Overview

Engagement Overview

RVshare began modernizing its data landscape to support real-time, analytics-driven decision-making across its growing RV rental marketplace. The objective was to reduce data silos, bring together booking and customer interaction data, and provide business and leadership teams with more timely and reliable insights.

As part of this initiative, a cloud-native data ecosystem was established on AWS, including a centralized data lake, scalable ETL pipelines, a Redshift-based data warehouse, and Tableau dashboards. This approach enabled faster reporting, more detailed customer segmentation for marketing, and improved access to executive analytics to support marketplace growth.

Our Client

Travel & Hospitality United States
RVshare is the largest and first peer-to-peer RV rental marketplace in the United States, offering a diverse inventory that spans from luxury motorhomes to compact campers. With more than 100,000 active listings nationwide and a trusted network of over 60,000 RV owners, RVshare has redefined the recreational travel experience. The platform also pioneered industry-first features such as comprehensive rental insurance and 24/7 roadside assistance, ensuring both owners and renters enjoy a secure, seamless, and worry-free journey.
RVshare

Business Objective

The data initiative focused on improving visibility and usability of marketplace data across teams.

01

Reduce fragmentation and improve consistency across booking, rental, and customer interaction data

02

Enable near real-time tracking of booking changes

03

Enable marketing teams with precise customer segmentation for targeted campaigns

04

Provide leadership with daily, reliable insights to monitor performance across multiple domains

05

Ensure dashboards reflect current and accurate operational data

Business Solutions

A cloud-based data platform was introduced to support analytics and reporting needs at scale.

  • A centralized data lake on AWS S3 captured real-time change data from multiple systems, creating a unified data foundation
  • Data pipelines were developed to extract information from transactional systems and APIs and load it into AWS Redshift for analysis
  • Apache NiFi processors were used to manage complex transformations and maintain consistent data flow
  • Tableau dashboards were connected to real-time and adjustment data to support operational and executive reporting

Business impact

The cloud-native data platform supported improvements across business, marketing, and leadership teams.

50% Automated pipelines slashed reporting times, enabling quicker insights

Real-Time visibility with instant access to booking adjustments and customer interactions

Unified data Source, a single, reliable view of rental, booking, and customer data eliminated silos

Targeted segmentation improved campaign effectiveness, boosting engagement

Future-Ready flexible architecture supports new data sources and evolving business needs

Tech stack

  • Data Engineering
  • Cloud & Storage
  • DevOps & Version Control
  • BI & Analytics
  • Workflow Management
Data Engineering
Cloud & Storage
DevOps & Version Control
BI & Analytics
Workflow Management