Making Data Analysts SelfSufficient at Amaysim

Technology Category

Analytics & Modeling - Real Time Analytics
Application Infrastructure & Middleware - Data Exchange & Integration
Infrastructure as a Service (IaaS) - Cloud Databases

Applicable Industries

Telecommunications

Applicable Functions

Business Operation
Sales & Marketing

Use Cases

Real-Time Location System (RTLS)

Services

Data Science Services
System Integration

The Challenge

Amaysim, Australia's largest MVNO with over 600k customers, was dealing with a massive amount of data. They had over 10 billion call data records to be analyzed, with 20-30 million call data records added daily. The velocity and complexity of data were high, with multiple data sources including Livechat, Zendesk, call data records, Google analytics/Website data, Point of sale data, and Exact target. The company had a small analytics team of three people covering a wide span of functions. They needed a solution that would enable line of business users to quickly build on a baseline of analytics, solve their own specific business problems quickly, and not have to wait on Business intelligence teams.

About The Customer

Amaysim is an award-winning low-cost mobile provider in Australia. The company is dedicated to delivering simplicity, fairness, and low prices. In just 4.5 years, Amaysim has grown from a startup to become Australia’s largest Mobile Virtual Network Operator (MVNO) with over 600,000 customers. The company has received numerous awards, including Money magazine‘s best of the best awards for 2012 and 2013, and Roy Morgan's #1 Mobile phone service provider award for 2013 and 2014. The company has a small but efficient analytics team of three people covering a wide range of functions including Finance, Marketing, Sales, Data warehousing, and HR.

The Solution

Amaysim implemented a combination of Alteryx, Amazon Redshift, and Tableau to enable data-driven decisions with real-time intelligence. Alteryx was used for its intuitive workflow for data blending and advanced analytics. Amazon Redshift provided the speed and robustness to store and analyze vast volumes of data. Tableau was used for its excellent visualization and collaboration capabilities. This combination of tools allowed Amaysim to quickly iterate and gain insights. The company also focused on engaging the business by addressing current business pain points with short proof of concepts with new tools, starting small and delivering continuously, and doing things that drive top line or bottom line performance.

Operational Impact

The implementation of Alteryx, Amazon Redshift, and Tableau significantly improved workforce productivity. The small analytics team was able to cover a wide span of functions more efficiently.
The time to insight was greatly reduced. Projects that would have taken 2-3 weeks were now completed in a day.
The solution enabled data-driven decision making. Line of business users were able to get direct access to their own data in an easy visualization, enabling them to solve problems faster.

Quantitative Benefit