The weekly weight index is a management tool that calculates sales weight analysis on a weekly basis based on historical sales data within a certain sales cycle. The following will combine the data of Amazon’s clothing category to explain the case of the weekly weight index.
Since Amazon sales still belong to the category of retail, they will fluctuate periodically on a weekly basis. If you directly compare the sales data, although you can see the fluctuations, it is difficult to summarize the rules. At this time, you need to reprocess the data.
Divide the daily sales by the total sales of the week to get the proportion coefficient, and then make a chart to clearly see that the store has the highest sales on Thursdays, the lowest sales on Sundays, and a slight recovery on Tuesdays and Saturdays. The rest of the time fluctuates more significantly. After data verification, it was found that sales do meet the characteristics of a weekly cycle, so the weekly weight index can be used as an indicator for analysis. The calculation method of weekly weight index is as follows:
Step.1 Collect the sales data of each store in the last full year;
Step.2 Eliminate outlier data such as Prime Day, Black Friday, and deals;
Step.3 Sort the remaining data by week, with the row label as week and the column label as week, and calculate the average daily sales;
Step.4 Take the sales data with the lowest average daily sales and set its daily sales weight index to 1. Divide the other 6 values by this number to get the daily sales weight index of the other 6 days;
step.5 Add up the daily weight index to get the final weekly weight index. The minimum weekly weight index should be 7, and the larger it is, the more unstable the sales are.
After statistics, the weekly weight index of the store/team is obtained.
Using the same method, the weekly weight index of a single store can be obtained. It should be noted that since the daily weight index of a single store has a short range of values (using 3 months of data to estimate 1 month’s sales, it needs to be updated once a month to ensure the reference of the data.
Finally, managers need to specify appropriate daily weight indexes for holidays and promotions. For the US site, most holidays are defined by week. Since different holidays will have pre-holiday shopping peaks and holiday troughs, most promotions will also be launched before the holidays. Therefore, you can simply compare the sales data of the past 2 to 3 years and use the following formula to determine the daily weight index of the promotion day and the week before the holiday:
Daily weight index = daily sales/unit weight (sales) value.
With a quantifiable weekly weight index, sellers can break out of the traditional Empirical operation ideas, track sales through data, set performance goals, and complete real-time tracking. Let’s take the data just now as an example. If the monthly target of store A is 30,000, you can calculate the sales target that store A should achieve every day:
Daily sales target = monthly sales target * (daily weight index/monthly weight index)
(where the monthly weight index is equal to the sum of the daily weight indexes of the whole month)
The performance target here will be specific to each day. If it is found that the operator’s store has failed to meet the target completion rate for 1 week, it is necessary to check the store products and the operator’s work status in time to ensure that the performance is completed in time. Assuming that today is July 18, there are 12 days left until the end of the month. Store A has completed a total sales of 17,510, with a completion rate of 58.4%. At this time, you can follow up in time according to the weekly weight index to observe the sales completion situation.
It is not difficult to see that if the current sales status is maintained, it is still a little short of completing the performance of this month. At this time, you can appropriately increase the number of orders through advertising and other means to ensure that the target is completed.
For some special sales nodes, such as the upcoming Prime Day and Black Friday Network One, etc., need to set performance targets separately. At this time, it is necessary to make a comprehensive comparison of sales over the years. In addition to setting appropriate goals, it can also guide reasonable stocking to avoid out-of-stock or redundancy.
In addition, by calculating and plotting the unit weight value of each day over a period of time, a unit weight value curve can be obtained. Huang Chengming called it the “Huang’s Curve” in his book “Data Management”.
The absolute weight curve should be a straight line, but in reality, this unit weight value curve will fluctuate around a certain value. By tracking and analyzing the distribution of these values, the impact of store promotion sales, special events, new product launches, out-of-stock and other factors can be analyzed and evaluated.
Since the “Huang’s Curve” needs to be updated and tracked in real time, it is not recommended for small sellers and single store operators to use it here. For sellers who already have their own team and store group and have been in operation for more than 1 year, you can try to use this method to use the weekly weight index, and you can solve the first type of problem through real-time tracking.