To analyze sales data for your 4,500 SKUs over the last two years, follow these steps:
Data Collection: Gather sales data from your sales records, eCommerce platform, or inventory management system. Ensure that your data includes information such as SKU, quantity sold, transaction date, and associated revenue.
Data Formatting: Structure your data in a manageable format, such as a spreadsheet or a database. Organize it into columns for SKUs, sales quantity, sales date, and perhaps revenue. Filtering by date will be essential later on.
Time Frame Selection: Filter the sales data to focus solely on transactions that occurred within the last two years. This will help you isolate relevant data points.
Sales Analysis: Using tools like Excel or data analysis software (e.g., Tableau, Power BI), analyze the filtered data:
Total Quantity Sold: Sum the quantity sold for each SKU.
Identify Non-Sellers: List SKUs with a sold quantity of zero over the selected time frame to find your non-selling items.
Performance Visualization: Create charts or graphs to visualize the sales trends over the two years. This can help you recognize patterns, such as seasonal fluctuations in sales.
Review Sales Trends: Look for trends like increasing or decreasing sales for specific SKUs, and analyze factors that might contribute to these trends, such as marketing efforts, seasonality, or competitor actions.
Reporting: Compile your findings in a report that details top-selling items, slow movers, and non-sellers, along with insights into trends and recommendations for product management moving forward.
Continuous Monitoring: Consider establishing regular reviews of your sales data to stay updated on inventory performance and make informed decisions regarding restocking, promotions, or discontinuation of SKUs.
By following this methodical approach, you will have a comprehensive understanding of which SKUs performed well and which did not over the last two years, enabling you to make strategic inventory decisions.
One response to “Analyzing Sales Data: Which of My 4,500 SKUs Sold Over Two Years?”
This is a great outline for analyzing sales data! I’d like to add a few thoughts that might further enhance your analysis and decision-making process.
1. **Customer Segmentation**: Beyond just examining SKU-level data, consider segmenting your customer base. Understanding who your top customers are and their buying patterns can provide valuable insights. You might find that certain products sell better to specific customer groups, which could inform targeted marketing strategies or promotions.
2. **Correlation with External Factors**: While you mention reviewing marketing efforts and competitor actions, it’s also essential to account for external economic factors, such as changes in consumer spending habits, industry trends, or even broader economic conditions (like recession or boom periods) that could influence your sales data.
3. **SKU Rationalization**: Identifying non-sellers is an excellent point, but consider developing criteria for SKU rationalization more actively. Some SKUs may have sentimental value or brand equity that could justify keeping them even if they are slow movers. Conversely, if an SKU’s maintenance costs outweigh its sales, consider discontinuation or bundling with other products to boost sales.
4. **Predictive Analytics**: If you have the capability, incorporating predictive analytics could be a game-changer. By forecasting future sales trends based on historical data, you could preemptively adjust procurement strategies, maximally aligning stock levels with anticipated demand.
5. **Feedback Loop**: Finally, create a robust feedback loop where you continuously refine your data collection and analysis process based on