In the 21st century retail marketplace, the importance of efficient in-store placement is greater than ever before. Whilst category optimisation techniques can prove invaluable in generating additional facings / sales / market share for retail suppliers, retail operators are increasingly looking beyond the primary category view.
Nunwood has developed a series of sophisticated solutions to get under the skin of this complex area…
In addition to the category performance suite of products, Nunwood Market Analytics has developed a toolkit to aid our retail clients in their store planning process:
Key Destination Drivers/Key Value Indicators: These techniques use transactional level EPoS data to help in the identification of the optimal product offering for a given retail estate/estate sub-group/store.
Key Destination Drivers: Understanding more fully which products drive footfall within a retailer/store. Conventional wisdom suggests that a well-priced range of essentials and everyday products are key to success – but how can a differentiated product offering help to drive footfall and increase customer value on each visit?
Key Value Indicators: Aimed at understanding the impact of price at the shop level, KVI analysis bridges the sizeable gap between primary research and EPoS data, to provide a holistic overview of the price dynamic within a given store, whilst identifying a “value basket” for retailers to maximise purchase frequency within a group of retail stores/individual store.
Store Environment Management: Shopping missions have been at the heart of retail strategy thinking for a number of years. Whilst resource has been freely given to investment in new store formats/product offerings, relatively little has been given to the format of existing stores.
Rather than basing a shopper mission hierarchy on motivations (eg emergency), use of transaction level EPoS data can help to reclassify shopping missions based on what products are purchased in combination with other SKUs.
This information can then be used as a basis of a cluster analysis to define shopping missions based on consumer purchase behaviour as opposed to motivations.
