Fractory is changing the landscape of manufacturing by offering on-demand metal fabrication. But with customers ranging from hobbyists to large corporations, Fractory needed a decision intelligence engine able to quickly surface the high-impact populations in its data critical for driving growth.
As the leading cloud manufacturing platform in the world, Factory is addressing challenges in the global supply chain by offering a one-stop shop for on-demand manufacturing services. Their unique solution streamlines the manufacturing process, allowing everyone from hobbyists to large corporations to order metal fabrication in seconds.
But appealing to a wide audience means needing to quickly sift through massive amounts of data to understand what factors are impacting Fractory’s key metrics. As VP of Demand Marketing and Marketing Operations at Fractory, Heikki Tilk both analyzes Fractory’s Sales and Marketing data and uses those answers to decide where to focus his customer acquisitions and optimization efforts. To do so in time to act, he needed a tool that would accelerate data exploration and identify what factors and combinations of factors mattered most in his data.
As the primary analyst reviewing Fractory’s data to find the factors impacting his high-value customer segments, Heikki couldn’t afford to spend days manually filtering and testing his data looking for insights. But to grow Fractory’s repeat business, Heikki needed to not only monitor their core KPIs but also drill down into the data to find the factors driving those metrics.
Heikki was looking for an analytics engine that could do that data exploration from him, allowing him to find answers quickly and drive decisions for the Sales and Marketing teams. Unfortunately, after searching more than twenty BI providers, he found that most didn’t have the core function for relational mapping between tables that he needed to make sense of his complex marketing and sales data. Heikki needed an analytics tools that was part of a modern analytics stack. That’s when he turned to Sisu.
“I could picture the days of work ahead of me if I had to hypothesize and execute the back and forth work in BI tools. When I heard about Sisu, the idea that I wouldn’t need to start manually filtering all the different scenarios in our data myself was really compelling.”
After reaching out to the Sisu team, Heikki and the Fractory team were quickly up and running in Sisu. With Sisu’s cloud-native architecture, Fractory quickly connected to their BigQuery data warehouse and began running analysis in seconds.
With their data connected to Sisu, Heikki worked to better understand the Sisu engine and how best to structure Fractory’s data for analysis. In under two weeks, Heikki was using Sisu to drill down into the factors and sup-populations driving changes in their core metrics and determine what was impacting customer behavior.
“After getting to know Sisu, I was able to look at the facts that Sisu surfaces in an understandable way. For example, Sisu could show me that people using a specific material have a higher than average LTV than people who are not using it. These were the meaningful moments to me that showed how Sisu would help me quickly understand client behavior.”
Despite having a smaller database than other companies, Heikki and the Fractory team realized they could rely on the confidence score in Sisu to understand what kind of questions they can ask of their data without wasting time looking for answers that were out of the scope of their data.
“Using the confidence level inside Sisu has also been very insightful. We can understand that for Fact X we can be very confident, but for Fact Y we may be directionally correct – which, for a startup, can be all the insight that’s needed.”
With a fast, comprehensive view of the factors driving customer behavior at Fractory, Heikki quickly identified trends with high confidence and used those recommendations to drive critical decisions. For example, based on the insights Fractory generated in Sisu, they determined how factors like material used for production, material process, and material treatment impacted high-value segments. These findings allowed them to find new approaches for cross-selling to existing customers and to identify new ways to improve customer onboarding that would reduce friction.
In one example, Sisu found that customers who use Fractory’s post-pay feature (the ability to pay within 30 days of receiving an order rather than paying upfront) had a 3-5x higher LTV than their non-postpay counterparts.
While Fractory leadership had a hunch this may be an important feature to promote, they were concerned investing in offering postpay at sign up would require significant upfront work. By surfacing this critical factor in Sisu, Heikki overcame internal debate on whether offering postpay after a sale or before was better to retain customers, creating confidence in investing in the work to provide this feature.
“Getting insights early with Sisu means we can start thinking about their execution early too. This gives us two strategic advantages: first, we can bring forward the starting date of strategic initiatives, and second, we get the context of what’s needed for execution.”
Today, Fractory relies on Sisu as their primary data exploration tool, allowing Heikki to analyze datasets quickly across a variety of use cases.
Looking ahead, Heikki wants to optimize his acquisition strategies by using Sisu to determine the most effective Facebook ad copy. With hundreds of ads where elements of the copy overlap, he plans to use Sisu’s keyword analysis to find patterns in the data and determine the most effective advertising campaigns to acquire new customers.