WayFair and Google Cloud Get Together to Raise the Bar on World-class Experience!
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On Dec 9th and 10th, Wayfair and Google Cloud came together for the inaugural Wayfair-Google Cloud Machine Learning Hackathon. Wayfair firmly believes that hackathons are a great way to fuel a culture of collaboration and experimentation. To fuel Wayfair’s incredible pace of innovation at scale, it’s team of more than 3,000 technologists is constantly experimenting and taking smart risks. It’s test-and-learn culture empowers everyone to think critically and creatively, and take big swings to raise the bar on its world-class experience.
The Wayfair-Google Cloud Machine Learning Hackathon was all about getting Wayfairians excited about and enabled on new technology. More specifically, this event was a contained test environment for Wayfair innovators to validate AI and ML tools in order to understand how to get better insight from their data. The projects worked on during this hackathon will help Wayfair build new use cases that could impact the business and the end customer in new and productive ways. Google Cloud’s focus for this Hackathon was to enable Wayfairains to harness the power of Machine Learning and AI to enable their own goals around continual improvement and relentless customer focus.
Prior to the event, Wayfair Data Scientists and Machine Learning Engineers who signed up and submitted ideas for the Hackathon were invited to optional Google Cloud enablement and training sessions. Google Cloud set up classrooms in Qwiklabs on topics including, but not limited to, BigQuery, Vertex AI, and Natural Language AI, so that all Hackathon participants could try out new technologies and tools in a learning environment. Google Cloud subject matter experts were available to field any questions Wayfairians had about the use cases they were hacking on.
The Hackathon was a hybrid virtual & physical event that hosted 67+ innovators, 49 of which registered for Google Cloud supported ideas. 15 judges from both Google Cloud and Wayfair oversaw the event. The esteemed list of judges included Steven Conine, Wayfair’s Co-Founder and Co-Chairman who has helped pave the way for the development of practical applications of next-generation technologies like augmented reality. The judges measured and evaluated the success of a project based on the following criteria:
- “Wow Factor”: How innovative is the project?
- Impact: How impactful is the project to Wayfair?
- Polish: How complete is the project?
- Presentation Quality: How clear and consistent is the demo?
The theme of the Hackathon was Machine Learning and AI. Teams were able to collaborate with participants globally, either in-person or virtually, and work together on projects in five categories: Relentless Customer Focus, Always Improving, Google’s Choice, People’s Choice, and Hackers’ choice.
At the end of the two days there were winners in all 5 categories. The Google’s Choice award went to “Entity Extraction for Order Matching”. Team “Project Clippy” was named both Hackers’ Choice and the winners of the Relentless Customer Focus category. See below for the results of the 5 categories:
- Relentless Customer Focus and Hackers’ Choice
- Winning Team: Project Clippy
- Hackers: Misha Balyasin, Alex Saad, Leo Smerling, and Gabriele Lanaro
- This project provided a gamified experience to make the process of leaving a product review even more seamless.
- Always Improving
- Winning Team: Customer Causal MetaLeaners
- Hackers: Colin Gray, Irene Wang, Huy Vo Tran, Wenhao Xu, and Santiago Velez Ferro
- Team Customer Causal MetaLeaners worked to build lightweight procedure(s) for computationally distributed, multi-target, customized loss functions to make causal meta-learners more applicable to real-world Wayfair problems.
- Google’s Choice Award:
- Winning Team: Entity Extraction for Order Matching
- Hackers: Roger Bock, Bradley West, Sina Moeini, and Jonathan de Melker Worms
- This team built out a solution to use text models to extract and identify the products that customers purchased from user reviews.
- People’s Choice:
- Winning Team: KNN and ANN on Vertex AI
- Hackers: Santosh Jhingade, Ashrith Marpaka, Nikhil Bhaip, Adam Schulze, and Brandon Sanders
- This team used Vertex AI to expand the impact they can have on suppliers and customers by providing accurate and real-time information of products that match either description or image.
Wayfair leaders reflected on the two days and shared their input. Matt Ferrari, Head of Ad Tech, Customer Intelligence, and Machine Learning; Engineering and Product at Wayfair said, “Wayfair has a lot of vendors, but very few strategic partners, and Google is that. Our Partner.” “Thank you all for the participation! I’m grateful to Google, and the many others for helping lead a successful event.”
Wayfair partners with Google Cloud to optimize performance and resiliency, support scaling data-driven decisions, and Increase employee productivity. “Our category is ripe for innovation, and our partnership with Google Cloud helps us ensure that great ideas can come from anywhere by empowering our technologists with cutting-edge products and solutions,” said Ferrari. “We’re proud to partner on efforts like hackathons that align with our team’s eagerness to work on complex, rewarding problems that push the envelope and challenge us to always think big.”
At first Google Cloud won Wayfair over with the speed, reliability and performance of their technology. Hackathons like this one exemplify that what is equally as important is Google Cloud’s willingness to work side-by-side with Wayfair at every level to enable a culture of innovation. Learn more about the Wayfair-Google Cloud partnership here.
Google Cloud’s Firebase Realtime Database and BigQuery AllowsCastbox to Ramp Up Customer Experience
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Demand for spoken audio content such as podcasts remains robust despite the proliferation of video services and other entertainment options for consumers. Shibin Li, Co-founder of Castbox, credits growth of the global podcast platform to the following: speed and availability, market-leading features, the proliferation of smart devices to deliver audio content, and activities — such as driving and working around the house — that make consuming video difficult.
Founded in 2016 and headquartered in Beijing, China, Castbox enables users to locate, access, and create spoken audio content. Available on iOS and Android, Castbox supports 50 million podcasts, on-demand radio programs, and audiobooks in 70 languages from 175 countries. The platform hosts about 2 million users per day and is the largest podcast platform on Android.
Castbox includes a range of features that build on its core service to provide a high-quality user experience. These features include curated podcast recommendations and in-audio search.
A combination of services
At its inception, Castbox relied on a combination of a multinational cloud services, as well as Google Cloud Platform (GCP) services, including the Google BigQuery analytics data warehouse and Cloud APIs to provide programmatic interfaces with Google Cloud services, and a range of services from the Google mobile development platform Firebase.
However, as Castbox matured and its user base expanded, the business increased its reliance on Google Cloud Platform and Firebase.
“We needed to access stable cloud services as we could not tolerate long periods of downtime that would compromise the user experience,” says Li. “Furthermore, we had to support up to 50,000 concurrent connections, and potentially more in future, without disruption.”
“Based on our analysis of the data in Google BigQuery, we can determine what type of content users are listening to, how long they like to listen to it, and when they like to listen to it. This allows us to recommend similar podcasts to each user based on the preferences he or she expressed, encouraging activity on and return visits to our platform.”
—Shibin Li, Co-Founder, Castbox
Competitive differentiation
Castbox also found machine learning-powered Google Cloud Platform APIs could help deliver features, such as in-audio search, that differentiate the podcast platform from its competitors. In addition, Firebase SDKs and Firebase A/B Testing would enable Castbox to create and analyze new applications, as well as make adjustments based on user feedback. Firebase Realtime Database would allow the business to support tens of thousands of concurrent user connections.
The diligence of the Google Cloud team in advising Li and her team about forthcoming products and services also swayed Castbox towards Google technologies. The business gained the opportunity with Google to join several programs that offered early access to Google innovations.
Signature in-audio search service
Castbox now uses Google Cloud Platform services in the Tokyo, Japan, and U.S. East regions. Cloud Speech-to-Text API plays a key role in delivering Castbox’s signature in-audio search service. This service enables users to search transcriptions of audio content on the platform for words or phrases. The search results incorporate the title of the podcast and the search term in context (for example, within the sentence or sentence excerpt in which it appears). Each use of the word or phrase is time-stamped so it can easily be found. The API enables Castbox developers to apply neural network algorithms to achieve audio-to-text conversion accuracy rates of greater than 96%, while search queries typically experience latency of just 50 milliseconds.
In addition, the latency of comparison data, converting audio to text, is only about 250 milliseconds, contributing to the processing of about 12 minutes worth of audio to text in just 10 minutes. “We can process about 20 hours of audio files in one day,” Li says. “This enables us to transcribe and index all the new episodes of a podcast in that period.”
50,000 concurrent connections
With Firebase Realtime Database, Castbox now supports up to 50,000 concurrent connections to its platform with an average latency per connection of just 10 milliseconds. “Firebase Realtime Database also allows us to continue operating in offline mode, which is extremely helpful if we experience any network disruptions,” Li explains. “When we come back online again, any data is simply synchronized with the database.”
Google BigQuery and the analytics capabilities of Firebase SDKs also enable Castbox to monitor and analyze user behaviors. “Based on our analysis of the data in Google BigQuery, we can determine what type of content users are listening to, how long they like to listen to it, and when they like to listen to it,” Li explains. “This allows us to recommend similar podcasts to each user based on the preferences he or she expressed, encouraging activity on and return visits to our platform.”
“Given our queries may span up to 40 days of data, we may be analyzing up to 1,200 GB at one time. We have no problem doing this with Google BigQuery.”
—Shibin Li, Co-Founder, Castbox
Castbox also uses its analyses of Google BigQuery data to amend banners and summaries on its platform to encourage users to listen to additional content. Furthermore, the service is prepared to make surprise recommendations of content to users based on the preferences and reactions of users with similar tastes.
“We analyze a pool of data growing at up to 30 GB per day,” Li explains. “Given our queries may span up to 40 days of data, we may be analyzing up to 1,200 GB at one time. We have no problem doing this with Google BigQuery.” These analyses also support Castbox’s decision to start creating original content, such as finance and economic news, for its platform.
Checking weekly changes
Castbox does not rely only on analyzing user data to deliver a high-quality experience. The business aggregates user feedback from emails and Google Play reviews to make weekly changes to its platform. It then uses Firebase A/B Testing to check whether these changes are met with a positive user response.
“We are extremely pleased with Google Cloud Platform and Firebase. We have been able to differentiate ourselves from our competitors and provide an attractive option for users at a time when content and entertainment options are exploding. We have a great opportunity with Google to continue to improve the value of our offering to users and build engagement and loyalty.”
—Shibin Li, Co-Founder, Castbox
Castbox’s positive experiences with Google Cloud Platform are encouraging the business to grow its use of the product. “We are trying to move some more services to Google Cloud Platform because it is very stable and scalable,” Li says. The business is keen to explore the capabilities of Cloud Pub/Sub to provide low latency messaging between applications, Cloud Spanner to deliver a distributed relational database service, and Cloud Dataflow to transform and enrich data in stream and batch modes.
“We are extremely pleased with Google Cloud Platform and Firebase,” Li concludes. “We have been able to differentiate ourselves from our competitors and provide an attractive option for users at a time when content and entertainment options are exploding. We have a great opportunity with Google to continue to improve the value of our offering to users and build engagement and loyalty.”
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Data Leaders in 2021 and Beyond: How to Prepare
As technologies and workplace environments are quickly evolving, how people and businesses leverage data in their day-to-day workflow is also changing.
Data leaders are an emerging force navigating and charting pathways forward towards new horizons for how people and organizations experience data.
In this presentation, Pedro Arellano, Product Marketing Director, Looker, will cover three areas:
- Underline that the value of data within an organization is no longer up for debate. Everybody needs data, and everybody’s job has the potential to be improved with data.
- Three trends that will help put into context how we’ve arrived at this particular moment of such great potential for data.
- Why data leaders, almost regardless of their title, are now in a position to really influence the direction of organization like never before.
Learn what’s guiding the thinking of data leaders in 2021 and beyond.
Southwire Completes SAP Migration to Google Cloud as a First Step of its Tech Evolution
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“Talk about tough times, right?”
That’s how Dan Stuart, Senior Vice President of IT Services at Southwire Company, refers to the months following a December 2019 ransomware event, and the COVID crisis that began in spring of 2020. Those events hit just as the company was preparing for an overhaul of their SAP environment. This comprehensive plan included three key elements. First, the company wanted to upgrade their SAP ECC environment to take advantage of the latest functionality available for this critical ERP system. Second, Southwire aimed to deploy SAP Business Warehouse on SAP HANA to accelerate vital reporting for all business users. Third, the company wanted to upgrade to the latest version of SAP Process Orchestration—an essential component that touches key manufacturing interfaces in all Southwire facilities.
Southwire had looked at multiple options for the upgrades, including remaining entirely on-premises, colocation, and full cloud migration. “Going to the cloud seemed a lot more compelling,” says Joe Schleupner, Southwire’s Senior Director of PMO & ITS planning and implementation. “We were going to the cloud eventually, so why take these intermediary steps? Let’s just get it done.”
After looking at several options, Southwire decided to migrate to Google Cloud. “We wanted to be on a platform for SAP that was flexible, scalable, and secure; that we could count on to get up and running quickly,” says Stuart. “We chose Google Cloud not only for those reasons, but also because we recognize that Google has other assets that we may be able to take advantage of down the line, such as technologies like artificial intelligence (AI).”
More stability, less worry
As one of the leading manufacturers of wire and cable used in the transmission and distribution of electricity, Southwire aids the delivery of power to millions of people worldwide. They have more than 30 manufacturing facilities across the United States running 24/7. Any downtime directly affects productivity and revenue. With help from Google Cloud and their implementation partner NIMBL, Southwire completed the SAP migration to Google Cloud over a planned maintenance weekend on July 4th.
The migration itself, while complex, went quickly and smoothly. “Just moving to the cloud was quite a feat because we were dealing with so much data, but in total the SAP system was down for only ~16 hours,” says Schleupner.
“As a project manager, I always felt that Google Cloud had my back” Schleupner says. The Process Orchestration (PO) migration was of particular concern, considering that it controlled all of Southwire’s manufacturing interfaces across the entire company. “Every critical piece of information that goes from SAP down to the manufacturing system goes through that system,” says Schleupner.
Even after migrating, Southwire discovered that making changes to the system was fast, easy, and resulted in no downtime. Normally, certain types of changes would have involved taking down SAP for at least an hour.
The Southwire team also appreciates the fact that the modern cloud architecture means spending less time on routine infrastructure maintenance. “It’s one less thing for me to worry about,” Stuart says, “I can focus on the business side of the house and move the technology and responsibilities to what we do within the Google Cloud Platform.”
What comes next?
While the cloud migration will increase stability, uptime, performance, and security, there is much more to come. Southwire is currently working on a disaster recovery implementation for their SAP environment on Google Cloud. Stuart and Schleupner are excited about where Google Cloud can further take Southwire. They are considering an SAP Hybris e-commerce implementation as well as connected factory and/or factory automation initiatives that can take advantage of artificial intelligence and machine learning.
To Stuart and Schleupner, the migration of Southwire’s SAP environment to Google Cloud, as important as it was, really represents the first step in the company’s tech evolution. Now that much of the heavy lifting is complete, Southwire’s digital transformation can begin in earnest. “There’s no shortage of areas where I think Google Cloud will come into play,” Stuart says, “and we intend to look at these things with an open mind to understand how we can leverage current investments to take our organization where we want to go.”
Learn more about Southwire’s SAP on Google Cloud deployment and how Google Cloud can transform the way you work with your SAP enterprise applications. Visit cloud.google.com/solutions/sap.
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Experiment: Machine Learning Helps IGG Acquire High-value App Users
What we set out to test
Can machine learning help app businesses efficiently acquire high-value users at scale?
The background
IGG (I Got Games) is a China-based online game developer with over 300 million players worldwide. They are always looking to acquire and grow a valuable user base for their games. They had previously done so by running manually optimized YouTube install campaigns. But manual optimization meant they had to analyse a vast amount of data (such as search, YouTube and Google Play data) across multiple channels to find the right gamers, build the perfect campaigns for them and find the best time and place to serve them.
The release of a new game “Lords Mobile” was a great opportunity to test the potential of Universal App Campaign to promote their new game application; a Machine Learning-powered solution that creates highly customized and targeted ads across multiple platforms, all done automatically and with real-time optimization.
How we set the experiment up
Experiment control and test groups split based on geography. Within the same market, control and test geographies were identified — roughly equal in terms of size and behaviour.
Control group: YouTube install Campaign
- Manually optimized
- On YouTube network only
Test group: Universal App Campaign (UAC)
- Uses Machine Learning to optimize for business goals (eg new app installs or in-app actions) and find your ideal audience
- Across Search, GDN, YouTube network
Solutions we used
What we learned
Universal App Campaigns can find high value app users, by selecting the right events to optimize for and by following creative best practices.
Competitive target Cost Per Acquisition bidding can strengthen Machine Learning predictions.
This case study is part of the Experiment with Google Ads Program.
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For as long as business intelligence (BI) has been around, visualization tools have played an important role in helping analysts and decision-makers quickly get insights from data.
In our current era of big data analytics, that premise still holds. To provide an integrated platform for building BI dashboards on top of big data, GCP offers the combination of Google BigQuery, a cloud-native data warehouse that helps you analyze petabytes of data quickly, and Google Data Studio, a free tool that helps you quickly create beautiful reports.
First, imagine that you work for an online retailer that bases important decisions on customer interaction data in the form of large amounts (multiple TBs) of usage logs stored as date-partitioned tables in a BigQuery dataset called usage. To get business value out of that data as quickly as possible, you want to build a dashboard for analysts that will provide visualizations of trends and patterns in your data.
The good news is that a connector is available that lets you query data stored in BigQuery directly from Data Studio.
Learn how to build a BI dashboard with Data Studio as the front end, and powered by BigQuery on the back end (with some help from Google App Engine for added efficiency). Download this how-to now.
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