Tag Archives for " Machine Learning "

Kicking off Black Friday and Holiday Shopping with Artificial Intelligence

Kicking off Black Friday and Holiday Shopping with Artificial Intelligence

US retailers are making final preparations for Black Friday in both their physical and digital stores to support the expectation of high volume shoppers. 2018 holiday sales are estimated to climb between 4.3 and 4.8 percent over 2017 to between $717.45 and $720.89 billion – all due to the rising health of the economy, low employment records, and increasing wage margins.

While the economy has been improving over the past year, technology has also been making progress – both online and offline. This is especially seen with making more personalized recommendations through the use of AI and machine learning.

AI-Driven Personalization takes priority

With retailers increasingly leaning towards AI and utilizing AI-driven platforms, they are choosing more sophisticated platforms to make more personalized recommendations for their customers, ultimately increasing revenues for retailers and brands.
Some studies even concluded that brands that invest in creating personalized experiences leveraging advanced digital technologies and proprietary data for customers see a bump in their revenue by 6% to 10% – two times faster than those brands that don’t.

For the holiday season, and the upcoming Black Friday shopping, AI can be a wonderful tool used to automate the process of helping online and offline shoppers find what they want to shop for. Shoppers often have trouble finding a memorable gift for friends and family, but do not have a clear starting point – this may need browsing extensively through different e-commerce websites and searching through several aisles in different stores to find the right gift.

AI simplifies this process by giving retailers and brands the ability to ask their customers questions about their gift recipients and offering personalized recommendations based on individual tastes and preferences.
The use of AI-driven personalization for e-commerce channels has increased over the past few years, but according to experts, the future of AI is limitless – especially in the physical store. Furthermore, the future physical retail is believed to be a mix of the speed and convenience offered by AI with a human touch.

Customers want to be engaged through human interaction rather than special effects using light and sound, so retailers can do well to create community events and use data to offer personalized in-store experiences.

In-store Personalization to Support Retail Employees

As more and more technology is being integrated into the store environment, retailers need to move towards an autonomous management reducing the dependency on manual management by store staff. Recent studies even predicted retailers providing in-store recommendations to shoppers through AI engines to be the most mainstream in-store technology in the coming years.

Though AI is often pegged as a technology of the future, it’s a concept that is slowly taking shape and is not too far into the future. AI capabilities enable retailers to pursue customer personalization in real time – which will soon become a top priority becoming important for shoppers. The capability to display prices and promotions, which are subject to change, also coincides with the concept of a more personalized consumer-friendly store.

Conclusion

As we approach Black Friday – the official kick-off for the holiday shopping season, it will be interesting to see in which ways retailers and brands will leverage AI into their shopping strategies this holiday season. While personalized offers and promotions to enhance shopper loyalty will definitely be in the mix in the months of November and December, retailers can also take advantage of the data they receive to encourage repeat business throughout 2019.

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A Tour of CatalogIQ for Grocery

Kick-start eCommerce sales with awesome product data

E-commerce requires awesome product data to support successful search and conversion. Product data for the online grocery market is currently being created manually. Retailers are struggling to acquire the rich product data necessary to support their online needs. Brands are struggling to generate good data.

ContentIQ Add Product

IceCream Labs CatalogIQ is designed to automatically extract attributes from product images. Using either label mechanicals or actual product images of the packaging, CatalogIQ can extract text from the labels. From there, the artificial intelligence in CatalogIQ understands what the text is and inserts it into the appropriate product attribute. The AI can also determine which images are the hero image, and front, back and side images.

CatalogIQ Extracted Attributes

CatalogIQ identifies brand names, sub brands and variants, normalizing the brand to the appropriate text. titles are generated from various attributes to create an SEO-rich title to optimize search. Other key attributes include feature/benefits, ingredients and nutrition facts.

CatalogIQ extracting content from a product

Sample CatalogIQ extraction (front/rear)

How complete is your data?

ContentIQ Catalog List view

CatalogIQ can score the data to help the merchandising and ecommerce teams understand which product records have been enhanced.

  • Missing attributes
  • Accuracy of attributes (are all of the attributes congruent?)
  • How unique are the attributes?
  • Is your product record SEO optimized?
  • Do you have relevant search keywords?
  • How well does your product data match up to customer site searches?
catalog IQ demo screen

Support for Grocery merchandising teams

Grocery merchandising teams have the chore of uploading new catalogs from suppliers and manufacturers. Often this data arrives in the form of a spreadsheet. CatalogIQ can easily upload a new catalog file (in spreadsheet form) to quickly and easily complete the ingestion process.

ContentIQ Add Catalog screen

Support for Grocery and CPG Brands

Grocery retailer and channel partners expect high quality product data to list and sell your products online. Can you deliver the content?

CatalogIQ allows brand and product managers to auto-generate high quality product data directly from product label mechanicals and/or product images. If you're currently using manual processes to create product content and to check the accuracy of product data, then let CatalogIQ help you automate the creation process. You'll be able to complete the data creation process much faster than manual methods. CatalogIQ can also validate the content and ensure that it matches what is contained on all of the product labeling.

CatalogIQ Features

  • Quality product images including relevant Nutrition Facts
  • Accurate meta-data, including attributes like: allergens, sugar free, Kosher certified, Non-GMO and other facets
  • Complete, standardized and SEO enabled titles
  • SEO rich descriptions
  • Correct product categorization

As a merchandising manager with a large product catalog, you know the difficulties of reviewing your product data and ensuring that everything in the catalog is ready to publish live to customers. There is always the nagging concern that something is inaccurate or missing when you push the “publish” button. Every time that you receive new data from your suppliers, it’s a chore to process the data. You have a long checklist to complete before you can publish data to the live catalog. Processing this checklist can consume all of your time.

CatalogIQ Benefits

  • High quality product data
  • Improve product page discoverability 
  • Increase product sales
lady using a credit card to make a payment online

Transforming the Payment landscape with AI

Transforming the Payment landscape with AI

People have increasingly become comfortable using technologies such as AI and machine learning in their day-to-day lives. Various companies also have increased their use of AI and machine learning into their product offerings as well as their processes. With the computer processing technology advancing increasingly, companies, institutions and even governments are gathering massive amounts of data as more consumer interactions move towards digital. This type of technology is already transforming the payments landscape in the following aspects -

Improving Efficiency

AI and machine learning have the potential to revolutionize the way payments are processed by enhancing operational efficiency and decreasing costs involved. For instance, AI enabled chatbots are reducing the load for customer service representatives.

With machine learning being incorporated into payments, learning algorithms play an important in helping speed along authorization of transactions and monitoring.
Furthermore, AI helps reduce the processing time for payments. It also helps eliminate human error and save time for correcting those mistakes.

For a business, processing large amounts of data to generate financial reports to satisfy regulatory and compliance requirements would involve a team that would perform repetitive data processing tasks. Leveraging AI would involve training the models to do these tasks, and the model can ensure completing the tasks faster and more accurately than humans. These technologies can improve efficiency while gathering important user insights in real-time.

Machine learning has already proved to be an invaluable part of fraud detection, but there are many opportunities that lie in product sales, customer care, and retention. Machine learning can draw vast amounts of available data and utilize it to profile customers to guess their product needs while offering new opportunities for upselling.

This model can also help identify which customers that companies are at risk for losing as well as halt customer loss before it happens. Machine learning can help automate many customer service interactions. This model which uses deep insights, cognitive engines and natural language processing is already widely available and the usage will only grow with time.

Fraud Prevention

There are various methods for customers to make payments today. They are no longer limited to paying with cash or even cards. There are new payment methods on the rise such as card-not-present (CNP) transactions, but as it gets popular come new opportunities for fraud. AI and machine learning are at the forefront of not only detecting fraud but also preventing it before it happens.

These technologies already have the capability to uncover patterns and drive hidden insights and are working towards fine-tuning these insights. Companies are choosing to move away from a static model where the reliance is on supervised learning with input towards unsupervised learning wherein the deep belief neural network does not require a labeled training set, but continuously updates the model as new patterns emerge, allowing for a more robust and flexible fraud prevention detection tool.

As more commerce and payments move online, more data is accessible. This new robust algorithm uses machine learning to decrease the false positives and more agile detection of the actual frauds.


AI and machine learning have come a long way in the past decade. These technologies have already been adopted by many sectors and have transformed many aspects of traditional processes. Though exciting new technologies have been adopted by businesses to improve and enhance the payment process and customer experiences, the scope for future implementation is endless.

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An Evolving AI retail experience: Transforming the way consumers shop

An Evolving AI retail experience: Transforming the way consumers shop

The retail experience of a shopper is the latest area that AI and machine learning are causing disruption. Most retailers recognizing the potential of these technologies have started aligning them into their business goals. Two crucial aspects - data and computing power have changed in the past few years in the space of AI, which has opened up new opportunities for retailers today.

Computing power is easy to see, with the advent and rise of smartphones which have phenomenal computing power when compared to the bulky phones and computers used decades ago. Businesses today have unlimited computing access to train their AI algorithms. Furthermore, the data available today is extremely rich and scalable. AI systems that leverage learning techniques such as Machine learning thrive on large, rich data sets. When fed appropriately, these systems discover patterns and correlations that would be otherwise difficult with a human intervention. These machine learning approaches automate data analysis, enabling users to create models that can then be used to make useful predictions about other similar data.

Retail is a perfect fit for AI, here’s why -

The speed at which AI can be deployed depends on specific critical factors. The first is the ability to test and measure. Retail giants can effectively deploy AI and test and measure consumer response. They can also leverage AI to measure the effect on their entire supply chain.

There is some innovative and interesting robot technology taking place in retail such as Grocery giant partnering with Nuro.AI to deliver groceries to the customers’ doorsteps. But most significant changes will come from the deployment of AI rather than the use of physical robots or autonomous vehicles.

Here are 3 AI-based scenarios that will transform the retail experience -

Shopping habits

AI can detect underlying patterns in the shopping behavior of shoppers from the products that they buy and the method used to buy them. This could be a simple weekly purchase of groceries from the supermarket, the sporadic purchases of wine from the liquor store or the complex midnight icecream cravings from the local convenience store.

At a larger scale, analysis of the behavior of millions of consumers would enable supermarkets to predict the number of households that cook pasta every week. This would then inform the inventory management systems, and automatically optimize the stock of pasta. This information can also be shared with the suppliers, enabling more efficient inventory management and organized logistics.

Pricing dynamics

The pricing challenge for supermarkets involves applying the right price and the right promotion to the right product. Retail pricing optimization requires data analysis at a granular level for each customer, product and transaction. To be effective, many factors need to be considered such as the impact of sales due to the changing price over time, seasonality, weather and competitors’ promotions.

A well-defined machine learning program can factor in all variations, including details such as purchase histories and product preferences to develop deep insights and pricing tailored to maximize revenue and ultimately, profit.

Customer feedback

In the past, customer feedback was collected through forms and feedback cards that were filled out and placed in a suggestions box. The feedback had to be manually read and acted upon appropriately. With the rise of social media, the platforms were leveraged to express feedback publicly. Retailers subsequently engaged in social media scraping software to respond, resolve and engage with customers.

With the growing innovations, machine learning will play a larger role in this space. Machine learning and AI systems will be able to analyze unstructured data from multiple sources such as verbal comments or video content.  

The evolving retail experience

As a shopper moves through various stages in life, the circumstances and spending habits change. AI and machine learning models will adjust and be able to predict the needs of the consumer before the consumer even searches for a product.

This shift to predictive marketing would change the way shoppers purchase products, bringing in suggestions and recommendations that they would not have even considered. The possibilities would widen, all because of AI - for both consumers and retailers alike.

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AI in Images and Video: How can it benefit e-commerce?

AI in Images and Video: How can it benefit e-commerce?

With the growing popularity of image-based social media platforms like Instagram and Snapchat, there has been a significant rise in UGC (user-generated content) on the internet. Users upload photos not only of their lives but also of interactions with different products or brands they encounter online. Retailers and brands can leverage this information to engage and interact with users building brand awareness. However, with the increased use of UGC, it has become a challenge for them to track and categorize unstructured information. This challenge can be addressed using image recognition and computer vision.

Helping with Product Search

If a user while looking for a type of furniture is unable to use the right terms to describe the item in the search query, he/she could always depend on voice assistants such as Alexa, Siri or Google Home. However, the voice command is really just fulfilling a text query.

Instead, by taking a few pictures of the object and uploading it online using image search, the user can find what he/she is looking for. Using image-based AI, the search breaks down different elements of the image and enables the user to choose which aspects of the results are important.

couch in the living room

For instance, there is a beautiful couch in the living room but it is missing a coffee table. The user can take a picture of the couch and upload it as a search item. The image AI picks up on the couch and detects the elements such as color palette, wooden legs, etc. It then provides results of coffee tables that can match and complement these elements. Furthermore, based on its database, the AI can also recognize elements such as the brand, price range, etc. of the couch allowing the AI understand what type of budget the user may be willing to spend on furniture items. This goes beyond the simple search that people see today. 

Personalized experience on social media

Social media is empowered by AI, and brands and retailers can now detect and analyze every mention on the social media platforms using image recognition. They can also view how the brand is portrayed through the various images and videos shared on a daily basis. This further allows brands to interact with the users as well as collect and reshare their images helping the users to develop a personal connection with the brand.

Brands are also leveraging computer vision to provide a more targeted ad experience for users. For example, after browsing through an Instagram feed of a famous fashion celebrity, the user may get ads of fashion lookbooks featuring some of the pieces worn by that celebrity. These type of ads provide a subtle recognition for the user, which in turn helps brands build awareness and engagement.

AI in video content

For video content, brands and retailers can use AI to scan the video and index objects, scenes and audio sounds such as a dress from a popular brand or a painting from a famous artist or a song from a famous musician. Leveraging these elements, brands can then promote their products that can relate to these items such as bags that may match the dress in the video.

For video advertisements, brands can insert their products into a “placeholder” dynamically. Video producers can mark areas in their videos that can easily incorporate an inserted image and depending on the geography, language, and demographic segregation of the audience, AI can dynamically insert an ad into the video. This personalized approach enables a more local advertising experience for the users.


The e-commerce landscape is evolving with technological innovations changing the way people shop online. Images and videos are a largely untapped resource for retailers and brands to get insights from but with image recognition and computer vision gaining momentum, it is now possible. Giants like Amazon have also recognized its potential and have incorporated image-based search into their shopping experience. The applications for AI in images and videos are still limited but with deep learning, it is evolving and has the potential to change the shopping experience completely.

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How AI helps to optimize e-commerce product content

How AI helps to optimize
e-commerce product content

With online sales growing faster and the e-commerce landscape changing with technological innovations, traditional retailers are increasingly investing in omnichannel strategies and doubling their efforts in order to meet consumer demands. An effective way to keep pace with e-commerce giants and stay relevant in the marketplace is to offer high-grade product discovery and selection. This requires providing detailed product content with product-specific attributes, along with semantic search.

The current product content problem

As more retail businesses are moving towards e-commerce, the need for quality information and powerful search platforms has become crucial in order to entice shoppers and help them make effective purchase decisions. However, this is a challenge as they are unable to easily deliver complete product content.

Retailers rely on the suppliers to provide all the coordinating images, videos, attributes, etc. for each of the products. Suppliers use various methods to provide content such as printed or digital catalogs or in different formats like Excel, PDF, etc., making it difficult for retailers to properly source and extract the right data required for the right product. In some cases, retailers even purchase content from third-party providers or online databases. However, the challenge here persists, as most of the time, content differs from suppliers to third-party providers and validation of the information becomes tedious.

Besides the price of a product, detailed product information along with superior quality-images, videos play an important role in a consumer’s buying decision. 

There are numerous technological challenges while extracting content from the product images - some including region segmentation, diverse product backgrounds, natural settings, typography and fonts, lighting conditions, and low-quality images. For instance, inconsistent product image sizes would limit the system to capture the product details completely from all the images.

Impact of poor quality data

Missing information and uncertainty are two leading factors for consumers to abandon their shopping journey. Consumers tend to leave their shopping journey when they sense that the product does not have clear or complete information. This could range from unclear product descriptions to missing or inaccurate product attributes such as size, materials used, ingredients, etc. or even product reviews.

While there is no definitive rule stating an optimal number of product images or videos or a recommended character limit for product information, the quality of product images and videos have a direct impact on the ability of the e-commerce business to generate sales. With complete and comprehensive product information (description along with attributes like size, or weight, etc.) and high-quality images and videos would enable shoppers with the information they may need to make a purchase decision.

Effective Extraction of Product Content

With IceCream Labs CatalogIQ, retailers can effectively address the problems they face while onboarding product content to their catalogs. Leveraging machine learning algorithms, Optical Character Recognition (OCR) systems, and Natural Language Processing (NLP) techniques, it can effectively extract the right information needed for the retailer to optimize their content as well as maintain their content health. Some of its capabilities include:

CatalogIQ extracting content from a product

Attribute Extraction: ​

Images would be clicked from all angles of the product and would be fed into the machine. Leveraging NLP techniques, brand attributes such as brand name, sub-brand, tagline, flavor, net weight/volume, and calorie information would be extracted.

Brand Name Detection (Logo detection): 

Leveraging OCR, the product image is scanned for text and the output is further sent to an NLP engine specifically to identify text logos (ex: for brand logos like Zara). If the text is not detected, image processing is further applied using the brand name parameters (ex: for brand logos like Nike)

Standard Certification Detection:

In this step, a preset database with standard food certification parameters is applied to detect and extract food certification labels such as “gluten-free”, “non-GMO”, “100% organic”. Here, the images are scanned using these parameters. This is similar to how the Brand Name detection functions.

nutritional label data extraction

Nutrition Facts Extraction:

Using OCR and region segmentation, nutritional facts text is extracted. This text is further corrected using a predefined vocabulary to streamline the content. A rule-based approach is then applied to the corrected text to extract nutritional values.

Product label images are a trusted source of product information for consumers. AI can ensure that the process would improve the quality of the information and maintain data consistency across all product pages. Retailers can further benefit from this as it would alleviate the burden of validating product data provided by various suppliers, online databases or third-party providers and can provide additional information that is critical for product discovery like brand or certification logo information.

The future of Product content

Applications leveraging AI and machine learning have projected tremendous potential for applying process automation to reduce data inconsistency and enhancing data quality and thereby, improving the product data extraction processes.


At IceCream Labs, we strive to address the challenges that businesses face in e-commerce using AI and machine learning. Are you ready to enhance your product content and take your e-commerce business to the next level? Reach out to us at sales@icecreamlabs.com for an AI-based solution for your business.


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Voice is changing the way consumers are shopping online

Voice is changing the way consumers are shopping online

It seems like, these days, you see an Amazon Alexa or a  Google Home everywhere. It’s not uncommon to see a person shout across the room to their voice device trying to turn the television on.

Amazon and Google have sold over 27 million voice command devices in the United States alone, and Apple’s Siri is available to more than 500 million users across the world. 

With the increased adoption of voice assistants, consumers are depending on them to do simple tasks like telling time, setting alarms or even making calls, so that they can focus their attention on some other tasks. However, it’s not just those simple tasks anymore, voice assistants are being increasingly used for online shopping, with users giving voice commands to the assistant about what products to purchase. Consumers are able to multitask without having to manually search different e-commerce portals and selecting products through each of their product categories, thereby, saving a lot of time.

Retailers, recognizing this trend, are slowly incorporating voice to further enhance the user experience. Incorporating voice in the shopping experience not only ups the convenience level of a shopper but also saves time lost in typing and searching for products. 21% of all Alexa and Home users have stated that they are shopping via their device today. Leveraging AI, voice recognizes language patterns such as dialects, intonations, and accents enabling them to converse with the user in a natural, conversational manner. The potential of turning the shopping world upside down is very high and the most immediate impact will be in the following areas:

Better searchability

SEO becomes beneficial for any retailer as it drives maximum traffic for e-commerce. However, there is a lot of difference between typing in search terms and using voice. Technology needs to evolve to differentiate voice commands from typewritten keywords. This will help to institute searchability and compatibility towards voice commands. Understanding the context is important as Voice is conversational. For example, auto-fill options must be provided for sentences or questions to understand the user intent.

With consumers increasingly moving towards voice search, e-commerce businesses must align their website and product pages to account for voice.

amazon echo dot

Ease of providing product reviews

The increase in voice searches eliminates having to browse through different categories and multiple pages. Furthermore, this has raised the importance of online reviews for products and services. The feedback loop between the retailer and the customer becomes more efficient and seamless.

For instance, imagine a customer ordered a pair of Nike Running shoes but never got around to filling out the review. The voice assistant would then ask questions like: “How would you rate your Nike Running shoes from one to five stars? Did it fit as you expected?” By answering these quick questions, the shopping experience can become increasingly personalized, providing better recommendations for the customer.

Online reviews will become increasingly important with almost 85% of voice-based customers trusting the recommendations provided by their assistants. These recommendations, in turn, are based on the top-reviewed products of that query making providing reviews more important than ever for retailers.

Shipment Tracking made easy

In the future, voice commands may not only be restricted to ordering products or proving reviews for them. Users may even get quick updates about their orders and their shipping status. There is a need for these complex processes to become more intuitive especially when consumers expect prompt responses. The retailer can enhance the shopping experience by connecting shipping operations with the voice app enabling users to get quick updates about their shipping status.

flat lay photography of coral Google Home Mini on black surface beside Apple AirPods

Although voice search and shopping is the next big thing, there are a number of challenges that are left unaddressed. The technology, in its current state, is yet to be equipped to handle complicated queries such as comparing different products. Many users still don't believe that the assistants can pick a product without choice, based on their query.

The consumer behavior is changing and as the popularity of using voice search grows, retailers must make decisions and act fast to cope with the change.

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Natural Language Processing: The future of e-Commerce product search

Natural Language Processing (NLP): The Future of E-Commerce Product Search

There is a plethora of content available today and is growing by leaps and bounds every day. There is a need to organize this content into categories and ensure that they show up if it is searched for. This is especially important for e-commerce businesses and retailers who have catalogs of products. Here is when search engines play a critical role – be it the Google, Bing engines or the on-site product search engines which helps users find what they desire.

It is important for retailers and e-commerce businesses to understand and analyze the needs and behavior of their target customers. Listening to what they express online via social media or forums becomes imperative for these businesses to provide a better customer experience. This also helps them to understand what the kind of language the user may use to buy a specific item.

While this may be an easy task for humans, it is time-consuming. Here is where  AI and machine learning fits perfectly. Using Natural language processing, machines can easily pick on what words or phrases humans would naturally use while looking for a particular item.

Natural Language Processing or NLP is the ability of a computer program to understand human language as it is spoken. Human speech is often ambiguous and the linguistic structure can depend on various complex variables, including the regional dialects and social context including colloquial terminologies.

Using a search engine is interacting with a system, and utilizing NLP helps customize the search for the user. Using NLP helps the system understand what kind of language was used and how the sentence was structured. Using these points, the system derives what the user is actually searching for, and provide results accordingly. Detecting patterns and creating links between the messaging is what it does best, and with Natural Language Processing, it is powered to derive meanings from unstructured text.

For instance, a search query for “sleeveless men’s shirts” would involve understanding the context of the words, and without NLP, search engines would unable to process the link between sleeveless and shirts and the results would end up looking like this –

Search results for sleeveless men's shirts

Here, the word “shirt” has not been taken into account, and the results have shown only sleeveless “t-shirts” or vests instead of the intended search – “sleeveless shirt”.

Why do users search for “top budget-friendly phones from 2018” on a search engine yet not on e-commerce websites directly?

An intent for a search would be to find discussions and do some research in the user’s purchase process. And while the word “top” is subjective, content creators and SEO agencies (providing product lists) usually pick words such as “top” or “best” in their communication.

Whereas, in an e-commerce store, users understand that using words like “top” or “best” is subjective. There is no rule that can translate “budget-friendly” being “less than $200” since it depends on the type of product as well as the perception of “budget-friendly”. The advantage of keyword heavy communication is that the format of communication is standardized – which works on most e-commerce sites.

What’s plaguing Natural Language Processing today?

The performance of the NLP model depends directly on the quantity and quality of the data that it is fed, as s the case with every ML model. Retailers and e-commerce businesses need to consider the problem with synonyms and slangs which works differently in different regions. Lexical databases such as WordNet can come in handy, but they are limited to English and therefore it may not work for international stores, catering to customers from different cultures and languages.

There is a high possibility of a discrepancy between – what a customer calls a product, and how the metadata describes the product. The words that customers use to describe the desired product often describes another product rather than the one they want.

Will NLP be the future of e-commerce product search?

NLP today has the ability to deliver valuable functionality such as identifying and separating a product (ex. shoes) from an attribute (ex. leather). Successful integration of NLP into online product search is still challenging.

In a typical retail eCommerce application, it would involve getting an algorithm to gather data about all the products being sold and put in a structure and normalize it. It would then find all linguistic attributes that would be used to describe each product. The challenge here is that leveraging NLP technologies put the burden on search engines and not on the consumer to make the experience natural.

Online product search will evolve in a manner in which the context understanding will be integrated with the search engine allowing humans to have conversations with them in a natural environment. For example, Customers searching for fashion products have a different way of phrasing requests as opposed to customers searching for home furnishing products. NLP platforms of the future would be able to contextually understand these variations.

As NLP gains momentum, the growth would give increase its capability to provide better customer experiences. NLP may very well be the future.

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person holding a phone with a laptop in the background

E-commerce is moving towards social commerce – How to get it right

E-commerce is moving towards social commerce - How to get it right?

Social commerce is often described as the intersection between social media and e-commerce. While this holds value, there is a lot of traffic with no direction. There’s no doubt that social selling is a powerful and an increasingly influential sales tool.

According to recent BI Intelligence, the top 500 retailers earned an estimated $6.5 billion from social shopping in 2017, up 24% from 2016.

There are various forms that social commerce adorns, from group buying to social shopping; from mobile apps to retailers adding social features, or shopping integrated into social media. All of these forms have one thing in common - the use of social technology to replicate age-old buying models in the digital sphere.

Whether it is girls going shopping together in a store or asking a friend for advice on power tools, moving them to online would result in them having a social commerce experience. Taking another instance of bartering, here, instead of the traditional method of trading goods or services, shoppers are trading personal data such as buying habits and preferences for access to easy shopping portals.

There are social platforms like Pinterest, Snapchat and Instagram which have incorporated a “Buy now” button that can turn a static image into a product with a click. However, since the story came about how social platforms are using and monetizing user data, there was a certain amount of wariness among the users about sharing their data on these networks.

The key here is to find a model of social commerce that would work the world over. Some of the things to keep in mind -

  • Provide the shoppers the ability to earn credit for sharing their own data and of their social network.
  • Enable retailers to own the relationship with their customers while also providing access to insights and goodwill from happy customers. 
  • Provide every individual the ability to turn into an influencer. 
  • Star
    Using the existing social media networks as a channel to interact with the brand itself.
person using laptop that is showing a webpage of images

How to make it work

Say a user wants to purchase a mobile phone. The ideal route would be to go the website of the retailer of their choice (assuming if the retailer provides a social commerce experience). They can then choose the selection of the models of their favorite mobile phones.

They now post a picture of the phones on their social networks and ask friends to vote on which phone they think the user should buy.

By setting up this vote, the user can then earn shop credits. Their friends who voted for the products can also earn shop credits by that action. In this scenario, there is no prerequisite of having a large social media presence to be valuable for the business. This action inadvertently turns the user into a micro-influencer.

The information gathered during the voting helps the retailer sell more effectively. They learn which of the products is most appealing and have the potential to become hot sellers, and then accordingly manage stock or change how they display their products. They also gain access to an expanded audience. This eventually, helps them to build a relationship with their customers which can help them build brand loyalty.

Summary

Social channels have a major role to play. Besides influencing purchase decisions, social media is a larger part of the product discovery and research phase in the shopping journey.

The next few years will see social commerce expand its influence if it efficiently benefits the consumers and businesses. The world of commerce is on the verge of disruption, thanks to technological innovations, data collection, and social media. If social commerce is achieved correctly, the future of retailers and shoppers will widen.

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Visual Search: AI tool for E-commerce

Visual Search: AI tool for E-commerce

AI has been empowering many of the world’s top technology solutions. Within e-commerce, AI is being used to make the industry increasingly customer-centric. This can be seen with sales forecasts, product recommendation engines, virtual personal assistants like Siri or Alexa, AI-powered chatbots, automated warehouses, etc.

The need for visual search in e-commerce


Large e-commerce companies offer catalogs with thousands of products and plenty of options. Customers, however, are becoming increasingly impatient during the buying process figuring out what they want to buy in the shortest possible time. This hence poses the question of how to make the search process short and seamless.

In an important use case, AI has enabled search engines to become smarter. This can be seen when using text as a search query, the search becomes more semantic and conversational. AI has also enabled enhanced voice search features. The latest popular feature being search via images.

A new way of search

E-commerce companies are investing largely in integrating all three search methods on their websites to make a more responsive search platform. With text and voice search being increasingly used for products such as electronics, visual search aids customers to find an easier alternative for fashion and lifestyle products which may be difficult to describe with words. For instance, when searching for outfits worn by celebrities, knowing the right keywords would provide the outfits that are indexed with those keywords. Most often than not, the right outfit is not found. With visual search combined with indexed images, the right outfit can be found in just a click!

Today, visual search has increased the level of engagement that customers have with e-commerce websites as well as offline retailers. Be it for searching a product page online or being provided with relevant product recommendations, smartphone apps are becoming more accurate and faster at predicting the customer needs.

Visual search has created new shopping experiences for online and offline retail stores. Customers can now scan images of their choice of products whether it is online or in a store. Providing relevant and accurate product results will ensure that users can shop from anywhere and at any time.

AI and visual search: The tech

Visual search is a very recent trend, and this has been possible only due to the recent advancement in this technology. Visual search is built using Deep Neural Networks, a subset of machine learning. This in fact built as a replica of the neural networks in the human brain.  To put it simply, Deep Neural Networks make machines intelligent to gather and categorize information in the form of text, images or videos like humans do, using their biological neural network.

For example, To make a machine understand a sofa using deep learning, it is first shown pictures of thousand sofas. The algorithm reads and extracts features that can collectively classify a sofa such as a backrest, armrest, cushions, etc. After this, if a new image of a sofa is shown, the machine would be able to now tell if the image has a sofa or not.

Furthermore, if a complete picture of a living room is shown, the machine can individually identify different objects that it has been trained via deep neural networks such as rocking chair, coffee table, rugs, side table, etc. This technology is very adaptive - it recognizes a user’s search pattern so as to provide accurate purchase predictions.

Deep learning technology, providing accurate results can ensure that users find exactly what are searching for, in a short search span. This helps enhance user experiences, which in turn, leads to an increase in conversion rates. Neural networks and deep learning provide the best solutions to problems being faced in image recognition, speech recognition, and natural language processing.

Visual search has become one of the most successful technological innovations in e-commerce and retail, in turn, boosting the effectiveness on a global scale.
With the tremendous emphasis on digitization and the rising economy, this holds a strong promise.

Today, companies are looking for product differentiation through tech and visual search advancements offer just what they are looking for.

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