Technology posts

How to Reduce Cart Abandonment with Machine Learning

How about what it takes to get a qualified shopper to your site that has the intention of purchasing today? Based on the average e-commerce conversion rate, magnitudes more time, money, and effort. Let’s see how machine learning can make the most of your efforts to maximize revenue. 

It’s Really Hard to Know WHY a Customer Abandons Cart

Getting shoppers to your site is half the battle. Next comes optimizing site layout, selecting lifestyle images, and making curated products discoverable. 

Shoppers start adding items to their cart. Perfect. But wait, where are you going? For a wide range of reasons, shoppers may be abandoning items they intended to buy in their cart. For example, they add an item to the cart  and continue to browse recommended items. But none of those products lead them back down the purchase path, so they get distracted and leave.

If you’re a clothing retailer, they could be concerned with fit or feel.  They may not be familiar with the size options, or they may be wondering how difficult it will be to exchange or return the item.  Is the shopper unaware that they qualify for free shipping and think they will have to pay?  Are they questioning if the item they’re considering is mainstream fashionable and will be popular amongst their friends? 

Imagine trying to put ALL the combinations of why a shopper abandons their cart into a set of rules. This is a task too big for even the greatest spreadsheet master among us. This is a job for machine learning.

Retargeting Should be Treated Only as a Last Resort Effort

Once a shopper leaves the site, retailers begin display, search, email, and social retargeting campaigns.

But what if the window of interest with that shopper closed? What if they already found a comparable product somewhere else to fulfill their need? Using machine learning while a shopper is still on your site reduces reliance, time, and money spent on retargeting.

Imagine a shopper walking into a physical store and loading up their arms with items they like. Then a store associate watches the shopper set down the items and walk out—without engaging the shopper. Unthinkable, right?

Letting a qualified shopper leave your site and not assisting them with a personalized message is the digital equivalent.

Machine Learning Reduces Cart Abandonment and is Actually More Cost Efficient

You are not alone. E-commerce cart abandonment happens more than 69% of the time. This means there’s a lot of room for growth, and even small improvements can have huge revenue gains. Shoppers abandon their cart for a variety of reasons and in different ways.

Using machine learning, Veltrod can predict when a shopper is exploring additional products vs losing focus. Without a timely message, the later shopper will wander away and lose interest in purchasing their cart today.

Based on the shopper’s digital body language, Veltrod can figure out why a customer may abandon cartand what message will help them stay on track. Identifying these digital objections can be challenging. The clues a physical store associate can easily notice are more-often undetectable in e-commerce analytics.

In these situations, Veltrod’s machine learning technology can interpret hundreds of data-points in real-time to take action. Show shoppers the information they need to see to move from consideration to purchase today before they leave your site.

If you wait until after the shopper leaves, you will have to invest more in retargeting. But if you can optimize for each customer in real-time, you can improve your customer acquisition cost. With the power of machine learning, it is possible to do this easily and at scale.

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


How to Make E-Commerce User Research Manageable

E-commerce has perhaps the most tangible relationship between UX and business impact, as a seamless experience translates into increased conversion rates and a healthier bottom line. The world leaders in online retailers such as Amazon, Nike, Neiman Marcus, and HSN all provide users with an experience that makes it easier than ever to purchase from their websites.

The USE Research Planning Method

We call this framework USE: User > Store > Ecosystem. As the goal is to focus your research efforts, it is visualized as a target. The User is kept at the core of the framework, as their needs should drive the design decisions. From there, the Store and Ecosystem make up the middle and outer rings. Each ring can, if you choose, be further divided into quantitative and qualitative halves.

Outer Ring: The Ecosystem

This layer of the target is the broadest in scope and generally offers the easiest immediate access to research materials. The ecosystem refers to the high-level aspects of the retail environment you are designing for: the vertical, the economy, the buyer attitudes, and the trends for the world your store and customers inhabit.

Middle Ring: The Store

What makes your store unique? How is your checkout process performing? Where are your shoppers coming from? Store-level research involves digging into the product, the pricing model, your brand, marketing efforts, and any other initiatives that could impact your users’ experience across channels.

When researching your own store, it is crucial to leverage experts in your organization who touch a range of aspects of the business (finance, merchandising, marketing, IT, and more). If you’re already involved in these areas, all the better! If not, go in with a clear ask of what you need to know, to optimize the time of the team members who will be collaborating with you.

The Center of It All: the Shopper

This level of research can be the most intense and requires some practice to gain comfort with. But direct user research is where you will gain the information that allows you to create the best possible experience for every shopper.

Identify your most important user personas or archetypes, but always remember that a shopper is a person, not a persona. Try to interview a range of current, past, or prospective shoppers and learn more about their goals, desires, and obstacles in shopping online. If you are working in an agile framework and building in tandem with ongoing research, have real people test your prototypes to learn what works and what doesn’t.

Putting It All Together

Plot the pieces of information you already have on the appropriate level of the target. To break it down one level further, we split the target into quantitative and qualitative halves, so we can visualize even more closely where we need to bolster our efforts.

Sometimes you might have several pieces of research pertaining to one level of the target while lacking in others. Perhaps all of your data is quantitative and none qualitative. Ultimately, plotting on this framework allows you to make assessments on the risks and rewards of pursuing more information in each category. If you have the time, budget, and wherewithal, you could spend years becoming the most knowledgeable UX designer the world has ever known!

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


How Artificial Intelligence is transforming the eCommerce Industry

Digitization of retail industry has unveiled new realms and opportunities for the retailers globally. Over time shopping has evolved drastically and is no longer just a utility of bartering money for a product. It is predicted that by 2018 75% of developer teams will utilize Artificial Intelligence in building one or more than one services or business applications.

What’s driving the trend?

The inclination towards artificial intelligence can be clearly noticed. But the question arises, what’s driving this entire flow? To answer this, we have here listed some ways in which AI is transforming the ecommerce industry and eventually gaining the attention:

Personalized experience: Artificial Intelligence is consistently proving its expertise in delivering a personalized experience to users worldwide. AI can help online retailers add personalized pages to their website and give personalized recommendations to their visitors. This not only makes the buying experience interactive but also increases the chances of generating more sales. This is because the recommendations are not just based on what user previously visited or liked, but it also includes complementary products that they might probably like.

Faster and reliable decision making: AL has simplified modeling and analysis drastically. AI has proved its expertise in handling customer data, analyzing their purchase pattern, predicting the behavior of visitors, and other data manipulation tasks. Also, it is easy for online retailers to AI solutions and automation in their businesses. It is believed that in coming days this will be the biggest way ecommerce industry will be changed through automation.

Sustainable customer service: With the increasing role of technology, competition has increased dramatically and with the increase in competition, customer service has become an invincible need of businesses. According to a survey, on an average 73%, customers prefer brands which avail good support. Now talking about customer support, it has been noticed that users prefer having human interaction for a quality However, employing human resource for the same can be expensive for entrepreneurs. With the advent of AI delivering interactive customer support has become easy and budget friendly. Use of conversational chatbots has been utilized by different companies all across the globe.

High-end security: It is believed that in the coming years AI will help ecommerce entrepreneurs to gain their customers loyalty by providing them prevention again online fraud activities. Apart from this, Artificial Intelligence will also assist in fetching more precise behavioral predictions and in fastening the checkout process. According to a report, 13% buyers accepted that security is their primary reason behind abandoning a cart. With the help of Artificial Intelligence entrepreneurs will be able to avail high-end security followed by peace of mind to its users. It’ll also be able to share the inconsistencies in purchase behavior and will also assist in mitigating fraud transactions.

Beyond meeting the needs: Data plays a crucial role in the growth of ecommerce businesses and undoubtedly the online retail industry is rich with data. However, not everyone is able to utilize the same to its full extent. This is where Artificial Intelligence plays the role. It can track and collect every bit of data related to customer’s order history, predictive analytics, and purchase frequency to identify purchase patterns and on the basis of same estimating when they might wish to order next. With the help these, the seller can schedule a notification email to increase the chances of generating sales.

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


Machine Learning + eCommerce – A Perfect Match

eCommerce, like traditional retail involves taking in a lot of data and trying to make the best decisions based on the data that you have. In a retail store data is relatively limited, you might know how many customers come in the store each day (or at least be able to estimate it), but it’s hard to keep track of how many customers try things on, how long the average customer stays in a store, or how many items they look at during each visit.

Online it is much easier to track shopper metrics because the data is easily accessible. You can tell exactly how long the average person spends on your site, which items they look at, what they add to their cart, and what they end up buying in the end. Unlike brick-and-mortar retail where the complexity comes in when you’re trying to collect data, online the complexity is making sense of all the data this is coming in, it’s like a fire hose and if your business is growing, so is the data.

Enter machine learning

Rewind five years ago and machine learning were two words you wouldn’t often hear used together in the same sentence, now it’s officially a hot topic, and with good reason. Machine Learning algorithms can help make sense of large amounts of data and provide actionable insights that eCommerce retailers can use to make decisions later, or even in realtime.

What if you could take data from current shoppers on a website and use that to improve the experience of future shoppers? It’s the future of hyper-personalization and it also happens to be what we spend a lot of our time on at Fashion Metric, we build technology that makes it easier for both retailers and shoppers to have a better experience online. We do this with math and machine learning, sure you’ll see the word “fashion” in our name but at our core we are a data science company that provides more data than apparel retailers have ever had before, this in turn can help increase conversions and reduce returns.

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 address attribution issues in e-commerce catalogs

A richly attributed and well-curated product catalog is the key asset of online retailers. However, products are frequently misattributed, which makes it a pain for customers to find the products they’re looking for.

Catalog product attribution issues are a major pain point in e-commerce. They lead to poor user experience, lost revenue, and high customer turnover. Luckily, new developments in Machine Learning in image recognition and text classification can help resolve these issues, improve catalog quality, and tailor user experience to individual customers.

The problem of misattribution & attribution gaps in product discovery

There are two main approaches to facilitating product discovery (the process of a consumer finding a product) in e-commerce:

  • Browsing category hierarchy
  • Keyword search

The success of these methods strongly depends on the quality and consistency of attribution within a catalog. For example, let’s say a customer searches for a Hawaiian shirt and none of the shirts in the catalog have been attributed as “Hawaiian”.

Machine Learning as a method for resolving attribution issues

Solutions based on image and text classification are a way to resolve attribution issues. Not only do they help with misattribution, they also improve catalog quality systematically and search-ability dynamically. Both of which result in a catalog and search experience tailored to actual individual customers based on real actions and insights.

Automated product attribution based on product images

As mentioned above, product attribution problems can be largely divided into two groups:

  • Misattribution
  • attribution gaps

Misattribution refers to the situation when a long dress is attributed as short, or a red tie is attributed as blue. Search engines are happy to trust indexed attribute data and retrieve completely irrelevant products as a result. Obviously, these kinds of situations lead to very frustrating customer experiences, where you just can’t find what you’re looking for.
Information processing and analysis

Attribution gaps
Attribution gaps are more subtle and harder to notice, yet by far more common. Dresses which are not attributed by length, style, or material are effectively invisible for corresponding queries, like “long dress” or “wedding dress”. In this case, retailers miss the opportunity to showcase these products to their customers. These situations lead directly to loss of revenue.

We can use Machine Learning classification based in a framework like Google’s Tensorflow to address these challenges. ML frameworks include libraries that make it easier for engineers to incorporate self-learning elements and AI features like speech recognition, computer vision and natural language processing into systems.

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


How machine learning is shaking up e-commerce and customer engagement

Now that machine learning has moved out of the hands of a select few and is accessible to a broader market and set of use cases; it’s good to look at how different technology providers are leveraging it to improve and expand their products in the name of a better customer experience. Adding value through machine learning.

  • There are two key focuses for Sitecore’s use of machine learning and AI:Efficiency in operations: How can you streamline the content authoring process?
  • Better insights to optimize: Are you achieving your desired outcomes?

Adding value through machine learning

Donovan pointed out that Sitecore is focused on providing machine learning-based features that provide real value (as opposed to a great demo). Machine learning (ML) supports digital marketing and improved content development (which improves engagement) in a few ways:

From a content perspective, it performs semantic analysis to:

  • Auto generate taxonomies and tagging
  • Help improve the tone of your content by analyzing for things like wordiness, slang, and other grammar-like faux pax







Open connectivity and a clear customer data strategy are key

Sitecore may be developing its own machine learning capabilities to do things like Path Analyzer, but it’s also making major investments in its product roadmap to support open connectivity with other platforms. The big one it’s working on now is building in support for Microsoft Azure machine learning. But Donovan pointed out that Sitecore wants to enable truly open connectivity to other best of breed platforms (e.g., Amazon ML).

Donovan spoke about the need to support open standards for data connectivity, to create what Sitecore refers to as “immersive engagement.” Immersive engagement is delivering the right experience in the right context across channels. That cross-channel experience and the sharing of context means you have to pass customer data back and forth between the technology products that support each channel; the key being bi-directional data exchange.

The evolution of e-commerce is not just about selling products

E-commerce is obviously about selling products, but it’s not all about selling products. That’s why it’s not as simple as turning on an e-commerce shopping cart, popping up a list of products and waiting for the money to roll in. There’s an entire experience you must create now that involves the right content, the right products, all channels in the purchase journey and understanding the shopper intimately.

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


How AI Powered Semantic Search Can Increase Your Ecommerce Conversion

The search functionality of your ecommerce site is one of the most important tools of conversion. About 88% people research online before buying a product. But often times, a simple spelling mistake can make you lose potential customers. Introducing Semantic search

Semantic search uses both Natural Language Processing and Artificial Intelligence to understand long search terms (like chocolate bars under 5 dollars). By using Machine Learning techniques it can also develop an ability to understand the customer’s’ intent (i.e. identify the typos, recognize synonyms, show related products) as opposed to focusing only on specific keywords.

The concept of Semantic search not entirely new. Apple’s Siri, Amazon Echo, and Google Assistant use similar technology to understand people’s speech.

Easy optimization

Text based search often gives the wrong or null result to a certain combination of terms. To improve customers’ experience, you need to manually correct the results for these cases. The process is time-consuming and difficult.

But by using Machine Learning techniques Semantic search can automatically optimize the search results and gradually improve in performance.


Better ranking

Based on click-through rate, the Semantic search can re-rank products according to customer interest. As a result, customers are likely to find most useful products at the top of the search page.

Relevant search suggestions and auto complete

Semantic engines can suggest related products by analyzing previous search terms and based on top queries. For example: If you are searching for pajamas, the suggested products will also include trousers.

Increasing conversion using Semantic search

Following strategies will help you get the best out of adding Semantic capabilities to your ecommerce site:

  1. Make the search box easy to spot

Onsite-search should be the central feature of your site. What’s the point of improving your search if customers don’t use it in the first place? Look for ways to make your site search stands out. You’ll find the following tips helpful in this regard:

Position: The search box should be visible across the entire site (along with the navigation bar). Ideally, it is placed at the top of the page.

Label: Label the search box with color and shade so that it’s easily distinguishable. The search button should have a magnifying glass icon or contain the text Search.

Text: The text in the search box should say something like – Search for products or What are you looking for? Make sure the in-box text disappears when the user clicks on the search box.

     2. Turn-on advanced Auto complete and spell check

Semantic search with its deep learning capability can produce smart search results and save the time of the customers.

  1. Show product images right from the search bar

Based on data, the intelligent search engine should know which products are most popular among your customers. Configure your search box in such a way that it displays those best-selling items right at the search suggestions when someone searches a related term.

       4. Add intelligent filtering options

The search result page is crucial for conversion. After reaching here, the customer will either leave or find something that she likes. So you must make the search results as customizable as possible.

The best way to do that is to give customers ways to filter the results. With a semantic search engine, it should be very easy to understand what type of filters are needed for a particular search term.

  1. Analyze data to learn more about your customers

With the help of Natural Language Processing and AI, it’s easy to learn more about your customers. By compiling data from search, you can know your visitors’ demographics, buying preferences etc.

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


5 Big Reasons Why Your E-Commerce Brand Needs to Use AI

E-commerce remains a highly competitive field, but don’t fret, you can make it big amongst the giants thanks to artificial intelligence and machine-learning tools.

Artificial intelligence and machine learning have far surpassed the “eye roll” reaction they once sparked…

They have become essential tools for many large e-commerce brands to operate at such scale — and both, while also becoming more advanced, are increasing in prominence with each year that goes by (in a multitude of sectors).

Predictive Marketing

As the number-one reason, the predictive nature of AI is not only its encompassing benefit, but one that can be finely tailored and applied to all use cases described in this guide (and more):

Before heading into a selection of those subsets and specifics, here are some examples of applications where marketing campaigns are concerned…

Through machine learning, AI can form marketing predictions such as:

  • What customers will buy next
  • What customers don’t want to see
  • How they want your website to be displayed
  • Their preferred device and channel
  • Their typical price threshold
  • When they will be more likely to buy

Based on the above conditions and numerous other data points — which of your planned marketing campaigns are set to perform (and those that aren’t)



For years, e-commerce brands have been sending email marketing campaigns with the customers’ names inserted at the beginning… This is personalization in its absolute primal state!

We’re heading into 2018 and if that’s your current method of personalization, it’s definitely time to shift gear. AI is the answer…

Super Searching

Up to 30 percent of e-commerce shoppers make use of on-site search functions. Approximately 88 percent of consumers research online before purchasing a product.

It’s not surprising that a superbly intelligent search function paves the way to immaculate customer experience and delight.

AI is also used to:

  • Provide relevant auto-complete suggestions
  • Rank search results based on user behavior
  • Display add-on items for products searched
  • Understand speech (voice search)
  • Analyze images (visual search)

Dynamic Pricing

When it comes to pricing, the online retail industry of today is consistently presenting fresh challenges to CMOs and COOs alike. Along with the sheer presence of Amazon, competition is fierce among e-commerce brands of all guises and sizes.

Superior Assistance

Once upon a time, personal shopping assistants were merely a luxury of the rich or famous… AI has completely shaken up this scenario and revolutionized e-commerce in the process. This intelligent, conversational technology cleverly extends to customer service, too.

Personal digital shopping assistants and chatbots can:

  • Suggest the best products to a new visitor in a human-like manner
  • Recommend fresh deals to your returning customers
  • Alert customers when items they may like come into stock or change in price
  • Answer customer queries and provide suggestions
  • Trigger personalization based on the above behavior to increase sales

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


How AI Is Challenging Ecommerce Relationships

Artificial intelligence (AI) is not just transforming ecommerce. It’s also breaking down barriers and challenging assumptions on buyer-seller interactions. Ecommerce came about because the internet made it easy to find what you were looking for.1 The digital age made the physical world appear closer, so you could buy things from distant countries.  Early adopters assumed ecommerce would be fueled by economics of scale and lower costs, but those weren’t the main drivers: It was convenience.

AI Refines Search

Consumers have to search, (re)search and deal with the paradox of choice. 

Enter AI, which is changing ecommerce by smoothing out the search glitch. AI is increasingly improving search functions, from using natural language processing to making the search more intuitive, right up to visual search. The company at the forefront of search — Google — is known for using AI to refine its search algorithms. 

Smarter Personalization

AI is also making personalization better and smarter. It allows marketers to get closer to behaviors in a scalable and practical way. AI can pick up on where loyalty programs have failed. They build loyalty by understanding your individual needs, the best times for various triggers, how sensitive you are to price differences and your preferred channels. 

AI has the power to orchestrate all the parts of ecommerce into one seamless, logical flow. What’s more, it can do it in a way that’s truly personalized and individualized for each customer. 

If you use AI on your ecommerce platform, you’ll need to use APIs to embed the buying ability within that customer journey. Your ecommerce bricks need to be tightly and consistently integrated, from CRM and ERP to product information management and payment system. The platform you use for ecommerce has to be robust to manage that kind of integration. 

Challenging the Interface

But beyond that, AI alters the way we react. And the interface may not be through designed webpages. 

If AI suggests products to us automatically and makes intuitive recommendations, how much longer will we need catalogues? The best ecommerce interface might well be no interface — AI bots act as personal shoppers to cater to your every whim and fancy, before you know it. 

cloudcraze-b2b-ecommerce-trends-consumerization-ai-cloud-1000x600Fusing Brands and Channels

AI is challenging conventional marketing by enabling connections that were previously difficult or impossible to make. 

One of the hardest things to do in ecommerce is to build seamless, consistent customer experiences across different channels, as they take varied journeys on desktops, mobile or physical stores. AI is already being used to smooth these customer journeys, by making logical connections and machine learning to create scenarios.

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


How Ecommerce Sites Can Leverage the Amazing Benefits of AI

It is no longer news that Artificial Intelligence (AI) and similar tech advancements such as augmented reality and virtual reality have come to stay.Ecommerce sites must be ready to take advantage of these developments if they want to remain competitive in their niche. But this readiness involves not just understanding the value of AI itself, but also the innovations that will enhance business growth and output for Ecommerce companies. It is often assumed that the role of tech is cut out for larger brands alone. Contrary to that, small businesses can use these new innovations to function competitively in their market.

Businesses must be ready to innovate

Innovation is the core of Ecommerce best practices. Retailers have become creative with the way they have tackled the concept of technology. For instance, Fitle is an app that allows shoppers test clothes on a 3D version of themselves. This automatically reduces time spent in stores.

However, Fitle is not the first store to explore the concept of 3D fitting. Bodymetrics developed their program few years earlier. While it requires you to visit the physical store for scanning, Fitle allows you to do your own scanning with your smartphone from anywhere.

Ecommerce businesses and AI interactions

AI is increasingly becoming integrated into the world of Ecommerce, and we can only expect the future to produce even more opportunities. In the examples below, you can get some ideas for incorporating AI into your online marketing strategy.


Your customers deserve a personal shopper

Did you know shopping feels like a chore to some people? By making it less hectic, you will go a long way to solve consumers’ pain points and endear them to your brand. A unique way to utilize AI is to design the user experience (UX) around pre-emptive personal assistance, instead of an impersonal search feature on your Ecommerce site.

 A site equipped with machine-learning algorithms can enhance UX and give shoppers a personal shopping experience as they would at an actual retail store.

Balancing customer privacy and trust

As concerns regarding online safety continues to grow, AI becomes a useful resource in the customisation of the individual’s “shopping” experience. From integrated calendars and traffic alerts, to suggested products with Google Search Ads, people can get the impression of enjoying unique UX and help for their technology.

 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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