How machine learning helps retailers optimize resources and improve sales

Technology is transforming the business models of different industries globally. Consumers now don't wait till Saturday to shop for their favourite brands or check the outlets for new products. As consumers exhibit their shopping habits and preferences online, retailers can leverage this information to maximize profit and stay competitive.

The retail industry is one of the early adopters of big data. The convergence of big data and technology offers unparalleled opportunities to retailers. It helps them delve deeply into customers buying behavior with the help of advance analytics and machine learning tools.

Leading retailers and consumer packaged goods (CPG) companies are using machine learning to personalize consumer experiences, forecast sales, and optimize production with different analytical models. The machine learning tools adroitly handle the large volume of data emanating from various sources and generate insights based on the following techniques:

Optimization to achieve maximum business value
The use of optimization and machine learning is helping retailers to lower cost, maximize profit and manage resources in an informed and decisive way. Retailers could set flexible prices for their products and services based on supply and demand constraints, competitor prices and other external factors. For instance, a retail firm can boost its sales by adjusting the brand price; reconfigure its distribution centers and use predictive information while still meeting inventory and demand through optimization. Linear and Kernel classification are the widely used methods in optimization. Popular examples of the classification include support vector machines (SVM) and logistic regression.

Recommendations for best products
Recommendation engines enable retailers to infer products that should be recommended to customers based on their online behavior. It can also be used to suggest products to customers they are planning to buy. A retailer can use content-based recommendation and collaborative filtering models to entice customers with best products. The use of recommendation engine is burgeoning in the e-tailer industry as it helps in making a dynamic suggestion on websites and boosts the marketing effectiveness.

Market Basket Analysis
To succeed in any business you must have a good product mix. Market basket analysis helps in identifying products that can be purchased together by customers. It uncovers the associations between specific products and enables retailers in cross-selling the same based on previous purchases by different customers. For example, people who buy flour, also tend to buy eggs as there is a high chance of them baking a cake. A retailer can use this information in placing these two products together in stores, drive recommendation engines and develop marketing campaigns effectively.

Embracing A/B Testing
A/B testing is a powerful technique to increase conversion rates of e-commerce websites and understand the online audience. The technique tests a version A of a website against a different version B to know which one is most successful based on different metrics. It allows retailers to learn which visitor segments perform better with specific content and helps in improving website optimization. A/B testing has become increasingly popular for e-commerce sites as it presents an array of opportunities for testing. More importantly, the conversion rates significantly impact the revenues of e-commerce companies and help in improving market share.

Predictive analytics to know the future
Predictive Analytics is becoming embedded in most of the strategic marketing departments of the retailers. The sales forecast is the backbone of many strategic decisions embarked by the retailers. Predictive models including neural network, ARIMA, dynamic regression are becoming the catalyst for machine learning-based forecasting. Accurate sales and demand forecasts aids in improving supply chain operations, production planning as well as after sales service of retailers. Evidently, the use of predictive analytics has been instrumental in the dominance of some of the major retailers in the online space including Amazon.

Conclusion
Machine learning and advanced analytics help retailers in automating complex processes and turn the massive customer and product data into actionable insights. These insights will not only catalyze business and operational changes but also help in gaining and sustaining customers for a longer period of time.

Machine learning is going to be the new weapon to drive competitive advantage and improve market share in the fiercely competitive retail arena. The battle for being on retail shelves or a website for a longer time will only be won by how well your organization implements machine learning techniques effectively. So do you see machine learning supporting the next-generation of retail and e-commerce growth?