Marketing engineering

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Marketing engineering is currently defined as "a systematic approach to harness data and knowledge to drive effective marketing decision making and implementation through a technology-enabled and model-supported decision process". [1]

Contents

History

The term marketing engineering can be traced back to Lilien et al. in "The Age of Marketing Engineering" published in 1998; [2] in this article the authors define marketing engineering as the use of computer decision models for making marketing decisions. Marketing managers typically use "conceptual marketing", that is they develop a mental model of the decision situation based on past experience, intuition and reasoning. That approach has its limitations though: experience is unique to every individual, there is no objective way of choosing between the best judgments of multiple individuals in such a situation and furthermore judgment can be influenced by the person's position in the firm's hierarchy. In the same year Lilien G. L. and A. Rangaswamy published Marketing Engineering: Computer-Assisted Marketing Analysis and Planning, [3] Fildes and Ventura [4] praised the book in their review, while noting that a fuller discussion of market share models and econometric models would have made the book better for teaching and that "conceptual marketing" should not be discarded in the presence of marketing engineering, but that both approaches should be used together. Leeflang and Wittink (2000) [5] have identified five eras of model building in marketing:

  1. (1950-1965) The first era of application of operations research and management science to marketing
  2. (1965-1970) Adaptation of models to fit marketing problems
  3. (1970-1985) Emphasis on models that are an acceptable representation of reality and are easy to use
  4. (1985-2000) Increase interest in marketing decision support systems, meta-analyses and studies of the generalizability of results
  5. (2000- . ) Growth of new exchange systems (ex: e-commerce) and need for new modeling approaches

How to build market models and how to develop a structured approach to marketing questions has been an issue of active discussion between researchers, L. Lilien and A. Rangaswamy (2001) [6] have observed that while having data gives a competitive advantage, having too much data without the models and systems for working with it may turn out to be as bad as not having the data. Lodish (2001) [7] observed that the most complicated and elegant model will not necessarily be the one adopted in the firm, good models are the ones that capture the trade-offs of decision making, subjective estimates may be necessary to complete the model, risk needs to be taken into account, model complexity must be balanced versus ease of understanding, models should integrate tactical with strategic aspects. Migley (2002) [8] identifies four purposes in codifying marketing knowledge:

  1. To facilitate the progress of marketing as a science
  2. To promote the discipline within its institutional and professional environments
  3. To better educate and credential the potential manager
  4. To provide competitive advantage to the firm

Lilien et al.(2002) [9] define marketing engineering as "the systematic process of putting marketing data and knowledge to practical use through the planning, design, and construction of decision aids and marketing management support systems (MMSSs)". One the driving factors toward the development of marketing engineering are the use of high-powered personal computers connected to LANs and WANs, the exponential growth in the volume of data, the reengineering of marketing functions. The effectiveness of the implementation of marketing engineering and MMSSs in the firm depend on the decision situation characteristics(demand), the nature of the MMSS (supply), match between supply and demand, design characteristics of the MMSS, characteristics of implementation process. Wider adoption depend on difference between end-user systems and high-end systems, user training and the growth of the Internet.

Market response models

All market response models include: [10]

Models

In marketing engineering methods and models can be classified in several categories: [1]

Customer value assessment

Segmentation and targeting

Positioning

Forecasting

New product and service design

Marketing mix

Related Research Articles

Marketing research is the systematic gathering, recording, and analysis of qualitative and quantitative data about issues relating to marketing products and services. The goal is to identify and assess how changing elements of the marketing mix impacts customer behavior.

Concept testing is the process of using surveys to evaluate consumer acceptance of a new product idea prior to the introduction of a product to the market. It is important not to confuse concept testing with advertising testing, brand testing and packaging testing, as is sometimes done. Concept testing focuses on the basic product idea, without the embellishments and puffery inherent in advertising.

In the field of management, strategic management involves the formulation and implementation of the major goals and initiatives taken by an organization's managers on behalf of stakeholders, based on consideration of resources and an assessment of the internal and external environments in which the organization operates. Strategic management provides overall direction to an enterprise and involves specifying the organization's objectives, developing policies and plans to achieve those objectives, and then allocating resources to implement the plans. Academics and practicing managers have developed numerous models and frameworks to assist in strategic decision-making in the context of complex environments and competitive dynamics. Strategic management is not static in nature; the models can include a feedback loop to monitor execution and to inform the next round of planning.

<span class="mw-page-title-main">Pricing</span> Process of determining what a company will receive in exchange for its products

Pricing is the process whereby a business sets the price at which it will sell its products and services, and may be part of the business's marketing plan. In setting prices, the business will take into account the price at which it could acquire the goods, the manufacturing cost, the marketplace, competition, market condition, brand, and quality of product.

In marketing, market segmentation is the process of dividing a broad consumer or business market, normally consisting of existing and potential customers, into sub-groups of consumers based on shared characteristics.

Marketing management is the organizational discipline which focuses on the practical application of marketing orientation, techniques and methods inside enterprises and organizations and on the management of a firm's marketing resources and activities.

<span class="mw-page-title-main">Conjoint analysis</span>

Conjoint analysis is a survey-based statistical technique used in market research that helps determine how people value different attributes that make up an individual product or service.

An executive information system (EIS), also known as an executive support system (ESS), is a type of management support system that facilitates and supports senior executive information and decision-making needs. It provides easy access to internal and external information relevant to organizational goals. It is commonly considered a specialized form of decision support system (DSS).

A feasibility study is an assessment of the practicality of a project or system. A feasibility study aims to objectively and rationally uncover the strengths and weaknesses of an existing business or proposed venture, opportunities and threats present in the natural environment, the resources required to carry through, and ultimately the prospects for success. In its simplest terms, the two criteria to judge feasibility are cost required and value to be attained.

Gary L. Lilien is Distinguished Professor of Management Science at the Smeal College of Business at Pennsylvania State University and is also the co-founder and research director of Institute for the Study of Business Markets ISBM, the world's leading institution focusing on fostering research in B2B markets.

<span class="mw-page-title-main">Market share</span> Relative market adoption

Market share is the percentage of the total revenue or sales in a market that a company's business makes up. For example, if there are 50,000 units sold per year in a given industry, a company whose sales were 5,000 of those units would have a 10 percent share in that market.

The following outline is provided as an overview of and topical guide to marketing:

Return on marketing investment (ROMI) is the contribution to profit attributable to marketing, divided by the marketing 'invested' or risked. ROMI is not like the other 'return-on-investment' (ROI) metrics because marketing is not the same kind of investment. Instead of money that is 'tied' up in plants and inventories, marketing funds are typically 'risked'. Marketing spending is typically expensed in the current period.

A target market, also known as serviceable obtainable market (SOM), is a group of customers within a business's serviceable available market at which a business aims its marketing efforts and resources. A target market is a subset of the total market for a product or service.

Demand forecasting refers to the process of predicting the quantity of goods and services that will be demanded by consumers at a future point in time. More specifically, the methods of demand forecasting entail using predictive analytics to estimate customer demand in consideration of key economic conditions. This is an important tool in optimizing business profitability through efficient supply chain management. Demand forecasting methods are divided into two major categories, qualitative and quantitative methods. Qualitative methods are based on expert opinion and information gathered from the field. This method is mostly used in situations when there is minimal data available for analysis such as when a business or product has recently been introduced to the market. Quantitative methods, however, use available data, and analytical tools in order to produce predictions. Demand forecasting may be used in resource allocation, inventory management, assessing future capacity requirements, or making decisions on whether to enter a new market.

A marketing information system (MIS) is a management information system (MIS) designed to support marketing decision making. Jobber (2007) defines it as a "system in which marketing data is formally gathered, stored, analysed and distributed to managers in accordance with their informational needs on a regular basis." In addition, the online business dictionary defines Marketing Information System (MKIS) as "a system that analyzes and assesses marketing information, gathered continuously from sources inside and outside an organization or a store." Furthermore, "an overall Marketing Information System can be defined as a set structure of procedures and methods for the regular, planned collection, analysis and presentation of information for use in making marketing decisions."

The fields of marketing and artificial intelligence converge in systems which assist in areas such as market forecasting, and automation of processes and decision making, along with increased efficiency of tasks which would usually be performed by humans. The science behind these systems can be explained through neural networks and expert systems, computer programs that process input and provide valuable output for marketers.

Product marketing is a sub-field of marketing that is responsible for crafting messaging, go-to-market flow, and promotion of a product. Product marketing managers can also be involved in defining and sizing target markets along with other business stakeholders such as business development and sales as well as technical functions such as product management. Other critical responsibilities include positioning and sales enablement.

Rajdeep 'Raj' Grewal is the Townsend Family Distinguished Professor of Marketing at Kenan-Flagler Business School, University of North Carolina at Chapel Hill. He is the editor-in-chief of Journal of Marketing Research. He is known for his work on marketing research, marketing strategy and business to business marketing.

SIMALTO – SImultaneous Multi-Attribute Trade Off – is a survey based statistical technique used in market research that helps determine how people prioritise and value alternative product and/or service options of the attributes that make up individual products or services.

References

  1. 1 2 Arvind., Rangaswamy; de., Bruyn, Arnaud (2013). Principles of marketing engineering. DecisionPro. ISBN   978-0985764807. OCLC   840607615.
  2. ↑ "The Age of Marketing Engineering". archive.ama.org. Retrieved 2017-05-31.
  3. ↑ Arvind., Rangaswamy (2005). Marketing Engineering : computer assisted marketing analysis and planning. Trafford. ISBN   978-1412022521. OCLC   731888669.
  4. ↑ The Journal of Operational Research Society, Vol. 51, No. 7 (Jul., 2000), pp. 891–892
  5. ↑ P.S.H. Leeflang, D. R. Wittink, Building models for marketing decisions: Past, present and future, International Journal of Research in Marketing, 2000
  6. ↑ Lilien, Gary L.; Rangaswamy, Arvind (2001-06-01). "The Marketing Engineering Imperative: Introduction to the Special Issue". Interfaces. 31 (3_supplement): S1–S7. CiteSeerX   10.1.1.421.5682 . doi:10.1287/inte.31.3s.1.9679. ISSN   0092-2102.
  7. ↑ Leonard M. Lodish, (2001) Building Marketing Models that Make Money. Interfaces 31(3_supplement):S45-S5
  8. ↑ David Migley, What to codify: marketing science or marketing engineering? Marketing theory 2002
  9. ↑ Lilien L.G., Rangaswamy A., van Bruggen Gerrit H.,Wierenga B., Bridging the marketing theory-practice gap with marketing engineering, Journal of Business Research 2002
  10. ↑ Lilien G. L., Rangaswamy A., De Bruyn A., Principles of Marketing Engineering, Decision Pro 2013
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