Jessica Papagno Module 6 Assignment Strategic Decision using Data Analytics Online Simulation 1) How much effort did you put into exploring the past data about Blue’s performance? Was it time well spent? What did you learn from it? - I spent some time researching Blue’s past data. It is important to know what you did best and worst in each year to better improve your future predictions. I thought the current situation of changing data in each category should be the most reliable pinpoint on making your decisions. If the social media accounts said the price to was too high or they wish the odor was less, then I immediately changed my decisions for the next year. It was time well spent exploring every tab because it is hard to predict what the consumers will want. The businesses don’t set the trends, the consumers do. So, it is important to know where your consumers stand with your product. 2) What filters made you change your decisions about improving Blue’s situation in the marketplace? This study source was downloaded by 100000858180007 from CourseHero.com on 02-02-2023 12:46:25 GMT -06:00 https://www.coursehero.com/file/51603612/Module-6-Assignmentdocx/ - I set the filters during the first year based on what I believed my consumer was based on the social media accounts. So, I stated no income or ethnicity specified, with a household size of 3 to 4 and under 35 years old. I thought this was true based on the social media parents talking about their 2-year-old kids and the very high performance in digital ads under the media consumed tab. I also looked at the changing map of the geographic demand tab to see where the regional demand was increasing. At one point in 2020 I changed my region from north east to south east to better target my consumers. 3) What was your overall strategy to turn around Blue’s performance in the marketplace? - My overall strategy consisted on looking first at the social media accounts. I tried to best please the consumer and hear directly from them to see what they wanted and needed. They also helped me predict their age, income, number of people in their household, and ethnicity. I then went to the data explorer tab to view the change over the most previous year in each category. The formulation demand showed which one of the pods, liquid, or powder to choose from based on the highest demand. In this case, pods remained the highest throughout the entire simulation. Under the media consumed tab, I changed the percentage of media spending based on the highest media consumed on the graph. Through the entire simulation, I focused heavily on digital ads, then equally on radio, TV, and print. Under the brand attribute demand tab, I saw odor elimination was the highest in demand. But if the social media accounts said something different, then I would change it since the demand on this graph are very similar to one another. The trade channel demand helped me set the spending for that category, even though it was equally distributed at times. The price point demand under the blue category consistently stated that the consumers wanted the price between $5 to $7. So, I kept my price for the first 2 year at $4 then increased it to $5.50. Lastly, I set the demand number based on the pulldown button showing the forecasted demands based on individual demand of each territory. 4) What specific factors (e.g., product formulation, product features/positioning, media channel spending, trade channel spending, etc.) did you manipulate in your decisions? What was the outcome? - I manipulated every variable in each year of the decision making. The consumer preferences change so frequently that we have to constantly keep improving how we are selling and targeting them. I stated exactly my strategy with each variable above in number 3. Specifically, I used formulation demand, media consumed, brand attribute demand, trade channel demand, and price point demand under the data explorer tab. The outcome was an increase in revenue, profit, and positive social media posts. Very beneficial and worth the time to research it and change your predictions. 5) How did social sentiment influence your decisions? - Social media influenced my decisions heavily in the make decisions tab. If a consumer said the product was not efficient or the odor was terrible, I was immediately changed the This study source was downloaded by 100000858180007 from CourseHero.com on 02-02-2023 12:46:25 GMT -06:00 https://www.coursehero.com/file/51603612/Module-6-Assignmentdocx/ decisions to help out that sector. The social media also helped me predict who I was targeting to in terms of household size, region, and age. 6) What lessons can you draw about the use of these types of analytics for branding decisions? - From this simulation I can notice that consumers change their perspective of a product and brand very quickly. It is important to know there is always more meaning behind her decision that straight data. In the real world, data analyst cannot make all the decisions off of only data, they need to take in reviews just as highly. This study source was downloaded by 100000858180007 from CourseHero.com on 02-02-2023 12:46:25 GMT -06:00 https://www.coursehero.com/file/51603612/Module-6-Assignmentdocx/ Powered by TCPDF (www.tcpdf.org)
