The McNamara Fallacy (Part 3)

For previous posts on the McNamara Fallacy, see these links

forethoughtsonretail.blogspot.com/2010/07/mcnamara-fallacy.html

forethoughtsonretail.blogspot.com/2010/08/mcnamara-fallacy-part-2.html

Why do I call FMCG’s in-store information resources a “data desert’? If we can agree that a very significant portion of a product’s sales (let’s say 40%-75%) are highly dependent on in-store factors (shelf position, pricing, display activity, POS etc.) then why on earth are there so few reliable in-store measures?

We probably would all agree that we have a good handle on sales, and also distribution.

The first step is to measure whatever can be easily measured. This is OK as far as it goes.

But beyond this, with in-store measurements we start getting into very arid data sets.

Let’s look first at out-of-stocks and distribution voids. There are NO standard on-going measures here. Retail chains may use DSR (Demand Signal Repository) software to indicate o-o-s, but these are notoriously inaccurate for this purpose, and manufacturers/suppliers may have their field forces gather some of this information. I would strenuously argue that self-generated o-o-s is often self-defeating (more on this subject in an upcoming post). The accepted world wide average in FMCG for out-of-stocks is 8%. Distribution voids (sku is listed in the account, but generally not carried in the store) can double that figure. Imagine the amount of lost sales if your product is missing 10%-15% of its expected distribution because these facts are ignored and not properly addressed. Yet the industry does not adequately track these basic fundamentals. The old adage “you can’t manage what you don’t measure” certainly comes to mind.

The second step is to disregard that which can’t be easily measured or to give it an arbitrary quantitative value. This is artificial and misleading

And what about planograms (POGs) and planogram compliance? The industry spends multi-millions annually, developing, changing, and implementing POGs. These are highly sophisticated computer generated shelf sets that theoretically provide the best (greatest sales, greatest profit etc.) alternative product list and shelf placement for each category for that account (and in some cases, store) based on various criteria. Yet who checks if they are ever implemented properly (properly being a rather loose term in relation to POGs)? And who (and how often) checks that the POG remains implemented properly? The truth is … no one. But in fairness, POGs have been notoriously difficult, and hence, expensive to monitor. Yet so much time, effort, and money are continuously invested in them! Presumably their proper implementation and maintenance is critical to category sales and profit. So why does no one care?

The reasons, I suspect, are four-fold
1) in-store has traditionally been second class (so-called below the line)
2) with high key account penetration and high supplier fees, the expectation is that implementation should be a fait accompli
3) if not, what do we do about it?
4) and anyway, it actually can be quite expensive and somewhat complicated to do properly

The third step is to presume that what can’t be measured easily really isn’t important. This is blindness

So there we have it. The McNamara Fallacy for FMCG. In-store determines product success to a greater degree than any other factor, and the packaged goods industry has developed no effective way of competently measuring and tracking it.

The fourth step is to say that what can’t be easily measured really doesn’t exist. This is suicide

And now we are ready to roll out the Shopper Marketing caravan into the data desert in a very BIG way. Perverse, yes?

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