This has become more challenging with the proliferation of Stock Keeping Units (SKUs), shorter product lifecycles and constantly changing customer preferences, and aggressive competition among retailers, all of which have led to increased volumes and manual iterations to be handled.
These evolving trends in retail and the opportunities they present underline the need for a localized and effective space planning solution. Space planning models and approaches have shifted gears over the past few years as retailers worldwide incorporate new channels, try new—often smaller—formats, expand categories and set aside areas of the store (like a snack area or an activity center) solely to improve the shopping experience. They derive their models based on the traditional foundation of sales and margins data, while discounting customer-centric parameters like opinions through social media feeds and demographic segment behavior. This defeats the purpose of the exercise and leads to no significant increments in returns. A large number of retailers are still struggling to identify appropriate, advanced and scalable technology-driven models to achieve the desired business results.
Effective macro space optimization has to be built on information that goes far beyond just sales numbers. Retailers know what shoppers buy, how they buy, when they buy and why they buy. They can learn how these patterns shift, sometimes multiple times in the same day. They can learn of the role their categories play (and how these roles shift) for those customers. This knowledge gives retailers an understanding about their clusters, categories and, most critically, their customers. Effective macro space planning needs to be proactive, taking into account factors like customer behavior, store characteristics, competitors, category role and more to predict ideal layouts that will increase your store revenue.
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