Check out how Machine Learning Revolutionizes the Retail Experience
  • byAIBridge ML
As retailers juggle with tens of thousands of items that are sold to a potentially large number of thousands or millions of customers, there are about billions of transactions – and enormous data is generated. A great shopping experience is one of the critical elements that form a buyer’s perspective of the retail brand.
Conventional Predictive Analytics allows humans to gauge and spot statistical trends from a vast collection of data. Machine Learning, on the other hand, is powered by algorithms to detect trends that help make quick predictions with explicit programming. As shopper data can generate real-time and actionable insights with robust analytics, Machine Learning promises to transform customer journeys into great hassle-free retail shopping experiences at the micro-level.
According to top-notch surveys, stores have increased their profit margins effectively by investing and transforming with AI and Machine Learning in Retail. Newest Machine Learning capabilities span shelf-pricing, merchandising, supply chain optimization, and a host of other operational metrics.
One of the world’s largest retailers – Amazon – applied Machine Learning huge customer data to fulfill business objectives. These include tailored product recommendations as well as identification and de-listing of fraudulent buyers. Likewise, a phone camera can be used to find or search for a specific item based on its appearance in real-time. Machine Learning drives a visual search engine embedded into a retailer’s app, which recognizes product shape, size, and color.
Based on a study from technology firm Infosys, 96% of retailers and 86% of consumers regard personalization as one of the priorities. As such, Machine Learning in Retail plays a significant role in identifying target segments and offering personalized products/services to retail buyers. Furthermore, Machine Learning models can learn from buying patterns based on seasonal buying patterns, and demographics such as age group, gender, social class, and financial status.
The learning models for Machine Learning in Retail will help businesses zero in on efficient staff optimization along with the most optimum product pricing with unique but realistic data sets. An uphill analysis of retail customer will soon become a thing of the past, as leveraging machine learning has allowed retailers to predict customer behavior on one hand, and improve own personnel productivity.
A smart store is equipped with Machine Learning to optimize merchandise and prevent shrinkage of assets. Powerful store-wise analytics driven by Machine Learning in Retail can analyze customer visits, most attractive passageways, the number of visitors, and footfalls at the retail store. Precision marketing and efficient store merchandising enabled a firm to accomplish a 30% increase in revenues from sales.
Autonomous Shopping
The evolution from brick-and-mortar shops to virtual stores has added new dimensions to shopping, wherein buyers enjoy more convenience. Machine Learning in Retail has now allowed buyers to check into cashier-less stores using mobile devices for ‘grab-and-go’ experience – with payment gateways and shopping transactions becoming steadier. Futuristic AI capabilities are now driving ‘Standard Cognition’ powered stores, enabling comprehensive autonomous buying.
360-Degree Customer View
Retail chains have deployed advanced Machine Learning models to gauge and direct customer-oriented actionable insights. The firms then take effective decisions on a 360-degree view of the customer, based on their preferences and buying patterns. AI-enabled capabilities power a well-known internet marketing firm to direct buyers to preferred products at the perfect time using data analytics.
Supply Chain Optimization
Retailers are increasingly relying on Machine Learning to easily replenish stocks and streamline the supply chain with powerful algorithms to maintain the desired item-by-store numbers every week. Large-size retail firms have adopted advanced techniques in managing warehouse logistics and predicting the precise level of stock needed down the line.
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