do not control we cannot manage, if we do not manage we cannot improve”.

Beyond the often provocative opinions expressed by  some authors about the supposed inadequacy of numbers, everybody knows that, in order to correctly “pilot” a company (especially if it operates in multiple sectors), a strategic control system (in addition to the traditional economic and financial control) is indispensable, and cannot be based only on qualitative opinions (see, for example the “Balanced Score-Card” approach: http://www.balancedscorecard.org/).

Who cares if the quantification of qualitative assessments with indices, scales, or percentages is not “precise”? We are not looking for precision, but for relevance: in any case, decisions are made for the future, and precise data about the future do not exist by definition.

Quantifying our estimates (imprecise by nature), assumptions, and hypotheses, is the only way we can make explicit our decision making process, communicate, discuss, and negotiate our conclusions, and control the discrepancies about projections and actual outcomes, in order to be able to adjust our strategies for the future.

An articulated and integrated “strategic and economic control dashboard”

Based on the four guidelines described above, it will be relatively easy to design an articulated “strategic and economic control dashboard” (“Strateco Dashboard”^® for short), made of the following basic elements (and not only of an approximate “value curve” that, as we have seen, is NOT “easy to understand and communicate for effective execution”, as stated in the Blue Ocean book):

1. An “investment curve” ^®: numerical and graphical representation of “actual” alternative investment or expense levels (we use the term “investment” in a broad sense) in the above mentioned resources (see figure  BOS-2). The investment indices for each resource or tool (e.g. raw materials, R&D, and production systems), will serve the purpose of assessing the weighted average investment in the various components of value perceived by the market (e.g. quality, in this case), based on the relative importance of the necessary resources or tools for managing the related components of value.

As we can see from the figure, alternative “investment” and pricing decisions (in practice, the strategies adopted in terms of resource allocation to the various tools) can be easily compared, thanks to a standardization on a scale 1-10 (based on the minimum and maximum levels previously estimated for each tool, and using, for the sake of simplicity, a linear function) of each individual decision: we assign to the minimum investment levels the index of 1, to the maximum investment levels the index of 10, while the indices for the intermediate levels are proportional to the actual min-max range. In this case, a relatively low investment in price correctly indicates a relatively higher price level, and vice versa. By the way, the function that interpolates the extreme levels could be more correctly represented by a logistic (S-shaped) curve, but normally we do not have sufficiently reliable data for interpreting it, and, in any case, the basic trend would not change significantly.

Finally, I should say that, even though the min-max ranges of investments are based on estimates, the figures presented in this graphical framework are quite precise, since they correspond to “actual” investment decisions.

For example, company B “invested”, respectively in raw materials, R&D, and production, € 0.45 per kilo (within an estimated min-max range of € 0.35–0.50), € 250k (range 100k–250k), and € 300k (range 100k–350k), obtaining, on a scale from 1 to 10, indices of 7.0, 10.0, and 8.2.

Next time, I will conclude my “revisiting” of the Blue Ocean approach with the presentation of other selected components of this dashboard.

(to be continued)

In the second post, we introduced the major methodological approach
proposed by BOS for the identification of the winning “strategic
move”, and suggested that, first of all, their “value curve” framework
does not distinguish between choice criteria adopted by the market,
and resources in which the firm invests in order to satisfy these
criteria.

Here, I will summarize the other three aspects that could be improved:

• The adopted scale (low or high investments, or, in any case, low or
high “degree of focalization”) does not allow meaningful comparisons,
even from a relative standpoint, for each component of the offer: does
stating that [yellow tail] invests (or focuses) a lot on “easy
drinking”, “ease of selection” and “fun/adventure”, totally
disregarding the “technicalities in communication”, make sense, while
the current competitors are at rock-bottom on the three first items?
Evidently, it is most of all (if not only) a matter of different
positioning from a communication standpoint, irrespective of the
amount of resources invested in communication. Besides, how could we
imply that any new or light wine (most of the “budget” wines) is not
“easy to drink”?. Furthermore, whatever the meaning of the vertical
axis (investment levels or degree of focalization), the “price” scale
should be reversed vs. that of the other components of the curve: a
low price means a high level of investment or
focalization/penetration, and vice versa for a high price, that allows
much better margins.

• But, most of all, it is totally impossible to hook up these value
curves to the offers’ economic profile (which is not taken into
consideration in any part of the book, together with methods for
estimating the market potentials): also because of the mixture and
confusion of the concepts of “investment” and “choice criterion”
(first problem), and the adoption of a purely qualitative scale
(second problem), it is impossible to estimate in advance whether or
not the value innovation will stand up from that viewpoint, i.e. if it
will be able to generate wealth, thanks to an appropriate balance
among value, prices, volumes, and costs.

• Finally, and this is not a minor aspect, the authors intentionally
exclude from the list of the possible components of the value curve
(i.e. the items on its horizontal axis) the “brand image”. The reason
is clear: since they maintain “by definition” that in the blue oceans
the competition does not exist, the “brand” is irrelevant.
Unfortunately, anybody can see that this a simplistic conclusion: the
brand image is a fundamental element for identifying the suppliers,
especially in “reddish” or “light-blue-but-not-so-much” oceans (Dell
case), but also in oceans that are really blue at the beginning, and
are subsequently entered by competitors: let us think again of the
mobile phones case, both at the dawn of the industry and, more
recently, with the “smart phones” complemented and improved by Apple
with significant innovations (touch screen, multitouch, accelerometer,
…). Apple succeeded, and still succeeds, also because of its brand
name. As a matter of fact, we see how important are also the synergies
among businesses (totally ignored by the authors) precisely thanks,
to a large extent, to brand awareness and image: the creation, by
Apple, of the “MP3” blue ocean with the launch of the iPod and the
iTunes store (that, by the way, canceled from the face of the earth,
in few months, the previous blue ocean created by Sony with the
walkman), enormously facilitated the development of the new blue
ocean, thanks to its iPhone.

In the last two posts, we will try to contribute to an improvement of
the value curve approach with some complementary frameworks and
guidelines, which, in our view, should eliminate most of the
weaknesses discussed above.

(to be continued)

[following the previous post: Blue Ocean revisited (1/5)]

The major logical and methodological (and, consequently, practical) weakness I found in the authors’ suggestions is that of grounding, to a large extent, the identification of the “strategic move” on the analysis of the “strategy canvasses” and the so-called “value curves”, which have the purpose of graphically describing the competitors’ strategies in any existing industry, as well as the strategy that instead could be adopted by a company which wants to distinguish itself and innovate, thanks to the selective approach described in the previous post.

An important specification, at this point: by “value innovation” we do not necessarily mean “technological innovation” (which could be irrelevant for the market, especially if it does not satisfy real and previously unmet needs and/or is too costly in relation to its value), but the proposal of a better and accessible value (vs. what is currently available in the market), thanks to new ways of configuring the offer, in relation to any choice criterion that is relevant for the (possibly, new) target market. Therefore, not necessarily better technical and qualitative performances, but, in case, broader choices, personalization, better service, more convenience and user friendliness, more affordable price without a reduction of the overall value, or, as we will see, better “appeal” from a psychological and emotional standpoint.

As an example, in figure “BOS-1”, adapted from the original Blue Ocean Strategy book (I added the “investment levels” label to the vertical axis, since it is explicit in the text, but not included in the original graph), the authors compare the strategies of two major “strategic groups” in the U.S. wine industry (premium and budget wines) to that of the innovative (or, at least, presented as such) Casella’s Australian wine [yellow tail], which focuses on new components of the offer (the three items at the bottom right of the figure), practically canceling the investments in other resources (the three on the left, price excluded): “a fun and simple wine to be enjoyed every day”.

The major problems of the “Value Curve” approach

I am afraid that I found, at least, four major problems, in addition to some simplistic comparisons: as far as I know, also in the U.S.A. the investments in media advertising (“a-t-l marketing”) are higher for the budget wines, compared to the premium wines which focus to a large extent their communication strategies on PR, connoisseurs’ endorsements, selective distribution and word-of-mouth.

·      First, this approach does not distinguish between choice criteria adopted by the market and resources or tools in which the company actually invests, but these two aspects are treated on a par and mixed together: “ease of selection” and “fun/adventure” could be considered as choice criteria (manageable with investments in product range and distribution on one side, and with appropriate communication approaches on the other), while “a-t-l marketing” is actually a tool in which the suppliers can invest (it is certainly not a choice criterion adopted by the market, but rather an element that could facilitate the choice, based on any possible criterion), and “wine complexity” or “aging quality” could be both criteria and investments, depending on the perspective from which they are considered (buyers or suppliers). An immediate consequence of this type of confusion is the impossibility of assessing the relative importance of the choice criteria on one hand, and that of the resources in which to invest on the other.

(to be continued)

The famous book by Chan Kim and Renée Mauborgne, Blue Ocean Strategy: How to Create Uncontested Market Space and Make the Competition Irrelevant (Harvard Business School Press, 2005) had an incredible worldwide success, and was translated in more than thirty languages.

Doing business in “blue oceans”, avoiding the blood flowing from the competitive war ( the “red oceans”), is apparently the new “must”, or at least an objective to pursue.

It certainly was a “technically” excellent marketing endeavor, based on the expedient of an original title, an intriguing subtitle, new labels for several old concepts, an intelligent and well targeted communication campaign, and the prestige of the sponsoring business schools (Harvard and INSEAD).

However, any reader of the book cannot avoid realizing that the statement “… make the competition irrelevant”  is, in the best case, a little bold, also in various cases mentioned by the authors in support of their thesis: for example, saying (page 203 of the original text) that “… [Dell computers’] U.S. market share grew from 2 percent to more than 30 percent..” in the period 1995-2003, assumes the existence, in the same period, of competitors that lost market share from 98 to 70%.

It goes without saying that gaining so quickly a market share of 30%, especially in “reddish” or “light-blue-but-not-so-much” oceans like that of personal computers is not so bad! But let us forget the deontological considerations about the manifest (yet commercially effective!) exaggeration embodied in the subtitle.

The major contribution of the book is that of having explicitly drawn the attention of the marketing world to the importance of replicating (or trying to replicate!) and systematizing the strategic process that allows the identification and exploitation of new industry sectors and market spaces, in which, at least initially, a competitive system does not exist by definition. Or, alternatively (and this is obviously the most frequent and relatively “within reach” case), the identification of new moves that displace the competitors at least for some time (this is precisely the example of Michael Dell: direct sales in an industry traditionally based on retailing via computer shops). 

In any case, it is clear that many industry sectors that did not exist until few decades ago (e.g. mobile phones, biotechnologies, etc.) are now well alive, even in a period of worldwide economic stagnation:

·      how were they born?

·      is it possible to replicate the strategic process that created them?

To this end, the authors suggest a series of methodologies, more or less practical and effective. In particular, I found useful the analytical method for identifying the “obstacles to the purchase or the use of the products or services”, based on the phases of the “clients’ experience cycle” (besides, largely known since the seventies under the label of “buyer behavior”: from the purchase to the use and disposal of the products), and the so-called “utility levers” (productivity, simplicity, convenience, risk, fun and image, environmental friendliness).

In short, the suggested methodologies have the purpose of allowing the identification and implementation of a “strategic move” based on the so-called “value innovation”, consisting in delivering a high value at affordable prices that, however, allow the “blue ocean firm” the creation of profits, thanks to a high selectivity and focus of its investments only in selected areas (choice criteria adopted by the market and/or resources that satisfy these criteria), definitely neglecting other areas.

It is almost exactly what was suggested by Michael Porter in 1996, i.e. ten years earlier (What is Strategy?, Harvard Business Review, November-December), without, however, talking about “irrelevant competition”: “... abandon or forgo some product features, services, or activities in order to be unique at others...”.

Next week we will start discussing the major logical and methodological weaknesses inherent in the way the authors address the issue of identifying the winning “strategic move” mentioned above.

(to be continued)

I read an interesting article by Roger Martin (Dean of the Rotman School of Management at the University of Toronto) and Jennifer Riel (Associate Director of the Desautels Centre for Integrative Thinking at Rotman) in the “Outside Shot” section of January 25, 2010 Business Week.

(by the way, they title “Innovation’s Accidental Enemies”, but I prefer “Worst”)

They say that “Innovation is killed with the two deadliest words in business: Prove it”, and that with both the deductive and inductive logic “we use existing information to understand the issue in play”, while the real breakthroughs happen without rules or pool of past data to provide certainty.

This reminds me the suggestions made by Blue Ocean Strategy’s proponents (and several other authors before them, although much less praised and fortunate, but we will cover this topic another time), who claim the need for “looking beyond the existing borders”, or the well-known De Bono’s “Lateral Thinking” approach.

Over my (unfortunately) long professional life, I had the opportunity, often together some friends and partners, of developing business plans for few innovative ideas which would have probably found venture capital financing, had we lived in a more civilized country than Italy (for example, the United States, where some of us had assimilated their taste for continuous innovation, backed by “adequate” funds).

As a matter of fact, the notion of “adequate funds” is not very clear in Italy and in other European countries, especially for ideas that look really innovative, and, therefore, “inevitably” risky (especially in the minds of the prospective funders).

Sometimes, it was also our fault, since we were too early in relation to the stage of assimilation of some technological developments (especially, ICTs): however, thanks to the growing diffusion of web 2.0, smart phones , and social networks, even in our countries, the time is probably ripe for an idea I had launched in a San Diego conference about twenty years ago, embryonically embedded in a subsequent Italian book (sold in three copies: my mom, my father, and my girlfriend!).

This idea, based on a combination of induction and abduction (“assembling of disparate experiences and bits of data that seem relevant in order to make an inference – a logical leap – to the best possible conclusion” in the words of Roger & Jennifer), can even count on some current “proofs” (can you imagine?), although not exactly the same that prospective funders would like to see.

But I will tell you more about that in a future post, as soon as we are able to get some concrete (I mean money!) recognition.