Cloud Retail Search, part of Discovery Solutions For Retail portfolio, helps retailers significantly improve the shopping experience on their digital platform with ‘Google-quality’ search. Cloud Retail Search offers advanced search capabilities such as better understanding user intent and self-learning ranking models that help retailers unlock the full potential of their online experience.

Google Cloud’s Discovery Solutions For Retail are a set of services that can help retailers improve their digital engagement and are offered as part of our industry solutions.

Executive Summary

Retailers are always working on trying to keep up with the ever changing consumer expectations and trying to forecast the next trend that can impact sales and revenue.

The pandemic brought its own (and largely new) set of challenges which further complicated the issue over the last two years. The retailers were forced to adapt to the new consumer (low physical touch) behavior in which the browsing and product research was largely digital (endless aisle) and accelerated other trends such as buy online and pick up in stores (BOPIS), curbside pick up and pick up lockers. According to a McKinsey Global Survey from early last year, the pandemic has accelerated the pace of digital transformation by several years.

The National Retail Federation (NRF) estimates that retail sales are expected to grow between 6% and 8% in 2022 (slower growth rate than in 2021), as consumers spend more on services instead of goods, deal with inflation and higher food & gas prices due to geopolitical disruptions in the world.

And the competition continues to be fierce as ever. Amazon continues its dominance in the U.S. retail world and new PYMNTS data shows that Amazon’s share of US Ecommerce sales hit an all-time high of 56.7% in 2021.

Customers now have more choices than ever on how they want to engage with the retailers, where they want to spend the money and make their purchase. They also have increased expectations from the retailers around providing a high quality product discovery experience, which is forcing the retailers to invest heavily on improving customer engagement on their digital platforms to boost conversion rate and overall customer loyalty.

This is where Retail Search can help by providing an enhanced search experience that uses Google-quality search models to understand the customer intent and takes into account the retailer’s first party data (such as promotions, available inventory and price) for ranking results.

How is Google Cloud Retail Search Different

The Ecommerce platform on-site search use case is not new and retailers have been trying to solve it effectively for the last two decades. Most retailers recognize that search is a critical service on the platform and have spent countless resources to improve and fine tune it over the years. Yet the challenge remains. According to a Baymard Institute study in late as 2019, 61% of sites still required their users to search by the exact product type jargon the site uses.

However, users now expect the same robust and intuitive search features as is offered by Google.com and other popular web platforms, who seem to have the uncanny ability to intelligently interpret and yield relevant results to complex search queries.

Google’s decades of experience and research in search technology benefits Cloud Retail Search solution and that is what differentiates it from the competition.

• Advanced Query Understanding: Retail Search can provide more relevant results for the same query due to better query understanding features and knowing when to broaden or narrow the query results. While most search engines still rely largely on keyword based or matching tokens results, Retail Search has the advantage of being able to leverage Google search algorithms to return highly relevant results for product listings and category pages.
• Semantic Search: Intent recognition is a key requirement for semantic search and identifying what the customers mean when they enter the query is a key strength of Retail Search. This is critical for retailers since this has a direct impact on Clickthrough rate, Conversion rate and the Bounce Rate.
• Personalized Search Results: Another key differentiator for Retail Search is its ability to leverage user interaction data and ranking models to provide hyper personalized search results. Retailers are able to optimize search performance to deliver desired outcomes: better engagement, revenue, or conversions.
• Self-Learning and Self-Managed Solution: Retail Search models get better over time because of the self-learning capabilities built into the solution. In addition, the service is fully managed, which saves precious resources needed to keep it running and managing its set up.
• Strong Security Controls: The service runs on Google Cloud and follows security best practices to keep our customers’ data secure. Google never shares model weights or customer data across customers using the Retail API or other Discovery Solution products. For more details about this data use, see a description of Retail API data use.

High Level Conceptual View

Here is a simplified high-level view of Retail Search API. Retailers can call the API for the given search query and get back the results which can then be displayed on their digital properties.

The returned results contains two types of information:

• Search results: Query search results including product listings and category pages based on advanced query understanding and semantic search.
• Dynamic faceted search attributes: Faceted Search is a feature that allows further refinement of the search by providing ways to apply additional filters while returning results.

Retail Search needs the following datasets as input to train its machine learning models for search:

• Product Catalog: Information about the available products including product categories, product description, in-stock availability, and pricing.
• User Events: This is the clickstream data that contains user interaction information such as clicks and purchases.
• Inventory / Pricing Updates: Incremental updates to in-stock availability and pricing as that information is updated.

(Keeping the product catalog up to date and recording user events successfully is crucial for getting high-quality results. Set up Cloud Monitoring alerts to take prompt action in case any issues arise).

Retailers also have the ability to set up business/config rules to customize their search results and optimize for business revenue goals such as Clickthrough rate, Conversion rate, Average size order etc.

How to get started

Retail Search is generally available now and anyone with a Google cloud account can access it. If you don’t already have an account, you can start with a trial account for free here.

Establish a Success Criteria: It’s important to establish a success criteria for measuring the effectiveness of Retail search. Get a consensus on which factor(s) you want to include in scope for measuring the effectiveness of Retail Search. This could include one or two from the following: Search Conversion Rate, Search Average Order Value, Search Revenue Per Visit and Null Search Rate (No Results Found).

• Initial Set Up: Create a Google Cloud Project and set up the Retail API. When you set up the Retail API for a new project, the Google Cloud Console displays the following three panels to help you configure your Retail API project:
  • Catalog: displays product catalog and a link to import catalog.
  • Event: displays user events and a link to import historical user events.
  • Serving configs: contains details on serving configuration and a link to create a new serving configuration.

• Measuring Performance: Retail dashboards provide metrics to help you determine how incorporating the Retail API is affecting the results. You can view summary metrics for your project on the Analytics tab of the Monitoring & Analytics page in Cloud Console.

• Set up A/B Experiments: To measure the performance of Retail Search with another search solution, you can set up A/B tests using a third-party experiment platform such as Google Optimize.

Summary:

As retailers try to navigate through the post-pandemic world where supply chain failures and digital transformation acceleration are major focus areas, they now also have to keep a close eye on the recent geopolitical challenges resulting in rising inflation and costs.

While we can all agree that in-store shopping will continue to be a major source of revenue, it is also important for retailers to tweak the in-store experience for the digital world. Trends such as buy online and pick up in stores (BOPIS), curbside pick up and pick up lockers are here to stay.

Given all the above, consumer engagement and digital experience is more important now than ever before. The cost of search abandonment is way too high and has both short and longer term impact. Retail Search is a great solution to help reduce churn, improve conversion and retention. It provides Google-quality search models to help understand customer intent and the retailers have the ability to set up business/config rules to optimize search results for business revenue goals such as Clickthrough rate, Conversion rate and Average size order.

B2B marketplace IndiaMART aims to help businesses escape the restrictions of traditional supply chains. By providing access to a digital platform optimized for access from desktops and mobile devices, businesses can improve their operations and generate more revenue. IndiaMART’s suite of services includes web storefront, enquiry support, priority listings, premium number services, a lead management system, and payment facilitation.

IndiaMART also provides “behavior-based matchmaking” that identifies the supplier best equipped to meet buyer needs by product or service, category and location. IndiaMART then matches the designated suppliers with the buyers. Finally, IndiaMART operates as a “horizontal marketplace”—enabling suppliers to market to a large number of potential buyers—presenting a compelling offering for both groups.

Amarinder S. Dhaliwal, Chief Product Officer of IndiaMART, says once IndiaMART grew to a certain size, it benefited from a network effect—as more buyers used the marketplace, more suppliers came on board, prompting yet more buyers to access the service and so on. Suppliers becoming buyers and using IndiaMART to purchase products is another growth driver. “There is not just a network effect, but a community effect as well, as a supplier becomes a buyer,” says Dhaliwal. “This increases affinity, and supplier and buyer ‘lock-in’ to the marketplace increases multifold.”

Positioned to address challenges

IndiaMART’s proactive approach positioned the business well to address the challenges presented by new trends and market conditions. Traffic from mobile devices to its business-to-business marketplace has grown from about 30% to 75% over the last four years.

“Mobile traffic has grown at a compound annual growth rate of almost 100% over the same period,” says Dhaliwal. “The proliferation of smartphones and other mobile devices has brought a considerable number of new users onto the internet and these users look for value—the right price from the right supplier,” he adds. “Furthermore, they can connect at any time and from any location they can access a network.”

The mobility revolution also challenged IndiaMART to provide a user interface and experience optimized for devices of various types and sizes—and that incorporated screens much smaller than the screens incorporated in desktops. The organization also had to help users overcome issues such as inconsistent network coverage and quality—particularly in remote areas.

Becoming a mobile-first organization

IndiaMART is responding by becoming, for buyers, a “mobile-first” organization that meets the group’s technical and user experience requirements.

IndiaMART is also adapting its marketplace to support two key trends:
• Buyers using long, conversational sentences to conduct online searches rather than simply typing in keywords
• Non English-users—the vast majority of people in India—stepping up their use of the marketplace

“We expect that, within a few years, we will have more non-English users than English users on IndiaMART,” says Dhaliwal.

IndiaMART is also benefiting from Indian government measures to reform taxation and stimulate the digital economy. “There has been a huge focus on areas such as digital payments and the digitization of identity,” says Dhaliwal. “We have embraced elements of this agenda by implementing a digital payment platform and are continuing to look at ways of providing new digital services to suppliers.

“Meanwhile, the Indian government’s recent implementation of GST allows us to validate suppliers’ businesses and bring more qualified, more verified suppliers on our platform—improving the experience for buyers and suppliers.”

Speed and reliability an issue

IndiaMART had started operations with servers, storage, networking, and associated systems co-located in a data center in the United States. However, as buyers and suppliers increasingly used mobile devices—over occasionally unreliable networks—to access the marketplace, access speeds and reliability became an issue. With most requests traveling between India and the United States, network latency was unacceptably high. Furthermore, business growth meant IndiaMART needed an environment that could scale to meet demand for the next five to 10 years.

IndiaMART opted for a multi-cloud architecture and established criteria for cloud providers to win its business. “We required an infrastructure that could scale and meet our demand for fast response time without putting our business at risk,” says Dhaliwal. “This meant taking a phased rather than one-shot approach to the migration. We also needed to minimize any wasteful duplication of infrastructure and reduce latency. In addition, as we scaled, we needed to protect our systems, transactions and information, including the details of buyers and suppliers.”

Google Cloud team’s high-quality support

The organization performed proof of concept with the three largest multinational cloud services providers and found Google Cloud was best positioned to act as the cornerstone of its multi-cloud architecture. “The Google Cloud team gave us considerable support in helping us run a proof of concept of its services,” says Dhaliwal.

“The proof of concept also illustrated that Google Cloud was superior to the other cloud services we looked at.

“We could run our marketplace across multiple geo-locations under a single IP address, avoiding duplication, and users in India could connect to Google Cloud via the closest access point, with their traffic passing quickly across the Google network.

“In addition, Google Cloud’s load balancing service would enable encryption between load balancing layers and back ends to ensure security, while all communication would move across Google’s own protected network.”

Being one of India’s early users of G Suite, the organization was also familiar with Google Cloud applications and services.

IndiaMART then opted to work with the Google Cloud team and a certified partner on a step-by-step implementation that minimized any risk of disruption.

Deep engagement from Google

“We engaged very deeply with both Google and the partner to complete this migration,” says Dhaliwal. “The Google team worked very hard to understand our requirements and provide a solution that catered to our needs and could be deployed in a phased manner.” The team ran workshops and technical sessions with IndiaMART and, at a Google Summit, connected the marketplace provider to Google experts in databases and infrastructure.

“These discussions really helped us formulate a strategy moving forward,” says Dhaliwal.

Based on input from Google and its own evaluation, IndiaMART developed an architecture comprising virtual machine instances delivered through Compute Engine, Google Cloud’s infrastructure-as-a-service offering; Cloud Load Balancing to support cloud resources distributed across multiple locations; Cloud Pub/Sub to provide enterprise messaging; and Cloud Dataflow to transform and enrich data.

Cloud Armor works with Cloud Load Balancing to defend against distributed denial of service (DDoS) attacks; and Geocoding API helps the organization convert geographic coordinates into readable addresses and vice versa. Cloud AutoML allows IndiaMART to train machine learning models to meet its requirements. With Geocoding API, IndiaMART can matchmake buyers and suppliers based on location—providing a high quality experience for both parties. Finally, AutoML Translation allows the organization to create a custom machine learning model that converts product names from English into Hindi and other languages, effectively opening up new markets for buyers and suppliers.

Phase one complete

IndiaMART has completed phase one of the migration that involved moving its web properties across to Google Cloud. The organization is now experimenting with moving its APIs and databases to the service and anticipates completing the exercise over the coming year.

Average page load time down

The initial phase of the project has already delivered considerable benefits to IndiaMART. The organization has cut average page loading time from five seconds to three seconds, and Dhaliwal attributes close to one second of that reduction to the move to Google Cloud. “With Google Cloud, buyers and suppliers can access our marketplace much faster than previously,” says Dhaliwal. “This impacts positively on engagement, time spent on our marketplace, and the user’s entire journey with us.”

DDoS attack repelled

Google Cloud’s security features have already passed their first test. As IndiaMART undertook stage one of the migration, the business experienced a DDoS attack that generated request loads more than 400 times greater than normal. “Because we were on Google Cloud infrastructure, we could develop a solution to combat this severe DDoS attack,” says Sunil Parolia, Sr. VP at IndiaMART. “From a security perspective, this really justified our decision to go with Google Cloud.”

Google Cloud is also helping deliver the availability required by IndiaMART and the scalability to support growing demand. “As the number of people in India who access the internet grows from about 500 million to 700-800 million over the next couple of years, we will continue to build our traffic and be the dominant business-to-business platform,” says Dhaliwal. “On the supplier side, we expect to see more and more businesses come onto our marketplace—ranging from small-to-medium businesses up to larger brands. Google Cloud will enable us to accommodate this traffic without compromising the experience we provide.”

The home lending journey entails processing an immense number of documents daily from hundreds of thousands of borrowers. Currently, home lending document processing relies on some outdated digital models and a high dependency on manual labor, resulting in slow processing times and higher origination costs. Scaling a business that sorts through millions of documents daily, while increasing efficacy and accuracy, is no small feat. When it comes to applying for a mortgage loan, consumers expect a digital experience that’s as good as the in-person one. Roostify simplifies the home lending journey for lenders and their customers.

No time to spare: Overcoming document processing challenges with AI

Roostify provides enterprise cloud applications for mortgage and home lenders. In order to empower its customers to deliver a better, more personalized lending experience, they needed to automate and scale their in-house document parsing functionality.

As a key component of its document intelligence service, Roostify is leveraging Google Cloud’s Lending DocAI machine learning platform to automate processing documents required during a home loan application process, such as tax returns or bank statements with multi-language support. This partnership delivers data capture at scale, enabling Roostify customers to automatically identify document types from the uploaded file and to extract relevant entities such as wages, tax liabilities, names, and ID numbers for further processing, and make things move faster in the cumbersome lending process. 

Roostify’s solutions leverage Google Cloud’s Lending DocAI, which is built on the recently announced Document AI platform, a unified console for document processing. Customers can easily create and customize all the specialized parsers (e.g., mortgage lending documents and tax returns parsers) on the platform without the need to perform additional data mapping or training. All Google Cloud’s specialized parsers are fine-tuned to achieve industry-leading accuracy, helping customers and partners confidently unlock insights from documents with machine learning. Learn more about the solution from the GA launch blog and the overview video.

Integrating Lending DocAI’s intelligent document processing capabilities into the Roostify platform means more innovation for their customers and tangible results: faster loan processing times, fewer document intake errors, and lower origination costs. Additional support in Google Lending DAI for other languages and more documents like global Know Your Customer (KYC) documents or payroll reports is in the near future.

Full integration of AI solutions

Working together with Roostify’s platform team, we were able to help them solve their document processing challenge through integration of various GCP products such as Lending DocAI (LDAI), Data Loss Prevention (DLP) for redacting sensitive data, BigQuery for data warehousing and analytics, and Firestore for API status. To make it very safe and secure, all data was encrypted end-to-end at Rest and in Transit. LDAI won’t require any training data to process. It is an easy plug and play API.

Here is a sneak peek in the high level deployment architecture for LDAI in Roostify environment:

Here are the steps for processing data:

1. Receives document processing request from the client.
2. API Function directs requests to the pre-processing service. For Async requests a processing ID is generated and returned to the caller.
3. Pre-processing service sends the request for further processing (Long/short PDF conversion), calling other microservices and receives back the responses. Any error in the response received is then sent to the response processing service. 
4. If the response is synchronous, the pre-processing service directs it to the LDAI Invoker service. 
   1. If the response is asynchronous, the pre-processing service feeds it into the Cloud Pub/Sub service.
5. Cloud Pub/Sub service feeds the response back to the LDAI Invoker service.
6. LDAI Invoker service routes the request to the Google LDAI API for classification if there are multiple pages in the document.
7. Document will be split based on LDAI response and then saved in a GCS bucket for temporary storage.
8. LDAI entity interface for single page processing and then LDAI Invoker sends LDAI results to LDAI Response Processing
9. If a request is a synchronous request the LDAI Response Processor sends results to the API Function so that it can complete the synchronous call and respond to the rConnect caller.
   1. If the request is an asynchronous request the LDAI Response Processor will respond to the caller’s webhook and complete the transaction.
10. Finally, Data stored in the GCP bucket will be deleted.

All the responses that come from the LDAI API can optionally feed into BigQuery via the Response Processor, after parsing it through Data Loss Prevention (DLP) API to redact the PII/sensitive information.  Throughout the processing of both asynchronous and synchronous requests all transactions are logged using Cloud Logging.  For asynchronous transactions, the state is maintained throughout the process using Cloud Firestore.

Roostify currently uses this technology to power two different solutions: Roostify Document Intelligence and Roostify Beyond™. Roostify Document Intelligence is a real-time document capture, classification, and data extraction solution built for home lenders. It ingests documents uploaded by borrowers and loan officers, identifies the relevant documents, and extracts and classifies key information. Roostify Document Intelligence is available as a standalone API service to any home lender with any digital lending infrastructure already in place. 

Roostify Beyond™ is a robust suite of AI-powered solutions that enables home lenders to create intelligent experiences from start to close. It combines powerful data, insightful analytics, and meaningful visualization to streamline the underwriting process. Roostify Beyond™ is currently available only to Roostify customers as part of an Early Adopter program and will be rolled out to the market later this year.

Through this partnership, Roostify has enabled its customers to adopt a data-first approach to their home lending processes, which will lead to improved user experiences and significantly reduced loan processing times.

Fast track end-to-end deployment with Google Cloud AI Services (AIS)

Google AIS (Professional Services Organization), in collaboration with our partner Quantiphi, helped Roostify deploy this system into production and fast-tracked the development multifold to generate the final business value.

The partnership between Google Cloud and Roostify is just one of the latest examples of how we’re providing AI-powered solutions to solve business problems.