Machine Learning (ML) has greatly benefited the retail industry by enabling companies to improve their bottom line. It is made possible by the generated data that helps unlock the opportunities to anticipate, adapt and meet constantly changing customer demands.
A typical machine learning model breaks large volumes of complex data into actionable insights with a better understanding of customer behavior and market trends. By leveraging these insights, an organization can estimate future demand, decide on competitive pricing and even personalize offerings for customers.
Through this article, we’ll discuss the various ways in which an organization can use machine learning technology to keep its retail business ahead of market competition by exploring the use cases & best practices.
Here’s what we’ll cover
It is a subset of artificial intelligence that enables computer systems to assess and learn from data while making accurate predictions and smart decisions, with minimal human intervention. A machine learning model for retailers efficiently review and break a big volume of complex data into actionable insights, enabling:
The retail industry is undergoing a continuous evolution on every front – customers are constantly changing their purchase patterns, the market is moving towards becoming complex ecosystems. The emerging technologies are disrupting the sector at a stunning pace. Meanwhile, the shoppers are being bombarded with luring offers competing for their attention on every channel, from online (web to mobile apps) and in-store.
By combining Machine Learning with marketing efforts, organizations can make the best use of their consumer data. AI functionalities like computer vision, visual search & NLP are proving to be game-changers by improving optimization & forecasting for the retailers.
Companies that are reluctant to implement this data-driven technology will severely lag in terms of KPIs. Those that are early to adopt will leapfrog ahead, regardless of their existing position. It makes perfect sense to actively catch up on this trend with a trusted technology partner like us.
We can help you develop custom data-driven solutions that boost your KPIs
Boost operational efficiency, cut inventory costs and adapt retail operations to respond to market shifts, now and in the future with this technology.
However, to reap these benefits of ML, it is important to ensure that the data is free from errors and inconsistencies. The cleansed data ensures making accurate predictions to arrive at the best decisions while keeping the customers coming back.
1. Staff-less Store
One of the leading Northern- American retailers has installed high-tech cameras equipped with radio frequency identification (RFID) to monitor the buying behavior of customers right from their entry until the payment is processed. This helps the store to determine which products are consistently in demand & which products stay the longest on shelves.
Additionally, these cameras segregate customers based on their height, weight, and other prominent physical characteristics. This helps in determining the most popular products for specific target groups. These are also connected to the store’s shelves and the warehouse equipped with sensors.
The cameras, sensors, inventory system, and point-of-sale system monitor every choice made by the customer including their reactions to change in rates. The machine learning algorithms then help draw precise conclusions from the consolidated data to increase sales.
2. Versatile Cosmetics Product App
A leading French multinational retailer of personal care and beauty products is pushing the boundaries of innovation with its AI-enabled app. It helps customers identify the best shades by uploading their photos. The accuracy of ML models helps the customer to select the right shades and buy the best products before trying them. Face analysis and augmented reality testing greatly enhances the online shopping experience for customers.
Shoppers can also upload their pictures to Facebook Messenger and chat with the help of a virtual assistant. It detects the most compatible shade and makes personalized suggestions from the current stock catalog using an intelligence mechanism. This gives the customer a clear idea of how they will look after applying the company product. This engaging app has increased the organization’s revenues significantly and continues to attract customers even after the outbreak of the pandemic.
Businesses can also target their potential customers based on their location and use technology to identify shorter and better routes. This location optimization also boosts customer satisfaction.
Having developed more than 50 retail software solutions for 10+ SMEs & 20+ Retail Specialists, we have machine learning use cases in retail that you can explore to learn how to deploy this phenomenal technology into your operations & transform the way you do business.
The projects presented below represent just a fraction of the potential and power of ML in the retail industry.
The Challenge: Although the client had launched new products and expanded their portfolio, they were unable to hit their sales targets.
The Solution: A custom-developed Hybrid Recommender Software based on Gated Recurrent Unit (GRU). It leverages the power of deep learning to track on-site buying behavior and provide personalized recommendations for an enhanced shopping experience.
Key Outcomes: The solution resulted in a massive 80% increase in transactions.
The Challenge: The uncertainty in market conditions created a wide gap in inventory management as stocks continued to sell rapidly.
The Solution: Our team created a custom Bayesian Optimal Stocking Solution that augmented human behavior to facilitate data-driven decision-making. It also delivered actionable insights periodically which enabled the merchant to replenish stock levels on time.
Key Outcomes: The gap in stocking predictions was reduced from 30% to 20%.
The Challenge: Owing to the global pandemic, the statistical forecast software was not able to estimate future trends and predict possible customer behavior.
The Solution: We developed and deployed a forecasting system capable of adapting to various customer trends, aligning consumer behavior with the priority sales segment.
Key Outcomes: After extracting months of accurate predictions, the client increased their sales revenue by 10%.
The Challenge: The existing system did not offer easy access to key consumer insights that were vital to business growth.
The Solution: Created a consumer clustering framework based on recency, frequency, monetary value (RFM) analysis. It helped them stay on top of buying patterns, average purchase rates, quality index of customer satisfaction, and more.
Key Outcomes: With enhanced visibility into consumer behavior and key business insights, the client was able to reduce the churn rate by 10%.
If you’re still not taking advantage of this technology, your rivals will surely stream ahead!
Machine learning retail use cases present a clear state of how ML offers a level playing field to retailers of all sizes with access to the same tools. If you’re still not taking advantage of this technology, your rivals will surely stream ahead!
Today’s shoppers expect a customized experience at a micro-level. This is especially crucial for growing businesses that are expanding their line of offerings. And a great customer experience can only be created when you blend the human touch with the power of tech to deliver a more personalized experience.
Rishabh Software recently concluded a webinar titled, ‘Leverage Machine Learning for Retail Business’. The premise of the session was to help companies leverage all the data lying with them to formulate profitable strategies with practical applications and use cases. You can explore the session to learn how we are making a stride for retail organizations by leveraging ML.