A global pandemic, a large ship blocking the Suez Canal, several international crises, and numerous other things have all affected the retail industry over the past few years.
Some repercussions of these events on retail, such as the sharp increase in online sales as a result of social alienation and a serious supply chain disruption, were quite expected. Others, such as the panic purchase of toilet paper and the ensuing shortages, demonstrated once more the inscrutability of the market and the human mind.
Fortunately, retailers called for a solution rather than giving up “like tears in rain” (yeah, another Blade Runner phrase). Machine learning was the solution to their prayers and, more importantly, their investments in technology. Learn how the retail sector may reinvent itself and find a more efficient method to operate with the aid of machine learning consultants.
Machine Learning in Retail
Machine learning is used in the retail industry to process large datasets, identify relevant metrics, recurring patterns, anomalies, or cause-and-effect relationships among variables, and thereby gain a greater understanding of the dynamics guiding this sector and the contexts where retailers operate. As more retail data is analyzed, machine learning algorithms become more effective at discovering new correlations and better organizing the business climate they are studying.
Two methods are typically used to make use of such capabilities. First, machine learning can be used to power augmented analytics solutions that, in comparison to conventional statistical analysis techniques, will look much deeper into data, identify even the most minute connections between data points, and be better able to handle new trends and constantly changing phenomena.
Second, pattern recognition in machine learning opens the door for important advancements in the field of artificial intelligence (AI) known as “cognitive technologies,” which enable machines to mimic some of the intrinsic human skills. This includes computer vision solutions that use algorithms to find visual patterns and link them to particular objects, as well as natural language processing systems that use machine learning to recognize the linguistic patterns of human communication to understand and duplicate them.
Machine Learning Opportunities for Retail Businesses
The ability for retailers to use machine learning and benefit from it in a range of business functions and scenarios is effectively translated by the aforementioned. Examples include:
- Market and consumer analytics serve for anticipating retail trends, such as shifts in product demand, and to create effective marketing, pricing, and restocking plans.
- A fully customized shopping experience that includes recommendation engines, niche marketing, dynamic pricing, and special offers based on consumer requirements.
- Through chatbots, virtual assistants, and contextual purchasing, interactive solutions for digital retailers can simulate the conventional in-store experience in a virtual setting.
- Using anticipatory shipping, intelligent route planning, self-driving cars, or drones, machine learning-enhanced logistics can speed up the delivery of goods.
- Use video surveillance to monitor your assets, employees, and customers. Anomaly detection based on machine learning is used to look for symptoms of fraud.
The whole range of other use cases opened by artificial intelligence and all of its sub-branches, varied in their potential business impact and viability, can be used to supplement these machine learning applications in retail.
AI Use Cases in Retail
Nowadays, machine learning algorithms are used in most AI-powered retail software solutions in one way or another. One of the key factors driving the growth of the global AI market in this sector is machine learning, which enables market participants to offer end users a more individualized and engaging shopping experience.
In this context, it’s important to point out that the increasing use of machine learning and associated technologies in retail hasn’t only been a reactionary move to more effectively address the problems described in our introduction. It has also been a proactive effort to take advantage of the opportunities presented by artificial intelligence, which ultimately served as a catalyst for general dynamics the retail industry had already undergone in previous years, such as the transition from a purely brick-and-mortar model to the coexistence of in-store shopping and eCommerce.
Machine learning-powered recommendation systems serve as a digital representation of a human sales assistant’s role in a real store, which is to identify the customer’s needs and match them with the appropriate merchandise. Recommendation systems do this by:
- Segmenting individuals based on their personal information, such as browsing behaviors, past purchases made on the platform, comments made on social media, past purchases, and more.
- Adding details about the product and contextual factors, including larger market trends or geography, to this research.
- Instead of letting customers become lost in a vast maze of products, offer them personalized choices that fit their consumer archetype and then fine-tune those recommendations based on user input.
Another technology that makes use of machine learning’s promise in eCommerce and is also easily capable of boosting in-store sales is targeted marketing. Its basic mechanism resembles that of recommendation systems in certain ways. To investigate pertinent indicators and reveal their relationships, a machine learning-based predictive analytics system can collect and evaluate user data from social media or eCommerce platforms.
Targeted advertising is only one of the potentials that machine learning in retail has opened up for marketers. Contextual shopping is another method for accelerating the virtual route that leads clients to the goods they seek. With the use of computer vision and machine learning algorithms, this highly engaging software solution can identify and highlight the products that appear in online content on popular social media platforms, directing consumers to your online store where they can make a quick purchase.
Chatbots are an example of machine learning in retail that focuses on interaction and contextual shopping. Natural language processing, a separate form of cognitive technology, is what gives them the ability to help clients around-the-clock with a range of tasks. This can entail providing users with assistance in locating a product they need, sending updates about new collections, proposing related products based on predictions made by recommendation engines, and so forth.
Such adaptable and untiring virtual assistants are widely available online and have already been used on the websites of numerous companies, including Victoria’s Secret and Burberry. Additionally, they’ve been integrated into social media sites like Facebook, which introduced its Messenger bots in 2016 and enabled companies to partially automate their customer support processes.
A smart retail solution is something businesses need to start implementing if they want to keep up with their competition. Depending on their products and services, the number of possibilities might vary, however, these solutions can all be customized to fit the needs of each business.