In the past companies, our sales analysis is divided into "overall sales", "store layout", "commodity structure" and "price system". Of course, the analysis between them is not completely separated, but based on the main focus. Let's design the analysis report again.
In overall sales, attention should be paid to the comparison of sales and sales volume over the same period, progress ratio, and benchmark ratio; in addition, seasonal analysis should be carried out in conjunction with time to distinguish between off-peak and peak season marketing strategies; and stores of different levels How to set up the product structure and so on...
In the store layout, analyze the company's sales stores and the performance of each store, and evaluate the geographic layout; analyze key stores to find their potential success factors; pay attention to stores with significant growth and decline, and make necessary early warnings ; Carry out an evaluative analysis of the product structure of stores at all levels, and make timely adjustments.....
In the product structure, analyze the product series and the single product structure, and find the trend of market performance (each product has a life cycle); conduct in-depth analysis of key products to grasp the performance in different stores and time; Correlation analysis to find the basis for the internal product line division.....
In the price system, analyze which stores and products the main profits come from; analyze the performance of different stores in promotional activities to provide a reference for future product pricing; analyze and compare the price bands of the overall and key stores as soon as possible Identify threats and risks.....
In summary, the purpose of sales data analysis is to find the problem and dig into the cause. First, we need to look at the overall company’s achievement rate, growth rate, growth rate, product structure, and membership size. When any indicator is found to be abnormal, we must analyze it from the three lines of "stores, products, and members":
1. "Which product caused the stagnant performance"-which store does this product perform poorly? Which members do not pay?
2. "Which store caused the slow performance?"-Which product is the store's problematic product? Or is it a member's problem?
3. "Which members have problems with purchasing power"-what product does the lack of purchasing power mean? How to adjust the marketing strategy?
The above is the simple experience sharing of Sematic software. It is difficult to mine the value of data. Just like the waves in the sand, data analysis requires not only proficiency in technology, but also familiarity with the company's business. "Dreams are not enough to let you reach the distance, but those who reach the distance must have dreams", encourage each data analyst!