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Black Friday and Demand Forecasting: What can retailers learn from the healthcare sector?

Black Friday is back and spending is once again set to soar. The annual event has gone from being a non-entity in European retail to becoming the sales event of the year over the last decade. The Covid-19 pandemic has challenged retailers like never before while also presenting an opportunity to learn important lessons from the healthcare sector. While the two are very different sectors with their own unique threats, there are some valuable lessons that retailers can glean from healthcare on one problem in particular: demand forecasting and operations planning.

How the NHS responded to the pandemic

Let’s start with some context. The Covid-19 pandemic has driven a massive surge in demand for critical resources across nearly every healthcare system in the world. As part of the data response to the pandemic, we worked with the NHS to develop a predictive tool called the Early Warning System (EWS). It is a first-of-its-kind AI solution that is based on a technique called Bayesian Hierarchical Modelling (BHM). Using aggregate data (e.g. Covid-19 positive case numbers, 111 calls and mobility data), the EWS is able to accurately forecast cases weeks in advance so hospitals can divert staff, beds and vital equipment, such as PPE, where it is needed most

To make accurate predictions, standard approaches to forecasting typically require years of historical data and consistent patterns and trends. Neither exists for a situation as unprecedented and rapidly-changing as the pandemic. What makes the EWS so special is its ability to bring together external contextual information and learn local patterns between geographies. This allows for accurate predictions, down to a hospital level, even as things are evolving extremely rapidly.

… and what this means for retailers

This is good news for retailers – and businesses at large. But moving from economic stagnation to a huge boom of economic activity over such a short timespan is not normal and introduces masses of uncertainty. Few brands (if any) have ever managed a transition like this before and it is still not clear which sectors will see the bulk of the benefit as the economy bounces back.

The truth is that many retailers are signing up for yet more complexity with Christmas sales, without the tools to maximise profit when faced with such uncertainty, especially on Black Friday and during the vital Christmas trading period. 

Inevitably, though, Black Friday involvement will bring supply chain complexity that’ll require an investment in additional technological resources to ensure business success at such a critical time of the year. The recent supply chain disruption and labour shortages exacerbated by COVID and Brexit has caught many retailers off guard. The Office for National Statistics recently reported that 27% of food and accommodation firms have experienced lower than normal stock levels and Richard Walker, Managing Director of Iceland, has warned that these shortages could even “cancel Christmas”

The data behind the big retail changes

One of the biggest obstacles for forecasting demand is data. Most demand forecasting approaches used by retailers are heavily reliant on historical data, using past trends and cycles in purchase behaviour to predict future trends. Some companies might have incorporated some external data sources to get a better handle on the influence of macroeconomic effects or seasonality, but even these companies still run into challenges. This is because traditional systems used for demand forecasting used by many retailers fail to account for the issues of data sparsity, scarcity, and uncertainty that inevitably exist in the real world.

The NHS response to the pandemic is a great example of how organisations can use data and machine learning to make accurate predictions about the future and to inform more effective decision-making – even during highly volatile conditions. The data techniques applied in the EWS – and the predictive analytics used to better manage life and death decisions – carry important lessons for retailers as the economy opens up.

The key to navigating uncertainty for brands is to focus on building the best possible understanding of the underlying factors that drive demand for their products. AI and machine learning are a great solution for this as they offer the possibility to incorporate a much broader range of internal and external sources of data than traditional techniques.

The latest generation of ML methods are able to drive as much as a 20 percent improvement in the accuracy of demand forecasts. For example by incorporating: high-street footfall, consumer search activity, website engagement, and weather data in addition to traditional internal data like sales data and marketing activity. Crucially, machine learning can learn the subtle correlations between products, geographies, and this rich range of data, which is extremely valuable in making more accurate and granular forecasts during times of uncertainty.

How demand forecasting can really make a difference

With the right scientific approach, and by that, I mean using Artificial Intelligence (AI) to forecast demand based on real-life, real-time factors that might impact shoppers’ decisions, such as weather and historical sales data, retailers will understand what else they can sell. Predicting how all of these uncertainties will play out poses a huge challenge for retailers. If brands want to navigate the uncertainty successfully, prepare effectively, price optimally and allocate resources accordingly, it’s critical that they set themselves up properly when it comes to the way they use data. Ultimately this means deploying similar capability that the NHS did in developing the Early Warning System.

The key to navigating uncertainty for brands is to focus on building the best possible understanding of the underlying factors that drive demand for their products. AI and machine learning are a great solution for this as they offer the possibility to incorporate a much broader range of internal and external sources of data than traditional techniques. The latest generation of ML methods are able to drive as much as a 20 percent improvement in the accuracy of demand forecasts. For example by incorporating: high-street footfall, consumer search activity, website engagement, and weather data in addition to traditional internal data like sales data and marketing activity. Crucially, machine learning can learn the subtle correlations between products, geographies, and this rich range of data, which is extremely valuable in making more accurate and granular forecasts during times of uncertainty.

Real time data, real solution?

With this knowledge, brands aren’t restricted to long-term forecasting based on a high-level understanding of customer behaviour and aggregate sales data. Instead, they can monitor these more granular metrics in real time, forecast short term demand fluctuations, and react accordingly by making optimisation decisions around pricing and logistics in a precise and highly-targeted way. That real-time insight is vital now more than ever; with the gap between restrictions having been eased but before any “new normal” being established likely to be the most uncertain period yet of the last two years.

The Covid-19 pandemic has exposed weaknesses in forecasting systems and the instincts of traditional ways of doing business across the world, but it has also presented an opportunity to re-set, innovate, and radically improve the economics and efficiencies of forecasting tools. If the success of the NHS Early Warning System is anything to go by, the best brands are getting ahead by using these techniques to predict demand in a way that is more robust, more flexible, and built on a solid foundation of data analysis. If it saved lives for the NHS, it will save money and improve customer experience for retailers. It pays to get the Black Friday period right because if a retailer can manage that well, there’s no reason why this good practice can’t be replicated all year round.

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