Retail Machine Learning & AI Solutions
Use AI insights to transform the retail customer experience
As online shopping replaces more and more brick-and-mortar retail stores, AI in retail is increasingly critical for enterprises. In the digital era where consumers are seeking personalized products and services, AI/ML solutions in retail are helping firms align their offerings with the expectations of customers. The disruptive impact of AI in retail is seen across the value chain and is emerging as a powerful tool for retail brands to gain a strategic advantage over their competition.
Companies like Amazon and Walmart have completely flipped the script on consumer expectations. Today’s customer expects personalized offers and deals, and if your firm is providing them this, you are likely losing wallet share. Responsive retail has peaked, and the World is entering the era of predictive commerce. It’s time for retailers to help people find products in their precise moment of need, and perhaps before they even perceive that need, whether or not they’re logged in or ready to click a ‘buy’ button. This shift requires designing experiences that merge an understanding of human behavior with large-scale automation and data integration.
Mosaic has helped our retail customers utilize AI & ML to transform every aspect of their business. These insights replace intuition with intelligence and gives executives a forward-looking perspective. Executives need to be pragmatic in their approach to implementing AI, it is a capital-intensive technology that provides results in the long run.
Our retail customers are using computer vision to customize search results in real time to deploying machine learning for inventory management decisions, and everything in between. Our data scientists’ partner with you to harness AI tools & techniques to better interact with your customers and operate more efficiently.
We have worked on multiple retail engagements where we develop a suite of tools and capabilities that facilitate ongoing learning from data to bring continuous value through machine learning and artificial intelligence. Our customers report sustained competitive advantage from working with Mosaic.
ML & AI for Retail Sheet
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Retail Success Stories
ML Price Optimization for Major Clearance Sales
Retail firms need to embrace the application of machine learning to forecast demand and set prices. If they don’t, they fail to remain competitive. Just take a look at Amazon’s predictive stocking program from 2014, needless to say, they have continued improving that capability. Retail executives need to think more like tech companies, using AI and machine learning not to just predict how to stock stores and staff shifts but also to dynamically recommend products and set prices at the individual consumer level.
Retail Demand Forecasting & Inventory Optimization
AI & ML helps retailers understand customer behaviors and trends that traditional forecasting tools might have missed, helping to forecast emerging demand. ML & AI help retailers improve demand forecasting, improve pricing decisions, and enhance product placement. As a result, customers discover the right products, in the right place, at the right time. Machine learning can help a retailer order the right amount of stock so that stores will not end up with excess or sparse products. Maintaining accurate inventory can be a major challenge for retailers. By connecting more parts of their operations and applying ML/AI, retailers gain a streamlined view of assets, shoppers, and products to help with inventory decisions.
Predicting Customer Churn & Identifying Growth Markets
Retaining customers is a must for a company’s bottom line. A company’s customers are its greatest asset, impacting business now, and becoming more valuable over time as they continue to invest in products and services. Customer churn can be costly, or even devastating, to growing and established organizations alike. The true cost of churn is often higher than business leaders generally estimate. Not only does it lead to lost revenue in the near term, but it also means your team must double down on acquiring new customers to fill those revenue streams to ensure continued success in the future. It is widely accepted that it can cost up to 5 times as much to acquire a new customer as it does to retain a current customer.
Deep Learning for Automated Cooking Operations
Restaurant labor is a growing point of concern for many quick service restaurants around the world. From the challenges of hiring and retaining high quality employees to the reality of increased minimum wage legislation across many marketing in the United States, the industry is under pressure to innovate. Couple this with the highly competitive nature of fast-food restaurants competing for consumer market share, and these chains needs to find any competitive advantage available to them.