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Top view image of an assortment of beauty care products representing digital merchandising

Leveraging digital merchandising to elevate grocery retail

Leveraging digital merchandising to elevate grocery retail

Merchandising is a core skill for both online and offline retailers. Merchandising is the skillful presentation of products in order to promote sales.  In the case of brick and mortar, merchandising mainly revolves around store displays in combination with assortment planning, packaging, pricing and offers, all done to entice customers into making purchases. 

But what is it about digital merchandising that grocers can leverage to impact sales? 

To begin with, utilizing tools such as cross-sell, they can easily set up basic online catalogs. Early themes and new virtual categories are then added to help as a guide for customers to navigate through large selections to find what they want.

What makes digital merchandising stand apart is its ability to generate data that gives a true insight into the customers’ shopping behaviors. It further enables retailers to track what their customers want and how they want it, scaling merchandising as a concept to new heights.

Why is Digital Merchandising Important for you?

Digital merchandising essentially mimics the in-store merchandising environment, only using a different set of tools to promote the sale of their products. Here, customers can understand more about the product without being constrained to its physical limitations. Digital merchandising allows grocers to impart more knowledge about the product via storytelling and more information about its usage. For grocery retail, digital merchandising can display several pieces of information including meal planning, complimentary food products, etc.

Here are some areas that digital merchandising differs from a brick and mortar setting:

  • Flexibility: Online content including digital imagery can be personalized at any time, unlike in-store displays which depend on store labor to manage.

  • Accessibility: Customers can access online content from anywhere such as from mobile phones or computers, and at any time. They are not restricted to the store timings and can do shopping right from their fingertips.

  • No Shrinkage: In the case of digital merchandising, replacing physical products with digital imagery eliminates the shrinkage that occurs with merchandising perishable products in the store. This way, grocery retailers can show the products the way it is meant to look like and are not restricted by the packaging of the products

How can Grocery retailers benefit from digital merchandising?

Digital merchandising is an essential part of a grocery retailer’s toolset.

Currently, grocery retailer websites showcase products by displaying rows after rows of individual images of products taken against a white or light background following up with a flashy introduction page. The challenge does not end here, grocery retailers must move beyond creating a product catalog.

For example, leveraging digital merchandising, grocers can efficiently market perishable products.

Real-time recommendations can encourage customers to buy items that have shorter shelf lives, thus enabling them to improve margins on perishables. Furthermore, they can elaborate on the products by educating the customer about where it comes from, who grows it, and how it can fit into a meal plan. The information does not end there, customers can even learn about health benefits and food preparation via video.

Connecting digital merchandising with your customers’ needs

Digital merchandising can help create environments to suit customer needs and interests. The advantage is the ability to understand customer behavior and even predict it to a certain extent. When the holiday season is in full swing, many grocery retailers out there would immediately pivot their marketing efforts toward ovens and bakeware.

If a customer has never bought or consumed a turkey, then the holiday theme can be centered around another protein. Also, selling salads next to raw meats in a store may be a problem, but online, they can be easily combined to create a meal plan or even a recipe!

Visually appealing product imagery already sets your product apart from that of your competitors’. That being said, the imagery alone cannot grab your customers’ attention. It needs to be followed up with a story that educates them about the farm that the produce is sourced from, the nutritional value of the meal and even recipes it can be used in or the story of the chef who came up with it. Social media can play a massive part here to help spread the word about the product as well as their journey in your online store. All of this information is to be organized in such a way that your customers can access it from one page. Finally, the online aspect ties to offline to the actual products that are delivered, making this a cohesive experience for your customer.

Conclusion

Content is clearly the king when you want to tell a story and connect with your customers. Digital merchandising takes into account how and why a customer will choose or like a particular product. Personalization is also another great opportunity presented by merchandising. Remember who forms your target audience while leveraging advertising. Grocery retail is all about selling ordinary products in the freshest and best way possible and we are here to help you elevate that by leveraging Digital merchandising.

Would you like to know more about us and how our category and catalog management solutions can your business? Click here to know more. 

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Shoptalk banner for Icecream Labs at the Startup street ss36

Shoptalk: Join IceCream Labs at retail’s community of innovators

Join IceCream Labs at retail's community of innovators: Shoptalk

IceCream Labs is going to be a part of Shoptalk 2019 which will be held on March 3 - 6, 2019, at the Venetian, Las Vegas.

At the world’s largest retail conference, ShopTalk, the entire retail ecosystem comes together to create the future of retail based on the latest trends, technologies and business models, including changes in consumer expectations.

Everyone who’s anyone is at Shoptalk! 8,000+ individuals attend Shoptalk each year from established retailers and brands, startups, tech companies, investors, real estate operators, media, Wall Street analysts and more - coming to learn, collaborate and create the future of retail with four days of relevant content, curated meetings, productive networking and facilitated social engagements.

Shoptalk covers the latest technologies, trends and business models as well as the rapid transformation of how consumers discover, shop and buy everything, ranging from apparel and electronics to grocery and luxury.

With over 100 groundbreaking sessions across more than a dozen tracks with important insights and perspectives shared by an unparalleled group of leaders and innovators, Shoptalk’s agenda leads the retail industry narrative.

Retailers or brands can also learn from peers in small roundtable settings by joining Tabletalks group discussions with the opportunity to improve knowledge of new technologies by joining the Hosted Retailers & Brands Program as well as Techtalks (open to all).

Thousands of individuals from hundreds of retailers and brands around the world attend Shoptalk every year to strengthen their knowledge of retail’s latest technologies, trends and business models as well as to collaborate with peers, startups, tech companies, investors, real estate operators and others in an open, friendly environment. Furthermore, more than 1,000 direct-to-consumer and tech startups redefining retail and ecommerce attend Shoptalk each year from major global hubs. Shoptalk provides startups with an unparalleled opportunity to form important fundraising, product, distribution and other partnerships with retailers, brands, tech companies, investors, real estate operators, media, analysts and more.

Join IceCream Labs at the Startup Street #SS36 to see some exciting AI-powered merchandising solutions for retailers.



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.

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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

###

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.

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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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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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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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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street view of a store front in LA

Transforming the Retail Customer Experience with In-store Analytics

Transforming the Retail Customer Experience with In-store Analytics

While online retailers have the advantage of tracking cookies and web analytics tools to calibrate different aspects of an online shopping experience, brick and mortar retailers aren’t as lucky. They have had to depend on much erratic customer insights.
However,in today’s date, even physical retailers are required to hold up to some very high expectations in shopping experiences.

In fact, in order for physical stores to remain relevant, they have to focus on improving the quality of the experience they deliver. This has led to the creation of entire businesses in retail experience innovation.

One of the ways a brick and mortar retailer can provide a quality experience is through the employment of in-store analytics that provides insights into the behavior of the customers and uses that information to engage with their customers as they shop. The use of in-store analytics has revolutionized how retailers understand their customers and how they communicate with them.

How will in-store analytics build up communication between shoppers and retailers?

In order to answer that question it is important to know how In-store analytics works.
For example, when a furniture store to offers free Wi-Fi, it may seem a bit strange.
Actually when the Wi-Fi is enabled on one’s phone, the device sends out a connection request every few seconds on every Wi-Fi channel available. It updates the list of the available networks after listening for a fraction of a second for a response to come back,

Interestingly, when a device probes the Wi-Fi spectrum, it broadcasts its unique MAC address to any device that’s listening. So, as one walks around in that furniture store, every Wi-Fi probe then acts as a beacon for the location. With multiple Wi-Fi access points available inside a single store, it becomes possible to considerably precisely locate each address. As far as the owner of the device is concerned, this happens passively without having to actually join a Wi-Fi network.



Although nothing about a device’s owner is being shared, the retailer can build a picture of what individuals do as they walk around a store. Such as, the number of customers who went to the first floor, the time people tend to spend in a particular region, the waiting period of customers before they come back to the shop.

This aids in understanding the broader shopping habits and interceding with informed in-store customer communication. Instead of having communication with customer transpire at the convenience of the retailer, it can happen at the customer’s convenience. 

Sending an SMS to inform of a sale as an effective marketing tactic. Sending emails every month or even good old direct post may increase customer movement towards a local store. However, a more customer-centric communication of a timely WhatsApp message offering assistance when the furniture store operator gets to know that the customer has spent over 20 minutes in the dining table department.

MAC address tracking to deliver a more personalised Customer Experience


Anonymously tracking a MAC address results in a more personalized customer communication and in understanding individual behavior in the retail experience. As the data increases, the MAC addressing question ceases to just be a randomly generated number and instead represents the behaviors of a real person. At this stage, there’s nothing to identify the individual who owns the phone but it’s possible to build a picture of who they are.

Whether gathered in multiple locations or over a longer time period in just one location, as the data builds it becomes useful in crafting more personalized communication, which can help increase sales and enhance the customer experience.

Relying on anonymized data can deliver only so much, though. And that brings us back to why the furniture store offers free Wi-Fi. As soon as someone signs up for that Wi-Fi, the store can associate the MAC address with whatever data they capture in the sign-up process. At the very least, that’s likely to be a name, email address and cellphone number. Again, that person never has to use the Wi-Fi: as long as they keep the same device, their MAC address and identity are linked.

Other retailers might not rely just on free Wi-Fi. They might have a loyalty or coupon-based mobile app that requires users to provide some personal data. Depending on the phone’s operating system, that app might be able to access the MAC address itself and make the connection for the retailer. Either way, retailers can incentivize shoppers to make their MAC address personally identifiable. And when that happens, communication can truly become personalized.

Respecting the shopper’s personal data

Either through inertia or without realizing it, most people publicly surfing the web are constantly being tracked. Sure, there are some loud voices of complaint but the vast majority of people accept it or don’t care.

As the company behind smart recycling bin advertisements in London and Nordstrom in the US discovered, people are less keen to have their physical location tracked. Even if it’s only an anonymized MAC address, such tracking could feel intrusive.

A value exchange for a richer retail experience


The answer, perhaps, is to take a tip from the loyalty schemes of large retailers: provide a genuine benefit to customers in exchange for gathering valuable data on their habits. Just as loyalty schemes such as Air Miles and Tesco Clubcard offer coupons, cashback, and exclusive store events, retailers can build similar value into retail location tracking and analysis. Rather than silently track customers, they can volitionally opt in to a mobile-phone enabled rewards program when they enter the store-a loyalty scheme for the 21st century.

Location tracking has the potential to transform how retailers communicate with their customers. It will provide the insight to know precisely when to engage and when to leave someone alone. However, it will work only if customers can see a tangible benefit to giving up some of their privacy.

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