Since the late 1980s, one of the key factors in the success story of speciality retailing has been the role of the merchandiser. This role emerged in main stream retailing at that time, driven by the desire to provide central control over the company’s stock range, its assortments and their relationships with the store network. Rather than continuing with the assumption that this was the responsibility of the store manager, who simply requested what they thought was needed, medium to large retailers realised that it would be more efficient to centralise this function.
Essentially, this was a move driven by the availability of technology, namely the emergence of lower cost mini-computers. The results were impressive, including tangible reductions in employee costs, less and more optimised stock holdings and a route to faster and more manageable store expansion.
For 30 years this role has been key in shaping shopping centres around the country, driving ‘identikit’ stores, and doing the best job possible to match demand with supply, across branch grade, style, colour and size dimensions. However, as much as this role emerged due to more cost effective and able technology, technology is now driving the role away from its original roots.
Technology has always driven retail processes and functions. Over the last 15 years it has changed the face of speciality retailing, online sales now significantly outperform even the largest store in the chain, customers have far greater expectations as to what stock they can buy, and how their orders can be fulfilled, whether by home delivery, or collection. In addition there have been exponential increases in both the type and amount of data that is available to retailers, relating to both store and customer based activities.
The role of the merchandiser is clearly moving away from its original process driven focus on allocation and replenishment, governed by departmental based store grading, to one that is essentially much more data driven and analytical in nature.
In the future, merchandisers need to take account of both the type of customer who is buying the stock and what data is being captured from each store, for example, the age and gender of customers entering the store, to drive their assortment, allocation and replenishment decisions.In many ways whether the allocation is exactly right or not is less important, as fulfilment models that will seek stock from all locations and either deliver directly to the customer or move to their selected collection point, is where the industry is heading.
Statistical forecast algorithms based on machine learning will assist the merchandiser, taking into account all this extra data, producing recommendations, and learning what the merchandiser accepts and rejects. In the future, the merchandising role is much more likely to be aligned with that of a data scientist, with an inherent understanding of the fashion industry they are working in.