Every user loves to find all their favorites assembled in a place. They want their belongings arranged with real-time prices and up-to-date stock levels. It sounds straight out of a movie, but that’s the revolution machine learning spearheads in the retail sector. 

Machine learning is a subset of artificial intelligence, and machine learning algorithms work hand in hand with AI tools to deliver more effective services to users and optimize company operations. Data from Extrapolate states that the AI in retail market size will experience a 22.5% CAGR between 2023 and 2032, seeing its valuation spike to about $43 billion.

Artificial Intelligence involves making software that mimic human intelligence, while machine learning consists of using relevant data to improve the performance of such software over time. In this article, we explored their relevance and how you can leverage them.

What is machine learning?

Machine learning is the branch of artificial intelligence that focuses on building computer algorithms that improve with time and exposure to relevant data. Machine learning programs are designed to scan through data and learn from them over time to improve their performances, especially in prediction and decision-making.

Machine learning programs differ from conventional software because developers don’t add predefined instructions while building the programs. Instead, they feed the algorithm with various data forms, allowing it to learn and improve with time. Machine learning retail solutions help provide business owners with market insights via prediction, streamline operations by aiding decision-making –again, with its predictions– and upgrade customer experience with informed suggestions.

Benefits Businesses Gain from Implementing Machine Learning

Machine learning and AI have recently revolutionized the retail business with modern retail tech trends, and the giants in the industry have been at the heart of implementing the technology in their operations. Here are some specific benefits eCommerce businesses gain from implementing machine-learning retail solutions:

• Increased efficiency, as the human error rate significantly reduces with the introduction of machine learning retail solutions. Typically, sorting through data can get dreary from routine, thus predisposing human operators to make more errors. Still, the algorithms (like chatbots) eliminate such events and improve the business’s overall output and productivity.
• Cost-effectiveness, especially in terms of operating costs where the machine learning algorithms take over some human roles, reducing the cost of labor incurred.
• Focused advertisements to drive conversion. The predictive abilities of machine learning retail solutions help to inform marketing decisions and improve conversion through well-targeted marketing campaigns. For example, Netflix saved about a billion dollars in 2017 by implementing personalized recommendations for its users.
• Machine learning also simplifies hiring processes in most firms, ensuring an unbiased process while eliminating ineligible candidates and saving the company time.

Common Applications of Machine Learning in Retail 

Machine learning in retail businesses serves the retailers' improved insights into market and consumer behaviors even as it offers the consumers enhanced shopping mechanics and a wholesome experience. 

Ultimately, the retailers make more well-informed business decisions and gain loyal customers, thus improving the company’s marketing campaigns and increasing revenue.

Here are some ways machine learning algorithms are applied in the retail sector today, with a few successful examples in each case.

#1 Specific customer recommendations

Machine learning algorithms crawl is typical data sorting features that crawl through individual customers’ history and shopping behavior to understand their needs, preferences, and most visited stores. The program then uses this information to make relevant, personalized suggestions for products and services in the store, thus increasing the likelihood of conversion and improving client/user satisfaction. Such information can also help classify users into more demographics than you will get through conventional analytics, thus helping marketers direct their ads and promotions to specific target audiences. 

This principle works across retail stores, from physical goods and products to services and entertainment. Companies like Amazon, Spotify, and Netflix have made the most of consumer data to customize their users’ shopping experiences, making them some of the most successful retail firms in the world.

#2 Stock management and sales predictions

With customer retail development software like machine learning, eCommerce stores can automate their inventory management systems, reducing error rates and improving efficiency by saving money and time. For example, stocked products remain at optimal levels since the machine learning programs are more efficient at keeping tabs on stock levels.

The algorithms also predict likely demand for certain products by analyzing market trends and consumer behavior, ensuring that the store stocks in-demand goods, avoids missed sales, and saves costs of overstocking/understocking.

#3 Upgrading consumer experience through customer service

Apart from analytics, machine learning retail solutions exist in chatbots and virtual assistants to provide customers with the aid they need to navigate the stores and make their choices. Well-developed customer retail software like that will hold insightful information about the store, including price changes and stock availability, and can replace human customer service operators at retail points, thus cutting running costs.

Machine learning chatbots and virtual assistants can also serve a large volume of customers simultaneously, thus making them more efficient than human-operated services.

#4 Fraud detection

Since machine learning algorithms are ‘trained’ using normal and abnormal data transactions, they can detect anomalies in transfers and transactions. The programs can flag such transactions and even prevent them from occurring when they recognize the patterns. 

The machine learning retail solutions for fraud detection are more efficient than traditional software because they can classify a transaction as illegitimate or abnormal based on a wide range of factors instead of preset instructions.

Challenges with integrating machine learning in retail 

As with most new technological innovations, particularly software, machine learning has significant challenges associated with current company policies and operating models. Businesses have different running models, but here are some of the common challenges a company might encounter while implementing machine learning:

• Limited, inaccessible, or insecure data and data sources could compromise the effectiveness and success of machine-learning retail solutions.
• Getting a team of expert data scientists to carry out the implementation/integration will be cost-intensive, as there are few data scientists in the talent pool.
• Many companies operate a rigid business model, while machine learning needs infrastructure and mindset flexibility to succeed.
• Insufficient analytical expertise in most companies will see them miss out on many insights that the machine learning retail solution will offer.
• Machine learning could take a while to implement, and many businesses might need to be patient for it to be fully operational.

Strategies to adopt while integrating machine learning into your retail operations

Adopting machine learning in your company/start-up should occur step-wise after careful examination of the state of the business and developing plans for how the customer retail software development will impact your operations. 

Below is a schematic you can follow for machine-learning-retail integrations:

• Assess the company’s needs for data analysis and how they will impact the retail operations.
• Check out popular machine learning frameworks and decide on the most compatible ones for your business.
• Plan the reintegration process and how the machine learning frameworks will sync with other customer retail software developments.
• Hire machine learning experts to build the algorithm. They will train company staff to interpret, analyze, and leverage data obtained from the software for business decisions.
• Test-run the machine learning algorithms with small-scale assignments and evaluate them before deciding to upscale the algorithms.

The entire machine learning integration process, from design to building and implementation, could span a long time, depending on the experts available and the current business model. It also entails several experiments, so patience is key to executing a successful machine learning implementation.

Partner with Pangea today for Machine Learning retail solutions

As we stated earlier, the pool of machine learning experts is tiny, as the skill is still new, even among techies. You also need to get the best hands for the job, and we know the palace for you.

Pangea is famous for its rigorous agency selection process, as we pick only the top 7% of development agencies for our talent pool. You can check our list of vetted RetailTech agencies here to begin your company’s journey to adopting machine learning. 

FAQs

#1 How is machine learning used in retail?

Machine learning in retail helps to analyze market and customer behaviors and detect/predict trends to help companies make better-informed business decisions and offer customers a personalized shopping experience.

#2 How is AI transforming the retail industry?

AI is changing the retail industry through machine learning and its predictive analytics, which offers businesses insight into customer and market trends. It is also helpful in stock/inventory management, automating routine tasks to optimize efficiency.

#3 Why is AI good for retail?

AI offers consumers a more wholesome shopping experience, increasing their satisfaction and conversions and returning customers. Businesses get a better analysis of the market and their users, which helps them streamline operations, manage stores and stock effectively, and optimize trends in the global market.