How Machine Learning In Retail Is Transforming Customer Experience

Here’s what we’ll cover

Why Machine Learning Matters?

Why do Retailers need ML to move forward?

Benefits of Machine Learning in the Retail industry

Machine Learning Applications in Retail

How Rishabh is helping businesses to use ML to up their Retail game

Custom retail software solutions we can help develop

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:

• Accurate estimation of future demands
• Optimization of inventory management
• Understanding customer demands with the right segmentation
• Personalizing products offerings
• Set the best prices to maximize revenue

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.

Boost operational efficiency, cut inventory costs and adapt retail operations to respond to market shifts, now and in the future with this technology.
Here’s how:

• Determine the Best Price for Products & Services
  It takes into account all the factors that impact product pricing to help vendors arrive at the best rates for their offerings.
• Predict the Inventory Levels Needed on Hand
  Based on the assessment of historical sales data and current purchase patterns, machine learning provides an accurate estimate of the orders you can expect.
• Provide Personalized Customer Services
  Intelligent AI-based chatbots enable assistance to customers 24×7, solving their queries promptly with personalized recommendations, without any human intervention.
• Identify Vendors Offering the Best Deals
  It can compare multiple vendor’s rate quotation against the market prices to help deliver the best deals and make profitable decisions.
• Customize the Shopping Experience
  ML can enable creation of hyper-personalized consumer personas in minutes to help customize deals & offers in real-time towards increasing conversions.
• Track Customer Journeys Across Touchpoints
  Can efficiently track in-store and online customer journeys to identify products that are consistently in demand & areas that receive higher traffic.

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.

Below are 10 practical applications of AI and Machine Learning in the retail industry:

1. Demand Forecasting & Stocking
   The way you price, promote, position, and sell your products can be changed dramatically with demand forecasting. For example, ML engineers can build models that can leverage historical data, regression, and time-series techniques to predict the expected sales for specific products in a certain period.
   The right pricing decisions can be made by analyzing consumers, products, and the competition. Additionally, with inventory and supply chain data, it is possible to anticipate future inventory needs, maintain adequate stock levels, and ensure consistent availability of in-demand items.
2. Making Profitable Pricing Decisions
   Making the best decisions on product pricing and changing it in response to consumer demands is challenging. For most sellers, seasonal trends, peaks in demand, and competitor’s pricing patterns are given priority in decision-making. However, many other factors can impact the price.
   Using Artificial Intelligence in the retail industry can help you identify the right time to reduce or push prices higher.
3. Tracking Customer Sentiment on Social Media
   With top brands having an active presence on popular channels, social media has transformed the way we all make purchases. Globally renowned brands are using these platforms as official contact channels to assist their customers.
   This helps to track customer sentiment and brand reputation on these important channels. Thanks to Lachine Learning and Artificial Intelligence, brands can now monitor their social presence on a large scale, analyze information about customer engagement, and get a precise understanding of what factors are driving traffic.
4. Determining Customer Lifetime Value (CLTV)
   Certain customers have a high lifetime value, which can be estimated by the amount they spend on the offerings, their consistency, payment history, and the number of times they make purchases. These insights can help you optimize your marketing campaigns. Subsequently, it would increase your share of the most valuable customers and generate a steady revenue stream.
5. Ensuring a Seamless Supply Chain
   The data on which ML algorithms are based can be highly beneficial in determining delivery routes. Smart systems ensure smooth logistics and simultaneously help to achieve two fundamental goals: enhanced customer service with fast delivery and reduced overhead cost.
6. Market Basket Analysis
   Market basket analysis is conducted to determine the connections between frequently occurring entities, making it an accurate technique for analyzing buying patterns. For example, someone buying milk may also grab sugar, tea, coffee, and biscuits. Machine learning efficiently processes vast retail data, uncovering valuable insights from these patterns.
7. Fraud Detection
   Machine learning and AI can detect and prevent credit card fraud in online and in-person transactions using autonomous learning. They can also block fraudulent activities, such as coupon fraud, by monitoring user behavior associated with specific IP addresses.
8. Chatbots and Virtual Shopping Assistants
   Chatbots, driven by Natural Language Processing and Machine Learning, serve as interactive retail assistants, offering 24/7 support for various tasks. They notify users about new collections, help them find desired products, suggest similar items, and much more. These versatile virtual assistants are deployed on brand websites and social media platforms to increase sales and enhance CX.
9. Recommendation Engines
   eCommerce platforms can leverage ML-powered recommendation systems to offer personalized product suggestions. These systems analyze user behavior, past purchases, and context to connect buying journeys with items that match customer preferences. Whether collaborative, content-based, or hybrid, these engines enhance the online shopping experience with personalized product recommendations that cater to individual needs and preferences
10. Cross-Sell Prediction
    By implementing predictive models, retailers can anticipate their customers’ specific product or service needs, enabling strategic cross-selling opportunities. This personalized approach enhances the shopping experience and maximizes transaction values. Customers benefit from tailored recommendations, while retailers enjoy increased revenue and stronger customer loyalty. This synergy between cross-sell prediction and Machine Learning is a game-changer for retailers looking to thrive in a competitive market.

Here are 2 amazing examples of using machine learning in the retail business:

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.

Business Case: Optimizing Growth for an eCommerce Website

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.

Business Case: Inventory Stocking Solution for a US-based Hardware Dealer

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%.

Business Case: Demand Forecasting for a US Based ‘Brick & Mortar’ Store

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%.

Business Case: Custom Omnichannel eCommerce Solution

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!