Headquarters: South Jakarta, Indonesia
- Modern Data Platform: Cloudera Enterprise
- Workloads: Data Warehouse, Data Science, Data Engineering
- Key Components: Apache Impala, Apache Spark, Cloudera Search, Kerberos
- BI & Analytics Tools: Kogentix AMP, Microsoft Power BI, Microstrategy, Tableau
- Database: Oracle, Teradata
- ETL Tool: Ab Initio
- Business Intelligence
- Self-service analytics
- Predictive analytics
- Network data
- Device data
- Subscriber data
- Retailer data
- Reduces customer churn through more targeted offers
- Increases sales through retailers
- Enables company to identify and fix network outages affecting most profitable customers sooner
Big data scale
- 2 PB with plans to expand within the year
An enterprise view of its customers is helping XL optimize customer acquisition, improve the customer experience and customer retention, and, ultimately, increase profitability.
PT XL Axiata Tbk. (XL) is one of the leading mobile service providers in Indonesia, with nearly 55 million subscribers. The company offers a range of data, voice, SMS, and other digital services to both retail customers and businesses across Indonesia.
“In the Indonesian telecommunications market, the customer churn rate is nearly 20 percent,” said Yessie Yosetya, CTO at XL Axiata. “People are basically using SIM cards just like data cards. They just throw away their SIM cards once they finish using them, and get a new one. If we can keep a fraction of those subscribers in our base, it’s a huge achievement for us. As a result, everything that we do has to be customer-centric.”
To better understand its customer experience, XL needed to create a single enterprise view of its customer data. “Previously, every business unit maintained its own data silos,” said Yosetya. “In our analytics journey, it’s important to put all this data together so we can understand each customer’s preferences and needs.”
Additionally, XL sought to include in its analytics an exponentially growing volume of network data along with new unstructured data sources, such as internet traffic and retailer data. “The data volumes are tremendous and typically marketing could only use samples of the data for their analyses,” said Yosetya. “We also were limited in the data sources we could analyze with our existing platform. It was a serious challenge in terms of how to grow and scale, because the cost of the traditional platforms no longer made sense.”
XL worked with Cloudera to create an enterprise view of its customer data for greater insight into the customer experience and increased profitability. The platform securely brings together and analyzes vast amounts of network, device, subscriber, and retailer data, including data from more than 100,000 base transceiver stations (BTS). IT staff can bring in new datasets quickly, easily, and cost-effectively to enable analysts with prompt and relevant datasets. They can also perform machine learning at scale for predictive analytics. This will allow XL to more accurately forecast potential customer attrition and more precisely target retention offers to reduce churn.
“We can see things from the customer point of view and do analyses that weren’t possible before,” said Yosetya. “Cloudera is the right partner for our analytic journey.”
As part of XL’s implementation, Yosetya plans to deliver self-service analytics across the company. “When I look at my team’s workload, much of their time is spent on creating ad hoc reports,” said Yosetya. “Instead, with Cloudera, we can empower users and give them the tools to create a report or look at the data and do the analysis themselves, rather than involving IT.”
According to Yosetya, XL’s analytics transformation is instrumental in helping the company reduce customer churn and grow. Marketing staff can easily understand the customer experience and see what offers they need to deliver to inspire customers to keep their contracts longer. They can also better influence customer acquisition through targeted promotions that draw on new insight into customer needs and the retailers that sell SIM cards.
“We are heavily dependent on our retail outlets; there are hundreds of thousands of Mom and Pop stores that sell our products,” said Yosetya. “We can now launch programs that tailor commissions to these shops based on each shop’s persona.”
An enterprise view of customer data is also fueling new operational efficiencies. For example, in the past, XL would dispatch service teams to repair network outages using a first-in, first-out model. Now, by overlaying customer profitability data with network outage information, XL can see which sites are most profitable and prioritize service accordingly. “The better we understand the facts, the better decisions we can make,” said Yosetya.