DAT-220 Milestone Two - Geever

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DAT-220

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Ryan Geever DAT-220 March 24, 2024 Professor Snyder Table of Contents Analysis Plan ............................................................................................................................................... Milestone One: Introduction ............................................................................................................. …….. Business Problem ................................................................................................................................ …... Analytic Method ................................................................................................................................... …. Milestone Two: Analysis Process, Tools and Visualizations ............................................................. ….. Analysis Tools ......................................................................................................................................... Data Visualizations .................................................................................................................................. Research Question ................................................................................................................................... Research Measurement ............................................................................................................................ Follow-Up Questions ............................................................................................................................... Research and Support .............................................................................................................................. References ........................................................................................................................................ …...

Analysis Plan Milestone One: Introduction Business Problem The first step in this analysis is to provide concise direction to our data mining initiative. It has been determined that after a period of rapid growth, thanks to the company’s inclusion in a blockbuster movie, sales have levelled off, and in the last two years, declined. We will use The Bubba Gump Shrimp Company’s recent creation of a Data Warehouse to answer this, “What types of natural clusters do the company’s customers fall into and how do we effectively target those clusters to increase sales efficiently?” Analytic Method We will use the data sources at hand, which include Bubba Gump’s restaurant point-of-sale (cash register, credit card) data, its customer database (collected from its restaurant loyalty program and online sales channel), its web store sales transaction data, and customer and sales data from third-party retailers to complete this analysis. Using JMP to create histograms of our customer demographics and cross-referencing their shopping habits, we will determine how different categories of customers prefer to shop. We will use bar graphs to see what segments of customers are buying less and which are more frequently spending money at The Bubba Gump Shrimp Company, this will be used to leverage marketing and advertising campaigns. With line plots we can see which sales channels are faltering and which are growing, and to which customer segments these are affecting. Milestone Two: Analysis Process, Tools and Visualizations Analysis Tools The main tool that will be used to analyze the data and solve the perceived problem will be JMP. This program gives us an easy way to import data from something as simple as an Excel sheet. The user is able to process the data and convert it into visualizations such as histograms, bar graphs, pie charts,

etc… JMP quickly identifies metrics such as mean, median, standard deviation, and the like while quickly identifying outlier data. This will be the tool used to work with the data, but other tools would include the database which houses the data, and an SQL front end to pull the data from the database. Data Visualizations We will be using JMP to produce visualizations such as histograms and bar charts. This will allow us to quickly identify patterns such as bell curves in the data which will show us a natural distribution, or if outlier data exists. Visualizations will be key to determining trends in customer data. Maybe the biggest advantage of well used data visualization is it’s ability to make a point with the audience, when trying to convince a client that your analysis is accurate, well put together data visualizations could be the key. Research Question Our basic research question asks if we can identify natural clustering in customer population. We want to be able to determine, first, if there are categories we can place certain customer populations in, and second, how can we use this information to help decreasing sales. All businesses of this type need efficient and effective marketing and advertising campaigns that are able to target subsets of customer populations. We first need to find those target populations and then the business can determine the best path forward for marketing to those populations. Research Measurement Progress will be gauged by accurately discerning shopping trends among our customers and verifying, via data and visual representations, the alignment of these customer groups with the predetermined categories. Metrics such as sales volume, sales dates, and sales channels will be employed to ascertain these categories.

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Follow-Up Questions The pivotal question that arises from this analysis is: "How can this information be leveraged to enhance sales?" The Product and Marketing division will be tasked with determining the most effective strategies for utilizing this customer population data to target these customers. This analysis marks the initial phase of a comprehensive plan aimed at increasing sales and capitalizing on the gathered data. Research and Support Several papers have been published concerning this subject, which will serve as guiding references for my analysis and report. One notable source is "Market Segmentation through Data Mining: A Method to Extract Behaviors from a Noisy Data Set" by Murray, Agard, and Barajas, which delves into customer segmentation using data mining techniques. Additionally, the same authors contributed another paper titled "Forecast of Individual Customer's Demand from a Large and Noisy Dataset," which will complement the insights from the first paper. Although these sources provide comprehensive information, they appear to necessitate a more detailed and granular level of data compared to what we have available from the Bubba Gump Shrimp Co. Despite potential limitations in direct relevance to our current dataset, I anticipate these papers will prove highly valuable for our forthcoming analysis.

References Murray, P. W., Agard, B., & Barajas, M. A. (2018). Forecast of individual customer’s demand from a large and noisy dataset. Computers & Industrial Engineering, 118, 33–43. https://doi- org.ezproxy.snhu.edu/10.1016/j.cie.2018.02.007 Murray, P. W., Agard, B., & Barajas, M. A. (2017). Market segmentation through data mining: A method to extract behaviors from a noisy data set. Computers & Industrial Engineering, 109, 233–252. https://doi-org.ezproxy.snhu.edu/10.1016/j.cie.2017.04.017

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