Dataiku > Case Studies > Buildertrend: Maximizing Data Project Speed to Value

Buildertrend: Maximizing Data Project Speed to Value

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Customer Company Size
Mid-size Company
Region
  • America
Country
  • United States
Product
  • Dataiku
Tech Stack
  • Python
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Productivity Improvements
  • Customer Satisfaction
Technology Category
  • Analytics & Modeling - Predictive Analytics
Applicable Industries
  • Construction & Infrastructure
Applicable Functions
  • Sales & Marketing
  • Business Operation
Use Cases
  • Predictive Maintenance
Services
  • Data Science Services
About The Customer
Buildertrend is a leading construction project management software company for home builders, remodelers, and residential contractors. Founded in 2006 and based in Omaha, Nebraska, the company has 550 employees and has been experiencing 40-50% year-over-year revenue growth. The company is on a mission to disrupt the residential construction industry, with data playing a key role in their strategy. They have a collaborative process inspired by agile principles, with regular sprint planning and showcasing meetings that allow them to ideate, align on project priority, and share progress and results in a meaningful way.
The Challenge
Buildertrend, a leading construction project management software company, was looking to disrupt the residential construction industry by leveraging data science to improve business operations and make residential contractors more efficient. They were seeking a data science platform that could enhance speed and agility in their data-to-insights process, enable company-wide collaboration on data projects, and empower their data scientists with the right tools and resources. The company was also keen on automating repetitive tasks, improving documentation practices, and increasing the amount of data included in their models. One of their key use cases was churn reduction, where they aimed to efficiently target at-risk accounts to drastically reduce churn.
The Solution
Buildertrend chose Dataiku as their data science platform after an extensive evaluation process. With Dataiku, they were able to drastically improve their speed to value, reducing model deployment time from three days to just three hours. The platform also enabled them to automate low-value tasks such as rerunning models on a weekly basis and enhance collaboration across various groups. Analysts could easily input on models, view version history, and make changes in a safe environment. In terms of use cases, Buildertrend leveraged Dataiku to create a churn probability model that automatically flagged at-risk accounts, leading to a significant reduction in churn. They also used the platform to develop a smart messaging system that identified the right content to push to a certain user at a certain time.
Operational Impact
  • Drastically improved speed to value, reducing model deployment time from three days to three hours.
  • Automated repetitive, low-value tasks such as rerunning models on a weekly basis.
  • Enhanced collaboration across various groups, with analysts able to easily input on models, view version history, and make changes in a safe environment.
  • Improved overall documentation best practices.
  • Increased the amount of data included in their models, moving from thousands of rows of data to millions.
Quantitative Benefit
  • 60% churn reduction for a targeted customer cohort.
  • Spread internal adoption of their churn reduction model.
  • Increased the amount of data they now include in their models, moving from thousands of rows of data to millions.

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