Gamification and 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.

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, an income statement A sentiment analysis with changing live feedback. After an error in inputting information in the first year, Pushkar and I managed to surpass Flash and Retailers in term of profitability in the following years of the game.

 

(our decisions history)

 

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

What we have learnt:

1) To follow the trends in the demand and stick to the facts more than trust information at face value. For example, we shifted our features optimization to odor control and pods from the classic more accepted powder pack and gave more attention to data analytics than the initial company brief.

2) The importance of listening more to the customer base and follow its dynamics. A marketer needs to enhance his active listening skill to create more insights.  In terms of qualitative data, social media feedback from the customer is useful when analyzing the most positive and negative reviews. Checking them is a good indicator of what can be fixed and what can be improved. On the quantitative data side, it is important to spot new opportunities to sell. Monitoring the market every year allows to reduce the risks associated with demand shifts and adapt the quantity of product sold. Quantitative data can also support qualitative data when it comes to developing a full-fledged sentiment analysis and to

3) Being aware of our client’s characteristics. We segmented our market to see the demographics of our current clientele.  This is necessary to understand future opportunities for the people we do not serve yet and to plan expansions on the current market target.

4) The importance of using forecasting tools to plan the demand. With the availability of more source of data and information, it is pivotal to use methods to estimate the quantities needed in the future. Having an indication of a probable future can improve a company decision making.

5) Importance of testing possibilities/products on the market. In our exercise, we had plenty of data to decide. This made us reflect that in case of lack of data it is really important to test product launches using pilot programs to see the actual client purchasing behavior against the declared one.

6) Importance of Speed in decision implementation. In big companies adopt solution fast is imperative to maintain or grow in market share and competitiveness. In the case study, the conditions changed each year and all we needed to adapt was to push a button on a dashboard. In real business decisions are impeded by internal politics, disorganization.Data can fasten the whole process.

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

 

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