At Quantum Metric, we’re in the business of bringing our customers business insights that are based on customer experience data and analytics for mid-market and Fortune 500 companies.

Our software, powered by big data, machine intelligence, and Google Cloud, helps our customers identify, quantify, prioritize and measure opportunities to improve digital experiences.

As companies move to a more agile product lifecycle, including continuous deployment and continuous integration, they’re finding that it’s critical to receive perpetual quantified feedback and insights from their data in real time to understand where the largest opportunities exist. 

Each year, billions of customer interactions are captured through browsers or mobile apps on PCs, tablets, and mobile devices. This data, fed into the Quantum Metric platform, can show if a customer had a password problem they couldn’t solve or struggled when trying to purchase something and abandoned their cart.

It also can show if the customer tried in vain to complete an online change to their service provider’s subscription, to reach tech support, or couldn’t find the size or color they were looking for while shopping online.

Most importantly, the Quantum Metric platform quantifies the business value of the issue, helping organizations prioritize where they can make the largest impact to their business.

Success overwhelms our initial architecture
Initially, the Quantum Metric experience analytics software ran on a MySQL open source relational database management system (RDBMS). The MySQL RDBMS worked great for simple queries, when there was a specific question to ask of the data.

Soon, though, we knew we needed to offer more advanced data science capabilities. Our bigger customers wanted to ask questions across very large data sets—days, weeks, months, and years worth of data. They wanted to pose iterative questions using complex filters to answer their most challenging business questions. 

With more complex queries across more data, response times from our RDBMS went from 100 to 500 milliseconds to as long as 20 minutes.

That delay was slowing down our ability and time to insights, which also reduced the value we could provide to our customers, since iterative exploration and analysis requires real-time query responses. Because of the need for real-time responses, there were certain questions that we just weren’t able to ask of the data. It became clear that we needed a much more robust data warehouse solution. 

There were also operational challenges with MySQL and massive-scale data ingestion. We spent a lot of time into the wee hours of the night and morning handling errors and recovering databases.

We tried to address these challenges by sharding, partitioning, and indexing the data to optimize for the types of questions customers were asking. But the problems were escalating and happening more often, from once a month across the customer base to monthly for at least 20 different customers.

We could tune the platform for today and tomorrow’s workload, with good guesses at where indexes could be used, but we simply couldn’t continue to horizontally scale MySQL in a cost-efficient and operationally efficient manner. 

Speed breeds innovation
Once we started exploring options that could better scale with our business, we looked at NoSQL technologies like Cassandra (a partitioned row-store database), MySQL’s Column Store (a columnar store database), and Vertica (a columnar store database)—each with unique ways of handling data storage and accessibility.

But with high volumes of complex queries across large data stores, all of these solutions began to fail, bogged down with multiple, simultaneous users. We could have solved the problems with more raw compute and storage, but it would have been prohibitively expensive to run and require a large team to operate. 

We then decided to try BigQuery, and it was transformative.

We connected our front end to BigQuery via APIs. Once data is 15 minutes old, it is automatically extracted, loaded, and transformed (ETL) to BigQuery.

We continuously update the legacy MySQL RDBMS so its data is integrated with BigQuery data when queries require real-time data. Most query response times are within 100-200 milliseconds, matching what we initially experienced with MySQL.

When traffic from our customers scales up, we can now scale on-demand to accommodate it, thanks to BigQuery’s hundreds of thousands of CPUs. Our customers no longer run into slow response times, and we’ve gained confidence that we can offer them—and their users—advanced insights and better experiences without delay.

More importantly, with this scale of query power, we were able to build data science algorithms into the platform, which iteratively query BigQuery based on the results, and help quantify the impact of a specific issue to a specific segment of users. Adding these capabilities was possible because of the massive scale of BigQuery. 

In addition to new insights and fast response times, we wanted our customers to be able to ask complex questions using very simple language.

For example: “Show me high-loyalty customers, located in specific geographic areas, who visited the web site at least five times, based on specific campaigns, and never booked a seat on a flight.”

This was exactly the kind of query that was used by a major U.S. airline to understand the multi-million dollar impact of a failure affecting their most valuable customers: their high-loyalty members.

And this was all done while maintaining the highest standard of care of customer data and privacy by default, using multiple layers of encryption of data in transit, at rest, and a unique military-grade encryption approach. This approach encrypts PII, including even session cookies, with a RSA-2048 key available only to a select few and used for use cases such as fraud analysis.   

It’s no exaggeration to say that BigQuery has totally transformed our business. It provides the petabyte scale and speed we were missing, in addition to taking care of operational maintenance, a task that was burying our team with MySQL.

We’re now able to support some of the largest companies in the world that require real-time, petabyte-scale analytics. That lets them serve more customers faster with higher quality, and take advantage of BigQuery’s power and scale to innovate.

There are other cloud solutions that can address petabyte analytics, but the most unique value proposition of BigQuery was its on-demand scaling and operational management, with extremely cost-effective pay-as-you-go billing. While today we are at a scale where we have round-the-clock querying needs, our early days had very sporadic query loads where we needed instant scale, then a long lull of nothing. The unique business model of BigQuery’s pay-by-bytes-scanned allowed us to have access to a massive-scale querying platform without breaking the bank. 

Using BigQuery powers better customer experience and reduces purchasing friction
Among the many features of Quantum Metric is the ability to replay online customer sessions. In the example below from a mobile e-commerce site, each action is displayed chronologically. Why did this customer’s transaction fail?

Diving deeper, the session replay shows that the user tried to change the item quantity in the checkout cart, which resulted in a failed API call. Powered by BigQuery, Quantum Metric can then show how many other end users had this issue, with a simple click of “Show More Errors Like This.”

With BigQuery’s massive scale, Quantum Metric will then quantify the impact of that issue, so companies can prioritize which issues need attention immediately. If this is the issue that’s impacting the business the most, our customer can use a single click to open a Jira ticket, forwarding the discovery to their product and engineering teams. Those teams can then re-engineer the experience in near-real time, addressing the failed API call and cutting out the frustrating time it takes for engineers to reproduce the issue.

Once we had a powerful back end in BigQuery, we realized that Quantum Metric’s platform could be used to ask complex questions from vast datasets. We built some of the processes that data scientists use to formulate those queries right into our product.  

For example, we added one series of processes to our platform to help customers understand whether a suspect issue is really impacting end-user experience. Is it something that should be prioritized and fixed? Does this really affect the user experience? Does it have financial impact? These and other questions can be pre-defined as a complex query in Quantum Metric to let our customer quickly gain insight on how an issue is impacting the business. Customers were blown away when they heard this was possible. It was the holy grail for what they were looking for in data science. It really sets us apart from our competition.

Today, with every company heavily dependent on data, those companies that can uncover and act on insights fastest are the ones that will succeed. BigQuery gives us the data warehouse platform we need to provide our customers with fast, reliable technology tools. It frees us from having to deal with the minutiae of technology infrastructure operations, so we can focus on finding and extracting the magic in customer data. With the power and scale of BigQuery, combined with the real-time capture of every user experience with 100% fidelity, we’re able to offer a self-service analytics platform that provides insights into digital journey friction points and acts as the indisputable arbitrator of truth. 

Hundreds of millions of residents of South East Asia have only limited access to banking and finance services. However, help is at hand. One of South East Asia’s largest fintech businesses, Ascend Money is using digital technologies to realize its mission of enabling as many people as possible to access innovative financial services and live better lives.

According to Ascend Money, 60 percent of the region’s 620 million residents do not have access to a full suite of financial services. Even a relatively small proportion of this market represents a big opportunity for the business.

Ascend Money’s offerings include the TrueMoney regional payment platform for underserved and digital consumers. The TrueMoney platform supports more than 40 million consumers across six countries: Cambodia, Indonesia, Myanmar, the Philippines, Thailand, and Vietnam.

“We found, based on value, ease of use, effectiveness, and the skills and adaptability of our own team members, BigQuery and other Google Cloud Platform services were the best fit for our business.”

– Abraham Jarrett, Head of Engineering, Ascend Money

TrueMoney also provides an e-wallet app that provides easy ways to top up mobile phones, undertake online shopping, and pay for products and services; a network of 65,000 branded shops; and international remittances, initially between Thailand and Myanmar. In addition, Ascend Money offers financial products to small to medium businesses.

Ascend Money is part of the South East Asian online business Ascend Group, which also operates e-commerce, e-procurement, data centers, cloud services, fulfilment, and digital marketing services.

Cost is a key criteria

Ascend Money initially started operations using physical infrastructure and on-premises workforce productivity applications. However, as the business grew its customer base and expanded into new markets, its technology leaders began to explore options to improve value for money; reduce the maintenance load on in-house team members; improve its data management and analysis; and collaborate more effectively.

“Operating our own on-premises infrastructure entails a higher cost of ownership and maintenance, as well as requiring us to scale up our hardware as needed,” says Abraham Jarrett, Head of Engineering, Ascend Money. “We conducted an evaluation and found that moving to the cloud would deliver a range of benefits.”

G Suite and Google Cloud Platform the best fit

The business evaluated solutions available in the market and opted to move to G Suite and Google Cloud Platform.

“Moving to Google Cloud Platform was an optimization exercise, with lowering our costs our primary goal, and achieving better performance an added benefit,” says Jarrett. Google Cloud Platform monitoring, diagnostic, and analytics tools have enabled Ascend Money to reduce its infrastructure spending, becoming more efficient and cost-effective in the process.

“Managing software is a big cost for us. Through G Suite, we are significantly reducing our software deployment, licensing, and repair costs.”

– Abraham Jarrett, Head of Engineering, Ascend Money

The Ascend Money team saw Google Kubernetes Engine as enabling a seamless way of transitioning from an on-premises data center to a cloud service. “We considered industry trends such as what people were adopting and who we could hire when making our decision,” says Jarrett. “Engineering teams are focusing on using Kubernetes open source container management at scale and as a way of doing business, which was attractive to us.

“Google Kubernetes Engine presented the easiest way to manage and orchestrate our containers, and drive us away from our existing infrastructure.”

BigQuery best for cost and ease of use

Ascend Money also reviewed data infrastructure solutions, with its business intelligence and data platform teams considering a range of options. The teams quickly ruled out an on-premises solution due to the capital investment required and opted for a cloud service. Following a rigorous evaluation of Google Cloud Platform against the services offered by another cloud provider, Ascend Money opted to run on Google Cloud.

“We found, based on value, ease of use, effectiveness, and the skills and adaptability of our own team members, BigQuery and other Google Cloud Platform services were the best fit for our business,” says Jarrett. The business is now running an architecture comprising a BigQuery analytics data warehouse; Cloud Storage to store raw and archive data; Cloud Dataflow to process stream and batch data, and Cloud Pub/Sub for event ingestion and delivery.

“We’re expanding our utilization of BigQuery and other services on a daily basis,” says Jarrett.

Ascend Money is also using a range of Google Cloud Platform services for the infrastructure outside its data platform, including Stackdriver logging and monitoring; Cloud KMS to manage cryptographic keys for its cloud services, Cloud Build to undertake continuous integration, delivery, and deployment; Cloud DNS to provide domain name system services; and Cloud Functions to enable its developers to run and scale code in the cloud.

With Google Cloud Platform, Ascend Money is now processing about 12GB of batch data per day and streaming data from about 200 data marts per day in Thailand alone.

Cost effective scalability and faster to market

Google Kubernetes Engine has enabled the business to scale and deliver to market faster. “We can build on the platform, take the application and container and deploy them to scale out,” says Jarrett. “The Google Kubernetes Engine orchestration mechanism that provides containerized, elastic scalability is extremely important in allowing us to manage costs and expend our effort efficiently. Through Kubernetes, we can write once and deploy everywhere.”

With Google Cloud Platform, the business has saved 3,000,000 THB (about US$90,000) in licensing costs and, through automation tools, reduced the time to complete infrastructure activities by 50 percent.

“We don’t have as many meetings since we deployed G Suite and we can work effectively in a distributed fashion.”

– Abraham Jarrett, Head of Engineering, Ascend Money

Ascend Money’s entire workforce is now using G Suite to collaborate and operate productively. The business is achieving a range of benefits including being able to get new team members up and running more quickly and reduced cost. “Managing software is a big cost for us,” says Jarrett. “Through G Suite, we are significantly reducing our software deployment, licensing, and repair costs.” Meanwhile, the shorter time to onboard team members to G Suite is paying off with improved productivity and an accelerated ability to collaborate.

Ascend Money team members primarily use Sheets, Slides, and Docs to create internal materials, including product requirement documents in Docs and workforce and project planning spreadsheets in Sheets.

The organization also uses Hangouts Meet to collaborate when working from different locations. “We don’t have as many meetings since we deployed G Suite and we can work effectively in a distributed fashion,” says Jarrett. “People can work from home and we have five regions plus Thailand where they can collaborate live on a document, such as a slide deck for board meetings or meetings with our chief executive officer, head of engineering, or other senior managers.”

20 hours per week saved

Jarrett describes the time savings of using web browser-based productivity software and G Suite as saving them an average of 20 hours per week. This has improved productivity and the quality of life for Ascend Money’s hard-working team, increasing overall staff satisfaction.

“The ability to work remotely is a big win for us,” Jarrett says. “Our team members can keep one computer at work and one at home and access the same information, they can work in a plane while it sits on the tarmac, or update a Google Doc or a Google Sheet in real time on their phone. They can work anytime, from any location, as long as they have an internet connection. They have less dead time, meaning more uptime.”

With Google Cloud Platform and G Suite, Ascend Money is ideally positioned to support growing demand for its fintech services in South East Asia, particularly among people with limited access to banking. “We have the agility and dynamism to support rising demand and look forward to continuing to work with Google to realize our business ambitions,” says Jarrett.