Retail Sales Dashboard Using Power BI

Retail analysis is a critical tool for businesses in the retail industry looking to stay ahead of their competition in today’s fast-paced retail environment. Retailers need to be able to analyse their own data and gather relevant information to make better business decisions about their operations and target performance efforts. This can involve analysing customers’ buying patterns, customer trends, examining sales trends over time, or assessing the effectiveness of various marketing campaigns. With careful retail analysis, businesses can leverage their existing datasets to gain strategic advantages over their competitors, identify new market opportunities, and make more informed decisions about pricing and inventory management goals. Ultimately, by leveraging the power of retail analytics through raw data effectively, businesses can stay ahead of the curve and succeed in today’s constantly evolving retail landscape.

The most successful retail companies worldwide solve these issues by efficiently leverage all of the data at their fingertips by following set processes to see data projects through from start to finish. It’s imperative that actionable insights are derived out of this huge data which facilitates retailers to strategize their business operations and maximize profits. Analyzing such data will help in identifying emerging buying trends which helps in categorizing products by various parameters such as price, season, geography, and so on. Retail Analytics help Retail companies to analyze customers and their buying patterns across several channels and geographies. We help in analyzing the products and transaction that occur at multiple channels. Additionally, we analyze the data from Dashboard.

1. DEFINE: Define your business question or business need: what problem are you trying to solve? What are the success metrics? What is the timeframe for completing the project?
2. IDENTIFY DATA: Mix and merge data from different sources for a more robust data project.
3. PREPARE & EXPLORE: Understand all variables. Ensure clean, homogenous data.
4. PREDICT: Avoid the common error of training your model on both past and future events. Train only on data that will be available to you when a predictive model is actually running.  Choose your evaluation method wisely; how you evaluate your model should correspond to your business needs.
5. VISUALIZE: Communicate with product/marketing teams to build insightful visualizations. Use visualizations to uncover additional insights to explore in the predictive phase.
6. DEPLOY: Determine if the project is addressing an ongoing business need, and if so, ensure the model is deployed into production for a continuous strategy and to avoid one-off data projects.

Things you will learn:

• Business Problem We are Trying to Solve
• Constraints
• Scope
• Objectives
• Data Preparation
• Dashboard Creation