Retail & CRM: Turning From Direct Profit By Products to Direct Profit By Customers

In rare occasions I have topics that link CRM, retail and strategy at the same time, but one of my last readings offered me the opportunity. I had the opportunity to read an old book (see the cover in picture right on the left) edited in 1998 which topic is how the retail business will look like in 2020.

I found it obviously funny to read a book written in the past about our future to see how the prediction is turning. A bit if I was watching back to the future 2 to see how 2015 looked like in 1985... Especially because at this time, Internet was almost not existent, especially in France.

But beyond the amusement point, I liked specifficaly one of the part of this book. You know I have written a lot about Direct Profit by Products as a concrete tool to manage both purchasing and category management. The idea to have a clear view of the true profitability of one product is key to understand how to deal with it. The author is able to talk about DPP linked to CRM.

Indeed, DPP is an old concept, which exists since the 80s. It was developped during an era where product was king: Rotations were high, sales were spiking, and there was no loyalty reward programs launched yet. But the era changed. Customers have taken back the power. Customers are more informed, have more competition to choose from, and obviously he is now the master of the relationship with retailers.

This is what the author explains:

"the profit per product" can not be the sole criterea of profitability. The equation must change. We can't think only products anylonger, but customers budget. The question is where the cusomer budget starts and where it stops. We should hence think about the household budget and propose him complete solutions matching all the specific products that matches a specific needs. Instead of trying to sell a sole tennis rackett, we should propose the balls, the shoes, the tshirt and so on. It could even go beyond that: the insurance, the club fees, video games...".

Now loyalty reward programs allow retailers to have extensive data which could help to define what the direct profit by loyal customers could be. It could also help customers meet the needs of one customers based on this idea.

I believe that this idea indeed could clearly be key in the way both category management and CRM could be managed in the future.

Some good food for thoughts.

Sales is what you buy. Demand is what you want. Growth comes from bringing the two together.

Interesting article as always of the Harvard Business Review. 

It reminds me of my customer decision making process, which I highly appreciated, and that gave me the love of customer relationship management. 

The article highlights two main ideas.

The difference between demand and sales

Of course, while working on category management plan or to forecast the outcomes of a new project, you need to set up some goals, backed up by data. Most of the time, you extrapolate data from sales figures. But as the article underline, sales data are slim to have the big picture. It does not take into account the impact  out of stocks, suboptimal assortment, pricing inefficiencies, difficult POS experiences, misaligned brands, and redundant innovation. The whole environment have an impact on sales.

Sales data provide you with figures that are difficult to take clearly due to all the factors that may alter one customer decision.

On the other hand demand data is also difficult to reach as we touch to the whole complexity of human emotions and complex decision making process. You need to have tools like brand mapping, that are difficult to transfert into figures. It is like working with your left and then your right brain. Easier said than done.

You need both information though to make the right answer. Same thing with the Direct Profit by Products analyze which can't be the single way to pick a product range, you need to understand all the strategical aspect of your demand, in order to make the right choice to suit your customers.

Working together with the different stakeholders

The second part of the article that I like is about integrating all marketing data. Indeed, there are three main actors that own a part of the information needed:

- Suppliers own customer data

- Retailers own shopper data

- Media own watchers data

It is the essence of category management: suppliers and retailers working together to leverage all these data in order to have the best respond to the demand. As the amount of data available is growing and becoming more precise, there is a lot of potential to unfold.

A New Article About Direct Profit by Product

Here is anew article I wanted to share with you about Direct Profit by Product. The Direct profit by product index is a method used in retailing to analyze the true profitability of one product, taken into account all the components of its margin and the cost related to its sale.

The article has been published in 1988, which proves that the idea of DPP has been there for quite a while, even though it is still not much used by retailers. This article shows the result of a study made in a supermarket chain on the mapple syrup categories. This is also the reason why the article is interesting, is that it gives a clear case study on how to use DPP.

Here are some of the highlights of the article.

There are 3 types of Direct Product Costs :

• Warehouse costs, 
• Transportation costs
• Store costs

The allocation of costs is a function of several factors:

•  Cubic volume of the unit and case
•  Case weight
•  Delivery schedule
• The cost of the product
• The inventory turn

The article also emphasize on the limits of the DPP analysis:” There are limitations to the use of DPP. Since DPP is a cost oriented approach to merchandising decision making, it does not take into account the consumer’s changing tastes or attitudes. The analysis also does not report how much shelf space should be changed. This problem is being addressed by the shelf space. One of these measures is the DPP of individual products. The integration of DPP and shelf allocation systems opens many possibilities for the effective management of grocery merchandising”.

The strategic approach of DPP

The article proposes a chart to charecterize the strategic interest of one product by its DPP.

Here is an example of how it works, and the axis of this strategy.

I also reconstructed a chart based on the same principle, with the same figures, to show how concretely we may picture the analysis. I have put the size of the circles by the size of the DPP/week.

Here are some charts showing the results of the study. It shows how to implement the technique.

I believe that this analysis may help to have a better view also about how to optimize the costs of goods sold in order to optimize profitability of product range.

Once again, DPP should not be taken solely, and you need to have a real category management strategy to properly manage your product selection.

How Does Direct Profit By Product Analysis Compare To Other SKU Productivity Measures

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. 

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

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.