Reflection Activity: Module $3$ Saving Acme Widget$$ Inc.

You and your classmates are on a business consulting team that has acquired a reputation in the industry for being able to analyze data effectively to help businesses make better decisions. Acme Widgets has a problem and, hearing of your team's reputation, has sought you out for help. The problengine Acme Widgets is losing sales and they are not sure why. The Widget market is strong, and their product is competitive, but they naturally have all the challenges common to the industry. As such, customer satisfaction, customer retention, quality control, sales, advertising, macro$-$economic factors, competitive price pressures, marketing channel selection, and many more are all valid considerations. The challenge is to predict where effort should be applied to yield the maximum positive results.

With the leadership's acceptance of Acme as a client, your team's ultimate goal is to develop a predictive model which will accurately forecast Acme's Widget sales growth $($or decline$)$ based on acquired data. To build this model, you will first need to know which data to acquire, where to acquire it$,$ and how much weight $($importance$)$ to assign to each category in your model. Consequently, each team member is tasked with the following action items:

Contribute five categories of data directly related to Acme's Widget product line. For each category, specify the source of the data noting whether you believe it is structured or unstructured, and rank the predictive value $($or weight$)$ of each one with respect to the other four. Your contribution must be unique with respect to your team members' but you may use the same category if you specify a unique source. Be specific with your contributions. For example, a category of "customer satisfaction" with a source of "customer" is of little value when actually building the model. However, a category of Identified Customer Endorsements in Current Month with a source of Facebook is much better. In other words, specifying a category and source implicitly requires that the source and data must be specific enough to be accessible. If you need to make assumptions, specify those assumptions. Finally, you are free to use any aspect of a product's lifecycle that you feel has value to the model.

Specify which data model from the Module $3$ reading, that would be the most appropriate to store the data in the predictive model. E$.$g$.$ do you see the data in this effort most applicable to a hierarchical, network, or relational model? Why? Note that there are multiple ways to organize data, the answer here is not as important as the reason behind it$.$

After posting items $1$ and $2,$ to your team's discussion board, $($i$.$e$.$ School's LMS$),$ take some time to review your teammate's contributions and select one to review and comment on$.$ For example, perhaps you agree with the categories that a team mate chose but differ on the weighting assigned to one or more of them.