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Post-Match Analysis: Retail Did Well, But Could Do Better
As I write, the country is still in recovery mode after the roller coaster drama of the World Cup. It’s time for reflection on what was achieved and what might have been.
Hopefully, many retailers are contemplating their performance too. Sky News reported that ‘beer, barbecues and big TVs’ lifted June’s sales figures, thanks to a combination of the warm weather and the football. According to Barclaycard, pub expenditure rose 9.5% during the world cup, with spending increasing 33% on the day of England’s first match against Tunisia.
And yet, Helen Dickinson, chief executive of the British Retail Consortium (BRC), commented: “June scored solid, but not sensational sales”. Just as the tournament was an amazing opportunity for the young England team, it was the big chance for many retailers to boost their sales. Things may have been good, but they could have been better.
So how can retailers achieve “sensational” sales next time there’s a major sporting or national event? Recent advances in machine learning and artificial intelligence suggest that, by the time of the next World Cup, retailers will be able to predict demand and prepare for it in precise detail. However, few can wait that long and emerging new products such as REPL’s ADAPT are already enabling the highly-sophisticated predictive analysis retailers need to improve their game.
Anyone can make a guess that warm weather and an international sports fixture means stocking up on beer and barbecue food, but these days stores need far more subtle intelligence. For example; do supporters of the losing team tend to drown their sorrows and drink more alcohol than those already elated by a win? Do England fans prefer pizza or curry with their beer? Does the weather create the peaks and troughs in sales rather than the event itself?
Models created by ADAPT will help retailers tailor staff resources, the supply chain and the warehouse. After all, the more awareness retailers can offer the supply chain, the more likely they are to have products ready, so helping to increase profit all round.
Machine learning can also take warehouse management to the next level. Currently systems are set up to pick by product type, however the technology can detect more complex patterns such as how and when items are ordered together. As a result, the system can store them depending on how often they are picked together, saving time and resources.
Dickinson of the BRC continued her note of realism telling Sky News: “The reality is that sales don’t grow on the feel-good factor alone.” However, what needs to be identified is what people do after their team has been knocked out. Do they just stay at home feeling miserable or go out and buy more food and drink to take advantage of the nice weather despite the football disappointment? There are many questions, thankfully we now have the technology to help find some answers.