The sight of empty supermarket shelves has now become a more regular part of shopping in the UK. The long-term disruption caused by COVID-19, paired with Brexit delays on logistics and the wider supply chain issues following global political instability has left supermarkets constantly playing catch up.
Whilst the pandemic created unprecedented and highly unpredictable challenges, in the months following the gradual easing of lockdowns, supermarkets have still been on the back foot. With the current inflationary pressures and the cost of living crisis, the consumer loyalty that brands previously took for granted, is being fiercely rivalled by the need for convenience and on the other hand, for cost savings amidst tightening budgets. Now more than ever, retailers cannot afford to slip up.
But what if there was a way for supermarkets to better prepare for changing circumstances, new shopping habits or potential disruptions in supply? Current methods of forecasting can only go so far and it can be difficult to truly keep track of an ever-changing world where the speed of changes in consumer behaviour now hugely outpaces standard predictive methods.
This is something the team at salesBeat have been helping FMCG brands overcome by harnessing the huge power of Machine Learning to make predictions based on a wealth of factors from weather to social media sentiment in order to better predict stock requirements for each store.
Now, they’re opening up these powerful tools to retailers too, to help maximise those efficiencies across the supply network.
They have recently launched a dedicated retail platform that will help eliminate both out-of-stocks and write-offs due to overstocking for the likes of supermarkets, convenience stores, grocery e-commerce and other quick commerce vendors.
Salesbeat’s recommendations take into account, real time and forecast, micro and macro factors to make predictive recommendations of how much of each SKU to stock at each store to meet demand without overstocking. SalesBeat’s algorithm replicates consumption behaviour based on numerous indicators; including but not limited to, information on air pollution levels, weather circumstances, water levels, consumer movements, current affairs, and popular trends.
Previously the tool showed optimal stock levels to salespeople who sell into supermarkets, but for salesBeat, they know how valuable access to predictive recommendations of stocking requirements on this scale can be for retailers too.
“Artificial intelligence and machine learning can be used by both FMCG companies and by retailers to process a large quantum of detailed data to predict consumer demand to within 5% – 10% of actual sales. It ensures that stock levels are optimal at the retailer level, and both stock write-offs and stock-outs can be avoided”, says Veena Giridhar Gopal, co-founder of salesBeat.
According to ECR Europe & Roland Berger Strategy Consultants, 30% of consumers feel stockouts hurt their shopping experience. That means, in the short term, consumers are more likely to start considering new brands and retailers, meaning in the long term stores will see a growing number of consumers switch away to brands or supermarkets they perceive they can trust more. Some studies show that nearly half of purchases may be lost by a retailer as a result of stock-outs.
Plus, according to a study in 2021, potential lost sales due to out of stocks and overstocking (due to writing off expired stock) can be up to 40% of a company’s revenues due to increased volatility in demand.
That’s why enhanced sales intelligence in FMCG is more important now than ever before – this tool will prevent these stockouts regardless of COVID, Brexit, Inflation, the Ukraine crisis, Monkey Pox or the next upset to hit us. Today’s technology allows this kind of data to be streamlined and affordable for a vast range of retailers and brands.
“Previously access to vast amounts of sales data would have meant hiring huge teams, and even then there would be a struggle to analyse and digest the data at the rates machine learning technology can offer. With AI platforms like salesBeat, this powerful data can be in the hands of a retailer in an instant,” said Veena.
salesBeat was founded in 2019 by Veena and Alex. They pivoted during the pandemic when they saw the magnitude of lost sales opportunities due to Covid-19, compounded by social media and unpredictable weather patterns over the past year, including a warmer than expected summer and a cooler than expected winter. They launched salesBeat to solve this problem.
They have applied the same technology and logic to predict consumer demand that is used by data science teams whose job is to predict events like wars and water shortages.
Veena worked for 20+ years within the FMCG industry including for companies like PepsiCo and Diageo. Alex has 25+ years in IT and in start-ups, with extensive experience in creating apps and advising start-ups on their tech strategy.