This is a predictive analysis of Mays Limited sales performance FY 2019 - 2020 to inform the year 2023 marketing strategy using Python, SQL and Tableau.
The libraries used in this project include:
- Pandas
- Numpy
- Matplotlib
- Seaborn
- Sklearn
- Xgboost
- The forecast analysis predicted the next one-year sales pattern similar to the previous years. The sales pattern maintained an increase in sales over the year with the highest sales happening in November and December.
- Marketing efforts should be focused on top profitable states such as California, New York and others to achieve a higher marketing ROI.
- Other modes of shipping such as First Class and Same Day shipping modes should be amplified to avoid delayed shipping as other modes such as Standard Class and Second Class recorded over 39% delayed shipping. Going forward, the root cause of delayed shipping should be investigated to find a lasting solution.
- The best-selling products are technology products with the highest sales and profits marketing efforts should be amplified for this category. In addition, further analysis should be carried out to determine why the products are performing better compared to the others. Could it be a product-target audience fit? This should be investigated further.
- As predicted, sales are expected to pick up during the holiday season, it would be recommended that we leverage this holiday period to reach out to potential customers. Different marketing tactics such as discount sales, referrals, content marketing, Google ads, influencer marketing and so on should be employed to reach potential and existing customers.
- To increase sales during the months/days predicted to witness low sales, we can employ different marketing tactics at intervals and seasonally themed marketing to reach customers and pump up the sales within this period and all year long.
The data visualization is designed using Tableau.