Analysis of turbulent market environments

EXECUTIVE SUMMARY

Analysis of Turbulent Market Environments 1

CHAPTER 1

INTRODUCTION

Besides the consumer demands and the business costs, the decisions of the firms

also depend on the number, size and behaviour of the other firms in the industry. The

strength of the competition faced by a company can profoundly affect its pricing, its

output decisions and its input purchases. Strong competitive pressures, sometimes taking

subtle forms, can severely limit the freedom pf choice by management in setting prices

and, in process, protect the interests of consumers. Giant corporations may also find

themselves under this sort of pressure, even where there are few rival domestic firms.

Industries differ dramatically in how populated they are and in the size of a typical firm.

Some industries contain a great many very small firms; others are composed of a few

industrial giants.


CHAPTER 2

MARKET AND ITS TYPES

What is a ‘market’?

Economists do not reserve the term ‘market’ to denote only an organized

exchange operating in a well defined physical location. In its more general and abstract

usage, ‘a market’ refers to a set of sellers and buyers whose activities affect the price at

which a particular commodity is sold.

With the development of transportation, communication and banking, the markets

have widened and dealings in come commodities worldwide. Therefore, the essential

feature of a market is that buyers should be able to strike bargains with sellers. According

to Wicksteed, “thus market is the characteristic phenomenon of economic life and the

constitution of markets and market prices is the central problem of Economics.”

The type of market in which the firm operates makes a great deal of difference for the

way in which it can and does behave. Under some market forms, for example, the firm

has no control over its price. In others, the firm has the power to adjust its price in a way

that adds to its profits and which, in the opinion of some, constitutes exploitation of

consumers.

Economist distinguish among different kinds of markets according to

(1) How many firms they include,

(2) Whether the products of the different firms are identical or somewhat different, and

(3) How easy it is for new firms to enter the market.

Perfect competition is at one extreme (many small firms selling an identical product),

while pure monopoly (a single firm) is at the other. In between are hybrid forms- called

monopolistic competition (many small firms selling products slightly different from the

others’) and oligopoly (a few large rival firms) - that share some of the characteristics of

perfect competition and some of the characteristics of monopoly. These kinds of markets

are explained in detail as follows.


I. Perfect Competition

A market is said to operate under perfect competition when following four

conditions are satisfied:

1. Numerous Small Firms and Customers. So many buyers and sellers that

each one constitutes a negligible portion of the market- so small, in fact,

that its decisions have no effect on the price. This requirement rules out

trade associations or other collusive arrangements strong enough to affect

price.

2. Homogeneity of product. The product offered by any seller is identical to

that supplied by any other seller. Because the product is homogeneous

product, consumers do not care from which firm they buy.

3. Freedom of entry and exit. New firms desiring to enter the market face no

impediments that the existing firms can avoid. Similarly, if production and

sale of the good proves unprofitable, there are no barriers preventing firms

from leaving the market.

4. Perfect information. Each firm and each customer is well informed about

the available products and their prices. They know whether one supplier is

selling at a price lower than another is.

Perfectly competitive industries have four characteristics:

1. The industry is fragmented. It consists of many buyers and sellers. Each buyer's

purchases are so small that they have an imperceptible effect on market price.

Each seller's output is so small in comparison to market demand that it has an

imperceptible impact on the market price. In addition, each seller’s purchases are

so small that it has an imperceptible impact on input prices

2. Firms produce undifferentiated products. That is, consumers perceive the

products to be identical no matter who produces them. When you buy fresh, cut

roses from a local flower shop, it probably does not matter to you that they were

produced by which firm. As far as you are; concerned, the roses from one grower

are just as good as the roses from any other grower. And because this is true for

you, it is also true for the flower shops and the wholesalers who buy the roses


directly from the growers. If the final consumer sees no difference in the roses

grown by the different growers then florists and wholesalers don't care who they

buy roses from either, as long as they get the best price. Roses are thus an

example of an undifferentiated product.

3. Consumers have perfect information about prices all sellers in the market charge .

This is certainly true in the rose market. The wholesalers and florists that buy

roses from the growers are keenly aware of the prevailing prices. In fact, as just

noted, these consumers need to be deeply knowledgeable about the prices because

the price is the main thing they care about when deciding which growers to buy

roses from.

4. The industry is characterized by equal access to resources. All firms-those

currently in the industry, as well as prospective entrants-have access to the same

technology and inputs. Firms can hire inputs, such as labor, capital, and materials,

as they need them, and they can release them from their employment when they

do not need them. This characteristic is generally true of the fresh-cut rose

industry: the technology for growing roses is well understood, and the key inputs

necessary to operate a rose growing firm (land, greenhouses rose bushes, and

labor) are readily available in well-functioning markets.

These characteristics have three implications for how perfectly competitive

markets work:

The first characteristic-the market is fragmented-implies that sellers and buyers

act as price takers. That is, a firm takes the market price of the product as given

when making an output decision and a buyer takes the market price as given when

making purchase decisions. Condition 1 also implies that a firm takes input prices

as fixed when making decisions about input quantities.

The second and third characteristics-firms produce undifferentiated products and

consumers have perfect information about prices-implies a law of one price: that

is, transactions between buyers and sellers occur at a single market price. Because

the products of all firms are perceived to be identical and the prices of all sellers

are known, a consumer will purchase at the lowest price available in the market.

No sales can be made at any higher price.


The fourth characteristic-equal access to resources-implies that the industry

is characterized by free entry. That is, if it is profitable for new firms to enter

the industry, they will eventually do so. Free entry does not mean that a new firm

incurs no cost when it enters the industry but that it has access to the same

technology and inputs that existing firms have.

II. Pure Competition:

Economists like Chamberlin and others often make distinction between pure

competition and perfect competition. The term ‘pure competition’ is used in a restricted

sense. It is also known as atomistic competition. In order that competition be pure it

requires the fulfillment of three conditions of perfect competition, namely, the existence

of large number of buyers and sellers, homogeneity of the product, and freedom of entry

and exit. These conditions together mean that no individual firm can exert any influence

over the market price. In short, the essential feature of pure competition is the absence of

monopoly element.

But the term perfect competition is a wider concept, in the sense that it includes

the features of pure completion and some additional conditions such as perfect

knowledge on the part of buyers and sellers, perfect mobility of factors of production and

absence of transportation costs.

This means that in addition to the absence of monopoly element i.e., absence of

any control over price by an individual firm, perfect competition requires that there

should be no imperfections in the market. Such imperfections arise due to imperfect

knowledge or immobility of the factors of production.

In fact, pure competition is a part and parcel of perfect competition. American

economists prefer to use the term pure competition, while English economists prefer the

term perfect competition. However, both the terms are used to analyze the features of

perfect markets.


III. Monopoly

The definition of pure monopoly is quite stringent. First, there must be only one

firm in the industry-the monopolist must be “the only supplier in town.” Second, there

must be no close substitute for the monopolist’s product. Thus, even the sole provider of

natural gas in a city is not considered a pure monopoly, since other firms offer close

substitutes like heating oil and electricity. Third, there must be some reason why survival

of potential competitor is extremely unlikely, for otherwise monopolistic behaviour and

its excessive profits could not persist.

These rigid requirements make pure monopoly a rarity in the real world. The local

telephone company and the post office may be examples of one-firm industries that face

little or no effective competition on some of their activities. But most firms face at least a

degree of competition from substitute products. Even if only one railroad serves a

particular town, it must compete with bus lines, trucking companies and airlines.

Similarly, the producer of a particular brand of beer may be the only supplier of that

specific product but the firm is not a monopolist by our definition. Since many other

beers are close substitutes for its product, the firm will lose much of its business if it tries

to raise its price much above the prices of other brands.

And there is one further reason why the unrestrained pure monopoly of economic

theory is rarely encountered in practice. Pure monopoly can have a number of undesirable

features. As a consequence, in markets where pure monopoly might prevail, the

government has intervened to prevent monopolization or to limit the discretion of the

monopolist to set its price.

Causes of monopoly: Barriers to entry and cost advantages:

The key element in preserving a monopoly is keeping potential rivals out of the

market. One possibility is that some specific impediment prevents the establishment of a

new firm in the industry. Economists call such impediments barriers to entry. Some

examples are:

1. Legal restrictions. Local monopolies of various kinds are sometimes established

either because government grants some special privilege to a single firm (for

example, the right to operate a food concession in municipal stadium) or prevents


other firms from entering the industry (for instance, by licensing only a single

cable television supplier.)

2. Patents. A special, but important, class of legal impediments to entry are patents.

To encourage inventiveness, the government gives exclusive production rights for

a period of time to the inventor of certain products. As long as the patent is in

effect. The firm has a protected position and is a monopoly. For example, Xerox

had for many years (but no longer has) a monopoly in plain paper copying.

3. Control of a scare resource or input. If a certain commodity can be produced only

by using a rare input, a company that gains control of the source of that input can

establish a monopoly position for itself.

4. Deliberately-erected entry barriers. A firm may deliberately attempt to make

entry difficult for others. One way is to start costly lawsuits against new rivals,

sometimes on trumped-up charges. Another is to spend exorbitant amounts on

advertising, thus forcing any potential entrant to match the expenditure.

5. Large sunk costs. Entry into an industry will, obviously, be very risky if entry

requires an investment of a large amount of money and if that investment is sunk-

meaning that one cannot hope to recoup the money for a considerable period of

time. Thus, the need for large sunk investment serves to discourage entry into an

industry, and many analysts therefore consider sunk costs to be the most

important type of “naturally imposed” barrier to entry.

Obviously such barriers can keep rivals out and ensure that an industry is

monopolized. But monopoly can also occur in the absence of such barriers to entry if a

single firm has important cost advantages over its potential rivals. Two examples of this

are:

6. Technical superiority. A firm whose technological expertise vastly exceeds that of

potential competitors can, for a period of time, maintain a monopoly position.

7. Economies of scale. If mere size gives large firm a cost advantage over a smaller

rival, it is likely to be impossible for anyone to compete with the largest firm in

the industry.


IV. Monopolistic Competition

The two market structures viz. perfect competition and monopoly are far from

reality. The conditions of neither perfect competition nor monopoly are experienced in

practice. We hardly come across a situation in which an indefinite number of firms

produce identical or homogeneous product Similarly, a situation in which there is only

one firm regulating the entire supply of a product is equally unrealistic.

In reality, we come across a market structure in which elements of both the competition

as well as monopoly are interwoven. In other words, the real market exhibits both

monopoly and competitive elements. Such a situation is called monopolistic competition.

Thus, there are a small number of firms producing similar product. According to

Chamberlin who introduced the concept "Monopolistic competition is a challenge to the

traditional viewpoint of economists that competition and monopoly are alternative and

that individual prices are to be explained in terms of either one or the other. By contrast,

it is held that most economic situations are composites of both competition and

monopoly". This argument makes it clear that perfect competition and monopoly are not

mutually exclusive situations. The real market presents both the elements.

Characteristics:

Monopolistic competition exhibits certain unique characteristics because of which

it distinguished from other market structures.

1. Large number of firms. Monopolistic competition is characterized by large

number of sellers. In this respect it is close to perfect competition. The number

may not be as large as that under perfect competition but it is also not very small.

In fact, the firms under this market structure are "Too many too small".

Consequently no individual has any significant control over the market.

2. Absence of interdependence. Since number of firms is sufficiently large and the

size of individual firms is small enough no appreciable interdependence exists

among the different firms. No single firm can influence or is influenced by the

others in the market. It means different firms cannot produce any significant

impact on market by changing their price policies.


3. Freedom of Entry. Like perfect competition monopolistic competition also grants

unrestricted entry to rival in the market. It means there are no restrictions. This

leads to occurrence of only normal profits in the long run. However, the nature of

this feature is not the same as that under perfect competition. Under perfect

competition new firms enter the market with an identical product while under

monopolistic competition the new firm may produce only similar but not identical

product. In other words new firm can start producing tooth paste or hair oil but it

cannot produce a ‘Colgate’ or a ‘Binaca’ or a ‘Tata Hair Oil.’ What it, brings is a

different product.

4. Product Differentiation. Under monopo1istic competition different firms produce

similar (but not homogeneous) products. It means the different firms produce

what may be properly described as a differentiated product. Thus, product

differentiation is the core of monopolistic competition. The firms produce a

product belonging to a particular class, say tooth paste; but individual product is

differentiated from other rival products. It is because of such product

differentiation that firms enjoy some monopoly power, that is, the power to

control the price in a narrow circle, but in the wider circle, it faces g the

competition from the rival firms. Hence, the firms may be called as ‘competing

monopolists’ and the situation may be rightly described as monopolistic

competition.

5. Selling Costs. Another feature of monopolistic competition is the existence of

selling costs which is absent under any other market situation. Under perfect

competition and monopoly, there is no need for incurring the expenditure on

creating demand (i.e. selling costs). Under perfect competition, a firm can sell any

quantity at a given price while under monopoly, the absence of close substitutes,

there is no need for incurring the selling costs. Thus, it is only under monopolistic

competition that the selling costs find a place


V. Oligopoly

The theory of perfect competition stands only as an ideal but has lost its

significance due to non-existence of its two basic features viz. large number of firms and

homogeneous product. The real market exhibits a number of departures from the ideal

market situation. These impurities or imperfections emerge due to deliberately

differentiated products and the less than large number of firms. Under monopolistic

competition, the number of firms is sufficiently large but the product stands

differentiated. The pricing policy of any firm depends upon two factors viz., the number

of rivals and the nature and extent of product differentiation.

Oligopoly is a distinct form of imperfect market which is characterized by the

existence of few sellers producing homogeneous or differentiated products. The term

'Oligopoly' owes its origin to two Greek words, 'Oligos' meaning 'a few' and 'pollen'

meaning to sell. It differs from monopoly (single firm) and perfect competition as well as

monopolistic competition (large number of firms), as it consists of a few firms. In a broad

sense this market form is described in a variety of ways such as, limited competition,

incomplete monopoly, multiple monopoly etc. A large number of products such as

automobiles, steel, cement, heavy electricals etc. are supplied by oligopolistic markets.

Definition and Features

Stigler defines Oligopoly in the following words:

"Oligopoly is that situation in which a firm bases its market policy in part on the expected

behaviour of a few close rivals".

This clearly reveals the fact that Oligopoly consists of a few firms as a result of

which there is a close interdependence among them in respect of price output policy. The

simplest way to clearly grasp the meaning and nature of

Oligopolistic Market is to analyze and understand the essential features of the same. The

study of the unique features of Oligopoly will be very useful in understanding the price

output determination under this market form.

1. Few Sellers. It is the number of sellers or firms that distinguishes oligopoly from

other market forms such as monopoly, perfect competition and monopolistic

competition. The number of sellers under Oligopoly is very small. Naturally each


individual firm has a sizeable share of the market demand. This leads to the fact

that action of each firm in respect of price and output has a close bearing on the

market. The decision of a single firm to expand or contract the output affects the

entire market under oligopoly. His decision to expand the output leads to fall in

price and profit while contraction of output produces an opposite effect. Since the

number of firms under Oligopoly is small, it is rightly described as 'competition

among few'. The product of these sellers may be homogenous i.e. perfect

substitute or just a close substitute.

2. Interdependence. Extreme interdependence among the Oligopolistic firms is an

unique feature of this market form. Under perfect competition or monopolistic

competition, under which the number of firms is very large, the question of

interdependence does not arise. An individual firm in a competitive market has

too small a share in the total market supply to produce any significant impact on

the rivals. In case of monopoly, which implies absence of a rival, the question of

interdependence is irrelevant. It is only under Oligopoly that one witnesses a close

interdependence among the different firms. Any policy decision of an individual

firm affects and is affected by the rivals. With close substitutes offered by

different Oligopolistic firms the products have high cross elasticity of demand as

a result of which every move by any single firm receives a sharp reaction from the

others. Thus, a close interdependence based on moves and counter-moves is a

distinct feature of Oligopoly. The interdependence is so intense that every firm

has to predict and analyze the possible reaction of the rivals before taking any

decision.

3. Indeterminate Demand Curve. It is difficult to derive a definite demand curve i.e.

AR curve of an Oligopolist. This situation arises due to extreme interdependence

among the firms. Such an interdependence generates uncertainty because none

can precisely predict the possible outcome of a particular decision. Demand Curve

is derived from the knowledge of various quantities of a product that can be sold

at different prices. This knowledge i.e. a demand schedule is difficult to obtain

under Oligopoly. This is due to the fact that the effect of any decision say price

reduction by a firm, cannot be precisely predicted. Neither the rivals nor the firm


reducing the price of its product can estimate exactly what will be the effect of

such an action on the demand for their product. The price cutting firm does not

know what will be the rise in demand, nor the rivals can estimate if there would

be a decline in demand for their product and to what extend.

All this leads to an intermediate or uncertain demand curve for an Oligopolist. In

this context an oligopoly firm differs from other market structures. Under perfect

competition an individual firm faces a definiate demand curve which is horizontal

straight line at a given price. Since the product is homogeneous and the price

given and constant an individual firm under perfect competition has hardly any

decision to make. Similar is the situation under monopoly which definite and

determinate demand or AR Curve. A monopolist can take his own decision

regarding price-output without any need to wait for the reaction of the rivals. It

can thus precisely predict its sales at different prices. An Oligopolist is caught in a

peculiar situation of an indeterminate demand curve for though he knows his

decision is bound to produce a reaction, he does not know what and how strong

that reaction will be. E.g. a firm cuts its price to acquire larger share of the

market. Now whether its sales will expand and if they do to what extent are the

key questions which lack definite answers. There may be different types and

intensities of reactions by the rivals. As such a firm is not in a position to locate a

definite demand curve. Similarly, his sales are affected by the decisions of other

firms. Some of them may begin the process through change in the price of their

products. Here again what will be the effect nobody can predict. Hence the

indeterminate AR curve.

4. Conflicting attitudes of firms. The uncertainty existing under Oligopoly is

intensified due to the conflicting attitude of the firms. It is often observed that the

Oligopolistic firms sometimes resort to cooperation amongst them while on

certain other occasions they take up fights and conflicts. The entire issue revolves

round the urge for profit maximization. In some cases the Oligopolistic firms

realize the declining profits due to undesirable competition. Hence they adopt the

strategy of cooperation and therefore unite together. This is known as the

tendency towards 'Collusion' among the firms for the purpose


of achieving the common objective of maximization of profits. Exactly opposite

behaviour is experienced in some other cases when they fight against each other

like enemies on the issue of distribution of profits and sharing of the markets.

Thus the two conflicting trends of cooperation and conflict are experienced in the

behaviour of the firms. This attitude enhances the unpredictable nature of this

market form.

5. Price Rigidity. Existence of price rigidity is a unique feature of Oligopoly with

product differentiation. The Oligopoly price appears to be ‘stuck up’ or ‘rigid’ or

‘fixed’ at a certain level. This implies that there is no departure in either direction

i.e., increase or decrease, from the prevailing price. No firm will resort to price

reduction as it benefits none. This is because an attempt by an individual firm of

price cutting for snatching away the customers of the rivals is immediately

followed by others. Hence hardly any benefits are received through price-cut.

What it really leads to, is a competitive price reduction, harmful to all. As against

it the policy of price-rise, for increasing the profits, is also not advantageous. This

is because the act of raising the price by one firm is not followed by the rivals. As

a result the price raising firm loses the customers to rivals and instead of

increasing the profits, faces the danger of fall in the same. Thus, no firm thinks of

changing the price from the existing level. Naturally there is price rigidity.

6. Element of Monopoly. With the existence of a few firms there is an element of

monopoly under oligopoly market. Small number of firms producing a

differentiated product naturally generates monopoly power. In its limited area

every firm enjoy monopoly as it commands an adequately large share of market.

Such monopoly power is exerted by the firm in respect of fixation of price-output.

The attachment of customers to a given product enhances the monopoly power of

the firm which enables it to have greater freedom in fixing price and output.


ATTIBUTES OF THE FIVE MARKET FORMS Table 1

Market

forms

Number of

firms in the

market

Frequency

in reality

Entry

barriers

Public

interest

results

Long-run

profit

Perfect

competition

Very many Rare (if any) None Good Zero

Pure

Competition

Many Frequent None Good Zero

Pure

Monopoly

One Rare Likely to be

high

Misallocates

resources

May be high

Monopolistic

Competition

Many Widespread Minor Inefficient Zero

Oligopoly Few Produces

large share

of GDP

Varies Varies Varies


CHAPTER 3

ENVIRONMETAL SCAN

One of the trademarks of the modern planning approach is its external orientation.

We have to address ourselves to the careful appreciation of environmental trends leading

to an understanding of the attractiveness of the industry in which the business resides. We

should be alert to all developments in our industry, especially to the behaviour of

competitors. Only a deep knowledge of the structural characteristics of the industry in

which the business operate along with a sound awareness of competitors’ actions, can

generate the high-quality strategic thinking required for the healthy long term

development of a firm.

Structural Analysis of Markets: The Five Forces Model

In order to select the desired competitive position of a business, it is necessary to

begin with the assessment of the industry to which it belongs. To accomplish this task,

we must understand the fundamental factors that determine its long-term profitability

prospects because this indicator embodies an overall measure of industry attractiveness.

By far the most influential and widely used framework for evaluating industry

attractiveness is the five forces model proposed by Michel E. Porter. Essentially, he

postulates that there are five forces that typically shape the industry structure:

1. Intensity of rivalry among competitors

2. Threat of new entrants

3. Threat of substitutes

4. Bargaining power of the buyers

5. Bargaining power of the suppliers

These five forces delimit prices, costs and investment requirements, which are the basic

factors that explain long-term profitability prospects and henceforth industry

attractiveness.


1. Intensity of rivalry among the competitors

The rivalry among the competitors is at the center of the forces contributing to industry

attractiveness. Out of the many determinants of rivalry, four of them stand out: industry

growth, the share of fixed cost to total value added to the business, the depth of product

differentiation, and the concentration and balance among competitors.

Analysis of Turbulent Market Environments

Industry Competitors/

Intensity of RivalrySuppliersSuppliers

New EntrantsNew Entrants

BuyersBuyers

SubstitutesSubstitutes

Threat of New Entrants

Bargaining Power of Suppliers

Bargaining Power of Buyers

Threat of Substitutes

Figure 1 The Five Forces Model

17

Case I: Monopolistic Competition: The Beauty Soap Industry in India

The population of India is over one billion. The market potential is very high. The

industry life cycle of beauty soaps is in the maturity stage in the urban areas and in the

growth stage in the rural areas. The fixed costs involved in setting up a business in this

industry are high, since the entire manufacturing department would be needed to set up,

but the value added to business is also high. There are a number of different kinds of

soaps available in the market, ranging from the imported branded ones to the locally

manufactured ones. The rivalry is intense as each firm is trying hard to get a substantial

market share.

Case II: Oligopoly: The Mobile Networking Industry in India

The increased popularity of using mobile phones to keep in touch with near and

dear ones has increased the attractiveness of this industry. Hence, there is an increasing

demand for mobile services. Also, there is a wide rural market which is untapped. Hence,

the industry life cycle is in the growth. But the number of firms in the industry is limited.

This is due to the high initial fixed costs. Each firm provides with different services.

2. Threat of new entrants

on many occasions, the most critical strategic issue for a given firm does not reside in

understanding the existing set of competitors and achieving an advantage over them, but

in directing the attentions to possible and sometimes inevitable new entrants.

Case I: The threat of new entrants is high in case of the beauty soap industry. This is

because of not very high fixed costs and low switching costs. We have already seen that

the potential market is huge and hence economies of scale can be achieved if properly

directed efforts are taken. If a product, which is not available, is introduced by the new

firm then its can achieve the desired sales in a short span of time.

Case II: The threat of new entrants in the mobile networking industry is low because of

huge initial fixed costs. The demand is huge and so is the potential market but the initial

setup costs are quite high that it makes the industry less attractive.


3. Threat of Substitutes

It is not only the firms participating in the industry and the potential newcomers that are

central forces in determining industry attractiveness; we have to add firms offering

substitutes, which can either replace the industry products and services or present an

alternative to fulfill that demand. Substitutes could affect in different ways the

attractiveness of an industry. Their mere presence establishes a ceiling for profitability,

whenever there is a price threshold after which a massive transfer of demand takes place.

Case I: The threat of substitutes is very high in this case. If one firm increases its products

price beyond a certain limit, then it is likely that the consumer will switch the brand.

There is increasing popularity of body wash as against the use of soaps. Hence, the threat

of substitutes is increasing.

Case II: The threat of substitutes is very low. This is because there are no substitutes

available for mobile phones.

4. & 5. Bargaining Power of Buyers and Suppliers

Porter’s wording “bargaining power of suppliers and buyers” suggest that there is a threat

imposed on the industry by excessive use of power on the part of these two agents. Porter

can be interpreted as indicting that a proper strategy to be pursued by a business firm will

have, as a key component, the attempt to neutralize suppliers’ and buyers’ bargaining

power. Moreover, buyers are the most important constituency of the firm, to be treated

not as rivals, but as the depositories of a long-lasting, friendly relationship based on

performance and integrity.

Case I: The bargaining power of the buyer is high as compared to the supplier as the

number of suppliers is high and also there are substitutes available for change. There are

more number of firms, hence there is increased competition.

Case II: The bargaining power of the supplier is high as compared to that of the buyer as

there are no substitutes for the products and the number of players is also limited.


Case Analysis

Barriers To Entry Table 2

Case I Case II

Economies of scale Small High

Product differentiation High Little

Brand identification Low High

Switching costs Low High

Capital requirements Low High

Barriers To Exit Table 3

Case I Case II

Asset specialization Low High

One time cost of exit Low High

Government and social restrictions Low Medium

Rivalry Among Competitors Table 4

Case I Case II

Number of equally balanced competitors Large Medium

Relative industry growth Fast Fast

Diversity of competitors High Medium

Strategic stakes Low Low

Capacity increases Large increments Small increments

Power Of Buyers Table 5

Case I Case II

Number of important buyers Many Many

Availability of substitutes Few Few

Buyer switching costs Low Low


Power Of Suppliers Table 6

Case I Case II

Number of important suppliers Many Few

Availability of substitutes for supplier’s products Many Few

Supplier’s contribution to quality and service Medium High


CHAPTER 4

BUSINESS PERFORMANCE

The performance of any business is based on two kinds of elements:

1. Key Certainties

They are those elements which will be constant irrespective of the changes in the market

environment.

The certainties in the business are as follows:

2. Key Uncertainties

They are those elements which will change with the change in time, place, industry and

market environment.

The uncertainties in business are as follows:

CHAPTER 5


Business Performance

Key Certainties Key Uncertainties

Figure 2 Business Performance

22

TURBULENT MARKET ENVIRONEMENT

Introduction

Modern organizations operate in an external environment in which conditions are

often changing rapidly and unpredictably. This type of environment is called ‘turbulent

environment’ by Emery and Trist. Turbulence arises in part from changes in various

elements that make up the environment. It occurs also as a result of interaction between

organizations that have conflicting objectives and that compete with one another for

benefits in the environment. Each of these organizations is seeking to progress from its

existing stage to one that is judged to be preferable relative to its objectives. However, no

organization can be sure that it can achieve its most preferred future position in view of

the competition from others in the environment.

Those responsible for dealing with complex decision problems in modern

turbulent environment experience uncertainty with regard to future conditions and with

respect to the future actions by others. Many of the formal decision making methods that

have been developed in disciplines of operational research and decision analysis do not

take full account of these factors. The basis for most of these methods is optimization of

the benefits of a single participant in a static environment. These methods themselves

consist for a search for a uniquely rational solution in terms of that single participant.

These methods have been developed for use in decision situations that are

relatively well understood, where firm and reliable data on the characteristics of the

situation are available and where such uncertainty as exists can be represented by the use

of simple probability distributions. They are clearly not applicable in turbulent

environments, in which interaction between objectives, intentions and actions of many

participants have to be taken into account in the resolution of decision problems.

CHAPTER 6


DEMAND FORECASTING METHODS

Significant gains have been made in forecasting for marketing in the past quarter

century. Advances have occurred in the development of qualitative methods such as

Delphi, role playing, intentions and opinions surveys, and bootstrapping. They have also

occurred for quantitative methods such as extrapolation and econometrics. An attempt is

made here to build on the experience in applying these methods by researchers so

generalizations can be made about which methods would be most appropriate to forecast

demand.

In general, experts advocate the use of structured methods that avoid intuition,

unstructured meetings, focus groups, and data mining. In situations where there is

sufficient data, use of quantitative methods is encouraged, including extrapolation,

quantitative analogies, rule-based forecasting and causal methods. In other cases, use

methods that structure judgement including surveys of intentions and expectations,

judgmental bootstrapping, structured analogies, and simulated interaction. Green &

Armstrong (2005) strongly advocate the integration of Judgmental and statistical

methods. Managers’ domain knowledge should be incorporated into statistical forecasts.

Methods for combining forecasts improve accuracy.

I. Econometric methods

"Econometric methods" are defined as quantitative approaches that attempt to use

causal relationships in forecasting. In particular, they refer to models based on regression

analysis and include all methods which forecast by explicitly measuring relationships

between the dependent variable and some causal variables.

For market demand forecasting, there is empirical evidence to support the use of

econometric methods rather than subjective methods for long-range forecasts. To date,

most econometric researchers have devoted their efforts to short-term forecasting, an area

that has yielded unimpressive or contradictory results. Econometric methods would be

expected to be more useful for long-range forecasting because the changes in the

causal variables are not swamped by random error, as in the short run. In fact,


econometric methods are more accurate. Armstrong reported seven empirical

comparisons of methods used in long-range forecasting. In all comparisons econometric

methods were more accurate than extrapolations. Also, there was a 3 to 0 advantage for

econometric versus subjective forecasts. Fildes located 20 studies on long-range

forecasting; he coded them as 15 showing econometric to be more accurate, 3 ties, and 2

showing econometric to be less accurate than other methods.

Thus it may be concluded that Causal econometric methods provide more

accurate long-range forecasts. While more expensive, the methods are expected to be

the most accurate method when large changes are expected. What must however be borne

in mind is that to improve predictive capacity, these causal models need not be

complex.

II. Naive versus Causal Methods

A continuum of causality exists in forecasting models. At the naive end, no

statements are made about causality; at the causal end, the model may include many

factors.

Causal methods are more complex than naive methods. First, data must be obtained

on the causal factors. Estimates of causal relationships are obtained from these data.

These estimates of the causal relationships should be, adjusted so that they are relevant

over the forecast horizon. Next, one must forecast the changes in the causal variables.

Finally, the forecasts of the causal variables and the relationships are used to calculate the

overall forecast.

Causal methods are of more obvious value in forecasting. However, naive methods

can be used in some phases. For example, naive methods can provide forecasts of

environmental factors.

III. Intentions Surveys


Theoretical literature in psychology that suggests that a good predictor of an

individual’s future behavior is his or her stated intention. However, the psychological

literature also suggests that past behavior is an important predictor of future behavior.

With intentions surveys, people are asked how they intend to behave in specified

situations. In a similar manner, an expectations survey asks people how they expect to

behave. Expectations differ from intentions because people realize that unintended

things happen. For example, if you were asked whether you intended to purchase a

particular product you might say no. However, you realize that a problem might arise that

would necessitate such a purchase, so your expectations would be that the event had a

probability greater than zero. This distinction was proposed and tested by Juster and its

evidence on its importance was summarised by Morwitz.

Expectations and intentions can be obtained using probability scales such as

Juster’s eleven-point scale. The scale should have descriptions such as 0 = ‘No chance,

or almost no chance (1 in 100)’ to 10 = ‘Certain, or practically certain (99 in 100)’.

To forecast demand using a survey of potential consumers, the administrator

should prepare an accurate and comprehensive description of the product, its benefits and

conditions of sale. He should select a representative sample of the population of interest

and develop questions to elicit expectations from respondents.

Purchase intentions are routinely used to forecast demand of existing products and

services. While past studies have shown that intentions are predictive of sales, they have

only examined the absolute accuracy of intentions, not their accuracy relative to other

forecasting methods.

For different products and time horizons, intentions-based forecasting methods

were more accurate than an extrapolation of past sales. Combinations of these

forecasting methods using equal weights lead to even greater accuracy, with error rates

about one-third lower than extrapolations of past sales. Thus, it appears that purchase

intentions can provide better forecasts than a simple extrapolation of past sales trends.

Purchase intentions are inexpensive to acquire and easily understood, which may

account for their widespread use.


Many studies have found a positive correlation between purchase intentions and

purchase behavior.

Buyer-Intentions and expectations surveys are especially useful in forecasting

demand when demand data are not available, such as for new or specialty product

forecasts.

The theoretical literature is equivocal about whether intentions-based forecasts or

past sales trends should be more accurate. Received wisdom suggests that the best

predictor of future behavior is past behavior. On the other hand, the social psychology

literature states that a good predictor of what individuals will do is their stated intentions

to perform the behavior.

Other research suggests that intentions data are useful for predictions under

certain conditions. Armstrong summarizes these conditions:

(1) The event being predicted is important,

(2) The respondent has a plan (at least the high intenders do),

(3) The respondent can fulfill the plan,

(4) New information is unlikely to change the plan over the forecast horizon,

(5) Responses can be obtained from the decision maker, and

(6) The respondent reports correctly.

Such conditions are likely to be met for purchase intentions of “high

involvement” goods and services. Once convinced of the utility, the consumer makes a

definite plan to buy the goods at a fixed time. With the increase in purchasing power, the

consumer in a position to convert potential demand into effective demand. It is also

possible to accurately obtain intensions information from the consumers. This suggests

that intentions data could potentially improve accuracy of forecasts based solely on past

sales behavior for these products.

A variety of survey questions have been used to measure consumers purchase

intentions. Among the most commonly used measures are Juster’s 11-point purchase

probability scale and a 5-point likelihood of purchase scale. Juster’s 11-point purchase

probability scale provides substantially better predictions of purchase behavior than

other types of intentions scales.


Purchase probabilities and expectations are broader than direct intentions questions

because they refer to actions that might be unplanned as well as planned.

Assessing purchase probabilities and expectations may be advantageous in

situations where people realize that they may purchase an item even though they have no

plans at the time of the survey. Therefore, a smaller proportion of respondents reports

“zero” on purchase probability scales than report “no” on intentions scales.

For many studies, most purchases are made by those who had reported no plans to

buy. This occurs because although non-intenders seldom purchase, they are often the

largest group of respondents. Models have been developed to describe how purchase

intentions relate to purchase behavior

Two commonly used methods to forecast sales from intentions predict that the

proportion of consumers who will purchase will equal :

(1) The mean intent (transformed to lie between zero and one to represent the mean

probability of purchase), or

(2) The proportion of respondents indicating a positive purchase intent.

Intentions, by themselves, provide only a crude way to predict sales. Several

studies have shown that these methods often provide biased estimates of sales,

overstating or understating actual purchasing. Thus, when possible, sales data should be

used to adjust for the bias in intentions. The simplest way to do this is to relate an

aggregate measure of purchase intentions to an aggregate measure of sales.

For new products or new product groups, intentions are sometimes used

directly to forecast demand. However, when sales figures are available, it is sensible to

calibrate intentions against them. In other words, we look at a category of intenders and

determine what percent actually did purchase in that period. This relationship is then

extended to the period to be forecast.

Morrison developed a descriptive model of the relationship between purchase

intentions and subsequent purchasing. Morrison proposed that there are three threats to

the predictive validity of purchase intention measures. First, intentions are measured with

error. Second, respondents’ purchase intentions might change over time because of

exogenous events. Third, average stated purchase intentions might be a biased estimate of

the proportion that actually buy the product because of systematic error.


However, it was uniformly observed that for different products, time horizons, countries,

and types of intentions questions, the intentions surveys when combined with prior sales

data, were more accurate than forecasts based solely on past sales.

Understanding the ‘Intention to Try’

The Theory of Trying developed by Bagozzi and Warshaw emphasizes consumer

uncertainty when achievement of a consumption objective is not entirely within one’s

volitional control.

Impediments can take several forms: outcome uncertainty, lack of

knowledge/information, distortion of market information, unfavourable earlier

experiences, time pressure and cultural differences, need to be self-reliant and satisfaction

with current behaviour, when new solutions require efforts in terms of search costs,

transaction costs, etc.


FIGURE Basic Framework for Attitude Building for a Product

(Source : Agarwal & Agarwal, 2003)


Outcome UncertaintyOutcome Uncertainty

Satisfaction with current behaviour

Satisfaction with current behaviour

Personal, Environmental Impediments

Personal, Environmental Impediments

Habits & InertiaHabits & Inertia

Information DistortionInformation Distortion

Lack of knowledgeLack of knowledge

Intention to try

Intention to try

Attitude towards Trying

Attitude towards Trying

Differed GratificationDiffered Gratification

Earlier Experiences

Earlier Experiences RecencyRecency

Being self reliantBeing self reliant

Self ExpressionSelf Expression

TryingTrying

Social Stigma, Cultural

Differences

Social Stigma, Cultural

Differences

30

These nine generic factors together affect the formation of an attitude towards

trying a product. Even if the consumer forms a favourable attitude towards trying the

product, this might not directly translate into an intention to try. Earlier experiences and

socio-cultural norms applicable to the individual may also influence to some extent the

intention to try.

After the consumer develops an ‘intention to try’, the next step is to actually try

the product. However besides the ‘intention to try’, actual trying is also affected by

‘recency’. If the consumer has tried out a similar product in the recent past he will be

more amenable to trying out the product now. An important point to note is that earlier

experiences affect both the ‘intention to try’ and ‘actual trying’. When a person is not

clear about his intentions to try out a product, he may rely upon his past experiences to

decide whether he wants to actually try out the product.

It becomes essential therefore, while using intension surveys, to understand and

appreciate the factors that affect the formation of an attitude towards the product /

product category.

IV. Delphi Technique

Since its design at the RAND Corporation over 40 years ago, the Delphi technique

has become a widely used tool for measuring and aiding forecasting.

Delphi is not a procedure intended to challenge statistical or model-based

procedures. It is intended for use in judgment and forecasting situations in which pure

model-based statistical methods are not practical or possible because of the lack of

appropriate historical /economic/ technical data, and thus some form of human

judgmental input is necessary. Such input needs to be used as efficiently as possible, and

for this purpose Delphi technique might serve a role.

Four key features may be regarded as necessary for defining a procedure as a

‘Delphi’. These are: rounds anonymity, iteration, controlled feedback, and the statistical

aggregation of group response.

Anonymity is achieved through the use of questionnaires. By allowing the

individual group members the opportunity to express their opinions and judgments


privately, undue social pressures – as from dominant or dogmatic individuals, or from a

majority – should be avoided. Ideally, this should allow the individual group members to

consider each idea on the basis merit alone, rather than on the basis of potentially invalid

criteria (such as the status of an idea’s proponent).

Furthermore, with the iteration of the questionnaire over a number of rounds, the

individuals are given the opportunity to change their opinions and judgments without fear

of losing face in the eyes of the (anonymous) others in the group. Between each

questionnaire iteration, controlled feedback is provided through which the group

members are informed of the opinions of their anonymous colleagues.

The number of rounds is variable, though it seldom goes beyond one or two

iterations (during which time most change in panelists’ responses generally occurs).

To forecast with Delphi the administrator should recruit between five and twenty

suitable experts and poll them for their forecasts and reasons. The administrator then

provides the experts with anonymous summary statistics on the forecasts, and experts’

reasons for their forecasts. The process is repeated until there is little change in forecasts

between rounds – two or three rounds are usually sufficient. The Delphi forecast is the

median or mode of the experts’ final forecasts. Software to guide you through the

procedure is available. Rowe and Wright provide evidence on the accuracy of Delphi

forecasts. The forecasts from Delphi groups are substantially more accurate than forecasts

from unaided judgment and traditional groups, and are somewhat more accurate than

combined forecasts from unaided judgment.

V. Unaided Judgement

It is common practice to ask experts what will happen. This is a good procedure to

use when experts are unbiased, large changes are unlikely, relationships are well

understood by experts (e.g., demand goes up when prices go down), experts possess

privileged information and experts receive accurate and well-summarized feedback about

their forecasts. Unfortunately, unaided judgement is often used when the above

conditions do not hold. Green and Armstrong, for example, found that experts were no


better than chance when they use their unaided judgement to prepare forecasts in

complex situations. Considering this, unaided judgment will not be used in this study.

VI. Game Theory and Role Playing

Game theory has been touted in textbooks and research papers as a way to obtain

better forecasts in situations involving negotiations or other conflicts. A Google search

for “game theory” and “forecasting” or “prediction” identified 147,300 sites. Despite a

vast research effort, there is no research that directly tests the forecasting ability of game

theory. However, Green tested the ability of game theorists, who were urged to use game

theory in predicting the outcome of eight real (but disguised) situations. In that study,

game theorists were no more accurate than university students.

Role playing is well-suited to forecasting how people will respond to exogenous

pressures (actions of those outside the firm).

The accuracy gain of game theory over unaided judgment may be illusory, and the

advantage of role playing over game theory is likely to be greater than the 44% error

reduction found by Green. The improved accuracy of role playing over game theory was

consistent across situations. For those cases that simulated interactions among people

with conflicting roles, game theory was no better than chance (28% correct), whereas

role-playing was correct in 61% of the predictions.

VII. Bootstrapping

According to Armstrong, Brodie & McIntyre bootstrapping (including related

approaches such as expert systems and conjoint analysis) is one of the more important

advances for forecasting in marketing over the past quarter century. It was also noted as

one of the most significant advances in the field of agricultural forecasting.

Bootstrapping has been widely applied in marketing. Occasionally it has been

used with experts, but typically it is consumer intentions that are modeled. Over 1,000

marketing applications had been made by indirect bootstrapping of consumer intentions


by the early 1980s. These applications have been done under the umbrella term “conjoint

analysis.” Bootstrapping is nearly always more accurate than judgment.

VIII. Focus Groups

One popular type of survey, focus groups, violates five important principles and

they should not, therefore, be used in forecasting. First, focus groups are seldom

representative of the population of interest. Second, the responses of each participant are

influenced by the expressed opinions of others in the group. Third, a focus group is a

small sample – samples for intentions or expectations surveys typically include several

hundred people whereas a focus group will consist of between six and ten individuals.

Fourth, questions for the participants are generally not well structured. And fifth,

summaries of focus groups responses are often subject to bias. There is no evidence to

show that focus groups provide useful forecasts.

IX. Neural Nets

Neural networks are computer intensive methods that use decision processes

analogous to those of the human brain. Like the brain, they have the capability of

learning as patterns change and updating their parameter estimates. However, much data

is needed in order to estimate neural network models and to reduce the risk of over-fitting

the data. There is some evidence that neural network models can produce forecasts that

are more accurate than those from other methods. While this is encouraging, our current

advice is to avoid neural networks because the method ignores prior knowledge and

because the results are difficult to understand.

X. Data Mining

Data mining ignores theory and prior knowledge in a search for patterns. Despite

ambitious claims and much research effort, we are not aware of evidence that data mining

techniques provide benefits for forecasting. In their extensive search and reanalysis of


data from published research, Keogh and Kasetty found little evidence for that data

mining is useful.

XI. Segmentation

Segmentation involves breaking a problem down into independent parts, using data

for each part to make a forecast, and then combining the parts.

To forecast using segmentation, one must first identify important causal variables

that can be used to define the segments, and their priorities. For each variable, cut-points

are determined such that the stronger the relationship with dependent variable, the greater

the non-linearity in the relationship, and the more data that are available the more cut-

points should be used. Forecasts are made for the population of each segment and the

behaviour of the population within the segment using the best method or methods given

the information available. Population and behaviour forecasts are combined for each

segment and the segment forecasts summed.

Where there is interaction between variables, the effect of variables on demand

are non-linear, and the effects of some variables can dominate others, segmentation

has advantages over regression analysis.

Efforts at dependent segmentation have gone under the names of microsimulation,

world dynamics, and system dynamics. While the simulation approach seems

reasonable, the models are complex and hence there are many opportunities for

judgemental errors and biases. Armstrong found no evidence that these simulation

approaches provide valid forecasts and there appears no reason to change this assessment.

XII. Rule Based Forecasting

Rule-based forecasting incorporates information from experts and from prior

research. The procedure calls for the development of empirically validated and fully

disclosed rules for the selection and combination of methods.


When large changes are expected, one should draw upon methods that

incorporate causal reasoning. If the anticipated changes are unusual, judgmental

methods such as Delphi would be appropriate. If the changes are expected to be

large, the causes are well understood, and if one lacks historical data, then

judgmental bootstrapping can be used to improve forecasting.

Other Forecasting Imperatives

I. Identifying causal variables

Environmental forecasts are useful as an input to strategic planning. The

identification of possible states of the environment and a forecast of their likelihood can

provide ideas on future demand trajectories. Environmental forecasts also can help to

provide better industry forecasts (e.g. the total demand for a product class in a given

market).

It is important that the forecasting methods first identify the possible states of the

future. For this, brainstorming among a variety of experts would be useful. Particular

attention would be given to the more important of these possible states. Importance

should be judged not only by the likelihood of the environmental change, but also by its

potential impact on the Industry if it does occur. It becomes important while forecasting

long range demand to assess the likelihood of this event occurring and therefore, its

potential impact on altering demand patterns of the industry.

There is some evidence to show that the accuracy of forecasts of environmental

variables is not as important as is identifying the key variables to include in the

market forecasting model. Measurement error in the causal variables (e.g., the

environmental inputs to a market forecasting model) had little impact on the accuracy of

an econometric model.

It is important to determine which are the important factors in the

environment that might affect the industry. It is also important to predict the

direction of change in the important factors, and to then get “approximately

correct” predictions of the magnitude of the changes in these factors.


For the direction of change in environmental factors, only general trends, not

cycles, should be considered. Other than recurrent events owing to the seasons of the year

(seasonality), cycles have been of little value for improving the accuracy of forecasts.

The reason? One must also predict the phases (timing) of the cycles. If the timing is off,

large errors can occur. Organizations should have a system for scanning the environment

to be sure that they do not overlook variables that may have a large impact on their

market. These variables can be tracked through marketing information systems. Periodic

brainstorming with a heterogeneous group of experts should be sufficient to identify

which variables to track. The key is to identify the important variables and the

direction of their effects. Once identified, only crude estimates of the coefficients of

these variables are typically sufficient in order to obtain useful forecasts. When large

shocks are encountered, more sophisticated approaches may be useful.

II. Estimating Uncertainty

In addition to improving accuracy, forecasting is concerned with assessing

uncertainty. This can help manage the risk associated with the forecasts. Much work has

been done on judgmental estimates of uncertainty. One of the key findings is that judges

are typically overconfident. Fischoff and MacGregor found that 95% confidence ranges

that are estimated judgmentally typically fail to include the true value. This bias occurs

even when subjects are warned in advance about the overconfidence phenomenon.

Nevertheless, judgmental expressions of uncertainty have been found to be useful.

One way to assess uncertainty has been to examine the agreement among

judgmental forecasts. For example, Ashton, found that the agreement among the

individual judgmental forecasts was a useful proxy for accuracy.

Probably the best way to assess uncertainty is to follow the track record of a given

forecasting method in actual use.

Traditional error measures, such as the mean square error (MSE), do not provide a

reliable basis for comparison of forecasting methods. The median absolute percentage

error (MdAPE) is more appropriate because it is invariant to scale and is not influenced

by outliers. When comparing methods, especially when testing on a small number of


series, control for degree of difficulty in forecasting by using the median relative absolute

error (MdRAE), which compares the error for a given model against errors for the naive,

no change forecast.

Statisticians have relied heavily on tests of statistical significance for assessing

uncertainty. However, statistical significance is inappropriate for assessing

uncertainty in forecasting. Furthermore, its use has been attacked as being misleading.

It is difficult to find studies in marketing forecasting where statistical significance has

made an important contribution.

Instead of statistical significance, the focus should be on prediction intervals.

Chatfield summarizes research on prediction intervals. Unfortunately, prediction intervals

are not widely used in practice. Tull’s survey noted that only 25% of 16 respondent

companies said they provided confidence intervals with their forecasts. Dalrymple found

that 48% did not use confidence intervals, and only 10% ‘usually’ used them.

In a survey of experts by Yokum and Armstrong half said that it was important ‘that

your forecasting methods provide confidence bounds on the forecasts’, while 20% said

this was not important.

III. Overlooked Discontinuities

Considering the wide range of random shocks that affect an industry, there is strong

agreement about the importance of discontinuities in forecasting. This was surprising

because this topic has been largely ignored in the forecasting literature.

Identifying areas of uncertainty or disagreement among experts, or disagreements

between researchers and practitioners, could help to guide further research. Also, the

opinions might aid in the development of expert systems for forecasting.

In a study of experts by Callopy and Armstong, 92% of the experts agreed that

“abrupt changes” are an important consideration while forecasting demand. This is

surprising given that time series forecasting research and practice have largely ignored

abrupt changes. Examination of a convenience sample of indices of 28 books that discuss

time series forecasting did not include any reference to ‘abrupt changes’,


‘discontinuities’, ‘erratic fluctuations’, ‘interruptions’, ‘irregularities’, ‘ramps’, ‘shifts’,

‘steps’, and variations on these terms.

The experts agreed that seasonality and recent trend were key features. The

experts also placed a heavy emphasis on the importance of abrupt changes in the

historical data patterns. This stands in stark contrast to forecasting methods and

forecasting research which have long ignored abrupt changes. We have no explanation

for this mystery of the overlooked discontinuities. Fortunately, software developers are

responding to this problem.

IV. Combining Forecasts

Considerable literature has accumulated over the years regarding the combination

of forecasts. The primary conclusion of this line of research is that forecast accuracy

can be substantially improved through the combination of multiple individual

forecasts.

Clemen is a milestone on the topic of combining forecasts. As noted by Clemen,

past research has produced two primary conclusions, one expected and one surprising.

The expected conclusion is that combined forecasts reduce error (in comparison with the

average error of the component forecasts). The unexpected conclusion is that the simple

average performs as well as more sophisticated statistical approaches.

Combining forecasts is more useful for long-range forecasting because of the

greater uncertainty.

The level of aggregation of the data was expected to be related to the relative

accuracy of alternative extrapolation methods by 88% of the experts. We speculate that

the level of aggregation may be important because different causal factors might affect

different components. Highly aggregated data are more likely to be subject to different

causal factors than are less aggregated data. On the other hand, the reliability of data

often improves when one uses larger aggregates. 83% of the experts with an opinion

believe that combining will produce more accurate forecasts.


Clemen advises forecasters to select a set of methods that differ substantially from

one another with respect to the data used and also with respect to the procedures for

analyzing the data. The experts believed that, in general, combined forecasts are more

accurate than those based on a single method: 73% of the respondents agreed and only

15% disagreed.

Combined forecasts improve accuracy and reduce the likelihood of large errors. In a

meta-analysis, Armstrong found an average error reduction of about 12% across 30

comparisons. They are especially useful when the component methods differ

substantially from one another. For example, Blattberg and Hoch obtained improved

sales forecast by averaging managers’ judgmental forecasts and forecasts from a

quantitative model. Considerable research suggests that, lacking well-structured domain

knowledge, unweighted averages are typically as accurate as other weighting schemes.

Callopy and Armstrong favored simple methods of preparing and combining

forecasts for stable and unstable situations, with a slightly stronger preference for their

use in unstable situations. Schnaars’ results implied that simple models are most

appropriate for unstable situations. The use of a simple average has proven to do as well

as more sophisticated approaches. An alternative simple approach, the median, might

offer additional benefits. It is less likely to be affected by errors in the data. Whether the

median is superior to the mean is an empirical issue. Meta-analysis may prove useful

here. Two studies that address this issue (Larréché and Moinpour, 1983, Agnew 1985,

cited in Armstrong, 1989) suggest that the median would improve accuracy. Certainly,

there are situations where one method is more accurate than another.

V. Value of Expertise in Judgmental Forecasts

An interesting issue is how much expertise is needed for judgmental forecasting.

Surprisingly, research to date indicates that high expertise in the subject area is not

important for judgmental forecasts of change. It is, however, important for assessing

current levels. An important conclusion, then, is not to spend heavily to obtain the best

experts in the field to forecast change. But one should avoid people who clearly have no

expertise.


Extensive research over the last two decades has examined biases that occur in

judgmental forecasting. Among these biases are optimism, conservatism, anchoring, and

an overemphasis on easily available data. While some sources of bias have been

identified, little knowledge exists as to how these biases affect marketing forecasts.

When using experts, it is essential to bear in mind that people who hold viewpoints

on an issue tend to perceive the world so as to reinforce what they already believe; they

look for "confirming" evidence and avoid "disconfirming" evidence. There is much

literature on this phenomenon, commonly known as "selective perception." In cases

where disconfirming evidence is thrust upon people, they tend to remember incorrectly.

Fischhoff and Beyth, for example, found that subjects tended to remember their

predictions differently if the outcome was in conflict with their prediction.

Experts are typically overconfident. In McNee’s examination of economic

forecasts from 22 economists over 11 years, the actual values fell outside the range of

their prediction intervals about 43% of the time. This occurs even when subjects are

warned in advance against overconfidence.

Fortunately, there are procedures to improve forecasts by experts. A commonly

used technique is to ask experts to write all the reasons why their forecasts might be

wrong. Alternatively, use the devil’s advocate procedure, where someone is assigned for

a short time to raise arguments about why the forecast might be wrong. However, playing

devil’s advocate does make the person unpopular with the group. Still another way to

assess uncertainty is to examine the agreement among judgmental forecasts. For example,

Ashton, in a study of forecasts of annual advertising sales for Time magazine, found that

the agreement among the individual judgmental forecasts was a good proxy for

uncertainty.

If we take Bayes’s theorem as the standard, people tend to adjust their predictions

less than they should when they receive new information. When they consider the

likelihood of an outcome from a multistage process (Hitler invades Belgium, he succeeds,

Britain declares war, Hitler attacks Britain) people have the opposite tendency: they act

as though their best guesses of what will happen at early stages are certainties.

Stewart found that judgmental forecasts are likely to be unreliable when

(1) The task is complex,


(2) There is uncertainty about the environment,

(3) Information acquisition is subjective, or

(4) Information processing is subjective.

People are willing to pay heavily for expert advice. However, expertise beyond

a minimal level is of little value in forecasting. This conclusion is both surprising and

useful, and its implication is clear: Don't hire the best expert, hire the cheapest expert.

"Expertise … breeds an inability to accept new views." - Laski (1930)

Figure Value of Expertise in forecasting

(Source: Armstrong, 1980)

Although experts are poor at forecasting, this does not mean that judgmental

forecasting is useless. However, since all available evidence suggests that expertise

beyond an easily achieved minimum is of little value in forecasting change, the most

obvious advice is to hire inexpensive experts. Also, look for unbiased experts – those

who are not actually involved in the situation. Finally, there is safety in numbers.


Robin Hogarth has suggested using at least three independent experts and preferably six

to ten.

VI. Using Multiple Hypotheses

Green used the method of multiple hypotheses. This is an important procedure in

ensuring objectivity and accuracy in forecasting.

Evaluating the utility of the Forecasting Model

The usefulness of a quantitative model depends on both "acceptability" and

"quality." Acceptability refers to approval by those who would actually use the model,

while quality refers to the ability to provide better predictions or decisions. A model must

score well on both characteristics if it is to be judged useful. A high-quality model that is

not accepted is of no value. Usually, some trade-offs must be made between quality and

acceptability.

A model is said to be "good" if it is better than alternative models. Quality and

acceptability are characteristics that may depend not only upon the model but also upon

the situation.

Research in forecasting has commonly assumed that accuracy is the primary

criterion in selecting among forecasting techniques. In fact, it has been used as the sole

criterion in many studies. In the sixteen 1992 International Journal of Forecasting papers

that compared the results of different techniques and series, only one used criteria other

than accuracy.

When asked ‘Relative to other considerations (e.g. cost, ease of interpretation,

cost/time, ease of use), how important is the accuracy of the forecasting methods that you

use?’ 29% of the experts said that accuracy was ‘extremely important’ and an additional

56% said that it was ‘important’. These results are similar to the opinions of practitioners

and researchers as reported in Carbone and Armstrong and with those of practitioners as

reported by Mentzer and Cox.


Table Rankings of criteria from previous studies (number of respondents)

(Source : Yokum and Armstrong, 1995)

However, this single-minded focus on accuracy is not completely reasonable.

To encourage diffusion, new techniques should be evaluated, not only in terms of

comparative accuracy, but also in terms of the "ease of use,” "ease of interpretation,” and

"flexibility.” "Cost savings" varied in rank depending upon its framing from a top

criterion if related to savings from improved decisions to a lower criterion if linked to

savings from technique development and maintenance.

Witt and Witt found that "speed" was most important for short-range forecasts,

while "accuracy" was most important for medium- and long-term forecasts.

The evaluation of overall quality of the model calls for an examination of four key

stages.

The first stage relates the "real world" to the assumptions of the model: Are the

assumptions reasonable and comprehensive? A review of written documents must be

carried out in order to develop an explicit listing of the key assumptions. This list may be

checked by conducting interviews with the advocates of the model. The assumptions are

then tested for reasonableness against:

(1) Empirical evidence,

(2) Judgments of managers, and

(3) Assessments by the evaluator.


Admittedly, this procedure is rather crude; however, the objective at this stage is

merely to identify “highly unreasonable" assumptions. Their appeal was strictly one of

face validity—that is, the assumptions seem reasonable.

The second stage relates the model's assumptions to the final form of the model.

Does the model follow logically from the assumptions? This is an examination of the

logical structure of the model. This stage of analysis is generally the most important one

for assessing the quality of a model. One possible approach is to assess the total costs

associated with the model [Initial development (money and time), Maintenance (money

and time), User (ease in understanding, time to get results, need for expert assistance)]

versus the total benefits derived [Predictive accuracy, Ability to assess uncertainty,

Identification of improved policies, Learning (the model improves as experience is

gained), Ability to assess effects of alternative policies, Adaptability (can adapt as the

environment changes)]

The third stage relates the model and its outputs: Given the same input data, can

the outputs be replicated?

And the fourth stage relates the outputs to the real world: Do the benefits of the

model (e.g., better predictions, better assessments of risk, or better decision making)

justify the costs of the model?

Based on the foregoing sections that review empirical forecasting literature, a

summary of general principles to be used while developing forecasting procedures is

summarized below:

Domain knowledge should be incorporated into forecasting methods.

When making forecasts in highly uncertain situations, be conservative. For

example, the trend should be dampened over the forecast horizon.

Complex methods have not proven to be more accurate than relatively simple

methods. Given their added cost and the reduced understanding among users,

highly complex procedures cannot be justified.

In case data on actual behaviour is unavailable, forecasts based on judgments or

intentions, may be used to predict behaviour.


Methods that integrate judgmental and statistical data and procedures (e.g., rule-

based forecasting) can improve forecast accuracy.

When making forecasts in situations with high uncertainty, use more than one

method and combine the forecasts, generally using simple averages.


CHAPTER

THE NEW COMPETITION

A profound, but silent, transformation of our society is afoot. Our industrial

system is generating more goods and services than at any point in history, delivered

through an ever-growing number of channels. Superstores, boutiques, online retailers and

discount stores proliferate, offering thousands of distinct products and services. This

product variety is overwhelming to consumers. Simultaneously, thanks to the propagation

of cell phones, Web sites, and media channels, consumers have increased access to more

information, at greater speed and lower cost than ever before. But who has the leisure and

the proficiency needed to sort through and evaluate all these products and services? The

burgeoning complexity of offerings, as well as the associated risks and rewards,

confounds and frustrates most time-starved consumers. Product variety has not

necessarily resulted in better consumer experiences.

For senior management, the situation is no better. Advances in digitization,

biotechnology, and smart materials are increasing opportunities to create fundamentally

new products and services and transform businesses. Major discontinuities in the

competitive landscape –ubiquitous connectivity, globalization, industry deregulation, and

technology convergence- are blurring industry boundaries and product definitions. These

discontinuities are releasing worldwide flows of information, capital, products, and ideas,

allowing nontraditional competitors to upend the status quo. At the same time,

competition is intensifying and profit margins are shrinking. Managers can no longer

focus solely on costs, product and process quality, speed, and efficiency. For profitable

growth, managers must also strive for new sources of innovation and creativity.

Thus, the paradox of the twenty-first-century economy: Consumers have more

choices that yield less satisfaction. Top management has more strategic options that yield

less value. Are we on the cusp of a new industrial system with characteristics different

from those we now take for granted? The emerging reality is forcing us to reexamine the

traditional system of company-centric value creation that has served us so well over the

past hundred years. We now need a new frame of reference for value creation. The


answer, we believe, lies in a different premise centered on co-creation of value. It begins

with the changing role of the consumer in the industrial system.


CHAPTER

THE INDUSTRY LIFE CYCLE

The nature of new competition leads us to one conclusion. The industry life cycles

are getting shorter. Every firm in every industry is trying to get a competitive advantage

over the other. It tries this in various ways- publicity, differentiated products, low prices,

huge advertising budgets, unique benefits of the products, degrading the competitors’

product, etc.

But one must realize that all these are ways to survive in the market. A firm needs

to give a product which is different from its competitor or else its products will not be

accepted. The consumer wants to know the marginal benefit he will receive by using

Company X’s product as against Company Y’s. He wants the product at low price with

additional features and services. He wants value for his money.

All these aspects have resulted in shorter industry life cycles. And hence, shorter

company life cycles. Then profit margins are reducing, the managers try to reduce the

cost of production yet not compromising on the quality of the product.

It is traditional to categorize enterprises as business-to-business (B2B) or

business-to-consumer (B2C), decidedly putting “business” first and taking a firm centric

view of the economy. But these conventions are challenged in today’s dynamic economy.

What if the individual consumer (whether an enterprise or a household) were at the center

and not the firm? What if we spoke of “consumer-to-business-to-consumer” (C2B2C)

patterns of economic activity?

Consequently, we challenge the traditional notion of value an its creation, namely

that firms create and exchange value with customers. The joint efforts of the consumer

and the firm-the firm’s extended network and consumer communities together-are co-

creating value through personalized experiences that are unique to each individual

consumer. This proposition challenges the fundamental assumptions about our industrial

system-assumptions about value itself, the value creation process, and the nature of the

relationship between the firm and the consumer. In this new paradigm, the firm and the

consumer co-create value at points of interaction. Firms cannot think and act unilaterally.


We will now analyze how the consumer’s role had changed in the new

environment and how a firm can operate in the using the new emerging managerial

principles and hence increase the company life cycles.


CHAPTER

THE CHANGING ROLE OF CONSUMER AND VALUE CREATION

The most basic change has been a shift in the role of the consumer-from isolated to

connected, from unaware to informed, from passive to active. The impact of the

connected, informed, and active consumer is manifest in many ways. Let us examine

some of them.

1. Information Access

With access to unprecedented amounts of information, knowledgeable consumers can

make more informed decisions. For companies accustomed to restricting the flow of

information to consumers, this shift is radical. Millions of networked consumers are now

collectively challenging the traditions of industries as varied as entertainment, financial

services, and health care.

2. Global View

Consumers can also access information on firms, products, technologies, performance,

prices, and consumer actions and reactions from around the world. Twenty years ago, the

two car dealerships (General Motors and Ford) in small towns in North America would

probably have influenced the driving aspirations of a local teenager. Today, a teen

anywhere can dream about owning one of more than seven hundred car models listed on

the Internet, creating a serious gap between what is immediately available in the

neighborhood and what is most desirable. Geographical limits on information still exist,

but they are eroding fast, changing the rules of business competition. For example,

broader consumer scrutiny of product range, price, and performance across geographic

borders is limiting multinational firms’ freedom to vary the price or quality of products

from one location to another.

3. Networking

Human beings have a natural desire to coalesce around common interests, needs, and

experiences. The explosion of the Internet and advances in messaging and telephony-the

number of mobile phone users is already over one billion-is fueling this desire, creating

an unparalleled ease and openness of communication among consumers. Consequently,

"thematic consumer communities," in which individuals share ideas and feelings without


regard for geographic or social barriers, are revolutionizing emerging markets and

transforming established ones. The power of consumer communities comes from their

independence from the firm.

4. Experimentation

Consumers can also use the Internet to experiment with and develop products, especially

digital ones. Consider MP3, the compression standard for encoding digital audio

developed by a student Karlheinz Brandenburg and released to the public by the

Fraunhofer Institute in Germany. Once technology-savvy consumers began

experimenting with MP3, a veritable audio-file-sharing movement surged to challenge

the music industry. The collective genius of software users the world over has similarly

enabled the co-development of such popular products as the Apache Web server software

and the Linux operating system.

Of course, the Internet facilitates consumer sharing in nondigital spheres as well:

Aspiring chefs swap recipes, gardening enthusiasts share tips on growing organic

vegetables, and homeowners share insights into home improvements. More crucial,

consumer networks allow proxy experimentation-that is, learning from the experiences of

others. The diversity of informed consumers around the world creates a wide base of

skills, sophistication, and interests that any individual can tap into.

5. Activism

As people learn, they can better discriminate when making choices;

and, as they network, they embolden each other to act and speak out. Consumers

increasingly provide unsolicited feedback to companies and to each other. Already,

hundreds of Web sites are perpetuating consumer activism, many targeting specific

companies and brands. The Web has also become a powerful tool by which groups

focused on issues such as child labor and environmental protection seek corporate and

governmental attention and promote reforms. Consumer advocacy through online groups

may have even greater impact than company marketing. When Novartis AG launched

clinical trials of a promising leukemia drug, Gleevec, word spread so fast on the Internet

that the company was inundated by demand from patients wanting to participate.

Activism by leukemia patients who were on the early clinical trials for this drug led to a


highly effective lobbying effort via Inter- net support groups to speed up its production,

and even get the Food & Drug Administration (FDA) to expedite its approval.

What is the net result of the changing role of consumers? Companies can no

longer act autonomously, designing products, developing production processes, crafting

marketing messages, and controlling sales channels with little or no interference from

consumers. Consumers now seek to exercise their influence in every part of the business

system. Armed with new tools and dissatisfied with available choices, consumers want to

interact with firms and thereby co-create value. The use of interaction as a basis for co-

creation is at the crux of our emerging reality.

Consumer- Company Interactions: The Emerging Reality of Value Creation

Consider the evolution of the health care industry. Innovations in

pharmaceuticals, biotechnology, nutrition, cosmetics, and alternative therapies are

creating various treatment modalities and transforming our concepts of health. As both

consumers and technologies advance, traditional medicine ("curing sickness"), preventive

medicine, and improvements in the quality of life are rapidly merging into a “wellness

space.” Let us examine the changing dynamics of interaction between a consumer and the

firms that participate in the wellness space.

Twenty years ago, when I was feeling ill and visited my doctor, I might have

undergone a battery of tests that would have informed my doctor’s diagnosis, which he

would explain to me only if he had to. He would then choose a treatment modality,

prescribe some medications, and schedule a follow-up examination. Health care back

then was generally doctor-centric, just as commerce was company-centric. Doctors

thought that they knew how to treat me, and since I wasn’t a physician I probably agreed.

Similarly, most businesses figured that they knew how to create customer value-and most

customers agreed.

Now, health care process is far more complex. As soon as I feel ill, I will tap into

the expertise and experience of other patients and :health care professionals. I can access

an abundance of information, some of it reliable, some not. I can learn what I want about


breast cancer or high cholesterol or liposuction. I can investigate alternative treatments

for any condition and develop an opinion about what might and might not work for me.

Ultimately, I can cut my own path through the wellness space, thereby

constructing a personal wellness portfolio. If I'm grappling with high cholesterol, then I

can include pharmaceuticals for blood pressure and cholesterol approved by the FDA,

health supplements not approved by the FDA, a fitness regimen developed with an

instructor, and genetic screening for hereditary heart disease.

Notice that my wellness portfolio does not fit neatly into any traditional industry

classification. Yes, I visit my doctor. I get tests and medications and submit the bills to

my medical insurance, provided through my employer. But other services in my wellness

portfolio fall outside the conventional doctor-based health care, pharmaceutical, or

insurance industries. My wellness space springs from my view of wellness, my biases,

values, expertise, preferences, expectations, experiences, and financial wherewithal. My

spouse, meanwhile, can construct her own wellness portfolio. Rather than rely solely on

my doctors’ expertise, I can seek experts among my peers-other health care consumers-

organized into thematic communities, such as a high-cholesterol group. This networked

knowledge encompasses not just the medical aspects pertinent to my condition but its

sociology, psychology, and likely impact on me, my family, and the community at large.

Thus, my next visit to the doctor can differ dramatically from the conventional

checkup. I can ask, Why did you prescribe this treatment? Why not the alternative that I

found through my exploration with other consumers and the Web? My doctor probably

won't enjoy my challenging his expertise and authority. After all, I’m asking him to

explain and defend his approach, which takes time and energy. What’s more, I’m testing

the depth, breadth, and currency of his knowledge. What if I’m experimenting with

alternatives-herbs, dietary supplements, and so on-that he may not yet understand? Will

he know of any complex interactions between these treatments modalities? Should he?

Of course, health care consumers have always shaped their own treatment to a certain

extend. Remember Grandma's prescribing a remedy such as chicken soup for a cold? But

with today's access to information, consumer war stories, and advice from an experienced

peer group, consumers are far more likely to network and experiment than ever before.


As a health care consumer, I can more actively determine the "value bundle" that is

appropriate for me, cutting across customary industry boundaries.

Now position yourself as a manager in a pharmaceutical firm. The commingling

of traditional industries into a complex, evolving wellness space challenges deeply

entrenched and implicit assumptions in managerial tradition, which have evolved over

decades. For starters, what constitutes or defines a product or service? Is an anti-wrinkle

cream with Retinol a cosmetic, a fashion, or a pharmaceutical product? With unclear

industry boundaries, how do we identify the nature of our competitive advantage?

More important, what value does the pharmaceutical firm provide in wellness

space of an active, involved consumer? How does the consumer’s increasing desire to

interact with both the providers and their provisions affect the various parties involved in

that consumer’s wellness space? Who bears the risk-the doctor, the hospital, or the

patient? Patients will likely hold doctors, as experts, accountable.

Let’s move beyond doctors and patients. What if consumers inappropriately use

or modify your products and then hold you responsible for any resulting damage?

Increasingly, consumers seem to want power without accountability. They want to

choose for themselves but not be liable for the consequences of their choice. Are you as a

manager responsible for the product's performance even though you cannot control the

consumer’s usage? How do you protect yourself? Is this risk a new cost of doing

business? No matter how the future unfolds in terms of the roles, rights and

responsibilities of companies and consumers, companies will have to engage consumers

in co-creation of value.

Thus we scrutinize consumer-company interactions and amplify the weak signals

reverberating in the wellness space, we glimpse the emerging reality of the active

involvement of consumers whether as thematic communities or as informed individuals.

This fundamentally challenges two deeply embedded, traditional business assumptions:

(1) that any given company or industry can create value unilaterally

(2) that value resides exclusively in the company's or industry's products and services.

Escaping the Past: The Traditional System of Value Creation


The traditional belief structure that has served business leaders so well for the past

hundred years is shown in the figure.

The relationships between the rows and the columns in the chart depict the

internal consistency of the traditional logic of value creation. Let us start with the

premises in the top row of the figure. Traditional business thinking starts with the

premise that the firm creates value. A firm autonomously determines the value that it will

provide through its choice of products and services. Consumers represent demand for the

firm's offerings.

The implications for business follow from these premises. The firm needs an

interface with consumers-an exchange process-to move its goods and services. This firm-

customer interface has long been the locus of the producer's extracting economic value

from the consumer. Firms have developed multiple approaches to extracting this value-by

increasing the variety of offerings, by efficiently delivering and servicing those offerings,

by customizing them for individual consumers, or by wrapping contexts around them and

staging the value creation process, as themed restaurants do.

These premises and implications manifest themselves in the perspectives and

practices of firms in the industrial system. Managers focus on the “value chain” that

captures the flow of products and services through operations that the firm controls or

influences. This value chain system essentially represents the “linear cost build” of

products and services. Decisions on what to make, what to buy from suppliers, where to

assemble and service products, and a host of other supply and logistics decisions all

emanate from this perspective. Employees focus on the quality of the firm’s products and

processes, potentially enhanced through internal disciplines such as Six Sigma and Total

Quality Management. Innovation involves technology, products, and processes.

Thus, we have a coherent system for value creation. The rows and columns are internally

consistent. If the firm creates value, then the value creation process is separate from the

market, where various parties simply exchange this value. The importance of efficiently

matching supply from the firm’s value chain with demand from consumers becomes

obvious. In fact, matching supply and demand has long been the bedrock of the value

creation process.


Consider the shifts in thinking identified thus far. Consumers are overwhelmed

and dissatisfied by the product variety available today. Armed with new connective tools,

consumers want to interact and co-create value, not just with one firm but with whole

communities of professionals, service providers, and other consumers. The co-creation

experience depends highly on individuals. Each person’s uniqueness affects the co-

creation process as well as the co-creation experience. A firm cannot create anything of

value without the engagement of individuals. Co-creation supplants the exchange

process.

The New Frame of Reference for value Creation

What might a new, internally consistent system based on co-creation of value

look like? We present such a system in figure.


The new starting premise is that the consumer and the firm co-create value, and so

the co-creation experience becomes the very basis of value. The value creation process

centers on individuals and their co- creation experiences.

New premises inevitably lead to new implications for business. The interaction

between consumers and firms becomes the new locus of co-creation of value. Since

millions of consumers will undoubtedly seek different interactions, the value creation

process must accommodate a variety of co-creation experiences. Context and consumer

involvement contribute to the meaning of a given experience to the individual and to the

uniqueness of the value co-created.

These premises and implications suggest new capabilities for firms.

Managers must attend to the quality of co-creation experiences, not just to the quality of

the firm’s products and processes. Quality depends on the infrastructure for interaction

between companies and consumers, oriented around the capacity to create a variety of

experiences. The firm must efficiently innovate “experience environments” that enable a

diversity of co-creation experiences. It must build a flexible "experience network" that

allows individuals to co-construct and personalize their experiences. Eventually, the roles

of the company and the consumer converge toward a unique co-creation experience, or an

"experience of one."

Notice what co-creation is not. It is neither the transfer nor out-sourcing of

activities to customers nor a marginal customization of products and services. Nor is it a

scripting or staging of customer events around the firm’s various offerings. That kind of

company-customer interaction no longer satisfies most consumers today.

The change that we are describing is far more fundamental. It involves the co-

creation of value through personalized interactions that are meaningful and sensitive to a

specific consumer. The co-creation experience (not the offering) is the basis of unique

value for each individual. The market begins to resemble a forum organized around

individuals and their co-creation experiences rather than around passive pockets of

demand for the firm’s offerings.


CO-CREATION

The co-creation process, demanding as it is, raises important questions for

managers:

In-depth dialogue with customers is very time-intensive. What if my firm has a

million consumers-or ten million? My staff can barely keep up with current

interactions with customers, suppliers, and each other. How can my firm interact

so intensely with each consumer and maintain operational efficiency?

Co-creation allows for an unusual degree of customer input into product design.

How do I maintain consistently high standards of product quality and cede some

control over design?

Transparency allows customers to interact with my firm in potentially intrusive

ways. How much access up and down the supply chain do I allow customers?

Individual consumers are at the very heart of the co-creation experience. How do I

deal with the heterogeneous demands of my customer base?

Discussing options openly gives customers a degree of control over the risks that

they assume-but not necessarily the liabilities. Where do I draw the line on

acceptable risks- and where do my legal responsibilities begin and end?

Co-creation moves the firm toward an individual-centered view of demand. How

does demand forecasting work under such unpredictable circumstances?

These difficult questions breach the very meaning of value and the process by which

a firm co-creates value. We will explore how that meaning and that process are evolving,

and we will consider possible answers for the important questions that manager must

confront.

The Building Blocks of Co-Creation

The company needs to concentrate on the total co-creation experience, as well as the

process of co-creation through its key building blocks: dialogue, access, risk assessment,

and transparency, which we will refer to by the acronym DART.


1. Dialogue

Dialogue means interactivity, deep engagement, and a propensity to act-on both sides.

Dialogue is more than listening to customers: It entails empathic understanding built

around experiencing what consumers experience, and recognizing the emotional, social,

and cultural context of experiences. It implies shared learning and communication

between two equal problem solvers. Dialogue creates and maintains a loyal community.

The dialogue involved in co-creation has several specific features:

It focuses on issues that interest both the consumer and the firm.

It requires a forum in which dialogue can occur.

It also requires rules of engagement (explicit or implicit) that make for an orderly,

productive interaction.

2. Access

The traditional focus of the firm and its value chain was to create and transfer

ownership of products to consumers. Increasingly, the goal of consumers is access to

desirable experiences-not necessarily ownership of the product. One need not own

something to access an experience. We must uncouple the notion of access from

ownership.

Access begins with information and tools. Access can also involve on-demand

resources such as computing. Consumers may also want access to a lifestyle. Access can

also transform the capacity for self expression.

3. Risk Assessment

Risk here refers to the probability of harm to the consumer. Managers have

traditionally assumed that firms can better assess and manage risk than consumers can.

Therefore, when communicating with consumers, marketers have focused almost entirely

on articulating benefits, largely ignoring risks.

Today, however, there is a growing debate about risk and the trade-off between risks

and benefits-a debate that the move to co-creation will intensify. Can firms unilaterally

manage risks in an environment of co-creation? On the other hand, if consumers are

active co-creators, should they shoulder responsibility for risks as well?


The debate about informed consent and the responsibilities of companies and

consumers will likely continue for years. However, we can safely assume that consumers

will increasingly participate in co-creation of value. They will not cede their right to

choose. However, they will insist that businesses inform them fully about risks, providing

not jus data but appropriate methodologies for assessing the personal and societal risk

associated with products and services.

4. Transparency

Companies have traditionally benefited from information asymmentry between the

consuerm and the firm. That asymmetry is rapidly disappearing. Firms can no longer

assumer opaqueness of prices, costs and profit margins. And as information about

products, technologies and business systems become more accessible, creating new levels

of transparency becomes increasingly desirable.

Combining the building blocks of DART enables companies to better engage

customers as collaborators. Transparency facilitates collaborative dialogue with

consumers. Constant experimentation coupled with access and risk assessment on both

sides can lead to new business models and functionalities designed to enable compelling

co-creation experiences.

Although many firms and industries are experimenting with these elements and the

evidence of the changing nature of value creation accumulates, many companies are

unable to embrace the new framework of co-creation. Why?

A large part of the answer is that co-creation fundamentally challenges the traditional

roles of the firm and the consumer. The tension manifests itself at points of interaction

between the consumer and the company-where the co-creation experience occurs, where

individuals exercise choice, and where value is co-created. Points of interaction provide

opportunities for collaboration and negotiation, explicit or implicit, between the

consumer and the company-as well as opportunities for those processes to break down.


THE CO-CREATION EXPERIENCE

We have seen many companies have begun to experiment with co-creation of value.

Still, most managers are reluctant to let go of familiar practices and tools-their comfort

zone. The result is an emerging tension between the traditional “company think” and the

emerging “consumer think” around the issue of whether the firm can unilaterally control

the choice of experience in the co-creation process. The answer is no.

Company Think Versus Consumer Think

Like all humans, business managers are socialized into a dominant logic- shaped by

the attitudes, behaviors, and assumptions that they learn in their business environments.

Unfortunately, most managers seem to forget that they are also consumers. Their thinking

is conditioned by managerial routines, systems, processes, budgets, and incentives

created under the traditional framework of value creation. They focus on technology road

maps, plant scheduling, product quality, cost reduction, cycle time, and efficiency.

Unsurprisingly, opportunities for interaction with consumers are approached in a similar

fashion.

Simply understanding the framework of co-creation will not suffice. We must

explicitly recognize how deeply etched ways of thinking limit our ability to shift into co-

creation mode. We must understand the differences between "company think" and

"consumer think" that will drive successes in the twenty-first century.

The disconnect between consumer think and company think is not new. However, as

we move toward co-creation, this disconnect becomes more pronounced at points of

consumer-company interaction, where choice is exercised and the consumer interacts

with the firm to co-create an experience.

Most managers implicitly assume that their physical product-the digital camera-is the

vessel of value. They rarely consider the aspirations, frustrations, and wishes of the

heterogeneous group of consumers who experience their product or service. Instead, they

focus on the efficiency of production and logistical systems or on advanced technology

for its own sake. Misled by company think, they clutter the marketplace with products

that are feature rich but experience poor. For the consumer; "technology convergence"

can create "experience divergence."


The continued dominance of company think explains why technological change is

creating more variety in devices, features, and formats than ever before-and more

confusion, doubt, and anxiety. Depending upon the consumer's level of competence and

sophistication, and her tolerance for irritants, her reaction will range from mild

annoyance ill deep cynicism and anger.

The tensions at the points of interaction result from the disconnect between company

think and consumer think. So how can we connect the two?

Dimensions of Choice in Consumer-Company Interaction

As we have seen, dialogue, access, risk assessment, and transparency- DART-form

the foundation for co-creation of value. But these factors alone may not produce

compelling experiences in co-creation. We also must attend to the dimensions of choice

in consumer-company interaction that condition the co-creation experience. We have

identified four such dimensions:

Consumers want the freedom of choice to interact with the firm through a

range of experience gateways. Therefore, the firm mm: focus on the co-

creation experience across multiple channels.

Consumers want to define choices in a manner that reflects their view of

value. Therefore, the firm must provide experience-centric options that reflect

consumer desires.

Consumers want to interact and transact in their preferred language and style.

They want quick, easy, convenient, and safe access to experiences. Therefore,

in consummating individual choices, the firm must also focus on the co-

creation experience through transactions.

Consumers want to associate choice with the experiences they are willing to

pay for. They want the price of thee experiences to be fair. Therefore, the firm

must focus on the totality of the price-experience relationship in co-creation.

Traditional Exchange Versus Co-Creation Experiences


We have identified the basic building blocks of co-creation-dialogue, access, risk

assessment, and transparency (DART)-as well as the dimensions of choice in consumer-

company interaction that determine the quality of co-creation experiences as seen from

the consumer's perspective-experiences across multiple channels and options,

transactions experiences, and a compelling price-experience relationship. In the process,

we have identified the quality of interaction between the consumer and the firm as the

critical link in the future of competition.

The emerging reality differs dramatically from the traditional approach to the

firm-customer interaction. The differences are summarized in table below. Note that:

In the traditional approach, the goal of the interaction is value extraction. This

occurs in the exchange process, which is the primary source of contact between

the firm and the customer. By contrast, in the co-creation approach, the goal of

the interaction is twofold: value creation as well as value extraction;

In the traditional approach, the locus of interaction is at the end of the value chain

activities. In the co-creation approach, interactions can take place repeatedly,

anywhere, and anytime in the system;

And most important, in the traditional approach, the notion of quality is

based on what the firm has to offer. In co-creation, it is about consumers co-

constructing their own experiences.

Traditional Exchange Co-Creating Experience

Goal of Interaction Extraction of economic

value

Co-creation of value

through compelling co-

creation experiences, as

well as extraction of

economic value

Locus of Interaction Once at the end of the value

chain

Repeatedly, anywhere and

anytime in the system

Company-Consumer

Relationship

Transaction-based Set of interactions and

transactions focused on a

series of co-creation

experiences


View of Choice Variety of products and

services, features and

functionalities, product

performance and operating

procedures

Co-creation experience

based on interactions across

multiple channels, options,

transactions, and the price-

experience relationship

Pattern of Interaction

Between Company and

Consumer

Passive, firm-initiated, one-

on-one

Active, initiated by either

company or consumer, one-

on-one-to-many

Focus of Quality Quality of internal

processes, and company

offerings

Quality of consumer-

company interactions and

co-creation experiences

The dimensions of choice in co-creation experiences suggest the potential richness of

the relationship through the quality of interactions between companies and consumers.

Business managers can compete in myriad ways by discovering new opportunities

through an individual-centric lens of choice in consumer-company interactions, and

carefully managing the quality of co-creation experiences. The possibilities are endless,

particularly if we gravitate toward innovating "experience environments" that

accommodate heterogeneous consumers who seek to interact in a multitude of ways.

INNOVATION

Experience environments are characterized by robustness, the capacity to

accommodate a wide range of context-specific experiences of heterogeneous individuals.

An experience environment facilitates a total experience for consumers. It includes

products and services as well as various interfaces for individual interactions with the

company, including multiple channels, modalities, employees and communities.

Since value increasingly lies in the co-creation experience, then business leaders

must shift focus of innovation away from products and services and toward robust

experience environments capable of facilitating compelling co-creation experiences. The

specific experience outcomes, by definition, cannot be detailed a priori. The focus on


innovating experience environemtnts, therefore, differs from the traditional focus on

innovating products and services.

The experience environments must:

Offer opportunites for consumers to co-construct their own experiences on

demand, in specific context of space and time;

Accommodate a heterogeneous group of consumers, from the very sophisticates

and active to the very sophisticated and passive;

Recognize that every consumer (including the active, smart consumer) does not

always want to c-create; sometimes they just want to consume passively;

Facilitates new ooprtunities afforded by the evolution of emerging technologies;

Accommodate the involvement of consumer communities;

Engage the consumer emotionally and intellectually; and

Explicitly recognize both the social and the technical aspects of co-creation

experiences.

The New Frontier

Experience innovation is a new frontier in co-creation, one that requires a

seamless integration of imagination, consumer insights and advanced technology. The

challenge of experience innovation is to combine the building blocks of DART with the

dimensions of choice in co-creation experiences (the quality of co-creation experiences

across multiple channels and options, the quality of the transaction experience, and the

perceived totality of the price-experience relationship) and the levers for experience

innovation (granularity, extensibility, linkage and evolvability) to create a rich new

experience space for co-creation.

Migrating to Experience Innovation

The transition for most firms is from product/service based, fimr centric view of

innovation to an experience-centric, co-creation view of innovation. The distinctions

between the traditional and new perspective of innovation are summarized in the

following table

Table Migrating to Experience Innovation

Traditional Innovation Experience Innovation

Innovation Goal Products and processes Experience environments


Basis of Value Product and service

offerings

Co-creation experiences

View of Value Creation Firm creates value; supply

push and demand pull for

firm’s offerings

Value is co-created;

individual-centric co-

creation of value

Focus of Development Cost, quality, speed and

modularity

Granularity, extensibility,

linkage and evolvability

View of technology Features and functions;

technology and systems

integration

Enablers of experiences;

experience integration

Focus of infrastructure Support fulfillment Support co-construction of

personalized experiences

As managers, we tend to think in terms of opposites-quality versus cost, variety

versus mass manufacturing, efficiency versus innovation. Such polarization often

misstates reality. A deep commitment to quality can lead to dramatic cost reductions.

Mass customization combines variety with large scale manufacturing. In the same way,

efficiency and experience innovation must go together. We can state three simple

propositions:

1. Discontinuities are destroying established industry and technology boudries,

thereby increasing the demand for experimentation.

2. Co-creation of value demands “de-risking” experimentation. This demands

efficiency in how we leverage resources, experiment in the marketplace, and

shape consumer expectations and evolving needs.

3. All experiments may not succeed. When some do, we should be able to scale and

expand the size of the market. That means creating systemwise efficiencies in

activities, whether in manufacturing, logistics, channels, customer service,

branding or community management.

We call this approach ‘efficient experience innovation.’

PERSONILATION


If every individual is unique, how do we innovate experience environments that

allow for co-creating unique value for each individual?

Dimensions of personalized experience

1. Events. Events form the basis for experiences. An event is a change of state in

space and time that affects one or more individuals.

2. Context of Events. Context in space and time is an inherent part of any event and

thereby experience. If an event is about what happened, then context is about

when it happened (time) and where it happened (space). These dimensions factor

into the meaning ascribed to the experience.

3. Individual Involvement. Individual involvement in events may take many forms,

based on interactions among the individual and various products, channels,

services and company employees, as well as with other individuals in various

thematic communities of interest.

4. Derivation of Personal Meaning. Personal meaning is about the relevance of an

event to the individual and the knowledge, insights, enjoyment, satisfaction and

excitement that emanate from it. Different individuals want different levels of

involvement, levels that affect the meaning assigned to the event by each

consumer.

The four dimensions just described put the individual at the heat of a co-creation

experience. This view suggests that firms can no longer dictate individual

experience outcomes. The challenge is to allow a high degree of personalization

of interactions with an experience environment as well as to accommodate

heterogeneous consumer interests, knowledge, needs and desires.

Migrating to Experience Personalization

Business must now contend with heterogeneity (defined by interaction) that is

more complex and subtle than an array of traditional market segments. “Segment-of-one”

thinking takes us only part of the way. When managers talk about segmentation of one,

they see the consumer as a marketing target. We sell to this single customer. We expect


him to learn our systems. We may allow him to configure the product from out menu or

offer him special discounts. But what we don’t do is actively engage the consumer in co-

creation. Instead, we tend to fight against co-creation.

Table Migrating to Experience Personalization

Traditional Customization Experience Customization

View of Customization Segment of one Experience of one

Focus of Customization One-off products and

services

Personalization of

interactions with the

experience environment

Approach to Customization Feature menus,

components, costs, speed

Events, context of events,

individual involvement and

personal meaning

View of Supply Chain Fulfillment of a variety of

customized products and

services through modularity

Facilitating a variety of

personalized experiences

through heterogeneous

interactions

Focus of infrastructure Configuration and

fulfillment services for

build-to-order processes

Infrastructure to support an

experience network

Starting from a traditional firm centric view of value creation, managers focus on

providing products and services to a single customer at low sot. This process leads to

mass customization, which combines benefits of “mass” (large scale production and

marketing and therefore low cost) with those of “customization” (targeting a single

customer). The focus on product feature development leads to increased product choice

for consumers.

Personalizing the co-creation experience means fostering individualized

experiences. A personalized co-creation experience reflects how the individual chooses to

interact with the experience environment that the firm facilitates. We are suggesting a


totally different process-one that involves individual consumers in personalized co-

creation experiences-a broad challenge that business leaders must face.


Analysis of turbulent market environments

Business

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