ML In Retail How It Works For Your Business

Machine learning is the branch of Artificial Intelligence that learns from past data and predicts and forecasts for the future.

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What is machine learning?

Machine learning is the branch of Artificial Intelligence that learns from past data and predicts and forecasts for the future. The preferences of data are trained and then learned from past data for prediction of future such as, if a customer bought blue jeans from Levi’s Ecommerce store ten times, the machine learning algorithm can detect the customer’s choice and recommend him the best blue jeans. 

Types of ML algorithm

Two basic algorithms used in machine learning are regression and classification.

Regression


Regression is the powerful machine learning statistical method of predicting and forecasting output variables depending on the past projection of input features or variables.

Example of Regression in Retail

In the retail sector, the demand for sweaters in the winter season can be easily forecasted using the Regression method by learning past year winter data.

Classification:

Classification classifies the input data or features into different groups of similar nature. The classification is used where different groups of things are needed to be separated according to their features. 

Example of Classification in Retail

In retail, the classification of customer’s example, the retail E-commerce and physical store wanted to classify customers into three categories basic, premium and executive from data of all customers in order to give them services accordingly.

What is machine learning in retail?

Machine learning operations (MLOps) in the retail industry means using past data of inventory, customers purchase, current stock position to predict various different things such as prediction of required inventory in future, forecasting of customer demands, the behaviour of customers, prediction of e-commerce store profit and revenue, optimization of revenue and profit by checking high revenue and profit products from past data. Machine learning empowers retail store managers to simply click and predict or forecast as required. 

Why do you think machine learning is important or useful in retail?

Machine learning besides helping from past data to the retailers and store manager also uses live sales data, inventory position and market condition in real-time. It can recommend the retail owner for purchase of inventory, pricing and demand.

How is machine learning changing retail?

There are various ways in which ML is changing the whole retail industry and its methods. Some of the examples are

  • ML provides a better and attractive interface to users
  • Personalized recommendations for users
  • Optimization of stocking and inventory
  • Optimizing operating cost
  • Automation and use of robots for efficiency
  • Smart solutions for customer
  • Ecommerce
  • Efficient Recommendation Engines
  • Customizing design
  • Payment facilitation with no use of paper money/card

How is machine learning used in retail?

Machine learning is used in various parts of retail processes and stores. Some of the Machine learning uses are given by

Customer service Management

Machine learning-based chatbots are powerful on E-commerce stores such that they can smartly take orders from customers, recommend products, talk to them and solve their problems. They provide greater efficiency, 24-hour working availability and are capable of learning from customer behaviour.

Workforce Management

Machine learning algorithms help retail stores with scheduling jobs, the use of their efficient employers at prime time, workers requirements and the prediction of work hours and workforce for the future.

Marketing

By checking customer behaviour on various social media sites and buying stores, the Machine learning algorithms advertise or email customers for E-commerce marketing of the same or related products.

Price forecasting and optimization

Machine learning algorithms analyze past and real-time data and help the retail manager in dynamic pricing. The price is suggested by checking real market demand, inventory position in-store and in market and supply position. The Ml algorithm changed prices automatically in order to get market conditions benefits and are optimized to make handsome profits for the retail company.

Supply chain and logistics optimization

Supply chains at retail stores are optimized at various levels such as demand-supply gap optimization, revenue-profit optimization, vehicle route optimization, filling of shelves optimization using robots.

Chabot and Virtual Assistants

Customer experience has been revolutionized due to AI algorithms such as now virtual assistant is available at any time without the wait, the robots interact with the customers for guiding and recommending products, robots use audio-video interaction with the customers for a better experience in their language. Now thousands of customers can talk to virtual assistants simultaneously without any delay.

Automation and Robotics

Robots are extensively used in retail stores for various purposes. The inventory is now placed on shelves using robots with better care than a human being. Robots are efficient enough that they clean the stores when it is closed and charge themselves when needed automatically.

Used cases of Machine learning in retail?

Now every retail store is using powerful machine learning algorithms for retail management, monitoring and prediction. Some of the examples are below

Nike

Nike has been using powerful AI algorithms for the past many years for E-commerce marketing and customization of products. Nike Machine learning based mobile apps are so powerful that you can design your shoe by self-according to your requirement of colour, shoe size, design and other features. It used customer’s data for marketing through messages and reminders about new products.

Walmart

Walmart is one of the best stores in the world and a pioneer in introducing Artificial intelligence in the retail sector. Walmart supply chain and logistics network is the biggest in the world and works on the principles of the Just in time (JIT) method. This method used a Machine learning based algorithm for truck route planning, optimization, filling of inventory and supply-demand optimization.

Benefits of ML in retail

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ML challenges in retail?

  • Data Security and Privacy
  • Machine Learning mistakes can cause serious damage
  • Complex Business Model Management and require Experts for implementation
  • Implementation is costly
  • Machine replacing retail workers job
  • Rapid changing in the ML systems and their allied infrastructure

Conclusion

There is no doubt that the retail industry benefited a lot from Artificial intelligence based methods such as machine learning for their inventory management, better forecasting, marketing, future predictions, price management and overall store optimization. This reduces the risk factors involved previously in the retail sector and optimizes the profit. However, the infrastructure of Machine learning is still costly for new industries and the issues of data privacy and insecurity would need to be solved in order to gain maximum customer trust.

Resource:

  1. The Guide to Machine Learning in Retail: Applications and Use Cases
  2. Machine Learning in Retail: 5 juicy examples
  3. How Machine Learning Is Reshaping Retail Pricing and Promotions
  4. Machine Learning
  5. Machine Learning: what every retailer needs to know to shift the dial

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