Close this search box.
Close this search box.

How Machine Learning Is Revolutionizing Retail in Emerging Markets

How Machine Learning Is Revolutionizing Retail in Emerging Markets


The retail sector has never been shy about throwing its hat in the ring with new technologies, and it’s not just the wealthiest regions of the planet that benefit from the forward-thinking mindset of businesses in this sphere.

The implementation of machine learning and artificial intelligence (AI) is an exemplar of this, bringing about perhaps the most significant shift in customer experiences that we’ve seen in a generation. 

Here’s a closer look at what this means from moment to moment in a variety of retail contexts across emerging markets.

Customer Relationship Management

In emerging markets, consumer data is vast yet often underutilized, in spite of the fact that 70% of the global population is accounted for by this group of countries and regions – so machine learning acts as a linchpin for transformative customer relationship management (CRM). Retailers adopting AI tools are seeing significant improvements in how they interact with and serve their customers. 

Here’s how this is achieved:

  • Predictive Analytics: Utilizing tools like Salesforce Einstein, retailers can predict buying behaviors based on historical data and enhance sales strategies accordingly. These insights help in crafting tailored marketing campaigns that speak directly to individual needs and preferences – which is seen as significant by 71% of decision-makers.
  • Chatbots and Virtual Assistants: Platforms such as HubsPot provide 24/7 customer service, handling inquiries and resolving issues instantly without human intervention. This automation increases customer satisfaction by ensuring that help is always available, thereby fostering brand loyalty.
  • Personalization Engines: Tools like Adobe Experience Cloud use AI to analyze browsing patterns and purchase history to provide personalized recommendations directly to the consumer. This not only boosts user engagement but also increases the chance of repeated sales. 

For instance, in South Africa a site like PC International is able to recommend laptops and other computing products according to the unique needs of each customer, doing so without having to dedicate vast resources to providing suggestions manually.

Inventory Optimization

Innovative machine learning applications are also transforming inventory management, making it more robust and responsive in emerging markets. This modern approach minimizes overstocking and understocking issues, resulting in significant cost reductions and improved product availability. 

Here’s how AI is reshaping this crucial area:

  • Automated Replenishment Systems: Tools like Blue Yonder enable retailers to automate their replenishment processes. These systems analyze sales data in real-time to forecast demand accurately, ensuring optimal stock levels at all times – and allowing for at least a 7% saving on annual operating costs.
  • RFID and IoT Integration: Incorporating technologies such as RFID (Radio Frequency Identification) and IoT (Internet of Things) lets platforms like SAP’s Advanced Track and Trace for Pharmaceuticals ensure that inventory tracking is precise and up-to-date. This integration provides retailers with a granular view of the supply chain, enhancing transparency and accountability.
  • Waste Reduction Algorithms: Machine learning also helps in identifying patterns that lead to waste. Solutions like Leanpath use automated tracking to monitor inventory lifecycles, helping businesses reduce spoilage through better shelf-life management and timely markdown strategies.


Machine learning is not just revolutionizing the operational aspects of retail; it is also redefining how customers engage with brands, particularly through personalized shopping experiences. 

We’ve touched on this briefly already, but it’s worth reinforcing the idea that in emerging markets, where consumer preferences can vary widely, AI’s role in delivering customized content and recommendations is worth shouting about. 

Here’s how this technology is being applied:

  • Augmented Reality (AR) Shopping Apps: AR technologies like Google’s ARCore and Amazon’s implementation enable retailers to offer virtual try-ons and in-room product visualizations, which help consumers make informed decisions without stepping into a physical store. This not only enhances the shopping experience but also reduces return rates.
  • Dynamic Pricing Models: Machine learning algorithms assist in setting optimal prices based on demand, competition, and user behavior insights. Tools like Revionics adjust prices in real-time, ensuring that both the retailer and the consumer get value from every transaction.
  • Product Customization: People love being able to have a say in how the products they buy look and feel. With generative AI, it’s a breeze for retailers to allow people to come up with their own designs and have them made to order.

The Last Word

While we might never get to the point where every aspect of retail is automated, the rapidfire rollout of ML and AI is making light work of many more tasks by the month – so for emerging markets as well as the global industry at large, this is a sea change that needs to be discussed and leveraged.

How Machine Learning Is Revolutionizing Retail in Emerging Markets