Author Archives: Udit Agarwal

How Artificial Intelligence is reshaping eCommerce Personalisation

Have you ever thought about learning in advance what products your customers would be most likely to buy? What if I told you that you can maximise your ecommerce profits by doing just that? Wouldn’t that be just great? Personalised and predictive retail is no easy job, but it may come sooner than expected.

eCommerce Personalisation is here to stay

eCommerce stores have been around for more than 20 yrs now (Amazon started its first online bookstore in 1995), but these websites have never been as big as they are nowadays. Online stores are now present across all sort of devices and have enveloped a great variety of product niches. As the eCommerce landscape grew, so did the customers pool, and in this regard, segmentation becomes the thing to create better engagement.  This is based on the fact that in order to make a visitor actually convert into a customer you need to first understand that customer in terms of age, gender and all sort of demographics before you communicate your offer. And the only tool to do this on a larger scale is the artificial intelligence that enhances your user’s segmentation using metadata and semantic analysis, then delivering predictive recommendations to increase conversions and grow your platform.


We’re about to enter in the era of convenience business and predictive eCommerce. It’s about time retailers help people find products that are the best fit for their needs, and perhaps before they even perceive that need. This shift will require creating experiences that understand human behaviour on the website and responds with large-scale automation and data integration.

Retail giants are already using machine-learning algorithms to forecast demand and set prices. Amazon declared that personalisation technologies help them to better address the users. eCommerce owners need to drive inspiration from this big retailers and learn to dynamically recommend products and set pricing methods that appeal to individual consumers.

What can AI do for your store?

But what does prediction mean? It means AI requires detecting patterns from a massive database you already have from your online store’s visitors such as purchase histories, product preferences, pricing preferences all the data used to forecast demand. This is the spot where AI kicks in. This is where the big global companies are investing right now.

The next generation of smart assistants and connected devices will learn from user habits and pick up on behavioral and environmental patterns in order to make these experiences more predictive. This is a huge potential for eCommerce to learn a thing or two from the IoT devices and incorporate predictive and personalisation offers into their websites. This will lead to a better experience a user has on the website on one sees personalised offers. Store owners need to create experiences that make this magic trick. People expect even faster and more intelligent services than they receive today. Soon, in the very near future, the expectation will shift to on-demand and predictive commerce. It’s time to get ahead of that change.

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

3 ways brands can use AI to transform the e-commerce shopping experience

The amount of behavioural and demographic data available for individual online shoppers has increased to staggering levels.

Coinciding with the increase in data, the use of artificial intelligence (AI) and machine learning techniques has become more mainstream as businesses discover the value of insights gained by these advanced techniques. With more data available than ever before, retail marketers have been given the opportunity to harness this data using machine learning algorithms that describe and predict shopper behaviour in real time.

These insights can be used to drive both automated email campaigns and interactive website experiences that are relevant and personalised, leading to the creation and conversion of lifelong customers that drive increased value. Thanks to AI-driven campaigns, marketers are no longer required to manually sort through massive amounts of data in order to discover behaviours and send personalised messaging that increase shopper loyalty.

Retailers who successfully scale and automate AI-driven experiences for their customers are already seeing significant results, including higher overall revenue and higher customer lifetime value.

These brands have been able to leverage a customer’s real-time online and offline transactional and behavioural data to automate individually personalised email campaigns that increase overall order values.


Here are three ways brands can use AI-driven campaigns to aid in personalising email and web marketing efforts:

Using ‘abandonment causality’ campaigns with customer service

By harnessing customer data, Value City Furniture is able to recognise when customers abandon items in their cart or leave before completing their checkout, which happens for a number of reasons.

To combat these types of abandonment, they send personalised email messaging to assist the shopper with the reason they left the site, whether it be shipping costs or a broken promotional code.

Using “mirroring” campaigns online and in-store

After consumers interact with your brand either online or in-store, retailers should personalise future interactions across all channels.

Brands can essentially “mirror” a shopper’s preferences and interests both on the website and inside the physical location of the store.

Using ‘model based’ campaigns to identify propensity to purchase and disengaging customers

When a customer returns to a retailer after previously interacting with specific brands or purchasing from the site, retailers can use machine learning algorithms to understand the shoppers habits and target them based on prior interactions.

For example, if a shopper normally makes their purchase one week after adding items to their cart, retailers should not send a discount to compel them to buy since their predicted propensity to purchase is high.

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

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.

Chatbots in Ecommerce – All You Need to Know

A chatbot is a software designed to simulate human conversation for different purposes and applications. There are two different types of Chatbots:

  1. ones based on pre-written scripts, functioning on a set of rules
  2. ones that use machine learning (AI-artificial intelligence) – The latter ones mimic human conversation and are constantly getting smarter and learning new things from the interaction. Bots make it possible for your brand to be more personal, proactive, and more streamlined.

The possibilities for chatbots usage are widespread. From advertising, virtual assistance, education, entertainment to the customer service/support and product ordering. Nowadays, chatbots have become so advanced they can even take care of food delivery, purchasing tickets, ordering Uber, scheduling appointments, money transactions or paying bills.


In e-commerce, chatbots can be very helpful on multiple levels and provide a complete shopping service. You can use them for accurate and quick product search. They can simultaneously handle more product orders from multiple customers, therefore speed up the ordering and shipping process. In addition, you can even pay your purchased products directly via chatbot so your customers have a complete and integrated shopping experience.


Chatbots maintenance and oversight depend on their type. Scripted ones correspond to predetermined answers only, and they are easy and simple to maintain. They don’t need a large amount of control and oversight.

Such bots can independently perform their tasks and communicate with customers, but still, they can run into a problem while conversing. For every query or problem which can’t be resolved solely by the chatbot, there’s an option to use specialized tools like email, help desk software or push notifications. If that’s the case, community managers kick in and take over the communication.

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.

Four Ways AI is already making ecommerce smarter

There’s no doubt that artificial intelligence (AI) will fundamentally change the world over the next few decades. What many do not realize however, is that in some fields, it has already become part of the status quo. One such example is e-commerce (EC).

Below are a few examples of how we are leveraging various applications of AI, specifically machine learning, to advance our EC business.

Predicting sales –Supervised machine learning is a form of AI in which “the machine,” or algorithm, is given sample data from the past that helps train it to process the data of the future. With 200 million products being traded, supervised machine learning algorithms allow us to use historical sales data to forecast the sales volume of products to a high degree of accuracy – and make surprising discoveries in a far more efficient way than a team of humans ever could.

Marketing to the right groups – We make use of so-called “unsupervised” learning algorithms when segmenting customer groups for marketing campaigns. Traditionally, marketers have defined market segments in ways that appeared to make sense to them – by age or gender, for example. But AI is demonstrating that those are not always the most effective approaches. An unsupervised learning algorithm, working from raw real-time data only, might identify alternative means of segmentation, such as online behavior or preferences, that can serve as a more accurate predictor of interests or tastes.

Classifying products – This can make categorizing a challenge. To solve the problem, we utilize a “semi-supervised” learning algorithm, which repeatedly resamples data until the algorithm learns how to process it in the most efficient way.

Analyzing ratings and reviews – Understanding user ratings and reviews is important, but it is also time-consuming. Applying “structural” machine learning algorithms, a method commonly used in the study of the structure and formation of words (morphology), we can efficiently collect and analyze product review text, both positive or negative. In addition, structural machine learning can help us mine valuable information data from page explanations and reviews.

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

How Machine Learning can be used to Predict Customer Behaviour

One of the biggest challenges for the modern business is learning to utilize all of the data available to them in a way that is both meaningful and actionable. However, the potential for using data generated by a website is often left unexplored, and as a result, the intentions and reactions of individual digital customers can be overlooked.

Focus is often placed on the broad strokes – key metrics such as the number of page views this month, or the number of unique visitors. While these figures have their place, we lose the ability to shape our individual customer’s journey, or to identify the customers who need engagement most. As a result, customers who may be on the verge of signing up for a trial, completing a checkout, or any other desirable outcome, can fall through the cracks. We know the outline of the picture, but we are missing all of the shades and complexities needed to understand our customers’ online experience entirely.

On the average website, there is an abundance of information to be collected about who interacts with your site and how. By leveraging all of this data, we can gain insights into customer behavior. Machine learning techniques can be used to determine which customers may be interested in achieving an outcome on your site.

For instance, if a customer is not en route to achieving a desirable outcome, a content offer or a chat offer could help to steer them in the right direction.

Predicting customer behavior can tell you which customers to reach out to on your site, in real time, to convert website visits into tangible outcomes.

So how do we do this? We can use machine learning techniques to create a model, using data collected about customer behavior to date. This model will then tell us how likely a customer is to achieve an outcome, based on what we know about that particular customer.

 Gather appropriate data

When trying to predict the likelihood of an event occurring, we look at what has happened so far. We begin by gathering data about every customer visit to the site. This includes demographic information such as location and device type, as well as behavioral data such as how many pages they have viewed and how long they were on the site. To data scientists, these are known as features. We also record whether or not a customer has achieved a particular outcome. These are known as labels.

From here, the premise is simple enough: if we are aware of the features of customers who have previously achieved an outcome, future customers with similar combinations of features are the most likely to also achieve this outcome.

Prepare and transform data

This step, while often overlooked, is usually the most work-intensive. Now that we have collected relevant data, we must change it into a form where it can be used with a machine learning algorithm. Categorical data, such as location or device type, usually needs to be binary-encoded. This is so that it can be recognized in a form that our algorithm can understand. Numerical data often needs to be normalized. Many machine learning algorithms perform better when numbers are scaled between 0 and 1. For instance, the number of pages a customer has viewed would be normalized. We use these techniques on both the features and the labels, with the labels requiring binary-encoding.

Sometimes, certain features can be detrimental to overall performance. It would be more advantageous to omit the feature from the model than to leave it in, as that particular feature does not give us much information. This is where feature selection comes in. Feature selection is the process of deciding which features to use for the model. While some techniques may not require feature selection in all cases, it is a key step in most machine learning algorithms.

Although different algorithms may require slightly different steps to prepare the data, the above process is common for the majority of them.

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

Artificial Intelligence (AI) Meaning and its Uses in Daily Life

The world has not come to that stage where machines have started dominating humans but it seems that the process has already started. Nevertheless, machines nowadays have a say and significant influence on the way we eat, sleep, work, live and almost whatever we do from morning till night. Starting from personal assistants, such as Cortana, Google Assistant, and Siri to even more advanced and complex platforms, the world is becoming more habitual to these applications and gadgets.

While we do believe that machines are progressing, it also needs to be kept in mind that they have not evolved fully. A lot of companies still do not believe in the end-to-end usage of machines.

What exactly is the meaning of Artificial Intelligence (AI)?

This again is a matter of debate and discussion. Some say that Artificial Intelligence is a piece of software that has some kind of specialized algorithm that responds according to pre-defined user behaviors or inputs. However, to some, AI is a kind of machine learning that has the ability to learn on its own and acts as a neural network that can connect and come up with conclusions based on behavior.

Premier companies like Google and Apple have already started introducing revolutionary changes based on AI. However, a lot of us are still not much aware of AI and its effects all over the world.

The following are some of the areas where artificial intelligence (AI) is being used in our daily life:

  • Virtual Personal Assistants: These platforms help users to find useful information through voice search. Google Assistant, Siri, and Cortana are some popular applications. Based on the information that you provide, the assistant finds results that are relevant to it. It also interacts with different other commands for better answers. It strives to learn more about the user and comes up with responses tailored to the preferences.
  • Video Games: This is where AI is being used quite a lot. The effectiveness has increased along with time. The gaming characters learn your playing behavior and respond accordingly. Some of the finest examples of AI have been done in first person shooter games like Call of Duty.
  • Smart Cars: Smart cars have brought about a revolution in the transport sector. Some of the notable examples are the autopilot cars by Tesla. Google has also come up with an algorithm where self-driven cars drive in the automatic mode like humans. The basic idea is that cars will make decisions on their own while driving just like humans.
    Mind Over Digit
  • Online Customer Support: Artificial Intelligence is also being used to provide customers with an opportunity to chat. However, in this case, a machine may chat with the customer and respond according to the queries. Advanced chatbots are equipped with automated responders that extract knowledge from the database and respond to customers according to the requested query.

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

How Artificial Intelligence Is Quietly Changing How You Shop Online

The promise of AI has seemingly been just on the horizon for years, with little evidence of change in the lives of most consumers. A few years ago, buzzwords like “big data” hinted at the potential, but ending up generating little actual impact. That’s now changing, thanks to advancements in AI like deep learning, in which software programs learn to perform sometimes complex tasks without active oversight from humans.

Deep learning algorithms have been powering self-driving cars and making quick progress in tasks like facial recognition. Now these innovations are beginning to find their way into the daily lives of consumers as well.

Other e-commerce sites are also adopting deep learning to help shoppers more easily find what they seek. Gilt deploys it to search for similar items of clothing with different features like a longer sleeve or a different cut. Etsy bought Blackbird Technologies last fall to apply the firm’s image-recognition and natural-language processing to its search function.

And notably, Amazon is planning to use the AI technology it offers on its Web Services in its new Amazon Go grocery stores. The company is operating only one store in Seattle, but Chief Financial Officer Brian Olsavsky said during a February earnings call that “it’s using some of the same technologies you would see in self-driving cars: computer vision, sensor, fusion, deep learning.”


While deep learning is becoming a part of the retail experience, it’s happening in fits and starts, as Facebook found with chatbots. Touted as a tool that could automate customer-service functions and deepen human engagement, chatbots were added to Facebook Messenger, with more than 11,000 of them available last year. But last week, Facebook scaled back its chatbot ambitions after they clocked a 70% failure rate.

As with the early days of the Web, there remains much work to do before deep learning can be seamlessly integrated into the daily lives of consumers. Compared to expectations of even a few years ago, though, things are a lot farther along than many expected. And that suggests Silicon Valley may again be ready to change how we shop.

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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