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Lion Parcel Gains Faster Data Insights on AWS to Enhance Customer Experience


The global logistics industry is projected to grow steadily from 2022–2030, at an annual growth rate of 6.8%, particularly due to the surge in ecommerce logistics, containers shortages, closure of major ports causing port congestion, and restricted capacity in the air freight market. While the sector has gone through major transformation over the last two years, it has adapted efficiently, continuing to evolve in line with multiple emerging trends.

Lion Parcel is bolstering its technology to make data-driven decisions that differentiate the company from domestic and international rivals. As a subsidiary of the Lion Group, Lion Parcel serves first, middle, and last-mile delivery to customers across Indonesia’s 17,000 islands. Lion Parcel also delivers to customers across 25 countries from more than 7,000 service points across Indonesia. “We see that technology can help us improve our existing logistics ecosystem efficiently,” says Probosetyo Krishna, head of information technology at Lion Parcel.

Until 2019, Lion Parcel was using on-premises infrastructure to run its business. The following year, it migrated its core systems to Amazon Web Services (AWS) to leverage its pay-as-you-go model and limitless scaling capabilities. Krishna elaborates, “We found the support from AWS and its partners to be top notch. We value this highly as we strive to advance in the industry by collaborating with trusted partners that share our values. This is important to us as we look to build and do more on the cloud.”

Lion Parcel Case Study

Implementation of the data pipeline was faster than we expected, and we’re able to do even more than we envisioned.”

Probosetyo Krishna
Head of Engineering, Lion Parcel

Delivering Reports in 1 Minute with Data Pipeline on AWS

At the start of 2021, Lion Parcel started forming a data team to build a data pipeline on AWS, working with AWS Partner Deloitte to assist with design and implementation. In the past, Lion Parcel had experimented with building a data lake on premises. However, the performance and data processing speed did not meet its business expectations. For example, to run a delivery report over a period of six months, Lion Parcel had to wait 15 minutes for the data to load. On AWS, the same report takes less than a minute.

Lion Parcel is using Amazon Simple Storage Service (Amazon S3) as a data lake to store structured and unstructured data, AWS Glue for data transformation and cleansing, and Amazon Redshift as a data warehouse.

Ideating to Implementing in Six Weeks

Within six weeks of starting discussions with Deloitte and AWS, Lion Parcel had its data lake and data warehouse up and running. “Implementation of the data pipeline was actually faster than expected, and we’re able to do more than we initially envisioned,” Krishna says. Improved visualization for internal reporting, and the application of machine learning (ML) using Amazon SageMaker to segment customers were identified as first use cases for the data pipeline.

Previously, reporting queries went via Lion Parcel’s transactional database. But as the volume of data increased, the business experienced bottlenecks that slowed information extraction. With a modern data pipeline, the transactional database remains isolated and “clean,” Krishna relates, and employees can extract the data they need directly from the data warehouse.

Gaining Higher Visibility with Proactive Customer Service

Running faster reports with more visibility into granular data is empowering Lion Parcel to shift to a more proactive customer service model. Management and customer service agents can quickly run package status reports during various stages of delivery. As a customer-centric company, Lion Parcel is currently creating alerts from its monitoring system on AWS to detect delivery disruptions promptly and take preemptive action before receiving complaints.

In addition to speed, Lion Parcel benefits from business intelligence dashboards on Amazon QuickSight to gain near-real-time insights into operations. Its IT team used to run daily performance reports for the previous day to present to management. Now, management can directly access current day reports, capturing data from just 1 hour prior.

“Visualization has improved dramatically with Amazon QuickSight. It’s easy to connect and has an excellent price to value ratio, with more features than our previous open-source visualization tool,” Krishna says.

Applying ML for Customer Segmentation

Following the visualization enhancements, Lion Parcel turned its attention to building ML models as an extension of its analytics pipeline. Segmentation was the first step in Lion Parcel’s ML journey. Management and marketing teams can now view distinct customer profiles through ML-driven customer behavior analysis, which they’re using to craft customer relationship management (CRM) initiatives.

Lion Parcel can analyze which purchasing stage customers are in, their likelihood to churn, and other characteristics that help with targeted marketing. The business has started using Salesforce CRM software and is able to integrate customer data seamlessly on the AWS Cloud.

Establishing a Route for Success on AWS

The scalability of the AWS Cloud is key to carrying out current ML initiatives—such as determining optimum locations for new service points—as well as future ones like route optimization and demand-driven pricing. “With AWS, we can scale high-performing servers to train and create data models then scale down after, which makes ML more cost efficient,” Krishna says.

He concludes, “Discovering the potential of AWS Cloud technology has been a really fun experience for my team. We’re learning a lot from AWS and Deloitte’s guidance, and our management team is also pleased with the results.”

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About Lion Parcel

Lion Parcel, a subsidiary of the Lion Group, is a logistics company in Indonesia providing integrated logistics delivery. As of 2022, the business has a market share of up to 98% in Indonesia and conducts international deliveries to 25 countries.

Benefits of AWS

  • Implements data lake and data warehouse in 6 weeks
  • Runs key reports in less than 1 minute, down from 15 minutes
  • Facilitates access to reporting data for all employees
  • Segments customers using ML-driven analysis
  • Retrieves near-real-time data for reporting
  • Projects business volumes up to 7 months in advance
  • Integrates data with third-party software

AWS Services Used

Amazon QuickSight

Amazon QuickSight allows everyone in your organization to understand your data by asking questions in natural language, exploring through interactive dashboards, or automatically looking for patterns and outliers powered by machine learning.

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Amazon SageMaker

Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.

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Amazon Redshift

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and machine learning to deliver the best price performance at any scale.

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Amazon Simple Storage Service

Amazon Simple Storage Service (Amazon S3) is an object storage service offering industry-leading scalability, data availability, security, and performance.

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