Archive for the 'Market planning' Category

Strategic Progamming

Over the last 40-odd years, a branch of Artificial Intelligence called AI Planning has developed.  One way to view Planning is as automated computer programming: 

  • Write a program that takes as input an initial state, a final state (“a goal”), and a collection of possible atomic actions, and  produces as output another computer programme comprising a combination of the actions (“a plan”) guaranteed to take us from the initial state to the final state. 

A prototypical example is robot motion:  Given an initial position (e.g., here), a means of locomotion (e.g., the robot can walk), and a desired end-position (e.g., over there), AI Planning seeks to empower the robot to develop a plan to walk from here to over there.   If some or all the actions are non-deterministic, or if there are other possibly intervening effects in the world, then the “guaranteed” modality may be replaced by a “likely” modality. 

Another way to view Planning is in contrast to Scheduling:

  • Scheduling is the orderly arrangement of a collection of tasks guranteed to achieve some goal from some initial state, when we know in advance the initial state, the goal state, and the tasks.
  • Planning is the identification and orderly arrangement of tasks guranteed to achieve some goal from some initial state, when we know in advance the initial state, the goal state, but we don’t yet know the tasks;  we only know in advance the atomic actions from which tasks may be constructed.

Relating these ideas to my business experience, I realized that a large swathe of complex planning activities in large companies involves something at a higher level of abstraction.  Henry Mintzberg called these activities “Strategic Programming”

  • Strategic Programming is the identification and priorization of a finite collection of programs or plans, given an initial state, a set of desirable end-states or objectives (possibly conflicting).  A program comprises an ordered collection of tasks, and these tasks and their ordering we may or may not know in advance.

Examples abound in complex business domains.   You wake up one morning to find yourself the owner of a national mobile telecommunications licence, and with funds to launch a network.  You have to buy the necessary equipment and deploy and connect it, in order to provide your new mobile network.   Your first decision is where to provide coverage:  you could aim to provide nationwide coverage, and not open your service to the public until the network has been installed and connected nationwide.  This is the strategy Orange adopted when launching PCS services in mainland Britain in 1994.   One downside of waiting till you’ve covered the nation before selling any service to customers is that revenues are delayed. 

Another downside is that a competitor may launch service before you, and that happened to Orange:  Mercury One2One (as it then was) offered service to the public in 1993, when they had only covered the area around London.   The upside of that strategy for One2One was early revenues.  The downside was that customers could not use their phones outside the island of coverage, essentially inside the M25 ring-road.   For some customer segments, wide-area or nationwide coverage may not be very important, so an early launch may be appropriate if those customer segments are being targeted.  But an early launch won’t help customers who need wider-area coverage, and – unless marketing communications are handled carefully – the early launch may position the network operator in the minds of such customers as permanently providing inadequate service.   The expectations of both current target customers and customers who are currently targets need to be explicitly managed to avoid such mis-perceptions.

In this example, the different coverage rollout strategies ended up at the same place eventually, with both networks providing nationwide coverage.  But the two operators took different paths to that same end-state.   How to identify, compare, prioritize, and select-between these different paths is the very stuff of marketing and business strategy, ie, of strategic programming.  It is why business decision-making is often very complex and often intellectually very demanding.   Let no one say (as academics are wont to do) that decision-making in business is a doddle.   Everything is always more complicated than it looks from outside, and identifying and choosing-between alternative programs is among the most complex of decision-making activities.




Vacuum cleaners generating hot air

Apparently, British inventor James Dyson has argued that more people should study engineering and fewer “French lesbian poetry”.    Assuming he is correctly quoted, there are a couple of things one could say in response.

First, all Mr Dyson need do is pay engineers more than the going market rates, and he will attract more people  into the profession.   Likewise, he could give students scholarships to study engineering.   He, unlike most of the rest of us, has it in his direct personal power to achieve this goal.   I think it ill-behooves someone who moved his manufacturing operations off-shore to bemoan any lack of home-grown talents.

Second, no matter how wonderful the engineering technology or novelty of the latest, jet-propelled, wind-turbine-bladed vacuum cleaner, the technology will not sell itself.   For that, even the vacuum cleaners of the famous Mr Dyson need marketing and advertising.  And, marketing needs people who can understand and predict customer attitudes and behaviours, people who have studied psychology and sociology and anthropology and economics.  Marketing needs people who can analyze data, increasingly in large quantities and in real-time, people who have studied mathematics and statistics and computer science and econometrics.  Marketing needs people who can strategize, people who have studied game theory and military strategy and political science and history, and can emphathize with customers and competitors.   As Australian advertising man Philip Adams once noted, Marxists and ex-Marxists are often the best marketing strategists, because they think dialectically about the long term.

And advertising needs people who can manipulate images, people who have usually studied art or art history or graphic design or architecture.  Advertising needs people who can take photos and use movie cameras and direct films, people who have studied photography and cinematography and lighting and film and theatre studies and acting.  Advertising needs people who can write jingles and advertising scores, and play the music required, people who have studied music and song and musical instruments.   Advertising needs people who can build sets, acquire props, and obtain costumes, people who are good with their hands or who have studied fashion.   And, finally, advertising needs people who can write ad copy and scripts – often people have studied history and journalism and languages and literature and poetry – even, at times, I would guess, the poetry of French lesbians.

One reason Britain is a such a world leader in marketing and advertising, despite the long-term decline and poor management of its manufacturing industry,  is because of its many leading art colleges and universities teaching the humanities and social sciences.  The name of Dyson would not be known to households across the country and beyond without the contributions of many, many professionals who did not study engineering.

 

UPDATE (2012-12-01):  And if you are still wondering why more people studying engineering would not be sufficient for business success, consider this from Grant McCracken:

Culture is the sea in which business swims. We can’t do good innovation without it. We can’t do good marketing without it. And we can’t build a good corporate culture without it.”

 




Markets as feedback mechanisms

I just posted after hearing a talk by economic journalist Tim Harford at LSE.  At the end of that post, I linked to a critical review of Harford’s latest book,  Adapt – Why Success Always Starts with Failure, by Whimsley.  This review quotes Harford talking about markets as feedback mechanisms:

To identify successful strategies, Harford argues that “we should not try to design a better world. We should make better feedback loops” (140) so that failures can be identified and successes capitalized on. Harford just asserts that “a market provides a short, strong feedback loop” (141), because “If one cafe is ordering a better combination of service, range of food, prices, decor, coffee blend, and so on, then more customers will congregate there than at the cafe next door“, but everyday small-scale examples like this have little to do with markets for credit default swaps or with any other large-scale operation.

Yes, indeed.  The lead-time between undertaking initial business planning in order to raise early capital investments and the launching of services to the  public for  global satellite communications networks is in the order of 10 years (since satellites, satellite networks and user devices need to be designed, manufactured, approved by regulators, deployed, and connected before they can provide service).  The time between initial business planning and the final decommissioning of an international gas or oil pipeline is about 50 years.  The time between initial business planning and the final decommissioning of an international undersea telecommunications cable may be as long as 100 years.   As I remarked once previously, the design of Transmission Control Protocol (TCP) packets, the primary engine of communication in the 21st century Internet, is closely modeled on the design of telegrams first sent in the middle of the 19th century.  Some markets, if they work at all, only work over the long run, but as Keynes famously said, in the long run we are all dead.

I have experience of trying to design telecoms services for satellite networks (among others), knowing that any accurate feedback for design decisions may come late or not at all, and when it comes may be vague and ambiguous, or even misleading.   Moreover, the success or failure of the selected marketing strategy may not ever be clear, since its success may depend on the quality of execution of the strategy, so that it may be impossible to determine what precisely led to the outcome.   I have talked about this issue before, both regarding military strategies and regarding complex decisions in general.  If the quality of execution also influences success (as it does), then just who or what is the market giving feedback to?

In other words, these coffees are not always short and strong (in Harford’s words), but may be cold, weak, very very slow in arriving, and even their very nature contested.   I’ve not yet read Harford’s book, but if he thinks all business is as simple as providing fmc (fast-moving consumer) services, his book is not worth reading.

Once again, an economist argues by anecdote and example.  And once again, I wonder at the world:  That economists have a reputation for talking about reality, when most of them evidently know so little about it, or reduce its messy complexities to homilies based on the operation of suburban coffee shops.




Vale: Sol Encel

I have just learnt of the death last  month of Sol Encel, Emeritus Professor of Sociology at the University of New South Wales, and a leading Australian sociologist, scenario planner, and futures thinker.    I took a course on futurology with him two decades ago, and it was one of the most interesting courses I ever studied.  This was  not due to Encel himself, at least not directly, who appeared in human form only at the first lecture.

He told us he was a very busy and important man, and would certainly not have the time to spare to attend any of the subsequent lectures in the course.  Instead, he had arranged a series of guest lectures for us, on a variety of topics related to futures studies, futurology, and forecasting.  Because he was genuinely important, his professional network was immense and impressive, and so the guest speakers he had invited were a diverse group of prominent people, from different industries, academic disciplines, professions, politics and organizations, each with interesting perspectives or experiences on the topic of futures and prognosis.  The talks they gave were absolutely fascinating.

To accommodate the guest speakers, the lectures were held in the early evening, after normal working hours.  Because of this unusual timing, and because the course assessment comprised only an essay, student attendance at the lectures soon fell sharply.  Often I turned up to find I was the only student present.   These small classes presented superb opportunities to meet and talk with the guest speakers, conversations that usually adjourned to a cafe or a bar nearby.  I learnt a great deal about the subject of forecasting, futures, strategic planning, and prognosis, particularly in real organizations with real stakeholders, from these interactions.  Since he chose these guests, I thus sincerely count Sol Encel as one of the important influences on my thinking about futures.

Here, in a tribute from the Australian Broadcasting Commission, is a radio broadcast Encel made in 1981 about Andrei Sakharov. It is interesting that there appears to have been speculation in the West then has to how the so-called father of the Soviet nuclear bomb could have become a supporter of dissidents.   This question worried, too, the KGB, whose answer was one Vadim Delone, poet.  And here, almost a month after Solomon Encel’s death, is his obituary in the Sydney Morning Herald.  One wonders why this took so long to be published.

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Complex Decisions

Most real-world business decisions are considerably more complex than the examples presented by academics in decision theory and game theory. What makes some decisions more complex than others? Here I list some features, not all of which are present in all decision situations.

  • The problems are not posed in a form amenable to classical decision theory.

    Decision theory requires the decision-maker to know what are his or her action-options, what are the consequences of these, what are the uncertain events which may influence these consequences, and what are the probabilities of these uncertain events (and to know all these matters in advance of the decision). Yet, for many real-world decisions, this knowledge is either absent, or may only be known in some vague, intuitive, way. The drug thalidomide, for example, was tested thoroughly before it was sold commercially – on male and female human subjects, adults and children. The only group not to be tested were pregnant women, which were, unfortunately, the main group for which the drug had serious side effects. These side effects were consequences which had not been imagined before the decision to launch was made. Decision theory does not tell us how to identify the possible consequences of some decision, so what use is it in real decision-making?

  • There are fundamental domain uncertainties.

    None of us knows the future. Even with considerable investment in market research, future demand for new products may not be known because potential customers themselves do not know with any certainty what their future demand will be. Moreover, in many cases, we don’t know the past either. I have had many experiences where participants in a business venture have disagreed profoundly about the causes of failure, or even success, and so have taken very different lessons from the experience.

  • Decisions may be unique (non-repeated).

    It is hard to draw on past experience when something is being done for the first time. This does not stop people trying, and so decision-making by metaphor or by anecdote is an important feature of real-world decision-making, even though mostly ignored by decision theorists.

  • There may be multiple stakeholders and participants to the decision.

    In developing a business plan for a global satellite network, for example, a decision-maker would need to take account of the views of a handful of competitors, tens of major investors, scores of minor investors, approximately two hundred national and international telecommunications regulators, a similar number of national company law authorities, scores of upstream suppliers (eg equipment manufacturers), hundreds of employees, hundreds of downstream service wholesalers, thousands of downstream retailers, thousands or millions of shareholders (if listed publicly), and millions of potential customers. To ignore or oppose the views of any of these stakeholders could doom the business to failure. As it happens, Game Theory isn’t much use with this number and complexity of participants. Moreover, despite the view commonly held in academia, most large Western corporations operate with a form of democracy. (If opinions of intelligent, capable staff are regularly over-ridden, these staff will simply leave, so competition ensures democracy. In addition, good managers know that decisions unsupported by their staff will often be executed poorly, so success of a decision may depend on the extent to which staff believe it has been reached fairly.) Accordingly, all major decisions are decided by groups or teams, not at the sole discretion of an individual. Decision theorists, it seems to me, have paid insufficient attention to group decisions: We hear lots about Bayesian decision theory, but where, for example, is the Bayesian theory of combining subjective probability assessments?

  • Domain knowledge may be incomplete and distributed across these stakeholders.
  • Beliefs, goals and preferences of the stakeholders may be diverse and conflicting.
  • Beliefs, goals and preferences of stakeholders, the probabilities of events and the consequences of decisions, may be determined endogenously, as part of the decision process itself.

    For instance, economists use the term network goods to refer to a good where one person’s utility depends on the utility of others. A fax machine is an example, since being the sole owner of fax is of little value to a consumer. Thus, a rational consumer would determine his or her preferences for such a good only AFTER learning the preferences of others. In other words, rational preferences are determined only in the course of the decision process, not beforehand.Having considerable experience in marketing, I contend that ALL goods and services have a network-good component. Even so-called commodities, such as natural resources or telecommunications bandwidth, have demand which is subject to fashion and peer pressure. You can’t get fired for buying IBM, was the old saying. And an important function of advertising is to allow potential consumers to infer the likely preferences of other consumers, so that they can then determine their own preferences. If the advertisement appeals to people like me, or people to whom I aspire to be like, then I can infer that those others are likely to prefer the product being advertized, and thus I can determine my own preferences for it. Similarly, if the advertisement appeals to people I don’t aspire to be like, then I can infer that I won’t be subject to peer pressure or fashion trends, and can determine my preferences accordingly.

    This is commonsense to marketers, even if heretical to many economists.

  • The decision-maker may not fully understand what actions are possible until he or she begins to execute.
  • Some actions may change the decision-making landscape, particularly in domains where there are many interacting participants.

    A bold announcement by a company to launch a new product, for example, may induce competitors to follow and so increase (or decrease) the chances of success. For many goods, an ecosystem of critical size may be required for success, and bold initiatives may act to create (or destroy) such ecosystems.

  • Measures of success may be absent, conflicting or vague.
  • The consequences of actions, including their success or failure, may depend on the quality of execution, which in turn may depend on attitudes and actions of people not making the decision.

    Most business strategies are executed by people other than those who developed or decided the strategy. If the people undertaking the execution are not fully committed to the strategy, they generally have many ways to undermine or subvert it. In military domains, the so-called Powell Doctrine, named after former US Secretary of State Colin Powell, says that foreign military actions undertaken by a democracy may only be successful if these actions have majority public support. (I have written on this topic before.)

  • As a corollary of the previous feature, success of an action may require extensive and continuing dialog with relevant stakeholders, before, during and after its execution.

    This is not news to anyone in business.

  • Success may require pre-commitments before a decision is finally taken.

    In the 1990s, many telecommunications companies bid for national telecoms licences in foreign countries. Often, an important criterion used by the Governments awarding these licences was how quickly each potential operator could launch commercial service. To ensure that they could launch service quickly, some bidders resorted to making purchase commitments with suppliers and even installing equipment ahead of knowing the outcome of a bid, and even ahead, in at least one case I know, of deciding whether or not to bid.

  • The consequences of decisions may be slow to realize.

    Satellite mobile communications networks have typically taken ten years from serious inception to launch of service.  The oil industry usually works on 50+ year cycles for major investment projects.  BP is currently suffering the consequence in the Gulf of Mexico of what appears to be a decades-long culture which de-emphasized safety and adequate contingency planning.

  • Decision-makers may influence the consequences of decisions and/or the measures of success.
  • Intelligent participants may model each other in reaching a decision, what I term reflexivity.

    As a consequence, participants are not only reacting to events in their environment, they are anticipating events and the reactions and anticipations of other participants, and acting proactively to these anticipated events and reactions. Traditional decision theory ignores this. Following Nash, traditional game theory has modeled the outcomes of one such reasoning process, but not the processes themselves. Evolutionary game theory may prove useful for modeling these reasoning processes, although assuming a sequence of identical, repeated interactions does not strike me as an immediate way to model a process of reflexivity. This problem still awaits its Nash.

In my experience, classical decision theory and game theory do not handle these features very well; in some cases, indeed, not at all.  I contend that a new theory of complex decisions is necessary to cope with decision domains having these features.

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GTD Intelligence at Kimberly-Clark

I started talking recently about getting-things-done (GTD) intelligence.  Grant McCracken, over at This Blog Sits At, has an interview with Paula Rosch, formerly of fmcg company Kimberly-Clark, which illustrates this nicely.

I spent the rest of my K-C career in advanced product development or new business identification, usually as a team leader, and sometimes as what Gifford Pinchot called an “Intrapreneur” – a corporate entrepreneur, driving new products from discovery to basis-for-interest to commercialization.  It’s the nature of many companies to prematurely dismiss ideas that represent what the world might want/need 5, 10 years out and beyond in favor of near-term opportunities – the intrapreneur stays under the radar, using passion, brains, intuition, stealth, any and every other human and material resource available to keep things moving.  It helps to have had some managers that often looked the other way.

Continue reading ‘GTD Intelligence at Kimberly-Clark’

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Footnotes of Mad Men

For those of you enchanted with Mad Men, this site, The Footnotes of Mad Men, provides superb annotation and tangential comments.




Bonuses yet again

Alex Goodall, over at A Swift Blow to the Head, has written another angry post about the bonuses paid to financial sector staff. I’ve been in several minds about responding, since my views seem to be decidedly minority ones in our present environment, and because there seems to be so much anger abroad on this topic.  But so much that is written and said, including by intelligent, reasonable people such as Alex, mis-understands the topic, that I feel a response is again needed.  It behooves none of us to make policy on the basis of anger and ignorance.

Continue reading ‘Bonuses yet again’




Social forecasting: Doppio Software

Five years ago, back in the antediluvian era of Web 2.0 (the web as enabler and facilitator of social networks), we had the idea of  social-network forecasting.  We developed a product to enable a group of people to share and aggregate their forecasts of something, via the web.  Because reducing greenhouse gases were also becoming flavour-du-jour, we applied these ideas to social forecasts of the price for the European Union’s carbon emission permits, in a nifty product we called Prophets-360.  Sadly, due mainly to poor regulatory design of the European carbon emission market, supply greatly outstripped demand for emissions permits, and the price of permits fell quickly and has mostly stayed fallen.  A flat curve is not difficult to predict, and certainly there was little value in comparing one person’s forecast with that of another.  Our venture was also felled.

But now the second generation of social networking forecasting tools has arrived.  I see that a French start-up, Doppio Software, has recently launched publicly.   They appear to have a product which has several advantages over ours:

  • Doppio Software is focused on forecasting demand along a supply chain.  This means the forecasting objective is very tactical, not the long-term strategic forecasting that CO2 emission permit prices became.   In the present economic climate, short-term tactical success is certainly more compelling to business customers than even looking five years hence.
  • The relevant social network for a supply chain is a much stronger community of interest than the amorphous groups we had in mind for Prophets-360.  Firstly, this community already exists (for each chain), and does not need to be created.  Secondly, the members of the community by definition have differential access to information, on the basis of their different positions up and down the chain.  Thirdly, although the interests of the partners in a supply chain are not identical, these interests are mutually-reinforcing:  everyone in the chain benefits if the chain itself is more successful at forecasting throughput.
  • In addition, Team Doppio (the Doppiogangers?) appear to have included a very compelling value-add:  their own automated modeling of causal relationships between the target demand variables of each client and general macro-economic variables, using  semantic-web data and qualitative modeling technologies from AI.  Only the largest manufacturing companies can afford their own econometricians, and such people will normally only be able to hand-craft models for the most important variables.  There are few companies IMO who would not benefit from Doppio’s offer here.

Of course, I’ve not seen the Doppio interface and a lot will hinge on its ease-of-use (as with all software aimed at business users).  But this offer appears to be very sophisticated, well-crafted and compelling, combining social network forecasting, intelligent causal modeling and semantic web technologies.

Well done, Team Doppio!  I wish you every success with this product!

PS:  I have just learnt that “doppio” means “double”, which makes it a very apposite name for this application – forecasts considered by many people, across their human network.  Neat!  (2009-09-16)

Article in The Observer (UK) about Doppio 2009-09-06 here. And here is an AFP TV news story (2009-09-15) about Doppio co-founder, Edouard d’Archimbaud.  Another co-founder is Benjamin Haycraft.

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A salute to Flo Skelly

Watching Season 2 of Mad Men with its arc of the rise of a female copywriter (Peggy Olsen, played by Elisabeth Moss), I was reminded of that real pioneer woman in advertising, Florence Skelly, who died in 1998.  I never had the good fortune to work with her, but I have worked with lots of people who did.  The stories about her were legion.    I recall especially hearing about a series of detailed presentations she gave in the mid-1990s on the attitudes and aspirations of teenagers — those in what we would now call late GenX and early GenY — a group she seemed to know better than any other researcher around.   The irony was that she herself was at the cusp of her eighth decade!

Interestingly, season 1 of Mad Men had a couple of scenes involving market researchers, but the one woman was a PhD psychologist with a Central European accent, apparently unable to be creative and clearly instantiating a different (albeit then-common) archetype to Flo Skelly.

On Mad Men,  a reminder that Ta-Nehisi Coates, mashing Karl Rove, last October captured the demographic of the typical viewer with great precision:

Even if I’ve never met you, I know you all. You guys are that dude at the country club with the beautiful date, holding a martini and a cigarette, standing against the wall and making snide comments about all the CSI-viewers who pass by. And you’re also a Muslim. Can’t forget Muslim.

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