MoneySupermarket Group runs the United Kingdom's leading price comparison websites serving nearly eight million families across the country. As part of their advanced analytics strategy, they wanted to scale their analytics and Machine Learning capabilities. Datatonic helped MoneySupermarket accelerate the building of a new fully-fledged analytics platform with Google Cloud Platform and optimise their cloud environment for advanced analytics and Machine Learning, created propensity models for their marketing team and upgraded analytics dashboards for ease of use.
Implemented an advanced Analytics Platform on Google Cloud
Reduced Machine Learning Pipeline Deployment from 11 hours to 5 minutes
Updated analytics dashboards to greatly reduce training time
“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 were working with a legacy analytics platform that was limiting their ability to scale and the speed at which they could deploy new technologies like machine learning. In order to optimise their marketing and customer communications channels, they wanted to build a feature-rich and scalable Data Analytics Platform supporting their machine learning process as well as traditional data analytics, which would enable advanced analytics and machine learning use cases.
Datatonic migrated MoneySupermarket’s existing dashboards and Machine Learning pipelines to Google Cloud within days. Our team started by assessing how the model build process and the weekly scoring process could be executed on Google Platform to leverage its scalable compute power and state of the art data tools.
Moving from a fixed size servers to a cloud environment enabled MoneySupermarket to reduce training and scoring time for Machine Learning models, streamlining 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 behavioural 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 models.
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.
Click here to read the Google Cloud case study.
“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.”
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