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Fresh Produce on a Table

The Impact of AI on Grocery Retail

The Impact of AI on Grocery Retail

In today’s age, grocery retailers no longer have to make guesses about what customers will buy; they can leverage artificial intelligence (AI) to predict customer shopping patterns and optimize their spending through attractive price and product promotion along with effective inventory management to meet the needs of their customers.

 

While AI in many forms has been available for several years, the coming few months could mark a tipping point in which the most compelling use cases for the technology will emerge. Grocers need to understand the behavior of the customers browsing through the aisles. If you think about it: while shopping for food, you don’t select random products but instead gather what you need to make whole meals, even if it is only pouring cereal and milk into a bowl for breakfast. Grocery retailers can capitalize on AI by deciding their business goals and then adopting the right technologies to better achieve them.

 

Here are some of the ways in which technology can impact a mundane chore such as grocery shopping:

 

 

Product Discovery

Product displays are often perceived as an art in retail, but AI brings a scientific element that will make grocery displays far more successful. By using the right approach to data analysis, AI can predict what customers would like to see in the product display, based on their purchase history. This ensures that you promote the best mix of products every week, thereby increasing discoverability and sales.

 

Also, it’s importance lies in being able to avoid overstocks on the wrong items by providing insight around product mixes, revenue, and profit margins, among other possibilities.

 

 

Smart Inventory and Intelligent Replenishment

Throwing out leftover food at home is wasteful but if it happens at the warehouse level in a business, that is a direct blow to the bottom line of the business. This is where the use of machine learning algorithms for grocery retailers’ inventory comes into the picture. Machine learning can analyze certain trends in spending behavior to predict future sales. It can also help in standardizing data for better clarity in inventory management.

 

You could create a workflow empowered with AI-driven automatic notifications about what needs to be restocked and when exactly to do it. This is possible due to advances in demand forecasting that gets constantly updated with real-time information. This results in fewer stock-outs, reduced waste and much greater profitability for grocery retailers.

 

 

Futuristic Smart Shelves

The idea of a digital shelf in retail has evolved over the past few years as technology has changed but AI can do more than the simple identification of the product on a shelf and its price.

AI can give customers more information about nutritional values, ingredients, recipe ideas and much more.

Additionally, product recommendations and other vital information can be customized to individual customers based on data they’ve shared with the retailer.

 

We have seen that AI is enabling grocery retailers to rapidly embrace innovative and compelling capabilities to optimize inventory, product discovery and smart shelves.

For grocery retailers, AI will discover new ways to drive profits higher, increase revenue and have a significant impact on operational efficiencies. In an industry that has traditionally operated with low-profit margins, AI is a breath of fresh wind, promising to enable greater net income growth.

The grocery retailers, however, are merely scratching the surface when it comes to AI. It remains to be seen how aggressively the industry will continue to explore AI to analyze and improve how they operate.

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

Digital Merchandising for Grocery Retail: How can it help boost sales

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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unsupervised learning represented by a mixed bowl of colourful candies

Primary Methods of Unsupervised Learning

Primary Methods of Unsupervised Learning

There are a variety of ways to create a new machine learning model. Supervised learning is the simplest of these learning processes, but it requires human input and curated data sets. For a supervised learning process, you classify data with labels, then build a machine learning (ML) model around it. This ML model can then be used to classify new data in real time.

But what if you only have unclassified data (i.e data without any labels)? Is it possible to train a model with a data set like this? Can this be done without human curation?

Yes, leveraging unclassified data sets for model training is known as “unsupervised learning”.

What is Unsupervised Learning?

Unsupervised learning is also known as self-organization. It is a machine learning process that uses an algorithm for datasets which are neither classified nor labeled. In unsupervised learning, algorithms are allowed to act on data without guidance and they operate autonomously to discover interesting structures in the data based primarily on similarities and differences.

Let’s take a look at two of the most popular clustering and anomaly detection methods in use for unsupervised machine learning algorithms.

Types of Clustering 

  1. K-means clustering

  2. Hierarchical clustering

K-means Clustering

K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (data without defined categories or groups). The goal of this algorithm is to find groups in the data. It is intended to partition “N” objects into “K” clusters in which each object belongs to the cluster with the nearest mean.

Algorithm

The Κ-means clustering algorithm uses iterative refinement to produce a final result. The algorithm inputs are the number of clusters Κ, and the data set. The data set is a collection of features for each data point. The algorithm starts with initial estimates for the Κ centroids, which can either be randomly generated or randomly selected from the data set.

          Clustering data into K groups where K  is predefined

  1. Select K points at random as cluster centers.
  2. Assign objects to their closest cluster center according to the Euclidean distance function.
  3. Calculate the centroid or mean of all objects in each cluster.
  4. Repeat steps 2 and 3 until the same points are assigned to each cluster in consecutive rounds.

Choosing K

In general, there is no method for determining the exact value of K, but an estimate can be obtained by finding an “elbow point”. Increasing the number of clusters will always reduce the distance to data points, i.e. increasing K will always decrease this metric. This metric cannot be used as the sole target because when K is the same as the number of data points, then the metric approaches zero. Therefore, it is ideal to plot the mean distance to the centroid as a function of K. Then identify where the rate of decrease sharply shifts (i.e. the "elbow point"), and use this to determine K.

Hierarchical Clustering

Hierarchical clustering is an algorithm that groups similar objects into groups of clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar. For example, the organization of the files and folders on your personal computer is a hierarchy. Stepping into each of these folders will reveal more folders and files.

Working of Hierarchical Clustering

  1. Start by assigning each observation as a separate cluster.
  2. Find the clusters that are closest together.
  3. Merge them into a single cluster, so that now you have one fewer cluster.
  4. Repeat steps 2 and 3 until all items are clustered together.

Types of Hierarchical Clustering

a. Divisive

b. Agglomerative

a. Divisive

In divisive (top-down) clustering method we assign all of the observations to a single cluster and then partition the cluster into at least two similar clusters. We proceed recursively on each cluster until there is one cluster for each observation. Divisive clustering is conceptually more complex and thus, rarely used to solve real-life problems.

b. Agglomerative

Agglomerative hierarchical clustering (bottom-up), is a clustering method where we assign each observation to its own cluster. Agglomerative hierarchical clustering starts with every single object in a single cluster. Then, in each successive iteration, it agglomerates (merges) the closest pair of clusters by satisfying some similarity criteria, until all of the data converges in one cluster.

To determine the closest pair of clusters, the distance between each point is calculated using a distance function. These distances are generally called linkage between the clusters. There are three methods to determine the distance (linkage) between the clusters.

i. Single LinkageIn single linkage hierarchical clustering, the distance between two clusters is defined as the shortest distance between two points in each cluster.

ii. Complete LinkageIn complete linkage hierarchical clustering, the distance between two clusters is defined as the longest distance between two points in each cluster.

iii. Average Linkage

In average linkage hierarchical clustering, the distance between two clusters is defined as the average distance between each point in one cluster to every point in the other cluster.

Final Thoughts

Leveraging unsupervised learning to generate a machine learning model is now an accepted and feasible process to operate on unclassified data sets. While it’s more complex to set up and tune an unsupervised learning process, the benefit is that the source data does not have to be curated by a human curation team. This is a beneficial process when it’s not feasible or economical to curate the source learning data. In this article, we’ve outlined the core clustering and anomaly detection methods which are used to set up an unsupervised machine learning algorithm. We use unsupervised learning at IceCream Labs as one of the many machine learning processes for our Intelligent Data Mesh at the core of our solution.

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brick and mortar store front

The Path to Upgrading Your Brick and Mortar Business

The Path to Upgrading Your Brick and Mortar Business 

Brick and mortar retail is often overlooked in the age of digital transformation. Many believed that the digital age would spell doom for physical stores. The “retail apocalypse” predicted the end of the brick and mortar retail model. However, this is not turning out to be the case, and a vast majority of shoppers still want to engage with retailers in a brick and mortar setting. A physical store gives customers the opportunity to get a feel for the products they are buying. It also presents retailers with a chance to provide customers with an unforgettable experience. Customers today want personalized shopping experiences.

Having a brick and mortar presence is also a good way to attract new customers. This is illustrated by the wave of innovative and trendy new retailers like Bonobos and Everlane who started off as online retailers but are looking to expand to physical stores across the country.

Omnichannel Experience

It is a mistake to believe that retailers are either only online or offline. Successful retailers today are operating across multiple different channels. The ability to deliver a highly engaging experience across all channels is the holy grail for retail success in the digital era. Brick and mortar form the cornerstone of this sophisticated omnichannel model of retail. However, physical stores must be able to provide customers with a multidimensional experience that touches all of their senses and enables them to connect with brands.

This retail experience begins the moment a customer enters the store for the first time. Customers intuitively react to the lighting, cleanliness, organization and flow of the store. Getting the physical design of the store right is crucial. So is the way products are arranged and displayed. Retailers today are also experimenting with technologies such as Augmented Reality(AR) and Virtual Reality(VR) to provide unforgettable in-store experiences to their customers.

Artificial Intelligence and Machine Learning

Artificial Intelligence(AI) and Machine Learning(ML) may sound like futuristic technologies, however, the reality is that these technologies are being widely applied in retail too. For example, AI and ML are already helping retailers make smarter choices when it comes to preventative maintenance. AI systems are used for product tagging and management, enabling retailers and employees to keep track of important products through a network of sensors. These technologies have changed the way retailers operate their business by enabling them to be able to understand what’s going on at all of their stores from an operations perspective.

There is no doubt that the brick and mortar retail model is here to stay. However, things are changing and the status quo is being disrupted. But this is only for the better. The importance of technology remains crucial for the success of any brick and mortar store. That is why you must understand and fully embrace the new technologies that can bring your business to a brighter future.

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woman scanning barcode in a warehouse

Top Retail Trends in Supply Chain Management

Top Retail Trends in Supply Chain Management

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


Artificial Intelligence (AI)

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


Immersive Technologies

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


3D Printing

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


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

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

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Two girls taking a selfie

Advantages of Leveraging User Generated Content for Retailers

Advantages of Leveraging User Generated Content for Retailers

Customer shopping habits have changed rapidly over the past decade. Customers today are looking for retailers that they can connect and engage with. Retailers must also change their marketing methods to interact with customers in an organic manner and provide an authentic brand experience.

One of the methods that is gaining popularity is the use of user-generated content (UGC). UGC is social media content created and shared between customers. Consumers want to be recognized for their input and as a result, retailers can benefit from the online discussion of their products. UGC can allow retailers to engage with their customers before, during and after a purchase. It provides customers with opportunities to promote the brands they like.

UGC in Marketing

UGC goes beyond mere likes or shares on social media. Customers often upload well-captured photographs or videos of the product. This content may even include comments and feedback about their experiences while using the product. Customers also leave reviews or offer recommendations of products they’ve purchased and used. UGC lets existing customers speak to future customers.

Given a choice between believing an advertisement by a retailer or a recommendation by another person, consumers tend to choose the latter. In fact, 93% of the consumers feel that UGC is helpful when making a purchase decisions.

Customers are continually bombarded by advertisements, but the impact is especially powerful when a customer feels a connection with another customer's experience. UGC works best when the message is real and authentic.

For example, cosmetics retailers benefit when their customers share pictures while wearing their products. Make-up artists are very popular on Instagram and they endorse the cosmetics brands they like. Followers of these make-up artists see these recommendations as credible and as a result, they are more likely to buy based on the recommendations. This user generated content showcases how the product works with a practical example, and without a heavy sell by the manufacturer/retailer. The result is that the credibility of the make-up artist is translated to the product and subsequently to the manufacturer/retailer.

UGC in Customer Retention

As a retailer, your job is to attract customers. However, it is equally crucial for you to nurture existing customers. How often are you creating rewards or special offers based on your customers purchase history? Have you considered utilizing UGC to showcase related or compatible products to existing customers?

You've made a big investment in attracting and nurturing your customer base. Don't lose it!

  1. Find UGC for your products where ever it exists and get it in front of new customers. Let your existing customers and fans do some of the hard marketing work for you. 
  2. Use social media to create an online community where users can discuss the products they’ve bought and offer recommendations to each other. Repost the best content on your official channels.
  3. Encourage customers to discover and share multiple uses of a product. Use UGC to showcase the versatility of your products. This retains old customers while drawing in. Retailers can also hold events like sales to boost customer interaction.

Artificial Intelligence in Curation of UGC

UGC is a key element of a successful marketing plan. With the growth of social media, content is being created every second. This can become an invaluable resource. The problem is that most of this content is uncurated as it's published. 

Learn to leverage Artificial Intelligence (AI) to augment your marketing team. You can employ AI to curate UGC and use it for your benefit, by helping your human curation teams to curate faster and with higher accuracy. A side effect is that you can quickly become aware of product issues or bad publicity from a user review, and avoid the business risk associated with that. New ways of employing the content are coming out as the digital channels continue to diversify. You must curate UGC if you want to harness its power.

Trust is key. The conversations initiated by UGC can boost your bottom line. Nurture and reward your best content creators, and you'll see your customer base grow.

Of course with a growing customer base comes increased brand awareness, customer reach, and revenue.

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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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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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