Technology posts

The time is now for AI in ecommerce

Siri, Amazon Echo, Google Home, etc. have made AI mainstream, but what if we could make retail shopping with AI mainstream as well?  For example, imagine walking into your favorite retailer and having the store not only recognize that you’re there, but then direct you to particular styles, brands, and products based on your unique user profile. Sound too good to be true? It’s coming — the time is now for AI in ecommerce.

Retailers today need to implement new technologies in order to future-proof their business, and AI should be at the top of that list. Ecommerce doesn’t lack ad hoc machine learning applications like up-sell and cross-sell recommendations or dynamic creative optimization. However, what ecommerce does lack is true AI that can aggregate and analyze thousands of attributes associated with millions of consumers with probabilities updated in real time.

Ecommerce needs AI to interpret consumer behavior and map it to structured data of predictive attributes, such as size, fit, color, brand preferences, price range, etc., to create a real and actionable customer relationship management (CRM) system. This holistic approach to understanding consumer preferences opens a whole slew of new and interesting applications for retailers from predictive merchandising to dynamic re-ranking of product listing pages to in-store personal digital concierges, etc.

Here are the top three benefits of implementing AI in ecommerce:

  1. Enable smarter merchandising and inventory planning

Decisions have to be done quantitatively across millions of data points, across millions of consumers. Merchandising teams need to be empowered with data and tools that can aggregate and compute such data. It cannot be just a log or historical summary of consumer behavior, but rather provide predictions of what will happen in the future based on millions of unique attributes for each individual consumer. 

  1. Create a personal concierge experience

Consumers can change their preferences on an hourly basis. Today a woman might be looking for pink pants; tomorrow she might want a blue dress. Yet there are certain preferences that are sticky like a woman’s size or her brand preferences or the fact that she likes three-quarter sleeves. Retailers need AI to take all this consumer behavior, map it to predictive attributes, and compute this data in real time. When a brand provides an optimized and unique consumer experience, the likelihood of customer engagement goes up. When consumers feel a connection with a brand on a more personal level, the probability they will turn into loyal customers increases.

  1. Use AI-computed data to generate more revenue

If you are computing aggregate demand data across all channels and building unique attribute data for each consumer, then you can create new revenue opportunities across the board by empowering each channel with this data. For example, your search can become smarter because it understands aggregate demand for different products and understands individual consumer preferences at the attribute level, like size, fit, color, brand preferences, etc. Investment in AI leads to exponential growth in revenue, where one investment can have a ripple effect on multiple channels.

Taking advantage of AI technology is the only way a brand will be able to differentiate itself and win in the hyper-competitive modern retail world.

As today’s consumers become more sophisticated and have higher expectations of how brands target them, using structured data based on predictive attributes will be key for retailers. Some players like Nordstrom Rack have already begun using structured data based on consumers’ predictive attributes to properly target customers.

We’ll see this adoption increase among other retailers moving forward. Artificial intelligence-powered predictions can provide a better shopping experience for your consumers, provide new insights about overall demand and trends, and ultimately result in new revenue opportunities for the retailer. It would be foolish to pass on it.

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


Why Your eCommerce Store Needs Artificial Intelligence To Survive

Artificial Intelligence (AI) has finally emerged from the shadowy realm of university labs and science fiction to splash down right in the heart of mainstream society.

Artificial Intelligence can go deep. Like, really deep. Its networks of software and hardware essentially mimic the neurons in our brains, analyzing huge swathes of digital data and drawing insights from the patterns they recognize. They adopt tasks, such as identifying pictures and recognizing voice commands, and then they leverage their biggest advantages—speed and volume—to quickly master these tasks, even outperforming humans in some cases.

As complex as AI can be, it can also be applied in service of simpler, more focused goals, such as making it easier for your customers find relevant products in your online store.

Here are four reasons why your eCommerce store needs AI right now:

  1. Refined Search Capabilities

When Artificial Intelligence learns and masters a skill quickly, it’s called deep learning. Deep learning is largely responsible for the huge strides that Facebook, Google and their ilk are making towards developing a true understanding of their individual users, even as their networks approach monumental scale.

One business area that is ripe for refinement and reinvention is your website’s search capabilities. Previously, websites have relied on static (human-guided) algorithms that did a poor job of adapting to new content and shifting user behaviors, and as a result, they did a mediocre job of narrowing down the user’s search queries.

AI can smooth out the search glitch and ensure that your results don’t lose relevance over time. One way that it improves search queries is through natural language processing. This allows the system to better understand and “infer” what a user is looking for based on the language they use to search. By better understanding what the user really means, the search engine can interpret more complex queries and respond with better, more individual shopper-specific results.

  1. Analyzing Big Data

The problem humans have when it comes to analyzing big data is that it’s just too big… there are simply too many data points coming in too fast to see all the patterns. Because Artificial Intelligence mimics the neurons in our brains to analyze large volumes of unstructured data, it can do what we do, only at tremendous scale.

AI’s ability to read, understand, analyze and break-down big data for you allows you to start trends and marketing styles before your competitors, and this kind of foresight can be the key to success in a highly competitive and crowded shopping environment.

  1. Personalized Online Experiences

It might sound odd to suggest that a robot can make your customers online shopping experience more personalized than a human being, but look at it like this: AI can’t replace a physical salesperson who greets your users with a friendly “Hi! Can I help you?” But it can provide intelligent engagement at every single customer touch point, something that would require way too much time for a human workforce.

It can do this by identifying clusters and patterns in information, such as similarities between customers, past purchasing behavior, browsing history and other common threads. With this information, your eCommerce store will be able to offer proactive guidance, such as providing your customers with a personal shopping assistant and customized the sales experience based on their behavior in real-time, while they are on your site.

Soon, AIs will even be able to handle more complex questions that typically require a human salesperson, but even if it can’t find the answer itself, the AI will be able to identify and “triage” these complex questions, directing them to the appropriate human salesperson and ensuring your customers stay in your store until they get what they want.

  1. Product Cataloging

There are many ways that you can improve your customer’s experience. Utilizing themes, such as those from Shopify, can make your eCommerce site more visually appealing and user-friendly. But if you can pair a beautiful website with highly efficient product cataloging, then you will really make some happy customers.

AI can play a vitally important role in product cataloging. Customers today expect more insightful and accurate product information from their retailers, and if you don’t meet their expectations, you could find yourself losing out to a competitor.  30% of American adults say they would purchase goods from an online retailer they’ve never even heard of before if they display detailed and accurate product info.

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


AI Is the Gladiator Saving eCommerce from Retail Disruption

Consumers increasingly prefer the convenience of online shopping over going to stores, right from buying groceries to shopping solitaires. Retailers have touched every aspect of our life and presented us with options like online shopping, omni channel experience, personalized marketing, etc. However, maintaining user loyalty to your business is always a challenge. Therefore, eCommerce businesses are trying something unique everyday to attract new users and engage buyers.

AI is turning out to be a weapon for bringing stickiness to the online stores. It could save the retail industry from the recent disruption by riding the innovation excellence to deliver data backed experience. So, here’s how AI could be a game changer in the space of retail and eCommerce.

#1 Next Generation Customer Experience

If the retail platform can predict a consumer style and size at the time of browsing, this will result in better sales and fewer returns. It will also help consumers to buy things quickly and easily, having a pleasant experience. Finally, AI can help sort data to profile individual shopper needs and make experience consumer-centric and relevant. Consumers won’t have to choose their style, rather it will be automatically selected matching their taste. The system will achieve this by looking into their sales history, online browsing, and social media activities.

#2 Find Your Look

Visual search is slowly emerging as the next-generation search. Customers can use their smartphone to take a pic of an item and their shopping app will display the similar items from the store’s inventory. This AI-empowered visual search will help shoppers to find desired things much earlier. For example, Pinterest’s recent search tool, Lens detects items on the web and suggest related items. Shazam does the same for products. Furthermore, retail brands are using the blend of visual search and AI to enhance the online shopping experience.

#3 Personal Stylist

Brands are garnering more information about their shoppers, their gender, their choices, their needs and even their device details. This information when put to work with artificial intelligence algorithms will result in far better and more personalized style recommendations. The shop app can act as their personal stylist who can analyze product images viewed and search the database to suggest the similar and liked products. This can be blended with the real time notifications for price drops and best deals.

#4 Intelligent Omni channel

Shopping websites are strengthening their presence by being available to customers at each touch point and every channel. AI could prove to be crucial for streamlining the communication between online inventory, multiple physical stores, boutiques, warehouse, delivery, and returns. Chatbots empowered by AI can engage consumers across all social media channels such as Facebook, Twitter, eCommerce website, in-store media devices and location based apps.

#5 Social Sales Assistant

Social media has turned out to be an important influencer in the space of shopping. All major brands are showcasing their products on their social media channels in addition to providing a buy button or redirecting them to websites and apps. They are also making use of the sponsoring option of social media channels to increase the reach of their products. The data can be employed to analyze whether the sponsored posts are resulting in sales. AI can help to profile people who are directed through the social media and their style and product they are looking for to customize the messaging.

#6 Trendstarter and Not a Spotter

You expect your retail outlets to update you with the latest and hottest trends in the industry. AI algorithms can make sense on the general data available on the social media sharing and likes, internet searches, fashion industry conversations and top notch fashion events to identify trends and also help brands tap the popular styles. This is especially relevant if you can turn these into real-time updates for your customers.

#7 Right Pricing Strategy

Pricing has always been a turning point for conversions in the retail industry. AI along with big data and machine learning algorithms can help brands create a pricing strategy. This also includes taking care of the factors like inventory and popularity of the product. It can also help in optimising the product prices according to the listings, deals, and discounts rolled by the competitors. Segmenting customers based on their user journey, dropout locations, and pricing preferences decreases bounce rates.

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



How Artificial Intelligence(AI) Is Helping Retailers Predict Prices

It safe to say that artificial intelligence (AI) is changing every aspect of modern living. From our phones to cars to healthcare and every other industry, AI is slowly becoming a common part of today’s environment, deeply embedded in everything we do. Retail is no exception, entering a new era of predictive commerce. Thanks to AI, retailers are able to cater to their customers to the tiniest detail possible, while also leveraging the technology to improve their business operations.

For this post, we’ll focus on how AI helps with one retail aspect that often troubles retailers the most – pricing. The tech behind AI, how it uses it to predict prices and what are the results – it’s all there in the following lines.

Inside AI

At the heart of AI is machine learning (ML), a process that has the ability to learn on its own without being explicitly programmed. Machine learning uses data to detect patterns in data and adjust actions accordingly so that, when it’s exposed to new data, it develops programs that adapt to that information. ML algorithms are closely related to a number of computational methods, such as computational statistics and mathematical optimization.

While this may sound rather boring, especially for those that never really liked math that much or were garbage at it, the reason why we are mentioning all the math babble is because ML is a standard method used to create complex algorithms that possess predictive powers. You might know this as predictive analytics, a number of analytical models that uncover insights through learning from trends and historical information in the data set.

Predicting prices

Machine learning has many approaches that constitute it – different types of learning if you will. It’s in your Facebook’s News Feed. It’s making Tom Cruise’s life a living hell in Minority Report. It can even predict when you are going to buy soup. Thus, we’ll spare you the nitty-gritty of it and focus on how the technology helps with price prediction.

In a nutshell, you have analytics software whose machine learning component is using a technique based on a certain statistical model to create algorithms that automatically identify patterns from the data and predict prices based on that information. Patterns from huge data sets range from competitors’ pricing and inventory, purchase histories, product preferences to product demand and anything closely related to pricing.

As you can imagine, these parameters are constantly in flux, which is where machine learning comes in and adds a bit of nuance to the whole process that goes beyond simple price history. Suddenly, you have an accurate prediction of customer behavior, a whole system built around the individual and its needs. All of this is followed by high levels of automation, where the execution of produced data-driven insights is instantly applied.

With prices, it’s a bit more complicated than that as there are more factors to consider. However, the basic principle is the same due to the highly complex and sophisticated nature of the technology. You have a pricing engine that helps you monitor competitor prices real-time. It then sorts them out and compares them to similar products, depending on the wide range of attributes selected, and ultimately optimizes prices.
AI and the DP2P clear

What’s in it for the retailers?

For once, they get price predictions and optimizations according to numerous market fluctuations that go in line with their pricing and volume goals, among others. Thus, the ever-elusive pricing is no longer a problem. There is no Indiana Jones-like adventure to find that pricing sweet spot that will attract and retain the customers whilst raising the level of profit margins. In a way, retailers get an effective guide for their retail life cycle decisions.

Also, they get precious time back. Automated solutions are the future of retailing, even if they are still on the margins. They handily reduce the enormous amounts of time need for manual labor regarding tracking the prices of your competition. By leaving everything in the hands of automated analytics software, retailers, both online and offline, have more time to focus on other important and time-demanding aspects of their business.

However, the benefits of technology don’t stop there. With the scope of modern technology, it would be foolish to think that there is only a single layer of this cake. Retailers also get to center their operations around customers and deliver them a personalized experience which matches their browsing history and wish lists with cross-sell and up-sell recommendations as it’s all part of the package. They get automated re-orders whenever certain product stocks fall below minimum required levels. Assortment optimization? Check. Various adjustments according to the occasion, competitor Intel, product, category, season and consumer behavior? Check.

In summary, they get real-time market intelligence with all bases covered – a detailed understanding of human behavior coupled with large-scale automation and data integration.

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



Four Big Ways AI Can Improve Customer Experience in Online Retail

As explored in our latest ebook, A no-nonsense guide to AI in ecommerce marketing, it’s only a matter of time before machine learning algorithms take over certain parts of an ecommerce marketer’s job.

Nothing can replace what human beings bring to the creative side of marketing (at least for now).

That said, it’s inevitable that artificial intelligence will have its part to play—but this is something to embrace, not fear. Because machines are created to help marketers, not replace them completely. 

From predictive replenishment to taste profiling, here are four ways AI can—and will— improve your brand’s customer experience.


Artificial intelligence enables retailers to make their marketing messages hyper-personalised. 
Here’s a closer look at how:
Creating and defining micro-segments | Machine learning algorithms can process vast sets of data and spot subtle patterns amongst customers.

Using a process known as “clustering”, an algorithm can then use the information obtained to split the data into micro-segments. Customers in each micro-segment will all have something in common; for example, they spend a lot of money but shop infrequently.

This enables retailers to not only tailor their marketing strategies to specific customer segments, but also to identify common traits in groups of customers that might be missed by the human eye.

Product recommendations

Whereas normal recommendation engines tend to focus on “latest” or “most popular” products, AI-powered ones use individual customer data, as well data from customers displaying similar traits, to learn the best product to put in front of each person. Predictive replenishment | This is an important point for retailers selling replenishable items, such as makeup, skincare and food and drink. It involves using artificial intelligence to predict when a customer will be about to run out of a product, and remind them to reorder.

Whilst this is possible without AI, resources are limited (a marketer will tend to rely on average repurchase rates, rather than individual customer data).

AI makes it possible to make the process far more refined, incorporating important factors like the amount of the goods purchased. It can also use the information its got to decide the right amount of reminder messages to send (as, often, retailers send too many!).

Greater efficiency

Like a calculator can carry out complex calculations faster than a mathematician, machine-learning algorithms can process huge sets of data faster than a marketer. This means that, thanks to AI, any form of communication between a brand and a customer will be swifter and more efficient than ever before.

An example of this could be campaign optimisation. Instead of a marketer needing to manually check in on campaign performance, and using methods such as A/B testing to try and optimise it, AI has the potential to do this for you. AI can also take care of the number of messages sent to a recipient, and ensure there’s no duplication.
All of this will, once again, ensure a customer is only sent the right material for them, at the right time and on the right channels.


Nothing will ever beat human rapport, but to write every single message to every single customer, at scale, is impossible. Which is why automation is such a godsend for ecommerce marketers. But there’s no denying that automation can make correspondence with a brand seem a bit cold and, well, automated.

Creates room for the human touch: As illustrated in the points above, artificial intelligence can free a marketer’s time by automating certain tasks.

This enables a marketer to invest more time in the customer journey. This could take the form of hosting in-store events or reading and responding to customer feedback. 

It’s all about making a customer’s experience as smooth and straightforward as possible whilst always showing how much you care.

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

Toward Digital Encryption

The next practical step in AI for eCommerce

I’m sure you would have been looking for ways to improve your eCommerce site experience. Now, you could gain ground in this area. Here, you’ll see how much Artificial Intelligence has evolved since chatbots.

According to recent estimates, eCommerce sales are set to top $27 trillion by the year 2020. Do you know what that translates to for you? Tremendous competition in the immediate future. Where do you stand now? Where will you stand in a year?

Imagine an F1 race car. Now, let’s say you’re racing against it with your car. Assuming both of you start at the same time, do you think you can ever catch up? Artificial Intelligence is the new race car. All you have to do is start using it.

Earlier, artificial intelligence took a u-turn with chatbots. This didn’t quite yield the expected profits. And not long after that, media turned its attention elsewhere. This rift in scrutiny gave the companies working in AI exactly what they needed. Now, they’ve come up with a host of e-Commerce techniques powered by AI that you can put in place on your website right away.

Out of the lot, these are the most useful and viable ones:

  • Intelligent Merchandising
  • Hyper-personalization

Intelligent Merchandising

This provides a smart curation of all your merchandise. Because of this, the merchandising algorithm optimizes your website to it’s greatest extent. It also upgrades your back-end optimizations to make it more effective.

Tools under Intelligent Merchandising:

  • Intelligent Search- Adds a contextual angle to your customers’ search terms. It provides more intelligent results in comparison to traditional search engines. The AI uses intuition to judge what the user means rather than simply processing keywords.
  • Assortment Tools- Tells you what products to carry. Compares your inventory to competitors’ and suggests unique products that you could provide.
  • Dynamic Pricing- Anticipates customers’ mindset and predicts the most profitable prices. You don’t need to plan product discounts anymore and still enjoy more sales and profits.
  • Intelligent Q&A agent – Acts as a go-between for brands and retailers.



Analyzes each of your customer’s behavioral patterns through web-based eye tracking. Using that, it suggests products based on intelligent 1:1 personalization algorithms. The AI provides these suggestions based on patterns, textures, and the like. Suggestions improve as customers show specific product tastes.

Tools under Personalization:

  • Visual Search- Your shopper, now gets to search for items just by taking a picture of it and uploading. The AI shows similar & related results by comparing texture, color, and other aspects of the product.
  • Product Suggestion Engine- Provides intuitive suggestions based on shoppers’ intentions. It uses Entity Detection and Computer Vision to determine shoppers’ purpose of visit. For example, the AI would identify “Chicago Bears” as intent. After which it displays products related to the team instead of typical suggestions based on keywords.
  • Virtual Dressing Rooms- Provides a virtual fitting room. Your customers can try out clothes and see how the products look on them. It is exclusively designed for fashion platforms.

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


Machine Learning: Simply Injecting Intelligence into B2B eCommerce

Efficiency in business takes many forms. It can mean doing the same job in less time or requiring fewer resources to accomplish a particular task. Technology is an enabler, helping companies maximize efficiency across numerous areas through automation and finding more ‘intelligent’ ways to sell more products, increase market share, and reduce costs. Today, many B2B E-Commerce firms are turning to machine learning for such intelligence.

Before we discuss how machine learning contributes to success in B2B E-Commerce, let’s first be clear on what it is. “Machine learning,” according to TechTarget, is “a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed”. For example, a system capable of machine learning could automatically analyze thousands of proposals a company sent to prospects. It could then determine which of these proposals ended in closed business and identify patterns (e.g., percentage discount offered and product type) common to successful proposals. Each new data point allows the system to enhance its model and, eventually, assess proposals before they are submitted, making recommendations where appropriate. B2B firms using machine learning in this way on their E-Commerce platforms can continuously improve and iterate their performance based on these insights.

Why Machine Learning is Important to You

Why should companies pay close attention to developments in this field and consider enriching their technology toolbox with solutions capable of machine learning? One important reason is the opportunity they offer to optimize omni-channel programs. Omni-channel interactions present challenges for B2B firms when it comes to maintaining consistency of pricing, brand, and messaging across all channels of customer interaction. Machine learning capabilities allow companies to automatically analyze multiple interactions with customers and coordinate communication accordingly. For example, a buyer might have received a special quote through live chat. If that buyer were to subsequently speak with another employee of the firm, a system capable of machine learning could direct said employee to offer this buyer the same price.

Machine learning capabilities can also optimize online ordering of diverse products (hard goods, services and digital goods), selections from extensive catalogs, Configure Price Quote (CPQ) functions for complex solutions and reordering. The key to effective online ordering and CPQ is configuring products or services in a way that caters to the precise needs of the buyer while also generating a price that is profitable, appropriate to the market, and in line with the buyer’s budget requirements. Use of machine learning helps B2B E-Commerce organizations automate this process. Once the company has captured the relevant data, machine learning insights can support the seller by automatically generating a quote that is a most likely fit with the expectations of that buyer, at a price point where the prospect is most likely to purchase.

Solutions capable of machine learning are versatile insofar as they will learn what the user of the solution wants them to learn. For example, if a company wants the solution to analyze customer conversations and identify which phrases during a phone call are most closely linked with customer churn, the application will do so. Similarly, the company might want the solution to determine which product images are most likely to generate a sale. The solution can then track countless web interactions, correlating images and outcomes, and begin making or testing image recommendations.

Overall, the opportunities provided by machine learning are virtually endless. To capitalize on these opportunities, all B2B firms need is the vision to know where to employ this capability. Once implemented and programmed properly, machine learning becomes the gift that keeps on giving, making B2B E-Commerce truly intelligent.

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


How to use a machine learning in mobile apps

Many businesses are now embracing new technologies. And machine learning is one of the most sought after. Machine learning is a technology that has been gaining quite a lot of attention lately. Some individuals have categorized machine learning under Artificial Intelligence or AI. Is that classification really accurate in describing machine learning?

Well, we will look at the machine learning in more detail to fully understand what it is really all about. We will also identify how it could be effectively implemented in various industries. Moreover, machine learning comes with its fair share of advantages and disadvantages, all of which will be clearly described.

What is Machine Learning and How it is used?

Machine learning is a term that was initially coined in the late 1950’s by computer gaming pioneer Arthur Samuel. It is a specialized area of computer science that studies the construction of an algorithm that can advance itself without any human interaction, basically, a computer that can discover more information without the need for programming.

Machine learning is involved with studying and creating algorithms that collect provided data, learn from it, and ultimately, make predictions with it. It takes the provided data and by using a particular algorithm makes comparisons. Machine learning then uses the various comparisons to make its actions more efficient.

There are several computing tasks that rely on machine learning. Most of these are tasks that require designing and programming very detailed algorithms that are not easily done. Some of these tasks may include:

OCR (Optical Character Recognition) – This is simply the conversion of the images of typed and handwritten into machine-encoded text,

MLR (Machine-Learned Ranking) – MLR deals with using machine learning to construct ranking models used for systems that retrieve data.

Machine learning can also be associated with computational statistics. This field is mainly concerned with making accurate predictions with the use of computers.

It also has several links to a field known as mathematical optimization. Mathematical optimization is specifically responsible for providing machine learning with important features, such as theory and method.

Machine learning advantages

There are so many applications that have features developed with machine learning technology. We have become so accustomed to this technology that it is hard to imagine our apps without it. The following are some of the best features of apps developed with machine learning technologies:

 Voice recognition

Many users of mobile devices like iPhone are familiar with either Apple’s Siri or Google now.  Voice recognition technology uses machine learning to enable the software to adapt to the commander’s voice and provide a response when interacted with.

 Image recognition

There is a wide range of applications that use image recognition. A good example of a type of app that uses this technology is one designed for editing pictures.

Other impressive features that have been created as a result of using machine learning technologies include fingerprint recognition, differentiating between male and female individuals, identifying a person’s retina and many other functions.

Advanced customization

Many companies deal with e-commerce value customer experience. One of the ways to assess the potential for success of a particular app is looking at its ability to be customized.

The machine learning algorithms used in customization enable individuals to customize their apps according to their liking. For example, some apps created for watching movies or sitcoms use an individual’s watch history to suggest similar shows or movies. A good example is Netflix.

Optical character recognition

This is one of the most useful features developed by machine learning technology. There are several algorithms that make it possible to identify certain important documents, make a translation of words on particular images, credit cards and so on.

It should be noted that text has so many different properties and developing an algorithm for optical character recognition should put all these properties under consideration.

Sensory data analysis

This feature is one of the most impressive as a result of advances in machine technology. In fact, it has made such an impact it is being used in medicine.

There are so many apps on either Android or iOS platforms designed to record the user’s activity. This activity includes the number of footsteps taken, heartbeat and other important physiological statistics.

Intelligent data analysis

In intelligent data analysis, machine learning is used in combination with Big Data. Together with Big Data, the machine learning collects vast amounts of information and process it respectively.

Machine learning also learns from this data, which is used to make predictions. Machine learning in intelligent data analysis can be used by companies to increase the knowledge about their audience. This knowledge can be used to create solutions to any problem and make better business decisions in the future.

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


Is machine learning good for small e-commerce business?

Machine learning, a part of the broader field of artificial intelligence, uses computers to spot patterns from data. Larger companies like Amazon, Google, Walmart, and eBay use machine learning in multiple areas of their businesses to great effect. But what about small businesses? Can they use machine learning as well?

What is Machine Learning Good For?

There are numerous blog posts out on the internet listing many different use cases. You have the more advanced examples like chat bots learning from customer support responses so you can improve your customer support quality without having to hire and train more staff, or image recognition to automate categorization of products (or spot categorization errors – such as “short dress”) based on your personal catalog. But there are many more mundane examples such as product recommendations “my customers who bought X typically also buy Y, so I should offer that a recommendation”.

I am not going to go into details of use cases in this blog post, but rather provide some considerations to think about when deciding if you should look at applying machine learning to your store.

Access to Technology

Access to the technology required for a machine learning project is definitely more accessible than just a year ago. Hadoop and Spark clusters can be spun up with a few clicks on cloud hosting providers; Google, Amazon, and Azure all have specialized machine learning offerings. Access to cloud hosted technology is easily accessible. Getting technology running is not the main problem.

The challenge is to learn how to use these technologies. Data scientists are in demand. What is your return on investment going to be on building up your own data scientist team? (I personally am wary of getting only one data scientist – single points of failure always scare me.) Are you going to get a return on such a staffing investment? The smaller the business, the less likely this is going to be true.

So smaller businesses are more likely to get benefit from one of the increasing number of vendors that use machine learning technologies within their product offerings. This can be a much more serious option for small businesses as it avoids the need to have in-house expertise. The vendor makes it cost effective by building up the expertise for you.

Volume of Data

So what area should you tackle first? Should you take on inventory forecasting, specific customer predictive personalization– there are many areas you could tackle.

One key point to remember is machine learning is primarily about learning patterns that emerge from data. If you don’t have much data, then the machine is not going to learn very well. For example, if you are trying to make personalized predictions based on an individual user’s behavior, but most customers do not return to your site often, the project is probably doomed for failure.

So don’t only look for use cases where you think you can get good ROI (which is clearly also important), think also about how much data you can collect in that area to feed into a machine learning algorithm. Do you have lots of anonymous customers or do they log in? Do you have a large catalog? Volume of data is one of the real challenges for smaller businesses that I don’t see talked about as often. If you don’t have volume, machine

Site Performance

Another consideration is site performance. If you have a more personalized experience you need to be careful of the performance impacts on your site. Personalized content caches less well. Is the improved experience you offer better than the performance hit customers observe?

Performance is more of a consideration of where it fits into your overall business. If you are using machine learning to optimize shipping expenses, then that will not affect the on-site experience of a user. If your site only has low traffic, again, caching may not be such a significant issue.

A/B Testing

And if you do decide to go ahead with a project, think about how you are going to test your new solution. If an on-site customer experience, are you going to use an A/B testing framework to make sure the offering is improving? This is more important with an external vendor as you won’t necessarily have the same access to data as you would with an in-house team. Shipping cost optimization however you can automatically compare by computing shipping using two strategies and comparing the results.

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



How machine learning can set fashion ecommerce strategy & product assortment

What amount of time, money, and effort goes into getting one new shopper to your e-commerce site?

Machine learning is having a big impact on fashion retail.

We recently caught up with senior data scientist Joe Berry at retail tech company Edited and asked him about trends in this area, as well as how the Edited product uses machine learning to help set retail strategy.

As the retail industry is highly visual, product categorisation is one of the most critical aspects where machine learning is used. How a retailer describes a product that customers consider to be similar can vary wildly, creating a lot of inconsistencies that make it hard for businesses to analyse information.

Edited builds systems that review millions of individual items every day to accurately and consistently categorise them. To perfectly categorise a garment, we look at more than just the words used to describe an item (text recognition). We need our machines to process and understand images as well as text. This entails knowing which parts of the picture are the model, identifying the background and differentiating it from the garment being retailed.

These tasks are often complex, as they often require separating a long-sleeved polo shirt from a short sleeved polo shirt, isolating a belt worn over jeans, or knowing what in the database was technical sportswear, versus athleisure, for example. 

Standardising the data in this way is transforming the industry as for the first time, retailers can run a direct comparison of their product assortment alongside every one of their competitors’ merchandise.

Will machines ever make pricing and merchandising decisions autonomously? 

The Edited product is about using machine learning to make better decisions in their retail strategies – and this includes approaches around pricing, assortments, merchandising and other specific insights. Machine learning represents a reliable way of categorising data and spotting patterns in data without a risk of making biased decisions. The more data a company can tap into, the better it can understand patterns based on past performance and trends. 

However, in order for machines to fully replace humans, computers would have to be fed information such as margins and inventory strategy, which are not only complex but also highly specialised making it difficult to generalise. The approach we use at Edited is to ensure that retailers have access to the world’s available data organized in a way where they can make strategic decisions based on variables suitable for their business.

For online stores, the customer experience is centered around ease-of-use and convenience. E-commerce can optimise conversion by adding image-based classifications to extract information from pictures, or make product searches much easier.


Another great UX example used across multiple retailers is adding customer-styled images next to the product image. ModCloth, for example, has an “Explore & Shop Outfit Photos” section where customers can see the product fit on other customers, which aims to boost purchases and reduce the rate of customer returns or exchanges.

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



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