Machine Learning and Predictive Analytics in Retail: Top 5 Opportunities for Business

Introduction

It should be no surprise that artificial intelligence along with machine learning considerably impacts the retail industry. Emerging technologies take businesses to an entirely new level through workflow optimization. To be a bit more precise, it results in better efficiency, cost saving, enhanced customer experience, better service, and more. 

In retail, supply chain efficiency is essential. This, in turn, depends on several equally important processes such as inventory management, picking, packing, and shipping. All these operations require a good deal of time and resources thus having a dramatic impact on a business’s bottom line.

All in all, retailers all over the world have been recently looking for ways to overcome challenges and remain competitive. Creating efficiencies in complex systems which involve multiple, often compartmentalized processes is the work for technology. Simply put, machines have the power to make lots of savings and boost efficiencies if implemented correctly. 

Some big players like eBay, Amazon, and Alibaba have successfully adopted AI-powered solutions across the entire sales cycle, from storage logistics to post-sale customer service. But if you think innovations are a privilege for the industry’s leaders, you’re mistaken. In fact, it does not take to be a big company or sell exclusively online to start benefiting from the tremendous power of ML and predictive analytics.

Addressing The Fortune Business Insights study, ML is currently retail’s largest subset of the worldwide AI market. ML is anticipated to experience growth from $5.84 billion in 2021 to $18.33 billion in 2028.  

In this article, we invite you to discover the peculiarities of using predictive analytics and ML in the retail sector, discover the biggest advantages and challenges, and learn about some successful use cases with us. 

Machine learning in retail

Be it clothes, groceries, household items, or whatever, the possibilities in the retail space are promising as never before.

Machine learning in retail involves the adoption of self-learning computer algorithms designed to process huge datasets and identify relevant metrics, recurring patterns, anomalies, or cause-effect relations among variables. As ML systems process data, their performance gets improved as they locate new correlations and better frame the business scenario they’re analyzing.

In layman’s words, the role of a machine learning model in retail is to quickly review and convert a large amount of complex data into valuable insights which, in turn, can later be utilized for: 

• Accurate forecasting of upcoming needs
• Enhanced inventory management
• Defining consumer needs through appropriate segmentation
• Unique and personalized offerings
• Setting the optimum prices to increase sales

The current industry state

AI is taking over the retail sector. In Artificial Intelligence in Retail Market – growth, trends, Covid-19 impact, and Forecasts (2023 – 2028), the market sizes and forecasts are provided for several crucial segments.

In terms of application, the top 6 segments are

If segmented by technology, the market benefits mostly from Machine Learning which is followed by Natural Language Processing, Chatbots, Image and Video Analytics, and Swarm Intelligence.

The benefits of using ML in retail

It goes without saying that ML brings a multitude of advantages to retail stores regardless of size, type, etc. It completely transforms the way we shop by bringing into reality things that seemed impossible not so long ago. 

See how this technology addresses retail challenges via large-scale data collection and analysis, which provides real-time insight into shopper behavior and helps to achieve a substantial return on investment:

• Higher customer Lifetime Value. Turning random customers into regular ones contributes to your overall revenue. But how do you increase return rates? You need to figure out which customers are most likely to return as well as what factors are deciding in achieving the highest customer lifetime value (CLV). Great news for you – it’s all up to machine learning. Sophisticated machine learning algorithms enable quick but accurate review of large inputs on purchase histories, internet, and social media behavior, customer feedback, production costs, product specifications, market research, and other data sources.
• Expand the customer base. You cannot rely on customer loyalty only. No matter how high your retention rates are, you must always work on attracting new customers. Advanced ML tools help to target consumers with the highest likelihood of conversion at the right time and place.
• Automating tedious manual tasks. Even though a lot of people consider ML a way to completely replace a human workforce, it’s far from the truth. ML is meant to unite forces with human workers thus helping them automate routine processes to free up their time for creative projects or in-depth problem-solving a machine is not capable of.
• Demand forecasting. Machine learning can be used to analyze sales data and predict future demand for products, allowing retailers to optimize inventory levels and reduce waste.
• Price optimization. Machine learning algorithms can analyze market trends and competitors’ pricing to help retailers optimize their pricing strategies and remain competitive. The technology takes key pricing variables into account to define an automatic pricing strategy with real-time, dynamic prices. Basically, the model helps to understand how clients react to items at different price points and estimate the best price for each product so it can be sold in a certain period, ultimately maximizing profit.
• Fraud detection. Machine learning algorithms can be used to detect and prevent credit card fraud and therefore reduce the risk of chargebacks and losses. 
• Improved supply chain management. Optimizing inventory planning and predictive maintenance is a concern of the greatest importance for retailers. Unless the supply chain is managed correctly, you will have to deal with negative UX, massive waste, and losses. Machine learning algorithms can use purchase data to predict inventory needs in real-time and provide a daily dashboard of suggested orders based on the day of the week, the season, nearby events, social media data, and customer past behavior.
• Marketing campaign optimization. The promotion has never been a piece of cake – it requires a deep understanding of the market and target audience demands. Machine Learning models offer precious assistance to decision-makers by leveraging historical data to forecast the ROI and provide optimal parameters for its execution. The model predictions are valuable for balancing the costs and profits when developing the campaign.

The opportunities ML provides to retailers

The opportunities that ML gives to retailers seem to be limitless. You say this is impossible – we say the technology is advancing and expanding the scope of applications continuously. What was a fantasy just yesterday is a common thing today. 

For retailers, leveraging the power of ML is a great leap forward. The technology is now widely applied across stores and chains – either small or large. ML has turned out to be highly efficient in a variety of business functions and scenarios. See when and how retailers implement machine learning:

• Market and customer analytics. Keeping an eye on the current trends and making predictions about product demand fluctuations is essential in terms of running a store. You know how fast things change out there so you have to catch up with the changes, otherwise, you’ll get beaten by competitors. ML helps to set up suitable marketing and pricing and restocking strategies.
• Create a unique and completely personalized shopping experience for your customers. We all like that feeling of care and attention, don’t we? Make use of recommendation engines, targeted advertising, dynamic pricing, and tailored promotions based on customers’ needs to ensure the best service ever.
• Interactive solutions for online stores. We cannot ignore the massive shift toward online shopping that has been taking place since 2020. People do find it more convenient and faster as they can shop without leaving the comfort of their homes.  ML has the power to digitize the typical in-store experience with chatbots, virtual assistants, and contextual shopping.
• Machine learning-augmented logistics is utilized to streamline product delivery via anticipatory shipping, smart route planning, and self-driving vehicles or drones.
• Retail security is crucial as a business’s entire reputation mostly depends on it. Retailers deploy video surveillance and spot signs of fraud via machine learning-based anomaly detection to ensure maximum security.

This list can go on and on but it’s impossible to bring together every use case – there may be dozens of them. The examples we’ve provided are the most common. They are often complemented with a full spectrum of additional use cases unlocked by AI and all its sub-branches.

ML in retail: Top 3 best use cases

Now it’s high time we moved on to some real-life examples. Let’s see how well-known retail brands are making the most out of machine learning. 

Staff-less stores in Amazon

Amazon has been experimenting with staffless stores through their “Amazon Go” chain of stores since 2016. These stores use a combination of computer vision, machine learning algorithms, and sensor fusion technology to allow customers to enter the store, take items off the shelves, and leave without ever interacting with a cashier or checkout terminal. 

While staffless stores have the potential to reduce labor costs and improve efficiency, they have also raised concerns about job loss and the impact on traditional brick-and-mortar retailers.

Netflix predicts demands

Netflix uses demand prediction to decide which TV shows and movies to license or produce, whether to release full seasons all at once, auto-play the next episode, and optimize their content recommendation algorithms. Demand prediction involves analyzing large amounts of data to forecast how popular a particular title will be with viewers.

Viewer behavior data is the key. This includes information such as how long a viewer watched a particular title, whether they watched it all the way through, and whether they watched it in one sitting or over multiple sessions. Netflix also collects data on viewer demographics, viewing devices, and time of day, which can all be used to help predict demand.

North Face takes ML to marketing

AI and ML create a highly personalized purchasing experience called “Shop with IBM Watson” on the store’s website. 

IBM Watson, an AI system, is a virtual assistant that guides users through a series of questions and based on the obtained data offers visitors the items as per their preferences and needs, just like a human salesperson helping you out in an offline store.

Let’s sum up

Machine learning in retail – yes or no? We definitely say yes to it! 

The retail sector is growing at a frantic pace and faces new challenges in an ever-evolving consumer paradigm. Tackling them with top-notch technology determines whether you succeed or are left behind.

Machine learning along with AI is smoothly invading the retail space, opening a bunch of unique and groundbreaking opportunities for retailers thus allowing them to stand out from their competitors. ML is a useful tool for expanding the customer base, bringing up customer loyalty, saving resources, and building a reputation as an innovative and committed retailer.  

Relying on ML solutions and breathing new life into old-fashioned predictive analytics techniques will affect your business positively. 

However, despite being extremely smart for technology, ML is still just a tool that must be used correctly. Proper human supervision, at least for now, is a necessity rather than a choice. 

It’s exciting to live in a world where humans and technologies go hand in hand and work together, isn’t it?