Returns Monster with AI – Algorithmic Retailing
- February 1, 2020
- Posted by: Aelius Venture
- Category: Business plans
Forecast returns after order placement
AI can build a contextual assessment of captured orders, customer profile, purchase history, credit scores, social media trends and merchandise performance from historical and current data to forecast returns. This helps retailers to plan for reverse-logistics beforehand , lowering costs for retailers. AI also can help identify and manage errant customers who abuse return rules by ‘wardrobing’.
Link return rate to other business areas
AI can turn returns into opportunities and convey efficiencies across the worth chain:
Sourcing: Intelligently link suppliers to product return rates with explainable AI to make sure right products at the proper price within the future
Marketing: Customize targeted marketing supported customer returns profile
Supply Chain and Logistics: Deploy AI/ML to pre-empt returns inbound for better depot and resource management
Merchandising: Leverage the facility of AI to optimize the ranges to scale back returns resulting from issues like fit and size. Right range at right price could mean higher customer satisfaction.
Eliminate reasons for returns
By assimilating, assessing, and drawing contextual insights from internal and external data sources, AI can help retailers understand customers better. this data are often wont to make sure that the customer buys confidently and their key reasons for returns are eliminated:
Make personalized recommendations: Leading fashion and sports brands are using AI to supply customers with customized recommendations when shopping online. as an example , customers can interact with Levi’s virtual stylist using Facebook Messenger to seek out the right pair of jeans.
Determine fit: Size and fit are the first deal-breakers. AI helps shoppers make informed decisions through multi-point sizing that delivers a bespoke fit. ML and AI enables brand-to-brand size translations, resulting in better fit recommendations for online buyers. UK’s top fashion retailer ASOS uses AI to form size recommendations supported the customer’s purchase history.
Predict price points: Tools like TCS’ Optumera™ apply real-time computational intelligence to trace and pre-empt competitor’s prices, assortment and inventory across channels to enable retailers to make winning competitive strategies and reduce returns.
Make accurate delivery promises: just in case of next day delivery, AI can have a holistic view of inventory positions in real time to assist retailers keep their fulfilment promise. Not only will this curb returns but also it’ll do wonders for enhancing customer confidence and loyalty.
Returns are the new norm
Retailers are slowing awakening to understand that rather than fighting returns head on, a more bottom-line driven approach will help business. Moreover, accepting returns as central to customer experience can help retailers affect it differently and it’s going to cease to be a drag . In fact, how you affect returns—pre and post purchase—can bring brand differentiation, cut competition, and even cause you to more profitable. With the proper set of tools, returns are often a source of loyalty and growth.
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