The retail industry is always in motion. Shifting macro-economic influences and changing customer expectations spark new business models, channel strategies, and strategic partnerships. To keep pace, retailers require a strong digital core that delivers powerful data-driven insights while staying compliant, maintaining security, and preventing fraud.  

Shree Venkat, chief architect at TCS, and GV Krishnan, Head of Solutions & Sales for the TCS Microsoft Unit in the UK and Ireland, note that business transformation, powered by artificial intelligence and machine learning (AI/ML), can help retailers innovate across four primary areas:  

Customer experience: Consumers want to engage with a retailer in a manner that eases their path through the shopping transaction.  TCS is a pioneer in delivering cloud-based solutions that facilitate hyper-personalization, such as Microsoft Dynamics 365 with Microsoft Dynamics 365 CoPilot, which turns service agents into superagents.  With Copilot, which brings next-generation AI capabilities and natural language processing to Dynamics 365, agents can quickly craft a draft email or chat response to customers with a single click. 

These insights can be used to deliver exclusive pricing offers, personalized call center access, and other rewards that build customer loyalty. AI/ML-based solutions like  Microsoft Cognitive Services helps further drive bespoke preferential services through sophisticated vision, language, speech, sentiment and contextual analysis to make bots more informed and more conversational.  

Retailers are also improving customer experiences by removing friction. Digitally driven innovations such as automated self-checkouts, pick and go, and contactless payments are helping in making happy customers. “When retailers embark on this journey, they gain more control in providing optimum customer experience and give their customers a sense of affiliation at the same time” says Shree Venkat. 

Store and channel operations: Customers have fully embraced ecommerce, social commerce, mobile commerce, and services such as buy online/pick up in store (BOPIS) and buy online/pick up at curb (BOPAC). 5G and edge computing technologies are helping retailers integrate and optimize in-store experiences. TCS specializes in AI-powered vision technologies like Microsoft Azure Percept to create a smart retail store solution that helps with visitor recognition, foot traffic analysis, store design planning, product quantity/quality checks, and more.  

Supply chain & sustainability transformation: Integration of data and digital systems with suppliers is a quintessential requirement for retailers to embark on intelligent supply chain initiatives. IoT & AI/ML-powered technologies can enable business capabilities such as just-in-time inventory management and auto-replenishment, which in addition to reducing waste and saving money also help retailers track, record, manage, reduce, and report emissions.  (In this whitepaper, TCS and Microsoft explore how to embark on supply chain decarbonization initiatives.)  

Employee experience: Shree Venkat believes the above three areas of business transformation seamlessly and quickly empower retail staff. Low-code/no-code technologies like Microsoft Power Platform, Azure Communication Services, and Azure OpenAI enable organizations to quickly capture customer sentiment, search for and respond to customer queries, and pass on the information to their frontline employees, providing real-time data about stocks, product insights, customer profiles, their buying patterns, and other behaviors to help them deliver better experiences across multiple channels. Training employees to use new digital tools and services is a critical step in the transformation journey for retailers. 

A powerful partnership 

Retailers face many challenges in keeping pace with rapidly changing markets and consumer demands. TCS along with Microsoft empower retailers to maximize the value derived from their Microsoft Cloud investments. The TCS Algo Retail™ framework, together with Microsoft Cloud for Retail, helps retail organizations transform into resilient, adaptable enterprises. 

TCS offers a rich portfolio of intellectual property using machine vision, conversational assistants, predictive analytics, machine learning, AI, and other capabilities on Microsoft Cloud. They include:  

TCS Optumera™— A strategic retail optimization suite that enables integrated, intelligent merchandising and supply chain decisions TCS OmniStore™— A future commerce platform that enables unified customer journeys catering to new channels and brand expansions TCS Optunique™— An enterprise personalization solution that delivers contextual hyper-personalization across omnichannel journeys 

These solutions help retailers enhance customer engagement, accelerate the launch of new products and services, build differentiation, and unlock business value.  

Learn how to master your cloud transformation journey with TCS and Microsoft Cloud.  

Cloud Computing, Retail Industry

In a bid to help retailers transform their in-store, inventory-checking processes and enhance their e-commerce sites, Google on Friday said that it is enhancing Google Cloud for Retailers with a new shelf-checking, AI-based capability, and updating its Discovery AI and Recommendation AI services.

Shelf-checking technology for inventory at physical retail stories has been a sought-after capability since low — or no — inventory is a troubling issue for retailers. Empty shelves cost US retailers $82 billion in missed sales in 2021 alone, according to an analysis from NielsenIQ.

The new AI-based tool for shelf-checking, according to the company, can be used to improve on-shelf product availability, provide better visibility into current conditions at the shelves, and identify where restocks are needed.

The tool, which is built on Google’s Vertex AI Vision and powered by two machine learning models — product recognizer and tag organizer — can be used to identify different product types based on visual imaging and text features, the company said, adding that retailers don’t have to spend time and effort into training their own AI models.

Further, the shelf-checking tool can identify products from images taken from a variety of angles and across devices such as a ceiling-mounted camera, a mobile phone or a store robot, Google said in a statement. Images from these devices are fed into Google Cloud for Retailers.

The capability, which is currently in preview and is expected to be generally available to retailers globally in the coming months, will not share any retailer’s imagery and data with Google and can only be used to identify products and tags, the company added.

Improving retail website experience

To help retailers make their online browsing and product discovery experience better, Google Cloud is also introducing a new AI-powered browse feature in its Discovery AI service for retailers.

The capability uses machine learning to select the optimal ordering of products to display on a retailer’s e-commerce site once shoppers choose a category, the company said, adding that the algorithm learns the ideal product ordering for each page over time based on historical data.

As it learns, the algorithm can optimize how and what products are shown for accuracy, relevance, and the likelihood of making a sale, Google said, adding that the capability can be used on different pages within a website.

“This browse technology takes a whole new approach, self-curating, learning from experience, and requiring no manual intervention. In addition to driving significant improvements in revenue per visit, it can also save retailers the time and expense of manually curating multiple ecommerce pages,” the company said in a statement.

The new capability, which has been made generally available, currently supports 72 languages.

Personalized recommendations for customers

In order to help retailers create hyperpersonalization for their online customers, Google Cloud has released a new AI-based capability for its Recommendation AI service for retailers.

The new capability, which is expected to advance Google Cloud’s existing Retail Search service, is underpinned by a product-pattern recognizer machine learning model that can study a customer’s behavior on a retail website, such as clicks and purchases, to understand the person’s preferences.

The AI then moves products that match those preferences up in search and browse rankings for a personalized result, the company said.

“A shopper’s personalized search and browse results are based solely on their interactions on that specific retailer’s ecommerce site, and are not linked to their Google account activity,” Google said, adding that the shopper is identified either through an account they have created with the retailer’s site, or by a first-party cookie on the website.

The capability has been made generally available.

Artificial Intelligence, Cloud Computing, Retail Industry, Supply Chain

Live shopping is one of the most exciting retail experiences in a long time. As shoppers become increasingly eager to buy via live shopping on social platforms such as Instagram and TikTok, retailers face new challenges: How to capture the shoppers’ attention on social media when the urge to buy hits? How can retailers create seamlessly interactive and immersive experiences that prompt consumers to hit the “buy button” during the live stream?

While retailers see the opportunity to pounce on this new trend, the hurdles they face are immense:

Connecting social commerce touchpoints like TikTok and Instagram, as well as built-in chat, video and voice functions, to commerce experiences is a must for seamless omnichannel experiences. Still, it’s not always easy to deploy new features and channels in their current commerce platform.Autoscaling traffic peaks from the live shopping experience is necessary, so consumers don’t face slowdowns or crashes when buying a product. When shoppers see a 404 error page, they’re not likely to come back. Unfortunately, most retailers still face scalability problems during sudden traffic spikes. Experimenting with new touchpoints enables brands and retailers to be ahead of changing customer demands now and in the future. Yet, experimentation is challenging to achieve in today’s IT environment.

These challenges stem from the rigid and monolithic nature of commerce platforms most retailers and brands still use today. These solutions, built for the desktop eCommerce era, lack the flexibility and scalability needed for spontaneous and high-volume sales powered by live shopping.

Flexible, scalable, agile: modern commerce starts with MACH

What’s the alternative for retailers looking at live shopping and beyond? As customer demands and market conditions change quickly, retailers and brands must move faster to capitalise on new ways to sell, such as omnichannel commerce, digital clienteling and personalisation. This means adapting customer experiences on the fly by adding new touchpoints, products, features, locales, currencies and every aspect of commerce without hassle.

This maximum flexibility and scalability philosophy is powered by the principles of MACH (Microservices-based, API-first, Cloud-native and Headless). In a nutshell, MACH-based architecture breaks down functionalities  such as integrating Instagram as a commerce channel  into modular pieces that can be easily customized, deployed, scaled and managed over time. In contrast to all-in-one legacy platforms, retailers using MACH can experiment, scale, change and adapt any functions at any time without disrupting their commerce backend or customer-facing storefronts. As 81% and 88% of adults under 55 years of age in Australia and New Zealand respectively shop online, and nine in 10 retail dollars spent offline during Australian peak season were influenced by digital, retailers are urged to rethink their digital commerce infrastructure to succeed in this new landscape.

For example, the Canadian menswear retail chain, Harry Rosenimplemented digital clienteling that sparked online traffic peaks. With MACH, the company had 0% downtime even as page views per session increased by 150%, coping with a three-fold increase in online sales without disruption.

Australian retail giant Kmart opted for MACH-based infrastructure to elevate personalisation and product categorisation, as well as autoscaling capabilities. During the COVID-19 pandemic, Kmart handled three times the online volume compared to pre-pandemic levels, and still, their eCommerce infrastructure was twice as fast. The company doubled the conversion rate and, just as importantly, operated at a third of its previous infrastructure costs.

Lastly, fashion retailer Express saw online traffic spikes that were three times higher than the busiest hour of Black Friday after a sales promotion went viral. Thanks to MACH, the company could avoid downtime and slowdowns to its webshop, fully capitalising on the sudden surge in sales.

Many retailers are shifting to MACH-based platforms to cope with traffic spikes from digital shopping. With modern commerce, retailers can add new touchpoints and experiment with new ways of reaching customers without constraints to the commerce infrastructure. 

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