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Data is key to helping retailers cope in challenging conditions

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By Gary Allemann, MD at Master Data Management

12 October 2020

With lockdown regulations ending, the retail sector is finding its feet and organisations are trying balance the ‘new normal’ with the old. Regardless of the fact that South Africa has moved to level two of the national lockdown the sector has felt the effects of demand surges, supply shortages and reduced productivity. Internet and mobile device usage has skyrocketed, introducing many customers to new buying behaviours. As a result, this has caused a rise of e-commerce and is forcing retailers into new avenues to reach those customers. However, retailers now struggle to maintain their competitive edge. In order to assist the retail industry in such challenging circumstances, planning models need to shift from steady-state models to making decisions based on current information. Agile, responsive planning in supply chain management is critical to reducing further disruption and this depends entirely on quality data.

Fast-forward transformation
In pre-Covid-19 days, businesses had time to tackle digital transformation at their own pace but this is no longer the case. Traditional retail demand forecasting based on historical patterns is no longer an option, because the new normal does not reflect history. Now, in a global pandemic we’ve reached a point where it literally is “do or die” when it comes to digital transformation and this is especially prevalent in the supply chain as South Africans continue to restrict movement and stay home. Today, companies that are able to adapt quickly to changing market dynamics are most able to survive and thrive post-lockdown.  Data agility going to determine how businesses come out on the other side of this lockdown – they’re going to need the right data, at the right time, in order to make the right decisions, and that’s where data governance becomes important to ensure quality and integrity.

An agile supply chain is data-driven to meet retail needs
Advanced data analytics is ready to change the face of supply chain management. New data analytics tools can shape the entire experience for retailers/ecommerce businesses. Instead of merely reacting to past events that might have happened weeks or months ago, predictive supply chains use data to analyse events as they happen and utilise this insight to anticipate and direct future demands in the retail sector.

Players in the ecommerce space will need to up their data analytics game, in order to compensate as much as possible for what is missing from the physical in-store experience. Product descriptions in ecommerce applications will depend more heavily than before on quality data, and the user experience will further hinge on data drawn from social media, such as product reviews. In a physically distant world, being able to utilise this type of data effectively  is what is going to drive the buying experience, and here the data quality (or lack thereof) could be the difference between a successful shopping expedition or a failure.

Profits depend on data-driven efficiency
On the physical side where profit margins are extremely tight organisations would benefit immensely from location-based data analytics such as driver and vehicle performance monitoring, real-time traffic updates and smart route planning. Driver and vehicle monitoring reduces accident risk, petrol consumption and vehicle wear and tear; while traffic data and smart route planning can ensure that the deliveries are optimised to reduce delays and wasted trips, by understanding when retailers are available to receive deliveries.

With the appropriate implementation of data analytics and management tools throughout the business, retailers can have a better understanding of their customers’ mobility and behavioural patterns. It can greatly assist the organisation’s drive for a holistic view of its audiences, delivering the enhanced understanding of the customer, pivotal to its business success.  However, this wouldn’t be possible without quality data.


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