How do fluctuations in sales affect turnover?
This publication is a real case from Datawiz.io , in which we will tell you how to find products and categories with large sales fluctuations, and how sales fluctuations affect customer behavior.
When analyzing data for the distribution network, we encountered a problem: with almost equal amounts of sales per day in two stores in the network, sales in one Shop1 store increased, while in Shop2 it decreased.
It was suggested that a sufficient number of key categories of goods (goods with a high frequency of entry into checks) were not delivered to the Shop2 store . Our task was to find such categories.
Then we calculated the coefficients of variation (V) for each product group in both stores:
V = std / avgwhere
std is the standard deviation of sales for a given period;
avg - the arithmetic average of the number of sales for a given period.
The coefficient shows fluctuations in sales.
The smaller the coefficient, the smaller the variation in the number of goods sold, the smoother (more stable) the sales schedule for the period looks.
The higher the value of the coefficient, the more unstable the sales and the fluctuations and failures in sales stand out sharply on the chart.
We calculated the coefficients of variationsales of goods of all categories for two stores per year. But this method of comparative analysis turned out to be ineffective, since sales of seasonal categories of goods are highly dependent on the season (we got the classic XYZ analysis). We are interested in the sale of categories of goods, the demand for which affects the turnover, regardless of the season.
Then we conducted a comparative analysis of sales fluctuations in two stores over the past month (in this example, September 2014). And they revealed the most problematic categories of goods for the Shop2 store:
Our assumption that an insufficient quantity of goods from these categories was brought into the store turned out to be true.
As can be seen from the table, with approximately equal average sales per month, the Shop2 store has a very large standard deviation in sales (std), and accordingly a high coefficient of sales fluctuation.
This means that there were days in the store when goods from key categories were either not on the shelves or there were insufficient quantities.
Let us consider in more detail the category “Dairy products”, since sales of this category account for a large percentage of the total turnover. The graphs below show the dynamics of sales of the category “Dairy products” by the number of units sold for each day of the period.
For the Shop1 store, the coefficient of variation in sales of this category per month is 0.09 or 9.0%. Such sales can be called homogeneous, since the fluctuations are small. Typically, in FCMG, the allowable coefficient of sales fluctuations is 0.2 (20%) for categories of products that form the basis of the shopping basket.
For Shop2, the coefficient of variation is 0.36 or 36.0%
Such sharp fluctuations in sales affect not only sales, but also customer behavior. Whenever the buyer does not find the product he needs, this negatively affects his loyalty, and as a result can lead to the loss of regular customers. Therefore, each subsequent failure in sales will be deeper and exit from it will be longer than from the previous one. In addition, every time a buyer does not find a key product in a store for which he came to the store, the likelihood of buying other goods of interest to him falls.
To determine the possible loss in sales of related categories of products, consider the key category of products “Dairy products” in the store “Shop2”, using associative rules. Using the APRIORI algorithm, we identify all paired relationships with this category. The table shows all the related categories of products that are bought with the key.
The indicator “Reliability” in the table means that consumers who buy goods from the category “Dairy products”
- in 34% of cases, buy goods from the category “Prom. goods";
- in 41% of cases - they buy goods from the category “Confectionery”, etc.
For example: a buyer buys milk or kefir 3-4 times a week at a supermarket closest to his house. Often, he buys cookies and fresh bread, as well as a bag at the checkout. The first few times not finding the dairy products he needs on the shelves, the buyer may continue to buy bread and pastries in this store. But over time, he will prefer another supermarket, even if it is not so close.
It is very difficult for retailers with large fluctuations in sales to retain regular customers, and even more so to attract new ones. Thus, the store is losing circulation not only due to the lack of key products, but the purchase of related categories is also significantly reduced. This problem can be solved as follows:
-daily analysis of data;
-identification of key products and tracking their coefficient of variation;
- timely response to changes in the dynamics of sales;
-use of associative rules to identify related products to increase profitability.
This is a very simple case, but we encounter such a problem in our work literally every day. Even large retailers do not always respond in a timely manner to the lack of goods in the store.