Machine Learning in Retail: A Game Changer in 2023
Updated on Aug 16th, 2023
Forecasted to become a market worth 18.33 billion USD by 2028, machine learning is all set to be a major technology in today’s digital transformation era. In fact, the many use cases of Machine Learning are expected to raise revenues and customer loyalty for various industries, including retail. The applications of machine learning in retail have been there for nearly a decade now, though, with time, these applications have evolved too. Presently, the technology is helping retail businesses by offering dynamic pricing facilities as well as automating the supply chain and inventory management. There are numerous other ways through which ML in retail is causing major disruptions, but before diving into that, let’s bring more illumination onto the technology itself.
- The machine learning market is forecasted to become worth 18.33 billion USD by 2028.
- In retail, ML is used for applications like recommendation engines and dynamic pricing.
- Through ML-powered recommendations, retail businesses can improve their AOV by 11%.
- Data collected for recommendation engines can also be used to optimize targeted marketing campaigns.
- ML in retail can also improve the security standards for the industry.
Table of Contents
What is Machine Learning?
Machine Learning or ML is a sub-domain of Artificial Intelligence (AI) that enables ‘machines’ to ‘learn’ the tasks that they are required to automate. Since the beginning, computers have been working on logic or programs developed by humans. These would include conditional and recurrent commands. The purpose has always remained to automate repetitive and mundane tasks that can easily be defined into the logic of if-else-then and loop-until.
This dependency on human-written programs has kept the evolution pace slow for computers, which through their high-performance and accurate computational capabilities, could achieve the determined goals much quicker. Machine learning is the profound solution to this impediment, as it allows computers to make logical sense of a circumstance by themselves. The machine can observe the input and output of any existing process and then learn the process all by itself. This capability has enabled developers to create miraculous applications like object recognition, personalized recommendations, autonomous vehicles, etc.
Difference Between AI and Machine Learning in Retail Industry
Since machine learning is all about automating tedious tasks by allowing computers to learn by themselves, its’ purview overlaps that of AI or artificial intelligence a lot. In fact, not many people can differentiate between these two disrupting technologies. However, the truth is that despite being very closely connected, they are not the same. In fact, Machine Learning is considered a subset of Artificial Intelligence.
In general terms, Artificial intelligence is the capability of a computer machine to simulate human cognitive functions by using mathematics and logic to make decisions. In contrast, Machine learning is the application of AI, which uses mathematical models of data to enable computers to learn these cognitive functions without direct instruction. The latter allows computers to learn and evolve without depending on human instructions, making it more efficient and transformable for various use cases.
So, in short, AI is the faux intelligence of a computer system to seem like thinking like a human and performing tasks on its own. Machine learning is the technology that allows computer systems to develop intelligence on their own.
Now, if you are interested in knowing about AI solutions in retail, this is the blog you’d like to read instead. However, if it is the applications of machine learning in retail you’re looking to learn about, follow through the current blog.
Also Read: Retail Chatbot Development Guide
Disruptive Trends and Use Cases of ML in Retail
Machine learning in retail requires algorithms that can process huge datasets to uncover hidden relationships between various variables, identify recurring patterns and anomalies, and derive a deeper understanding of the various entities involved in the retail world. The more an ML algorithm is trained for the retail use case, the more robust and reliable it will be to automate various existing and opportune tasks. Here are some of these exciting ML solutions that are all set to disrupt the retail industry in 2023.
That’s why leading companies hire machine learning developers to develop, train, and scale custom machine learning models for business improvement. You can also do that same by connecting with a reliable staff augmentation service provider and hiring talented ML engineers for building the below mentioned ML solutions that are all set to disrupt the retail industry in 2023.
The recommender system is one of the most popular applications of ML in retail. These systems are already helping multiple eCommerce businesses improve their conversion rate by 32% and AOV (Average Order Value) by 11%. They are popularly known as recommendation engines, and they function based on user behavior on a given website. For example, if your retail eCommerce in fashion deals in formal dresses, and a particular customer has shown keen interest in a red dress, the engine will automatically filter out more dresses like that one. Not only that, based on behavioral data collected from previous users, the engine can even predict what other accessories this user will most likely be interested in.
Such in-depth analysis of user requirements and generating personalized results would ultimately help customers find what they would want from a given store and help increase their AOV for the retail business. The experience of finding the wanted things without even looking for them will also help customers feel a better affinity with the brand, helping increase brand loyalty. No wonder almost all the big names in the eCommerce realm, including Amazon, BestBuy, and Alibaba, have been using recommendation engines for years now!
The world of online retail is highly competitive. Global audiences can explore retail web stores from around the world to find a desired product at the best cost. In such a scenario, there are only two ways for retail businesses to make sales and remain profitable, establish a strong brand name and loyalty, and offer items at the best cost. Later, the retailers can use the dynamic pricing application of machine learning in retail software.
ML algorithms can analyze data to identify patterns and detect new trends. These findings can then be applied to the inventory of the retail business to further customers’ purchase behavior and the pricing trends in the niche. This proactive approach will help a retail business to always stay ahead of the game and offer the most profitable yet competitive price for their merchandise at the right time. Other than the demand and price prediction in the macro, the system can also go into the micro to offer dedicated rebates and discounts to users with the potentially high average order value. This will help businesses target and retain high-value customers.
Supply Chain and Inventory Management
Machine learning-augmented supply chains can help retailers optimize their logistics and inventory management for improved efficiency, availability, and cost savings. Retailers can predict demand in the future and ensure that there is enough stock to meet the expected demand. Similarly, they would overstock on inventory that is not going to remain in demand in the near future. Other than predicting demands, machine learning also enables retailers to automate inventory management in real time. It allows the website admin to update the inventory levels in the back end with every successful order placement. Therefore, as soon as a certain inventory reaches a threshold, the warehouse would be alerted to raise a purchase order to prevent stock-out cases.
Other than inventory, ML in retail applications can also help retailers optimize the supply chain itself. Through advanced applications like route optimization, smart supply touchpoint audits, and autonomous vehicles, they can bring unprecedented efficiency to their logistics processes. This will further improve the delivery timelines for the brand helping them enhance the overall customer experience.
Most retail businesses have an adequate surveillance system in their stores. However, this has not yet brought a sufficient reduction in the cases of thefts and shoplifting. As per the 2020 National Retail Security Survey, theft was at a record high of $61.7 billion in 2019, and the number is still growing up since then. As such, advanced solutions are the need of the hour to help retail businesses save on their annual bottom line.
ML-powered video surveillance would prove a great help here. By automating surveillance through object and face recognition, the surveillance cameras can detect anomalies like suspicious user behavior and notorious shoplifters. What’s interesting about this particular use case is that it requires very little investment. Already there are a number of facial, image, and behavioral recognition machine-learning libraries available for commercial use. Retailers can simply embed the same onto the edge devices and invest in retail software development services to enhance their surveillance capabilities. Such software can help them manage the warehouse as well, helping them achieve optimization in multiple back-office processes.
Another interesting potential of machine learning in retail is about increasing the effectiveness of marketing efforts. As discussed above, machine learning algorithms are good at identifying patterns and predicting trends and user requirements. These insights can then be used to retarget website visitors for dedicated items. For example, if a user has consistently shown interest in baby products on a retail website, the company can then trigger email campaigns for the user, giving them product recommendations, updates on product launches, and even free relevant content to help their engagement rate.
Other than email, this data can also be fed to search ad engines to optimize your search and display ad campaigns. For example, in the previous case, if the retailer is not running email campaigns or does not have the email details of the given user, they can instead show ads of relevant products on partner websites and mobile applications. If the retailer has a physical store, the customers can be sent rich notifications through Bluetooth beacon whenever the person is in the vicinity.
Other than recommending similar products, the retailer can also identify whether the user has certain specifics in their purchasing behavior, like their budget or days when they prefer to shop, like the end of the month. This way, they can further optimize their marketing campaigns for improved conversion rates.
Machine learning is indeed transforming the retail industry, enabling businesses to deliver superior value to customers. But it’s not just customer experience; from raising brand awareness to automating back-office processes, machine learning algorithms are making retail businesses more efficient every day. If you’re a retail business looking to transform it through any of the machine learning use cases mentioned above, you should look for a machine learning consulting services provider to explore all the possible opportunities for your business. And while every machine learning consulting and development service provider agency should be able to offer you the required aid, you would like to go with one that has experience in the retail niche.
Matellio, for example, has over a decade worth of experience in developing custom retail software as well as expertise in machine learning and other sub-domains of artificial intelligence. Therefore, even if you’re only looking to test an opportunity or understand its feasibility, you can simply book a free consultation with our experts. Alternatively, if you’re looking to implement ML in retail use cases through software or app development, you can even request a free quote by filling up this form with your requirements.
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