With unemployment application submissions reaching record numbers over the past year, state and local agencies in the United States have faced the challenge of processing unprecedented numbers of claims per week. The digital infrastructure most agencies have in place is unable to handle this volume, resulting in constituents waiting longer, and bad actors taking advantage of vulnerable systems. The Department of Labor Inspector General estimates that $63 billion in claims distributed is either an improper payment or fraud. 

Validating claims also requires secure data sharing with other agencies for document and identity verification. Government leaders need a way to allow case adjudicators to quickly and confidently release backlogged claims, integrate with existing systems, and segment legitimate claims from potentially fraudulent ones — all within limited government budgets — securely and at scale.

Implementing a fraud detection solution on Google Cloud

States were under pressure to release payments, while also filtering out potentially fraudulent claims. SpringML and Google Cloud developed a framework to give adjudicators a reliable verification process that quickly filters potentially fraudulent claims, while processing the remaining claims so benefits reach citizens in a timely manner. SpringML and Google Cloud, applied AI-powered machine learning models to detect anomalous patterns in large datasets. Using Google Cloud tools, SpringML implemented a solution to streamline workflows, improve efficiencies, automate processes and identify potentially fraudulent claims.

SpringML used a variety of Google Cloud products to deliver a fraud detection solution, including:

• Google Cloud Storage to store and manage data
• BigQuery to store tabular data and BigQuery Machine Learning (BQML) to conduct machine learning on that data
• AutoML solutions to build predictive models and risk scoring
• Visualization tools such as Looker and Data Studio to present data and help government leaders make informed decisions.

Implementing machine learning to detect improper payments allows agencies to classify claims as “fraud” or “not fraud” based on the number of flags, as well as prioritize the most urgent claims. Deploying intelligent virtual agents to handle frequently asked questions meant that live agents could focus their time on more challenging cases. 

Even once the pandemic is behind us, there will be bad actors trying to take advantage of overwhelmed or legacy systems. We’ve identified a few best practices for agencies managing enormous case loads and looking to improve improper payment analytics: 

• Move your systems to the cloud. Many on-premises legacy systems can’t update their applications and scale to meet the volume of claims. Moving to a cloud environment enables rapid solution deployment and ingestion of large amounts of data without fear of overloading the system. The cloud scales with you–cost-effectively and securely. 
• Understand patterns in the data. The answer is always in the data — we used deep analysis to help uncover suspicious patterns in large data sets. We implemented unsupervised machine learning to learn behaviors and create configurable rules that adjust to new information that comes into the system. We can uncover patterns that are likely associated with fraud – ones that a human might have missed. 
• Use AI/ML tools to automate your existing systems and teams. These tools enable humans to work smarter and more efficiently. We automate anomaly detection and create dashboards for adjudicators to rapidly process claims. We are enabling the Wisconsin Department of Workforce Development by implementing automatic calculations and processing of recharge amounts, resulting in faster processing times and fewer human errors. Proactive fraud detection and timely calculation of recharge payment allowed DWD to ensure the benefits reached the right individuals.
• Build flexibility into your systems. We discovered that fraud patterns change over time. For instance,flags for fraud during March-May 2020 were vastly different from those we found in June-July 2020. Google Cloud tools make it easy to continually update algorithms to detect patterns and integrate external data sources.

Using Google Cloud tools, we can update digital infrastructure and incorporate machine learning best practices to help organizations efficiently process large volumes of claims and identify high probability fraudulent ones. SpringML provides consulting and implementation services and industry-specific analytics solutions that deliver high-impact business value to accelerate data-driven digital transformation. Learn more about fraud detection and how to improve improper payments analytics by watching our webinar. 

For more than 15 years, developers have used Google Maps Platform to deliver location-based experiences to their end users and used location intelligence to optimize their businesses. Along this journey, we’ve made a variety of changes to better support our community as needs have changed and new industries and technologies have emerged. We started rolling out a new website experience, at https://mapsplatform.google.com, to help you better understand the products and solutions best suited to address your objectives. Plus, now you can directly connect to the developer documentation for each product to get started quickly, and you can visualize usage and associated costs to have a better idea of what to expect before getting started. 

Getting to your solution faster 

Maps, Routes, Places are building blocks that let you develop implementations for any use case. Building for specific use cases, however, typically requires using a combination of APIs and SDKs. To help you quickly understand what’s possible and what you need to build for your use case, you can now visit the solutions tab to select from a list of popular use cases or industries. Once you’ve selected a use case or industry, you’re taken to a page where you can explore relevant products, read helpful blog posts, see how other customers have deployed for similar use cases, and more.  

Direct access to developer documentation

Did you know there are more than a thousand pages of developer documentation created to help you get started, unblock you when you’re stuck, and share best practices? Now when you explore a product or solution from the Google Maps Platform website, you can easily navigate back and forth between our website and documentation. Just tap on JS, iOS, Android or API under the product name to get to the documentation you need. 

Budgeting for your project

To help you calculate pricing for your project, we’ve introduced a new pricing calculator. Once you find the product and API or SDK you plan to use, pull the slider to reflect your estimated number of monthly requests. This will automatically update the “monthly cost” column for each product and API or SDK you plan to use. If your estimated monthly requests exceed the slider limit, contact our sales team to ​​learn about volume discounts that start at 20% off. 

We hope our new website makes it easier to discover our products and solutions, estimate your budget, and start building with our documentation so you can deliver helpful experiences to your users and optimize your business. 

For more information on Google Maps Platform, visit https://mapsplatform.google.com.

Editor’s note: Today we’re hearing from Ryan Mason, Director, Head of DTC Growth & Strategy, at alcoholic beverage firm, Constellation Brands on the company’s shift to Direct-to-Consumer (DTC) sales and how Google Cloud’s powerful technology stack helped with this transformation. 

It’s no secret that consumer businesses have been up-ended in a lasting manner after 18 months of the pandemic. Consumers have been forced to shop differently over the past year – and as a result, they’ve evolved to be more comfortable with online spending and have grown to expect a certain level of convenience. While the e-commerce share of consumer sales has grown steadily over the past decade, the pandemic was the catalyst for the famous “10 years of growth in 3 months” which many argue is here to stay. 

Facing this reality head-on,  we placed a new emphasis on Direct-to-Consumer (DTC) with our acquisition of Empathy Wines, a DTC-native wine brand that sells directly to consumers via e-commerce. To accelerate our innovation in the DTC space, we added headcount and new functions to the existing Empathy team and empowered the newly-minted DTC group to apply their digital commerce operating model across the rest of the wine and spirits portfolio, which includes Robert Mondavi Winery, Meiomi Wines, The Prisoner Wine Company, High West Whiskey, and more. 

One pandemic and one year later, DTC sales have surged in the wine and spirits category with Constellation positioned as a leader armed with a unique and powerful cloud technology stack, best-in-class e-commerce user experiences, modernized fulfillment solutions, and data-driven growth marketing. 

Benefits of Going DTC 

A report from McKinsey estimates that the strategic business shift to DTC has been accelerated by two years because of the pandemic and argues that consumer brands that want to thrive will need to aim for a 20% DTC business or higher, which is already taking shape in the market: Nike’s direct digital channels are on track to make up 21.5% of the total business by the end of 2021, up from 15.5% in the last fiscal year, and Adidas is aiming for 50% DTC by 2025. But outside of the clear revenue upside, the auxiliary benefits of going DTC are robust.

For Constellation Brands, each of these four pillars ring true, and our shift toward DTC is as much about margin accretion and revenue mix management as it is about consumer insights and data. The added complexities of the alcohol space add wrinkles to our DTC approach and manifest in many areas like consumer shopping preference, shipping and logistics hurdles, and more. In order to win share early and continue to lead the category, we recognized the need to harness the immense amount of first-party data to power impactful and actionable insights. 

Our DTC technology architecture has fostered a value chain that is completely digitized: website traffic, marketing expenditures, tasting room transactions, e-commerce transactions, logistics and fulfillment events, cost of goods sold (COGS) and margin profiles, etc. are recorded and stored in a data warehouse in real time.  For the first time, at any given moment, we can easily and deterministically answer complex business questions like “what is the age and gender distribution of my customers from Los Angeles who have purchased SKU X from Brand.com Y in the last 6 months? What is the cohort net promoter score? Did that increase after we introduced same-day shipping in this zip code? By how much?” 

The ability to answer these questions and understand the root causes allows us to stay nimble with product offerings and iterate marketing strategies at the speed of consumer preference. Further, it enables us to optimize our omnichannel presence in the same manner by leaning on DTC consumer insights to develop valuable strategies with key wholesale distribution partners and 3-Tier eCommerce partners like Drizly and Instacart. At its core, Constellation’s DTC practice is designed to be the consumer-centric “tip-of-the-spear” responsible for generating insights from which all sales channels, including wholesale, can benefit. 

Constellation’s DTC technology approach prioritizes consumer-centricity and insights generation

We have taken a modern approach to building a digital commerce technology stack, leveraging a hub-and-spoke model built around Shopify Plus and other key emergent technology providers like email provider Klaviyo, loyalty platform Yotpo, Net Promoter Score measurer Delighted, Customer Service module Gorgias, payments processor Stripe, event reservations platform Tock, and many more. For digital marketing and analytics, we use Google Cloud and Google Marketing Platform, which includes products like Analytics 360, Tag Manager 360, and Search Ads 360.

To help gather, organize, and store all of the inbound data from the ecosystem, we partnered with SoundCommerce, a data processing platform for eCommerce businesses. Together with SoundCommerce, we are able to automate data ingestion from all endpoints into a central data warehouse in Google BigQuery. With BigQuery, our data team is able to break data silos and quickly analyze large volumes of data that help unlock actionable insights about our business.  BigQuery itself allows for out-of-the-box predictive analytics using SQL via BigQuery ML, and a key differentiator for us is that all Google Marketing Platform data is natively accessible for analysis within BigQuery. 

But data possession only addresses half of the opportunity: we needed a powerful and modern business intelligence platform to help make sense of the vast amounts of data flowing into the system. Core to the search was to find a partner that approached BI in a way that fit with our future-looking strategy.

Our DTC team relies on the accurate measurement of variable metrics like Customer Acquisition Cost (CAC), Customer Lifetime Value (CLV), Churn, and Net Promoter Score (NPS) as a bellwether of the health of the business and monitoring these figures on a daily basis is paramount to success. To enable us to keep an accurate pulse on strategic KPIs, we considered several incumbent BI platforms. Ultimately we selected Google Cloud’s Looker for a range of benefits that separated it from the rest of the pack.

From a vision perspective, in this particular case we felt Looker was most aligned with our belief that better decisions are made when everyone has access to accurate, up-to-date information. Looker allows us to realize that vision by surfacing data in a simple web-based interface that empowers everyone to take action with real-time data on critical commercial activities. Furthermore, Looker’s ability to automate and distribute formatted modules to a myriad of stakeholders on a regular cadence increases data literacy and business performance transparency.

From a product perspective, we chose Looker for it’s cloud offering, web-based interface, and  centralized, agile modeling layer that creates a trusted environment for all users to confidently interact with data — without any actual data extraction. While other BI tools have centralized semantic layers that require skilled IT resources, we’ve experienced that those can lead to bottlenecks and limited agility. With Looker’s semantic layer, LookML, our BI Team, led by Peter Donald, can easily build upon their SQL knowledge to add both a high degree of control as well as flexibility to our data model.  The fully browser-based development environment allows the data team to rapidly develop, test, and deploy code and is backed by robust and seamless Git source code management. 

In parallel, LookML empowers  business users to collaborate without the need for advanced SQL knowledge. Our data team curates interactive data experiences with Looker to help scale access and adoption. Business users can explore ad hoc analysis, create dashboards, and develop custom data experiences in the web-based environment to get the answers they need without relying on IT resources each time they have a new question, while also maintaining the confidence that the underlying data will always be accurate. This helps us meet our primary goal of providing all businesses users with the data access they need to monitor the pulse of key metrics in near real-time.

Impact and future of DTC BI at Constellation

In short order, taking a modern and integrated approach to the DTC technology stack has delivered economic impact across the portfolio, helping our team understand and combat customer churn, increase conversion rates, and optimize the customer acquisition cost (CAC) and customer lifetime value (CLV) ratios. Perhaps most important is the benefit it can provide to the customer base. Mining customer data and consumer behavior generates data into what our customers are seeking, giving us insights to supply more, or less of it. For example, observing sales velocity and conversion rates by SKU or by region can help us better understand changes in customer taste profiles and fluctuations in demand, providing the foundation for a more powerful innovation pipeline and more effective sales and distribution tactics in wholesale. Our team has also been an early pilot tester for Looker’s new integration with Customer Match, which contributes to the virtuous cycle between data insight and data activation. In the future, our plan is to leverage this cycle to amplify the impact of Google Ads across Search, Shopping, and YouTube placements for the wine and spirits portfolio. 

The operational impact of Looker is also substantial: our team estimates that the number of hours needed to reach critical business decisions has been reduced by nearly 60%, boosting productivity and accelerating the daily operating rhythm. A thoughtfully curated technology stack together with a modern BI solution allows us to stay at the vanguard of the industry. While the DTC sales channel is not designed to surpass the core business of wholesale for Constellation in terms of size, the approach enables unparalleled insights and measurement abilities that will pay dividends for the entire business for years to come.

Whether they meet customers online, offline, or in some combination, retailers share a big problem: How can they offer the right choices, when and how the customer wants, without overwhelming (and often losing) the buyer?

More than anything, this is an information problem. As such, it’s a good candidate for using artificial intelligence (AI) for greater success. Here’s how Richemont tackled the problem.

Richemont owns a portfolio of leading luxury goods brands, recognized for their distinctive heritage, craftsmanship and creativity. It has strengths and specialties in jewelry (Cartier, Van Cleef & Arpels), luxury watches (IWC, Jaeger-LeCoultre, Panerai, Vacheron Constantin), and fashion & accessories (Chloé, Montblanc, dunhill).

People shop for such goods in a number of ways, from online searching to individual meetings in boutiques, and Richemont must be prepared for every context. Understanding which shoppers are likely to buy or repurchase, when to engage directly, and what creation to suggest enables sales associates to spend quality time with clients, engaging at the right time with meaningful advice. Richemont solves these retail challenges with an integrated Client Platform leveraging Google Cloud and its AI/ML capabilities.

Enticing peoples’ desires with Machine Learning

Richemont began by posing two questions:

1. Which prospects or clients need extra attention? Specifically, who is likely to convert or to repurchase?
2. What would be meaningful items to suggest to each client and prospect?

Both questions were addressed with machine learning algorithms. Their challenges included deploying and monitoring algorithms at scale for several brands across the globe, while addressing the specific business needs for each brand. For instance, it may be more relevant to recommend in-season items for fashion brands, while for watchmakers it is more about cross-fertilization across each brand’s iconic creations.

This graph summarizes the prediction process implemented by Richemont:

Engagement data (email opened, clicked, SMS/MMS, website visits…) was found crucial to predict conversion of prospects for whom per definition no transaction history is available. For website interactions Richemont leverages the Google x Salesforce Connector.

To deploy the Machine Learning algorithms and to monitor them, Richemont leveraged Vertex AI, along with BigQuery, Cloud Functions and Google Storage, all orchestrated with Google Cloud Composer.

The role of product recommendation algorithms

Richemont used the deep learning library TensorFlow Recommenders to perform the product recommendation tasks. This library enables companies to build state of the art deep learning algorithms to achieve relevant and robust predictions.

Unlocking client value with integrated technology

Richemont’s innovations show how technology that considers many parts of the customer experience creates more value. In this case, the company used in store applications to invite people with a strong propensity to buy for boutique visits, while others at a different point in the purchasing journey were offered different options more suited to their tastes and inclinations.This solution, now deployed across 11 brands in over 25 countries, shows just one way that AI can improve customer experience, for better customer loyalty.

Key to the process, here and elsewhere, is the way a retailer and its partners put customer understanding at the center of the process. As AI becomes more important not only in retail, but in every industry, this human understanding will become even more important as a fundamental organizing principle. Much is changing, but once again, the winners will be the companies that focus best on their customers.

When solving a new ML problem, it’s common to start by experimenting with a subset of your data in a notebook environment. But if you want to execute a long-running job, add accelerators, or run multiple training trials with different input parameters, you’ll likely find yourself copying code over to a Python file to do the actual computation. That’s why we’re excited to announce the launch of the notebook executor, a new feature of Vertex AI Workbench that allows you to schedule notebooks ad hoc, or on a recurring basis. With the executor, your notebook is run cell by cell on Vertex AI Training. You can seamlessly scale your notebook workflows by configuring different hardware options, passing in parameters you’d like to experiment with, and setting an execution schedule, all via the Console UI or the notebooks API. 

Built to Scale

Imagine you’re tasked with building a new image classifier. You start by loading a portion of the dataset into your notebook environment and running some analysis and experiments on a small machine. After a few trials, your model looks promising, so you want to train on the full image dataset. With the notebook executor, you can easily scale up model training by configuring a cluster with machine types and accelerators, such as NVIDIA GPUs, that are much more powerful than the current instance where your notebook is running.

Your model training gets a huge performance boost from adding a GPU, and you now want to run a few extra experiments with different model architectures from TensorFlow Hub. For example, you can train a new model using feature vectors from various architectures, such as Inception, ResNet, or MobileNet, all pretrained on the ImageNet dataset. Using these feature vectors with the Keras Sequential API is simple; all you need to do is pass the TF Hub URL for the particular model to hub.KerasLayer.

Instead of running these trials one by one in the notebook, or making multiples copies of your notebook (inceptionv3.ipynb, resnet50.ipynb, etc) for each of the different TF Hub URLs, you can experiment with different architectures by using a parameter tag. To use this feature, first select the cell you want to parameterize. Then click on the gear icon in the top right corner of your notebook.

Type “parameters” in the Add Tag box and hit Enter. Later when configuring your execution, you’ll pass in the different values you want to test.

In this example, we create a parameter called feature_extractor_model, and we’ll pass in the name of the TF hub model we want to use when launching the execution. That model name will be substituted into the tf_hub_uri variable, which is then passed to the hub.KerasLayer, as shown in the screenshot above.  

After you’ve discovered the optimal model architecture for your use case, you’ll want to track the performance of your model in production. You can create a notebook that pulls the most recent batch of serving data that you have labels for, gets predictions, and computes the relevant metrics. By scheduling these jobs to execute on a recurring basis, you’ve created a lightweight monitoring system that tracks the quality of your model predictions over time. The executor supports your end-to-end ML workflow, making it easy to scale up or scale out notebook experiments written with Vertex AI Workbench.

Configuring Executions

Executions can be configured through the Cloud Console UI or the Notebooks API.

In your notebook, click on the Executor icon.

In the side panel on the right specify the configuration for your job, such as the machine type and the environment. You can select an existing image, or provide your own custom docker container image.

If you’ve added parameter tags to any of your notebook cells, you can pass in your parameter values to the executor.

Finally, you can choose to run your notebook as a one time execution, or schedule recurring executions.

Then click SUBMIT to launch your job.

In the EXECUTIONS tab, you’ll be able to track the status of your notebook execution.

When your execution completes, you’ll be able to see the output of your notebook by clicking VIEW RESULT.

You can see that an additional cell was added with the comment # Parameters, that overrides the default value for feature_extractor_model, with the value we passed in at execution time. As a result, the feature vectors used for this execution came from a ResNet50 model instead of an Inception model.

What’s Next?

You now know the basics of how to use the notebook executor to train with a more performant hardware profile, test out different parameters, and track model performance over time. If you’d like to try out an end-to-end example, check out this tutorial. It’s time to run some experiments of your own!