Precise size optimisation is key to exceptional inventory management, driving sell-through rates and reducing waste across retail operations. In this second instalment of our series exploring data applications across a product’s critical path, Jennifer Gaskins, PhD shares her experience on how robust product data, granular analysis and machine learning can transform how retailers approach size-level buying decisions.
Precise product attributes are the key to exceptional customer experiences, driving sales and fostering brand loyalty. Natalie Theo, Dressipi’s Style Director shares her experience on how investing in robust product attribution, allows merchandisers to outperform competitors and meet the needs of discerning shoppers.
In the rapidly evolving landscape of fashion retail, the intersection of data and artificial intelligence is creating unprecedented opportunities. As we navigate 2024, let’s explore how cutting-edge AI technologies are transforming the way fashion retailers operate, while acknowledging the critical challenges posed by unstructured data in this domain.
Product data enrichment or attribution for fashion and apparel retailers is the process of enhancing and expanding the information associated with clothing and accessory items to describe the item in the language of the customer and to show exactly what they are purchasing. Done well, product enrichment will improve the efficiency of marketing budgets, improve the digital shopping experience, and deliver more accurate demand forecasting.
We ended up having a profound discussion about the evolving landscape of personalization and its critical role in shaping the future of retail. Most retailers today have too narrow a view of the use and potential of personalization: they think of personalization in terms of segments, widgets on a site, and possibly some AB testing.
