It is stating the obvious to say that success in retail can be measured by the amount of profit generated in relation to the working capital invested – the return on investment. What is perhaps less obvious is what a retailer can do to manage this pro-actively. Certain costs in any business are fixed, or at least are not easily flexed. Shop rents and head office costs fall into this category. Merchandise margins and product mix, however, are variable, and their management can either enhance or destroy profitability. In this paper I shall explain the use of a key performance measurement, Gross Margin Return on Investment (G.M.R.O.I.) that can provide invaluable insight into merchandise performance.
Many retailers use the performance indicators of gross margin % (after mark-down) and weeks cover to measure performance. These are very commonly available, but used in isolation from each other they are of limited value. Gross margin % gives us a measure of relative profitability without taking into account the costs of stockholding investment. Weeks cover tells us how effectively we turned our stock without informing us about relative profitability. What we need is a measure that combines these two indicators into an indicator of real profitability. G.M.R.O.I. gives us this.
So, what is G.M.R.O.I.? In simple terms it tells us how many times over a year we get our stock investment returned with a given margin . It is calculated as follows
(Gross Margin% / (100% – Gross Margin %)) x (52/Weeks Cover)
So a product with a gross margin of 50% and an average 26 weeks cover would give us a G.M.R.O.I of 2.0
(50/50) x (52/26) = 1 x 2 = 2.0
If we compare this with a product with a gross margin of 40% but an average of 17 weeks cover we see that the G.M.R.O.I. is also 2.0
(40/60) x (52/17) = 2/3 x 3.01 = 2.0
Simple gross margin measurement would indicate that the first of these products was a better investment. GMROI shows us a fuller picture that shows that the second product provided an equal return on stock invested. We can see from this that we can use G.M.R.O.I. as a powerful measure of historical performance, but it has an equally powerful application in merchandise planning.
In this instance we might well apply the measure at a summary level, perhaps sub product group by branch, to give us an indication of those sub product groups that have greater potential than others in specific branches. From this we can make better informed decisions as to which should have more space allocated to them, be better supported by stock or have ranges expanded or contracted.
For example, products with low cover and high gross margin will probably have experienced stock outs and fragmentation of ranges, and were therefore not fully exploited in terms of their ability to generate profit. This combination would result in a relatively high G.M.R.O.I.. Assuming that this performance were not the result of a fashion “blip”, it would make sense to increase the stock support for this area and maybe increase the space allocated to it. We might also look at increasing the number of options available.
Conversely a product with high cover and a low gross margin was obviously over supported with stock, and failed to generate a reasonable return in spite of this. It would therefore make sense to reduce its space allocation ,and to channel the stock investment to a more appropriate area, maybe reducing range width at the same time. In extreme cases we might decide to remove the product area from the range altogether.
In between these two extremes lie other combinations.
Firstly products with high margin and high cover where me might reduce the level of stock support without impacting the margin. Secondly there are products with low margin and low cover. This could result from a combination of marking down fragmented ranges due to inadequate stock support. In this instance we might profitably increase stock levels. If the low margin is planned and not due to mark-down, then we might increase our intake margin percentage as the products performed comparatively well even with low cover. Finally there are those products with an average margin and average cover where we might leave things pretty much as they are.
The above examples show that GMROI requires some interpretation in the light of knowledge of the market and an understanding of retail dynamics. It provides a realistic measure of potential that must be read in the light of the relativity of its constituent parts, margin and cover, and of market movements. It is a decision support measure and not a decision making one. It is also important to realise that it is a relative measure, both within different parts of one business and also between different types of business. Thus one branch may have a higher than average GMROI due to a city centre location with a small stock area giving it a low average cover. Equally a shoe retailer would expect a lower average GMROI than a general clothing retailer due to the greater number of size options he has to carry to avoid stock-outs, resulting in comparatively higher cover requirements.
Finally, even though it extends the insight that we have into stock performance using information that most retailers already have to hand, it still does not tell the full story. To get this we have to go still further and take into account such costs as head office, distribution , space and selling staff. This brings us into the realms of relatively complex Direct Product Profitability modelling. G.M.R.O.I may not be the complete answer, but it can take us a long way with very little extra effort or investment.
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