This is a research paper aimed at studying the diversity in the trends through which marketing management systems support has evolved. It’s a study focus aimed to relate the different phases in this technological evolvement with the relation in the market systems. Since the emergence of the marketing management support systems, various structural changes have endowed changing activities in marketing. Managers have now become more concerned about the importance of standard technological systems that would adequately help them to achieve their goals in the most substantial manner.
This paper is an analytical research study aimed at studying the respective influence of technological development. It will deeply evaluate the process encountered in the changing marketplace scenario about the technology. This is a research focus on the changing systems of marketing. The hypothesis to be researched is whether a technology change has any influence on these marketing systems.
Marketing has evolved from a simple business function into its science-like field over the past century. While the basic is to find a need and filling its basis is still the core problem to marketing, it has evolved into a much more complex operation that has companies trying to maximize its value to the utmost potential. A common textbook definition of marketing is “the process of planning and executing the conception, pricing, promotion, and distribution of ideas, goods, services, organizations, and events to create and maintain relationships that will satisfy individual and organizational objectives.”(Boone, 1998).
For this paper I will break marketing down into two primary segments; 1) Market Research, how research and information are gathered about customers and 2) Marketing Management, how marketers then implement that information to make strategic decisions about the four P’s, product, place, promotion, and price. While there are several Marketing Information Systems (MIS) concerned with the collection, storage, and analysis of information and data for marketing decision making purposes that have been developed and implemented for market research, the focus of this paper will center on Marketing Decision Support Systems (MDSS) that managers have access to and that enable them to better utilize the information gathered from market research. I will review their development and discuss what the future of MDSS may look like.
Market Management Support Systems
Despite the preponderance of this research paper focuses on Marketing Management Support Systems and their use in the usefulness of market research, marketing information systems can not be overlooked. The information gathered must be thoroughly analyzed to ensure that it is of high quality and pertinent to the business or managers who want to use it. As with most systems that analyze data, the information coming out is only as good as the information fed to the systems as input. Philip Kotler, a distinguished scholar of international marketing at Northwestern University, offers ten easy questions that can be utilized to determine the information needs of marketing managers. These questions act as a guideline for managers to ensure that the information they are receiving is of value to them.
- What decisions do you regularly make?
- What information do you need to make these decisions?
- What information do you regularly get?
- What special studies do you periodically request?
- What information would you want that you are not getting now?
- What information would you want daily? Weekly? Monthly? Yearly?
- What magazines and trade reports would you like to see regularly?
- What topics would you like to be kept informed of?
- What data analysis programs would you want?
- What are the four most helpful improvements that could be made in the present marketing information system?
Based on these questions, eight different management systems have been synthesized by Wierenga and Van Bruggen for use in management decision-making in marketing. They are as:
- Marketing Models – which are the mathematical representations of marketing problems that are used for finding optimal marketing instruments. The underlying philosophy of these systems is that one can find the objectively best suggestion.
- Marketing Information Systems – systems for storage, retrieval, and statistical analysis of data. Through the manipulation of quantitative information, marketing information systems assist in analyzing what has occurred in the market and the possible foundation of the event.
- Marketing Decision Support Systems – provide managers with the ability to answer hypothetical questions by performing simulations. Where marketing information systems are mainly passive systems that only provide information to marketing managers, marketing decision support systems are interactive and help managers make marketing decisions. Through logic, managers will produce possible courses of action and will have the system produce predictions for the outcomes of the actions. The manager’s logic and judgment is the decisive factor in selecting the most appropriate course of action.
- Marketing Information Systems – a system used to capture the knowledge from a marketing expert in a specific field and make that knowledge available in a computer program for solving problems in that field. The goal of expert systems is to replicate the performance levels of the human expert. It focuses on the best solution available.
- Marketing Knowledge-based Systems – obtain knowledge from any source available, from human experts as well as textbooks, cases, and the like. Knowledge is represented in many forms for this system to include rules and using semantic networks and frame-based hierarchies. Knowledge-based systems stress the logical methodology of decision-makers instead of focusing on the best solution.
- Marketing Case-based Reasoning systems – focus on solving problems based on analogical reasoning, a way of solving problems in which solutions from past similar problems are used as a starting point to solve the current problem. These systems store and access
There are several different Marketing Decision Support Systems available commercially to managers. This forms two main designs under MDSS. Firstly, is the store placement model which applies the impact of demographics, and secondly, the store site characteristics will have on the projected financial success of the store. These models are utilized for the initial set up of the store and are decisions that are outside of the general everyday decision-making of marketing managers and will not be presented in this paper.
History of Market Management Support Systems
Utilizing maximization and minimization functions and linear equations were viable tools for businesses in the early part of the 20th century. However, only the simplest mathematical equations could be utilized by every day practicing managers. More advanced quantitative statistical tools like multiple regression, discriminant analysis, and factor analysis were beyond the abilities of everyday managers and could not be easily changed for “what ifs”. The study of how computers and analytical models could be combined to help managers make recurring key business planning decisions began in the mid-1960s.
The viability of building computerized decision support systems was likely influenced by the pioneering work of people like George Dantzig, Douglas Engelbart, and Jay Forrester (Power, 2007). George Dantzig began implementing linear programming on experimental computers of the Rand Corporation in 1952 where he was a research mathematician. In the mid-1960s, Douglas Engelbart and colleagues developed the first hypermedia—groupware system called NLS (oNLine System). NLS assisted in the creation of digital libraries in the storage and retrieval of electronic documents using hypertext. NLS also made on-screen video teleconferencing possible and was a precursor to group decision support systems. Jay Forrester helped build the SAGE (Semi-Automatic Ground Environment) air defense system for North America, which was then completed in 1962. SAGE is probably the first computerized data-driven DSS. Professor Forrester also started the System Dynamics Group at the Massachusetts Institute of Technology Sloan School (Power, 2007).
Another important figure in the advancement of management support systems was J.C.R. Licklider who published a paper titled “Man-Computer Symbiosis” in 1960 about the future role of multi-access interactive computing. He predicted that man-computer interaction would enhance both the quality and efficiency of human problem solving and his paper provided the groundwork for decades of computer research to follow.
Researchers started studying the use of management support systems using computerized quantitative models to assist in decision making and planning heavily in the 1960s (Raymond, 1966; Turban, 1967; Urban, 1967, Holt and Huber, 1969). With the advent of stronger microcomputers like the IBM System 360 and other more powerful mainframe systems, it became more practical and cost-effective to develop Management Information Systems (MIS) for large companies (Davis, 1974). However, MIS at this point focused on providing managers with structured, periodic reports and the information primarily from accounting and transaction processing systems, but did not provide interactive support to assist managers in decision making.
Ferguson and Jones reported the first experimental study using a computer-aided decision system in 1969 when they investigated a production scheduling application running on an IBM 7094 (Power, 2007). However, Michael S. Scott Morton’s 1967 dissertation at Harvard University involved building, implementing, and then testing an interactive, model-driven management decision system. Scott Morton conducted an experiment in which managers used a Management Decision System (MDS) in 1966 as part of his research. Marketing and production managers used an MDS to coordinate production planning for laundry equipment. The MDS ran on an IDI 21 inch CRT with a light pen connected using a 2400 bps modem to a pair of Univac 494 systems (Power, 2007).
Beginning in the 1970s, business journals started to publish articles on management decision systems, strategic planning systems, and decision support systems (Sprague and Watson 1979). For example, Scott Morton and colleagues McCosh and Stephens published decision support-related articles in 1968. The first use of the term decision support system was in Gorry and Scott-Morton’s Sloan Management Review article in 1971. They argued that Management Information Systems primarily focused on structured decisions and suggested that the supporting information systems for semi-structured and unstructured decisions should be termed as “Decision Support Systems”. At the beginning of the 1980s, MMSS gained a more advanced development where such systems were attained. Perhaps, development of the group decision support system (GDSS) was the most attributable development in MMSS in the 1980s. This was software whose purpose was to aid in decision-making for groups, which was done by researchers from the University of Arizona. Development to this system got empowerments when different scientists continued to advance this software even more.
In 1985, Kersten developed Collaborative Decision Support Systems (CDSSs), which could support negotiation from a group. This led to an easy way of formulating decisions in marketing groups. This system was further got developed in 1989 when Lotus introduced another software that worked to strengthen the (CDSS).
In the 1970s and 80’s the DSS used document –Driven storage systems and processing methods, which involved the provision of a document retrieval system and easy use of databases to analyze large document databases, with this development representation and processing of text could now be done through document outputs.
The evolvement of this technological era promoted marketing systems where data processing could now be done as text pieces. Swanson wrote the first article about this DSS system in 1978. This article was however a review of the then existing document-driven system which would help more in the market management. In mid of 1990s, extensive work to develop the system was done 1993. This could now help managers to fully have supportive documents for any decision they entered into. Due to the current development in the web system, the document-driven DSS got a full-strength in which case documents are often available to the managers about their work.
The knowledge-driven DSS was a computer system that was significantly useful in expertise relating to problem-solving. It usually used knowledge-driven concepts within a particular area or skill for solving problems (power 2002). It was developed by Artificial Intelligence (AI). This was developed useful in the late 1980s and early 1990s. The knowledge continued to achieve strength when researchers introduced the connecting systems of expert technologies based on the knowledge-driven DSS.
The system comprises a series of an expert system that has been developed through technological research. They are highly useful in forming different suggestions or even comments of actions to the marketing managers. The system mainly comprises of person-computer, which can help in solving problems. High knowledge DSS has led to the development of Artificial Intelligence (AI) that helps to detect any possible fraud cases and inconsistencies in financial transactions. The works of Huntington in 1983 highly helped to improve this technology. Such knowledge has also helped managers in scheduling their different manufacturing options.
Perhaps, the biggest development in the MMSS was the establishment of web-based DSS which began in 1995. It consisted of the decision support internet system which could easily help transmission of marketing message through the internet. Its biggest development was in the establishment of an Internet-based HTML 2.0, which comprised of tags and other, marketing tables. The development of the worldwide web in 1995, involving several software’s helped to boost decision support systems. The development of the Internet system was highly useful in Supporting Management Decisions. Application of software and decision support through technical infrastructure was bolstered in 2000, during the beginning of Application Service Providers (ASPs). Currently, the web-based DSS has undergone numerous developments where other software on MMSS has been developed basing their technology on web-based DSS.
Indecision support, the development of the International Society for Decision Support (ISDSS) in 1995 gave decision-making an elaborated boost. Various corporate intranets have been developed between 1996and 1997, which have helped in information support and also the management of knowledge. This decision support is comprised of various tools such as reporting tools, various mathematical models using optimization criteria and process simulations, analytical processing through the online, and various data mining methods. Different experiments were continued to be done relating to the development of electronic markets where technologies could be used in such markets. The essence of the web-based support for decisions making was the main aspect in the development of computerized DSS. In organizations, TCP protocol helps the users of computers in an organization to have a network through a computer server.
Application of Market Management Support Systems
Since the start of development in MMSS, they have been of varied application in the marketing system. Perhaps, the greatest marketing relations can be enhanced relatively faster and more efficiently between parties. International marketing has been highly efficient through the web-based DSS where parties to it can efficiently dispense marketing relations within a short period. Other software useful in marketing has been enhanced through MMSS.
Every aspect of marketing involves a series of decision-making between groups or collaborating parties. Due to the developing nature of business recently, marketing decisions have called for more sophisticated, effective, efficient, and economic methods of marketing in respect to the stringent regulation whose documentary system can only be realized through a more developed system. However, the solution to fast decision-making in marketing has now been achieved through MMSS. Due to the development in the Group Decision support system (GDSSs) and Collaborative Decision Support Systems (CDSSs), decision-making in marketing has been made simpler. Using such programs, marketing groups and parties can easily involve themselves in their decisions. Such decisions upgraded through MMSS usually yield more viable results. Marketing models such as linear, metric, and other statistical tools have been achieved adequately through the use of MMSS concepts. Organizations can thus adequately and efficiently involve themselves in easier decision entries.
Different tools for decision-making in marketing have been achieved through the use of MMSS. The software has been developed to deal with the marketing environment forecasting, building up sales functions, analysis of cost, and variances of expected revenues against actual receipts. Elsewhere, product development and search for markets have been achieved through the use of a computerized system which can easily be used to formulate strategic functions that can be used in formatting different hypotheses regarding the marketplace. Such achievements have adequately been recognized through the developments of a software system and integrated computer shortcuts that make these formulations easier. (Nelson, 1999).
Perhaps the greatest benefits in the MMSS world are the technological efficiencies that have been brought up by the MMSS. Such efficiencies are however of a varied nature running from macroeconomic and specific organizational economies that may have been brought by this aspect. With the growth of MMSS, marketing organizations have continued to realize optimality in their costing activities. The technology has led to high efficiencies in their activities. The activities themselves have been of a quality nature, with a high-efficiency variable in their costing structures. Marketing transactions can now be done at a higher efficiency in the sourcing of material inputs, regards to the products line within the market, and the use of systems to study the environmental requirement of the marketplace. With such technology, managers can offer their products to the market given the economies that may prevail in the market structures got through such market surveys by use of MMSS.
With the growing nature of the marketing system, the system has been involved with diversity in its transactions. It involves various huge concerns from a different aspects. To ensure efficiency in these systems, a workload of databases has helped to formulate a strategy that ensures comprehensive documentation of these figures. Such a requirement has adequately been achieved through the use of computer databases for marketing strategies. With this database system, marketing inventories of voluminous intercepts can adequately be kept. This enables efficiency in location, updating, evaluation, and monitoring schemes relating to marketing modes. Most of the marketing schemes involve intensity in transactions that may capture the variable of legal transitions in the marketing system. This can only be ensured efficiently through database support where issues can easily be entered down to in an organized manner. (Nelson, 1999).
The Future of MMSS
With the long trailing development in the history of MMSS, it would be practicable and viable for one to comment that the future of this technology calls for a highly synthesized system. In trying to focus on the changes in the DSS over the years, the analysis reveals that technology comprises a series of changes that have triggered high developments in this concept.
Scientific technology is developed through the machinery of research. Research is subject to evolvement and consequent development. Throughout since 1960, research has seen the MMSS technology acquire a different scenario in its entire establishment where advancement in such technology has always propagated from a lesser school of thought. During the birth of this technology, starters to it only came up with low-profile technology systems, which were less efficient and reliable. However, advancements have been made on these technologies to even capture highly computerized systems that do not compromise efficiency and reliability. For example, the availability of five well-specialized DSS was perhaps a key framework to focus the future of this technology especially if we try to relate it with the past. The evolution of the DSS framework is a historical pedigree, which can be used to formulate the future status of the system. (Griffin, Mcarthur, 1997)
Perhaps, we can use two historical approaches to define the future of the MMSS. Firstly, projection in trends or even use of analogy reasoning. This will be enabled by decision support systems through research and development, which will help this technology to continue exploiting its horizons for expanded ones. In which case, new technology developments, which would be more beneficial will then be sought into.
Following the past development, the DSS trend suggests possible faster development into more complex DSS aspects which form integrated systems that can capture larger databases and strong storage capacities. Perhaps, the DSS will depict a more complex structure with its systems built through intensified simulations analysis and possible visual displays. Video communication would even be developed as support communications and larger repositories for data access in Document Driven DSS. As a whole, sophistication will be got, which would perhaps be more complicated and comprehensive. Such advancements in the knowledge–driven DSS will call for better performance and broad domain in their applications. (Thierauf, 1999)
Due to the broad, variety of the origin of DSS, their way out challenges has however seen the recent developments. The future of the MMSS is direct congruent to the regards in the past perspective in which case, this future is seen to accommodate highly expensive technological relationships that will foster more attraction to service provision by the development.
Broadly the DSS system is characterized to have a long trend in the structure of its developments. The trend shows that one level of DSS technology has led to another. With the long trailing nature of the DSS, it would be possible to even yield a move developed MSS in the future. Perhaps, the system to be developed will be highly efficient, using high database integration and with larger access to information. This will be perhaps a future development that may be seen in the data-driven DSS. To the knowledge-driven DSS, the system will be perhaps of high in intensities, easily understandable, and using various simulations accompanied by various sets of visual displays. In all this, future technology will evolve through a series of various research undertakings that will help more technological advancements in the MMSS. Although the past of the MMSS is just short, great inventions have already been achieved. It would be logical to say that its future is bound to accommodate extensive technological inventions whose efficiency and use would be subject to high result orientations in the marketing scenario. This would however be captured by highly technical MMSS developments that raise sophisticated methodologies to application and problem-solving schedules.
Since the discipline of MMSS is categorically academic, it will not hesitate to follow the paths so embraced by academic studies to crown it in high motivating scales, which would be highly sophisticated.
Like any other sphere of effect to mankind, marketing has embraced support systems that help to make it visualize a more realistic approach in achieving its goals. Due to the growing and advancement nature of the marketing system, research adventures have undergone a study that has focused on the computerized system to help the objectives of the marketing systems.
At large, MSS has passed through a series of changes that even call for one to believe in a more fountain future expectation in the development of these systems. With the growing state in the MMSS, managers have been possible to efficiently organize the structures of their organization to adequately suit the changing character of the market systems.
Globally, marketing has highly expanded its horizons to capture the changing demand and supply schedules of goods and services within their organizational structures. Development in MMSS has placed the marketing discipline in a place in which product distribution is subject to high market requirements.
As its leading consequence, MMSS has led to optimality in the market structures globally. Consequently, the marketing system is comprised of high optimal marketing techniques which have continued to increase its benefits to managers. Through this, managers have adequately been able to structure their marketing system better suit some economies and relationships between customers and products through the demand and supply variables in the market environment.
MMSS has played a contributory role in ensuring the success of the marketing system in which roles, duties, and decisions have been arrived at in a simpler perspective. The future developments remain an aspect of attribute towards further integration and success of this entity.
Thierauf, R (1999) Knowledge Management System for Business; Westport,CT, Quorum Books.
Griffin., T and Mcarthur., D (1997) A Marketing Management View of Integrated Marketing Communications: Journal of Advertising Research, Vol. 37.
Nelson, K (1999) The New World of Power Marketing: Management Quarterly, Vol. 40.
Crunk, J., North, M. (2007). Decision Support Systems and Artificial Intelligence Technologies in Aid of Information Systems Based Marketing: International Management Review, Vol.3
Power, D.J. A Brief History of Decision Support Systems. (2007) DSSResources.COM. Web.
|1945||Bush proposed Memex|
|1947||Simon book titled Administrative Behavior|
|1952||Dantzig joined RAND and continued research on linear programming|
|1955||Semiautomatic Ground Environment (SAGE) project at M.I.T. Lincoln Lab uses first light pen; SAGE completed 1962, first data-driven DSS|
|1956||Forrester started System Dynamics Group at the M.I.T. Sloan School|
|1960||Simon book The New Science of Management Decision; Licklider article on “Man-Computer Symbiosis”|
|1962||Licklider architect of Project MAC program at M.I.T.; Iverson’s book A Programming Language (APL); Engelbart’s paper “Augmenting Human Intellect: A Conceptual Framework”|
|1963||Englebart established Augmentation Research Center at SRI|
|1965||Stanford team led by Feigenbaum created DENDRAL expert system; Problem Statement Language/Problem Statement Analyzer (PSL/PSA) developed at Case Institute of Technology|
|1966||UNIVAC 494 introduced; Tymshare founded and Raymond article on computer time-sharing for business planning and budgeting|
|1967||Scott Morton’s dissertation completed on impact of computer-driven visual display devices on management decision-making process; Turban reports national survey on use of mathematical models in plant maintenance decision making|
|1968||Scott Morton and McCosh article; Scott Morton and Stephens article; Englebart demonstrated hypermedia—groupware system NLS (oNLine System) at Fall Joint Computer Conference in San Francisco|
|1969||Ferguson and Jones article on lab study of a production scheduling computer-aided decision system running on an IBM 7094; Little and Lodish MEDIAC, media planning model; Urban new product model-based system called SPRINTER|
|1970||Little article on decision calculus support system; Joyner and Tunstall article on Conference Coordinator computer software; IRI Express, a multidimensional analytic tool for time-sharing systems, becomes available; Turoff conferencing system|
|1971||Gorry and Scott Morton SMR article first published use of term Decision Support System; Scott Morton book Management Decision Systems; Gerrity article Man-Machine decision systems; Klein and Tixier article on SCARABEE|
|1973||PLATO Notes, written at the Computer-based Education Research Laboratory (CERL) at the University of Illinois by David R. Woolley|
|1974||Davis’s book Management Information Systems; Meador and Ness article DSS application to corporate planning|
|1975||Alter completed M.I.T. Ph.D. dissertation “A Study of Computer Aided Decision Making in Organizations”; Keen SMR article on evaluating computer-based decision aids; Boulden book on computer-assisted planning systems|
|1976||Sprague and Watson article “A Decision Support System for Banks”; Grace paper on Geodata Analysis and Display System|
|1977||Alter article “A Taxonomy of Decision Support Systems”, Klein article on Finsim; Carlson and Scott Morton chair ACM SIGBDP Conference DSS Conference|
|1978||Development began on Management Information and Decision Support (MIDS) at Lockheed-Georgia; Keen and Scott Morton book; McCosh and Scott Morton book; Holsapple dissertation completed; Wagner founded Execucom to market IFPS; Bricklin and Frankston created Visicalc (Visible Calculator) microcomputer spreadsheet; Carlson from IBM, San Jose plenary speaker at HICSS-11; Swanson and Culnan article document-based systems for management planning|
|1979||Rockart HBR article on CEO data needs|
|1980||Sprague MISQ article on a DSS Framework; Alter book; Hackathorn founded MicroDecisionware|
|1981||First International Conference on DSS, Atlanta, Georgia; Bonczek, Holsapple, and Whinston book; Gray paper on SMU decision rooms and GDSS|
|1982||Computer named the “Man” of the Year by Time Magazine; Rockart and Treacy article “The CEO Goes On-Line” HBR; Sprague and Carlson book; Metaphor Computer Systems founded by Kimball and others from Xerox PARC; ESRI launched its first commercial GIS software called ARC/INFO; IFIP Working Group 8.3 on Decision Support Systems established|
|1983||Inmon Computerworld article on relational DBMS; IBM DB2 Decision Support database released; Student Guide to IFPS by Gray; Huntington established Exsys; Expert Choice software released|
|1984||PLEXSYS, Mindsight and SAMM GDSS; first Teradata computer with relational database management system shipped to customers Wells Fargo and AT&T; MYCIN expert system shell explained|
|1985||Procter & Gamble use first data mart from Metaphor to analyze data from checkout-counter scanners; Whinston founded Decision Support Systems journal; Kersten developed NEGO|
|1987||Houdeshel and Watson article on MIDS; DeSanctis and Gallupe article on GDSS; Frontline Systems founded by Fylstra, marketed solver add-in for Excel|
|1988||Turban DSS textbook; Pilot Software EIS for Balanced Scorecard deployed at Analog Devices|
|1989||Gartner analyst Dresner coins term business intelligence; release of Lotus Notes; International Society for Decision Support Systems (ISDSS) founded by Holsapple and Whinston|
|1990||Inmon book Using Oracle to Build Decision Support Systems; Eom and Lee co-citation analysis of DSS research 1971–1988|
|1991||Inmon books Building the Data Warehouse and Database Machines and Decision Support Systems; Berners-Lee’s World Wide Web server and browser, become publicly available|
|1993||Codd et al. paper defines online analytical processing (OLAP)|
|1994||HTML 2.0 with form tags and tables; Pendse’s OLAP Report project began|
|1995||The Data Warehousing Institute (TDWI) established; DSS journal issue on Next Generation of Decision Support; Crossland, Wynne, and Perkins article on Spatial DSS; ISWorld DSS Research pages and DSS Research Resources|
|1996||InterNeg negotiation software renamed Inspire; OLAPReport.com established;|
|1997||Wal-Mart and Teradata created then world’s largest production data warehouse at 24 Terabytes (TB)|
|1998||ACM First International Workshop on Data Warehousing and OLAP|
|1999||DSSResources.com domain name registered|
|2000||First AIS Americas Conference mini-track on Decision Support Systems|
|2001||Association for Information Systems (AIS) Special Interest Group on Decision Support, Knowledge and Data Management Systems (SIG DSS) founded|
|2003||International Society for Decision Support Systems (ISDSS) merged with AIS SIG DSS|