Gamification to learn Data analysis with Harvard Business Publishing

The business world is revolving around the idea of big data.

The most successful companies strive to collect everyone’s data in order to make optimal decisions and predict the outcome of future profits.

Trinity Business Schools choose to use gamification, the application of game-design elements and game principles in non-game contexts.

More information about gamification and how it can impact real case scenarios in this video:

Trinity Business School chose to use a Harvard Business Publishing case presented as a game: a Detergent Company trying to get the best possible profit in a period of 5 years.

The Case

In the game, we were put in the shoes of Blue’s Marketing directors, a fictional US detergent company with a slowly declining market share and a traditional Marketing and Manufacturing process. 

The company had recently decided to use forecast and reports to improve its profits against the main player of the Market, Turbo, Flash and Retailer’s Stores.

A brief with the history of the industry was given to flesh out the business case

First Run of the game First Failure

We were given 10 minutes to figure out the data from the company  and how to maximize the profit a lot of information was given at the same time:

 a Market report,

Market situation Blue

 an income statement

A sentiment analysis  with changing live feedback,

An explorable database,

explorable database

A decision dashboard,

We started with a market share of 11 %, our initial thought process was to differentiate ourselves and serve the market range where we were strong.

So we checked every market area and jotted down on paper

Region – Top player for the region – Household composition – Income-Age

We found out that in most of the markets we were popular with an age range from Under 35 to 44 years old, families with 3-4 components and income from under 20.000 $ to 40.000 $.

So we decided to market softness as an attribute since no one in the market was doing the same move, use liquid detergent as we thought that an innovation would be understood by the younger costumer base.

We used the innovation in the product to justify an increase in price by  1 dollar since competitors already could ask a higher price with newer formulations.We also wanted to differentiate from the cheaper powder private labels.

The real mistake that we did was using the forecast tool.

We thought that the number shown in the cell was the increase from base production. So we inputted 3 mln thinking that it was adding up that quantity to the current sales. We passed from producing 32 mln of units in 2018 to 3 mln total in 2019 !!!

The following turns to do decision lasted 10 minutes each. With so little time we were unable to detect the error we made until the last round of the game where we understood that the forecasting tool was also used to set up production.

Our other choices were influenced by the big loss in revenue on the first turn.

In 2020 we switched back our formula to powder seeing that the customer could not understand how to use the liquid formulation in the sentiment analysis and to reduce costs. In the sentiment analysis, we spotted that most of our customer did not like our old advertising so we optimized the media spending by reducing spending in Radio and Print to increase the one in Digital Advertising.

We also noticed that our product was selling more in Convenience and Club trade channels, therefore, we invested more there in 2020 and 2021.

In 2021 we saw that there was more demand than supply for pods and odor elimination features. We changed the attributes to sell more on the market and finally saw a rise in profit. 

Finally, for 2022 we understood that we were producing fewer units than we were meant to. We upped the production and went back to a market share of 11%, the same condition as the start year.

                                             (our decisions history)


( Despite the loss  in the first year at the end we were able to have more profitability than Fresh and Retailer stores)

What we should have done instead

Other than the mistake in the production, we should have noticed sooner the trends in the Demand. We should have shifted to odor control and pods, give more attention to data analytics and weigh less the initial company brief.

What we have learned

We need to listen more to the market and follow its dynamics. Both Social Media and quantitative data on the market are fundamental to listen to the customer.

Social feedback from the customer is useful when analyzing the most positive and negative reviews and ratings. Checking them is a good indicator of what can be fixed and what can be improved.

Quantitative data is important to spot new opportunities to sell and monitoring the market every year allow to reduce risks associated with demand shifts and adapt the quantity of product sold.

In business, companies should strive to gain near-perfect information, to increase their profits and having a competitive edge against adversaries.


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