Thursday, June 23, 2016

Serious Gaming With Data Analytics For Strategic Decision Making


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The Data Analytics Simulation: Strategic Decision Making, created by Professor Tom Davenport, renowned thought leader on big data, for Harvard Business Publishing has won silver honors in the 2016 International Serious Play Awards competition under the Higher Education category.

The browser-based, single-player simulation teaches students about the power of analytics in decision-making.

Acting as the brand manager for a laundry detergent, players use sophisticated analytic techniques to determine the best strategy for improving brand performance. Players are asked to predict market demand, set the channel price, make formulation decisions, determine promotional spending strategy, and communicate their strategy effectively to their managers.

Those who have ever formatted new offerings for market (re)/positioning or played the traditional spreadsheet-based equivalent business games, as I have, are to be amazed by the most playable, engaging and compelling solution Professor Tom Davenport has come up with. Professor Davenport has been extremely successful in aligning game context with market dynamics, creating a unique experience to show players how big data can be translated into actionable information and how leaders can harness that information to make better strategic decisions.

The data set used in this simulation is based on actual consumer data from a multinational consumer goods company. The simulation takes players approximately one hour of gameplay and is ideal for courses in management, marketing, and analytics at the graduate, undergraduate, and executive education levels.

Game Background and Context

Departing from the background presumption that leaders are constantly bombarded with headlines about “big data,” but few understand what it actually means for them and their organizations, Data Analytics Simulation: Strategic Decision Making addresses this topic by allowing players to apply the insights gained from such a data set.

Instead of teaching students about data modeling or algorithms, the Serious Game shows them how big data can be translated into useful, actionable information about a business or market.

The simulation accommodates a variety of class types and sizes, learning environments, and instructor goals, and can be played in class or assigned as homework. An accompanying Teaching Note contains an overview of the theory, simulation screens, and reference materials, as well as instructions for teaching and debrief.

In the simulation, players begin by analyzing a dashboard that provides metrics on their laundry detergent’s market share, profitability, competitor pricing, and demand by geographic region.

After reviewing the dashboard, students dive deeper into the data before making strategic decisions. 




Players then carefully review reports and manipulate demographic filters to drill into data segments by income, ethnicity, household size, region, and age. After reviewing all screens and reports, students devise their strategy for their detergent brand--"Blue"--by forecasting demand and then make decisions about production, pricing, positioning, promotional spending, and communication activities.



Players make these decisions over 4 simulated annual cycles. Between each annual cycle, students review their results and learn the impact of their decisions. They can then communicate their overall strategy to the professor and class in a short, open-ended text box. Once the simulation is over, the students compare and contrast their results in a class debrief session and learn how their experience can be tied to the learning objectives.

Players make these decisions over 4 simulated annual cycles. Between each annual cycle, students review their results and learn the impact of their decisions. They can then communicate their overall strategy to the professor and class in a short, open-ended text box. Once the simulation is over, the students compare and contrast their results in a class debrief session and learn how their experience can be tied to the learning objectives.

Takeaways

•        Illustrating that understanding some of the underlying factors and segments in data helps develop a coherent marketing approach over several years
•        Showing that analytics and decision-making are iterative processes and after each new decision there is typically new data to analyze and understand
•        Suggesting that successful financial performance is the result of several possible factors - rarely does a single variable explain an outcome
•        Communicating that all predictions and forecasts are based on probabilistic assumptions resulting in a range of possible results.

Subjects Covered

Analytics; Decision analysis; Decision making; Improving performance; Market analysis; Marketing strategy; Product management

Setting:

Geographic: United States
Industry: Soap & detergents