IceCream Labs at NRF Big Show 2019

ICECREAM LABS LAUNCHES SLURP.AI AT NATIONAL RETAIL FOUNDATION TRADESHOW

FOR IMMEDIATE RELEASE:

ICECREAM LABS LAUNCHES SLURP.AI AT NATIONAL RETAIL FOUNDATION TRADESHOW SLURP.AI automates the meal planning and grocery shopping process

New York, New York – Jan 14, 2019 – IceCream Labs, the leader in intelligent merchandising solutions for e-commerce providers, launched SLURP.AI today at the 2019 NRF Big Show. SLURP.AI enables consumers to quickly plan meals, purchase meal ingredients and personalise their shopping carts based on price, availability and dietary needs from their favorite grocers.

“SLURP.AI delivers a new generation of intelligent content matching by combining complex machine learning and product graph technologies. SLURP.AI uses machine learning, natural language processing and semantic analysis to create a dynamic a data mesh which links the product graph, ingredient graph and recipe graph. This allows for automatic mapping of products to recipe ingredients and helps suggest substitute and complementary products. SLURP.AI is able to fulfill several retailer use cases that weren’t possible with existing shoppable content offerings.”, said Madhu Konety, CEO of IceCream Labs. “SLURP is able to dynamically recommend products, both to the consumer, and to the picker”

“Our objective is to to help our grocery retailers and brands improve the consumer experience in a hyper competitive grocery market by improving product search, recommendations and personalisation”, said Konety. “When we showed this to our grocery partners, they were excited by the prospects of leveraging machine learning to process new recipe and product data in real time”.

For grocers, a key application for SLURP.AI is in helping ensure customer orders are fulfilled by ensuring pickers find the best products during the pick and pack operation and handle “stock out” situations. Typically, a stock out will lead to a lost order and lost revenue, in an already low margin operation. With SLURP.AI, the pickers can now quickly find substitutes that will still satisfy the customer’s needs.

In addition, SLURP.AI makes recipe ingredient data shoppable. SLURP.AI uses machine learning and a deep ingredient taxonomy to match ingredients to products within a grocer’s product catalog. Once the model is trained, SLURP.AI is able to automatically update ingredient references as new products are added to the grocer’s product catalog. SLURP.AI’s auto suggest feature allows consumers to dynamically personalise carts based on price, availability and dietary needs with just a click of a button. Reducing shopping time and improving customer satisfaction.

SLURP.AI leverages a model driven recipe parser to ingest both structured and unstructured recipe files. The parser is fundamental to dissecting a recipe for ingredients, measures, utensils and instructions. This parser uses the same unique, model driven technology as our market leading CatalogIQ solution (which can read and understand CPG product packaging).

Lastly, SLURP.AI can leverage its data mesh to help suggest complementary products. The use case here is in meal planning, when the consumer may want to know what courses (recipes/ingredients) go well together and/or to pair wine and beer with a meal/ingredient.

“As we advance the ability of the Intelligent Data Mesh to build relationships between different types of data, we’re finding new and unique applications for that information”, said Konety. “We believe that the grocery market is evolving quickly right now and SLURP.AI is a great example of the synergy between IceCream Labs and our grocer clients”

Leveraging AI-based retail solutions from IceCream Labs help to improve retail customer experience

We are evaluating partners for our upcoming SLURP.AI beta release. To get on the beta list for Slurp, go to https://slurp.ai/


About IceCream Labs

IceCream Labs is the only AI powered platform which provides on-demand Intelligent Merchandising solutions for e-commerce retailers, brands and marketplace sellers. We help you realize the maximum potential of your product catalog by boosting the quality of your product content to create an immediate impact on revenues and operations.

Several of the world’s largest retailers have benefited from our Intelligent Merchandising platform. Deep learning applications on the platform have delivered results with absolute precision, accelerating revenues up to 4X for our customers.

Our new catalog data quality platform consumes product data coming in through various sources. Applications on the platform continuously process and profile the content quality of over 100 million products and 50 million images empowered by our big data algorithms. This data is interpreted by our multiple patent pending Deep Learning models to cleanse, enrich and optimize your product content. The output of the models can be integrated seamlessly with your existing solutions to help you reach your business goals.

At IceCream Labs, we believe in the value of technology and its ability to disrupt traditional business models. Our Culture – like technology – is open and without any boundaries. We believe in the power of providing simplicity while managing the complexity behind it, by keeping our focus constantly on Innovation and Execution.

CONTACT:

Mike Oitzman

IceCream Labs

mike.oitzman@icecreamlabs.com

Web: icecreamlabs.com

Ph: 530-270-9466

###

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.

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The Rise of Conversational Commerce

The Rise of Conversational Commerce

Conversational AI is breaking the frontiers of retailer-customer interactions in today’s world. E-commerce used to be about selling products without offering customers a way express needs. However, conversations and personal interactions are essential in e-commerce. It helps retailers understand the customers’ requirements and offer customized products for them.

Today, about 5 billion users across the globe use messaging apps, with a fast-growing adoption rate. Social media and messaging apps are the preferred communication channels for millennials. Retailers can reach their customers by leveraging these messaging apps. Artificial intelligence is one medium that enables conversational commerce to instantly connect with customers. It helps engage and personalize communication to customers, thus driving sales for the business.

Conversational commerce is a two-way discussion between retailers and their customers through chat, messaging apps or voice technology, leading to a fruitful interaction that results in a value-based transaction. It allows retailers to create and nurture a relationship with their customers.

 

Enhancing customer shopping experience

Conversational commerce offers new avenues to connect with customers and improve the user experience.

Suppose a customer is planning to buy a Mother’s Day present, he/she would ideally step into a store and tell an associate about his/her requirement and get recommendations to buy an ideal gift.

Conversational commerce lets retailers take the learnings from the experience and automate the entire process. Retailers can integrate chatbots on their websites or use Virtual Personal Assistants to converse with customers through their online stores. From instantly answering questions to offering personalized choices, automation in commerce boosts interactions.

Additionally, conversational commerce can enable a follow-up experience for customers who abandon their cart. Instead of sending them an email that takes them through a long process to finish shopping, a direct message is a better approach. With a simple message, retailers can inquire whether the customer is willing to purchase the product, ask queries or requires to be reminded later. This allows the customer to take action and complete a purchase, all within the messaging app.

After a customer has purchased an item, retailers can notify about the shipment of the order, allowing customers to easily track their package. Once the order is delivered, retailers can connect with the customers to rate the overall experience, submit reviews or share pictures, with the click of a button.

 

Driving online sales

“Conversations are the driving force behind Conversions”

Customers want an easy and simple platform to purchase their desired products. Having a direct line of communication with their customers helps retailers make sure of sales. Conversational commerce is the way to engage customers at the point of sale to increase the rate of conversion. It opens the door for retailers to have a deeper interaction with customers during the crucial period between winning or losing a sale.

Conversational commerce reduces sales & support costs, overcoming the challenges of mobile browsing. It is a direct, personalized, dialog-driven approach to establishing long-term relationships, collecting data and increasing sales.

 

The purpose of conversational commerce is to provide personalization and convenience throughout a customer’s journey, from sales to service. It helps create a positive experience for the customer and earn loyalty. Moreover, it represents a customized online presence that lets customers request information they need rather than depend on browsing aimlessly through an online store.

Conversational commerce is a big deal as it represents a paradigm shift in the way retailers interact with their customers. It will have a huge impact on the entire customer shopping experience.

 

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Woman shopping for shoes

Retail Trends Prediction for 2019

Retail Trends Prediction for 2019

From online shopping expanding to social media to customer-controlled delivery and returns, 2018 saw an exciting time in retail which drove a massive shakeup in the traditional retail landscape. Besides instant gratification, today’s connected customers expect newer experiences and hyper-personalization in retail. As we enter 2019, retailers are increasingly faced with the challenge to push boundaries and meet the evergrowing expectations of their customers.

Here are 5 retail trends that will gain momentum in 2019 -

Customer Datasets

Data plays a crucial role in the growth of any business. Accurate data will serve as a vital tool to enable retailers to understand customer behavior and reward their loyalty. This data will further enable retailers to deliver personalized shopping experiences to each customer. Retailers can target product offers more effectively due to access they have to customer datasets, AI and Machine Learning. Closer customer engagement will provide insights that can be valuable for retailers who want to grow their businesses. Forming stronger connections will be beneficial for both the customers and retailers.

Cognitive Technology

Cognitive Technology has led to automation across different parts of the retail sector, and this is a trend that will grow exponentially. Retailers will leverage cognitive computing to provide better customer service technology that can analyze enormous amounts of data. Retailers who integrate cognitive computing in their customer service can offer faster service. Setting computers and robots that can actually understand natural language and respond to simple questions will free up employees to attend to more complex queries. Robots can work alongside humans in many areas of retail increasing efficiency and productivity.

Voice-activated Shopping

As of 2018, Amazon and Google have together sold about 27 million voice-activated devices in the USA. 29% of these customers are already using the devices to shop online, and this number is slowly increasing day after day. Customers also expressed confidence in the recommendations provided by these digital assistants. These devices have a large following that can’t fully embrace their shopping abilities due to the lack of visuals. Companies are combating this by adding screens to their voice assistant devices. For example, Amazon Echo Show allows its users to view the products on the screen which increases confidence in their purchase.

Shopping anytime, anywhere

Consumers today are on the move and are going to expect services that could be available in transit. Social commerce will gain momentum by providing users/customers more choices for making purchases on the go. Retailers will be able to do targeted marketing and offer new ways to make online shopping more convenient, social and instantaneous. Customers will be able to shop from their vehicles while commuting. New innovative social commerce solutions will also emerge throughout 2019.

Augmented Reality

Augmented reality is quite popular with many apps that offer users a chance to blend the digital with the real world. These interactive experiences are not just fun but also offer retailers an opportunity to use them in shops. 

For example, if customers want to buy furniture, it could be quite difficult to picture the store items in their own house setting. If they pick wrong, they could end up ruining the aesthetics and ambiance of a room. Augmented reality apps can place 3D models of furniture right inside the customer’s home.

The digital furniture is also resizable and observable in any light, from any angle. Customers can even save room designs for the future. There is a huge level of customer engagement that can be achieved through these apps. They will allow customers to buy the best products and consequently come back to buy more in the future.

These trends may prove useful by providing retailers with some foresight. The landscape of the retail industry is evolving at a fast pace, and with Gen Z driving seismic shifts in shopping behavior, 2019 can be a very interesting year 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

shopping cart filled with groceries in a supermarket aisle

What to Expect from Online Shopping in 2019?

What to Expect from Online Shopping in 2019?

Retail is changing at lightning speed and as we move towards the end of the year, as consumers begin anticipating what their shopping experience will look like in 2019. Retailers continue to evolve in a highly competitive world where delivery, customer experience, and convenience are the main factors that seal the fate of any store - forcing some into bankruptcy and propelling some into profits.

Here are five things to look forward to in retail next year, and most of them include technology:

More online grocery shopping

Despite having a small portion of consumers using online grocery shopping, industry experts expect digital sales to reach 20 percent of the total grocery market by 2025. Many retailers are partnering with third-party delivery companies such as Shipt and Instacart, enabling many consumers to order groceries from anywhere in a click or tap of a button. Soon, consumers will increasingly order online.

This includes both delivery and ordering online to pick up in store. It’s also expected that social media platforms like Instagram will continue discovering new ways to convince consumers to buy online.

Voice Retail

Experts say shoppers will increasingly pick up voice shopping through smartphones, Amazon devices, and vehicles.

Consumers with Alexa-enabled devices are already able to purchase their groceries, home goods, and gifts through Amazon and Whole Foods Market. But other retailers are starting to get in on the action.

Kroger recently announced plans to roll out voice ordering through Alexa-enabled devices and Amazon has released software that allows developers to integrate Alexa in vehicle infotainment systems.

More private labels

 Private labels have proven successful in the eyes of consumers this year. Dozens of retailers including Target, Kroger, Walmart, Aldi, and Amazon have expanded private label offerings this year.

Private labels are notorious for adding exclusivity that builds customer loyalty, all while keeping profit margins high without suppliers taking their cuts. Many of the retailers have passed the savings to the consumer with low-cost private labels that are increasingly growing in popularity.

Growth in artificial intelligence

Retailers have used artificial intelligence to learn consumer and market habits. The technology becomes increasingly beneficial for online retailers looking to upsell without a physical salesperson. Different subscription services like Stitch Fix and Kidbox have used AI to analyze subscriber data to recommend products that increase relevance and are more likely to be purchased.

Retailers are trying to use AI to expand holiday shopping earlier as well, learning what consumers will want most around the holidays as early in the year as possible. The intelligence can help spread out orders so delivery systems won’t become as congested close to the holidays.

More interactive aisles

As consumer shopping habits shift to favor experience, retailers are scrambling to find ways to draw crowds into stores. In 2019, augmented reality and virtual reality are likely to take a stronger foothold in all types of brick-and-mortar stores.

 For example, Kettering-based Marxent has developed augmented reality technology for Macy’s to show how furniture could look without having to purchase the items.

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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. 

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view of a grocery aisle

Technologies shaping the Supermarket of the Future

Technologies and Trends shaping the Supermarket of the Future

Food businesses will have to change to stay competitive – online, in-store, and at sorting and processing plants too.

The technological boom and the increasing adoption of Industry 4.0 among retailers are creating disruption across all industries. This change is coming to supermarkets which will have an immediate impact on the entire food industry supply chain. Technological innovations – both online and in-store along with the shifting consumer demands will re-shape the supermarket of the future.
Traditional brick-and-mortar supermarket chains are strengthening their own e-commerce capabilities to stay on par with their digitally native competitors. The global grocery e-commerce market is forecasted to expand from an annual value of 43 billion pounds to 135 billion pounds by 2025.

Another aspect that e-commerce players must note is while they are making efforts to establish a strong foothold in the US and European markets, they may face serious challenges because the existing grocery market is saturated and the margins are low. This indicates that the global growth in food e-commerce will be driven by Asia, where there is a willingness to purchase groceries online, along with rapid urbanization, low labor costs, and a newer retail market.

Rising consumer expectations

Widespread food shopping online and fast deliveries to customers’ front doors will only just be the tip of the iceberg in the new world. Computer codes and algorithms will further enable supermarkets to collect data about shopper preferences and habits and use this to personalize their offerings to customers. Recommendation engines further help nudge customers to make purchases similar or related to the products that they have already purchased or been looking for via the “Recommended for you” web pages.

The growing number of people with moderate incomes and lifestyles will become more aware of food safety and more curious about how their foods are sourced and screened. Moreover, food shoppers will develop higher expectations and become critical when buying fresh fruits and vegetables. More will want to know how fresh the produce is and whether or when it is ready to eat.

Consumers will further have the ability to check information about the origins and nutritional value of produce and will be able to see suggestions for recipes and food pairings. This will attract a greater number of customers while making each feel as if they are being provided with individual shopping experiences.

The ad-hoc demand created through the online ‘nudge’ will challenge the traditional food supply chain. Processing lines will need to know precise details about the food – where it is coming from and what is in the storage to meet the demand.

Technology to ensure quality and safety

Grading and inspection equipment – at point-of-origin, prior to shipment to the supermarket, or from the on-line dispatching warehouse – can ensure that the fresh produce has the desired size and ripeness without bruising or mold. In addition, sorting equipment at different stages in the supply chain will be able to provide essential information on sizing, quality and other quality markers.

Traditional supermarkets fight back against the online disruptors – and information about shoppers’ preferences and habits will be an important weapon. Consumer-facing technologies, such as shopping-cart-mounted devices or smartphone apps, will steer shoppers towards the aisles and shelves where they are more likely to make purchases. Sensors in the store’s shelves will keep track of the items customers put in their carts and bill their mobile payment system as they exit the store.

Looking ahead

Another likelihood is that supermarkets will remain the same size but change in concept, becoming destinations for click and mortar shopping. Retailers need to offer consumers a consistent omnichannel experience, stores will connect the physical and digital worlds. Here, consumers can see and feel products they might order online. Here, too, the online product offering could also be accessible via interactive screens.
These changes align with the forecast growth in consumer demand for healthier, high-quality produce, more choice, and greater convenience – a demand which will increase massively as household incomes rise in developing nations, bringing 70 million more people globally every year.

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woman in store window looking at cellphone for product matching

Tackling Product Matching for E-commerce with Automation

Tackling Product Matching for E-commerce using Automation

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.

Here's when product matching becomes extremely important. 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.

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.

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.

Product matching - Identify and Sort

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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