customer picking out clothes from a shopping rack

Enhancing the customer experience beyond shopping

Enhancing the customer experience beyond shopping

With consumers changing how they purchase and engage with a brand, retailers have leveraged technology to re-engineer stores and experiences. The opportunities that technology such as AI and machine learning has brought forth has been tremendous, as are the business benefits for the retailers that are deploying them. This can be especially seen in the case of the grocery retail industry. AI has enabled brands to completely rethink and reinvent  the store, and to continuously entice and engage the new wave of tech-savvy shoppers. 

In the near future, it would come as no surprise if stores have AI-powered chatbots assisting shoppers with the location of their desired products. Taking customer service to a whole new level has been in discussion for years, but now the time has come for action. AI and machine learning technologies can help shoppers but benefit the staff as well. Hease Robotics has rolled out mobile digital kiosks in retail locations throughout Europe, enabling 20 times more interactions than stationary kiosks. With these kiosks, 'Heasy the robot' can scan a customer's loyalty card and show deals relevant to a shopper, as well as point customers in the right direction. The robot provides business benefits too, collecting data that can identify shopper pain-points and ultimately help businesses increase revenue.

The further development of self-service is also inevitable, and essential, as the checkout and paying process remains the most time-consuming aspect of shopping in the physical world. The future is in automation, and it is inevitable that the checkout process will become nearly entirely robotic. Eliminating shopping and queue times may seem like small initiatives, but given that the average person will spend nearly two-thirds of a year (235 days) waiting in queues over the course of a lifetime, these small knock-on time savings add up to big differences.

AI technologies don't just bring benefits to customer service; they can also help improve profitability in other ways. One is helping retailers set better, more competitive prices. There are many factors to take into consideration with product pricing. Machine learning, the core element of artificial intelligence, can analyse massive amounts of data quickly and correctly. Foxtrot, an omnichannel consumer electronics retailer, used AI-driven price optimisation software to do just this. As a result, the firm's revenue, and volume of transactions, increased by 16% and 13.6% respectively.

The multi-channel maze

While bricks-and-mortar stores and online operations bring their own benefits and pain points, an omnichannel strategy can help retailers boost their business. It ensures that companies align their channels, and recognise that customers shop in a range of different ways.

Having a multi-channel presence and joining them up means better opportunities to cross-promote. For example, online can showcase offline with virtual tours and images, whilst also providing helpful information like opening hours, events, etc. Offline can utilize online for in-store ordering and facilitate functional benefits like click and collect.

Superdry is one example of a retailer extolling the benefits of a customer-facing multichannel strategy, having made concerted efforts to join up various channels to better track products and orders. This has allowed customers to combine convenient online shopping purchases with visiting shops that provide inspiration and experience.

Superdry's multi-channel strategy has been enabled by ‘invisible', sophisticated technology. The company uses radio frequency identification tags to track stock and make sure that there are always plentiful supplies on the shelves. This helps ensure that enough products are available for shoppers. In addition, Chip and PIN iPads, available in all of their locations, eliminate queue times, as buyers can pay at any location in a store. They can also order anything out of stock for home delivery.

It's all about you

Retail has changed from being product-focussed to customer-focussed, and shoppers now want an experience that goes beyond shopping. Creative an immersive retail experience can be powered by augmented reality (AR), which enhances what a customer sees and experiences within a retail environment.

One example is Ikea Place, an app launched in 2017 that allows users to place virtual Ikea furniture into their own home to see how everything might look once assembled. The app is 98% accurate in scale, rendering 3D images to react to light and shade. This gives consumers a much more realistic portrayal when imagining new purchases in their home.

Beauty retailers are also recognizing the importance that AR is going to play in shopping. Leading make-up firm Sephora launched Sephora Virtual Artist, an app developed in partnership with AR company ModiFace that scans your face, figures out where your lips and eyes are, and lets you try on different looks. Users can buy any ‘looks' they like, and also benefit from virtual tutorials.

Augmented reality is still unique and surprising – but the brands that start to embrace its benefits will also be set to reap them. To create a sustainable strategy that will pay dividends, the technology should be implemented long-term – rather than just being used as a flash-in-the-pan marketing stunt.

The underlying technology

Underpinning these technologies and tools will be a reliable, resilient network that facilitates and supports them. Retailers must have a robust IT infrastructure that supports their variable business demands — whilst keeping costs down, to avoid eradicating profitability. This can be achieved by selective outsourcing, which will enable brands to focus in-house resources on value-added activities and help drive a competitive advantage.

Data maintenance is also essential for businesses to consider. As businesses gather more and more customer information in today's post-GDPR implementation landscape, they need to protect that data from network breaches. They must also maintain the intelligence to use certain information, like customer data, for certain things such as consented contact. Converged managed service providers can play a key role in the protection of this information, as well as ensuring that systems are operating at an optimum level.

Downtime will be crucial for retailers to minimize — and businesses must be mindful that this can strike in a number of ways. If a shopper can't process an online order, they will likely turn to a competitor to purchase from instead, given the wealth of vendor choices on offer. Similarly, if buyers are faced with shopping inconveniences, they are unlikely to buy from that brand again. In essence, anything that results in poor customer service will alienate customers — possibly forever.  

In order to survive in today's challenging high street, be it physical or online, retailers must create an experience that goes beyond shopping. They must also place equal priority on the systems that underpin these initiatives — or risk business failure.

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customer using loyalty card for paying

Importance of AI in customer loyalty

The expectations of consumers today from their favourite brands has increased ten-fold, and with big companies such as Amazon providing more personalised experiences tailored to individual user interests, have disrupted customer expectations. 

The loyalty aspect of the business is no exception. Traditional loyalty programs wherein consumers collect points to earn special rewards, offers or discounts has become universal. Brands now need to leverage new technologies to provide an experience that their customers expect or risk them losing interest. 

Here is when artificial intelligence (AI) comes into play. AI is paving the way in providing new opportunities for brands to offer to their customers with hyper-personalised experiences, leading to greater customer loyalty. 

Increase in AI involvement

From data-mining to powering intelligent insights to automating manually-intensive processes, organisations have seen the potential that AI can bring. 

The variety of potential uses of AI is vast. The companies that invest in AI technologies to specifically improve customer experiences. 


Role of AI in loyalty

There is a lot of data on the internet. Every activity done by users generate data. Organisations, therefore, can obtain a lot of useful data about their customers. For instance, there are different data points that Amazon collects about their customers -

  • The frequency of customers logging on and when
  • Their browsing history
  • The frequency of their purchases
  • The time of day of the purchase
  • What they purchase


Collecting and categorising these pieces of information as well as storing it in the right place for a human would become an arduous and a time consuming task but not for machines. Through automation, AI can easily complete these tasks. Furthermore, it can even gain insights for the data.


This combination of AI and big data provides exactly what is needed to take customer experiences to the next level. Insights can be drawn and used to drive loyalty through greater personalization such as recommendations based on customer lifestyle, interests and activity.


Enhanced customer insights

Developments in AI have now made it possible to analyse this data to provide insights into users lifestyles and interests. AI can teach a mobile device to analyse its on-device image gallery to produce insights about its likes and dislikes, their desires, and their intentions for the future. Photos become a data source that can be used to align your customers with what your loyalty scheme offers.


AI as a differentiator

Metadata and understanding visual information will be the main weapons in the battle for customers’ attention. By leveraging visual information from customers’ photos, it provides experiences and recommendations that are personal, targeted and exactly what customers desire.

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Man with a checklist

What is GS1 Verified?

​Live from the GS1 Connect 2019 Event

With the GS1 Connect 2019 event happening this week in Denver, Colorado, we think that it’s important to review the core principles of GS1 and how IceCream Labs is contributing to the success of GS1 and the companies who leverage GS1 standards in their business. This article will recap the show and introduce the technology that IceCream Labs is announcing to support GS1 standards.

Making the Pitch in a New Venue

IceCream Labs is competing in the startup pitch competition this week at GS1 Connect 2019​. We have a booth on Startup Row at the event and are thankful for the invitation to compete as one of eight startups participating in the GS1 Startup Lab and Pitch competition. The GS1 Connect event is one of the first events to take place in the brand new Gaylord Rockies Convention Center, and the event is bigger this year than ever before.

​Our CEO, Madhu Konety was live on stage with the other Startup Lab CEO's competing in the startup pitch competition.

GS1 Connect Startup Lab Participants

Image Courtesy of GS1 Connect Startup Lab

Quality Data is the Core of the Verified by GS1 Program

Accurate product data is key for every step of the supply chain process. Consumers expect that the information which they see on a product description page accurately describes the product they are about to purchase. Since an online consumer can’t touch the physical product that they are considering, the online product description page must present all of the information that the consumer needs to make their decision. This data includes all of the images, video and text which describe the product.

One side effect of online retailing that we’ve observed is “Attribute Creep” and growth in the total number and variety of product attributes displayed for consumers. Product images and videos also complicate the process of sharing information along the supply chain. Media files have historically been difficult for merchandising teams to version and track. IceCream Labs technology enables the merchandising team to verify the content in a product image.

All along the supply chain process, there are a variety of expectations which define the product attributes necessary for a given transaction. Any incorrect or incomplete piece of information can add cost to the entire transaction. Merchandising and purchasing teams at a retailer need accurate data to buy from a supplier or manufacturer. Logistics managers need accurate product data to plan and move cargo. But it all starts at the manufacturer, who is creating and launching a new product for sale. Failure to create and deliver accurate product data at this point, will destroy trust through all subsequent supply chain interactions.

This is why ecommerce executives are starting to look closely at the product information process and putting the necessary steps in place to ensure that each transaction maintains the highest level of data quality.

GS1 Verified Image

Image courtesy of GS1 US

The Creation of a Minimum Product Data Set

While there can be hundreds of different product attributes necessary to move an item from the manufacturer to a consumer, there is a small set of attributes which are required at every step of the process. GS1 has worked diligently to distill this long attribute list down to the following 7 core attributes:

Unique Product Identifier (Global Trade Item Number or GTIN)

  • Brand Name
  • Product Description
  • Product Image URL
  • Global Product Classification
  • Net Content & Unit of Measure
  • Target Market

It’s these 7 attributes which make up the basis for the the Verified by GS1 program​. GS1’s role in the process is to help create and manage barcode data for product packaging, along with managing the GTIN registry. This ensures that each new product gets a unique product number which isn’t replicated anywhere else.

Managing the Product Data Creation Process

This is where IceCream Labs comes in. With artificial intelligence, IceCream Labs is able to automatically extract product content from product images with the IceCream Labs Catalog Management solution.

Next, the extracted product content can be compared to existing product data to ensure that the product packaging images match the existing digital product data. In this process, the existing product content and the extracted product content can be verified with the official product GTIN and then normalized to ensure that all of the data (images and text) is consistent. This validates that the images match the rest of the content (item 4 in the core GS1 attribute list above).

Enriched Product Content for Consistency

Unlike a rules based engine, AI is able to intuitively process a variety of product content and selectively enrich content differently, based on the market and usage of the content. The output is a verified and normalized product data set that is ready for publication in your production product catalog. This process helps to augment the tasks of a brand manager at the manufacturer or the merchandising team at a retailer.

IceCream Labs is one of the first technology companies to apply artificial intelligence to this problem and we are helping ensure that product data is consistent across all channels and types of content. The end result is the presentation of the best information to the consumer so that they can make the right choice.

Handheld phone with image of products

DataPorts and Why they Matter

Improve the Quality of Product Content

​Retailers today struggle with managing ​the product content necessary to publish and maintain their online product catalog. Assembling content from manufactures is difficult when information ​comes in different forms. Likewise, manufacturers struggle to publish new product content and syndicate content throughout their channel.


Standardization is one approach and it is important for critical attributes such as the global trade identification number (GTIN). However, information such as schemas remain difficult to implement throughout the value chain and across the various different markets and regions.

Recently, IceCream Labs become involved with the Consumer Goods Forum (CGF). One core goal of the CGF is to improve data exchange throughout the value chain. The momentum within the CGF comes from the attention of executive leadership from many of the largest worldwide retailers and manufacturers. As a result, the CGF is starting to see momentum build from its initiatives.

Make it Simple

The executives within the CGF want to achieve the goal of reducing the pain of sharing information and improving interactivity (while reducing the costs of managing this data). From this need has emerged the idea of DataPorts. Conceptually, DataPorts deliver a method for peer-to-peer data exchange between any two points in the value chain. This removes the need for data aggregation or hub and spoke interactions. Any point in the supply chain can talk to any other point.

At the heart of the DataPort implementation is the use of Graph Query Language (GraphQL) and GraphQL schemas. The significance is that the GraphQL is emerging as a performant solution to the need for quickly finding related information in a network of related data. GraphQL has evolved to meet the needs of social media giants Facebook and LinkedIn.

What is a DataPort?

At its simplest implementation, a DataPort is a method for publishing information and then discovering and using the information using data virtualization rather than data federation. There is a common programming model for DataPorts, and this allows for peer-to-peer integration.

For example, a manufacturer can release a new product line, publish the content to a DataPort and then make that DataPort available for any retailer to query (with their product catalog DataPort). A retailer can request the new product content, and specify the schema that they desire and normalize any values in the process.

The services delivered by a DataPort ​are broken down (currently) into three broad areas:

1. Abstraction is the process of virtualizing the source data.
2. Transformation operates on the data to do things such as unit conversion.
3. Composition takes a set of results and creates a response from the DataPort.

At IceCream Labs, we are actively working on applying our existing expertise and experience in  data extraction to use machine learning models in the abstraction and transformation processes. We are already normalizing ​and extracting data from source images and unstructured information to generate high-quality product content.

Stay tuned, as we continue to explore more ways that ​DataPorts are changing the way that data moves through the supply chain, and improve the entire end-to-end process.


Dataport Whitepaper Cover

​Download your copy of the latest DataPorts whitepaper: Solving End-to-End Value Chain Content Integration from the Consumer Goods Forum

Grocery Storefront

3 ways Grocery retailers can survive in the age of Amazon

3 ways retailers can survive in the age of Amazon

Retail giants like Amazon’s ability to effectively address the ever-changing customer demands has enabled it to make its mark in the market and eventually dominating it.

However, even though it poses a threat to other grocery retail players due to their delivery capabilities and effective customer service, the changes in customer expectations, especially in the last mile of the customer’s shopping journey, present a tremendous opportunity. Grocers willing to adapt their operations to that of the bigger players can see a gradual impact on their profits.

Here are some important methods that grocers can use to capture a larger market share, with greater efficiency and satisfied customers:

Strengthen customer relationships by focusing on customer service

Almost every customer values good customer service, hence it becomes imperative that the grocery stores live up to customer expectations. Small developments in the manner in which grocers deliver and engage with their customers can provide significant results. 

It is important for grocers to provide complete visibility into their orders at all times, meaning that customers can track the delivery from the beginning to the end. Here, customers may be able to make specific results as well as the ability to rate drivers after the products have been dropped off. With fulfillment increasingly becoming an important aspect of customer experience, features such as this become a necessity. 

Leverage brick and mortar setups

Grocers’ physical stores are the biggest advantage over a giant like Amazon, which still does not have a significant brick-and-mortar presence. This can be seen especially for deliveries that require faster turnarounds. 

Even though the larger retail giants have more capital to invest and have a significant amount of experience in the e-commerce field than most grocers, it is still tough to compete with a store that is located near the customer’s home. A physical footprint also aids the grocers to offer their customers options such as in-store pickup or curtsied pick up. 

Scale operations by digitizing the supply chain

Many grocers still use outdated technology for their operations, however, if they want to effectively scale and manage their supply chain, they require a scalable solution that provides a true status on demand. This especially holds true if the grocers are dealing with high delivery volumes. 

Grocers are gradually investing in automation to expedite fulfillments and deliveries. This, in turn, will also enable an increase in profit margins over 5% which is a great margin as the grocery sector has smaller margins and higher volumes. 


As with any disruption, bigger players like Amazon’s entrance into the grocery market brings with it not only a threat but a great opportunity as well. Those that act quickly to implement the latest technologies and strategies both in their stores and throughout their delivery ecosystem will likely find themselves on the path to becoming market leaders and customer favorites.

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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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woman holding up a basket of fresh produce

Three Important Aspects That Can Influence Your Grocery Business

3 Main Factors That Will Shape Grocery Retail

Grocery retail has seen four major shifts since the 1950s, starting with a focus on brand in the 1950s, moving to category in the 1970s, then the customer needs in the middle of 1990s, with the focus settling on giving the customer control that emerged in 2010. The shift to giving customer control began with the infamous Amazon Effect that disrupted the world of online retail.

This latest shift in the way customers purchase their grocery has upturned brands and grocery retailers, pitting them against sudden changes powered by the latest in technology and unpredictable socioeconomic conditions. In the new age, only grocers with the most adaptability and agility in the face of change can survive.

Let us explore some of the driving factors of grocery retail and how you can leverage them to improve your business:

Convenience is Top Priority

These days, customers are pressed for time. Amazon understands this well and uses this knowledge to build success in every category. Convenience happens to be a decisive factor in the highly competitive world of grocery retail. Even brick and mortar grocery retailers are turning to new in-store experience solutions such as mini-stores within a store (e.g.: meat and frozen foods, wine and specialty cheeses etc) and modular product displays, keeping in mind customer’s insistence on convenience.

Long check-out lines are a big turn-off for customers. Amazon has innovated and eliminated this inconvenience by introducing Amazon Go stores which are cashierless and run solely on technology. Walmart has introduced an option to pay from their app, easing the check-out process for customers. Your customers are looking for the simplest and fastest way to buy grocery products from you. Since most of the grocery shopping includes repeated products, you can easily provide better service by allowing your customers to repeat their orders, or increase sales by pushing out offers on their regular purchases.

Easy Discoverability

Artificial Intelligence(AI) can be leveraged to analyze customer data and insights, to help grocery retailers and CPG brands make shopping easier and more logical. As an example, product sections are now being organized around the needs of customers rather than solely based on brand merchandising. 

Grocery retailers and CPG brands working together to create singular themes under which they can market and sell affiliated and affinity products. For example, a theme such as “breakfast time” groups together products such as eggs, fruit juices, and loaves of bread, etc. This not only helps customers to discover new products easily but also helps retailers increase gross merchandise value (GMV) whether online or offline.

Omnichannel Experience

Persuasive in-store merchandising can entice store customers to go online or vice versa. For example, an offer on an in-store display of chips sends customers online for a free dip coupon. Likewise, an online offer on select dips sends customers into a brick and mortar store for nachos at a discounted price. These kinds of promotional offers not only bridge the gap between online and offline retail but also serve to deliver an omnichannel experience.  

Research conducted by Dunnhumby research found that 70% of an online shopping cart is populated with the usual products from an in-store spend of a customer. Utilizing customer intelligence such as this, you can drive profits up by making the shift from offline to online shopping smoother for your customers.


We’ve covered some of the current driving factors behind grocery retail. You can apply these concepts to improve your grocery retail. Remember that to become a successful online grocery retailer, you need to implement a structural shift at the very foundation of your retail existence. This applies across all parts of your business, from merchandising to displaying and selling products at your online or brick and mortar store. Using the latest technology such as AI to analyze customer data and gain useful insights could help boost your business like never before.

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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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shopping bags

Frictionless Shopping: Futuristic Retail

Frictionless Shopping: Futuristic Retail

Customers today want the gap between online browsing and in-store purchasing to bridged in a seamless experience. Potential inhibitors of the customer shopping journey include inconveniences such as having their preferred choice unavailable in-store or losing items in an online basket. It is a disappointing experience when customers face inconvenience whether real or perceived when shopping in-store or online.  It is critical for brick and mortars to make necessary adjustments to stay ahead in the competitive arena of retail. A frictionless shopping experience is one that seamlessly incorporates checkout and payment options, real-time customer service and, customer delivery preferences. This is quickly turning out to be a fundamental aspect of any retailer’s business. Offering frictionless shopping is a great way to connect with new customers.

What Defines Frictionless Shopping?

Frictionless shopping is the idea to connect customers and retailers so that customers instantly find the products they need and then buy it without any interruptions. Frictionless shopping ensures that the customers are in control. The concept has evolved with technology and now customers expect these experiences to be made available through their smartphones. Frictionless shopping also requires the elimination of retail interactions that negatively impact customer experience, such as, having to wait for paper receipts to print or fetching loyalty cards to get a discount.

The implications for you include the way you package and market goods, down to the ease with which your customers can complete the payment and checkout process. You must incorporate customer-friendly ordering options, as well as click and collect services. If you have a brick and mortar store, you must find a way to enable mobile payment options and optimize inventory systems to attract customers into the store.

Why is Frictionless Shopping Important?

In the digital age, customers are spoiled for choice and habituated to getting what they want delivered instantly. Customers prefer not to have to stand in long queues at checkout counters or wait long for their online orders to be delivered. Information is also always at their fingertips and they can easily find what they need/want with one quick search on their smartphones. 

A good example of a great frictionless shopping experience is Amazon Go. The cashierless stores are at 4 locations in the US. Amazon Go uses hundred of cameras and lots of data to allow customers to simply walk in, pick up whatever they need and walk out without any checkout queues. Amazon Go has a smartphone app that automatically adds items to a virtual shopping basket while customers select them. The customers are charged to their Amazon account for the products they walk out with as they leave the store.

Impact of Frictionless Shopping on Retailers

If you want to stay ahead of the game, then you must embrace technology and data to provide a frictionless shopping experience from start to finish. From product innovation to improving customer experiences in-store, data and technology play a big role in helping you understand how customers respond to display and packaging. It is important to reorder inventory based on demand, keep shelves stocked and ensure efficient distribution.

For example, push notifications sent on smartphones to customers while they are shopping could alert them to offers and provide helpful information to smooth their shopping journey. An essential element in frictionless shopping is an easy mobile checkout process. Mobile friendly, simple forms and single payment option will get more customers. It is also crucial to allow customers to easily and quickly find help through FAQs, direct calling or live chat.

We have seen some tips for implementing the concept of frictionless shopping whether offline or online in your business.  In this busy world, customers are drawn to retailers who understand the value of their time. Stay ahead in the competition by giving your customers a smooth and frictionless shopping experience.

 

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