You may have noticed that I have been discussing a lot about category management and the way to analyse product range productivity via the Direct Product Profit Index, and the Cost Of Goods Sold.
Information that may twist the DPP analysis
DPP can not be taken solely into accounts. Hence, different components may impact its analysis, that may be set in prospective:
Profitability is linked to the pricing strategy, and if the pricing is not accurate, or may be changed, it could affect greatly the DPP of one product.
Moreover, DPP don't take into account strategical marketing assets (traffic builder products may have a low DPP, but are mandatory to sell other goods, and to keep your clientele).
DPP should also take into account operational issues that can not be perceived in its analysis, such as the minimal stock of one product.
You should also take into account lastly how promotion may impact sales and profit of one product. And this needs to be analyzed seperately.
The most important components of direct profit by products.
The results show that the best single predictor of DDP is gross margin dollards, followed by:
- Sales in dollar,
- Package unit sales
- Then, square feet of space allocation
These 4 components account for over 98% of the variability of SKU DPP. The study hence explains that, in order to keep a viable DPP and to use it properly, you should also focus on specific variables. If you have too many, the DPP index become too difficult to be used.
This is the reason why the authors discuss about an MAI (merchandise attractiveness Index) that reduces the number of variables needed. For a extensive DPP analysis you need 75 costs components, but after the weights are computed, only 2 to 4 variables must be gathered to recalculate an MAI.
I'll keep on looking for information on the topic to share with you, especially on how big data may give a lift to these analysis.
I believed that those concepts were somehow new in the industry, but as I have been looking for information on the topic, I found out the issue was there for quite a while now. I actually read a great article on how the Direct Profit By Product Analysis Compare To Other Traditionnal SKU Productivity Measures. You should read carefully this article, as it gives a lot of information on that topic.
The whole Philosophy about DPP
"Some SKUs may earn high gross margins, but excessive handling and storage costs can outweigh their net contributions to overall profit. DPP reflects inter-item differences in sales, margins and costs associated with storing, transporting, shelving, and labor intensive merchandising activities (such as pricing individual items)."
That also highlights the fact you need to constantly reevaluate DPP, as some changes may occur in the cost of goods sold structure, either on the supplier or the retailer end:
- Investments in more optimized supply chain
- New ordering policies that would lower the cost of inventory for example.
Especially, what is interesting is to see how the analysis of Direct Profit By Product may influence category management decisions, upon other traditionnal way to estimate category performances.
It
The whole Philosophy about DPP
"Some SKUs may earn high gross margins, but excessive handling and storage costs can outweigh their net contributions to overall profit. DPP reflects inter-item differences in sales, margins and costs associated with storing, transporting, shelving, and labor intensive merchandising activities (such as pricing individual items)."
That also highlights the fact you need to constantly reevaluate DPP, as some changes may occur in the cost of goods sold structure, either on the supplier or the retailer end:
- Investments in more optimized supply chain
- New ordering policies that would lower the cost of inventory for example.
Especially, what is interesting is to see how the analysis of Direct Profit By Product may influence category management decisions, upon other traditionnal way to estimate category performances.
It
Information that may twist the DPP analysis
DPP can not be taken solely into accounts. Hence, different components may impact its analysis, that may be set in prospective:
Profitability is linked to the pricing strategy, and if the pricing is not accurate, or may be changed, it could affect greatly the DPP of one product.
Moreover, DPP don't take into account strategical marketing assets (traffic builder products may have a low DPP, but are mandatory to sell other goods, and to keep your clientele).
DPP should also take into account operational issues that can not be perceived in its analysis, such as the minimal stock of one product.
You should also take into account lastly how promotion may impact sales and profit of one product. And this needs to be analyzed seperately.
The most important components of direct profit by products.
The results show that the best single predictor of DDP is gross margin dollards, followed by:
- Sales in dollar,
- Package unit sales
- Then, square feet of space allocation
These 4 components account for over 98% of the variability of SKU DPP. The study hence explains that, in order to keep a viable DPP and to use it properly, you should also focus on specific variables. If you have too many, the DPP index become too difficult to be used.
This is the reason why the authors discuss about an MAI (merchandise attractiveness Index) that reduces the number of variables needed. For a extensive DPP analysis you need 75 costs components, but after the weights are computed, only 2 to 4 variables must be gathered to recalculate an MAI.
I'll keep on looking for information on the topic to share with you, especially on how big data may give a lift to these analysis.
