Understanding the Relationship between Sales Volume, Parcel Volume, and Distribution Chain
The channels of distribution contribute greatly to the volume of sales obtained on various products and services. Depending on the nature of the product or service being distributed, a company can either use intermediaries for the delivery or manage the distribution process independently. Whichever method a company chooses, however, it must have a clear picture of the expenses incurred to determine whether they make economic sense in regards to the volume of sales driven to the company. Some of the most common channels of distribution a company may consider include the use of wholesale relationships, direct sales force, and distributor relationships.
This study explores the associations between the volume of sales, the volume of the parcel, and the distribution chain (ACV). The aim is to find out I want to make a story like to know where is there is a chance to get more revenue and where is better to sell ata discount or without. A database with more than 150,000 product is inspected, and we study variations in terms of equalized units sale, base-dollar sale (dollar sales when the products are not discounted - no feature, display, or temporary price reduction), and revenue on trade (dollar sales when the products are discounted - either feature, display, or temporary price reduction)
The correlations between these indicators are however extremely high in the data set. They actually are between .999 and 1.000, which means that irrespectively with each of the variables we work, the results are substantially identical. Therefore, to simplify the analysis, only equ (equivalized units sale) is considered as the main variable of interest.
Figure 1. Histogram of equivalized units sale
Figure 1 shows the distribution of the equalized units sale (equ) over the over 153,824 products. In order to keep the figure readable, only the products with sales under 100000 equ are displayed in the figure. Figure 2 includes all products and shows the same type of distribution from which Figure 1 depicted more accurate details. About a third of these products have very low sales (under 60 equ), and those under equ 1000 are making half of the sample. Therefore we have a very skewed distribution, with a long right queue.
Figure 2. Barplot of recoded equ
To find out the covariates of the sales, we start by inspecting the volume of the package. For most of the products, the volume is small, but some packages are quite large, leading to a very skewed distribution.
Figure 3. Histogram of the volume
Figure 4. Histogram of the volume: detailed view
Restricting the range of the Ox axis, one can observe that most of the products are in the 0-10 range. A categorical variable can be computed, in order to illustrate the connection between the volume of the package and the other two variables of interest.
The ACV distribution indicates the percentage of stores selling the product, with the stores being weighted based on their size. For our database, as Figure 5 shows, the distribution includes many products sold only locally, in very few stores, then an even number of products sold in each small range of ACV, with some more products being sold nationwide. For professional assistance with this topic, avail our covariance assignment help.
One may also observe that products with more comprehensive distribution chains typically come more often in larger parcels (Figure 6). However, there is no clear pattern of association between the two variables, so it makes sense to analyze the variation in equ across the levels of both ACV and per unit volume.
Figure 6. Association between ACV and Per Unit Volume
The association of equivalized units sale and per unit volume is not straightforward (Figure 7). The graph reveals a larger volume of equivalized sales when the per unit volume is 6-10, with a decrease at 11-50 and much smaller values for all other registered sizes. However, in the case that no information is available on the per unit volume, the equivalized volume sale is much larger than in any of the other situations.
Figure 7. Equ depending on volume per unit
Figure 8. Equ depending on ACV
The relation to the distribution chain, depicted in Figure 8, is clearer. The equivalized sold units are higher when the ACV is large, and the ACV is small when sales are low. In fact, there is a huge increase when getting almost national as coverage, but the increase in sales with the size of the distribution chain was clear even from the soother changes observed for the first categories of ACV. There is a tiny increase in equ when passing from 2% or less ACV to 2-5%, and the increase continues to 5-10%, and the subsequent categories, with the above-mentioned steady jump for the category of very high ACV (80-100). Take our data visualization assignment help and have all the complex tasks on this topic handled by an expert.
In summary, we have noticed little association between ACV and per unit volume. Studying the variation of equ across the levels of both variables lead to different conclusions. On one hand, equ seems larger for the medium size volumes, but three is a lot of missing information for the corresponding variable, and one should further investigate the relation. On the other hand, there is an extremely clear association between equ and ACV, with nationwide products selling much better than the local ones. One may consider focusing on ACV if the aim is to increase equ.