Natural Language Processing posts

How natural language processing and machine learning are changing eCommerce

For the average online shopper, it’s easy not to notice how much ecommerce has evolved over the years, or how that evolution is continually gathering pace. We can all appreciate that online shopping is a far less frustrating experience than it was just a few years ago, but for the majority of shoppers, the technological changes that are taking place behind the scenes are invisible. The adoption of natural language processing (NLP) and machine learning technologies is one of the key drivers of change in ecommerce today, and the impact that these technologies are having on customer experience is both far-reaching and powerful. In this article, we examine some of the key ways that NLP and machine learning are being used within ecommerce, to drive increased sales and improved conversion rates.

Smart on-site search

Online shopping is now part and parcel of everyone’s life, and something we have quickly come to take for granted. Alongside the rapid adoption of ecommerce, the growth of smartphone usage has been exponential, with more than two-thirds of the UK population now owning a smartphone. As mobile devices overtake laptops as the preferred device for accessing the Internet, it’s vital that ecommerce sites adapt, to satisfy the needs of time-pressed consumers who want to shop from their phones or tablets. This has pushed on-site search to the forefront of retailers’ attention, as search has become the main navigation tool for many online shoppers, replacing cumbersome menu-driven navigation structures.

Historically, on-site search has focussed on keyword matching. This has often produced results that were less than optimal, with seemingly random results generated for more descriptive queries, or even no results at all. That’s all changing though, as ecommerce retailers look to NLP-driven site search engines to power their on-site search. NLP search is a game-changer in ecommerce, because it has the capability to base results on semantics, rather than keywords, which produces more relevant results. This smart, meaning-based approach to search is coupled with machine learning abilities, to create an engine that becomes smarter over time, learning more and more about query intent, concept extraction and shopper behaviour. The data mined from an NLP search engine is ploughed back into the system to improve future search performance, but it can also be used in other areas of ecommerce operations, to drive additional benefits.

Product recommendations

Product recommendations represent a very important part of an ecommerce store today, particularly for merchants with larger catalogs. Product recommendation blocks are used across all page templates, primarily for up-selling and cross-selling.

The immediate benefits of smarter product recommendations is improved customer satisfaction and improved average order value and conversion rates, which leads to increased sales. The data derived from product recommendations has huge value too. By analysing customer behaviour on a store, understanding much more about shopper intent, and looking at aggregate data for products viewed and similar customers, it is possible to present a highly personalised experience to each individual shopper.


As an example, if a customer searches a fashion website for ‘red dresses’, machine learning technology could enable the store to dynamically recommend highly targeted products to that customer, such as red shoes or a matching bag, even if the recommended products don’t actually contain the keyword ‘red’. This extraction of meaning and context means that the needs and desires of customers can be interpreted in more detail and more accurately than ever before.

Veltrod is specialized in providing Artificial Intelligence based solutions for an ecommerce spectrum. Write to for free consultation and quotes for business needs.

How Machine Learning is Changing Digital E-Commerce

Machine learning (ML) emerged as a derivative of artificial intelligence. It is enabling computers to be self-learning, evolving intelligent systems by providing relevant data sets from the past and recognizing patterns that exist in them. Today, since data in large volumes is available from various domains through digitalization, be it customer demography or product inventory, it is much easier to observe repeated patterns which can then be used to improve system efficiency and accuracy.

How is Machine Learning Different From Traditional Programming?

Traditional programming involves developing computer code for each task. This did help automate and speed up operations significantly compared to manual operations. However, there are several areas where it is quite difficult to come up with a single algorithm for a task. Consider language processing and speech translation as examples. Using an ML-based approach ensures that the system improves itself through observation. The idea is to constantly improve a system’s output using accurate and complex algorithms through multiple iterations on information that is already available.



How Does Machine Learning Work?

The machine learning process starts by obtaining useful domain-specific raw data, which is then converted to prepared data. Analytical models are then developed by processing and identifying patterns in this prepared data using complex algorithms through multiple iterations. The models are then deployed into the application, and the result obtained would allow a data scientist to make safe and reliable predictions in future data behavior. Today, graphic tools are available for data scientists to develop appropriate models that can then be deployed into the applications to provide the probability of future events.

Benefits of Machine Learning

Having sensed the immense potential of ML applications in their businesses, large corporations have invested heavily in ML and are already reaping its benefits:

  1. Businesses are able to gain the coveted edge among their competitors with the help of ML by accurately and quickly predicting future market changes
  2. Revenue predictions of organizations are more accurate with the help of algorithms generated by observing past financial investments
  3. By studying consumer behavior more deeply, companies are able to target prospective customers and ensure better customer satisfaction
  4. Production units get better insights to improve and develop new product lines
  5. Companies can now improve their candidate requisition and employee retention programs

Veltrod is specialized in providing Artificial Intelligence based solutions for an ecommerce spectrum. Write to for free consultation and quotes for business needs.

Future-proof Your Ecommerce Marketplace: Applications of Machine Learning

Anyone can start an e-commerce marketplace and let vendors list their products, but what after that? However, as the technology is moving, a product or a service popular today can easily become redundant in the near future. Why will people want to come to your e-commerce marketplace year after year? The job of an e-commerce marketplace owner does not stop after seeing few products getting sold.
To ensure a steady supply of buyers, you need to future-proof your marketplace because if you do not, your competitors will overrun your business and you will not be able to satisfy the growing demands of experience-hungry buyers.

To keep your ecommerce marketplace advancing with changing times, it is recommended for you to know about cutting-edge technologies like machine learning. This post talks about several areas where machine learning can help in redefining future of ecommerce sector.

Smart Recommendations

Small and big e-commerce portals alike are already using product recommendations based on search and buying history. Amazon and Google are pushing the limits of machine learning to recommend the most relevant products, which is personalized for each user.

The personalized recommendations are made on the basis of data collected on each customer. Needless to say that using such technology requires superior data collection techniques.

Better Customer Segmentation

Creating customer segments is essential to maximize the reach of your ecommerce store. Customer segmentation analysis, in most cases, is based on age, gender, demographics and more but, it is important to consider various other indicators as well. Machine learning can help e-commerce marketplaces to uncover new customer segments with similar behavior.

When new patterns are discovered among customers or potential customers, you will run your e-commerce marketplace in a more profitable way. Machine learning based customer segmentation adds value to the marketing effort as implementing such complex models is not possible without the help of fast and efficient algorithms.

Real-time Solution to Customer Queries

Answering queries of buyers and sellers in real-time requires an e-commerce marketplace to hire a team that can provide support around the clock. Machine learning based solutions, such as chatbots, can prove to be a good return on investment as they can answer the questions of buyers and sellers instantly.

By integrating this solution with marketplace’s knowledge base, chatbots can instantly answer questions like the status of delivery and refund. Examples of machine learning driven smart assistants include Apple’s Siri, Google Assistant, and Amazon Echo.


Fraud Pattern Analysis

E-commerce marketplaces get spam orders across the board and are prone to online frauds. No technology is completely secure and this is why it is important to take preventive measures. Machine learning technique can be used to fight online fraud.

With machine learning, the algorithms can learn from the knowledge base as well as observe patterns to figure out the probability of fraud. Since machine learning involves constant learning, the algorithms can be programmed to optimize its accuracy.

Customer Churn Predictions

Most e-commerce marketplaces spend a lot of money on acquiring new customers, but they fail to understand that retaining old customers can be highly profitable and economical. Excessive churn rate can make an owner take drastic steps that can prove catastrophic for the health of the marketplace.

Traditionally, all one can do to lower the churn rate is to initiate different marketing campaigns, establish the reasons behind high churn rate and tackle them accordingly. Whereas machine learning can be used handle problem of higher churn rate with performance comparison, testing different algorithms, and more.

Demand Estimation

Why spend hours in reviewing monthly, quarterly and yearly sales to estimate the growing demand? Getting the right demand estimates is vital for an e-commerce business as it is important to know if your marketplace has limited stock or in bulk. In both cases, the owner suffers a financial loss.

Demand and supply go hand in hand, but foreseeing the growing demand can only be done with machine learning algorithms. Not just demand, machine learning can be used for delivery route optimization, orders grouping, warehouse space optimization, and more.

Veltrod is specialized in providing deep learning solutions across the domain. Write to for free consultation and quotes for business needs.

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