High Tech Product Strategy

Phillip T. Meade

Xodus Business & Technology Solutions, Inc. 5703 Red Bug Lake Rd. #405

Winter Springs, FL 32708 407.538.0478

www.xodusbts.com Introduction Product strategy is extremely critical to high-tech companies. The market continuously evaluates and rewards or punishes competitors based primarily on the success or failure of their products. Because of the dynamic nature of high-tech markets, product strategy becomes even more critical than in other industries. This strategy cannot be static, but must be constantly adapting to the changes in technology and the market. This rapid change causes corresponding rapid changes in competitive advantage. Yesterday’s winning strategy is likely today’s prescription for obscurity. There are many challenges faced by high-tech companies attempting to define their product strategy. High-tech companies must constantly be building new markets, as technologies evolve and obsolete existing markets. Additionally, these firms must learn to manage short and rapidly changing product and market lifecycles. This environment of rapid change creates instability and uncertainty which can paralyze strategy development. Furthermore, high-tech firms must constantly be on the lookout for the next discontinuous innovation or disruptive technology that will revolutionize their industry. It is imperative that they learn to harness these emerging technologies while simultaneously adapting to the collapse of the current paradigm. As can be seen, there are severe challenges faced by high-tech companies with respect to product strategy. The forces acting on high-tech industries result in a need for adaptive product strategy. The strategy required to succeed with a given product is highly dependent on the constantly changing external forces of the market. The technology adoption lifecycle is currently the best means for dictating what strategies are required at a given time. State of Product Strategy Current product strategy focuses on a process of managing existing products and new product introductions within a strategic framework. This framework aligns the firm’s business objectives with its technology strategy. This framework is very powerful and decomposes product strategy into its fundamental supporting strategies. These strategies include:

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o Strategic Vision o Platform Strategy o Product Line Strategy o Competitive Strategy

Differentiation Strategy Price-Based Strategy Supporting Strategies

• Time-Based • Cannibalization • Global Product Strategy

o Growth Strategy Expansion Strategy Innovation Strategy

This integrated strategic framework has been presented pictorially as seen in figure 1.

Figure 1. Product Strategy Integrated Framework [1]

Strategic Vision, Platform Strategy, Product Line Strategy, and Competitive Strategy are mandatory components of product strategy, while Growth Strategy can be optional depending on the growth goals of the company. The supporting strategies of product strategy proceed from general to specific, guided by the preceding strategy. For example, Strategic Vision is the overriding strategy, and Platform Strategy is based on the goals set forth by it. Correspondingly, Product Line Strategy is constrained by and guided by Platform Strategy.

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Technology Adoption Lifecycle The technology adoption lifecycle is based on the socioeconomic reaction of customers to a discontinuous innovation or product. It is these discontinuities in the high-tech industry which create the constant turmoil and challenges to developing product strategy.

Chasm

Figure 2. The Technology Adoption Lifecycle [2] The phases of the technology adoption life cycle dictate who the customer is, what the customer’s motivation for purchasing is, what the basis of competition will be, what pricing strategy should be employed, what marketing strategy should be used, what the product focus should be, and what organizational behaviors are required. The proper strategy for each of these changes and, in fact, often reverses at each inflection point of the life cycle. As an example, the required strategies for the various stages of the life cycle are presented in the following table. The information for this table was compiled from Moore’s book Inside the Tornado[2].

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Chasm Early Majority Late Majority

Customer Focus on the economic buyer and the end user

Ignore the economic buyer and end user and focus exclusively on the infrastructure buyer Focus on the end user

Product Focus Emphasize ROI Ignore ROI; focus on timely deployment and reliability

Focus on maximizing product usability

Differentiation Differentiate the whole product for a single application

Commoditize the whole product for general-purpose use

Differentiate the commoditized whole product with +1 campaigns targeted at specific niches

Distribution

Partner with a value-added distribution channel for customized solution delivery

Distribute through low-cost, high volume channels for maximum market exposure

Continue to distribute through the same channels, but focus on merchandising to communicate the +1 marketing message

Pricing Value-based pricing

Use competition-based pricing to maximize market share

Leverage +1 value propositions to gain margins above the low cost clone

Competition Avoid competition to gain niche market share

Attack competition to gain mass market share

Compete against your own low-cost offering to gain margin share

Positioning Position product within vertical market segment

Position product horizontally as global infrastructure

Position product in niche markets, based on the individual preferences of end users

Organizational Imperatives Customer Intimacy and

Product Leadership Product Leadership and Operational Excellence

Operational Excellence and Customer Intimacy

Table 1. Life Cycle Strategies [2] Positioning within the TALC Obviously, the technology adoption life cycle has a great impact on the strategic direction of the firm. One of the primary drawbacks to this model, however, is the difficulty in determining where in the life cycle a given product currently resides. One of the primary means of determining a product’s position within the technology adoption lifecycle is to draw a correlation with the product’s diffusion into the market. The literature is flooded with models for forecasting a product’s diffusion into the market. These models range from empirical to theoretical to normative in nature, and

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focus on varying contributors to the diffusion process. However, the bulk of the models have at their root the Bass (1969) model. This model is the best known and most widely used model for diffusion. Other well known models include the Fourt and Woodlock (1960) and the Mansfield (1961). These models are actually accounted for as special cases of the Bass (1969) model. The Bass (1969) model is a differential equation expressed as:

)]()[()]([)( tNmtNmqtNmp

ttN

−+−=∂

∂

where N(t) = cumulative number of adopters t = time m = potential population p = coefficient of innovation q = coefficient of imitation As stated, most models are based on this basic model and include additional marketing-mix factors as parameters. For example, the Robinson and Lakhani (1975) model introduces pricing into the Bass (1969) model. Xt;t+1 = (α + β’Xt)(N – Xt)e-cp(t) c = parameter, p(t) = price as a function of time Some of these derivative models and the factors that they are based upon are included in the following chart:

Classification of Diffusion Models

Variables Included Author(s) Model Type

Basic Logistic and exponential models

Fourt and Woodlock (1960) Mansfield (1961)

Empirical Empirical

Social system

Bartholomew (1976) Mahajan and Peterson (1978) Mahajan et al. (1979) Karmeshu and Pathria (1980a) Jeuland (1981b) Eliashberg, Tapiero,and Wind (1985)

Theoretical Empirical Empirical Theoretical Theoretical Theoretical

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Personal influence

Bass (1969) Nevers (1972) Dodds (1973) Teece (1980) Heeler and Hustad (1980) Easingwood, Mahajan, and Muller (1983)

Empirical Empirical Empirical Empirical Empirical Empirical Empirical

Adoption process

Heibert (1974) Bartholomew (1976) Dodson and Muller (1978) Stoneman (1981) Feder and O'Mara (1982) Jensen (1982) Karmeshu and Pathria (1980) Mahajan, Muller, and Kerin (1984)

Theoretical Theoretical Theoretical Theoretical Theoretical Theoretical Theoretical Normative

Innovation Characteristics Peterson and Mahajan (1978) Empirical

Marketing actions Price

Robinson and Lakhani (1975) Bass (1980) Dolan and Jeuland (1981) Kalish (1983a) Simon and Sebastian (1982)

Normative Empirical Normative Normative Empirical

Advertising Horsky and Simon (1983) Simon and Sebastian (1982) Mate (1981)

Empirical Empirical Normative

Personal Selling Lilien, Rao, and Kalish (1981) Empirical

Competitive actions Mate (1981) Eliashberg and Jeuland

Normative Normative

Table 2. Classification of Diffusion Models [3] These models suffer from numerous problems, which account for the continual creation of new diffusion models leading to the current proliferation of models. A primary problem experienced by these models is the set of assumptions upon which they must be founded. Mahajan and Wind [3] detail these common assumptions:

The diffusion process is a binary process. That is, members of a social system are either adopters or nonadopters (Midgley 1977a; Dodson and Muller 1978).

The size of the population of the potential adopters remains constant over time

(Mahajan and Peterson 1978).

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There is only one adoption by an adopter (Lilien, Rao, and Kalish 1981).

Coefficients of innovation and imitation (in the Bass model) remain constant over the diffusion process (Kalish and Lilien 1986;Easingwood Mahajan, and Muller 1983).

Adoption of an innovation does not complement, substitute for, detract from, or

enhance the adoption of any other innovation (and vice versa). That is, an innovation diffuses in isolation (Peterson and Mahajan 1978).

Innovation itself (or perception of its attributes) does not change over the

diffusion process (Kalish and Lilien 1986; Roberts and Urban 1984).

Geographic boundaries do not change over the diffusion process. That is, the innovation is confined to one geographic area or market segment (Mahajan and Peterson 1979b).

Innovation is introduced by only one manufacturer and there is no competition

(Eliashberg and Jeuland Forthcoming).

The impact of marketing strategies employed to diffuse an innovation are implicitly captured by the model parameters (Robinson and Lakhani 1975; Bass 1980; Horsky and Simon 1983).

In addition to the limiting assumptions, there are other problems which these models are plagued with:

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Key Problems and Opportunities

The Factor The Problem Opportunities/Suggested Solutions

1. Structure of Model

Inflexible structure

Develop models that: -are parsimonious -have flexible point of inflection -incorporate both symmetric and asymmetric diffusion patterns

2. Data

Inconsistent and inappropriate selection of data

Develop models which: -incorporate managements' subjective judgment -help identify the expected diffusion pattern before the launch of a new product

3. Estimation Mostly focused on the OLS procedure Use multiple approaches

4. Assumptions

Too many restrictiveassumptions, which reduce the value of diffusion models to potential users

Develop models that relax assumptions and allow conceptually sound diffusion models

5. Usage

Too many models focus on the forecasting aspect ofthe model

Use models more for diagnostic insights

Table 3. Diffusion Model Problems [3]

In response to these problems, researchers have developed alternative models that focus on factors such as price point and Bayesian models. One example of a price point model is proposed by Golder and Tellis [4] in the form of: S = f(Pt, It, CSt, MPt) S = sales P = price I = income CS = consumer sentiment MP = market presence

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The resulting model is then:

εββββ eMPCSIPS ××××= 4321

The basic concept underlying this model is that the diffusion of a product depends primarily on a consumer’s ability to purchase the item. This, then, is a function of the price of the product, the consumer’s income, and the consumer’s sentiment about the continued health of the economy. To this point, the models discussed have been time invariant. That is to say that a forecast is generated at time zero and is not modified as time passes. For the purposes of determining the current position within the diffusion curve, or for greater accuracy it is necessary to incorporate current data into the model as it becomes available. This class of model is time variant. There are several types of these models which include:

Adaptive Filter Meta Analysis ( Sultan, Farley and Lehmann, 1990) Hierarchical Bayesian Augmented Kalman Filter

These models still suffer from a time bias since discrete data is used as the input to a continuous function. This means that the models will over estimate prior to the inflection point and under estimate after the inflection point. Furthermore, prediction of where in the lifecycle the product currently is still depends on accurately estimating the total sales for the product over its lifecycle. Many researchers feel that the forecasting methods suffer from a lack of fidelity in the interactions of the diffusion process. Most models ignore the dynamic interactions underlying diffusion. To combat this, some researchers have begun using system dynamics modeling to simulate the interactions and feedback loops inherent in the system. An example feedback structure from a system dynamics model is presented here:

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Figure 3. Feedback Structure of a System Dynamics Model [5]

Still other methods for determining a product’s diffusion include the Norton and Bass (1987) model for modeling successive generations of a technology product. One researcher suggests that the rate of diffusion of a technology is a function of the age of the target market, with diffusion occurring faster in younger consumers and slower in older ones.[6] A very simplistic method is to merely estimate the total sales for the life of a product and the product’s expected life and fit a logistic curve.[7] Geoffery Moore in his books Inside the Tornado and The Gorilla Game, suggests that the primary means for determining position within the lifecycle is to look at external environmental factors. These consist of news articles and press releases. As the product moves through its lifecycle, the language changes from technology focused to product focused, to customer focused. Additionally, he points to price points as being critical factors in achieving rapid adoption in the growth phase. Another indicative factor is the availability of the complementary technology required to create the whole product. Prior to entering the growth phase, all restricting factors must be removed from all components of the whole product.

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As can be clearly seen, no single model exists which provides an accurate estimation of diffusion in all situations. Most models have received criticism for the fact that their performance is limited to a specific set of situations meeting the necessary assumptions and data characteristics. Furthermore, an obvious problem experienced by most of the models is the fact that a forecast is being made which is based upon parameters which themselves must be estimated. Additionally, the majority of the known models are based on time. Therefore, rather than providing insight into the current position of the product based on current environmental factors, the model merely indicates where in the diffusion curve the product should be at a given time. For time invariant models, this forecast of where in the lifecycle the product should be was made at the very beginning of the lifecycle and based upon estimates for the parameters. Finally, very few of the reviewed models are designed specifically for high-tech products. It is a generally accepted fact that high-tech products experience a slightly different adoption lifecycle, with shorter life spans and greater product volatility. None of the models account for the presence of a chasm, or the substantive influence of the existing technology infrastructure and OEM’s on adoption. Rather than modeling the technology adoption life cycle as being a function of risk tolerance versus value proposition as most high-tech marketing experts claim, the existing models view adoption as a function primarily of influence of other users and marketing-mix. Finally, several important points about the use of the technology adoption lifecycle must be mentioned. First, the strategies and life cycle model apply for a product category across the entire industry. Consequently, it is not sufficient for a firm to merely analyze the performance of their own products. Additionally, a product does not necessarily have to proceed through each phase of the life cycle. Some products can skip the early market and chasm and go directly into the mainstream market. Likewise, some products never experience the hypergrowth typically associated with the early majority, and move directly into the mature market phase.

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The Whole Product One final concept of product strategy that must be discussed is that of the whole product. Described by Levitt in his book The Marketing Imagination, [8] this tool helps us to see the product in the context of the total services and products required for a product to completely meet the requirements of a given user. Obviously, the whole product differs by market segment and evolves throughout the technology adoption life cycle. The model is depicted as a series of concentric circles expanding out from the generic product (Fig. 4).

Generic

Expected

Augmented

Potential

Generic

Expected

Augmented

Potential

Figure 4. The Whole Product Model

The layers of the model have the following meanings:

1. Generic product – the basic product which defines a given product category 2. Expected product – the minimum set of products and services required for a

customer to purchase the product. 3. Augmented product – the expected product plus additional services, capabilities

and 3rd party products which enhance the product. 4. Potential product – what the product has the potential to grow to become as the

market, technology, and consumers mature. This particular model has great significance to product strategy. Much of the platform and product line strategy can be based on this particular model. Additionally, a prudent company will account for the variations between the different products, and even within a single product as it evolves throughout its life cycle. These variation points can then be

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designed into the product at the start, allowing a quicker and cheaper implementation of the features when required. This concept has particular significance when attempting to cross the chasm. The prevailing strategy for crossing the chasm is to pick as single beachhead on the other side of the chasm to deploy the product into. This represents a single market segment or market niche. The product should be designed to fulfill the whole product requirements for this target consumer. However, we also know that the goal of early market strategy is to deploy a product into as many segments as possible. By ensuring a leading market share at the beginning of the large-scale industry wide infrastructure conversion a company can be assured of being propelled to a leading position within the market with corresponding large-scale payback. Therefore, the ability to reduce time to market with a unique whole product for multiple market niches is critical to the long-term success of a company. By determining the variation points and insulating them from the rest of the product design, it becomes much easier to re-deploy a product into a new segment. Moreover, it is much less costly because the degree of rework required to deploy is greatly reduced. This is a great benefit to a cash flow constrained company competing in the early market. Obviously, to succeed at such a strategy it takes a great degree of forethought. It also takes a high degree of customer intimacy. The challenge is to correctly guess those features which will be of value to a given customer base. An even greater challenge, however, is to forecast the evolution of the product and estimate the future product variations. To some degree we can rely on the fact that the basis of competition for a product changes throughout its lifecycle. When first introduced, the most important product attribute is the functionality which it can provide. The generic product is the primary concern of the consumer. However, as the product is commoditized and competent clones with equivalent functionality become available the basis of competition moves to reliability. The most reliable product commands the premium price, and is selected over competitors, assuming price proximity. The next phase of the lifecycle brings competition based on the service provided. A product with free customer support, or who is renown for hassle-free warrantee fulfillment will be preferred. Next, the basis of competition will be based upon convenience. This includes both the convenience of purchasing and the convenience of use. There is a lot of potential at this stage in the lifecycle to differentiate along the lines of user friendliness and ergonomics. Finally, the product will be completely commoditized with all products possessing all possible features, services and equivalent reliability. At this point competition becomes purely price based. In this final stage of the lifecycle, a company should move to a “+1” marketing strategy if possible. This is a strategy deployed when the product has reached maturity and is an effort to add value based pricing to an otherwise commoditized, price based product. In +1 marketing, the strategy returns to that of the early market and the firm searches for

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niche markets and segments who would place value on particular product extensions. This whole product +1 is then marketed as being an added value over the commodity product. From this discussion, it should be clear that, just as product strategy was not static, neither is the product itself. Companies must take this fact into account in the early design stages for the product. Architectural decisions which are made assuming a static product will result in redesign costs later in the lifecycle. Every effort should be made to determine the variation points and account for these in the basic product architecture. It is important to note that it may not be necessary to know what all the variations will ultimately be. In many cases, just knowing where the variations will occur will allow for design flexibility at those points. Conclusion The task of creating an effective product strategy for is critical for the success of high-tech companies. Additionally, the strategy developed must be dynamic and continually updated throughout the product’s lifecycle. The basic building blocks for high-tech product strategy have been explained within this paper. However, it is important to note that there is no single method or framework which can successfully develop product strategy. Rather, it is important that those individuals charged with developing product strategy understand the various frameworks, as well as the high-tech market to develop strategy inferentially. The various frameworks merely serve as guideposts on the path of strategic development and no single framework should serve as the basis for strategy. The optimal strategy comes from understanding the strengths and weaknesses of each strategic framework and developing a final solution based on their combined inputs, and rooted in experience in the high-tech industry.

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