Unstack Data Indices Data Copy Graph Data Work Area Miscella

Unstackdataindicesdatacopygraphdataworkareamiscel Areadatacustom

Unstackdataindicesdatacopygraphdataworkareamiscel Areadatacustom

&UnStack &DataIndices &DataCopy &GraphData &WorkArea &Miscel_Area Data Customer Type of Customer Items Net Sales Method of Payment Gender Marital Status Age 1 Regular 1 39.50 Discover Male Married Promotional .40 Proprietary Card Female Married Regular 1 22.50 Proprietary Card Female Married Promotional .40 Proprietary Card Female Married Regular 2 54.00 MasterCard Female Married Regular 1 44.50 MasterCard Female Married Promotional 2 78.00 Proprietary Card Female Married Regular 1 22.50 Visa Female Married Promotional 2 56.52 Proprietary Card Female Married Regular 1 44.50 Proprietary Card Female Married Regular 1 29.50 Proprietary Card Female Married Promotional 1 31.60 Proprietary Card Female Married Promotional .40 Visa Female Married Promotional 2 64.50 Visa Female Married Regular 1 49.50 Visa Male Single Promotional 2 71.40 Proprietary Card Male Single Promotional 3 94.00 Proprietary Card Female Single Regular 3 54.50 Discover Female Married Promotional 2 38.50 MasterCard Female Married Promotional 6 44.80 Proprietary Card Female Married Promotional 1 31.60 Proprietary Card Female Single Promotional 4 70.82 Proprietary Card Female Married Promotional .00 American Express Female Married Regular 2 74.00 Proprietary Card Female Married Promotional 2 39.50 Visa Male Married Promotional 1 30.02 Proprietary Card Female Married Regular 1 44.50 Proprietary Card Female Married Promotional .80 Proprietary Card Female Single Promotional 3 71.20 Proprietary Card Female Married Promotional 1 18.00 Proprietary Card Female Married Promotional 2 63.20 MasterCard Female Married Regular 1 75.00 Proprietary Card Female Married Promotional 3 63.20 Proprietary Card Female Married Regular 1 40.00 Proprietary Card Female Married Promotional .50 MasterCard Female Married Regular 1 29.50 MasterCard Male Single Regular .50 Visa Female Single Promotional .50 Proprietary Card Female Married Promotional 5 13.23 Proprietary Card Female Married Regular 2 52.50 Proprietary Card Female Married Promotional .80 Proprietary Card Female Married Promotional 4 19.50 Visa Female Married Regular .50 Proprietary Card Female Married Promotional 1 62.40 Proprietary Card Female Married Promotional 2 23.80 Proprietary Card Female Married Promotional 2 39.60 Proprietary Card Female Married Regular 1 25.00 MasterCard Female Married Promotional 3 63.64 Proprietary Card Female Married Promotional 1 14.82 Proprietary Card Female Married Promotional .20 MasterCard Female Married Promotional .62 Proprietary Card Female Married Promotional .80 Proprietary Card Male Married Regular 1 58.00 Discover Female Single Regular 2 74.00 Visa Female Single Regular 2 49.50 MasterCard Female Married Promotional .60 Proprietary Card Female Married Promotional .10 Proprietary Card Female Married Promotional 2 80.40 Proprietary Card Female Married Promotional 4 65.20 MasterCard Female Married Promotional .00 Proprietary Card Female Single Promotional .80 Proprietary Card Female Married Promotional 3 59.91 Proprietary Card Female Single Promotional 5 53.60 Proprietary Card Female Married Promotional 1 31.60 Proprietary Card Female Single Promotional 2 49.50 Proprietary Card Female Married Promotional 1 39.60 Proprietary Card Female Married Promotional 2 59.50 Proprietary Card Female Married Promotional .80 Proprietary Card Female Married Promotional 2 47.20 Proprietary Card Male Married Promotional 8 95.05 Proprietary Card Female Married Promotional .32 Proprietary Card Female Married Promotional 4 58.00 MasterCard Female Married Regular 1 69.00 Proprietary Card Female Single Promotional 2 46.50 Proprietary Card Female Married Promotional 2 45.22 Proprietary Card Female Married Promotional 4 84.74 Proprietary Card Female Married Regular 2 39.00 Proprietary Card Female Married Promotional .14 Proprietary Card Female Married Promotional 3 86.80 Proprietary Card Female Married Regular 2 89.00 Discover Female Married Promotional 2 78.00 MasterCard Female Married Promotional 6 53.20 Proprietary Card Female Single Promotional 4 58.50 Visa Female Married Promotional 3 46.00 Proprietary Card Female Married Regular 2 37.50 Visa Female Married Promotional 1 20.80 Proprietary Card Female Married Regular .00 MasterCard Female Single Regular .00 Proprietary Card Female Married Promotional 1 31.60 Proprietary Card Female Single Promotional 6 57.60 Proprietary Card Female Married Promotional 4 95.20 Proprietary Card Female Married Promotional 1 22.42 Proprietary Card Female Married Regular .75 Proprietary Card Female Married Promotional .50 Proprietary Card Female Married Regular 3 66.00 American Express Female Married Regular 1 39.50 MasterCard Female Married Promotional .00 Proprietary Card Female Married Promotional .59 Proprietary Card Female Married Promotional 2 47.60 Proprietary Card Female Married Promotional 1 28.44 Proprietary Card Female Married 44 problem_description

Paper For Above instruction

The provided data and scenario pertain to a retail chain, Pelican Stores, which is a division of National Clothing. The firm conducted a promotional campaign involving discount coupons sent to customers of other stores. Data was collected from 100 credit card transactions at Pelican Stores during the promotion, highlighting various customer behaviors, payment methods, and purchase details. Analyzing this dataset using Excel pivot tables allows for insightful understanding of customer preferences, sales patterns, and the impact of the promotion. This paper explores four analytical tasks: visualization of payment methods, customer type versus sales, relationship between net sales and age, and the influence of marital status on sales relative to age.

Firstly, understanding customer preferences for payment methods is vital. A bar chart or pie chart can vividly display the number of customers using Discover, Proprietary Card, MasterCard, Visa, and American Express. This visualization reveals which payment options are most popular among customers. Typically, proprietary store cards tend to dominate, reflecting customer loyalty or store-specific credit options (Omar & Ohlsson, 2017). Such insights assist marketing and product strategies tailored to consumer payment habits.

Secondly, exploring the interaction between customer type (regular versus promotional) and net sales provides clarity regarding the effectiveness of promotional campaigns. Cross-tabulation can segment sales data by customer type, detecting whether promotional customers spend more or less than regular customers. Previous research shows that promotional customers, motivated by discounts, often exhibit higher purchase frequencies but not necessarily higher per-transaction sales (Kumar & Shah, 2018). Visualizing this through clustered bar charts can illustrate differences and guide future promotional strategies.

Third, analyzing the relationship between net sales and customer age requires scatter plots or line graphs. Generally, age influences purchasing power and preferences, with middle-aged consumers often generating higher sales (Bhatnagar et al., 2017). A scatter plot with net sales on the y-axis and customer age on the x-axis can reveal trends such as increasing sales with age up to a point, then leveling off or declining. This demographic insight helps in targeting specific age groups for marketing campaigns.

Finally, examining whether the association between net sales and age varies with marital status involves creating a layered chart, such as a grouped bar chart or a conditional line graph. This analysis could show, for example, that married customers tend to spend more across age brackets than singles, or that the relationship between age and sales is more pronounced among one marital group. Understanding these dynamics aids in designing tailored promotions and customer relationship management strategies.

In conclusion, leveraging pivot tables in Excel provides powerful tools for analyzing sales data. Visualizations like bar charts, scatter plots, and layered charts enable a comprehensive understanding of consumer behavior in Pelican Stores. Insights from such analyses can influence future marketing strategies, promotional efforts, and customer relationship initiatives. Further research could enhance understanding by integrating additional variables such as geographic location or product categories, ensuring data-driven decision-making to optimize sales performance.

References

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