The volume and velocity of consumer data is accelerating the need for a major transformation in Category Management.
Shoppers are undertaking ever more complex journeys when buying goods, which straddles numerous channels, while also becoming increasingly demanding in terms of how they expect to be serviced by retailers. This includes both a need for immediacy of delivery and tailoring offers to specific preferences.
The result of this revolution in customer behaviour is that the old ways of category management are reaching their sell-by-date. From traditional task-driven methods, based on past sales, the industry is gradually shifting towards a more customer-centric methodology that adapts to real-time demand and customer behaviours by utilising a myriad of data sources.
This is a marked strategic change in the retail industry that has largely recognised category management as simply a spreadsheet juggling exercise that uses scant amounts of data beyond the basic information that flows from a Point-of-Sale system.
The profusion of data sources and the growth of omnichannel retail are combining to drive significant changes. Consumers want retailers to meet their needs regardless of whether they are in-store, at home, or on the move using their mobile devices.
Mobile devices are playing an increasingly impactful role and the use of mobile devices to research products while in-store is also a common activity before the purchase. This ‘always-on consumer’ is fuelling an immediacy that is forcing retailers to adapt to remain competitive. To meet the needs of the new customer requires radical action in the supply chain.
Making sense of data
This boils down to three key points: an ability to anticipate demand; the alignment of assortments with local demographics and shopping patterns; and the translation of assortment decisions into executable space plans that optimise space allocation by store. Subsequently, category management needs to involve the entire supply chain to deliver the results on the shop floor calling for greater collaboration between brand owners and retailers. They both need to work together on a new breed of KPIs as well as work together to better understand the segmentation of customer bases.
Fundamental to this is customer data – regardless of whether it is sourced from an in-house CRM system or loyalty programme, or bought in from a third-party data provider – that contributes to the ‘secret sauce’ of the retailer or brand owner.
But it is not just about access to data that is an imperative today. It is a blunt instrument unless rich insight can be derived from the raw data. Hence data science needs to be thrown into the mix. New capabilities like machine learning are developing at a rapid pace and will have a growing impact on category management as it will provide the indicators of what actions to take.
It will help create a better understanding of the market and enable a greater sense of demand through more predictive capabilities. In fact, retailers need to leverage the available consumer insights to support increased localisation and personalisation as well as dynamic pricing and improved merchandising.
The major grocers have recognised the need to take on board this thinking as they come under increased pressure from discount chains. They need to efficiently manage their inventories across different store formats by localising their assortments.
Such moves towards localising the offer is putting a strain on retailers and manufacturers who are finding that the old way of using planograms is simply not sustainable. The need for constant generation of new planograms to support their increasing desire for localisation strategies highlights the fact that automation needs to be introduced into their systems to reduce labour-intensive human touch-points and lead to real-time capabilities within the category management function.
This is not some dream-like scenario that will only come to fruition many years into the future because, for instance, we are already seeing the early signs of assortment planning being re-invented.
Next-generation retail planning
The next generation of retail planning solutions will enable retailers to step into the shoes of their customers and think as they do. By using data science scoring of individual items in the assortment by customer segment and cluster and by using historical buying behaviours to forecast their predicted performance, planners will be able to align product selection with customer preferences while maximising sales, margin and inventory productivity.
To help the industry progress along this journey towards creating the next generation of category management solutions, we are starting to see increased cross retailer collaboration, as they strive to create a framework of best practice.
Although such collaboration is an acknowledgment of the magnitude of the challenge ahead, it also highlights that the category management industry is working hard to create future solutions that will deliver on the increasingly complex demands of both customers and retailers.