by Antonio Calvo. Recent world events, including the COVID-19 pandemic and the war in Ukraine, have challenged retailers’ supply chains and product availability. Combined with changing consumer behaviour driven by social distancing, this has challenged most retailers in not only demand planning but designing an appealing customer experience. The key to getting both right lies in harnessing data.
For the last two years, retailers have contended with rapidly accelerated digital transformation. Some have moved digitalisation projects forward by as much as a decade as customers become increasingly comfortable with virtual interactions.
In general, consumers have become much more demanding in their expectations of product availability and delivery time, but retailers are having to contend with both uncertainty about future consumer behaviour and supply chain difficulties. Some key developed markets have already begun to relax social distancing regulations, which has led retailers to question which consumer behaviours will persist and which will revert to pre-pandemic patterns. The short answer is that the only way to deal with these changes is to harness the data provided to retailers by customers and to use it in real-time.
Customers are now much more aware that they are providing retailers with data through every engagement point and expect retailers to use it constructively in improving the customer experience. If I as a customer have been buying DIY tools, for example, I would expect that the retailer will then recommend other products in line with this behaviour – cross-sell, up-sell and tailor my engagement in line with my buying history and behaviour.
Amazon’s move to physical retail stores and Target’s rollout of drive-through collection points is a forward-thinking move. We don’t know what customer behaviours will come next as regulations on movement ease, so anyone in retail appealing to commuters or physical customers will need to be agile. In Amazon’s model, customers can engage online before and during the physical store visit, pre-selecting a range of products to try when they get there. Amazon then has the opportunity to recommend others in real-time while the customer is present. This real-time, interactive response is the key to agility and competitiveness – gone are the days of pushed follow-up emails and text messages after a sale, it is now time to listen.
The best retailers are looking at how to bring technology and automation that much closer to the customer – and must consider two options. Firstly, improving the stock visibility in real-time, in order to have the right product in the right place at the right time. And secondly, helping with lower costs in a global environment where inflation is on the rise, supply chain costs are increasing, and sustainability demands are forcing businesses to optimise their routes to market.
The interface between collected data and demand planning is the critical adaptation retailers will need to make. It is now impossible to rely on historical customer purchase patterns for forecasting because markets and operating conditions have shifted so markedly. What is required is machine learning and artificial intelligence to analyse customer behaviour patterns in real-time and to build infrastructure and product sets to meet them.
Customers in 2022 are that much more demanding of a brand’s promise of delivery and reliability, as well as relevance. We are seeing some fashion brands offering deliveries within one hour. That is driven by growing customer dependence on direct-to-consumer models. This is an opportunity for forward-thinking retailers to take a big leap ahead of competitors.
Machine learning models have dramatically improved forecast accuracy for fashion retailers when adequate product attributes are available. Retailers that have developed strong product attribute data management capabilities are significantly improving inventory placement for both online and in-store fulfilment. Plus, they are swiftly adjusting their inventory planning practices across fulfilment networks by using new cloud-based analytics capabilities, from AI and machine learning forecasting to just-in-time supply chain optimisation.
When demand unexpectedly increases, retailers with an insufficient supply may lose customers to competitors that have the desired product available. Once lost, a customer may not come back. Companies that better understand what influences historical product performance are able to better predict future demand. POS data is key to retail and consumer products forecasting because it closely reflects true consumer demand. With analytics, companies improve forecasting using machine learning algorithms that automate collection and data cleansing for both internal and external data in real-time, then display focused results to predict consumer demand. This is what will drive the next phase of retail competition, which we believe will be hyper-personalisation of the shopping experience.
Main image credit: Pixabay.com.
Antonio Calvo, senior manager, Global Retail and CPG Practice SAS.
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