Artificial Intelligence And Merchandising.
How can Retailers Like CHANEL benefit from AI Powered Merchandising
Design, build and deploy AI and machine learning tools that make your operations smarter.
Merchandising and the role of the merchandiser has always been about creating a relationship between consumer and brand, to the extent that a storefront design or advertisement can evoke strong emotions and behaviour. Our ability and desire to identify with new trends created by a respected person or brand is extremely important.
Merchandisers are the creative fuel that drives the store’s vision for their inventory. They think about the future and look to create trends.
So how can we harness the power of AI and combine it effectively with the role of the creative merchandiser to take the consumer-brand relationship to a new level – and what sort of impact will this have on the future role of the merchandisers themselves?
Three ways to optimize your merchandising process with AI
Execution
Apply automation to highly predictable occurrences. For processes that require minimal human judgement, automation can help reduce errors, save on costs and free up time.
Intelligence
Embrace advanced analytics to enable precision at scale. Now actionable data insights will direct merchant’s curation and segmentation of merchandise selections.
Strategy
Put execution and intelligence capabilities in place. By using these capabilities, merchants like CHANEL can invest brainpower in inspiring actions that deliver differentiated value propositions.
1. Assortment
Product line reviews across categories require effective taxonomy classification and mapping. With AI, you have the ability to match and compare them with competitors’ corresponding products. This will allow retailers the ability to review many more categories and be more relevant to customers.
2. Content
Online retailers are striving to include more attributes in their product content to inform confident purchase decisions. In addition, product images also form a key part of product content. Deep learning algorithms can be used to classify images based on the retailer’s requirements — think identifying fraudulent CHANEL images and promotional text on images at scale.
3. Pricing
Relying on data from historic sales-based trends and forecasts to make pricing decisions isn’t enough these days. Allowing machines to identify price patterns, allows retailers like CHANEL to make smarter, more timely pricing decisions at scale.
4. Search
Product discovery requires products to be discoverable in multiple nodes across a catalog.. Multiple taxonomies or a routinely changing taxonomy for product discovery is a key governance that retailers need to reckon with in the future. This simply can’t be done manually. Instead, a combination of classification techniques and constant learning algorithms combined with human intelligence will be the future need for many ecommerce retailers.
CONCLUSION
AI engines will continue to evolve and become “super recommendation engines” that will put ecommerce sites on steroids. The organizations that invest wisely in these applications of AI will understand and serve their customers more effectively, gain market share and become formidable competitors in their space.
For more information on how Machine Learning is integrating into the digital landscape check out this article:
McKinsey’s State Of Machine Learning And AI, 2017
https://www.forbes.com/sites/louiscolumbus/2017/07/09/mckinseys-state-of-machine-learning-and-ai-2017/#3a77187475b6
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