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.

Like countless other industries, farming is going digital and undergoing big changes—driven by access to more actionable information. The agriculture business can now gather and analyze georeferenced data from satellites, combined with data from IoT sensors in fields, crop rotation and yield histories, weather patterns, seed genotypes and soil composition to help increase the quantity and quality of crops.

This is essential for businesses in the agriculture industry, but it’s also critical to address growing food shortages around the world. 

At S4, we create technology to de-risk crop production. We provide customers seeking agricultural risk management solutions with the tools to make better, data-driven decisions for their crop planning, based on machine learning and proprietary algorithms.

We interpret plant evolution on a global scale with predictive modeling and analytics, and offer super-efficient risk-transferring solutions. Our multi-cloud platform includes a petabyte-scale database, an open source stack, and—after 50 proof-of-concept evaluations—BigQuery for our data warehouse and the Cloud SQL database service to handle OLTP queries to our PostgreSQL database.

These PoCs included, among others, Microsoft Azure Data Lake Analytics, IBM Netezza, Postgres/PostGIS running on IBM bare-metal servers with SATA SSDs and on Google’s Compute Engine with NVMe disks, and on-premises memSQL, CitusData and Yandex ClickHouse. 

Weeding out risk in an uncertain market

According to recent research, climate extreme events like drought, heat waves, and heavy precipitation are responsible for 18-43% of global variation in crop yields for maize, spring wheat, rice, and soybeans. This is a clear trend for other crops as well. Such variation poses risks of food shortages as well as large financial risks to farmers, insurers, and regions dependent on successful crop yields. Also, it creates vast humanitarian difficulties.

Our mission at S4 is to help de-risk crop production by matching the right data with analytics tools so farmers and other participants in the agricultural value chain can plan better, resulting in more reliable food supplies.

In a nutshell, we create indices out of biological assets. These indices measure yield losses on crops that are caused by the effects of weather and other factors, which are then used as underlying assets for products, such as swap/derivative contracts and parametric insurance policies, to transfer risk to the financial markets.

We enable insurers and lenders to buy and sell agricultural risks through the futures market. Also, our other products help farmers and seed and fertilizer companies provide customized genotype recommendations and fertilization requirements. This helps to optimize planting by geography, resources, and crop species, monitor phenological, pests and humidity evolution throughout the crop season, and estimate yields.

Local communities benefit from S4’s technology, as the ability to manage weather risks allows farmers to stabilize their cash flows, invest more to produce more with fewer risks, and develop in a more sustainable manner.

Growing data sources, reducing costs, accelerating performance

With the volume of diverse data sources and analytical complexity both growing at a very fast pace, we decided that using a major cloud services provider with a broad roadmap and global partnerships would be beneficial to S4’s future evolution.

At the same time, we wanted to bring our services to users faster and cut costs by consolidating our on-premises technology stack. When we started evaluating providers, our leading criteria included a powerful geospatial database and data analytics tools along with excellent support, all at a competitive price. GCP prevailed in nearly all criteria categories among the 50 companies we measured. 

Our previous platform architecture included a hybrid relational database that used Compute Engine for virtual machines and Cloud Storage for database backup. The RDBMS was slow. Maintaining our own data warehouse was complex and expensive.

We wanted to use machine learning and neural networks, but couldn’t do so easily and affordably. The complexity of that system meant that products or services requiring small changes or additions to the data model translated to expensive expansions of infrastructure or project time.

Also, agronomical or product teams couldn’t test these changes by themselves, always requiring the intervention on no small part of the IT team, which led to further delays.

We added GCP services like BigQuery as S4’s cloud data warehouse and use BigQuery GIS for geospatial analysis, Cloud Dataflow for simplified stream and batch data processing, and Cloud SQL for queries to the S4 database platform, which have all made a huge impact on our services and bottom line.

Database and analytics costs have decreased by 40% and customers are receiving our analytical results 25% faster. In addition, we’ve eliminated the time-consuming downloading of images, reducing storage and processing costs by 80%, because we no longer need expensive tools licenses, and have greatly reduced classification processing times.

Our customers working in the agriculture industry are also benefiting from this infrastructure change. They are now able to speed up their data analytics using our GCP-based platform.

“S4 products and technologies unlock the full potential of satellite imagery for crop prescriptions, monitoring and yield estimates,” says Nicolás Loria, Manager of Marketing Services, Southern Cone, Corteva Agriscience.

“We’ve worked with S4 for the last three (and starting year number four) crop seasons as its team capabilities, data integration capacities, and analytics insights have allowed Corteva to perform an entire new solution. Thanks to S4’s customized 360° approach, fast response and delivery times, we have safely outsourced our remote crop analytic technical needs.”

Also, this new architecture has allowed us to scale our models and databases with almost no limits, at a fraction of the cost vs. the previous models.

We’ve saved a lot of time on executing processes and reduced work needed by our internal teams to do certain tasks, like preparing images, converting them, validating results, and more. Using Google Earth Engine has decreased the execution time of daily tasks anywhere from 50% to 90% of the previous time, going from an average time of 30 minutes to between four and 15 minutes, depending on the task.

In addition to saving money and time, we are able to focus on innovation with the GCP performance and features we’re using. We’re able to seamlessly add satellite data to analytics using both public datasets and our own private data, and deliver GIS data management, analytics, crop classification and monitoring in real time.

We can do semi-automatic crop classification and classification using spectral signatures with Google Earth Engine. Later this year, we’ll be using neural networks for pattern recognition and machine learning in new applications to improve crop yields and fine-tune risk models. And using GCP and Google Earth Engine infrastructure means we can run models for customers in South America and around the world, since Google Earth Engine has global satellite imagery available. 

We’ve heard from our customer Indigo Argentina that they’re able to bring customers data insights faster.

“We are working with S4 in the development of two different applications for satellite crop monitoring and yield assessment,” says Carlos Becco, CEO, Indigo Argentina. “S4’s technology allowed us to manage and analyze multiple sources and layers of information in real time, letting us uncover valuable insights in Indigo’s own microbiome technologies, and at a very competitive cost.” 

Analytical products and app development thrive with GCP

With GCP, we are updating and improving algorithms that we built manually with machine learning processes to develop drought indices for upcoming crop seasons. Algorithms can recognize specific phases of crop phenology (e.g., bud burst, flowering, fruiting, leaf fall) and correlate them with photosynthetic activity, light, water, temperature, radiation, and plant genetics factors. Other analytical products like crop monitoring, pre-planting recommendations, financial scoring, and yield estimation can now do a lot more for users by offering multiple layers and datasets, faster image processing, and real-time access via APIs.

We also replaced our bare-metal S4 app deployment with the App Engine serverless application platform. It provides tighter integration between the S4 platform and our BigQuery data warehouse for integration with marketplaces and third-party solutions.

We get all of these Google Cloud features with all the benefits of managed cloud services, from multiversioning and security to automatic backups and high availability.

At S4, we trust technology to decode plant growth and help protect farmers and their communities from climate change. With growing food shortages due to increasing populations and intensifying weather, data and analytics can have a huge impact in lowering financial risks and improving agricultural yields. It’s one sector where cloud, database, analytics, and other technologies are combining to improve business outcomes and affect the lives of billions of people. Learn more about S4’s work and learn more about data analytics on Google Cloud.

Over the last two years, we’ve seen a significant uptick in the number of people using messaging to connect with businesses. Whether it was checking hours of operation, verifying what was in stock, or scheduling a pick-up, the pandemic caused a significant shift in consumer behavior.

• 45% of users are spending more time on messaging services because of the pandemic 
• 85% of consumers would like to message brands directly

With the rise in demand for messaging, consumers expect communication with businesses to be  speedy, simple, and convenient. For businesses, keeping up with customer inquiries can be a labor-intensive process, and offering 24/7 support outside of store hours can be costly.  

To help businesses seamlessly deliver helpful, timely, and engaging conversations with customers when and where they need help, we introduced AI-powered Business Messages.

https://youtube.com/watch?v=fcgP3RHjBLY%3Fenablejsapi%3D1%26

What is AI-powered Business Messages?

With AI-powered Business Messages, you can connect with your customers in their moment of need, in the places they’re looking for answers—such as Google Search, Google Maps, or any brand-owned channel. For instance, check out how Walmart customers in the US are able to receive real-time information on product availability, straight from a search results page.

People turn to Google when they are searching for answers to their questions, looking to buy something, or trying to accomplish a particular task with one of our many tools. In fact, 68% of all online experiences begin with a search engine.

At Google, we know how important it is for interactions with a brand to be personalized, helpful, and simple. With AI-powered Business Messages, customers are able to chat with virtual agents that understand, interact, and respond in natural ways.

We are also combining smart automation with the ability for customers to chat with live agents when it’s really needed. This approach saves your customers precious time, while also saving you money. And with Business Messages automatically handling many customer inquiries in the background, businesses have the option to distribute their human customer service agents to address other needs. 

Getting started with conversational AI is easy with Bot-in-a-Box

We know it can be difficult to get started with AI. That’s why we are utilizing existing Google AI tools like Dialogflow—part of Google Cloud Contact Center AI—to create the capability within Google’s Business Messages called Bot-in-a-Box, which makes getting started with Conversational AI easy. Bot-in-a-Box allows for fast and effective adoption of automation for businesses of all sizes. 

Enabling Business Messages with Bot-in-a-Box can be as simple as leveraging an existing customer FAQ document you already have, whether it’s from a web page or an internal document. And since the conversational AI is powered by Business Messages and Dialogflow working together, your chat bot is able to understand and respond to customer questions automatically without the need to write code. 

Bot-in-a-Box also supports other critical journeys like “Custom Intents.” That means that your bot is able to understand the different ways customers express a similar question and respond accurately by using machine learning capabilities.

Finding success 

In April 2021, Wake County courthouses in North Carolina partnered with Tango Technology to implement Business Messages when it became apparent that being able to provide the public and attorneys with around-the-clock access to information would significantly reduce the pressure on courthouse staff. Using Bot-in-a-Box, Tango Technology was able to customize a solution for Wake County Courthouse, Justice Center, and Clerk of Superior Court in just four days.

“With the combination of Google’s Business Messages, GCP, and Dialogflow, we were able to spin up an AI-driven bot for the courts in days. And the technology stack allows us to continually improve by adding functionality in an agile process.”— Mike Lotz, Co-Founder, Tango Technology

With Business Messages, North Carolina courthouses saw a 37% decrease in the call volume handled by courthouse staff.  With 398,298 fewer phone calls during the first year of operation, the AI-based messages helped Wake County Courthouse work more efficiently and productively.

We’ve seen many brands benefiting from AI-powered Business Messages. For instance, Levi’s saw a 30% increase in off-hours shoppers and surpassed 85% customer satisfaction scores after implementing Business Messages. They also drove 30x more store-related questions than Levi’s website chat. 

Bring Google’s conversational AI to your storefront with Business Messages

Google’s Business Messages makes it easier for businesses of all sizes to engage their existing or potential customers in a virtual conversation, when and where they need it. 

To learn more, watch our Cloud Next session here or visit us at g.co/businessmessages. We have specialized services to help you get started  and can share the wisdom of our channel partners and dedicated experts who specialize in unleashing the potential of conversational AI.