Marketing engineering

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 and the use of computer models for making marketingdecisions. Marketing managers typically use “conceptual marketing”, that is they develop a mental modelof the decision based on past experience, intuition and reasoning. That approach Has Its limitations though: experience is one to every individual, there is no objective way of choosing the best entre of Judgments in multiple Individuals Such a position and furthermore judgment Can Be Influenced by the person position in the firm’s hierarchy . In the same year Lilien GL 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 modelswould 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 APPROBATION five era of model building in marketing:

1. (1950-1965) The first era of applications of 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-analyzes and studies of the generalizability of results
5. (2000-.) Growth of new exchange systems (eg e-commerce ) and need for new modeling approaches

How to build market models and how to Develop a structured approach to marketing issues has-been an outcome of active discussion entre Researchers L. Lilien and A. Rangaswamy (2001) ^[6] -have Observed That while HAVING data Gives a competitive advantage, HAVING Too much data with the models and systems for working with the data. Lodish (2001) ^[7] Observed que la MOST complicated and elegant model will not Necessarily be the one adopté in the firm, good models are the ones Who captures the trade-offs of decision making , subjective Estimatesmay be necessary to complete the model, model 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] identified 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 be better educated and credentialed to 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 into practice and using the planning, design, and construction of decision aids and marketing management support systems (MMSSs)”. One of the driving factors towards the development of marketing engineering is 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 and MMSSs in the firm depends on the decision situation characteristics (demand), the nature of the MMSS (supply), and the characteristics of the process. Wider adoption depends on the 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]

• Inputs: price , advertising , selling effort, product design , market size , competitive environment
• Response Model: links to products, sales , profits
• Objectives: used to evaluate actions such as sales

Models

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

Customer value assessment

• Objective measures: internal engineering assessment, indirect survey questions, field value-in-use assessment
• Perceptual measures: focus groups , direct survey questions, importance ratings , joint analysis , benchmarking
• Behavioral measures: choice models , data mining

Segmentation and targeting

• Reducing data: factor analysis
• Association measures: cluster analysis
• Outlier detection and removal
• Forming Segments: cluster analysis
• Profiling Segments: discriminant analysis

Positioning

• Perceptual maps : similarity-based methods, attribute-based methods
• Preference maps: ideal-point model, vector model
• Joint-space maps: averaged ideal-point model, averaged vector model, external analysis

Forecasting

• Judgmental methods: sales force composite estimates, jury of executive opinion, Delphi method , scenario analysis
• Market and Survey Analysis: Buyer Intentions, Product Testing , Chain Ratio
• Time Series: naive methods, moving averages , exponential smoothing , Box-Jenkins method , decompositional methods
• Causal analyzes: regression analysis , econometric models , input-output models , multivariate ARMA , neural networks
• New product forecasting models: Bass Model , ASSESSOR model

New product and service design

• Creativity software: idea generation, GE / Mckinsey portfolio model , spouse analysis

Marketing mix

• Pricing : classic approach, cost-oriented pricing, demand-oriented pricing, competition-oriented pricing
• Promotion : affordable method, percentage-of-sales method, competitive parity method, objective-and-task method
• Sales force decisions: intuitive methods, market response methods, response functions

References