>Business >ML in the retail industry – Use cases, applications, and the future Part 4

ML in the retail industry – Use cases, applications, and the future Part 4

This is the fourth part of the blog series on ML within the retail space leveraging AI/ML.

ML-driven demand forecasting, AI-voice assistance, customized shopping assistants and virtual fitment rooms are just some answers that can be beneficial to the retail space to furnish a noteworthy shopping experience to clients.

As the COVID-19 pandemic reaches a sort of plateau and the general populace starts to go outside with their shopping hats on, retailers have to be equipped to manage the crowd in brick-and-mortar stores. This is the time when their marketing, product coverage, pricing, and client service are evaluated to fresh limits.

To ignite their top line through interpretation of the changing client shopping preferences, retailers are taking up data analytics solutions that harness emergent tech like Artificial Intelligence (AI), Data Science, and Machine Learning (ML). Such answers assist retailers collect actionable insights from the massive amounts of client data available and develop techniques and strategies to entice new and drive retention of current clients.

Understanding Client Behaviour

They can identify trending patterns in historical purchases, made on the internet, and in-store visits, price range of preference, branding, etc. and can predict what clients may look for in the future and plan their campaigns in a relevant fashion.

With the influence of COVID-19 on purchase trends for instance. An ET Brand Equity reveals that 72% of new users started consumption of health and immunity-boosters during the course of the pandemic. It demonstrated that 60% of customers would go on to do so, after the pandemic as well. With the capacity to analyse and comprehend these trends in a deeper manner, retailers can plan out their product line-ups to satisfy dynamic client demands.

When businesses are conscious of client-centric patterns, formulation of marketing techniques are simpler which will produce appreciation in their top line for their enterprises.

Enhance Client Experience

AI/ML are have a critical part to play in assisting retailers provide standout client experience cross differing channels. AI, for example, facilitates them to understand everyday modifications in client behaviour with each interaction and communication. It drives them to filter through parameters like historical purchase patterns, hobbies, interests, etc. furnishing appropriate product recommendations to every client.

For instance, when a customer purchases a smartphone on the internet, they obtain automatic suggestions on buying accessories such as screen guards, covers, etc. that are available to be delivered on that same day. By putting product suggestions and recommendations in an intelligent manner, retailers have the capacity to improve and supplement their shopping experience.

Analytics additionally facilitate retailers to enhance in-store experiences. Long standing queues for billing purposes is a spoiler for several shoppers. With data-led insights and predictions on expected client rush at differing hours of the day, retailers can go about planning on including additional staff or billing kiosks to make sure that a smooth shopping experience.

Conventionally, retailers were reliant on trial and error to handle inventory, which had the outcome of piled-up stocks and product wastage. Currently, with AI/ML on their side, retailers have the capacity to streamline inventory administration. They obtain precise forecasts on how demand for specific products is probable to fluctuate in the times to come. With precise predictions with regards to product demand, they can go about planning purchases and stock movements to make sure there is max consumption, saving on warehouse expenditure to minimize wastage.

Practical Examples and Deployments that add value

It is wise to be in-the-know with regards to the part that Machine Learning plays within the retail industry. This portion of this blog article will enable you to know more about applications of ML within the retail industry, use cases and advantages of the technology which ultimately serve as a potent value addition, supercharging your retail presence and .

Machine Learning (ML), has conferred massive advantages upon the retail space by facilitating enterprises to improve their profits. It is made feasible through the generated information that assists in unlocking the avenues to predict, adapt, and meet consistently evolving client demands.

A common ML model breaks massive volumes of advanced data into insights that can be pro-actively acted upon with an enhanced understanding of why a client behaves in the manner that they do. Market trends also need to be understood and adapted to. Through extraction of these significant insights, a business can arrive at estimations for demand in the months and years to come, determine a market-competitive, and fair pricing scheme – additionally, they can also be used to keep a check on compliance in the various international markets, and devise lucrative offers and schemes for both existing clients (to prevent churn) and to pull in new ones.


The market has witnessed a considerable shift towards personalization and experiences over products, and in keeping with this spirit, we’ll elaborate on the several ways in which a business can leverage ML technology to maintain the pace with its retail-based business ahead of market contestants through exploration and deployment of the use cases and vetted best practices.

What’s the fuzz about ML?

Machine Learning, or ML for short, falls under the greater umbrella of ‘Artificial Intelligence’, which is a constellation of technologies, a wide array of emergent tech which includes Robotic Process Automation (RPA), ML, Natural Language Processing (NLP), and neural networks all integrate Artificial Intelligence within operations.

Machine Learning, in particular, facilitates computer systems to evaluate and train from data while rendering precise forecasts and smart decision-making, with no requirement for a human agent to intervene. An ML model for retailers effectively undertake review and break a massive volume of sophisticated data into actionable insights, facilitating:

What is the role of ML in driving the retail space?

The retail space is experiencing an ongoing evolution on all fronts – clients are persistently altering their purchase patterns, giving rise to new trend patterns, the market is witnessing a shift in the direction of evolving into advanced ecosystems. The emergent tech, being classified as Industrial Revolution 4.0, are altering the domain in ways that make it unrecognizable. In this environment, it is crucial to stay agile. At the same time, shoppers are having their inboxes flooded with attractive offers making a bid for their interest on every channel available, ranging from internet (web and smartphone apps) and brick-and-mortar stores.

With the marriage of ML and marketing efforts, businesses can obtain the best use of their client’s data. AI functions such as computer vision, NLP, and visual search are serving as game changing revelations by enhancing optimization and forecasting for the retailers.

Businesses that are hesitant regarding the implementation of this data-driven tech will experience considerable log with regards to KPIs. Those that are early adopters will rocket jump ahead, notwithstanding their current scenario or position.


That brings us to the conclusion of the fourth part of this multi-part blog series. We had a deeper look into understanding and interpreting customer behaviour, improving experiences, and also took a look at practical examples and use cases that inject value into the business. The next part will explore the same subject in deeper detail, while touching on other topics.

Add Comment