Tag Archives for " AI "

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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AI is Redefining Experience in Customer Support Centres

AI is Redefining Experience in Customer Support Centres

Businesses need to understand the complexities of individual transactions and customer behavior over multiple touch points and channels, now more than ever. With AI in the fore, and technological integrations becoming increasingly popular and customer support centres or contact centres have the opportunity to stand as industry leaders and reimagine every aspect of their business.

Data mining now has the ability to look at every single customer and personalise the brand’s interaction with each of them. Harnessing the massive rise in unstructured data through AI will play a crucial role in helping reshape contact centres into customer experience centres, helping them provide insights into customer needs which would drive increased efficiency and drive profitability, greater customer satisfaction and create more valued and meaningful work.

A seamless, individual customer experience

Digital convenience is a huge motivator for consumers. Several companies such as Apple, Google, Facebook and Amazon have set the bar for integrated customer experience that provides individual customer service across multiple channels. Consumers today expect to move seamlessly through the different channels seamlessly.

While the customer expectations are high, their brand/company loyalty is not as much - while customers will cross channels if they cannot complete a task on their first channel of choice, they only want to engage through the channels they want to use. This is one of the integral reasons why retail businesses must understand the intricacies of individual transactions, as well as the context of customer behavior over multiple channels.

Businesses that are cognisant of their customers’ issues, moulding their experiences and creating meaningful engagement creates value for customer and company. Leveraging AI, businesses can receive immediate feedback - systematically and quantitatively, from every interaction without creating any points of friction or customer effort at an individual customer level or aggregated to the level of your choice. It links all channels to create an individual yet seamless customer experience.

Multiple channels fuel customer contact

Customers are increasingly demanding choice and control and even expect brands and retailers to anticipate their needs without invading their privacy. While digital touch points are becoming the interaction channel of choice for customers, there is still a significant amount of customer support centres that do not use data analysis tools, despite analytics being voted the top factor to change the shape of the industry in the next five years.

Furthermore, customers have reported that the phone as a channel is the most frustrating contact option, an industry study found that its dominance has not declined as quickly as expected. In 2017, almost half of the customer support executives have utilised phone and digital channels. Moreover, it is predicted that more than 50% of organisations would manage a multichannel customer support centre in the immediate future.

Augmenting Intelligence 

While AI can help augment human behavior, there is still a very real bias for humans to want to talk to other humans. Customer support is still an important competitive point of difference for business, with success gauged on customer experience outcomes. A key challenge is maintaining integration levels across all channels while providing consistent service.

Today, customer support centres are experiencing an offloading of transactional activities into alternate channels. Calls are more complex and add more value for the customer as well as the business. 

This means AI will take the the existing analysis techniques of those calls to the next level. It will have the ability to map word and concept level relationships within conversations and then deduce business specific intelligence and insights. Speech analytics will be able to measure everything from the reason the person called to their mood at any stage of the call or contact.

AI can link key words and phrases and carry out semantic matching (which matches phrases on their similarity of meaning). This will enable customer support centres to improve the customer experience, monitor contact centre quality, reduce operational costs and gain critical business insights. Critically, it will do this seamlessly from the conversation, not through set questions or a survey. Today’s data, informs tomorrow’s decisions.

The road ahead

There is no denying that contact centres are entering a period of intense disruption. The rise of cloud-based infrastructure will see new forces enter the market and force existing operators to become more flexible.

For large established businesses, offering a frictionless multi-channel offering will not be something new but something expected by customers. So much so, customers won’t think about dealing with different channels within a company but simply with the company. Accurate, consistent and personalised interactions with customers will be essential.

AI software will be instrumental in helping contact centres reimagine their role from contact to resolution. It will free staff to work on meaningful, more complex and intuitive scenarios with customers as AI performs transactional and predictable tasks. The elevation of work in a contact centre has the potential to create a more stable workforce with improved corporate culture.

Ultimately, people still want to interact with other people. A contact centre is a fine example of that. Utilising AI will allow contact centres to focus less on mundane, transactional activities and more on its interactions with its customers. It will see far more opportunity for meaningful human interaction beneficial to customer and company.

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Kicking off Black Friday and Holiday Shopping with Artificial Intelligence

Kicking off Black Friday and Holiday Shopping with Artificial Intelligence

US retailers are making final preparations for Black Friday in both their physical and digital stores to support the expectation of high volume shoppers. 2018 holiday sales are estimated to climb between 4.3 and 4.8 percent over 2017 to between $717.45 and $720.89 billion – all due to the rising health of the economy, low employment records, and increasing wage margins.

While the economy has been improving over the past year, technology has also been making progress – both online and offline. This is especially seen with making more personalized recommendations through the use of AI and machine learning.

AI-Driven Personalization takes priority

With retailers increasingly leaning towards AI and utilizing AI-driven platforms, they are choosing more sophisticated platforms to make more personalized recommendations for their customers, ultimately increasing revenues for retailers and brands.
Some studies even concluded that brands that invest in creating personalized experiences leveraging advanced digital technologies and proprietary data for customers see a bump in their revenue by 6% to 10% – two times faster than those brands that don’t.

For the holiday season, and the upcoming Black Friday shopping, AI can be a wonderful tool used to automate the process of helping online and offline shoppers find what they want to shop for. Shoppers often have trouble finding a memorable gift for friends and family, but do not have a clear starting point – this may need browsing extensively through different e-commerce websites and searching through several aisles in different stores to find the right gift.

AI simplifies this process by giving retailers and brands the ability to ask their customers questions about their gift recipients and offering personalized recommendations based on individual tastes and preferences.
The use of AI-driven personalization for e-commerce channels has increased over the past few years, but according to experts, the future of AI is limitless – especially in the physical store. Furthermore, the future physical retail is believed to be a mix of the speed and convenience offered by AI with a human touch.

Customers want to be engaged through human interaction rather than special effects using light and sound, so retailers can do well to create community events and use data to offer personalized in-store experiences.

In-store Personalization to Support Retail Employees

As more and more technology is being integrated into the store environment, retailers need to move towards an autonomous management reducing the dependency on manual management by store staff. Recent studies even predicted retailers providing in-store recommendations to shoppers through AI engines to be the most mainstream in-store technology in the coming years.

Though AI is often pegged as a technology of the future, it’s a concept that is slowly taking shape and is not too far into the future. AI capabilities enable retailers to pursue customer personalization in real time – which will soon become a top priority becoming important for shoppers. The capability to display prices and promotions, which are subject to change, also coincides with the concept of a more personalized consumer-friendly store.

Conclusion

As we approach Black Friday – the official kick-off for the holiday shopping season, it will be interesting to see in which ways retailers and brands will leverage AI into their shopping strategies this holiday season. While personalized offers and promotions to enhance shopper loyalty will definitely be in the mix in the months of November and December, retailers can also take advantage of the data they receive to encourage repeat business throughout 2019.

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Chatbots: Boon for e-commerce businesses

Chatbots: Boon for e-commerce businesses

E-commerce is an intensely competitive market where businesses need to keep innovating and adopting new technologies to sustain. These technologies come in packages, large & small, to help optimize the systems and processes, ensuring seamless experiences for the customers.

One of these technologies - Chatbots - has been a popular topic of discussion in the retail and e-commerce industries. Some discussions focus on how the user experience is improved while the remaining provides a detailed view of implementing and adopting chatbots for businesses. In this blog, we try to provide a holistic view of how chatbots help scale e-commerce businesses and subsequently consumers by dividing the benefits into three categories - predictive recommendations, engaging consumer engagement, reduction in the purchase process

Predictive recommendations 

E-commerce industries are user heavy and need to cater to different segments of audiences and their requirements. Whenever there is a discussion about the role of chatbots, one quote stands out -

“Goal is to turn data into information and information into insights.” – Carly Fiorina, Former CEO of HP

Imagine chatbots to be the one that hoards information and turn information into insights. They help customers find the products they’re looking for without extensive browsing, thereby providing users an incentive to stay on the platform and reduce drop-offs.

For instance, Amazon provides best-in-class search for users to find the products they want. The search takes into consideration a number of factors including user dialects, ease in product categorization and user buying behavior & patterns.   

Simple & Quick Consumer Engagement 

The primary function of chatbots is to be conversational by nature using text, buttons, and images and understand typed natural language requests. This is the reason that the most famous chatbots run inside messaging applications such as Facebook Messenger or Skype.

Hence, chatbots provide a huge benefit to e-commerce businesses to connect with the users.  In a high volume business of e-commerce, chatbots provide a highly scalable system to manage individual user conversations simultaneously for millions of users while gaining user insights to improve the product flow and experience.  

Reducing Purchase Time 

Chatbots help consumers to interact with the products at critical stages of their journey, increasing customer satisfaction, loyalty, and engagement. Chatbots provide the assistance to access product information quickly and make informed decisions to purchase the products.

Moreover, customers do not need to look at any other source to gain information on the products. Chatbots process information in the form of notifications, reminders, product updates to fuel conversions and enhance social experiences.

Image credit: Photo by Matan Segev from Pexels

A Tour of CatalogIQ for Grocery

Kick-start eCommerce sales with awesome product data

E-commerce requires awesome product data to support successful search and conversion. Product data for the online grocery market is currently being created manually. Retailers are struggling to acquire the rich product data necessary to support their online needs. Brands are struggling to generate good data.

ContentIQ Add Product

IceCream Labs CatalogIQ is designed to automatically extract attributes from product images. Using either label mechanicals or actual product images of the packaging, CatalogIQ can extract text from the labels. From there, the artificial intelligence in CatalogIQ understands what the text is and inserts it into the appropriate product attribute. The AI can also determine which images are the hero image, and front, back and side images.

CatalogIQ Extracted Attributes

CatalogIQ identifies brand names, sub brands and variants, normalizing the brand to the appropriate text. titles are generated from various attributes to create an SEO-rich title to optimize search. Other key attributes include feature/benefits, ingredients and nutrition facts.

CatalogIQ extracting content from a product

Sample CatalogIQ extraction (front/rear)

How complete is your data?

ContentIQ Catalog List view

CatalogIQ can score the data to help the merchandising and ecommerce teams understand which product records have been enhanced.

  • Missing attributes
  • Accuracy of attributes (are all of the attributes congruent?)
  • How unique are the attributes?
  • Is your product record SEO optimized?
  • Do you have relevant search keywords?
  • How well does your product data match up to customer site searches?
catalog IQ demo screen

Support for Grocery merchandising teams

Grocery merchandising teams have the chore of uploading new catalogs from suppliers and manufacturers. Often this data arrives in the form of a spreadsheet. CatalogIQ can easily upload a new catalog file (in spreadsheet form) to quickly and easily complete the ingestion process.

ContentIQ Add Catalog screen

Support for Grocery and CPG Brands

Grocery retailer and channel partners expect high quality product data to list and sell your products online. Can you deliver the content?

CatalogIQ allows brand and product managers to auto-generate high quality product data directly from product label mechanicals and/or product images. If you're currently using manual processes to create product content and to check the accuracy of product data, then let CatalogIQ help you automate the creation process. You'll be able to complete the data creation process much faster than manual methods. CatalogIQ can also validate the content and ensure that it matches what is contained on all of the product labeling.

CatalogIQ Features

  • Quality product images including relevant Nutrition Facts
  • Accurate meta-data, including attributes like: allergens, sugar free, Kosher certified, Non-GMO and other facets
  • Complete, standardized and SEO enabled titles
  • SEO rich descriptions
  • Correct product categorization

As a merchandising manager with a large product catalog, you know the difficulties of reviewing your product data and ensuring that everything in the catalog is ready to publish live to customers. There is always the nagging concern that something is inaccurate or missing when you push the “publish” button. Every time that you receive new data from your suppliers, it’s a chore to process the data. You have a long checklist to complete before you can publish data to the live catalog. Processing this checklist can consume all of your time.

CatalogIQ Benefits

  • High quality product data
  • Improve product page discoverability 
  • Increase product sales
lady using a credit card to make a payment online

Transforming the Payment landscape with AI

Transforming the Payment landscape with AI

People have increasingly become comfortable using technologies such as AI and machine learning in their day-to-day lives. Various companies also have increased their use of AI and machine learning into their product offerings as well as their processes. With the computer processing technology advancing increasingly, companies, institutions and even governments are gathering massive amounts of data as more consumer interactions move towards digital. This type of technology is already transforming the payments landscape in the following aspects -

Improving Efficiency

AI and machine learning have the potential to revolutionize the way payments are processed by enhancing operational efficiency and decreasing costs involved. For instance, AI enabled chatbots are reducing the load for customer service representatives.

With machine learning being incorporated into payments, learning algorithms play an important in helping speed along authorization of transactions and monitoring.
Furthermore, AI helps reduce the processing time for payments. It also helps eliminate human error and save time for correcting those mistakes.

For a business, processing large amounts of data to generate financial reports to satisfy regulatory and compliance requirements would involve a team that would perform repetitive data processing tasks. Leveraging AI would involve training the models to do these tasks, and the model can ensure completing the tasks faster and more accurately than humans. These technologies can improve efficiency while gathering important user insights in real-time.

Machine learning has already proved to be an invaluable part of fraud detection, but there are many opportunities that lie in product sales, customer care, and retention. Machine learning can draw vast amounts of available data and utilize it to profile customers to guess their product needs while offering new opportunities for upselling.

This model can also help identify which customers that companies are at risk for losing as well as halt customer loss before it happens. Machine learning can help automate many customer service interactions. This model which uses deep insights, cognitive engines and natural language processing is already widely available and the usage will only grow with time.

Fraud Prevention

There are various methods for customers to make payments today. They are no longer limited to paying with cash or even cards. There are new payment methods on the rise such as card-not-present (CNP) transactions, but as it gets popular come new opportunities for fraud. AI and machine learning are at the forefront of not only detecting fraud but also preventing it before it happens.

These technologies already have the capability to uncover patterns and drive hidden insights and are working towards fine-tuning these insights. Companies are choosing to move away from a static model where the reliance is on supervised learning with input towards unsupervised learning wherein the deep belief neural network does not require a labeled training set, but continuously updates the model as new patterns emerge, allowing for a more robust and flexible fraud prevention detection tool.

As more commerce and payments move online, more data is accessible. This new robust algorithm uses machine learning to decrease the false positives and more agile detection of the actual frauds.


AI and machine learning have come a long way in the past decade. These technologies have already been adopted by many sectors and have transformed many aspects of traditional processes. Though exciting new technologies have been adopted by businesses to improve and enhance the payment process and customer experiences, the scope for future implementation is endless.

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Revolutionizing grocery retail with artificial intelligence

Revolutionizing grocery retail with artificial intelligence

While there is a lot of chatter around artificial intelligence and the potential it has to transform retail, it has the capability to impact the most fundamental shopping experiences: the grocery store. Most retailers are moving towards building a better grocery store experience, with the rise of subscription meal kits and competition from pure-play grocery delivery services. With the abundance of customer data and product information available, grocers today are in a special position to apply machine learning and AI in other areas of the business as well.

The grocery industry has a heavy reliance on the movement of perishable goods and with supermarkets struggling to plan, promote and sell goods in a short period of time, it is not efficient. Furthermore, there is a lot of food wastage that happens in this industry - while some of that waste happens in consumers’ homes, a good amount is also lost in the supply chain - anywhere between the farm and the store shelf. And with the various options for when, where and how to buy groceries, grocers compete on prices that are most likely to be profitable.

While grocers are faced with these challenges, they have a fairly good idea of their customer base, who they are and what they want to buy at what price point. With the data available, there is a lot of opportunities utilizing the data in the right manner. This is where AI comes in. Using machine learning capabilities and analytics, more grocers are leaning towards adopting this technology to strengthen the relationship with customers, as well as address some of the biggest challenges they are faced with today.

Leveraging the abundance of customer data

The grocery industry was one of the first industries to collect shopper buying data through programs such as loyalty programs and in-store promotional offers. These methods helped grocers gather information about their key shopper demographic as well as brand preferences. This information is already leveraged to provide discounts and special promotions, the new technology can help enhance the relationship between the grocer and shopper even further.

AI helps grocers to provide a deeper understanding of context and intent by answering the questions behind customers' shopping decisions. It also enables the grocers to parse the customer data and automate the ability to offer targeted promotions to each customer.

Enhanced inventory management

AI can change the entire way of managing inventory. AI can help stock shelves with the right mix of products and ensure that the supply chain is aligned to avoid out of stock products using point-of-sale information and inventory visibility.

Machine learning can build on grocers' rich customer data and combine that with contextual data such as weather, climate, holidays and events - providing a more accurate forecast compared to traditional methods.

Reducing waste

With better inventory management and data analytics, AI can provide better visibility on produce and perishables. Automation can help stores dynamically re-adjust orders based on demand or automate product promotions for the items that are not performing well or selling fast, and helping stores to protect margins, and reducing the amount of food that goes into landfills.

As AI and machine learning advances, grocers should begin to position their systems for a seamless transition towards a highly automated future.


AI must integrate into the commerce platforms and connect across systems to maximize its effectiveness throughout the business. While AI is a valuable tool for customer service, the impact of it will come through its ability to reward loyalty, understand consumer behavior, ensure reduced wastage and increase revenue for the retailers.

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The growing importance of customer loyalty programs in grocery retail

The growing importance of loyalty programs in grocery retail

Grocery loyalty programs have been around for years, but with the changing landscape as well as customer shopping behavior, it is now more important than ever. The goal for loyalty programs is to be relevant and timely to shoppers and their preferences and to create a continuous dialogue between the customer and the brand or retailer.

The benefit retailers get utilizing loyalty programs is the access to customer data, shopping patterns and behavior with direct engagement with the shopper, elevating the communication and optimizing the offers to meet their needs. This ultimately leads to help drive sales for the retailers.

The current scenario

Customer loyalty programs build large customer databases and retailers can leverage this information to create more personalized and targeted promotions tailor-made for every customer. A successful loyalty program is a well-crafted blend of rewards and recognition features that change shopper behaviors. However, grocery loyalty programs are challenged not only by the economics of the industry but also the limited opportunities to differentiate the customer experiences for each customer.

However, compared to other industries, the grocery retail industry today still does not have a completely organized, leveraged and utilized customer data to deliver the optimum level of personalization and relevance to the customer, unlike in industries such as travel or banking.

Limited impact on shoppers

The grocery retail industry operates at small margins, which limit the rewards grocers can offer to the program members. Furthermore, compared to other retail loyalty programs, grocers depend on subsidizing their programs with discounts provided by the CPG manufacturers they are partnered with. Most loyalty programs have followed suit and use the same strategy, enabling shoppers to spread their loyalty across different brands.

Digital coupons - a primary feature of most grocery loyalty programs are offered by most of the retailers in an equal capacity. Another perk is the member pricing feature is an attractive way to entice shoppers into joining the program, but this feature too is replicated across all of the programs, limiting the impact these programs can create.

One feature of the grocery program that does create an opportunity cost and loyalty is reward points. Some shoppers even consolidate their shopping to a single brand in order to maximize the points earned and get a chance to get more discounts with every purchase. While most grocery programs prefer CPG subsidies, retailers should not limit the programs only to create more loyal customers. With e-commerce retail giants like Walmart and Amazon leaving no stones unturned to engage with shoppers, grocers need to double up on creating successful customer loyalty.

Looking ahead


Grocery retailers are facing several options today, they must decide among differing formats, both online and in-store, and seek the best combination of program features available to them.

A growing demographic - Millennials, in particular, seek immediate gratification, support, rewards, and recognition. Moreover, they do not invest a lot of time while planning grocery lists as they center their grocery visits around recipes more than replenishing a set stock. They are also far less price-sensitive in their menu planning as compared to previous generations. Additionally, as shoppers are increasingly opting for healthier lifestyles, produce as well as non-processed foods have become important aspects for grocers to drive retention and loyalty.

With the rising popularity of Walmart and Amazon among shoppers, grocers must foresee the future from both physical stores as well as digital capabilities. A well-managed loyalty program uses analytics and insights to enhance customer experience and elevate shopper journeys. Another major trend is the development of artificial intelligence. Leveraging AI, grocers can use unused retailer data and create additional value from it and tailor more relevant communication and improved personalized pricing and promotions.

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An Evolving AI retail experience: Transforming the way consumers shop

An Evolving AI retail experience: Transforming the way consumers shop

The retail experience of a shopper is the latest area that AI and machine learning are causing disruption. Most retailers recognizing the potential of these technologies have started aligning them into their business goals. Two crucial aspects - data and computing power have changed in the past few years in the space of AI, which has opened up new opportunities for retailers today.

Computing power is easy to see, with the advent and rise of smartphones which have phenomenal computing power when compared to the bulky phones and computers used decades ago. Businesses today have unlimited computing access to train their AI algorithms. Furthermore, the data available today is extremely rich and scalable. AI systems that leverage learning techniques such as Machine learning thrive on large, rich data sets. When fed appropriately, these systems discover patterns and correlations that would be otherwise difficult with a human intervention. These machine learning approaches automate data analysis, enabling users to create models that can then be used to make useful predictions about other similar data.

Retail is a perfect fit for AI, here’s why -

The speed at which AI can be deployed depends on specific critical factors. The first is the ability to test and measure. Retail giants can effectively deploy AI and test and measure consumer response. They can also leverage AI to measure the effect on their entire supply chain.

There is some innovative and interesting robot technology taking place in retail such as Grocery giant partnering with Nuro.AI to deliver groceries to the customers’ doorsteps. But most significant changes will come from the deployment of AI rather than the use of physical robots or autonomous vehicles.

Here are 3 AI-based scenarios that will transform the retail experience -

Shopping habits

AI can detect underlying patterns in the shopping behavior of shoppers from the products that they buy and the method used to buy them. This could be a simple weekly purchase of groceries from the supermarket, the sporadic purchases of wine from the liquor store or the complex midnight icecream cravings from the local convenience store.

At a larger scale, analysis of the behavior of millions of consumers would enable supermarkets to predict the number of households that cook pasta every week. This would then inform the inventory management systems, and automatically optimize the stock of pasta. This information can also be shared with the suppliers, enabling more efficient inventory management and organized logistics.

Pricing dynamics

The pricing challenge for supermarkets involves applying the right price and the right promotion to the right product. Retail pricing optimization requires data analysis at a granular level for each customer, product and transaction. To be effective, many factors need to be considered such as the impact of sales due to the changing price over time, seasonality, weather and competitors’ promotions.

A well-defined machine learning program can factor in all variations, including details such as purchase histories and product preferences to develop deep insights and pricing tailored to maximize revenue and ultimately, profit.

Customer feedback

In the past, customer feedback was collected through forms and feedback cards that were filled out and placed in a suggestions box. The feedback had to be manually read and acted upon appropriately. With the rise of social media, the platforms were leveraged to express feedback publicly. Retailers subsequently engaged in social media scraping software to respond, resolve and engage with customers.

With the growing innovations, machine learning will play a larger role in this space. Machine learning and AI systems will be able to analyze unstructured data from multiple sources such as verbal comments or video content.  

The evolving retail experience

As a shopper moves through various stages in life, the circumstances and spending habits change. AI and machine learning models will adjust and be able to predict the needs of the consumer before the consumer even searches for a product.

This shift to predictive marketing would change the way shoppers purchase products, bringing in suggestions and recommendations that they would not have even considered. The possibilities would widen, all because of AI - for both consumers and retailers alike.

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AI and Automation are transforming the E-Grocery experience

AI and Automation are transforming the E-Grocery experience

The concept of e-grocery is not new, with existing e-commerce businesses like Walmart To Go, Amazon Fresh and Instacart, but with Amazon acquiring Whole Foods, all the players in the grocery retail industry have realized that grocery shopping is at the brink of transformation and are changing their strategies to accommodate and incorporate online shopping into their business goals. 

The focus is on providing solutions to enhance customer engagement, optimize inventory management and upgrade logistics for accurate and speedy delivery. To address these concerns, businesses are investing in technologies like artificial intelligence (AI), machine learning (ML), big data, internet of things (IoT), cloud computing, autonomous robots, virtual and augmented reality (VR/AR).

Challenges of e-grocery inventory management

The success of an e-grocery business essentially depends on inventory management. The fundamental problem that needs to be addressed is stocking: with grocers finding the right balance between understocking and overstocking. 

Overstocking uses up costly warehouse space and locks up capital which could be otherwise made available for resources. Products decay over time and with perishables, the decay is often quicker, in some cases, by the end of the day. Every wastage affects the business, increasing the costs and making it unproductive.

While on the other hand, understocking hinders the growth of the business. No grocer wants customers abandoning their shopping carts because of the inability to supply an item. While there are options to pre-order products that are not in stock, groceries are fast moving products that customers need on a regular basis. Grocers cannot list fruits, vegetables, cereals, soaps, detergents, and personal care items as out of stock. The demand for them is instant.

Optimizing inventory is crucial for the survival of the grocery industry. It’s no surprise that e-grocers are leaning towards innovative technologies to enhance and optimize their inventory management processes.

aisle with fresh fruits

Emerging trends in e-grocery

Big Data, AI and Machine Learning

E-grocery businesses generate a significant amount of data about purchasing patterns which can be useful to predict future trends. However, this data needs to be examined and categorized to make it efficient and useful. Here’s where data analytics and machine learning come in to help grocers extract relevant insights which help them make strategic business decisions.

Businesses are leveraging AI to predict operational failures and improve warehouse management. As machine learning models get smarter, the systems get more efficient over time.

Automation and use of robots

Besides inventory maintenance, the physical movement of the inventory is another challenge for the grocers as it requires a considerable amount of human labor. Technologies like automation and robotic systems are helping businesses by taking over these manual tasks.

The robotic systems are automating operations for customer orders and are also helping businesses to build space-saving warehouses and utilize the complete area efficiently without wastage. There are rails between aisles for robots to move around, stock and fetch products. Robotics and automation go hand in hand towards reducing the size of real estate investments.

Self-Drive logistics

Another challenge that e-grocers face is delivery of the products to the customers.

Groceries differ from regular e-commerce products such as shoes, or furniture items in two ways: the quick turn around time expected by the customer and the perishable nature of grocery items. As the order volume of the e-grocery business grows, the logistics system needs to scale along with it. This, in turn, increases the delivery cost that further affects the business.

Businesses are turning towards self-drive vehicles to deliver groceries to customers and with startups like AutoX with self-drive car deliveries and Marble with a sidewalk delivery robot coming in the market. As transport technology advances, self-driving automatons can become the next big thing to look out for.

The future of e-grocery

As online grocery businesses are adopting the latest technology to solve the supply chain, inventory management, and logistics problems, even small grocers are able to leverage these technologies to scale their businesses through automation and predictive analytics.

Moving grocery online has been a major problem with the high demand for operational excellence and the low margin of the products. This is a hard sell for many businesses, but with the advancements in AI, Machine Learning, Automation, Robotics, this is changing. This can be seen from the growth of e-grocery ventures that are emerging around the world.

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