Fivetran > Case Studies > Pitney Bowes Revolutionizes Parcel Tracking with Fivetran

Pitney Bowes Revolutionizes Parcel Tracking with Fivetran

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Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Analytics & Modeling - Real Time Analytics
Applicable Industries
  • E-Commerce
  • Transportation
Applicable Functions
  • Facility Management
  • Logistics & Transportation
Use Cases
  • Last Mile Delivery
  • Real-Time Location System (RTLS)
  • Hardware Design & Engineering Services
  • System Integration
About The Customer
Pitney Bowes is a global technology company that simplifies the complexities of e-commerce, shipping, and mailing. The company manages 400 million mail parcels annually across 16 distribution facilities and serves 750,000 businesses worldwide. With over 11,000 employees, Pitney Bowes is a major player in the e-commerce, shipping, and mailing industry. The company's services are critical for businesses worldwide, and its ability to manage and track parcels effectively and efficiently is key to its success. The company's recent data stack modernization has enabled it to track every individual parcel and predict changes in mail volumes, thereby forecasting labor needs on a per-facility basis.
The Challenge
Pitney Bowes, a global technology company that simplifies e-commerce, shipping, and mailing, was facing significant challenges with its data management. The company lacked high-quality, real-time data necessary for critical business decisions. Its Enterprise Information Management (EIM) team was grappling with siloed data, lack of scalability, and inefficient tech spending. Employees were resorting to pasting data into Excel spreadsheets for executive reporting and analytics, which often exacerbated the issues. The company was also experiencing downstream problems, such as late-arriving packages that impacted Service Level Agreement (SLA) targets. They lacked the sophistication to detect delays and notify customers in time, causing reputational risk. The COVID pandemic magnified these data challenges when online shopping increased tenfold, leading to a tenfold increase in parcel volume. The company's legacy data infrastructure was unable to handle event- and email-based data operations for 800 million packages per day. The data captured was critical, but aggregating and consolidating it to the central analytics warehouse took days, making it outdated by the time it reached the leadership team.
The Solution
Pitney Bowes decided to modernize its data stack by implementing Fivetran and Snowflake. Fivetran replaced all of Pitney Bowes' custom batch scripts and extract, transform, load (ETL) processes. The team used Fivetran’s out-of-the-box connectors to quickly build pipelines for several business-critical apps like SAP, Salesforce, Facebook, Kafka, and Kinesis. Fivetran was able to decrease one batch load from 31 hours to under two hours, and another from days to under one hour. This new data flow efficiently collected and aggregated data from 700,000 IoT devices at 16 facilities. Fivetran’s log-based change data capture (CDC) connectors captured all data changes and eliminated the processing load time and impact on the source systems. Fivetran Local Data Processing helped Pitney Bowes move high volumes of data and eliminated its infrastructure bottlenecks. The team also leveraged Fivetran Local Data Processing to sync SAP data, which was previously a challenge. Fivetran’s SAP connector performed full syncs of high volume data to Snowflake in under seven hours, a significant increase in performance.
Operational Impact
  • The implementation of Fivetran and Snowflake revolutionized Pitney Bowes' data management, providing real-time data and eliminating performance issues. The company was able to create a real-time operational analysis dashboard, providing a comprehensive view into volume, labor, and efficiency across all facilities. This allowed executives to understand exactly what was happening across the entire business at any given time. Facility managers could drill down to see parcel volume and assess corresponding labor capacity. The company was also able to build predictive dashboards for facility volume, network volume, estimated delivery and return dates, harmonized system (HS) product classification, and delivery truck scheduling. This led to increased operational efficiency and the ability to proactively mitigate risks. The updated data architecture also provided the foundation for a new revenue-generating program called “Guaranteed Delivery.” The company could predict facility volumes and delivery dates, providing downstream delivery assurances through their vendors. They could even spot churn behaviors and mitigate that risk proactively.
Quantitative Benefit
  • Gained near real-time visibility and analytics for 16 global distribution centers and 800M+ packages per day
  • Achieved a 93 percent delivery estimate accuracy after a period of missed delivery SLAs, leading to new revenue-generating delivery guarantee program
  • Reduced batch load times by 94 percent from 31 hours to under two hours

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