Unveiling Maven Toys' Sales Performance

Hired as a Lead Business Intelligence Analyst, I prepared a new monthly dashboard for the Regional Sales Managers with the following objectives:

• Track monthly revenue trends
• Determine store ranks in revenue generation
• Find out top and bottom performing products

Process

• Filter: I determined the filter to be used to explore the dashboard. Since, each regional manager will be interested in his/her own region, I decided to use Region as a filter. I also used current month, current year, previous month, previous year as filter for charts.
• I used Excel's "Name Manager" for the Filter values. This was for easy retrieval of filter values.
• KPIs: I calculated monthly revenue, previous month (MoM), and current month in previous year (YoY). The MoM and YoY values were needed to add context to the monthly revenue.
• Revenue trend: I needed the regional managers to see the revenue trends across month to give a bird eye view of the revenue generated. I used a line chart to display this trend.
• Performance across store: I used bar chart to compare performance across stores.
• Top and Bottom performing products: These tables show products driving the main increases and decreases in revenue.
• Set up dashboard layout: Here, I removed gridlines, planned the layout (determined where each chart should be), and moved in the charts.
• Used colour to tell a story.

Findings

• Sales Performance Over Time: Sales revenue and profit showed a significant increase from January to April 2020. Units sold also increased, reflecting higher demand or successful sales strategies.
• Performance by Region: Chicago region led in terms of revenue, profit, and units sold, followed closely by New York and then Los Angeles. This suggests that the market in Chicago might be larger or more receptive to the products being sold.
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• Seasonality in Sales: Sales revenue and units sold peak around April, slightly dip but remained strong through June, and then showed a notable decrease in August. This pattern could be indicative of seasonal buying behavior.