South Africa's relationship with Black Friday has evolved rapidly.
What began as a single-day phenomenon imported from the US now extends across the better part of November, with major retailers launching promotions weeks in advance and consumers planning their purchases accordingly.
In 2021, NielsenIQ's analysis found that the Black Friday week had surpassed the biggest festive season purchase week in December, with FMCG basket sales 23% higher than the average of all other month-end weeks that year, and the percentage of volume sold on promotion 55% higher.
Goods worth more than R4bn were sold on discount deals in that single promotional period.
By 2022, the event had grown further still, with Pick n Pay alone reporting R800m in sales on the day itself.
NielsenIQ noted that the Black Friday week and the one immediately following it had begun to outpace the two key December month-end weeks.
This is a trend with significant implications for how suppliers should be thinking about their full year performance, and where they should be focusing their promotional investment.
The scale of the event creates an equally scaled set of risks. In a single week, a supplier can either close the gap in their annual sales targets or fall far short of them.
The difference, in most cases, is not the quality of the product, the depth of the discount, or even the strength of the retailer relationship.
It is the quality of the data that informs the decisions going into the event — and the speed at which the data coming out of it is acted upon.
Some brands have become so reliant on Black Friday to achieve their annual targets that missing the event, or executing it poorly, now carries year-end consequences from which it is difficult to recover.
The problem is not the data – it’s what happens to it afterwards.
Every supplier participating in Black Friday generates useful data during the promotional period: daily sales by store and by stock keeping unit (SKU); stock levels, pricing compliance, promotional uplift plus rate of sale. In theory, this data contains answers to questions that could directly improve next year's planning, negotiation, and ultimate results.
In practice, most of it is never properly interrogated.
The promotional period ends, the commercial team moves on to the next priority, and the analysis that is completed tends to be high-level, namely total sales performance against target, perhaps a top-line view of which products sold best.
The store-level, day-to-day picture that would reveal where stock ran out and when, which stores never activated the promotion correctly, and which SKUs dramatically outperformed their planned rate of sale, often remains unexamined.
This is not a failure of intent, but a structural problem. When the data required to conduct that analysis is not being captured in a format that makes it accessible and actionable in real time, the window to act closes well before anyone has a chance to look at it properly.
By the time a supplier knows that a cluster of high-volume stores ran out of their key promotional SKU on day two of the event, the revenue is already gone.
The moral of the tale: The post-event debrief is only as good as the data that was being tracked during the event. If you were not watching daily during Black Friday, there is nothing useful left to analyse afterwards.
Black Friday data can be pure gold if analysed correctly. Approached correctly, the data generated during Black Friday can be the most commercially valuable insight a supplier collects all year.
The following questions are the ones that separate suppliers who use Black Friday as a learning opportunity from those who simply survive it.
Where did we run out of stock — and what did that cost us? Out-of-stock rates are already elevated during normal trading conditions, averaging approximately 8% across developed FMCG markets globally. During promotional periods, that figure can double, with some research placing the promotional OOS rate at more than 13%.
On Black Friday specifically, where the entire purpose of the event is to drive volume, running out of a promoted SKU mid-week is not a minor inconvenience.
It is a quantifiable revenue loss, a promotional compliance failure, and a wasted investment in the discount that was never fully realised.
To understand exactly where stock ran out, after the event, a supplier needs store-level stock and sales data tracked daily throughout the promotional period and not a weekly aggregate that masks when the stock actually ran out.
Through the availability of this data, it becomes possible to calculate not just that an OOS occurred, but how many units would likely have sold on each day the product was unavailable, based on the store's established rate of sale.
That is a number that can be taken into a buyer negotiation, used to build the case for increased stock allocation next year, and used to model the additional revenue that better execution would have captured.
A promotional price agreed at head office level does not automatically appear consistently across a retail estate of hundreds of stores.
Pricing discrepancies during Black Friday are more common than most suppliers realise.
Moreover, they distort the data in two directions simultaneously: stores where the promotion was not correctly applied will show lower-than-expected sales uplift, making the promotion appear less effective than it was.
Whereas stores where the price was incorrectly dropped further than agreed may show strong volume but at a margin that was never sanctioned.
Daily monitoring of average selling price at the store level, throughout the promotional period, is what makes these discrepancies visible while there is still time to correct them.
This is what makes it possible to calculate a true measure of promotional ROI once the event is over, rather than one that is distorted by inconsistent execution.
In a second article on Black Friday data, Heath Batt will reveal how retailers can mine what is absolute gold dust from their Black Friday drives.
