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In
today’s marketplace, successful apparel retailing
means understanding more about the customer –
faster than ever before. And those short selling
cycles present unique problems. Often with less
than three months to move merchandise, fashion retailers
know that having a keen understanding of their customer’s
behavior plays a significant role in determining
merchandise assortments.
Having the capability to define and understand product attributes, along with understanding customers’ buying behaviors, all play a role in the selling success of the assortment. Getting the right fashion merchandise to the right location at the right price is getting more difficult. Successful fashion retailing will become more dependent on the ability to predict the performance of new items while accurately defining assortment for existing items. The reality is that retailers will come to rely more heavily on quick and accurate forecasting accounts for product and location attributes.
How do you define actionable
customer centricity?
AIS predictive forecasting engines run multiple models with product, location and customer data in a multi-dimensional fashion, finding the most relevant demand patterns. Taking into account that customer behavior will differ between stores, these engines clearly define how much inventory should be purchased in a selling season and make recommendations to specific locations.
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