MKT356 Extra Credit Mini Project Spring 2018 Please Complete

Mkt356 Extra Credit Mini Project S2018please Complete The Following

Analyze a transaction dataset similar to your term project, focusing on industry-level sales trends over three years, including seasonality, pricing patterns, and channel use. Identify the top three companies in this dataset, compare their sales trends, pricing patterns, and channel utilization on a monthly basis. Incorporate zip code-level median income data into this dataset via a vlookup-style operation and interpret the customer bases of these top companies, particularly which serve more affluent neighborhoods. Using data from 2005 for each of these top companies, categorize their customers into top, medium, and bottom groups, and examine the proportion of sales from existing versus new customers during that period. Additionally, analyze whether the top customer group of each company has migrated out of the industry or remains purchasing through different channels.

Paper For Above instruction

Analyzing industry sales dynamics through data-driven approaches provides crucial insights for strategic decision-making. The dataset in question, comparable to a term project dataset but smaller in scale, offers an opportunity to examine multiple facets, including industry-wide sales trends, seasonal behaviors, pricing strategies, and channel effectiveness over a three-year period. A comprehensive analysis begins with aggregating sales data annually and monthly, depicting trends with line charts to detect overarching growth or decline patterns. Seasonality can be assessed via decomposition methods or seasonal indices, revealing cyclical fluctuations aligned with periods such as holidays or promotional campaigns.

Pricing patterns over time can be visualized through trend lines of average or median prices at the industry level. Changes in pricing relative to industry trends hint at strategic price adjustments responding to market conditions or competitive pressures. Channel use analysis involves categorizing sales by distribution channels—be it online, in-store, or through partners—and evaluating shifts over time via stacked bar charts or heat maps. This information guides understanding of channel preferences and evolving consumer behaviors.

Identifying the top three companies within the dataset involves ranking by total sales volume or revenue. Once identified, their sales trends, pricing, and channel use are examined at a monthly granularity, using line graphs for sales trajectories, scatter plots for pricing patterns, and pie charts for channel distribution. Comparing these metrics across companies reveals competitive positioning and operational differences.

The integration of zip code median income data involves augmenting the transaction dataset with geographical socioeconomic indicators, enabling analysis of customer demographics. Using VLOOKUP or equivalent functions, median income data associated with customer zip codes is matched to each transaction or customer record. This allows assessment of whether each company predominantly serves higher-income neighborhoods, indicating targeting strategies or market segmentation.

Analyzing the customer bases of the top three companies involves segmenting customers based on their total purchase amounts, dividing them into top, medium, and bottom tiers—potentially through percentile ranking or clustering techniques—using 2005 data. Once categorized, the proportion of sales attributable to existing versus new customers is computed to evaluate customer retention and acquisition efforts. Trends in customer groups over time indicate stability, growth, or attrition, informing long-term strategy.

Further, tracking where the top customer groups have migrated—whether they remain within the industry, switch brands, or cease purchasing—provides insights into customer loyalty and competitive threats. This involves analyzing customer purchase histories over multiple periods, identifying whether top-tier customers have shifted to competitors or reduced buying frequency. Such insights can inform targeted retention initiatives or re-engagement campaigns.

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