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.

May was a very busy month for data analytics product innovation. If you didn’t have the chance to attend our inaugural Data Cloud Summit, video replays of all our sessions are now available so feel free to watch them at your own pace. 

In this blog, I’d like to share some background behind the innovations we released in May, why we built them the way we did, and the type of value they can bring your company and your team.

But first, a huge thank you!

This week, we had the honor to announce that Google has been named a Leader in The Forrester Wave™: Streaming Analytics, Q2 2021 report. Forrester gave Dataflow a score of 5 out of 5 across 12 different criteria, stating: “Google Cloud Dataflow has strengths in data sequencing, advanced analytics, performance, and high-availability”. 

Google has more than a decade of experience in building real-time and internet-scale systems for its own needs, and we are excited to see that our ability to provide customers with a reliable, scalable, and performant platform is bearing fruit. 

This announcement comes on the back of the release of The Forrester Wave™: Cloud Data Warehouse, Q1 2021 report, which also named Google Cloud as a Leader.

We couldn’t be more excited about the recognition and appreciate all your feedback and trust in the work that we do to support your goal in accelerating data-powered innovation.

Innovation galore

Your feedback and your passion is the fuel that drives our ambition to deliver more and better services to you. That’s why, this year, we didn’t want to wait until Google Cloud Next to share some great products we have been working on. On May 26, our team announced a slew of new products, services and programs. Watch a quick summary below:

https://youtube.com/watch?v=DG1mOPMXJvw%3Fenablejsapi%3D1%26

Meeting you where you are

An important design principle behind all of our services is “meeting you where you are”. This means we aim to provide you with the tools and software you need to innovate on your own terms. Here are three new services that will help you do just that:

Datastream

Datastream, our new serverless change data capture (CDC) and replication service, allows your company to synchronize data across heterogeneous databases, storage systems, and applications reliably and with minimal latency to support real-time analytics, database replication, and event-driven architectures. Datastream delivers change streams from Oracle and MySQL databases into Google Cloud services such as BigQuery, Cloud SQL, Cloud Storage, and Cloud Spanner, saving time and resources while ensuring your data is accurate and up-to-date. 

• Under the hood, Datastream reads CDC events (inserts, updates, and deletes) from source databases, and writes those events with minimal latency to a data destination. It leverages the fact that each database source has its own CDC log—binlog for MySQL and LogMiner for Oracle—which it uses for its own internal replication and consistency purposes. 
• Datastream integrates with purpose-built and extensible Dataflow templates to pull the change streams written to Cloud Storage, and create up-to-date replicated tables in BigQuery for analytics. It also leverages Dataflow templates to replicate and synchronize databases into Cloud SQL or Cloud Spanner for database migrations and hybrid cloud configurations. 
• Datastream also powers a Google-native Oracle connector in Cloud Data Fusion’s new replication feature for easy ETL/ELT pipelining. By delivering change streams directly into Cloud Storage, customers can leverage Datastream to implement modern, event-driven architectures.

Looker and BigQuery Omni on Microsoft Azure

Research on multi cloud adoption is unequivocal — 92% of businesses in 2021 report having a multi cloud strategy. We want to continue supporting your choice by providing the flexibility you need to see your strategy through. 

• This past month, we introduced Looker, hosted on Microsoft Azure. For the first time, you can now choose Azure, Google Cloud, or AWS for your Looker instance. You can also self-host your Looker instance on-premises.
• We also introduced BigQuery Omni for Azure, which along with last year’s introduction of BigQuery Omni for AWS, will help you access and securely analyze data across Google Cloud, AWS, and Azure. 

The cost of moving data between cloud providers isn’t sustainable for many, and it’s still difficult to seamlessly work across clouds. BigQuery Omni represents a new way of analyzing data stored in multiple public clouds, which is made possible by BigQuery’s separation of compute and storage. By decoupling these two, BigQuery provides scalable storage that can reside in Google Cloud or other public clouds, and stateless resilient compute that executes standard SQL queries. 

• Unlike competitors, BigQuery Omni doesn’t require you to move or copy your data from one public cloud to another, where you might incur egress costs. You also benefit from the same BigQuery interface on Google Cloud, enabling you to query data stored in Google Cloud, AWS, and Azure without any cross-cloud movement or copies of data. 
• BigQuery Omni’s query engine runs the necessary compute on clusters in the same region where your data resides. For example, you can query Google Analytics 360 Ads data stored in Google Cloud and query logs data from your ecommerce platform and applications that are stored in AWS S3 and/or Microsoft Azure. 

Then, using Looker, you can build a dashboard that allows you to visualize your audience behavior and purchases alongside your advertising spend. 

Dataplex

We understand that most organizations still struggle to make high-quality data easily discoverable and accessible for analytics, across multiple silos, to a growing number of people and tools within their organization. 

They are often forced to make tradeoffs. For instance, moving and duplicating data across silos to enable diverse analytics use cases or leaving their data distributed but limiting the agility of decisions. 

• Dataplex provides an intelligent data fabric that enables you to centrally manage, monitor, and govern your data across data lakes, data warehouses, and data marts, while also ensuring data is securely accessible to a variety of analytics and data science tools. 
• One of the core tenets of Dataplex is letting you organize and manage your data in a way that makes sense for your business, without data movement or duplication. For that, we provide logical constructs like lakes, data zones, and assets. These constructs enable you to abstract away the underlying storage systems and become the foundation for setting policies around data access, security, lifecycle management, and so on. 
• For example, you can create a lake per department within your organization (e.g. Retail, Sales, Finance, etc.) and create data zones that map to data readiness and usage (e.g. landing, raw, curated_data_analytics, curated_data_science, etc.). 

Once you have your lakes and zones setup, you can attach data to these zones as assets. You can add data from different types of storage (e.g. GCS Bucket and BigQuery dataset) under the same zone. You can also attach data across multiple projects under the same zone. You can ingest data into your lakes and zones using the tools of your choice, including services such as Dataflow, Data Fusion, Dataproc, Pub/Sub, or choose from one of our partner products. Dataplex comes with built-in 1-click templates for common data management tasks. 
To find out more about Dataplex, head to cloud.google.com/dataplex or watch the video below:

https://youtube.com/watch?v=bbFeAt7cw1g%3Fenablejsapi%3D1%26

Helping you innovate everyday

Sharing data is hard. Traditional data sharing techniques use batch data pipelines that are expensive to run, create late arriving data, and can break with any changes to the source data. These techniques also create multiple copies of data, which brings unnecessary costs and can bypass data governance processes. They also fail to offer features for data monetization, such as managing subscriptions and entitlements. Altogether, these challenges mean that organizations are unable to realize the full potential of transforming their business with shared data.

Analytics Hub

To address these limitations, we are introducing Analytics Hub, a new fully managed service that helps organizations unlock the value of data sharing, leading to new insights and increased business value. 

This new service is built on the tremendous experience and feedback we have received over the years. For example, BigQuery has had cross-organizational, in-place data sharing capabilities since its inception in 2010—and the functionality is very popular. Over a 7-day period in April, we had over 3,000 different organizations sharing over 200 petabytes of data. These numbers don’t include data sharing between departments within the same organization.

Analytics Hub takes sharing to the next level, making it easy for you to publish, discover, and subscribe to valuable datasets that you can combine with your own data to derive unique insights. 

This includes: 

• Shared datasets: As a data publisher, you create shared datasets that contain the views of data that you want to deliver to your subscribers. Data subscribers can search through the datasets that are available across all exchanges for which they have access and subscribe to relevant datasets. In addition, the publisher can track subscribers, disable subscriptions, and see aggregated usage information for the shared data.
• Curated, self-service data exchanges: Exchanges are collections used to organize and secure shared datasets. By default, exchanges are completely private, but granular roles and permissions make it easy to deliver data to the right audience—whether internal or public. 

This is just the beginning for Analytics Hub. Please sign up for the preview, which is scheduled to be available in the third quarter of 2021.

Dataflow Prime

At Google Cloud, we have the great privilege of working with some of the most innovative organizations in the world. And this work provides us with a unique perspective into the future of big data processing. Dataflow Prime is a new platform based on a serverless, no-ops, and auto-tuning architecture that brings unparalleled resource utilization and radical operational simplicity to big data processing. This new service introduces a large number of exciting capabilities but I’d like to highlight three key aspects of the product:

• Vertical Autoscaling: Dataflow Prime dynamically adjusts the compute capacity allocated to each worker based on utilization, detecting when jobs are limited by worker resources and automatically adding more resources. Vertical Autoscaling works hand in hand with Horizontal Autoscaling to seamlessly scale workers to best fit the needs of the pipeline. As a result, it no longer takes hours or days to determine the perfect worker configuration to maximize utilization. 
• Right Fitting: Each stage of a pipeline typically has a different resource requirement than the others. Until now, either all workers in the pipeline would have had the higher memory and GPU, or none of them would. Pipelines either had to waste resources or suffer slower workloads. Right Fitting solves this problem by creating stage-specific pools of resources, optimized for each stage. 
• Smart Recommendations: Smart Recommendations automatically detects problems in your pipeline and shows potential fixes. For example, if your pipeline is running into permissions issues, a Smart Recommendation will detect which IAM permissions you need to enable to unblock your job. If you are using an inefficient coder in your job, Smart Recommendations will surface more performant coder implementations that can help you save on costs.

What’s next

We’re excited to hear your thoughts and feedback about all these exciting new services. I would also highly recommend that you connect with members of the community to learn more about their story and journey. A good example to start with is the Data To Value customer panel we produced at our inaugural Data Cloud Summit with the Chief Data Officers of Keybank and Rackspace. You can watch it for free below:

https://youtube.com/watch?v=ITI2Q3MkxuA%3Fenablejsapi%3D1%26