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This Data Analytics Simulation: Strategic Decision Making case study introduces the power of analytics in decision-making. As the brand manager for laundry detergent, one must implement decisions to boost brand performance through the application of sophisticated analytic techniques. This is geared to determining issues and strategies which could help a company in the long run.
Thomas H. Davenport
Harvard Business School Publishing (7050-HTM-ENG)
Feb 24, 2016
Case questions answered:
- Based on the data available to you prior to making your first set of decisions, and every set thereafter, what problems did you uncover?
- What role did social media play as informative data in helping you make decisions, specifically note some examples?
- Based on round to round evidence, what was your strategy (in other words, how did you use the data analytics available to you for simulation to make decisions prior to each year?
- What lessons can you draw about the use of these types of analytics? How easy is it to use them? What factors might make them more valuable within an organization?
- How difficult do you think it would be to assemble and integrate all the data for a system like this?
- Can you see any downsides to this type of management? What might invalidate the data-driven lessons that you learned?
- What’s another example of a real-world application of analytics for managing a company?
- 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, and combinations, of 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.
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Case answers for Data Analytics Simulation: Strategic Decision Making
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Data Analytics Simulation Case Study
In Harvard’s Data Analytics Simulation, players make choices for the brand Blue for four years with the intent of increasing their market share. Blue started using a new system that provides market, financial and operational performance information.
Using this system and the information, it provides players to make strategic decisions for the business. Each round shows how the decisions made affect the market, allowing players to learn from their mistakes.
At the start of the data analytics simulation, there was a very vague and rough plan for Blue. This made it a little more difficult when making decisions for the simulation.
As the rounds when on and I explored the data and information provided more, and the plan became clearer, making me more confident with decisions each round as I was able to explore their effects and learn what worked and what did not.
Overall, this was a very helpful simulation for data analytics because not only are players able to see the types of data used in decision making, but they also get to see it in play as the numbers change each round according to the decisions made.
After completing this simulation, I do wish I had made more drastic changes. I was too afraid to change too much, so even when the correct segments were targeted, and the decisions increased market share and profit, they did not change much.
After reading the case study for the data analytics simulation, I was able to see that Blue was a very generic brand. Every other brand was known for something, but there was nothing special about Blue that set them apart from the others.
Using this information, I decided to pick a target market. I chose consumers 54 and under. They would be in the market for a while and not stuck in their ways.
The top media consumed for ages 54 and under was digital ads and then TV. Radio was consistently last, so when it came to promotional spending, I took 10% from radio and gave 7% digital ads and 3% to TV.
Looking at the graph for brand attributes, odor elimination was the top demand overall and within the different age groups targeted, so I chose to focus on it. Looking at what social media was saying helped a little bit, the comments were pretty negative, but most weren’t clear, making it difficult to know what to fix.
For the price, I kept it at $7 since there is a big jump in demand from the $5-$7 section to the $7-$9 section. Raising the price just a dollar could cause the demand to drop substantially.
Since I am focusing on a younger generation, Pods were most popular with them, which is why they were produced over the other forms. The predicted demand was about 32 million, but with the new advertising strategy, I hoped to change that, so I increased production to a 40million.
The result of round one increased Blue’s market share to 12.7%, and revenue increased as well. The demand increased more than I had anticipated and was higher than what was produced.
Sticking with my strategy from round done to appeal to ages 54 and under, I kept the pods, price, and promotional spending the same. But the social media reviews weren’t much better, and demand in the Northeast is very low. In an attempt to change these, I focused on scent rather than odor elimination.
Since most negative reviews weren’t very helpful, I decided to stop trying to use social media in my data analytics simulation because even if it is negative, demand and revenue still went up.
Instead of choosing all regions, I focused on the Northeast, Central, and West because demand was low in those regions. Last year’s demand was 47 million, which was 7 million more than produced so this year 51 million were produced.
The result of round two ended with the market share going down to 12% and demand in the West and Central region decreased. Social media was a lot more positive after this round, but the numbers were worse.
After doing worse in round two, I went back to odor elimination since it is still the highest demand for each age group. The media focus was changed. The print was increased to 28% TV was at 28% Digital went up to 34%, and Radio had 10% of the budget.
One problem I noticed in the simulation of this data analytics was that in the Northeast, only two attributes are demanded (softness and scent) both are the lowest demanded overall, so I will either have to accept that I cannot meet their demands or try to get the Northeast by sacrificing the other region which I did not want to do.
Grocery still had the lowest demand, so I continued to focus on club and convenience. The forecasted demand was about 37 million, and there is extra inventory from last year, so only 34 million were produced.
The result was…
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