Importance of machine learning in Retail Analytics

It becomes extremely difficult at times for a retailer to make a choice between two great products on which deserves more promotion. Retailers face such situations every day when it comes to defeating the competition or creating a dominance in the vicinity.

In such situations, we believe what can help a retailer, is the use technology like machine learning, etc to make amplifying decisions. With the evolution of technology, consumer behaviour also tends to evolve. When managers and retailers think or hear the machine learning in retail, the first image in their mind of robots or automated processes like conversational agents. This imagination is going to reality and this is the future of the retail industry but it takes some time. Though the admirable growth of Artificial Intelligence(AI) and Machine Learning(ML) in the past few years have fueled a belief in retailers that Al might become capable of challenging and replacing human intelligence and their decisions. But the retailers know that it takes time and especially in the retail sector, AI and ML falls short while making the decision where there are many context and variable involved.

Now, the retailers know how to take advantage of technology with the help of retail analytics. The definition of Retail Analytics is “any information that allows retailers to make smarter decisions and manage their businesses more effective.” The retail analytics market has been experiencing good market growth by following good relationships with customers, which has resulted in increased competitive advantage and growth benefits. Due to digitization and technological awareness among customers, it makes it easy for them to purchase through a variety of options. The variety and bulk volume of data are projected to have a remarkable effect on the retail analytics market. With big data analytics, companies are capable to generate meaningful and achievable visions and information that facilitates revenue generation and reach untapped locations for promotions and increase conversion rate. 

The retail analytics market is segregated based on solution, service, business function, deployment model and location. Solutions are divided into data management software, analytical tools, and reporting & visualization tools. Based on business function, the market is segmented into marketing & customer analytics, merchandising & in-store analytics, supply chain analytics, and strategy & planning.

Before knowing the importance of machine learning. Let’s just talk about what is machine learning and how machine learning is used in retail. 

What is Machine Learning?

First of all, don’t confuse Machine Learning technology with Artificial Intelligence technology. While Artificial intelligence is the acquisition of knowledge intelligence is defined as an ability to acquire and apply knowledge, Machine Learning is defined as the acquisition of knowledge or skill. In simple words, AI stimulates natural intelligence to solve complex problems whereas ML is to learn from data on certain tasks to maximize the performance of a machine on this task. 

Machine learning refers to the way a computer learns the human logic, behavioural patterns and preferences from their interactions with the computer and various computing software applications. Machine Learning technology helps a computing machine to update itself continuously by learning about the users through interactions, computing behaviour, and individual choices. Machine learning is increasingly being used to understand customers, it has become highly popular among the retailers for targeted marketing and also for delivering customer-centric shopping experience.

Machine Learning In Retail

There is a lot to talk about machine learning in retail. We are still unaware of how it works, but there is a typical lifecycle of machine learning algorithms that involve data preparation to getting the desired output. 



Key Benefits of Machine Learning in Retail

Stocking & Inventory 

Stock optimization is a key factor in running a business or cutting losses for the store. Retailers aim to provide a proper product at the right time, at a proper place. Every retailer wants to provide all the products according to their customer’s needs & preferences. To provide this, retailers need to analyze stock and inventory deeply. While analyzing, managers can easily track their running commodity and also take care of the purchases data set and its prices. This analytics report is shown daily to purchase manager to plan inventory accordingly, considering weekends, festive seasons, and inviting consumers by hosting events. In short, purchase managers can get accurate and real-time estimates of product, so they can find an unusual pattern in their inventory data and notify others regarding suspected thefts. One can also create a sales summary and find which product is selling more and gives benefits to the store. Many retailers are having experience in their industry and know that understanding their inventory needs, ordering appropriately can be a huge win for controlling costs. 

Marketing & Product Placement

 If stock optimization is about to cutting losses then marketing and product placement are about to driving sales. By adopting retail analytics, one can easily track all the valuable information and increase revenues in new ways. In a retail environment, CCTV cameras are used for analytics by preparing certain models in it and delivering values to businesses with humans to watch the footage. Detecting suspicious behaviour or detecting unusual patterns in-store, all of this is possible through AI-powered camera analytics. There are a number of retail chain stores that already has adopted the technology and they are fully focused on using it in marketing and driving revenues. Their investment in machine vision is potentially giving them ROI and they are making history in their books by making important marketing lessons for their multiple stores. 

Customer Sentiment Analysis

Customer sentiment analysis is not a new brand tool. It is a traditional tool for checking…… Are they doing good in the market? Are their customers satisfied? Are they attracting new customers? The traditional method is expensive but after implementation of machine learning and artificial intelligence, it has become less expensive and time-consuming. The analyst can perform customer sentiment analysis through feedbacks and comments on a website or social media platforms, no need to focus on groups and customer polls is no longer needed to collect data. It is done by tracking words bearing positive and negative attitudes of customers. The analyst performs sentiment analysis on the basis of text analysis to define neutral, positive and negative sentiment. The algorithms go through all meaningful speech, letters or feedbacks. The output is the sentiment rating in one of the categories mentioned above and the overall sentiment of the text or we can say feedback.

From the above points, it is clear that increasing the businesses are now relying on the machine learning technology for pushing conversion, marketing, increasing revenues, growth and customer engagement. One does not need to panic as adopting this technology doesn’t happen overnight, there are certain things you need to plan. 

One needs to slowly shift towards measuring their traffic data in a particular store and also compare traffic from other stores. Also make real-time decisions based on analysis data, relying less on traditional ways and more on constantly adjusting the retail environment to cut losses and increase gains 

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