Markstrat

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1 Introduction

Markstrat is the product name of an educational business simulation environment, more precisely a strategy marketing simulation. It is developed and distributed by StratX. Its Strategic marketing simulation with Markstrat web page (retrieved April3, 2019) describes the product in the following terms: “Markstrat is a marketing simulation software which offers MBA students and professionals a risk-free platform in order to test theories and make decisions. StratX founder, Jean-Claude Larréché, developed MarkStrat on the simple theory: if you give MBA students a way to apply under real market conditions the theory they learned during their lessons, they will not only devote more energy but will also learn from their mistakes and successes alike.”

On the Insead web site, Markstrat page (retrieved April 3, 2019) describes Markstrat as a “marketing strategy simulation available in B2C Durable Goods, B2C Consumer Goods and B2B versions. Markstrat is a very effective tool for learning strategic concepts such as: brand portfolio strategy, segmentation and positioning strategies, and operational marketing.”

According to Kinnear & Klammer (1987) [1], it can be used both as an educational tool to teach an array of marketing skills and as a tool to conduct experimental studies. It also can be used as assessment tool.

According to Ikeda et al. (2015) [2] “Participants take a series of marketing decisions implementing concepts such as segmentation and positioning, consumer behavior, marketing mix (4 P’s), and marketing research. Moreover students have to analyze and interpret data in order to acquire more and better market knowledge about customers and competitors. Students use Markstrat information and scenario to develop a Marketing plan. After presenting the plan for one year they start the simulation and take decisions for a long term result (commonly, 7 or 8 rounds)”

2 History and general principles

Markstrat was founded by Insead professor Jean-Claude Larréché and has been developed since the late 1970s. As of April 2019, the current version is Markstrat 7. It can be used both in academic (e.g. MBAs) and executive education, and for a variety of courses like strategic marketing, product planning and management, brand management and advertising.

The Markstrat page at Insead [3] emphasizes that Markstrat lets learners “experience essential Strategic Marketing concepts and tools in highly challenging B2C or B2B environments.” and add that “in Markstrat, Teams must plan not only for short-term profits, but also for long-term objectives; the name of the game is not just tactics, but long-term strategy. Every aspect is real: from competitive forces to the effects of sales, distribution, R&D and advertising. Each team’s actions have direct consequences on the market, making competitive analysis a must. Competitor actions and reactions, new product launches, sales and distribution strategies all define how teams will manage their own product portfolio, R&D projects, positioning, pricing and distribution channels.”

According to Wikipedia (April, 2019), “Between 1974 and 1977, working with an assistant, Hubert Gatignon, Larréché developed his work on marketing modelling to create a teaching simulation called Markstrat (Mazur & Miles 2007, p. 50). Markstrat is a game where teams of students compete against each other in an artificial world under realistic market conditions. [4] It is claimed that Markstrat is now used in 8 out of the top 10 business schools in the world and 25 of the top 30 schools in the US.”

According to Sharif & Ranchhod (2009) [5], in 2008, “MarkStrat, has been in use for over 25 years and continues to be the worldwide leader of interactive marketing simulations in education, having been used at more than 500 educational institutions across a wide range of undergraduate and postgraduate courses throughout the world (Markstrat, 1997). The simulation software itself arose out of a pedagogic desire to increase and improve the efficiency and reflectivity of understanding strategic decision-making behaviour within the focal area of marketing; also addressing the need to apply theoretical strategic concepts (portfolio mix, market analysis, corporate strategy, market research, forecasting, team planning and inter-team dynamics) in a “safe” simulated environment (Larreche and Gatignon, 1990a).”

In 1987, Kinnear and Klammer (p. 491) [1] describe the Markstrat simulation environment as follows: “Markstrat is a marketing-strategy simulation game in which five firms compete against each other. This competition is based upon the utilization of the classic marketing variables: product development and management, distribution, promotion, and price. Decision makers in competing firms utilize marketing research studies and develop strategies based upon effective segmentation and positioning. Their performance in the game is based upon such measures as market share, sales, contribution margin, and return on marketing investment. In many respects, then, it is a classic strategic marketing situation that a real-world manager would face.”

Sharif & Ranchod (2009:3-4) [5] describe the philosophy, the design and the rules of the simulation environment as follows:

  • Teams of (students) “compete against each other under semi-realistic synthetic business conditions, to design, innovate, brand and market a set of products across two markets in an artificial world with a given budget and a target to maximise shareholder returns (Burns, 1997; Gatignon, 1987).”
  • Participants do not only explore a simulated market but also confront real, human competitors who are interacting and setting business strategies. To do so, the “platform itself essentially provides a suite of decision-making and forecasting tools available as a suite of “management dashboards” such that each team attempts to meet the needs of five different (virtual) consumer groups.”
  • The “game progresses through a series of up to 12 –15 virtual “rounds” over a period of 3 –4 days, whereby each team –hence company – have to make strategic decisions on product R&D, production, market research, HR costs, distribution and so forth.”. More precisely: In a given round, student groups look at results for their team and the "world", discuss and then input team decisions (i.e. a complete marketing budget).
  • The simulation environment collects decisions decisions from all participant groups on a periodic basis, computes a result and transmits these under the form of management reports (e.g. shareholder price, rate of inflation, product drift, consumer satisfaction and other indices). Participants also receive the result of marketing studies ordered in the previous period.

3 Markstrat 7

Disclaimer: The following information has been collected from the literature and various other sources and may not be totally reliable since the author has neither access to Markstrat nor experience in teaching marketing subjects - Daniel K. Schneider (talk) 16:16, 3 April 2019 (CEST)

3.1 Overview

Markstrat is usually played in direct competition (team against team) with 2-5 days of running time, but it also can be played a single team against computer.

The three key concepts that define strategic marketing in Markstrat are [3] brand portfolio management (analyse and plan on how it is perceived in the market), segmentation (divide a market of potential customers into groups based on different characteristics) and positioning strategies (distinguish a brand from the products of the competitors). This implies the following types of learner actions [6]: manage established and emerging markets, conduct market and competitor analysis, (use) essential marketing tools, (conduct) research and development projects, (create) product portfolio and launches, and (plan and launch) sales and distribution strategies.

The product exists in three versions

  • B2C (Business to Consumers) - durable goods, electronics. This "classic" version “allows participants to design and implement a marketing strategy in a completely fictitious electronics market. The new web platform features modern vocabulary and industry settings matching today's consumer goods market realities.” [3]
  • B2B (Business to Business) - mechatronics. “This version features vocabulary, market and industry settings that are adapted to B2B situations. It addresses challenges of B2B markets, such as direct VS indirect distribution.”[3]
  • B2C - consumer goods, cosmetics. “This version features vocabulary and industry settings that are adapted to fast moving consumer goods markets. It addresses challenges behind consumer goods marketing strategies such as repeat purchases, retention rates, private labels or share of shelf place.” [3]

A simulation game is usually organized in the following way:

(1) Students are placed into 4-6 competing teams representing a firm. Each firm can compete in two product categories (established and emerging) and market up to five brands in each.

(2) Each group will have to build a successful marketing strategy for up to 10 simulated periods, typically representing a year. The overall direction is defined in three dimensions: product portfolio strategy (brands to be developed and sold), segmentation and positioning, marketing.

This includes:

  • Specifying and ordering new Research & Development projects
  • Managing their Brand Portfolio
  • Setting Production levels, Price & Advertising Initiatives
  • Allocating Sales Force & Distribution Resource
  • Analyzing up to 23 Market Research Studies

Each team (firm) has the same winning opportunity, but start from different positions.

(3) The simulation goal is to maximize the share price index of the firm, composed of market share, sales, improved profit. It is calculated at the end of each round with the team’s net contribution, brand market shares, ability to grow the firms’ revenues, quality of R&D projects successfully completed, etc.

(4) Teachers will organize debriefing sessions after simulation rounds and after the simulation game. Learners should reflect on their experience in order to achieve a real and deep learning effect.

3.2 The Markstrat world

The Markstrat simulated "world" is composed of 80 million inhabitants living in a highly developed economy. It includes five large cities (40%), smaller cities (25%) and 35% rural areas. Both the economic and political situation is stable. The Markstrat world is described in various training materials, e.g. the older Handbook-SM-B3C-DG [7].

Given the three versions (B2C in electronics, B2B, and B2C in cosmetics) outlined above, there are a total of six types of markets. Learners, in a simulation game usually develop one established and one related merging market.

Overview of the six types of Markstrat markets
Version Established market Emerging market
B2C electronics Sonite (electronic device, e.g. TV) Vodite (space industry)
B2B mechanics Squazol (electromechanical, e.g. brakes) Trigol (e.g. robotics, etc.)
B3C cosmetics Clinite (e.g. sun-care oil) Nutrite (e.g. food-base beauty care)

Each of the established market's brands are characterized by a few dozen attributes. There are five to six main attributes, e.g. efficacy, safety, packaging, pleasure, simplicity and price for the established cosmetics market. Each of the used attributes is rated on scale of 10 (poor week) to 100 (excellent or strong). "100" does not necessarily mean "better", e.g. total sun protection is not desired for getting a tan.

In a similar way for each established market, consumer types have been segmented, e.g. explorers, shoppers, professionals, high earners, savers for consumer electronics. For traditional cosmetics (clinite customers), high earners, affluent families, medium income families, low income families and singles. Each of these have specific needs that can be addressed.

Decisions will affect attributes by consumer segment.

3.3 Architecture and tools overview

Markstrat has a client-server architecture using standard web browsers. Some former versions used a special purpose thin client. On the client side, students and tutors can access data and make decisions. The simulation engine is hosted on a server.

Tutors can monitor various groups of learners, i.e. multiple industries, using charting tool and coaching spread sheets. They also can administer online surveys.

Interactions with the system are accessible from a main panel. It includes company results (second column), market and competitors (third column), market research (cols 4 and 5) and finally decisions (last column).

Main Markstrat 7 panel, Copyright StratX, reproduced with permission by StratX

3.4 Analysis of reports and market studies

As we explained above, at the beginning of each round, participants look at reports and market studies and have to sort interpret information. Reports and studies include:

  • company results, e.g. sales, research and development, production
  • information about the market
  • information about the competitor's performance
From Data to Strategy. Copyright StratX, reproduced with permission by StratX

Typically, the learner has to understand various interactive visualizations, and also be able to identify information needs (including new studies).

3.5 Decisions

In round one, the learner team must make decisions in the following areas:

(1) Commercial team: Size, allocations across distribution channels and brands (who deals with orders by whom and for what)

(2) Order market research, either industry wide or maker-specific. Studies have a cost.

In rounds two and later, the team can revise decisions of round one and in addition must act in the following areas:

(3) Develop research and development

  • Initiate new brands
  • Decide to continue or kill uncompleted projects

(4) Redefine the brand portfolio

  • Launch new brands
  • Upgrade or kill brands

(5) Define a marketing mix

  • Production planning
  • Pricing
  • Advertising and segmentation
  • Perceptual objectives, i.e. identify attributes of brands that consumers should perceive. Consumers may not have the same perception as firms...
Marketing mix decisions for each brand, Copyright StratX, reproduced with permission by StratX

The production plan for each brand defines the number of units to be produced. The system can later adapt production, but to some extent only (20% up or down). The system also will compute the inventory (left overs) and lost sales (not enough produced).

3.6 Coaching sheets and grading

The system provides instructors with key performance indicators for each team, both at firm and brand level. This allows individual coaching (e.g helping a team that is behind, or reflecting on a team's strategy) or the organization of classroom discussions.

Markstrat includes a tool to compute a grade in function of selected and weighted key performance indicators.

4 Educational benefits and conditions

The Markstrat page at Insead [3], identifies the following pedagogical objectives:

  • Learn fundamental strategic marketing concepts
  • Experience essential marketing tools, such as: marketing plan, perceptual mapping, conjoint, regression, portfolio analysis
  • Master market and competitive analysis
  • Combine tactical implementation with long-term strategy

A number of educational benefits are expected from simulations like Markstrat. We can distinguish motivational aspects (enthusiasm, engagement), development of general and metacognitive skills (awareness of skills, strategy development, time management, decision making, team work, problem solving), concept learning (develop core marketing principles) and transfer (bridging the gap between theory and practice). Asiri et al. (2017) [8], in a literature review that maps expected employability skills with simulation features conclude that employability skills can be improved using business simulation games. Like many other authors in simulation and gaming, Lahneman and Arcos (2017) [9] refer to Kolb's experiential learning learning theory and model that puts emphasis on learning activities that include, in stages, concrete experience, reflective observation, abstract conceptualisation, and active experimentation.

Some studies are target specifically the Markstrat environment. Already decades ago, Haskell and Taylor (1985) [10] report that “that participants' confidence in applying learned marketing concepts and tools to real-world situations increases significantly due to participation in the simulation.”

Sharif & Ranchhod (2009:8) [5] postulated that the “Markstrat simulation represents one of the most effective ways to confront students with real-life business situations, offering them the possibility to apply their theoretical knowledge, to interact with other people, and to take responsibilities for implementing business decisions. All these aspects of the Markstrat exercise determine the development of essential skills, at theoretical, practical, individual and inter-personal levels.” A user-based survey of 210 UK undergraduate students within a business and management degree somewhat corroborated this position: “The survey responses found that on average most students viewed the business simulation game experience as “somewhat” effective, being slightly higher than average on the ordinal Likert scale of 1-7 (with a dataset average of 5.22 across the sample of 210 students). The remainder of the descriptive statistics highlighted a positive view on how the students understood core marketing, strategy, operations management and macro-economic principles (such as those relating to pricing, customer satisfaction etc) –and the fact that the simulation itself afforded a “safe” environment to take pseudo-business risks with a level of immersion which the students also found to be conducive to learning.”.

In a similar survey study, Ranchod (2014) [11] designed an Educational Value Generation Model to measure the educational outcomes of Markstrat. It includes experience generation, conceptual understanding, skills developments, and affective evaluation. “The application of structural equation modelling indicates several significant relationships: experience generation has a strong impact on conceptual understanding, and both of them have medium to high direct impacts on skills development. On the other hand, the participants’ perception regarding the professional skills developed during the simulation game determines their affective evaluation of the Markstrat exercise.” [11]

A number of publications investigate under which conditions, a simulation is effective. For example:

Focus should be on learning, not gaming (Doyle and Brown 2000)[12].

Learning with a business simulation like Markstrat takes time. Redmond (1989) [13] finds that “The effective lower boundary for MBA students using Markstrat appears to be five periods”, i.e. students have to complete five full cycles.

Business simulations seem to be effective when learners expect to perform well. Caruna et al (2016) [14] report that Performance Expectancy and Effort Expectancy drive Learner Satisfaction, which in turn is related to Recalled Performance.

5 Markstrat as research medium

The environment can be used to conduct a variety of research, e.g. in behavioral economics or learning psychology.

For example, Lukas et al. (2016) [15] argue, based on a simulation using Markstrat, that organizational performance feedback influences individual decision-maker cognitions and thereby changes a team’s attention focus in terms of strategy.

Pathak and Lim (2016) [16] use a Markstrat simulation to study (1) how do performance-linked rewards influence the decision making styles of marketing managers who have to be innovative in dealing with competition and (2) whether the promise of attractive performance-linked rewards helps or hinders performance of a marketing unit.

Lant (1992:18-19) [17] concludes that “games like Markstrat are especially valuable tools for studying strategic decision making” since it is very difficult to determine how, when, and by whom strategic decisions are made in a real organization. “Because simulation games are closed systems with few participants who repeat similar cycles of decision making over time, they are particularly well suited to the study of decision making.”

6 Links

6.1 Official

6.2 Other

7 Bibliography

7.1 Cited with footnotes

  1. 1.0 1.1 Kinnear, T. C., & Klammer, S. K. (1987). Management perspectives on Markstrat: The GE experience and beyond. Journal of Business Research, 15(6), 491-501. https://www.sciencedirect.com/science/article/abs/pii/0148296387900348
  2. Ikeda A.A., Campomar M.B., Campomar M.C. (2015) Using Simulator Markstrat in Marketing Planning Courses in Brazil. In: Dato-on M. (eds) The Sustainable Global Marketplace. Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Springer, Cham
  3. 3.0 3.1 3.2 3.3 3.4 3.5 INSEAD, Faculty & Research, MARKSTRAT, web page retrieved April 3 2019 from https://www.insead.edu/faculty-research/simulations/markstrat
  4. Oliver Meixner & Rainer Haas, “Markstrat Simulation: Distance Learning for Marketing Students”, Paper presented at the 6th Annual EDEN Conference – Budapest, June 1997, http://www.boku.ac.at/mi/markstrat/artikel.html
  5. 5.0 5.1 5.2 Sharif, Amir M. & Ranchhod, Ashok. 2009. "Using the markstrat business simulation to develop strategic management behaviours".European and Mediterranean Conference on Information Systems. http://bura.brunel.ac.uk/handle/2438/4233
  6. Strategic marketing simulation with markstrat, web page, retrieved on April 3, 2019 from https://web.stratxsimulations.com/simulation/strategic-marketing-simulation
  7. StratX (undated). Handbook-SM-B2C-DG, retrieved April 4 from http://www.stratxsimulations.com/latest_materials_markstrat_web/enu/Handbook-SM-B2C-DG/DocToHelpOutput/NetHelp/default.htm#!WordDocuments/voditeproducts.htm
  8. Asiri, A., Greasley, A., & Bocij, P. (2017, July). A review of the use of business simulation to enhance students' employability (wip). In Proceedings of the Summer Simulation Multi-Conference (p. 39). Society for Computer Simulation International. https://dl.acm.org/citation.cfm?id=3140104
  9. Lahneman, W. J., & Arcos, R. (2017). Experiencing the art of intelligence: using simulations/gaming for teaching intelligence and developing analysis and production skills. Intelligence and National Security, 1–14. https://doi.org/10.1080/02684527.2017.1328851
  10. Haskell, N. A., & Taylor, J. R. (2015). The Knowledge-Related Confidence Effects of a Marketing Simulation Game. In Proceedings of the 1985 Academy of Marketing Science (AMS) Annual Conference (pp. 130-132). Springer, Cham.
  11. 11.0 11.1 Ranchhod, Ashok, Călin Gurău, Euripides Loukis, and Rohit Trivedi. “Evaluating the Educational Effectiveness of Simulation Games: A Value Generation Model.” Information Sciences 264, no. 20 (2014): 75–90
  12. Doyle, Declan & Brown, F. William. 2000. "Using a business simulation to teach applied skills – the benefits and the challenges of using student teams from multiple countries". Journal of European Industrial Training 24 (6), pp. 330-336.
  13. Redmond, W. H. (1989). On the Duration of Simulations: An Exploration of Minimum Effective Length. Journal of Marketing Education, 11(1), 53-57.
  14. Caruana, A., La Rocca, A., & Snehota, I. (2016). Learner satisfaction in marketing simulation games: Antecedents and influencers. Journal of Marketing Education, 38(2), 107-118.
  15. Lucas, G. J., Zijlmans, M. H., Meeus, M. T., & Blettner, D. P. (2016). The Effect of Organizational Performance Feedback on Team Attention Focus. In Uncertainty and Strategic Decision Making (pp. 171-190). Emerald Group Publishing Limited.
  16. Abhishek Pathak & Lewis K S Lim, 2016. "Marketing Decision Making Behavior under the Influence of Attractive Performance-linked Rewards," International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, vol. 7(3), pages 81-90, May.
  17. Lant T. K., and Montgomery D. B. (1992). Simulation Games as a Research Method for Studying Strategic Decision Making: The Case of MARKSTRAT.Stanford GSB: Research Papers, https://www.gsb.stanford.edu/faculty-research/working-papers/simulation-games-research-method-studying-strategic-decision-making

7.2 Other

  • Cook, V. J. (1987). Introduction to strategic studies in Markstrat. Journal of Business Research, 15(6), 467-468.
  • Green, D. H., & Ryans, A. B. (1990). Entry strategies and market performance causal modeling of a business simulation. Journal of Product Innovation Management: AN INTERNATIONAL PUBLICATION OF THE PRODUCT DEVELOPMENT & MANAGEMENT ASSOCIATION, 7(1), 45-58.
  • Ikeda, A. A., Campomar, M. B., & Campomar, M. C. (2015). Using Simulator Markstrat in Marketing Planning Courses in Brazil. In The Sustainable Global Marketplace (pp. 211-211). Springer, Cham.
  • Lant, T. K., & Montgomery, D. B. (1992). Simulation games as a research method for studying strategic decision making: the case of Markstrat (No. 1242). Graduate School of Business, Stanford University.
  • Larreche, J. C. (1987). On simulations in business education and research. Journal of business research, 15(6), 559-571.
  • Larreche, J. C., & Gatignon, H. (1977). Markstrat: A Marketing Strategy Game, Palo Alto: Course Technology.
  • Larreche J-C., Gatignon H (1990a). Instructor's Manual: Markstrat -A Marketing Strategy Game, Palo Alto: The Scientific Press.
  • Larreche J-C., Gatignon H (1990b). Markstrat -A Marketing Strategy Game; Participant's Manual. Palo Alto: The Scientific Press
  • Sharif, Amir M. & Ranchhod, Ashok. 2009. "Using the markstrat business simulation to develop strategic management behaviours".European and Mediterranean Conference on Information Systems. https://bura.brunel.ac.uk/handle/2438/4233

To sort out:

8 Acknowledgement

Thanx go to StratX for providing us with images.