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.

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.

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:

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.

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.

​

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.

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.

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.

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.

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.

Social commerce is often described as the intersection between social media and e-commerce. While this holds value, there is a lot of traffic with no direction. There’s no doubt that social selling is a powerful and an increasingly influential sales tool.

According to recent BI Intelligence, the top 500 retailers earned an estimated $6.5 billion from social shopping in 2017, up 24% from 2016.

There are various forms that social commerce adorns, from group buying to social shopping; from mobile apps to retailers adding social features, or shopping integrated into social media. All of these forms have one thing in common - the use of social technology to replicate age-old buying models in the digital sphere.

Whether it is girls going shopping together in a store or asking a friend for advice on power tools, moving them to online would result in them having a social commerce experience. Taking another instance of bartering, here, instead of the traditional method of trading goods or services, shoppers are trading personal data such as buying habits and preferences for access to easy shopping portals.

There are social platforms like Pinterest, Snapchat and Instagram which have incorporated a “Buy now” button that can turn a static image into a product with a click. However, since the story came about how social platforms are using and monetizing user data, there was a certain amount of wariness among the users about sharing their data on these networks.

The key here is to find a model of social commerce that would work the world over. Some of the things to keep in mind -

How to make it work

Say a user wants to purchase a mobile phone. The ideal route would be to go the website of the retailer of their choice (assuming if the retailer provides a social commerce experience). They can then choose the selection of the models of their favorite mobile phones.

They now post a picture of the phones on their social networks and ask friends to vote on which phone they think the user should buy.

By setting up this vote, the user can then earn shop credits. Their friends who voted for the products can also earn shop credits by that action. In this scenario, there is no prerequisite of having a large social media presence to be valuable for the business. This action inadvertently turns the user into a micro-influencer.

The information gathered during the voting helps the retailer sell more effectively. They learn which of the products is most appealing and have the potential to become hot sellers, and then accordingly manage stock or change how they display their products. They also gain access to an expanded audience. This eventually, helps them to build a relationship with their customers which can help them build brand loyalty.

Summary

Social channels have a major role to play. Besides influencing purchase decisions, social media is a larger part of the product discovery and research phase in the shopping journey.

The next few years will see social commerce expand its influence if it efficiently benefits the consumers and businesses. The world of commerce is on the verge of disruption, thanks to technological innovations, data collection, and social media. If social commerce is achieved correctly, the future of retailers and shoppers will widen.

AI has been empowering many of the world’s top technology solutions. Within e-commerce, AI is being used to make the industry increasingly customer-centric. This can be seen with sales forecasts, product recommendation engines, virtual personal assistants like Siri or Alexa, AI-powered chatbots, automated warehouses, etc.

The need for visual search in e-commerce

​
Large e-commerce companies offer catalogs with thousands of products and plenty of options. Customers, however, are becoming increasingly impatient during the buying process figuring out what they want to buy in the shortest possible time. This hence poses the question of how to make the search process short and seamless.

In an important use case, AI has enabled search engines to become smarter. This can be seen when using text as a search query, the search becomes more semantic and conversational. AI has also enabled enhanced voice search features. The latest popular feature being search via images.

A new way of search

E-commerce companies are investing largely in integrating all three search methods on their websites to make a more responsive search platform. With text and voice search being increasingly used for products such as electronics, visual search aids customers to find an easier alternative for fashion and lifestyle products which may be difficult to describe with words. For instance, when searching for outfits worn by celebrities, knowing the right keywords would provide the outfits that are indexed with those keywords. Most often than not, the right outfit is not found. With visual search combined with indexed images, the right outfit can be found in just a click!

Today, visual search has increased the level of engagement that customers have with e-commerce websites as well as offline retailers. Be it for searching a product page online or being provided with relevant product recommendations, smartphone apps are becoming more accurate and faster at predicting the customer needs.

Visual search has created new shopping experiences for online and offline retail stores. Customers can now scan images of their choice of products whether it is online or in a store. Providing relevant and accurate product results will ensure that users can shop from anywhere and at any time.

AI and visual search: The tech

Visual search is a very recent trend, and this has been possible only due to the recent advancement in this technology. Visual search is built using Deep Neural Networks, a subset of machine learning. This in fact built as a replica of the neural networks in the human brain.  To put it simply, Deep Neural Networks make machines intelligent to gather and categorize information in the form of text, images or videos like humans do, using their biological neural network.

For example, To make a machine understand a sofa using deep learning, it is first shown pictures of thousand sofas. The algorithm reads and extracts features that can collectively classify a sofa such as a backrest, armrest, cushions, etc. After this, if a new image of a sofa is shown, the machine would be able to now tell if the image has a sofa or not.

Furthermore, if a complete picture of a living room is shown, the machine can individually identify different objects that it has been trained via deep neural networks such as rocking chair, coffee table, rugs, side table, etc. This technology is very adaptive - it recognizes a user’s search pattern so as to provide accurate purchase predictions.

Deep learning technology, providing accurate results can ensure that users find exactly what are searching for, in a short search span. This helps enhance user experiences, which in turn, leads to an increase in conversion rates. Neural networks and deep learning provide the best solutions to problems being faced in image recognition, speech recognition, and natural language processing.

Visual search has become one of the most successful technological innovations in e-commerce and retail, in turn, boosting the effectiveness on a global scale.
With the tremendous emphasis on digitization and the rising economy, this holds a strong promise.

Today, companies are looking for product differentiation through tech and visual search advancements offer just what they are looking for.

Commerce has evolved over the span of 100 years. Over the century, it went from being driven by demographics in the 1890s - with retail catalogs offering a limited product selection to being driven by brands in 1990s - with stores and malls offering a rising product selection. With the advent of the world wide web, commerce was driven utility - with the rise of now e-commerce giants eBay and Amazon. In the 2010s, data began taking over as the driving trend.

The current trend

An indication of data-driven e-commerce are personalized e-commerce offering, curated product discovery and 24/7 recommendations. Many e-commerce and retail companies now have incorporated some level of personalization whilst engaging with their customers.

 The best examples can be seen with websites like Netflix - from the time a customer watches their first movie or show, Netflix’s algorithm starts working. They have been so successful at tracking customer preferences that they are spending almost 8 million dollars to keep up with the demand. They’re even using e-commerce personalization to affect the graphics for their shows.  Customers can get to see one of many different graphics of a show based on their preferences that Netflix knows about.

Another example on the other spectrum would be Subscription boxes like Stitch Fix, a personalized style service. Using the preferences provided by the user, Stitch Fix would deliver the best 5 items to suit them in the comfort of their own homes. Based on what customers keep and what they return, they personalize the clothes even further.

Personalisation completely relies on data gathering to be effective, which depends on the customers as they have to be willing to trust and share their information with a brand. The brand can then, enhance their experience and engagement using this data. It becomes a win-win situation when carried out efficiently, the use of data will correlate positively with customer satisfaction.

While this is the current scenario of how companies are using some elements of personalization today, here are some predictions about how personalization will play a more substantial role in the near future.

The rise in Subscription services

Customers are slowly evolving from product purchases to subscribing. This is due to the rise in personalization - the leading factors driving the growth of subscription-based services. There is plenty of evidence suggesting that subscription services are the future of e-commerce or at least a big portion of it.   

Digital subscription services such as Netflix, Amazon Prime or even Spotify are now the driving our entertainment consumption, while retail subscription services like Birchbox, Dollar Shave Club and Stitch Fix have started becoming more and more popular among consumers.

We have already demonstrated with Netflix, how personalization is breaking ground, another example could be Spotify, with their paid subscribers rose from 0% in 2008 at its launch to 45% in 2017. In other words, if a service is available for free, yet its paid counterpart is providing a better and compelling experience, consumers will not hesitate to invest.

Spotify uses personalization to effectively nurture its recommendation engine - which provides daily discoveries of music based on a user's preference of songs, artists, genres, etc. This has not only increased the app preference amongst users but also increased the exposure of new artists enlisting their albums & singles on the platform.

Automation to support omnichannel marketing

Automation and personalization largely go hand in hand.
In the next few years, automation will play a massive role especially with the growth of omnichannel marketing. A study conducted by The Harvard Business Review among 46,000 shoppers in the span of a year stated that only 7% of that group solely shopped online, 20% shopped solely in stores and a whopping 73% shopped using multiple channels.

Omnichannel marketing has challenges yet tremendous opportunities that companies will be able to leverage to utilize automation in the near future and the companies that would be able to leverage it would only be those that can efficiently utilize automation so as to offer personalization across all channels.

Personalised deals and pricing

Shoppers today not only differ on what they want to buy but also on what they would be willing to pay for. Traditionally, companies had to study different price ranges so they would pick on what would give them the most profit, even if this meant that they’d turn down potential customers.

This is now slowly changing. Personalisation is giving way for companies to charge individual shoppers. For instance, Orbitz used to charge Mac users more - based on the assumption that that demographic tended to have more spending power. Hence, it stood to reason that they were willing to pay more.

With the advent of AI and machine learning, companies in the future will easily be able to get into the granular details. This, in turn, will enable more people to get the products and services that they require at an accurate price point based on their past buying decisions. This approach will help e-commerce companies to build attractive bundles or tailor-made promotions for individuals.

AI and Machine Learning

Leveraging AI and machine learning, personalization can scale new heights by anticipating customer support needs before they even have them.

Furthermore, machine learning will also change the language that websites use based on the visitors it gets. In a scenario where a user needs help, machine learning would allow the site to find the best answers based on the information instead of the standard replies that most websites give now.

If the site knows that a returning shopper is looking for some products with respect to a previous purchase, they will be able to recommend the best products but also further provide them with relevant content that would help them make the purchase.

Looking ahead

E-commerce personalization is already here, so if retail or e-commerce businesses have not utilized it yet, they must start now. With more and more innovations coming in, the horizons are widening day by day. These predictions can ensure that your business is on the right path towards personalization.

Drafting creative SEO strategies is a crucial point for retailers. Having rich data like clear images, or detailed product descriptions is imperative as the content is one of the main traffic drivers for e-commerce portals. Implementing the right product description strategies can impact and enhance site searches and increase sales.

Images play a pivotal role

Site searches and site data are directly related. This means that if the site contains poor data, the results received will be insufficient and may not meet visitor expectations. Consumers are more willing to engage with content that includes relevant images, and hence mapping images to rich attributes is important so that the relevant results appear on the website. This would show products that would better interest the customer.

This is especially important for e-commerce businesses because the product overview and its appearance play a very important role during a purchase. A research even shows that over 90% of consumers consider that the images play an important role during the purchase journey.

Automated descriptions are accurate descriptions

Many consumers when searching to find a specific product, often get irritated when the results show irrelevant products. For instance, while searching for a “little black dress”, maxi dresses in the results should not show up.

Search engines rank images against several factors. These include file name, the captions provided with the image, the alt tags, how the product is categorized, etc.

AI solutions based on image recognition have started to gain momentum. They help generate deeper and accurate attributes from product pictures in an automated manner.  These solutions are based upon recognizing the shape and size of objects. In terms of fashion, this can help in identifying clothing - the type of clothing (shirt, blouse, jumpsuit), the color, its fabric (denim, lace, cotton), the patterns (stripes, chevron, floral) and its shape (short sleeved, cowl neck, etc.)

The aim of this solution is to function in a similar manner to that of a human brain. The AI solutions can classify and extract very specific information from an image. From a product provided by a customer, the machine uses algorithms to recognize specific patterns and arrive at certain conclusions. It also utilizes patterns from previous experiences, as a way to learn by itself.

Homogenizing product attributes and categorization

Loading products onto the website along with each product categorization can be extremely time-consuming. If this is done manually, there is a chance that typing errors or duplicate entries in categorization may happen. These mistakes can highly affect SEO rankings, as Google factors in linguistic accuracy and classification.

Furthermore, the more specific and descriptive the product attributes are, the easier the task becomes to match long tail searches and provide relevant products to the users entering a search query.

Powerful and intuitive image recognition AI solutions help to avoid categorization mistakes and provide richer attributes. This makes online catalog management efficient and enables search engines to find appropriate products - ultimately, enhancing product discoverability with automated content.  

Leveraging AI and machine learning capabilities, CatalogIQ can help your e-commerce or retail business by strengthening your content and improve your search rankings.

Are you looking for boosting your e-commerce business? At Icecream Labs, we aim to provide solutions for your business problems. To know more on how AI can impact your business you can get in touch on LinkedIn, or mail us — sales@icecreamlabs.com

There is stiff competition between Asian e-tailers and their American counterparts for the battle of global leadership. This is an extremely probable future as the e-commerce giants Alibaba and Amazon are already gaining momentum in India and Australia.

Nearly two-thirds of the US e-commerce industry is dominated by retailers such as Walmart, eBay, Best Buy and Amazon (that covers this space by over 40%), the local market is becoming more consolidated and less flexible. This is in comparison to the agile, data-driven, and fragmented Asian e-commerce industry.

Can the mature American retail market learn something from the comparatively young and rapidly growing Asian e-commerce market?

Modify selling strategies to Local markets

US-based brick and mortar stores and online retailers focus on domestic and English-speaking homogeneous markets. On the contrary, the nascent Asian e-tail market is constantly expanding beyond borders to markets with varying population sizes, purchasing powers and cultural backgrounds.

Here’s something to ponder upon — Amazon is the world’s third-largest retailer, it uses its universal selling strategy regardless of the market it scales up to, while its Asian competitor, Alibaba, the world’s sixth-largest retailer, acts on a different vision — “Act Local, Think Global”. This strategy works well in the rather fragmented Asian market and therefore, by extension, in the global market. Alibaba acts through local players or players that know the local market by offering a variety of affiliate programs.

For instance, Alibaba acquired a majority stake in Lazada, a major player in the South East Asian marketplace, an ideal platform for introducing Chinese vendors to non-Chinese buyers. Furthermore, another great example of Alibaba’s adjusting to local markets would be the expansion of Chinese marketplaces Taobao and Tmall in Russia. Alibaba’s strategy envisions adaptations to various local markets and finds ways of making the local systems reinforce each other.

Cross-Border selling is the name of the game

Alibaba is not the only e-commerce company that wants to increase cross-border sales. Recently, South Korean retailer GS Retail made a $29 million equity investment in the U.S. e-tailer Thrive Market. US e-tailers are not far behind. Even in the US, retailers have begun to realize that buyers outside the English-speaking markets can also generate revenue. This trend could be seen when Walmart, one of the biggest retail chains in the US, acquired a majority stake in India’s largest online retailer, Flipkart, making the transaction “the world’s biggest purchase of an e-commerce company.”

To function in an unfamiliar environment, retailers need expertise, which in today’s market, only the Asian players have. They can soon be well equipped to battle American retail in the global market with optimized operations and the ability to cover different markets with subsidized prices.

Retailers who are tech-savvy, stay ahead

Innovation is a crucial element for businesses to compete in the market whether it wants to play internally, externally or globally. More and more businesses are opting for technology including AI and machine learning to gain an edge over the competition.

ML and AI are disrupting retail by enabling businesses to observe competitive prices and monitoring trends, helping them to react to changes and forecast demand and sales. This way, retailers can boost their revenue and can build data-driven strategies and make better business decisions. No algorithm can be useful if the data it processes is not of high quality. Trained on the data, can it recommend optimal pricing and forecast sales which directly affects the business performance.
The better the data is, the better the outcomes are.

Conclusion

Successful businesses will be those that recognize and adjust their strategies and offerings to that particular market. Moreover, building several channels of communication with customers and leveraging the marketplace as a way of accessing consumers as well as integrating innovations and data into their operations will further strengthen their success.

It’s a no-brainer — Data-driven companies are already dominating the market. The other retailers need to jump on the bandwagon if they want to stay competitive.

We have already established that there is cutthroat competition present in the e-commerce and retail industries. This is forcing many brands to re-evaluate how they deliver value and stay relevant to customers. Retailers want to understand what drives their customers to visit their stores and make purchases, and how to reinforce those loyal behaviors.

Loyalty programs is one method to help achieve these goals like increased foot traffic, repeat visitors, build deeper engagement and reap a successful financial return on this loyalty investment. Customer loyalty programs are becoming increasingly popular and they offer a lot of benefits, to both the retailer as well as the customer.

Nielsen found that 84% of consumers are more likely to choose retailers that offer such a program, and 59% report that they’re available where they already shop.

Retailers need to capitalize on this interest in loyalty programs and create an active user base that eventually keeps them engaged with the brand for a long time.

What is a Customer Loyalty Program?

The idea is simple: The more you shop and spend, the more you receive in return. Nielsen describes them as “marketing programs that reward members with purchase incentives.”
With these programs, retailers can track and incentivize purchasing behavior and reward customers for their loyalty to their brand. This is a powerful customer retention tool as it motivates existing customers to continue engaging with the brand and therefore, spend more.

Different types of Customer Loyalty programs

Customer Loyalty programs come in different forms. Some retailers use only one type while some others create combinations of two.

Loyalty Points
This tactic can be seen especially in the grocery chains where customers get points for making purchases or perform certain actions such as providing some personal information.

Social Media
In this approach, retailers abandon the traditional approach to purchasing a product. They award certain points to their customers for social engagement with their brands such as sharing, liking or commenting on an ongoing campaign. Many brands even run contests and raffles that reward loyal fans with amazing prizes.

Paid Programs
Not every reward program is free. Some of these programs require their customers to pay a certain fee that could be a one-time payment or a recurring payment in order to enroll. Amazon Prime is a great example of this type of model.
Furthermore, these programs can also include partnerships with credit card companies who may provide special benefits and offers in exchange for reward points. Some of these benefits may include discounts, cash-backs, free shipping, access to exclusive shopping events, free services, upgrades.

Retailers may use these programs to modify buying behavior by incentivizing the action they want their customers to take. These programs also provide data to help retailers understand their customers more deeply. With this kind of data on purchasing behavior, it’s easier to segment, create personas, and gain insights to help guide new initiatives.

AI has found its way to many retail companies across different verticals and now have slowly made their way into loyalty and marketing programs as well.

Customers as well, to an extent have become familiar and comfortable with using these technologies. A research states that customers are increasingly willing to rely on algorithms and smart devices for enhanced and personalized retail experiences. This, in turn, fosters an expectation for convenient, low-friction shopping experiences with loyalty programs. AI and machine learning may help in streamlining customer experiences, but they are apt for managing and interpreting customer data captured by loyalty activities and customer interactions. A marketing strategy with an integrated AI and machine learning technology can create a single customer view dynamically, in real time. This can help brands and retailers with large footprints or multiple locations can understand their customers faster and predict trends and offers accordingly. Moreover, this helps them to stay ahead of the competition. Of course, enthusiasm for these technologies is at a high point, and there are many varied predictions about the impact AI will have on the world at large.

Summary

A research shows that retailers spend 5 to 10 times more to capture a new customer than to retain the currents ones. With Customer loyalty programs, engaging with the existing customers could cost less, and reap larger benefits in the future. Effective execution of these programs can increase the customer lifetime value and ROI. There is a huge chunk of consumers that modify their spending amounts in order to maximize points. Hence, program members are more likely to shop on a regular basis. Furthermore, they also activate word of mouth marketing as one customer’s experience with a brand can influence another’s choice or preference for a brand.