Back to Blog

Machine Learning in Retail: Using Deep Learning to Drive Growth

Deep Learning in Online Retail

Implementing machine learning in retail, specifically deep learning, is a game changer that powers growth and scalability for online retailers. Retail machine learning allows for frictionless customer experiences by collecting consumer behavior data and providing valuable insights retailers can use to show customers real-time, deep-learning-driven product recommendations relevant to their searches.  

Deep learning, also known as deep structured learning, is a sub-field of machine learning, wherein models are trained using neural network architectures and labeled data. The term “deep learning” was first introduced by Rina Dechter in the year 1986, and “deep” refers to the number of layers within basic neural networks. 

Deep learning focuses on the scalability of neural networks, which indicates that more data, larger models, and enhanced computation can drive better results. Hence, with deep learning algorithms, performance continually improves as more data is being fed.  

Traditional machine learning models have always faced the challenge of feature extraction. However, with the evolution of deep learning techniques, it’s easy to train computers to accomplish tasks that human brains can perform naturally.

Deep learning helps machines generate and understand sensory data, including video, text, sound, images, music, or speech. For instance, HomeLuv, a pioneer visual search platform that uses deep learning to match builders and homebuyers, and Google Duplex, a voice assistant that helps people book appointments telephonically across 43 US states, use advanced machine learning techniques.

Impact of Deep Learning in the Online Retail Industry

Brick and mortar retail stores faced a tough time in 2019 with many businesses going bankrupt, declining share prices, and poor holiday sales. During this period of turmoil, in-store foot traffic declined considerably. However, online retail proved to be a global success, generating approximately trillion in sales. In such a scenario, the question remains:

 “How are online retailers driving business growth?” The answer? Machine learning. 

Machine learning makes use of the vast amounts of consumer data available on social media channels and websites to draw relevant conclusions about buying patterns. Deep learning algorithms help retailers analyze what sells, what captures customers’ attention, and what keeps customers coming back for more. 

Major e-commerce giants use machine-learning-driven product recommendations, creating customer service forecasting systems that help online retailers determine product demand well in advance. Amazon, which has the best recommendation engine in the retail segment, harnesses shopper data to increase sales. If there’s a Mother’s Day sale, for instance, Amazon’s recommendation engine will display items based on that user’s previous buying behavior. Amazon’s recommendation engine will also showcase items that other buyers are purchasing for Mother’s Day. In this way, Amazon can estimate demand and maintain sufficient stock during such sales.

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

Machine learning supports retailers in price optimization, customer data collection, and efficient logistics processes, streamlining the retail industry by reducing costs and building stronger customer relationships.

How Machine Learning Can Be Used in Online Retail Industry is a scalable SaaS platform that handles your end-to-end machine learning workflow. The platform allows data science teams to effectively adopt and sustain machine learning workflows with more visibility, accuracy, and collaboration. Skyl creates and trains models without coding, using fine-tuned, state-of-the-art neural networks. Following are four ways is used by online retailers to grow business:

1. Tagging Visual Information

Visual searches have gone a long way with top retail giants like eBay and Amazon, attracting more customers with the help of visual commerce. A quick example is the “Shop a Look” feature introduced by many major fashion brands, which makes shopping for celebrity looks easier. Another example is accessory recommendations for a particular color palette. For instance, if a user buys a pair of beige trousers, that user might see recommendations for a formal shoe or tote bag in a similar color to complete the look.’s Image Classification attribute helps businesses add more depth to their retail business by adding descriptive tags regarding the products’ color or material. provides a ready-to-integrate inference API that syncs directly to an application to begin making predictions.

2. Analyzing Customer Behavior

Major retailers like Walmart and Amazon have been using data analysis to improve their offerings and increase revenue generation. Using’s text classification template, users can create machine learning models that analyze customer personality traits, sentiments, and behavioral patterns. Multiple model pipelines can be created to cater to different areas of consumer and retail analysis and help predict future patterns.

3. Personalized Recommendations 

According to a recent survey, 45 percent of users are more eager to shop on an e-commerce site that offers personalized recommendations than one that doesn’t. Amazon shoppers receive multiple recommendations based on their previous purchases. Amazon uses content-based filtering, collaborative filtering, and hybrid filtering to make these recommendations possible. With, you can create similar models to help you validate the accuracy of invoice data.

4. Text and Image Interpretation from Invoices 

In the past, invoice reconciling involved =manual ledger entry, a time-consuming and tedious task. Today, modern technology makes it possible to digitally interpret text from invoices. For instance,’s text extraction template scans and analyzes invoices, lowering back-office expenses, reducing human effort, increasing employee productivity, and eliminating invoice errors.

Business Case: ‘Women’s Apparel Categorization’ Problem for Online Retail Industry implemented a use case to predict multiple categories of women’s apparel, such as color, type, neck style, dress length, and so on, from images. This type of prediction can be used anywhere in the e-commerce industry where clothing items need to be tagged and classified. We collected various images of women’s apparel and created a project named “Female Fashion Aspect Extraction.”

1. Creating a Project provides multiple templates in Computer Vision and NLP for a guided machine learning workflow, which can be chosen depending on the use case that is being implemented. For this project, we chose the image classification multi-label template. An image classification multi-label template categorizes an image into two or more classes.

Sreenshot 1 - border

2. Designing the Data Set Schema

We then designed the schema of the data set through the guided flow of We provided the data set name and description and designed our schema as per the requirements of the project. We provided class names as “categorization,” “identification,” “color,” “dress_length,” “neck_style,” “occasion,” and “sleeve_length.” The options for each class were as follows.

  • Categorization: shorts, jumpsuit/romper, sweater, skirt, jacket/coats, dress, swimwear/bikini, active_wear, top
  • Identification: bottom, one_piece, top
  • Color: green, blue, brown, black, red, pink, white, purple, multi-color, gray
  • Dress_length: above_knee, long, knee_length, mini, below_knee
  • Neck_style: scoop_neck, v_neck, crew_neck
  • Occasion: casual, work, party, any, summer
  • Sleeve_length: long_sleeve, short_sleeve, sleeveless, 3/4_sleeve

3. Collecting Data

We then uploaded the data using the “CSV upload” feature of You can easily see the format in which the CSV file needs to be uploaded by downloading the schema from the button provided on the top right-hand side of the drag-and-drop window.

Screenshot 2 - border

Screenshot 3 - border

4. Creating the Feature Set

In no time, the data was uploaded to the data set and a feature set was created for machine learning training. A feature set is a subset of your data set, which is used as the input to your machine learning algorithm. also provides a summary of the feature set you are creating so you can analyze whether your feature set is properly balanced and ensure there are no biases or bad data. After all, your machine learning model is only as good as the data it is being fed with.

Screenshot 4 - border

5. Model Training

We initiated the model training using’s suggested algorithms and parameters. allows you to tune parameters such as batch size, number of epochs, and learning rate and suggests the best possible optimized training parameters for your model training.

Screenshot 5 - border

6. Model Deployment

As soon as the training finished, a model was created and listed under “Model Deployment.” The model achieved an accuracy of 87 percent. Training reports for the model were also generated for metrics such as loss, accuracy, recall, precision, and so on. allows one-click model deployment for your models, thus eliminating all the work required for setting up a model deployment pipeline.

Screenshot 6 - border

7. Inference API

Inference APIs were generated once this model was deployed. These APIs could be hooked into any application and used for predictions.’s inference APIs are easy to use and available in all major programming languages for seamless integration.

Screenshot 7 - border

You can check out a similar model in the featured models section of website.

Machine learning in retail, specifically deep learning, plays a pivotal role in customizing the customer shopping experience and predicting sales conversions. It’s time for online retailers to take competitive advantage of new-age technologies to drive business growth. 

Check out’s demo to see how the platform will  enhance your business, or opt for a customized subscription plan to create a deep model of your own.