Tag Archives for " Ecommerce "

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Impact of Great Product Content in Fashion Retail

Impact of Great Product Content in Fashion Retail

 When it comes to e-commerce, merely displaying a robust collection of products on your website will no longer do. Customers today want to do their due diligence before committing to any purchases. This initial evaluation of product content is considered one of the most essential parts of a customer shopping journey. It is also a crucial prerequisite for you to make sure of sales. Incomplete, missing, error-riddled and misspelled product information causes customers to lose faith in your e-commerce website.

In a physical store, consumers are used to touching, feeling and trying on clothing before buying. However, in the case of online shopping, you need to provide rich descriptions along with complete details about the product for a customer to make up their mind about buying a fashion item.

Engaging imagery is key for selling a fashion garment, however, accurate descriptions help educate the consumer and help search engines present accurate results. However, adding a title, description, facets, proper categories and relevant attributes to your product are essential to guide your customers from Google search results or your website’s search bar to the product description page.  Textual content actually makes your products easier to discover.

Here is how great product content can impact fashion retail:

Effect on Customer Experience

Product descriptions answer questions that your potential customers have as they are shopping. Customers comparison shop on multiple websites to find what they are looking for, hence providing all the relevant information is important. A majority of customers will abandon a site or even a partially filled cart if they are not able to find what they are looking for.

Inadequate product content will fail to convert the shopper if it doesn't do a convincing job of communicating the features and capabilities of a product. Fashion Retail is now competitive online. You need to create an engaging experience for your customer that communicates your brand, both visually and with rich information about the product.

Drive Conversions

In the world of e-commerce, enriched product content is the difference between making a sale or losing customers to your competitors, which makes it a top priority for any online retailer. Merely offering content that shows price, short description, sizing and color, doesn’t provide anything for your customers to get excited about. 

We have explored how your product description page is the key for a customer to make a purchase. It is a virtual salesperson who is present at all times to pitch to your customers. Any missing or misleading information will have a negative impact on sales as customers will abandon a purchase . Having rich content will help your customers to understand your products, building trust to find what they’re seeking. This will, in turn, increase sales for you.

Build Credibility

Inconsistent or misspelled product content adversely affect your credibility. The language you use on your website reinforces your identity. Incorporating a specific style and tone-of-voice that is unique to you will help you differentiate your website from competitors, strengthening your brand image in the process.

It is important to be consistent in the ontology and terms you use for categories and items, and avoid misspellings, typos and grammar mistakes. Auto-generated product attributes and categories could help avoid these issues and help build better credibility.

Displaying vibrant, engaging product content on your website ensures that your product listings rank higher in search engines. When your website is the first one that customers find, it leads them to believe your site is more popular and reliable. It also lets you outrank your competitors, getting your site more clicks as a result.

When you take the time to produce great online content for your products, your customers take notice and reward you with their purchase. In the same way that a smart and personable retail clerk can convince a customer to buy in the store, your web store content must be convincing for an online customer. Your products need complete and well-crafted content if they are going to sell.

Related e-commerce articles:

Impact of Great Product Content in Fashion Retail
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woman scanning barcode in a warehouse

Top Retail Trends in Supply Chain Management

Top Retail Trends in Supply Chain Management

As we move forward into 2019, it is only fitting to look at the latest trends in supply chain management. The supply chain is the backbone of e-commerce and in today’s age, it is more diversified than ever before. New technological innovations are being introduced in an effort to dilute this complexity. The supply chain is changing from being technologically enabled to being technologically driven. The high complexity of online markets, as well as the ever-changing and increasing customer demands, requires proactive stances from retailers. Here are some trends that you should consider in your supply chain to stay ahead of your competition.


Artificial Intelligence (AI)

AI is the technology of the future. A wide array of self-learning algorithms are available in the market today. Huge amounts of data are generated, analyzed, linked and patterned by data scientists. With machine learning operating on this big data, decisive knowledge can be derived from it, regardless of the amount of data. AI also has multiple applications in the supply chain. Logistical coordination and organization in warehouses is being driven by intelligent and autonomous technology such as drones, robots, and self-driving vehicles. Chatbots can also be a function of logistics, for example, they can be programmed to give information such as "Your package has just been shipped", or "your order is going to be delayed". Zion Market Research published a report stating that global AI in supply chain management is estimated to cross 6,548 million USD by the year 2024. AI is an all-encompassing solution that will fundamentally revolutionize and influence the future of the supply chain.


Immersive Technologies

In the supply chain, the use of augmented, virtual or mixed realities isn’t a novelty anymore. Augmented Reality (AR) offers various assistive technologies that facilitate the employees in their day to day work. Hands-free picking aided by AR could take care of simple picking tasks. Employees wearing smart glasses enhanced by AR can immediately view barcode data on the screen and improve their productivity. Tasks in the future of the supply chain could be significantly simplified using smart glasses, displays and simulators. From the customer’s vantage, AR makes virtual trial and test of products possible without the need for actual physical products. This customer behavior could impact the supply chain in the long run with fewer products being kept at store locations and speedy shipping from larger storages in warehouses. Gartner forecasts that by 2022, 70% of enterprises will be experimenting with immersive technologies with 25% having deployed them in production.


3D Printing

This technology has found its way into almost every imaginable industry and continues to provide limitless opportunities. 3D printing is not only used for prototypes and small series products but is being utilized in mass production of items such as car parts. The advantages of 3D printing will be even better utilized in the future. 3D-printed parts can be manufactured easily, at a cheaper cost and without compromising on quality; these factors make it especially attractive for the production of cost-intensive individual parts required in the aerospace industry, for example. Ever since its introduction, 3D printing has been evolving and improving, which makes it a viable addition to the supply chain of the future. 3D printing can not only meet rising customer requirements but also significantly shorten the supply chain. With the possibility of faster prototyping and reduced cost of production, the applications are endless, particularly in the field of consumer goods and medical care. 


These are only some of the trends that you can keep in mind while upgrading your supply chain. Artificial intelligence currently holds the highest potential as it is being rapidly innovated and integrated into every aspect of e-commerce. 3D printing and Immersive Technologies have their own upsides and will only help shape the efficiency and ramp up the dynamics of the future supply chain. Keeping up with these trends will give you a significant edge over the competition and assure lasting viability in the market.

Read more about how leveraging AI could improve supply chain efficiency for Grocery Retailers.

Related e-commerce articles:

Impact of Great Product Content in Fashion Retail
Impact of Great Product Content in Fashion Retail  When it comes to e-commerce, merely displaying a robust collection of products[...]
Top Retail Trends in Supply Chain Management
Top Retail Trends in Supply Chain ManagementAs we move forward into 2019, it is only fitting to look at the[...]
Impact of Stock-Out on Retailers
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Product Discovery: Enhancing the customer shopping experience
The Path to Better Product DiscoveryDiscovery is essentially the most important part of product management in e-commerce. The search functionality[...]
Augmented Reality v/s Image Recognition – The better bet for your business
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Virtual Reality: Playing a growing role in retail
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scrabble keys form the word search denoting product discovery element in ecommerce

Product Discovery: Enhancing the customer shopping experience

The Path to Better Product Discovery

Discovery is essentially the most important part of product management in e-commerce. The search functionality differs across different websites, but overall, customers must be able to access relevant results quickly and easily in order for you to raise conversion rates for your business.

In this article, we shall explore how you can optimize your website for better product discovery.

Product Discovery: The Key Factor

‘Product Discovery’ is the process through which a customer comes to your online store and discovers a relevant product. The time taken to get to the product is called ‘Product Discovery Time’. As a retailer, quick product discovery is imperative, because unless your customer can find a product he/she wants to buy, no amount of traffic or conversion optimization will be able to drive revenue. 

In a study conducted by ClickTale, it was observed that for e-commerce sites without an optimized search functionality, shoppers spent more time attempting to find their desired product, but left because they were unable to find it. Alternatively, on e-commerce sites with better search functionality, they spend less time but actually find the desired product faster. As a result, the customer sticks around to make a purchase despite spending less time on the site. If a customer has a sub-par experience while searching for products, they are likely to ditch the store resulting in an overall disappointing experience.

Up The Product Discovery Game

Now that you understand how product discovery drives revenue, let’s take a look at how you can optimize the e-commerce experience for your customers. Relevance is an important theme to base an e-commerce site on. The more relevant your site is to each individual shopper, the better your product discovery experience will be.

Begin with an optimized homepage that features current promotional offers, current sales, and time-sensitive offers to drive urgency. After this, you can add:

  1. Personalized product listings based on recently viewed items to save time for a shopper looking to make similar purchases.

  2. Showcase trending and popular best-selling products to draw in new customers visiting for the first time that lack browsing history.

  3. Display new arrivals to give your customers a chance to view and possibly purchase your newest offerings.

  4. Include educational content such as guides, product reviews, and product videos. This kind of content pre-sells your products to customers who are merely browsing through your online store.

Hook By Navigation

Customers use navigation when they are searching for products but are undecided on what exactly they are looking for. So the navigation on your site needs to be both fast and intuitive so as to not lose a shopper at the start of their search journey. 

Here are a few points to bear in mind:

  1. Navigation menus must display a varied collection of categories and sub-categories to narrow the range from which a shopper can search for a product.

  2. Fly-out menus i.e, menus that "fly out" from the left or right side of the screen, and are hidden until the mouse is moved into the area of the screen, are a popular option used by retailers for this purpose.

  3. Adding filters to the search menu can help shoppers to selectively filter products based on necessity.

  4. Make filterable and sortable attributes per product as complete as possible, such as tags for colors, sizes, brand etc.

  5. Allow mix and match of the filter and sort tags to make search easier. For example, You can allow customers to search for pink and black dresses in one view instead of making them go through two separate searches for each color.

Faster, Accurate Results

Customers expect to get exact results from quickly typing in words into a search box. Your site search must be capable of handling input such as product names, product types, categories, FAQs, product features, keyword variations, and misspellings for an optimized solution. Create and optimize dedicated landing pages for users coming in from search engines. In these cases, it is critical to feature relevant products immediately and include a call to action or conversion action. As an example, if a user searches for black boots, they should ideally be redirected to a page showcasing boots under shoes with black pre-selected as a filter. You must also make sure to create dedicated landing pages for top search terms relating to your products and display the correct product listings.

Error 404 pages are an often-overlooked opportunity in e-commerce. For such pages, instead of discouraging a user away from your site, you may include best-sellers, current sales, promotions, trending product lists, etc that help customers discover products they want to buy.

Dedicated Landing Pages and Secondary Product Discovery

For customers who are looking for gift ideas, landing pages dedicated to best-sellers and trending product listings can be a boon. These pages are also great to include in subscriber email series. Popular products are automatically more desirable and dedicated landing pages are a great way to take advantage of that. 

All of the above takes place fairly early in the customer shopping journey. On the other hand, there is a further possibility of upselling using product discovery that you can place at the time of purchase. You can add product recommendations and items that are frequently bought together, and include products to ‘complete the look’. Another enticing option for online shoppers is to provide them with an opportunity to get free shipping by adding extra products to increase the size of the order or provide discounts for bundled products. These practices can aid in driving up average order values by cross-selling and upselling products.

We hope this was a helpful guide to optimizing your product discovery. From creating an optimized home page to offering secondary product discovery options, it is up to you how to implement these ideas in your online store to drive revenues.

Now that we’ve seen how product discovery can be a crucial tool for conversion, here’s how leveraging AI for product categorization can help narrow search results.

Related E-commerce Articles:

Impact of Great Product Content in Fashion Retail
Impact of Great Product Content in Fashion Retail  When it comes to e-commerce, merely displaying a robust collection of products[...]
Top Retail Trends in Supply Chain Management
Top Retail Trends in Supply Chain ManagementAs we move forward into 2019, it is only fitting to look at the[...]
Impact of Stock-Out on Retailers
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Virtual Reality: Playing a growing role in retail
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man holding the smart phone, using the Augmented Reality buy some food in the supermarket

Augmented Reality v/s Image Recognition – The better bet for your business

Augmented Reality vs. Image Recognition - The better bet for your retail business

There's a lot of chatter around how Augmented Reality will change the way people shop. While Augmented Reality holds value, every technology created has its specific use case. Retailers and brands must bear in mind the several aspects each of the technology provides and select those that align to their objectives and goals. Let's delve deeper into each of these technologies -

Image recognition

Image recognition technology enables consumers to search for products by just taking a picture of them. These visual experiences are usually more flexible in nature when compared to Augmented Reality experiences for the following reasons -

No requirement for users to download an extension or app 

While there are some versions of AR applications out there for mobile websites, it's still a long way from delivering a seamless experience for its users. AR experiences that perform well often require a user to download an app. Image recognition, here plays a pivotal role as it enables interactive experiences within a retailer's mobile web, and not just the native app.

There is no need for creating 3D models 

Developing 3D models for AR experiences can often be time-consuming and expensive. Due to its complicated nature, it even requires technical skills to deliver the experience. Image recognition can be used with the existing marketing and web collaterals and can be implemented with ease. Moreover, the changes made to the content will automatically be updated in the apps, keeping the experience up-to-date.

Providing a universal and inclusive experience for shoppers 

Devices play an important role when Augmented reality is concerned. The experience may differ between low-end and high-end user devices, with the highest quality devices getting the best results. This is not an issue with Image recognition as it allows brands and retailers to ensure that their content is delivered to their customers in the same, interactive manner, irrespective of the user's device.


Limitations of Image recognition vs AR

While Image recognition provides the aforementioned benefits, there are certain aspects that set Augmented Reality apart from Image recognition:

Content is visualized in a three-dimensional manner

The type of content linked to Image recognition often includes videos, promotions, product information pages, etc. which often aids the customer's purchase journey by allowing them to learn more about the products and it offers at one go. In AR, the content is represented in a three-dimensional format. The content visualized is not three-dimensional, unlike what many Augmented Reality experiences build upon.

Image recognition provides a transactional experience, not immersive

If a user/customer aims to visualize objects in their environment, Augmented reality can be a good option to choose from, as Image recognition limits the user or customer to place digital content into the real world. This comes especially handy while buying expensive furniture - with a 'try before you buy' functionality. The customer can use the functionality and get a feel of how it may appear against a realistic setup - and nudge him or her towards a purchase.


Conclusion

To put it briefly, image recognition helps create a smooth transition between the physical and the digital worlds and help customers through a shopping journey. It allows them to interact with real products with the help of the images and the product information. For instance, it allows the user to learn more about a product's nutritional values, the user ratings, allergens, check for its alternatives, similar products, complementary products, etc.

On the other hand, Augmented Reality goes from digital to physical. It lets customers interact with virtual products in their own environment when, in fact, are not present.

While they may support different use cases, both technologies can provide customers with different kinds of engaging experiences.

Related e-commerce articles:

Impact of Great Product Content in Fashion Retail
Impact of Great Product Content in Fashion Retail  When it comes to e-commerce, merely displaying a robust collection of products[...]
Top Retail Trends in Supply Chain Management
Top Retail Trends in Supply Chain ManagementAs we move forward into 2019, it is only fitting to look at the[...]
Impact of Stock-Out on Retailers
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Product Discovery: Enhancing the customer shopping experience
The Path to Better Product DiscoveryDiscovery is essentially the most important part of product management in e-commerce. The search functionality[...]
Augmented Reality v/s Image Recognition – The better bet for your business
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Virtual Reality: Playing a growing role in retail
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people on their smartphones

Social Media: A New Way to Shop Online

Social Media: A New Way to Shop Online

 E-commerce has changed the way people shop, giving retailers and businesses new avenues to interact and engage their customers. Millennials are currently the most valuable target demographic for modern retailers. As they are also the most avid users of social media, a crossover was all but inevitable.  

Retailers, realizing the power of social media, have used these platforms for a while now to engage with their customers online. Social media also lets retailers market their products in a more interactive environment.

Recently, social media platforms like Instagram and Facebook have been investing in retail. This captures wide consumer interest and boosts both revenues as well as followers. As for retailers, they can use social media platforms to better understand consumer behavior and trends.

UGC or user-generated content is one of the most valuable data sources for retailers. Millennials put a lot of trust and faith in peer evaluation of products. Studies show that reviews and recommendations by fellow shoppers, rather than brand messaging, motivate customers to buy products online.

Here are a few instances of how social media platforms are leveraging the retail space:

Facebook Shop and Marketplace

Facebook has a feature called Facebook Shop. Retailers can add the Shop tab to their business page. It lets retailers display their products and sell directly from their company page itself. Considering the huge number of users on Facebook, this offers retailers a wide audience to convert into customers. Retailers can upload a product catalog to their page and customers can browse the products and buy them without having to leave the page. It also allows retailers to manage orders, and mark them as shipped or canceled right on the page itself.

Facebook also introduced Facebook Marketplace, an online market for retailers to display products. This offers free organic distribution for retailers’ products. It curates content and provides product recommendations based on user preferences and relevance. This ensures higher conversion from a user to a consumer. Like Facebook Shop, there is no listing fee involved. It is an online platform for retailers to sell to their customers.

#Instashopping on Instagram

Instagram is one of the most popular social media platforms with more than 1 billion active users; and about 60% or a whopping 600 million people, seek out and discover products on the app. Instagram has introduced shoppable posts which allow customers to go from discovery to buying without having to leave the app. Retailers can add up to 5 product tags per picture on business accounts only. They can only tag products from their Facebook Shop catalog. These tags redirect the customers to a product page that allows them to buy the products.

This seamless and hassle-free shopping experience has a wide appeal for the customers. In June 2018, they added Shoppable Stories which are an added advantage as about 400 million users view Stories every month. As of late 2018, Instagram also added a shopping channel to the Explore page, which is in its testing phase.

Shop and Cop by Snapchat

Snapchat  has introduced ads and product catalogs through its self-service ad buying platform. Recently, they released a dedicated shopping channel called Shop and Cop on the app which will feature exclusive offers and limited time deals through Shopify. These channels will be available in the Discovery section of the Snapchat app. Shop and Cop will feature social influencer posts and content. Shopify capabilities will allow in-app purchases ensuring a smooth shopping experience without the user leaving the app. Snapchat will curate products while Shopify will take care of the buying end. Moreover, Snapchat and Amazon have announced a partnership on a visual search tool. This will allow customers to use the Snapchat camera to search for products on Amazon.



2018 saw a lot of innovations with social media intersecting with e-commerce to give the rise of social commerce.  Retailers stand to make big bucks off of these popular platforms by better understanding shopper purchase behavior and using it to their advantage. They can conduct market research, market their products better and even sell, all in one place. With these innovations, it's only a matter of time before social media takes over the world of retail.

Related e-commerce articles:

Impact of Great Product Content in Fashion Retail
Impact of Great Product Content in Fashion Retail  When it comes to e-commerce, merely displaying a robust collection of products[...]
Top Retail Trends in Supply Chain Management
Top Retail Trends in Supply Chain ManagementAs we move forward into 2019, it is only fitting to look at the[...]
Impact of Stock-Out on Retailers
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Product Discovery: Enhancing the customer shopping experience
The Path to Better Product DiscoveryDiscovery is essentially the most important part of product management in e-commerce. The search functionality[...]
Augmented Reality v/s Image Recognition – The better bet for your business
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Virtual Reality: Playing a growing role in retail
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fork truck loading a pallet into a tractor trailer on a loading dock

Leveraging AI to Improve the Supply Chain Efficiency for Grocery Retailers

Leveraging AI to Improve the Supply Chain Efficiency for Grocery Retailers

Food companies are increasingly prioritizing supply chain transparency and efficiency. IBM expanded its food supply chain network, IBM Food Trust, with Carrefour rolling out the solution to all of its brands worldwide by 2022 and Topco Associates, Wakefern, and suppliers Beefchain, Dennick Fruit Source, Scoular and Smithfield joining the blockchain traceability program.

Half of U.S. grocery retailers are turning to artificial intelligence to improve supply chain efficiency. Nearly two thirds of the 50 retailers surveyed, most of which were grocery executives and managers, struggle with a disconnect between systems, and 48% rate their forecasting technology as average to very poor. While they would prefer that each supply chain component work together, few retailers have established a unified process.

The challenge for grocery retailers is that they lack connected systems, with consumers indicating they have separate demand planning, replenishment, allocation and order management systems for store and e-commerce orders. Combined with the fact that a small portion of consumers indicating they don’t manage each of their modules on the same platform, disparate demand replenishment systems appear to be a significant burden to efficiency.

Retailers are being pressured to push past barriers and produce more accurate demand forecasts. The pace of innovation is a significant issue, with 43% of grocery retailers saying their technology can’t keep up with business demands. Forty-two percent describe less-than-optimal synchronization between their inventory and channels, and nearly as many worry about fulfilment complexities, stocking inefficiencies and high product lead time.

When they do invest in needed technology, grocery stores are most inclined to spend on supply chain systems that increase stock availability and decrease stock holding, as 44% invest in new technology because their existing systems are unable to sustain new growth.

In an effort to keep reasonable service levels, food retailers often tend to overstock, but then over course-correct and understock instead. While 43% say they’re challenged by lack of real-time visibility of overall supply chain inventory, six in 10 say they are actively taking steps to address this hurdle and increase inventory visibility.

AI and machine learning hold a lot of potential to improve supply chain efficiency, and forward-looking retailers are already investing in these technologies. Grocery retailers say AI’s greatest potential to improve supply chain management relates to quality and speed of planning insights, while nearly 50% identified demand management as one of the top three areas for AI in the next five years.

One in three food retailers incorporate AI capabilities into their supply chain management processes, and one in four are working toward that goal. Artificial intelligence has the possibility to provide faster, more reliable demand insights, quality management capabilities and real-time updates along the way, the study noted.

Tree Branches

How can I use AI to Categorize Product Data

Is there a best way to leverage AI to categorize product data?

Have you ever tried searching for a product on your favorite online shopping site, only to be disappointed when you couldn’t find the product that you’re looking for? Most product site search engines leverage accurate product categorization attributes to help narrow the search results for a user.

In this article we’re going to look at the impact that proper categorization has on search and how it’s now possible to automate product categorization with a machine learning model.

What is Categorization?

Categorization starts with a well designed product category taxonomy. The product taxonomy defines how each product type is related. The first couple levels of a product taxonomy contain broad category labels. For a grocery taxonomy, the top levels might be organized by departments within the store. It’s a logical representation of the way that a shopper would look for a given product in the physical store. A taxonomy is often referred to as a “Product tree”, with each product category referred to as a “branch” and each individual item referred to as a “leaf” on that branch.

Grocery taxonomy example:

  1. Meat & Seafood

    1. Fresh Meat

      1. Ribs

      2. Smoked Ham

      3. Specialty Meat

      4. Kosher Meat

      5. ...

    2. Fresh Seafood

    3. Packaged Meat

    4. Packaged Seafood

  2. Produce

  3. Deli

  4. Bakery

  5. Adult Beverages

  6. Beverages

  7. Floral

  8. ...

For a new product to be put into the online product catalog, it first needs to be categorized appropriately into the correct level of the product taxonomy. This is easy enough for a human to complete the product categorization, however, when you have thousands and thousands of products, this can be a tedious process.

Why is Categorization Important?

The science of search has evolved over the last two decades. Trying to determine the searchers intent from one or two words is not a simple process. We’re not going to dive into that in this article. However, in the specific use case of product search for an ecommerce website, most shoppers will generally include the object of their intent as part of the search input. In most cases this data can be used to quickly narrow the results set based on the product taxonomy. After all, the consumer isn’t looking for organic lettuce in the seafood section, nor would they be looking for seafood in the produce section. So one method to quickly close the search breadth is to narrow the search to specific sub-branch of the product taxonomy.

One downside to improper categorization is that improperly categorized products can become “lost”. When a product is mis-categorized on an improper branch of the taxonomy, the search engine may either (1) not find the product or (2) relegate the mis-categorized product to the bottom of the search results.

Don’t believe me? Try this: go to your favorite ecommerce provider, search for something, and then go to the last page of the search results. What do find there? Don’t let this happen to your product catalog.

In addition, the product category for a given catalog item can help define the product schema that should be employed to display the product information for the consumer on the product data page. The schema can also help define the meaning of generic product attributes, depending on the product type.

What is ATOM?

ATOM is the product categorization service from IceCream Labs. We developed ATOM as an API service which can be accessed automatically from your product information manager. ATOM takes a product title or description as an input and outputs the recommended product category for the item. ATOM is powered by a machine learning model that has been trained on millions of product records. It’s constantly learning as it processes new data.

With ATOM, you can properly categorize or validate a new product item before accepting it into your production product catalog.

To learn more about ATOM, or see a demo, contact our sales team: sales@icecreamlabs.com

PHOTO CREDIT: Photo by Min An from Pexels

three trays filled with salad ingredients

Personalization: The important role it plays for Grocery Retailers

Personalization: Why is it important  for Grocery Retailers


In today’s hyper-local and hyper-personalized consumer demands, delivering a tailor-made and individualistic message becomes extremely important.

They can be put off by irrelevant messages and the likelihood of them seeking products elsewhere increases. They want to buy from innovative companies who create better experiences tailored to their preferences and previous behavior.

While grocery has often been a leader in data and personalization, the focus was not entirely on creating a genuine and valuable customer experience.

To keep up with the ever-changing customer expectations and to stay a step ahead, food companies need to facilitate a consumer’s needs before they arise, and the retailers that capture on this trend, are more likely to succeed in the future.

Personalized recommendations is not a new concept. Spotify creates playlists based on songs that a user has previously enjoyed and Amazon’s recommendations based on previous purchases.

Personalized recommendations are not news. YouTube is recommending which songs we should listen to next, Spotify is creating playlists based on songs we enjoyed in the past, what day of the week it is or time of day, Amazon is letting us know which books we might like based on what’s in our cart, but we feel frustrated if the recommendations feel impersonal.

In a society with a unique sense of self, search with the term “for me” is growing exponentially and food companies are looking for ways to create food recommendations that will not let the consumer down.

Grocery retailers have recognized the need for creating personalized shopping experiences as well, but are still struggling to implement every step of a connected and delightful consumer journey.

Leveraging both the data provided by the consumer and past purchase behaviors can help grocery retailers deliver more personalized and meaningful shopping experiences, thus increasing customer loyalty and basket size.

In the blog post, we will explore more about why consumers expect Personalisation from grocery retailers. 

Related e-commerce articles:

Impact of Great Product Content in Fashion Retail
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Top Retail Trends in Supply Chain Management
Top Retail Trends in Supply Chain ManagementAs we move forward into 2019, it is only fitting to look at the[...]
Impact of Stock-Out on Retailers
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Leveraging AI and Machine Learning for Product Matching

Leveraging AI and Machine Learning for Product Matching

There is a vast number of products sold online through various outlets all over the world. Identifying, matching and cross-checking products for purposes such as price comparison becomes a challenge as there are no global unique identifiers.

There are many situations where accurately identifying a product match is essential. For instance, stores may want to compare competitor prices for the same product they may offer. Similarly, customers may use comparison tools to get the best deals. Amazon allows different sellers to offer the same products only after ensuring that they are the same before listing the sellers in a single, unique product page.

Numerous products but no method to match them across different stores

Product titles/descriptions do not have a standardized format. Each store, as well as different sellers within a store, might have different titles and descriptions for the same products. Another challenge comes in with respect to attribute listings as different e-tailers follow different formats. The product images of the same product also differ across different e-tailers.

While there are standardized unique identifiers like UPC, MPN, GTIN, etc, they, however, may not be mentioned in the product page in all stores selling them. The attributes themselves may be described differently - for instance 9" and 9 inches. Images may be included but they can differ in perspective, clarity, tone, etc. The brand name may also be referred to in different ways like GE and General Electric.


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It is an impossible task for a human to visit different product pages to ensure if they are matching the same products. Although, if the process is to be automated, how can it be ensured that the system makes sense of all the information. This is when AI and machine learning come into the picture. 

Machine Learning for Product Matching

In machine learning solutions for product matching, the solution provider must initially build a database with billions of products. This can be done by collecting information through web crawls and feeds. The system then has to come up with a universal taxonomy. This especially is a unique challenge as different retailers use different classifications for their products, and the same product might be listed in more than one category. For instance, a particular shoe model might be listed under casual shoes as well as dress shoes. The system first must design a standardized taxonomy, irrespective of how a particular store classifies its products.

There are standard classification models such as Google Taxonomy, GS1, and Amazon but a product match solution may devise its own taxonomy. The universal taxonomy is designed by identifying patterns and signals from titles, product descriptions and attributes, and from images.

Once a universal taxonomy is in place, the next step is making particular product matches. Here, there is a need for precise comparisons to ensure a particular product is indeed the same unique product, despite the differences in titles, images, descriptions, etc. First, there is a search for unique identifiers such as UPC or GTIN on the product page. Then, the product titles need to be compared. It needs to be noted that no two product titles are the same across different stores for the same product, for example:

Neural networks play a key role

Neural networks and deep learning techniques are extensively used to identify and learn from similarities, to identify and learn from differences, and produce word-level embedding to create a system of representation for common words. This involves teaching the system to recognize different references to a unique entity such as 'GE' and General Electric or 7" or 7 inches, to come up with one unique representation for each entity.

A product can be identified using its title, description, images and attributes or its specifications list. In many cases, the product title itself will yield a lot of information and the system needs to be trained to differentiate the product name (for instance, brand model) from the attributes.

<Phone model images>Samsung Galaxy Note 8 (US Version) Factory Unlocked Phone 64GB – Midnight Black (Certified Refurbished)Samsung Galaxy Note 8 is the phone model, and the title provides additional information like the memory size, US version, Factory Unlocked Refurbished, etc. 

Identifying and sorting product matches 

The information then needs to be extracted and sorted into the appropriate slots - Phone model, version, memory size, etc. Different techniques might be used to help the system learn to parse and sort the different sets of information. 

The next comparison comes in the form of more information about the product such as the title, description containing additional information and a specs table. These help add more knowledge about the product, and the machine will be better able to identify an exact product match or mismatch in the following comparison.

The standard identifying signals are similar results or positive matches for unique identification numbers (UPC or MPN), classification, brand, title, attributes, and image. For each comparison, the system follows a long procedure of checks or safety valves. The checks pass through a search for the unique identification number, a test for keyword similarities, brand normalization and match (for example, HP is the same as Hewlett Packard), attribute normalization and match ( 9 inches is the same as 9in, 9"), image matching, etc. There is also a check for variation in attributes such as:

For the best product match result, there has to be at least 99% of positive results. It will be considered a mismatch, even if it is a variation of what is essentially the same product. Different product match solutions employ different techniques and training methods, and it is a complicated process. Although, there is an advantage that neural networks and machine learning learn over time, and get better with each use.

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AI is Redefining Experience in Customer Support Centres

AI is Redefining Experience in Customer Support Centres

Businesses need to understand the complexities of individual transactions and customer behavior over multiple touch points and channels, now more than ever. With AI in the fore, and technological integrations becoming increasingly popular and customer support centres or contact centres have the opportunity to stand as industry leaders and reimagine every aspect of their business.

Data mining now has the ability to look at every single customer and personalise the brand’s interaction with each of them. Harnessing the massive rise in unstructured data through AI will play a crucial role in helping reshape contact centres into customer experience centres, helping them provide insights into customer needs which would drive increased efficiency and drive profitability, greater customer satisfaction and create more valued and meaningful work.

A seamless, individual customer experience

Digital convenience is a huge motivator for consumers. Several companies such as Apple, Google, Facebook and Amazon have set the bar for integrated customer experience that provides individual customer service across multiple channels. Consumers today expect to move seamlessly through the different channels seamlessly.

While the customer expectations are high, their brand/company loyalty is not as much - while customers will cross channels if they cannot complete a task on their first channel of choice, they only want to engage through the channels they want to use. This is one of the integral reasons why retail businesses must understand the intricacies of individual transactions, as well as the context of customer behavior over multiple channels.

Businesses that are cognisant of their customers’ issues, moulding their experiences and creating meaningful engagement creates value for customer and company. Leveraging AI, businesses can receive immediate feedback - systematically and quantitatively, from every interaction without creating any points of friction or customer effort at an individual customer level or aggregated to the level of your choice. It links all channels to create an individual yet seamless customer experience.

Multiple channels fuel customer contact

Customers are increasingly demanding choice and control and even expect brands and retailers to anticipate their needs without invading their privacy. While digital touch points are becoming the interaction channel of choice for customers, there is still a significant amount of customer support centres that do not use data analysis tools, despite analytics being voted the top factor to change the shape of the industry in the next five years.

Furthermore, customers have reported that the phone as a channel is the most frustrating contact option, an industry study found that its dominance has not declined as quickly as expected. In 2017, almost half of the customer support executives have utilised phone and digital channels. Moreover, it is predicted that more than 50% of organisations would manage a multichannel customer support centre in the immediate future.

Augmenting Intelligence 

While AI can help augment human behavior, there is still a very real bias for humans to want to talk to other humans. Customer support is still an important competitive point of difference for business, with success gauged on customer experience outcomes. A key challenge is maintaining integration levels across all channels while providing consistent service.

Today, customer support centres are experiencing an offloading of transactional activities into alternate channels. Calls are more complex and add more value for the customer as well as the business. 

This means AI will take the the existing analysis techniques of those calls to the next level. It will have the ability to map word and concept level relationships within conversations and then deduce business specific intelligence and insights. Speech analytics will be able to measure everything from the reason the person called to their mood at any stage of the call or contact.

AI can link key words and phrases and carry out semantic matching (which matches phrases on their similarity of meaning). This will enable customer support centres to improve the customer experience, monitor contact centre quality, reduce operational costs and gain critical business insights. Critically, it will do this seamlessly from the conversation, not through set questions or a survey. Today’s data, informs tomorrow’s decisions.

The road ahead

There is no denying that contact centres are entering a period of intense disruption. The rise of cloud-based infrastructure will see new forces enter the market and force existing operators to become more flexible.

For large established businesses, offering a frictionless multi-channel offering will not be something new but something expected by customers. So much so, customers won’t think about dealing with different channels within a company but simply with the company. Accurate, consistent and personalised interactions with customers will be essential.

AI software will be instrumental in helping contact centres reimagine their role from contact to resolution. It will free staff to work on meaningful, more complex and intuitive scenarios with customers as AI performs transactional and predictable tasks. The elevation of work in a contact centre has the potential to create a more stable workforce with improved corporate culture.

Ultimately, people still want to interact with other people. A contact centre is a fine example of that. Utilising AI will allow contact centres to focus less on mundane, transactional activities and more on its interactions with its customers. It will see far more opportunity for meaningful human interaction beneficial to customer and company.

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