Chief Executive Officer Terry Lundgren attributed the results to better margins, disciplined inventory management and Macy's program to allow stores greater leeway in catering to local tastes. These factors also prepared the company to woo shoppers despite the threat of a new dip in consumer spending.
Although the department store chain has centralized its buying, planning and marketing operations, it also has local managers on the ground who examine sales data and travel to a specific handful of stores to study customer buying at those locations. Based on their work, individual Macy's stores may decide to stock more of a particular dress size in a particular style, for example.
Other store chains make similar inventory moves, but without the granularity of Macy's approach. For example, a store may ship more of a specific size for a whole range of clothing to a store. That may be all it takes to make more apparel sell at a full price rather than linger on the rack and be marked down.
Kudos to Macy's.
Based on our work for several US women's apparel retailers, I can tell you that assortment localization can be achieved through good analytics. Retailers don't need many local managers to observe the shelves to achieve localization.
The key aspects of analytics for assortment localization include:
- Collating the transactional sales data (gross sales ($,units) , net sales ($, units), cost ($)) at a weekly level across stores for the past few quarters -- typically hundreds of millions of rows
- Adjusting the sales data for stock-outs and negative margin sales (to get a true picture of demand for each style at a store level
- Rolling up the sales data to the desired level of product hierarchy (Department/category/class/section/end-use/Color/Size/etc)
- Using the rolled-up adjusted sales data across stores to decide assortment and allocation for the future.
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