With nearly 2,300 stores, The Home Depot is the world’s largest home-improvement chain — a brand that professional contractors and DIYers alike have come to depend on. The home improvement industry continues to experience unprecedented demand and dramatic increases in online ordering accompanied by expanding consumer expectations for things like curbside pickup and same day delivery. The Home Depot’s decision to migrate to cloud-based infrastructure, including the migration of the company’s SAP applications on Google Cloud which began in 2017, has set it up for success in an increasingly digital world, and helped the company adapt to changing market conditions quickly. 

Interconnected retail at scale

Building on a strong customer-first philosophy, The Home Depot aims to create what it calls interconnected retail—allowing customers to shop however, whenever, and wherever they want. “So many companies are focused on omni-channel retail,” explains Sam Moses, Vice President of Corporate Systems. “At The Home Depot, we wanted to take it to the next level. Interconnected retail puts the customer at the center of everything and enables them to shop in store, online, or both. Customers can begin a transaction online and continue in-store, or vice-versa.”

To support this strategy, the company’s SAP environment needed to be more agile. Running  everything on-premises, from central finance to POS systems, meant that The Home Depot’s IT teams experienced redundancy and repetitive, manual processes. Their data warehouse needed an upgrade to process and analyze growing and increasingly diverse data sets. The Home Depot chose to migrate its SAP environment to Google Cloud to support both the velocity and scale needed for the business as well as critical analytics capabilities needed for its bold digital initiatives. “We chose Google Cloud to support our SAP implementation. Our decision had a lot to do with the relationship between Google Cloud and SAP and also for the applications and services that are offered by Google Cloud, like BigQuery, which are helping to enable data and analytics within our organization,” Moses explains.

After migrating its SAP applications—including S/4HANA, its customer activity repository (CAR), general ledger, e-commerce system, enterprise data warehouse and more to Google Cloud, the company now has the speed, scale and flexibility to tackle enormous spikes in the business, all while staying fully available for their customers. Additionally, The Home Depot was able to transform its financial systems and make them more agile to deliver critical information across multiple business functions in real time.

Maximizing data insights to support customer experiences

By migrating to Google Cloud, The Home Depot is leveraging Google Cloud analytics to build   the industry’s most efficient supply chain including more robust demand forecasting, supplier lead times, estimated delivery times and more, all while maintaining better security than before. “We experienced unprecedented change in our customers’ behavior and their buying patterns, which puts a lot of pressure on our supply chain,” explains Moses. “So having the ability to leverage data and analytics gives us insights to know exactly what it is that our customers need.” 

The company’s analysts now use BigQuery ML for machine learning directly against the company’s BigQuery data and use AutoML to determine the best model for predictions. The Home Depot’s engineers have also adapted BigQuery to monitor, analyze, and act on application performance data across all its stores and warehouses in real time—capabilities that were not as seamless in the on-premises environment.

With hundreds of projects on Google Cloud, The Home Depot’s cloud journey is well on track, but the company is always looking to the future. “As our customers’ needs have continued to evolve, and as technology has continued to evolve, our relationship with Google will continue to advance — to be able to innovate together, to be able to find new solutions together, to better serve our customers.”

Learn more about how The Home Depot is renovating its retail operation with SAP on Google Cloud.

Over 2.14 billion people worldwide are expected to buy online this year, according to Statista. Online retail sales will account for 22% of all purchases by 2023. But in a competitive retail landscape, positive interactions can mean the difference between a sale and an abandoned shopping cart.

One of the leading global marketplaces – Kinguin.net is a haven for gamers. Their bustling ecommerce business conducts over 500,000 new transactions monthly. Users will encounter over 50,000 unique digital products, from video games, gift cards, in-game items to computer software and services. With over 10 million registered users, Kinguin improved their experience by helping users find items quickly and deliver service at scale.

Helping customers find what they want, fast

Because of Kinguin’s high volume of users—both buyers and sellers—and breadth of digital products, browsing and shopping can be challenging. “Customers shop online for choice and convenience, but it can sometimes be overwhelming. We want anyone who shops at Kinguin to find what they are looking for quickly and easily,” says Viktor Romaniuk Wanli, Kinguin CEO and Founder.

Today’s retailers know that creating personalized shopping experiences is crucial for establishing and maintaining customer loyalty. Kinguin discovered their users were getting a rather standard retail experience. They wondered how they could offer them a more tailored, personalized experience.

They knew product recommendations were a great way to personalize experiences because they help customers discover products that match their tastes and preferences. But it’s not that easy to recommend products. Various shifting factors make recommendations much more complex:

• Customer behavior. Understanding customers is tough. How do you recommend something to a cold start user who’s never been to your site before? What happens when their behavior changes?
• Omnichannel context. According to Harvard Business Review, 73% of all customers use many channels when they buy. What happens when they go from desktop to mobile or from social media shopping to a proprietary app?
• Product data challenges. How do you recommend new products within a large catalog of items? What if your product data has sparse labeling or unstructured metadata?

Data wasn’t a problem for Kinguin. They had data orders, history, wishlists, and could collect events based on their platform interactions. It was the machine learning model expertise they lacked. So rather than building their own solution, they determined it was more cost effective for them to find a reliable partner. It was also essential that the solution integrated easily with Kubernetes, which enabled their global network.

With these considerations in mind, they applied for the Google Recommendations AI beta program. Kinguin became the first gaming e-commerce platform in Europe to use Recommendations AI when it launched in 2020.

Pro gamer move: using a fully managed AI service 

Google Recommendations AI uses algorithms to deliver highly personalized suggestions tailored to a customer’s preferences. Google Cloud based these algorithms on the same research that powers models by YouTube search and Google Shopping. Algorithms are always being tuned and adjusted to focus on individuals themselves—not just items.

Many shopping AIs rely on manually provisioning infrastructure and training machine learning models. Instead, Recommendations AI’s deep learning models use item and user metadata to gain insights. It processes Kinguin’s thousands of products at scale, iterating in real time. First, Kinguin pieces together a customer’s history and shopping journey. Then, using Recommendations AI, they can serve up personalized products—even for long-tail products and cold-start users. 

By leveraging internal tools, Kinguin didn’t need to start implementation from scratch. After a few trial sessions with Google Cloud engineers, they got started right away. Due to the fast-paced nature of a marketplace—i.e., price changes, out-of-stock items—Kinguin needed their recommendations to be as close to real time as possible. They used internal event buses to stream events and their product catalog directly to the recommendations API.

Kinguin rolled out in high-traffic areas, including their home page, product page, and category pages. They analyzed heat maps and scroll maps to figure out where to test placements. They also experimented with different recommendation models such as “recently bought together” and “you may like.” Engineers also factored in where they were implementing the models. For example, the “others you might like” model would fit best on the homepage, while “frequently bought together” made sense at checkout.

Understanding how product recommendations influence financials is critical for demonstrating the impact of personalization. Using BigQuery, Kinguin could analyze different cost projection models. BigQuery helped them dig into specific financial data to understand their margins and revenue gains.

Playing to win: enhanced customer experience

Since adopting Recommendations AI, Kinguin has improved both customer experience and satisfaction. Search times have shortened by 20 seconds. Additionally, their average cart value has increased by 5 EUR. Conversion rates have quadrupled since the outset. Click-thru rates have doubled, increasing by 2.16 on product pages and 2.8 times on recommendations pages.

“Google Recommendations AI has helped us evolve our service, increase customer loyalty and satisfaction. It has also contributed to a significant rise in sales,” says Wanli. Kinguin is already thinking about other ways of enhancing user experiences with recommendations. Ideas include their checkout process, other landing pages, and email marketing.

Kinguin’s journey with Google Cloud shows how companies can leverage AI to optimize sales and deliver high-performing, low-latency recommendations to any customer touchpoint. 

Learn more about Recommendations AI and Google Cloud AI and machine learning solutions.

It may be surprising to know that U.S. natural catastrophe economic losses totaled $119 billion in 2020, and 75% (or $89.4B) of those economic losses were caused by severe storms and cyclones. In the insurance industry, data is everything. Insurers use data to influence underwriting, rating, pricing, forms, marketing, and even claims handling. When fueled by good data, risk assessments become more accurate and produce better business results. To make this possible, the industry is increasingly turning to predictive analytics, which uses data, statistical algorithms, and machine learning (ML) techniques to predict future outcomes based on historical data. Insurance firms also integrate external data sources with their own existing data to generate more insight into claimants and damages. Google Cloud Public Datasets offers more than 100 high-demand public datasets through BigQuery that helps insurers in these sorts of data “mashups.” 

One particular dataset that insurers find very useful is Severe Storm Event Details from the U.S. National Oceanic and Atmospheric Administration (NOAA). As part of the Google Cloud Public Datasets program and NOAA’s Public Data Program, this severe storm data contains various types of storm reports by state, county, and event type—from 1950 to the present—with regular updates. Similar NOAA datasets within the Google Cloud Public Datasets program include the Significant Earthquake Database, Global Hurricane Tracks, and the Global Historical Tsunami Database.  

In this post, we’ll explore how to apply storm event data for insurance pricing purposes using a few common data science tools—Python Notebook and BigQuery—to drive better insights for insurers.

Predicting outcomes with severe storm datasets

For property insurers, common determinants of insurance pricing include home condition, assessor and neighborhood data, and cost-to-replace. But macro forces such as natural disasters—like regional hurricanes, flash floods, and thunderstorms—can also significantly contribute to the risk profile of the insured. Insurance companies can leverage severe weather data for dynamic pricing of premiums by analyzing the severity of those events in terms of past damage done to property and crops, for example. 

It’s important to set the premium correctly, however, considering the risks involved. Insurance companies now run sophisticated statistical models, taking into account various factors—many of which can change over time. After all, without accurate data, poor predictions can lead to business losses, particularly at scale.  

The Severe Storm Event Details database includes information about a storm event’s location, azimuth (an angle measurement used in celestial coordination), distance, impact, and severity, including the cost of damages to property and crops. It documents:

• The occurrence of storms and other significant weather events of sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce.
• Rare, unusual weather events that generate media attention, such as snow flurries in South Florida or the San Diego coastal area.
• Other significant weather events, such as record maximum or minimum temperatures or precipitation that occur in connection with another event.

Data about a specific event is added to the dataset within 120 days to allow time for damage assessments and other analysis.

Driving business insights with BigQuery and notebooks

Google Cloud’s BigQuery provides easy access to this data in multiple ways. For example, you can query directly within BigQuery and perform analysis using SQL. 

Another popular option in the data science and analyst community is to access BigQuery from within the Notebook environment to intersperse Python code and SQL text, and then perform ad hoc experimentation. This uses the powerful BigQuery compute to query and process huge amounts of data without having to perform the complex transformations within the memory in Pandas, for example.

In this Python notebook, we have shown how the severe storm data can be used to generate risk profiles of various zip codes based on the severity of those events as measured by the damage incurred. The severe storm dataset is queried to retrieve a smaller dataset into the notebook, which is then explored and visualized using Python. Here’s a look at the risk profiles of the zip codes:

Another Google Cloud resource for insurers is BigQuery ML, which allows them to create and execute machine learning models on their data using standard SQL queries. In this notebook, with a K-Means Clustering algorithm, we have used BigQuery ML to generate different clusters of zip codes in the top five states impacted by severe storms. These clusters show different levels of impact by the storms, indicating different risk groups. 

The example notebook is a reference guide to enable analysts to easily incorporate and leverage public datasets to augment their analysis and streamline the journey to business insights. Instead of having to figure out how to access and use this data yourself, the public datasets, coupled with BigQuery and other solutions, provide a well-lit path to insights, leaving you more time to focus on your own business solutions.

Making an impact with big data

Google Cloud’s Public Datasets is just one resource within the broader Google Cloud ecosystem that provides data science teams within the financial services with flexible tools to gather deeper insights for growth. The severe storm dataset is a part of our environmental, social, and governance (ESG) efforts to organize information about our planet and make it actionable through technology, helping people make a positive impact together. 

To learn more about this public dataset collaboration between Google Cloud and NOAA, attend the Dynamic Pricing in Insurance: Leveraging Datasets To Predict Risk and Price session at the Google Cloud Financial Services Summit on May 27. You can also check out our recent blog and explore more about BigQuery and BigQuery ML.