In the last year, there’s been an upward trend in cloud adoption in India. In fact, NASSCOM finds that cloud spending in India is estimated to grow at 30% per annum to cross the US$7 billion mark by 2022.

At Google, in our conversations with customers, discussions have evolved beyond cost savings and efficiencies. While those are still very relevant reasons for adopting cloud technologies, Indian enterprises are looking to Google Cloud to help them drive digital transformation, identify new revenue generating business models, reach previously untapped consumer markets, and build customer loyalty through greater insight and personalization.

Here are some companies and their stories.

Tata Steel: Mining data and maximizing its power

Tata Steel is a great example of an established enterprise from a traditional industry that is modernizing and embracing cloud computing. With an ambition to be a leader in manufacturing in India and a digital-first organization by 2022, Tata Steel believes smart analytics is key to enhancing operational efficiency and gaining business advantage. 

To organize data from siloed systems across the organization and make it easily accessible to all employees, Tata Steel is using Cloud Search and plans to scale it to more than one million documents and 28 disparate enterprise content sources including enterprise resource planning (ERP) and SharePoint. In fact, Tata Steel is one of the first Indian enterprises to harness the power of Cloud Search to meet some of the most aggressive ingestion demands, with indexing durations reduced from weeks to seconds.

They are also leveraging Google Cloud Platform (GCP) services like Google Cloud Storage and BigQuery to build their data lake and enterprise data warehouse so they can take advantage of advanced analytics and machine learning. Managed services such as AI Platform further enable Tata Steel to manage end-to-end AI/ML workflows within the GCP console. This complements their existing on-premise reporting and analytics tools, and brings data management to the forefront of everything they do—from forecasting market demand to predictive equipment maintenance.

“Digital is not just a goal, it’s become a way of life. We are digitizing everything from the deployment of factory vehicles to improving material throughput to marketing and sales. As a result, we have petabytes of structured and unstructured data that is not only waiting to be mined, but that we can generate intelligence from to create opportunities across our multiple lines of business using GCP,” said Sarajit Jha, Chief Business Transformation & Digital Solutions at Tata Steel.

Helping L&T Financial Services reach customers in rural communities

In rural communities, quick access to financial services can make a tremendous difference to livelihoods. L&T Financial Services provides farm-equipment finance, micro loans and two-wheeler finance to consumers across rural India backed by a strong digital and analytics platform. Their digital-loan approval app, which runs on GCP, makes it significantly faster and easier for people to apply for financial assistance to purchase important things such as farming equipment and two-wheelers. It also helps rural women entrepreneurs get quicker access to funds for their businesses through micro loans.

L&T Financial found G Suite to be a far better collaborative tool to help staff work together efficiently. Employees can interact with each other in real time using Hangouts Meet, and the task of information sharing is more seamless and secure through Drive. BigQuery also helps L&T Financial Services generate behavior scorecards to track credit quality of its micro-loan customers.

“Cloud is the technology that enables us to achieve scale and reach. Today there are countless data points available about rural consumers which enable us to personalize our products to serve them better. With access to faster compute power, we can also on-board consumers more efficiently. Our rural businesses have clocked a disbursement CAGR of 60% over the past three years.” said Sunil Prabhune, Chief Executive-Rural Finance, and Group Head-Digital, IT and Analytics, L&T Financial Services.

Creating conversational connections for Digitate’s customers

Digitate, a venture of TCS (Tata Consultancy Services), has integrated Dialogflow into its flagship brand ignio, an award-winning artificial intelligence platform for driving IT operations, workload operations and ERP operations for diverse enterprises. This integration is the next step in ignio’s product development journey, and will enable users to chat or talk with ignio to detect issues, triage problems, resolve them and even predict system behavior.

“ignio combines its unique self-healing AIOps capabilities for enterprise IT and business operations with Dialogflow’s AI/ML-based, easy to use, natural and rich conversational capabilities to create an unparalleled, intuitive and feature-rich experience for our customers,” says Akhilesh Tripathi, Head of Digitate.

Indian enterprises going G Suite

The base of Indian enterprises that are making the switch to G Suite to streamline their productivity and collaboration also continues to grow. Sharechat, BookMyShow, Hero MotorCorp, DB Corp and Royal Enfield are now able to move faster within their organizations, using intelligent, cloud-based apps to transform the way they work.

A hybrid and multi-cloud future in India

IDC predicts that by 2023, 55% of India 500 organizations will have a multi-cloud management strategy that includes integrated tools across public and private clouds. (IDC FutureScape: Worldwide Cloud 2019 Predictions  — India Implications (# AP43922319). We look forward to sharing more success stories of Indian enterprises that have taken the next step in their digital transformation journey.

In the United Kingdom, the popularity of online grocery shopping is expected to surge from about 6% of the market today to 9% by 2021, according to market research firm Mintel. One of the pioneers of online-only grocery retailing is Ocado, based in Hatfield, Hertfordshire in the U.K. Since starting commercial deliveries in 2002, the company has grown to 600,000 active customers, 260,000 weekly orders, and £1.39 billion in annual revenue.

Ocado takes supermarket trips out of the equation by enabling shoppers to purchase items online through its convenient web and mobile applications. Items are then picked and packed in automated warehouses and shipped directly to customers in a one-hour time slot of their choosing. Ocado’s delivery punctuality is 95%, order accuracy is 99%, and its service footprint now reaches more than 70% of the U.K. population.

“Google Cloud Platform gives us the flexibility and performance to tackle the large and complex data challenges unique to our business.”

—Paul Clarke, Chief Technology Officer, Ocado

The company achieved its success by building in-house almost all the technology and automation that powers its end-to-end e-commerce, fulfillment, and logistics platform. Ocado also developed a new platform, the Ocado Smart Platform (OSP), which offers large brick-and-mortar grocery retailers around the world access to a best-in-class solution for online grocery.

Democratizing machine learning

The shopping journey for online grocery retailing differs significantly from other e-businesses. Customers often buy dozens of products at once, a single household may have multiple buyers using multiple devices, and product shelf life may only be a couple of days.

“We often say that having built an end-to-end platform that can do online grocery scalably and profitably, we can do other forms of online retail; but the reverse does not necessarily follow,” says Paul Clarke, Chief Technology Officer at Ocado. “Google Cloud Platform gives us the flexibility and performance to tackle the large and complex data challenges unique to our business.”

The Ocado business model takes advantage of consumers’ shifting preferences and the links between digital technology and shopping experiences.

“Google Cloud Machine Learning Engine gives us the agility we need. Our developers were able to try out TensorFlow and see firsthand the benefits of machine learning in the cloud.”

—Paul Clarke, Chief Technology Officer, Ocado

The company has been building machine learning into its systems for over five years. Until recently, Ocado machine learning applications required specialist data scientists, typically with PhDs in machine learning, who would build these solutions from the ground up. It also required the specialist who set up the system and costly on-premises infrastructure to train and run these systems.

However, working with Google as a private alpha testing site for Google Cloud Machine Learning Engine accelerated its adoption of artificial intelligence (AI).

“We’ve been talking about how the cloud could democratize AI for some time,” says Paul. “Google Cloud Machine Learning Engine gives us the agility we need. Our developers were able to try out TensorFlow and see firsthand the benefits of machine learning in the cloud.”

TensorFlow is an open source software library for machine learning developed by the Google Brain team. Ocado developers, engineers, and data scientists now use TensorFlow for many of their machine learning projects. They deploy the models they build on Google Cloud Machine Learning Engine, which lets them train models faster across servers, desktop computers, and mobile devices through a single application program interface (API). Additionally, Google Cloud Machine Learning Engine integrates easily with the other Google Cloud Platform products used widely at Ocado.

What do customers really want?

One of the first TensorFlow models Ocado created was a machine learning algorithm that tags and categorizes customer emails and then prioritizes them for response.

The contact center receives thousands of emails each day and Ocado wanted to automate determining which ones needed to be answered immediately and which ones could wait.

For example, a first-time customer expressing their delight in using Ocado doesn’t need to be responded to with the same urgency as a customer who is missing an item from their order or who won’t be home to receive the delivery.

“Enabling agents to respond without having to sort through less-urgent emails improves Ocado’s responsiveness and customer service.”

—James Donkin, General Manager, Ocado

“We get a lot of emails from customers saying, ‘Our service was great,’ or ‘The driver was very courteous,'” says James Donkin, General Manager, Ocado. “But when issues like weather or road conditions potentially affect delivery, we often get surges of urgent questions. Enabling agents to respond without having to sort through less-urgent emails improves Ocado’s responsiveness and customer service.”

Using Google Cloud Machine Learning Engine, TensorFlow, and a large data set culled from several years’ worth of manually categorized customer emails, Ocado experimented on which kind of neural network architecture would best prioritize emails. After testing its models, Ocado implemented the highest-performing one and has been able to respond to urgent messages four times faster. The company also discovered that 7% of its emails don’t require a response at all, which means call center representatives now have more time to devote to higher priority messages.

“Without Google Cloud Machine Learning Engine, it would have been a lot harder to succeed on a project like email classification,” says Roland Plaszowski, who has recently managed several big data projects and initiatives at Ocado.

“Even if we invested significantly in infrastructure, it would be difficult to manage because of the computational intensity. It’s challenging and expensive to run machine learning projects at the same time without infrastructure that you can scale easily.”

Ocado also uses machine learning to predict customer behavior and improve experiences. By analyzing order data, Ocado makes shopping as frictionless as possible. For example, the ordering system can pre-populate customers’ shopping carts with items they are most likely to purchase, remind customers about items they may have forgotten, and notify them of multi-buy offers they haven’t completed, for example, only buying one of a buy one, get one free offer. Based on machine learning from previous purchase data, the Ocado system can also offer new products that are likely to delight customers.

“You will regularly see items that are more personally relevant to you instead of items that are being promoted more generally,” says James. “I’m a vegetarian, so I’m offered specials for vegetarian products that I normally buy and new ones that I’ve never bought. I’m also less likely to see things that I’m not interested in.”

Machines and machine learning

Within the Internet of Things (IoT), Ocado is looking to enhance its warehouse robots with machine learning. An integral part of the OSP, thousands of robots continually stream data into Google Cloud Storage and Google BigQuery.

Ocado data scientists apply machine learning to create a type of swarm intelligence that enables warehouse robots to work cooperatively to achieve a common goal. Projects include modules to search robot telemetry data, such as whether a battery pack is operating within standard tolerances or whether firmware has been successfully loaded, and use it to optimize maintenance schedules or detect patterns in wear and tear.

“Another challenge we’re looking at is how to embed machine learning directly into robots so they become smarter in terms of self-testing, exception handling, and error recovery,” says Paul. “This is a challenging combination of IoT, data analytics, and machine learning that we believe Google BigQuery and Google Cloud Machine Learning are particularly well suited to helping Ocado achieve.”

Scaling for new business

Scalability is also a major reason behind some of Ocado’s cloud initiatives, including the migration of all its on-premises data to the cloud. Ocado wanted to improve customer experiences, empower business teams with greater insight, and reduce IT overhead, so it consolidated onto Google Cloud Platform.

“The old databases just weren’t fast enough,” says Paul. “We needed a solution that could scale with the amount of data we generate and how we use it. Google Cloud Storage and Google BigQuery now provide the backbone, from a data point of view, for the Ocado Smart Platform.”

Ocado estimates its business, product, and transaction data is approaching two petabytes. Combining customer and supply chain data helps both internal Ocado operations and the company’s ambitions to commercialize OSP.

“When compared with other options for expansion internationally, selling OSP as a managed service lets us turn companies that could have been competitors into customers,” says Paul. “We want to build OSP once and then turn it on for multiple business-to-business customers.”

Each time Ocado adds a new hosting customer to OSP, it will launch a customized instance to fit that customer’s requirements. The capacity and performance of each new OSP instance must be able to scale quickly as the backend platform for established retailers with large numbers of products, customers, and transactions.

Ocado’s first OSP customer, Morrisons, is already benefiting from this first-of-a kind solution. Morrisons is one of the UK’s four largest supermarkets and uses OSP to power its online retail business. Using Google Cloud Platform, Ocado has stored, processed, and analyzed terabytes of Morrisons’ data using a dedicated data lake and Google BigQuery.

In addition to using Google Cloud Platform for OSP, Ocado also adopted it for its own online grocery retail business operation. Ocado originally used the Apache Spark and Apache Hadoop open-source frameworks on Google Compute Engine for its data platform. Moving to Google BigQuery frees Ocado business analysts from the complex query setup and workflows associated with Spark and Hadoop. Plus, it lets Ocado share data analytics with suppliers and partners.

Google BigQuery is well integrated with TensorFlow on Google Cloud Machine Learning Engine and Google Cloud Dataproc, the Apache Spark and Apache Hadoop service that lets Ocado use open source data tools for batch processing, querying, streaming, and machine learning. Google Cloud Dataflow and Google Cloud Dataproc handle cluster management, and provide an easy-to-use framework so developers can spend less time and money on administration and more time on delivering valuable business features.

Switching from Hadoop to Google BigQuery revealed a series of cost and performance improvements. For example, Ocado no longer needed to decide how many instances to bring up in a cluster or wait for the instances to spin up. Google handled everything.

“We simply ran our queries and paid for the resources that we use,” adds Roland. “One big win with Google BigQuery is we don’t have to do maintenance. Best of all, we saw Google BigQuery outperform our Hadoop cluster by over 80 times on our largest dataset, and for only two-thirds the cost.”

How many times has a shopper searched for a product on a store’s website only to get results that aren’t relevant—or worse, provided no search results at all? While most ecommerce sites have search capabilities, few accomplish their ultimate goal: making it easy for customers to discover the products they want.

Some 94% of U.S. consumers abandoned a shopping session because they received irrelevant search results, according to a 2021 survey conducted by The Harris Poll and Google Cloud. It’s a phenomenon known as “search abandonment.” Indeed, poor product discovery experiences can stop a purchase in its tracks and leave shoppers frustrated. Retailers miss out on a staggering $300 billion each year due to search abandonment in the U.S. alone.

With the announcement today of our latest discovery solution, Retail Search, now generally available, retailers worldwide can supercharge their websites and mobile apps with Google-quality search. Built on Google’s technologies that understand user intent and context, the solution helps businesses improve the search and overall shopping experience across all of their digital touchpoints.

Already, early adopters like Lowes, Fnac Darty, and Casas Pernambucanas have been using Google Cloud’s discovery solutions to increase sales conversions, boost basket sizes, and improve customer engagement.

Understanding user intent in search is a hard problem

While we’ve come a long way from the days when search was largely based on keywords and boolean rules, shoppers still struggle to find what they’re looking for. They often have to come up with a perfectly-worded query that a retailer’s site search engine will understand, sometimes rephrasing several times before getting the results they want—if they find anything relevant at all.

Traditional search technologies don’t work in the modern age of online retail, where tens or even hundreds of thousands of items are available on a single ecommerce site.

Today, people expect search engines to understand their intent more deeply, return relevant results faster, and help them discover new products easily with personalized recommendations.

Fortunately, product discovery experiences on retail sites can now offer superior, contextual experiences for consumers, combining marketing and data science insights along with advanced search technologies based on machine learning and artificial intelligence.

Now, through the power of Retail Search, when a shopper searches for a “long black dress with short sleeves and comfortable fit” on an ecommerce site, they should immediately get results for precisely that—rather than refining their search multiple times, or worse, giving up their shopping journey.

Helping retailers solve their search experience woes

Retail Search transforms the shopping experience and makes it easier for shoppers to find relevant products by surfacing results that are intuitive and contextual.

This fully managed service is easily customizable, enabling organizations to craft shopper-focused search experiences. Our site search solution builds upon decades of Google’s experience and innovation in search indexing, retrieval, and ranking. Retailers can make product discovery even easier for shoppers, while optimizing for their business goals with advanced capabilities, including:

• Advanced query understanding that produces better results from even the broadest queries, including non-product searches.
• Semantic search to effectively match product attributes with website content for fast, relevant product discovery.
• Optimized results that leverage user interaction and ranking models to meet specific business goals.
• State-of-the-art security and privacy practices that ensure retailer data is isolated with strong access controls and is only used to deliver relevant search results on their own properties.

Retailers can now shape product discovery into a shopping experience that is timely, relevant, and personalized—all powered by Google Cloud.

Early adopters seeing immediate impact

Several leading retailers around the world have been able to quickly create better online shopping experiences for their customers and capture more digital and omnichannel growth using Google Cloud’s product discovery solutions.

“With limited customer signals and no historical data, descriptive long-tail searches are some of the most challenging queries to understand,” said Neelima Sharma, senior vice president, technology, e-commerce, marketing and merchandising at Lowe’s. “We have been partnering with Google Cloud to give our customers relevant results for long-tail searches and have seen an increase in click-through and search conversion and a drop in our ‘No Results Found’ rate since we launched.”

“Making constant improvements to our website search engines has always been a priority for us as we aim to give our customers a simpler, more customized and enhanced online shopping experience,” said Olivier Theulle, Fnac Darty’s chief ecommerce and digital officer. “As we implement Google Cloud’s site search solution on our Fnac Darty websites and are the first French retailer to do so, we expect the solution to deliver increased conversion rates while also offering greater customer satisfaction.”

“Google Cloud has helped improve the indexing and quality of search results on Pernambucanas’ digital platforms, providing a better experience to our customers and improving sales conversions,” said Fabiano Rustice, chief information officer at Pernambucanas. “On Black Friday in November 2021—the largest retail date in Brazil—we saw a 20% reduction in search refinements per user, for which Retail Search was instrumental. We are proud to be an early adopter and to have Google Cloud as a strategic innovation partner.”

In addition to Google Cloud’s efforts working directly with customers, our ecosystem of key partners, including GroupBy, Lucidworks, GridDynamics, SpringML, and others are also leveraging Google Cloud discovery solutions to provide value added services to their customers.

“In the fast paced and extremely competitive ecommerce environment, we are seeing that forward-looking and innovative retailers are prioritizing product discovery,” said Roland Gossage, CEO of GroupBy. “GroupBy has proudly partnered with Google Cloud to launch its product discovery solutions across several client environments. Already we have seen a more than 10% increase in online revenues, which equates to millions of dollars, and this is just the beginning.”

To learn more, visit Discovery Solutions for Retail or contact your Google Cloud field sales representative.