Buildertrend: Maximizing Data Project Speed to Value

Buildertrend uses Dataiku to infuse speed and agility in their data-to-insights process, notably reducing model deployment time to three hours instead of three days, contributing to a 60% churn reduction for a targeted customer cohort.

Buildertrend is on a mission to disrupt the residential construction industry with data playing a key role. We sat down with SVP of Data and Research Luke Christiansen and Data Strategist Preston Badeer to understand how they ensure their data initiatives drive business impact for the company and their customers, the role Dataiku now plays in their data projects (think: drastic time savings), and their key use cases.

Luke Christiansen Buildertrend preston buildertrend

About Buildertrend

  • Founded in 2006, Buildertrend is a leading construction project management software for home builders, remodelers, and residential contractors.
  • 550 employees, U.S.-based in Omaha, Neb. 
  • 40-50% year-over-year revenue growth.


The team at Buildertrend has a very collaborative process, inspired by agile principles. Every three weeks, they have sprint planning and sprint showcasing meetings with the executive and management team, allowing them to ideate in a multi-persona context, align on project priority, and iterate as a team to share progress and results in a meaningful way. So far, the principles are proving to be a very effective way for the team to deploy impactful data science projects.

They were looking for a data science platform to further improve speed and agility, as well as enable all parts of the company to collaborate on data projects. They want to leverage data science to do business in a better way and make residential contractors more efficient at their work. Simultaneously, they wanted to empower their data scientists to be in a business problem-solving mindset, with the right tools and resources at their disposal. They embarked on an extensive evaluation process for choosing a data science platform and ultimately ended up choosing Dataiku.

Thanks to Dataiku, Buildertrend was able to:

  • Drastically improve speed to value, as it took three hours instead of three days to deploy the churn prediction model with Dataiku, which automatically flagged at-risk accounts.
  • Continuously reduce churn probability among its customer base.
  • Increase the amount of data they now include in their models. They have moved from thousands of rows of data to millions of rows of data, whereas previously, they were limited in terms of sharing power.
  • Automate repetitive, low-value tasks such as rerunning models on a weekly basis.
  • Enhance collaboration across various groups. Analysts can easily input on models, view version history, and make changes in a safe environment.
  • Improve their overall documentation best practices.

“We went through a very thorough selection process when we looked at data science platforms and Dataiku was the best fit by far.”Preston Badeer, Data Strategist

Use Cases

Buildertrend is intent on disrupting the more than $514 billion U.S. residential construction market with a data-driven approach. Two of their main focuses currently include churn reduction and smart customer messaging. With regard to the former, the team at Buildertrend efficiently targeted at-risk accounts to drastically reduce churn by 60%, a number that has only increased since they implemented Dataiku. Initially, the data scientists created a churn probability model in a Python notebook, identifying some customers to be in a control group to test its accuracy. They would take a list of the potential churners manually created by the data scientists and encourage them to check in with their customer service representative. 

Over time, the process was wholly automated by Dataiku — customer service representatives now receive automatic notifications of potential churners and an estimate of how soon they are going to churn — and the team has been able to spread internal adoption of their churn reduction model.

Additionally, Buildertrend leverages Dataiku to help identify the right content to push to a certain user at a certain time. The smart messaging system (which is tied to Salesforce) measures feature usage across the entire customer base and the health of individual users relative to optimal usage thresholds. Through Dataiku scenarios (which involve automating a sequence of actions), Buildertrend knows when people are crossing those identified thresholds and uses them to trigger the “Next Best Action” with personalized content delivery.

Thanks to Dataiku, the ease and speed at which the team is able to get a solution to a business problem is impressive. With limited knowledge at the outset, the entire team learned the platform quickly and now has it tightly integrated with mission-critical systems across the company. It’s incredible how this tech enables us to go fast and speed is critical to our success as a company.” – Luke Christiansen, SVP, Data and Research

A Collaborative Approach: Buildertrend and Dataiku

When the Buildertrend team was assessing data science platform providers, they were curious to see how much better their results could be with a specialized tool, notably how much time they would be able to save. 

Uniquely, Dataiku was recommended (and resold) to Buildertrend by Contemporary Analysis (CAN), a data science consulting firm that provides data science services to organizations from fast-growing technology startups to Fortune 500s. As builders of data science teams themselves, those involved from CAN were impressed with the speed and success that Buildertrend was able to implement data science. CAN attributes their success to Buildertrend purposely picking the right people, the right software that will grow with them, the right partners, the right first projects, and, of course, a culture of demand for data science insight.

“This combination, which Buildertrend executed better than any we have seen, should serve as a blueprint for other companies wishing to improve their sophistication of problem-solving, increase their company’s value, and improve their employee’s overall happiness.” – Nathan Watson, President, Contemporary Analysis

Sample Project: Predict the Risk of Customer Churn

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