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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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supermarket with fresh produce

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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AI in Images and Video: How can it benefit e-commerce?

AI in Images and Video: How can it benefit e-commerce?

With the growing popularity of image-based social media platforms like Instagram and Snapchat, there has been a significant rise in UGC (user-generated content) on the internet. Users upload photos not only of their lives but also of interactions with different products or brands they encounter online. Retailers and brands can leverage this information to engage and interact with users building brand awareness. However, with the increased use of UGC, it has become a challenge for them to track and categorize unstructured information. This challenge can be addressed using image recognition and computer vision.

Helping with Product Search

If a user while looking for a type of furniture is unable to use the right terms to describe the item in the search query, he/she could always depend on voice assistants such as Alexa, Siri or Google Home. However, the voice command is really just fulfilling a text query.

Instead, by taking a few pictures of the object and uploading it online using image search, the user can find what he/she is looking for. Using image-based AI, the search breaks down different elements of the image and enables the user to choose which aspects of the results are important.

couch in the living room

For instance, there is a beautiful couch in the living room but it is missing a coffee table. The user can take a picture of the couch and upload it as a search item. The image AI picks up on the couch and detects the elements such as color palette, wooden legs, etc. It then provides results of coffee tables that can match and complement these elements. Furthermore, based on its database, the AI can also recognize elements such as the brand, price range, etc. of the couch allowing the AI understand what type of budget the user may be willing to spend on furniture items. This goes beyond the simple search that people see today. 

Personalized experience on social media

Social media is empowered by AI, and brands and retailers can now detect and analyze every mention on the social media platforms using image recognition. They can also view how the brand is portrayed through the various images and videos shared on a daily basis. This further allows brands to interact with the users as well as collect and reshare their images helping the users to develop a personal connection with the brand.

Brands are also leveraging computer vision to provide a more targeted ad experience for users. For example, after browsing through an Instagram feed of a famous fashion celebrity, the user may get ads of fashion lookbooks featuring some of the pieces worn by that celebrity. These type of ads provide a subtle recognition for the user, which in turn helps brands build awareness and engagement.

AI in video content

For video content, brands and retailers can use AI to scan the video and index objects, scenes and audio sounds such as a dress from a popular brand or a painting from a famous artist or a song from a famous musician. Leveraging these elements, brands can then promote their products that can relate to these items such as bags that may match the dress in the video.

For video advertisements, brands can insert their products into a “placeholder” dynamically. Video producers can mark areas in their videos that can easily incorporate an inserted image and depending on the geography, language, and demographic segregation of the audience, AI can dynamically insert an ad into the video. This personalized approach enables a more local advertising experience for the users.


The e-commerce landscape is evolving with technological innovations changing the way people shop online. Images and videos are a largely untapped resource for retailers and brands to get insights from but with image recognition and computer vision gaining momentum, it is now possible. Giants like Amazon have also recognized its potential and have incorporated image-based search into their shopping experience. The applications for AI in images and videos are still limited but with deep learning, it is evolving and has the potential to change the shopping experience completely.

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How AI helps to optimize e-commerce product content

How AI helps to optimize
e-commerce product content

With online sales growing faster and the e-commerce landscape changing with technological innovations, traditional retailers are increasingly investing in omnichannel strategies and doubling their efforts in order to meet consumer demands. An effective way to keep pace with e-commerce giants and stay relevant in the marketplace is to offer high-grade product discovery and selection. This requires providing detailed product content with product-specific attributes, along with semantic search.

The current product content problem

As more retail businesses are moving towards e-commerce, the need for quality information and powerful search platforms has become crucial in order to entice shoppers and help them make effective purchase decisions. However, this is a challenge as they are unable to easily deliver complete product content.

Retailers rely on the suppliers to provide all the coordinating images, videos, attributes, etc. for each of the products. Suppliers use various methods to provide content such as printed or digital catalogs or in different formats like Excel, PDF, etc., making it difficult for retailers to properly source and extract the right data required for the right product. In some cases, retailers even purchase content from third-party providers or online databases. However, the challenge here persists, as most of the time, content differs from suppliers to third-party providers and validation of the information becomes tedious.

Besides the price of a product, detailed product information along with superior quality-images, videos play an important role in a consumer’s buying decision. 

There are numerous technological challenges while extracting content from the product images - some including region segmentation, diverse product backgrounds, natural settings, typography and fonts, lighting conditions, and low-quality images. For instance, inconsistent product image sizes would limit the system to capture the product details completely from all the images.

Impact of poor quality data

Missing information and uncertainty are two leading factors for consumers to abandon their shopping journey. Consumers tend to leave their shopping journey when they sense that the product does not have clear or complete information. This could range from unclear product descriptions to missing or inaccurate product attributes such as size, materials used, ingredients, etc. or even product reviews.

While there is no definitive rule stating an optimal number of product images or videos or a recommended character limit for product information, the quality of product images and videos have a direct impact on the ability of the e-commerce business to generate sales. With complete and comprehensive product information (description along with attributes like size, or weight, etc.) and high-quality images and videos would enable shoppers with the information they may need to make a purchase decision.

Effective Extraction of Product Content

With IceCream Labs CatalogIQ, retailers can effectively address the problems they face while onboarding product content to their catalogs. Leveraging machine learning algorithms, Optical Character Recognition (OCR) systems, and Natural Language Processing (NLP) techniques, it can effectively extract the right information needed for the retailer to optimize their content as well as maintain their content health. Some of its capabilities include:

CatalogIQ extracting content from a product

Attribute Extraction: ​

Images would be clicked from all angles of the product and would be fed into the machine. Leveraging NLP techniques, brand attributes such as brand name, sub-brand, tagline, flavor, net weight/volume, and calorie information would be extracted.

Brand Name Detection (Logo detection): 

Leveraging OCR, the product image is scanned for text and the output is further sent to an NLP engine specifically to identify text logos (ex: for brand logos like Zara). If the text is not detected, image processing is further applied using the brand name parameters (ex: for brand logos like Nike)

Standard Certification Detection:

In this step, a preset database with standard food certification parameters is applied to detect and extract food certification labels such as “gluten-free”, “non-GMO”, “100% organic”. Here, the images are scanned using these parameters. This is similar to how the Brand Name detection functions.

nutritional label data extraction

Nutrition Facts Extraction:

Using OCR and region segmentation, nutritional facts text is extracted. This text is further corrected using a predefined vocabulary to streamline the content. A rule-based approach is then applied to the corrected text to extract nutritional values.

Product label images are a trusted source of product information for consumers. AI can ensure that the process would improve the quality of the information and maintain data consistency across all product pages. Retailers can further benefit from this as it would alleviate the burden of validating product data provided by various suppliers, online databases or third-party providers and can provide additional information that is critical for product discovery like brand or certification logo information.

The future of Product content

Applications leveraging AI and machine learning have projected tremendous potential for applying process automation to reduce data inconsistency and enhancing data quality and thereby, improving the product data extraction processes.


At IceCream Labs, we strive to address the challenges that businesses face in e-commerce using AI and machine learning. Are you ready to enhance your product content and take your e-commerce business to the next level? Reach out to us at sales@icecreamlabs.com for an AI-based solution for your business.


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Voice is changing the way consumers are shopping online

Voice is changing the way consumers are shopping online

It seems like, these days, you see an Amazon Alexa or a  Google Home everywhere. It’s not uncommon to see a person shout across the room to their voice device trying to turn the television on.

Amazon and Google have sold over 27 million voice command devices in the United States alone, and Apple’s Siri is available to more than 500 million users across the world. 

With the increased adoption of voice assistants, consumers are depending on them to do simple tasks like telling time, setting alarms or even making calls, so that they can focus their attention on some other tasks. However, it’s not just those simple tasks anymore, voice assistants are being increasingly used for online shopping, with users giving voice commands to the assistant about what products to purchase. Consumers are able to multitask without having to manually search different e-commerce portals and selecting products through each of their product categories, thereby, saving a lot of time.

Retailers, recognizing this trend, are slowly incorporating voice to further enhance the user experience. Incorporating voice in the shopping experience not only ups the convenience level of a shopper but also saves time lost in typing and searching for products. 21% of all Alexa and Home users have stated that they are shopping via their device today. Leveraging AI, voice recognizes language patterns such as dialects, intonations, and accents enabling them to converse with the user in a natural, conversational manner. The potential of turning the shopping world upside down is very high and the most immediate impact will be in the following areas:

Better searchability

SEO becomes beneficial for any retailer as it drives maximum traffic for e-commerce. However, there is a lot of difference between typing in search terms and using voice. Technology needs to evolve to differentiate voice commands from typewritten keywords. This will help to institute searchability and compatibility towards voice commands. Understanding the context is important as Voice is conversational. For example, auto-fill options must be provided for sentences or questions to understand the user intent.

With consumers increasingly moving towards voice search, e-commerce businesses must align their website and product pages to account for voice.

amazon echo dot

Ease of providing product reviews

The increase in voice searches eliminates having to browse through different categories and multiple pages. Furthermore, this has raised the importance of online reviews for products and services. The feedback loop between the retailer and the customer becomes more efficient and seamless.

For instance, imagine a customer ordered a pair of Nike Running shoes but never got around to filling out the review. The voice assistant would then ask questions like: “How would you rate your Nike Running shoes from one to five stars? Did it fit as you expected?” By answering these quick questions, the shopping experience can become increasingly personalized, providing better recommendations for the customer.

Online reviews will become increasingly important with almost 85% of voice-based customers trusting the recommendations provided by their assistants. These recommendations, in turn, are based on the top-reviewed products of that query making providing reviews more important than ever for retailers.

Shipment Tracking made easy

In the future, voice commands may not only be restricted to ordering products or proving reviews for them. Users may even get quick updates about their orders and their shipping status. There is a need for these complex processes to become more intuitive especially when consumers expect prompt responses. The retailer can enhance the shopping experience by connecting shipping operations with the voice app enabling users to get quick updates about their shipping status.

flat lay photography of coral Google Home Mini on black surface beside Apple AirPods

Although voice search and shopping is the next big thing, there are a number of challenges that are left unaddressed. The technology, in its current state, is yet to be equipped to handle complicated queries such as comparing different products. Many users still don't believe that the assistants can pick a product without choice, based on their query.

The consumer behavior is changing and as the popularity of using voice search grows, retailers must make decisions and act fast to cope with the change.

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Automation and AI in Retail: How are stores coping with the change?

AI & Automation in Retail: How are stores coping with the change?

The retail shopping experience is constantly evolving. The stores of the future may look completely different than the ones that customers visit these days. A large contribution to what would make them different would be the integration of AI and automation. Almost every retailer in the world is competing against e-commerce giants like Amazon, and with the introduction of its physical store, Amazon Go, which uses cameras and sensors to charge customers, there is more competition. Tech companies are seeking to roll out powerful automation tools for retailers, but will these tools be able to entice customers?

Automation is a preferred choice

The benefits of automation have already been experienced on a smaller scale with supermarkets offering self-checkout kiosks. While retailers invest considerably in these innovations, some of the benefits can be decreased reliance on the staff, and ensuring that the customers complete their shopping journey seamlessly. Furthermore, a study also concluded that a majority of US consumers believe that self-checkout kiosks enhance the store experience.

The checkout process can be made faster and simpler using a smartphone, and customers may no longer need to stand in long queues. They can use apps that are designed to read barcodes of  products and automatically generate the bill for the customer within the app itself.

It is important to note, however, that these innovations do not replace the human effort and it simply supports the staff and makes the logistics process more efficient.

robot standing near luggage bags

Going beyond checkout

Automating the checkout is only one application of automation. Some other applications can be bots that can automatically stock shelves or smart devices that can provide signals for products going out of stock or nearing expiration dates. These applications can help store managers to efficiently manage the inventory with the right data. 

Automation as Customer Service

Retailers are increasingly using Robotic Process Automation to make transactions and providing answers for simple customer queries faster. This ensures that the customers are provided with the help that they need, without the need for engaging the store staff. The US retailer Lowe, as an excellent example of application of this idea, introduced the LoweBot - an in-store robot that converses with customers and helps them locate items in the store using real time inventory tracking. Furthermore, it acts as an alternative source of information for customers who have simple questions, freeing up employees to attend to customers who need more guidance with their queries.

Automation and Pick-Up Trends Merge

Many retailers, such as Target, provide customers with the option of placing an order online and having it picked up at a nearby store without having to get out of the car, much like Drive-Thru at fast food chains.

This facility in its current model, however, requires human involvement. . As soon as the store receives an order, a staff member is required to bag the order, while another may be required to take them to the customer’s vehicle. While this is a convenient option for the customer, it creates  labor inefficiency for the store. Automating these processes would free up the staff allowing them to help the customers in-store and streamline the pickup routine for customers.

selective focus photography of group of people selecting vinyl record sleeves

Despite the stiff competition online, in-store shopping will survive, however, it needs to change to meet changing customer needs and preferences. Retailers must realize the importance of AI and automation and capitalize on it to stay ahead. The goal should be not to out-do e-commerce giants like Amazon but to provide an involved face-to-face customer service in a welcoming environment.

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Natural Language Processing: The future of e-Commerce product search

Is Natural Language Processing (NLP) the Future of E-Commerce Product Search?

There is a plethora of content available today and is growing by leaps and bounds every day. There is a need to organize this content into categories and ensure that they show up if it is searched for. This is especially important for e-commerce businesses and retailers who have catalogs of products. Here is when search engines play a critical role - be it the Google, Bing engines or the on-site product search engines which helps users find what they desire.

It is important for retailers and e-commerce businesses to understand and analyze the needs and behavior of their target customers. Listening to what they express online via social media or forums becomes imperative for these businesses to provide a better customer experience. This also helps them to understand what the kind of language the user may use to buy a specific item.

While this may be an easy task for humans, it is time-consuming. Here is where  AI and machine learning fits perfectly. Using Natural language processing, machines can easily pick on what words or phrases humans would naturally use while looking for a particular item.  

Natural Language Processing or NLP is the ability of a computer program to understand human language as it is spoken. Human speech is often ambiguous and the linguistic structure can depend on various complex variables, including the regional dialects and social context including colloquial terminologies.

Using a search engine is interacting with a system, and utilizing NLP helps customize the search for the user. Using NLP helps the system understand what kind of language was used and how the sentence was structured. Using these points, the system derives what the user is actually searching for, and provide results accordingly. Detecting patterns and creating links between the messaging is what it does best, and with Natural Language Processing, it is powered to derive meanings from unstructured text.

 For instance, a search query for “sleeveless men’s shirts” would involve understanding the context of the words, and without NLP, search engines would unable to process the link between sleeveless and shirts and the results would end up looking like this -

Search results for sleeveless men's shirts

Here, the word “shirt” has not been taken into account, and the results have shown only sleeveless “t-shirts” or vests instead of the intended search - “sleeveless shirt”.

Why do users search for “top budget-friendly phones from 2018” on a search engine yet not on e-commerce websites directly?

An intent for a search would be to find discussions and do some research in the user’s purchase process. And while the word “top” is subjective, content creators and SEO agencies (providing product lists) usually pick words such as “top” or “best” in their communication. 

Whereas, in an e-commerce store, users understand that using words like “top” or “best” is subjective. There is no rule that can translate “budget-friendly” being “less than $200” since it depends on the type of product as well as the perception of “budget-friendly”. The advantage of keyword heavy communication is that the format of communication is standardized - which works on most e-commerce sites.

What's plaguing Natural Language Processing today?

The performance of the NLP model depends directly on the quantity and quality of the data that it is fed, as s the case with every ML model. Retailers and e-commerce businesses need to consider the problem with synonyms and slangs which works differently in different regions. Lexical databases such as WordNet can come in handy, but they are limited to English and therefore it may not work for international stores, catering to customers from different cultures and languages.

There is a high possibility of a discrepancy between - what a customer calls a product, and how the metadata describes the product. The words that customers use to describe the desired product often describes another product rather than the one they want. 

Will NLP be the future of e-commerce product search?

Successful integration of NLP into online product search is still challenging. In a typical retail eCommerce application, it would involve getting an algorithm to gather data about all the products being sold and put in a structure and normalize it. It would then find all linguistic attributes that would be used to describe each product. The challenge here is that leveraging NLP technologies put the burden on search engines and not on the consumer to make the experience natural. 

Online product search will evolve in a manner in which the context understanding will be integrated with the search engine allowing humans to have conversations with them in a natural environment. For example, Customers searching for fashion products have a different way of phrasing requests as opposed to customers searching for home furnishing products. NLP platforms of the future would be able to contextually understand these variations.

As NLP gains momentum, the growth would give increase its capability to provide better customer experiences. NLP may very well be the future.

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Types of product imagery that drive eCommerce sales

Types of product imagery that drive e-commerce sale

Touching and feeling a product has always been the sign of a savvy shopper. The softness of a fabric, the firmness of a fruit has been the cornerstone of the tactile experience of shopping and we have traditionally held in esteem, the men and women who are skilled at establishing quality just by their sense of touch. This age-old process, however, is eliminated in the increasingly digital world and Retailers are scrambling to innovate so as to provide the customers an experience that engages their other senses.

This is where good product content comes into play - a detailed product description paints an image which allows the customer to get to know what the product is, what it can do and what it potentially feels like. Labels, ingredients, and materials used to expand the connection we have with the product or similar products and the customer is given as many tools as possible to create the sensations needed to make that purchase.

And finally, product images connect the textual dots to let the customer see what the product would look like and complete the full picture

Product images play a pivotal role

Uncertainty and missing information are two of the top reasons why people decide not to buy a product online. Customers tend to leave their shopping journey when they feel the product doesn't have enough or clear information. This could range from product reviews and the materials used to the size specifications. However, product imagery takes the cake in being the primary driver of sales. Studies have shown, 92% of consumers are driven to a purchasing decision based on product imagery, may it be images or videos.

Therefore naturally, the quality of product images directly affects the ability to generate sales making it crucial for brands and retailers to use high-quality images and HD videos while showcasing their products.

So what exactly do shoppers want from a brand or retailer's product imagery?

Product Angles

Customers generally like to view a  product from every angle. A study conducted by the Nielsen Norman Group found that consumers were better informed after viewing clear, high-quality product images. This helps them “see” and extrapolate on what the features could be. The quantity of images also plays an important role. For example, for a floral dress, the imagery should reflect the color palette, the draping, and how it would look like from the front, sides and the back. This would bring the user’s attention to small features such as a button or a sleeve detailing.

Walmart - product page for floral dress with different product images

In the case of electronics, besides the ability to view the product from every angle, the images can also show the buttons or outlet sockets. This would come in handy for the user as he may not need to refer to the manual every single time, looking for a function.

Size matters 

If customers need a magnifying glass to view product images, there is something wrong. The imagery should be able to show enough details that the customers don't need to go to a store and look at the actual product in person. If the imagery shows the detailing from afar as well as when zoomed in, it would imitate feeling the texture of the product. Furthermore, the size of the image in the product catalog also plays a large role in piquing the interest of a shopper.

The VWO blog reported an A/B test comparing catalogs with smaller vs larger images. The results showed that larger product images led to a 9.46 percent in sales in comparison to smaller product images.

So when it comes to product imagery, size does matter.

Presentation

The battle does not end with putting up images of the product. How it is being shown also affects the shopper’s buying decision.

While some shoppers prefer to see the product image against a plain background, there are some that want to see the products used contextually. There is another set of shoppers who would like to see user-generated images from people who have purchased the product in the past.

When providing product images for millennial shoppers, in addition to quality sized images, social media tends plays a pivotal role. Millennials get attracted to products that are socially endorsed. This also gives shoppers an impetus to share their purchases online.

While shoppers have always valued brand shopping experiences above all, the rise of innovations integrating online and in-store is intriguing. VR technology and AI integration help shoppers get a better understanding of how to use the products. Shoppers also depend on VR to help product images or product labels come to life.

Piled up Polaroid photos


The fact that consumers want quality product imagery is not something new, but it is valuable to know what kind of imagery the target customers are looking for. This ensures that retailers and brands create product content that meets the needs of the customers inadvertently driving sales and an increase in customer base.

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Online Shopping and Grocery – building consumer trust

Online Shopping and Grocery - building consumer trust

Online shopping has skyrocketed in the past few years. While it will continue to grow, majority of consumers still prefer to shop in-store - especially when it concerns grocery shopping. It’s no secret that the grocery industry has been the slowest major retail sector to join the e-commerce bandwagon.

As grocers are investing their resources to move their businesses online, there is competition among them to provide the best customer experience.

There have been a lot announcements made recently in this space. Retail giants like Walmart, announced the pilot of a new robotic back-end which would manage online orders in its super centers. Amazon added curbside grocery pickup for online orders as a Amazon Prime membership perk at Whole Foods. Whereas Target, rolled out same day delivery for groceries and other categories.

Consumers are concerned

While there is a lot of talk about omnichannel and online efforts, research suggests that people today still go to grocery stores.

According to a Gallup survey of 1,033 US adults, 84 percent said that they would never buy their groceries online. About 11 percent order groceries online for pickup or delivery twice in a month or lesser. While only 4 percent order once a week or more.

The survey highlights the fact that people still use the traditional means to purchase grocery. To ensure that the majority of the purchases are made online, it is imperative for the online retailers to provide an incentive to engage and help users purchase groceries online. The incentives could be in the form of timeline delivery of goods, competitive pricing, trusted & reliable products.    

The familiar feeling of a traditional store infrastructure cannot be replaced. Hence, the need of hour is to make the underlying technology for groceries feel human, comforting and intuitive.

lady buying apples at a store

Enabling technology for groceries 

By investing in technology and infrastructure, along with access to instant delivery channels, online grocers can build trust among consumers. 

Detailed product information 

Consumers have often been able to verify the quality of groceries such as fruits and vegetables with stores. When not physically present in the store, the consumer is unable to verify the quality of the products, thereby, increasing risk and uncertainty.

In an online platform, the product page must provide the right data of the particular product in terms of the images, the product description, the product specifications (size or weight/volume) along with the date it has been manufactured and the expiration date to be clearly mentioned.

This helps the consumer get the right information to make a decision. When consumers gain more knowledge about the product, and gain trust towards the platform, their uncertainty towards purchasing decreases. Furthermore, products that are from familiar brands also help reduce the perceived risk as the consumer already knows what to expect from a product that he/she is already familiar with.

Making reviews count

It is important to engage customers and community to rate the service of online retailers -   delivery of goods, the ease of ordering groceries through the platform, pricing, availability of groceries, etc.

Reviews are one way of building trust amongst existing users and new users alike to use the platform for their requirements. The more positive the reviews, the more users are likely to purchase through the online grocer.  

Instant Deliveries to instant gratification

Unlike appliances, groceries are mostly perishables and need to be consumed as early as possible. Sometimes, the need for groceries is almost instantaneous and requires delivery at the earliest. One way to make it readily available is strengthening the supply chain and the underlying technology to ensure instant delivery. 

Conclusion

While consumers today still prefer the traditional method of buying groceries, technology is fast catching up to cater to the needs and convenience of the consumer. There is tremendous scope for innovation and increase in grocery technology that can solve the problems for the retailers who want to move online and provide value to consumers. 


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