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.”

Data science can be applied to business problems to improve practices and help to strengthen customer satisfaction. In this blog, we address how the addition of Looker extends the value of your Google Cloud investments to help you understand your customer journey, unlock value from first-party data, and optimize existing cloud infrastructure.    

Data is a powerful tool 

While everyone doesn’t need to understand the nuts and bolts of data technologies, most people do care about the value data can create for them — how it can help them do their jobs better. Within the data space, we are seeing a trend of ongoing failure to make data accessible to “humans” –   the industry still hasn’t figured out how to put data into the hands of people when and where they need it, how they need it. 

What if everyone in your organization could analyze data at scale, and make more-informed, fact-based decisions?   Data and insights derived from data are valuable but only if your users see it.  We think Looker helps solve that. 

Solutions tell a larger story of how it all fits together 

Across all verticals and industries,  businesses benefit from knowing and understanding their customers better. Many business goals are to increase revenue by improving product recommendations and pricing optimization, improving the user experience through targeted marketing and personalization, and reducing churn while improving retention rates.  To help reach  these goals, key strategies should focus on understanding customer needs, their motivations, likes and dislikes, and using all available data – in other words, put yourself in the shoes of your customer.    

Our goal with Looker solutions is to offer the right level of out-of-box support that allows customers to get to value quickly, while maintaining the necessary flexibility. We aim to offer a library of data-driven solutions that accelerate data projects. Many solutions include Looker Blocks (pre-built pieces of code that accelerate data exploration environments) and Actions (custom integrations) that get customers up and running quickly and lets you build business-friendly access points for Google Cloud functionality like BQML, App Engine and Cloud Functions.  

Below, you’ll find a sampling of the newest Looker solutions. 

Listening to customers by looking at the data

Looker’s solution for Contact Center AI (CCAI), helps businesses gain a deeper understanding and appreciation of their customers’ full journey by unlocking insights from all their company’s first-party data. Call centers can converse naturally with customers and deliver outstanding experiences by leveraging artificial intelligence. CCAI‘s newest product —CCAI Insights — reviews conversations support agents are having, finding and annotating the data with the important information, and identifying the calls that need review. We’ve partnered with the product teams at CCAI to build Looker’s Block for CCAI Insights, which sets you on the path to integrating the advanced insights into your first-party data in Looker, overlaying business data with the customer experience.

Businesses can better understand contact center experiences and take immediate action when necessary to make sure the most valuable customers receive the best service. 

Realizing full business value of First-Party data

Looker for Google Marketing Platform (GMP) provides marketers the power to unlock the value of their company’s first-party data to more effectively target audiences.  The Looker Blocks and Actions for GMP offer interactive data exploration, slices of data with built-in ML predictions and activation paths back to the GMP.  This strategic solution continues to evolve with the Looker Action for Google Ads (Customer Match), the Looker Action for Google Analytics (Data Import) and the Looker Block for Google Analytics 4 (GA4).

• The Looker Action for Customer Match allows marketers to send segments and audiences based on first-party data directly into Google Ads. Reach users cross-device and across the web’s most powerful channels such as Display, Video, YouTube, and Gmail.  The entire process is performed within a single screen in Looker, and is able to be completed in a few minutes by a non-technical user. 
• The Looker Action for Data Import can be used to enhance user segmentation and remarketing audiences in Google Analytics by taking advantage of user information accessible in Looker, such as in CRM systems or transactional data warehouses.
• The Looker Block for Google Analytics 4 (GA4) expands the solution’s support with out-of-the-box dashboards and pre-baked BigQuery ML models for the newest version of Google Analytics.The Looker Block offers up reports with flexible configuration capabilities to unlock custom insights beyond the standard GA reporting. Customize audience segments, define custom goals to track and share these reports with teams who do not have access to the GA console.

From clinical notes to patient insights at scale

Taking a look at the Healthcare vertical, the Looker Healthcare NLP API Block serves as a critical bridge between existing care systems and applications hosted on Google Cloud providing a managed solution for storing and accessing healthcare data in Google Cloud. The Healthcare NLP API uses natural language models to extract healthcare information from medical text,  rapidly unlocking insights from unstructured medical text and providing medical providers with simplified access to intelligent insights.  Healthcare providers, payers, and pharma companies can quickly understand the context and relationships of medical concepts within the text, such as medications, procedures, conditions, clinical history, and begin to link this to other clinical data sources for downstream AI/ML.   

Specifically, the natural language processing (NLP) Patient View (pictured below) allows you to review a single selected patient of interest, surfacing their clinical notes history over time. It informs clinical diagnosis with family history insights, which is not currently captured in claims, and captures additional procedure coding for revenue cycle purposes.

The dashboard below shows the NLP Term View which allows users to focus on chosen medical terms across the entire patient population in the dataset so they can start to view trends and patterns across groups of patients. 

This data can be used to: 

• Enhance patient matching for clinical trials 
• Identify re-purposed medications
• Drive advancements for research in cancer and rare diseases
• Identify how social determinants of health impact access to care

Managing Costs Across Clouds

Effective cloud cost management is important for reasons beyond cost control — it provides you the ability to reduce waste and predictably forecast both costs and resource needs. Looker’s solution for Cloud Cost Management offers quick access to necessary reporting and clear insights into cloud expenditures and utilization. 

This solution brings together billing data from different cloud providers in a phased approach: get up and running quickly with Blocks optimized for where the data is today (Google Cloud, AWS or Azure) as you work towards more sophisticated analysis for cross-platform planning and even cloud spend optimization with the mapping of tags, labels and cost centers across clouds.

The Looker Cloud Cost Management solution provides operational teams struggling to monitor, understand, and manage the costs and needs associated with their cloud technology with a comprehensive view into what, where and why they are spending money.

Making better decisions with Looker-powered data

Leading companies are discovering ways to get value from all of that data beyond displaying it in a report or a dashboard. They want to enable everyone to make better decisions but that’s only going to happen if everyone can ask questions of the data, and get reliable, correct answers without using outdated or incomplete data and without waiting for it. People and systems need to have data available to them in the way that makes the most sense for them at that moment.  

It’s clear that successful data-driven organizations will lead their respective segments not because they use data to create reports, but because they use it to power data experiences tailored to every part of the business, including employees, customers, operational workflows, products and services.   

As people’s way of experiencing data has evolved, more than ever before, dashboards alone are not enough. You can use data as fuel for data-driven business workflows, and to power digital experiences that improve customer engagement, conversions, and advocacy. 

From Nov. 9 – 11th, Looker is hosting its annual conference JOIN, where we’ll be showing new features, including how we help to:

• Build data experiences at the speed of business
• Accelerate the business value with packaged experiences 
• Unleash more insights for more people in the right way – Deliver data experiences at scale

There is no cost to attend JOIN.  Register here and learn how Looker helps organizations build and deliver custom data-driven experiences that goes beyond just reports and dashboards, scales and grows with your business, allows developers to build innovative data products faster, and ensures data reaches everyone.

The combination of a vital need for IT experts among businesses and a digital skills gap is making lifelong learning increasingly critical. Beyond professional development, learning new skills offers additional rewards from building peer connections to boosting your creativity. That’s why in 2020 Krishna Deepak Nallamilli and I launched KIMO.ai to reimagine how people approach learning, especially in developing markets. Our team is building the artificial intelligence needed to generate individual learning paths through a wide range of quality digital learning content.

Google Cloud and its Startup Program have been instrumental in connecting our team with the tools, people, processes, and best practices to grow our business. 

Existing learning platforms lack engagement

Outside of traditional education settings, massive open online learning courses (MOOCs)—often modeled after university courses—can provide a flexible and affordable way to upskill or reskill. But the vast majority of people who participate in MOOC programs fail to complete courses. Based on our research, the challenge with existing learning platforms is a lack of engagement, primarily caused by limited direction on which skills to learn, whether AI, fintech, blockchain, or other in-demand disciplines.   

We’ve also received feedback that many corporate learning management systems–developed as online training systems to upskill employees–tend to be poorly designed and time-consuming to use. 

Overall, a significant challenge with most existing learning platforms is that they’re generic. For example, suppose you’re interested in learning about AI. In that case, you need AI-related coursework that applies to your industry and the job you want because AI in medicine is vastly different from AI in financial services. Today’s online learning options typically take a one-size-fits-all approach and fail to capture the nuances of what learners really need to get ahead. 

Building a future-proof learning platform 

The commitment to highly personalized, accessible learning inspired KIMO.ai, a platform that we believe is the future of education. Depending on your goals, current skills, location, and other factors, our AI-based platform will identify which coursework (and where to find those classes) to build the skills you need. The more personalized, relevant learning recommendations even take into account people’s preferences for podcasts, MOOCs, books, articles, videos, courses, publications, and more.  

In a mix of cooperation and competition we call “coopetition,” KIMO.ai will regularly recommend courses from other established online learning systems if, based on our automated assessment, it’s the best option for a learner. There’s also the option to access free content only. 

Google cultural alignment fosters trust

Our platform started with one developer exploring NLP models and Google APIs. As we’ve grown our team and launched our beta to 110,000 users in developing markets, we discovered there is a lot of interest in our platform, and we believe we can make a significant impact. In feedback forums, we also learned that we need to focus our efforts on the mobile experience to improve engagement since 99% of the beta testers use mobile devices. 

Beyond our team’s high level of trust in Google Cloud solutions, our team also appreciates the cultural alignment with Google. We value Google’s developer-centric approach and rely on tools like Dataflow for batch data processing and Cloud TPU to reliably run machine learning models with AI services on Google Cloud. We also build all of our deployments on Google Kubernetes Engine (GKE), which makes it easy to manage all our containerized workloads 

On the front end, Google App Engine makes it easy to deploy apps and experiment, and it integrates seamlessly with Firebase for authentication and more. BigQuery is our serverless data warehouse that efficiently scales to support the millions of articles, videos, and other learning resources we need to analyze to provide the targeted coursework recommendations our learners require.

As we grow our business having a network of trusted advisors is also extremely valuable. By working closely with DoIT International, the 2020 Google Cloud Global Reseller Partner of the Year, our team has access to their cloud, Kubernetes, and machine learning expertise. DoIT has already helped us quickly resolve IT issues and create analytics dashboards that give us insights to continually enhance our services. 

Building for a growing industry

The dynamic edtech market is growing rapidly and estimated to become an $11B industry by 2025. We’re proud to be part of the next wave of personalized education that has the potential to empower people in developing markets and beyond to grow their skills with coursework tailored to their exact needs and how they like to learn. This year, we will deliver our platform to at least 400,000 more people. We’re excited to see how they use it and where it takes them. 

If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

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.