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.

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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.

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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.

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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.

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.

 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.

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.

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.

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.

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

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.