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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.
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
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