Machine learning is an increasingly common concept. The term has been around since the 1950s, but recent tech developments are now making it extremely important. A cornerstone of artificial intelligence, machine learning is designed to help computers learn without having to be programmed.
Machine learning is based on algorithms and helps companies analyze large quantities of big data automatically to gain insights. According to data from Growth: the cost and digital imperative, a report published by Deloitte in 2017, CPOs believe that the impact of automation will increase from 50% to 88% in 2020 and to 93% in 2025. To date, retailers analyzed customer behavior using CRM databases, purchase histories, loyalty programs, market trends and so on. But they can now process large quantities of data automatically and reliably thanks to machine learning algorithms.
Machine learning algorithms are classified into two main categories:
– Supervised learning, where prior knowledge helps understand collected data. Supervised learning involves providing basic learning guidelines to enable the automatic identification of relevant data. This helps make predictions and decisions.
– Unsupervised learning, where artificial intelligence has no prior experience when it comes to analyzing the data. Here, the machine finds and connects patterns and anomalies in large quantities of data, and segments customers by analyzing these behaviors and trends. Machine learning is an extensive field, increasingly linked to scientific data, whose possibilities continue to expand as more algorithms are developed.
Machine learning applied to artificial intelligence is a retail trend allowing businesses to provide an online shopping experience in physical stores (online-to-offline/O2O). E-commerce allows businesses to collect useful data, including website user clicks, movements and behaviors. Analyzing this data allows businesses to optimize marketing, placement and stock management, and to predict online advertising success in real time. Now retailers can use machine learning to collect the kind of data they obtain online in physical stores. The Artificial Intelligence: Current Situation and Future Impact study by Metriplica, the digital analytics and optimization consultant, concludes that artificial intelligence is expanding rapidly from the online to the offline world, via mobile phones and apps. In an omnichannel environment, the ability to react in real time and in a personalized way is accelerating penetration of this technology.
Machine learning can help retailers optimize their store stock and inventory management. Retailers will be able to analyze online and offline purchasing data in real time to predict store inventory needs based on the day, season, activity at other stores, etc. This is possible thanks to predictive analysis, an advanced analytics tool designed to make predictions about the future. Predictive analysis uses a range of data mining techniques to allow companies to leverage the advantages of big data, making them more proactive and future-focused. All this information can be used to produce daily reports for purchasing managers including suggested products a store may need. Artificial intelligence can even be set up to place small orders based on the needs it detects automatically. Guided by machine learning, artificial intelligence can also make stock estimates for all of the products in a store using cameras and sensors.
Data from the McKinsey & Company study: Artificial Intelligence, the Next Digital Frontier? shows that these capabilities can allow businesses to make better supply chain predictions and to design better offers. Companies can therefore predict demand, store only the products they will need to meet demand, anticipate trends to order more potential best-sellers, and even warn of suspected robberies.
Retailers can also track in-store customer behavior anonymously using cameras and sensors, allowing them to reorganize products to make them more attractive. These behaviors include in-store routes, where customers look at displays, etc. These cameras can also analyze customer profiles, demographics and seasonality to inform businesses whether most shoppers are older during the week and younger on the weekend, for example.
Machine learning allows artificial intelligence to detect patterns, and also to suggest actions to improve store performance. It can suggest how to restructure stores around best-sellers or areas of high traffic, propose discounts, or even recommend clothing in line with the weather.
Machine learning allows retailers to precisely predict customer purchasing behavior by understanding which products they interact with and how. Retail businesses now not only need machine learning to personalize customer experiences, it also helps them make strategic decisions. Artificial intelligence capabilities will continue to expand as this technology develops.