MoneySuperMarket: Helping customers find the right price with Google Cloud
About MoneySupermarket Group
An established member of the FTSE 250 index, the Moneysupermarket Group comprises MoneySuperMarket, MoneySavingExpert, TravelSupermarket, and Decision Technologies providing customers with services, tools, and products they need to grow their money.
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About Datatonic
Google Cloud partner Datatonic delivers big data and machine learning solutions for telecommunication, media, retail, and finance clients.
MoneySuperMarket uses BigQuery and Google Kubernetes Engine to build a scalable data analytics platform that speeds up development and helps its marketing stay relevant to its customers' needs.
Google Cloud results
- Automates, streamlines, and speeds up data pipeline deployment with an event-driven architecture built using Google Kubernetes Engine
- Collates and unifies data from multiple sources into a single platform for the first time with BigQuery
- Empowers data engineers to spin up instances and deploy solutions without requiring IT resources, reducing time to market
Cuts ML pipeline deployment from 11 hours to 5 minutes
MoneySupermarket Group runs the United Kingdom's leading price comparison websites serving nearly eight million families across the country. In 2018, they helped users to save an estimated £2.1bn on their household bills. Their brands include MoneySuperMarket, MoneySavingExpert, TravelSupermarket, and Decision Technologies.
"We had a legacy analytics platform that was limiting our ability to scale and the speed with which we could work with new technology like machine learning. We knew we needed a different approach to analytics, so we turned to Google Cloud."
—Harvinder Atwal, Head of Data Strategy and Advanced Analytics, Moneysupermarket Group"MoneySuperMarket offers a very broad range of products and services to our customers. Everything from insurance to financial products to energy suppliers. We can help save them money in all kinds of different ways," says Harvinder Atwal, Head of Data Strategy and Advanced Analytics at MoneySupermarket Group. Along with the benefits of success came the challenge of handling ever-increasing loads of data. When it was time to overhaul the company's analytics solution, MoneySuperMarket looked to Google Cloud for the answers.
"We had a legacy analytics platform that was limiting our ability to scale and the speed with which we could work with new technology like machine learning," he says. "We knew we needed a different approach to analytics, so we turned to Google Cloud."
Scalable, cloud-based analytics with BigQuery
Price comparison sites can be tricky to manage, with lots of moving parts needing to work in harmony. As well as the prices of the products, identifying customer preferences is vital to delivering a relevant and personalized service. With the breadth of its offerings and the size of its business, handling data is a special challenge for MoneySuperMarket. Five years ago, the company shifted its data warehouse to the cloud to mitigate the costs of storage while still keeping hold of its existing on-premises analytics solution. This solution proved short term as MoneySuperMarket's data needs grew.
On-premises solutions can become very expensive as a business scales up. Not only do new servers have to be installed and configured, they also have to be maintained and managed afterwards. In addition to the cost, MoneySuperMarket found that this was beginning to impact its employees' workflows. On the one hand, only the data scientists had access to the data creating a bottleneck within the company. On the other hand, any time the data team wanted to add capacity or make changes they had to wait for the IT department to make time for them.
When it was clear that an on-premises solution would not be sustainable, MoneySuperMarket attempted to build an analytics platform with its existing cloud provider. However, the management requirements were too high. "There were a lot of open source tools we needed to seamlessly integrate across the data and model lifecycles for," says Harvinder. "Developing, maintaining, and prioritizing new development for all of it was a major management overhead on top of the day job." In addition, MoneySuperMarket had started to develop its own machine learning models to optimize its marketing and customer communications channels. Any new solution for MoneySuperMarket would have to handle the demands of an increasingly important machine learning process as well as traditional data analytics.
"For the first time, we've been able to collate offloads from our enterprise warehouse with other kinds of data in a single place. BigQuery is really the beating heart of our platform."
—Harvinder Atwal, Head of Data Strategy and Advanced Analytics, MoneySupermarket GroupGoogle Cloud, with its flexibility, scalability, and ease of use, was a natural contender for MoneySuperMarket's new analytics platform. What's more, the company had already used Google products, such as Google Analytics within the Google Marketing Platform and Google Ads, so was aware of Google's capabilities. "We'd worked with Google on the ad performance side of things, so were quite familiar with the environment," says Harvinder. In January 2018, MoneySuperMarket worked with Google Partner Datatonic to accelerate the building of a new fully-fledged analytics platform with Google Cloud.
The majority of the data for the new platform was held in MoneySuperMarket's existing enterprise data warehouse. Using Cloud Data Transfer, the company extracted the data to Cloud Storage buckets. From there, MoneySuperMarket used Google Kubernetes Engine and Cloud Pub/Sub to orchestrate a process of cleaning the data with containerized applications and loading it into BigQuery. BigQuery also started to serve as a repository for other kinds of information such as events data from different products and services.
"For the first time, we've been able to collate offloads from our enterprise warehouse with other kinds of data in a single place," says Harvinder. "BigQuery is really the beating heart of our platform."
Google Cloud also helped MoneySuperMarket to streamline its machine learning pipeline for its marketing campaigns. The company's proprietary machine learning models would be regularly refreshed with new data, such as demographics or customer behavioral events like purchases and website clicks. This helped ensure that customers would receive the most relevant offers and advice as quickly as possible. With Cloud Composer, Cloud Functions, Container Registry, and Google Kubernetes Engine, MoneySuperMarket could break up the process into distinct phases of data extraction, pre-processing, and scoring before loading the results into its machine learning model.
"Chunking up the model scoring process into discrete tasks lets us scale and develop different elements in isolation, which was really useful," says Harvinder. "The ability to scale big jobs with Google Kubernetes Engine and an event-driven architecture has been really powerful for us."
Greater power, greater empowerment
With its new analytics platform, MoneySuperMarket has benefited most from the speed of development and running big tasks. According to Harvinder, the most notable change has been the deployment time for its machine learning pipelines. "We went from eleven hours down to about five minutes," he says. That meant that the models could be updated every day instead of once a week, which, in turn, led to more relevant communications and offers, ultimately helping customers to save more money.
"Google Cloud has helped our analytics team become much more independent and self-servicing. We don't have to worry about infrastructure, engineers, or scaling up. We can be up and running very quickly."
—Harvinder Atwal, Head of Data Strategy and Advanced Analytics, MoneySupermarket GroupUsing managed services with Google Cloud enabled MoneySuperMarket to scale up its analytics without having to worry about sinking costs into hardware, which might lay idle in slower periods or be expensive to maintain. Meanwhile, using Cloud Identity and Access Management has helped the company simplify its access and permissions protocols without sacrificing security or control. The lack of infrastructure management, in general, means that the data team at MoneySuperMarket can work on projects they want without having to jump through administrative hoops to build new environments.
"Google Cloud has helped our analytics team become much more independent and self-servicing," says Harvinder. "We don't have to worry about infrastructure, engineers, or scaling up. We can be up and running very quickly."
New tools, new ways of working
With the new analytics platform in place, MoneySuperMarket has not stopped looking for ways to see what else Google Cloud can help with. As well as improving its data warehouse and ML pipelines, the company is using Google Cloud to build new pricing models to predict the likelihood of customers buying insurance and has just launched a Credit Monitor app that will help its customers hit their financial goals. MoneySuperMarket also is currently exploring TensorFlow and Cloud Machine Learning as alternatives to its existing machine learning tools, helping to make its processes even more efficient. Meanwhile, MoneySuperMarket is keen to expand its work with APIs managed with Cloud Endpoints or Apigee API Management Platform to help bring other departments closer to the data that means the most to them.
"People have been really excited about what we can achieve with APIs," says Harvinder. "Finally, we can open up some of the data that has been sitting in our warehouse and make use of it. It's a cheesy phrase, but it definitely has helped to democratize our data."
Tell us your challenge. We're here to help.
Contact us
About MoneySupermarket Group
An established member of the FTSE 250 index, the Moneysupermarket Group comprises MoneySuperMarket, MoneySavingExpert, TravelSupermarket, and Decision Technologies providing customers with services, tools, and products they need to grow their money.
About Datatonic
Google Cloud partner Datatonic delivers big data and machine learning solutions for telecommunication, media, retail, and finance clients.