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Marketing Research of Broadcom -
April 1st, 2011
Broadcom Corporation is a leading semiconductor company in the wireless and broadband communication business. The company is headquarted in Irvine, California, USA. Broadcom was founded by a professor student pair Henry Samueli and Henry Nicholas from University of California, Los Angeles (UCLA)at Los Angeles, California in 1991. The company was moved to Irvine, California three years later. In 1998, Broadcom became a public company and now employs over 8,900 people worldwide.
Broadcom is among the Worldwide Top 20 Semiconductor Sales Leaders. In 2010, Broadcom’s total revenue was $6.82 billion.
IMPROVING SPEED, EFFICIENCY,
New products often fail because of unanticipated market shifts that result in missed opportunities and misused channels of distribution. Failures also occur because companies miscalculate their own technological strengths or the product's technological challenges. These potential problems often crop up in the latter stages and result in delays, redesigns, or poor quality products.
Companies are constantly seeking ways to avoid these pitfalls. One solution is new product development maps that chart the evolution of a company's product lines. This historical perspective helps the firm to identify and analyze functional capabilities in a systematic, repetitive fashion that allows for the development of linkages and the identification of resources for new endeavors. These maps can direct the firm to new market opportunities and point out technological challenges.
Aggregate plans for projects offer another solution. Rather than viewing each new product development project individually, they consider all of the new product development projects under consideration by the firm. This is particularly important in firms with hundreds of new product development projects going on at the same time. Projects are categorized according to resources required and contribution to the firm's bottom line. Aggregate project plans enable management to improve the management of new product development by providing greater control over resource allocation and utilization. These plans help to point out where capabilities need to be improved, how sequencing projects may help, and how projects fit with the firm's development strategies.
Return maps graphically represent the contributions of all team members to product success in terms of time and money. Their focus is on the point at which product sales generate sufficient profit so that the firm's initial investment in development is returned. Return maps show team members the time and money needed to complete their tasks in the development process so that they may estimate and re-estimate their investment in the process. In doing this return maps illustrate the impact of their actions on the project's overall success.
Another way to improve the speed and efficiency with which new products are introduced is to involve purchasing in the development process. When purchasing expertise is introduced into the development project team, quality may increase, time to market entry may decrease, investment in inventory may diminish, and costs may significantly decrease.
Technology continues to change and create new opportunities and threats. Customer requirements and expectations continue to shift and create new demands. Old channels of distribution are becoming obsolete and new channels are opening new opportunities. Some competitors are falling by the wayside while others are surging to the forefront by making new and unexpected moves to gain advantage. The very structure of industry is changing. A key to success in this tumultuous environment will continue to be the ability to sustain a competitive advantage through innovation. However, speed, efficiency, and quality in product development will be paramount. Building capabilities in all aspects of product creation and implementation, overcoming uncertainty and facilitating decision-making, ensuring these innovations are strategically linked to the firm's vision, and doing this on a continuous basis is the challenge of new product development in the next century.
2.Optimal Distribution System.
Let’s suppose a coffee company wants to create a distribution system that maximizes profitability within given DMAs. The coffee company can deliver directly to the store (DSD, direct store delivery) or ship coffee to the food retailers’ warehouses that in turn move the product from warehouse to retail shelves (i.e., warehouse distribution). What are the major variables to consider across DMAs?
a. Out-of-stocks. What level of out-of-stocks is associated with DSD versus warehouse distribution?
b. What is the shelf space (number of facings) and shelf position implications of DSD versus warehouse?
c. What warehouses, trucks, employees, and infrastructure is required to support DSD versus warehouse distribution, and what would be the comparative costs?
d. What is the tradeoff between spending more of the budget on media advertising with warehouse distribution versus better in-store merchandising and control with DSD?
e. What is the relative cost of media advertising?
f. Does DSD provide a product freshness (or product quality) advantage, and is this advantage significant enough to positively affect market share?
g. Does the same solution apply equally to all markets, or are some markets better for DSD and some markets better for warehouse distribution?
h. If DSD is the preferred distribution method, then what is the optimal way to route trucks and service accounts?
As before, this is a complex set of questions. Time would be spent riding trucks, visiting warehouses, and conducting depth interviews with warehouse and store employees, DSD truck drivers, and executives at the coffee company and their retail customers, to develop an understanding of the key variables and the probable relationships among the variables. An analytical database would be assembled and studied. The work would include product testing, measurement of out-of-stocks, and brand share analyses. Transportation models, inventory models, and advertising response models would be used to help derive the final solutions.
3.Optimal Product Line.
Let’s suppose an automotive manufacturer wants to create an optimal product line to help it succeed over the next 20 years. What variables might be considered in creating an OR/MS optimization model?
a. What is the range of market conditions (scenarios) the manufacturer might face over the next 20 years?
b. What are the probabilities of these market conditions or states?
c. What are the long-term trends in fuel prices? Fuel types? Technological probabilities?
d. What are the boundaries of consumer acceptance, given extreme scenarios?
e. How much variation in product line is permissible before brand image begins to weaken? That is, what are the practical limits of brand elasticity?
f. What is the optimal mix of cars, trucks, and other types of vehicles, given different scenarios?
This project is challenging because it involves long-range forecasting of the economy and future technologies. Econometric models would be part of the solution, as would forecasts of demographic trends. Depth interviews and surveys would be conducted among industry experts and executives to identify new technologies and the future probabilities of each. Choice modeling would be used to measure consumers’ “product line’’ preferences and elasticities, given different market conditions. Lastly, all of this would be pulled together in an integrated model to identify optimal solutions.
4.Optimal Positioning and Advertising Messaging.
What if an Internet dating service wants to optimize its television advertising? Communicating in an optimal way with the target audience via a particular media is a very complicated problem because the communication partially defines the audience, and the audience partially defines the communication. An optimal solution would involve some of the following variables:
a. What is the architecture of target-audience possibilities? Demographic? Attitudinal? Behavioral?
b. What are the strategically viable positionings?
c. What are viable themes, messages and taglines?
d. What imagery and colors correspond to the various positionings?
e. What sounds and music best reinforce the advertising themes and messages?
f. What characters and voices best support the messaging?
g. What are the interaction effects among the variables?
h. What contextual variables moderate the influence of particular positionings, themes and messages?
i. What mix of advertising media optimizes profits?
In this example, some good old-fashioned qualitative research would be used to help define the range of possibilities (positionings, themes, messages, taglines, colors, imagery, etc.). Survey research would be employed to provide a first approximation of target-audience definition. The final optimization model would involve choice modeling experiments among the broadly-defined target audience to identify a set of optimal solutions, which would also precisely define corresponding optimal target audiences.
Other types of marketing applications for OR/MS methods include:
• Route or delivery system optimization
• Promotional optimization
• Package design optimization
• Product features optimization
• Pricing optimization
• Industry and category forecasting
• Inventory optimization
• Retail category optimization
• Store design optimization
The concept of a multidiscipline team in OR/MS has tended to fade away over the years as the glitter of advanced quantitative techniques has garnered most of the attention. Despite all of the mathematical advances and software improvements, the multidiscipline team approach should not be forgotten.
The value of different educational and experiential backgrounds and different viewpoints in solving complicated problems is time-tested and proven. Marketing research is a member of the team and plays an important role in bringing new information and new perspectives into the modeling process.
Depth interviews, focus groups, ethnography, and surveys can bring the experience and knowledge of customers, truck drivers, shelf stockers, warehouse workers, store managers, and senior executives into the analyses and modeling, and lead ultimately to much better and more accurate OR/MS solutions.
The great challenge facing marketing executives at all levels is how to make better decisions (i.e., decisions that maximize the long-term returns on marketing investments). Rarely are these major decisions simple and obvious, even when they appear to be.
As the examples in this article suggest, many complex and interacting variables must be understood and modeled to find the ultimate answer. OR/MS methods, combined with marketing research, can be a valuable ally in the search for long-term optimal solutions.
Last edited by netrashetty; April 1st, 2011 at 03:53 PM..