NextGen emporium is an online retail store that seeks insights from its data for informed business decisions. However, they recently observed that they do not have adequate data insights to answer pressing business issues on sales improvement.
Disclaimer: All datasets and reports do not represent any company, institution or country. All info are dummy dataset to demonstrate my capabilities in Tableau.
NextGen, an e-commerce company wants to optimize its online sales by analyzing its sales data and customer behavior to improve its product offerings and marketing strategies. I split the analysis into 3 major categories. Sales Performance, Customer Insights & Product Performance
- How are sales trending over time?
- What are the peak sales months/seasons?
- How do sales vary across different product categories?
- Metric: Monthly/Yearly Sales Growth
- Who are our top customers in terms of purchasing frequency and spending?
- What is the average customer lifetime value?
- How can we segment our customers based on their purchasing behavior?
- Metrics: Customer Lifetime Value (CLTV), Purchase Frequency
- Which products are the top sellers in terms of revenue?
- Are there any products with consistently low sales? How can we improve their performance?
- How do products spread across the world?
For this project I will be using Tableau to clean my dataset, run my hypothesis and finally plotting my analysis on visualization.
- Line Chart
- World Map Chart
- Butterfly Chart
- Heat Map
I did basic data cleaning using the filter function in tableau to filter out the Null values. I also made sure that each column is appropriate format (Date, Text, Number) etc.
This analysis when plotted reveals the sales trend over time and I am able to see the peak months and years. I further analyzed the data and I was able to forecast sales trend for the coming year.
This world map shows the general distribution of products all around the world. I round up sales in each country against total sales and made the answer to be in percentage for easier understanding.
This chart was created using my advance skills in Bar Chart. With this chat we are able to see how each product sells by category while I filter it by top and bottom 10 countries.
Top 10 countries
Bottom 10 countries
This chart made it easy of us to understand customer behaviours in regards to which product they buy and how often they purchase such product. We plotted Customer ID against count of products sold to derive this wonderful insights. The product Manual is the most sold product
This line chart shows us that sales generally picks up from January with few downslopes but steady climbing till August, By August there is a sharp surge in products sales and it hits the peak by November. By Mid November we start to notice sharp downslope again until January when sales will gradually start picking up.
This is how we represent all our analysis on visualization, while some can not be shown indept above, we explain the analysis below.
- Total Revenue which was calculated by Sum(Sales Prices)
- Total Customer which was calculated by Distinct Count of Customer ID
- Number Of Countries which was calulated by Distinct Count of Country
- Average price which was calculated by Avg(Price)
- Minimum and Maximum Price were both calculated as MIN(Price) and MAX(Price) respectively.
For more info you can Email me
