The retail is one of the most dynamic industry in the world as retailers proactively and constantly looking for ways to attract, retain and support customer through data and analytics at each stage of the shopping journey.
Retailers that could effectively utilize machine learning, which include recognizing known patterns and optimizing and planning will be able to make the right decision as what to stock, how much to buy, and what products to suggest to repeat customers.
Machine learning enables retailers to analyze the data to better understand and meet their customers’ needs. So rather than programming software to accomplish a specific task, the machine uses Big Data and sophisticated algorithms to learn how to perform the task itself.
Machine learning allows applications to “think” and independently make a determination or prediction going beyond what predictive analytics and Big Data analytics can do, and often beyond what humans can do.
A popular retail example of machine learning is a recommendation engine in an online retail environment.
Machine learning is central to cognitive computing and critical to its application in retail marketing.
It simply means that a machine adjusts its algorithms based on outcomes of its recent efforts to successively improve its understanding, reasoning, predicting, and prescribing capabilities under more varied conditions.
Learning enables another distinguishing capability, the ability to perform a task the machine has not been explicitly programmed to perform.
Machine learning predicts and optimizes performance based on results from previously manual tasks, using algorithms that learn more every time they run.
Over half the data in a typical enterprise is unstructured in images, videos, and correspondence, and until recently, these sources were difficult to mine for insight.
Machine learning empowers software algorithms to learn from history how to handle unstructured data through the recognition of patterns. With machine learning, an application can learn how to initiate appropriate action without being explicitly programmed and expand its know-how with each case.
Sales promotions are on the rise, and retailers are struggling to make better prediction and design promotion campaign that improve sales performance and increase returns.
There are many parameters such as brand quality, product category, shelf life, sales volume, price optimization and promotion frequency that should be analyzed before planning and executing a sales promotion. In the new forecasting world of machine learning, retailer can build a customized model for every category or sub-category or brand.
Instead of a few decision trees, machine learning algorithms randomly create thousands of decision trees based on sub-groups of explanatory variables.
The algorithm then combines the thousands of trees to make a single predictive model that incorporates all the variables.
Once “trained,” the algorithm is able to automatically predict sales at the product level during any promotion. And it continues to learn as it takes in more data and results.
With many positive outcomes already being realized, machine learning is quickly moving into the retail enterprises.
However, without proper due diligence, machine learning will bring adverse and unforeseen consequences that can exert significant disruptions on employees, customers, and business partners.
Mike Ghasemi is the Research Director for IDC Retail Insights & Hospitality Asia Pacific, where he leads the definition, creation and production of IDC market intelligence solutions for countries across the region. Before joining IDC, Mike spent 15 years in the IT industry, with 10 years in retail information technology software solutions.