Artifact Uprising is engaged in the online retail of photo books, photo cards, prints and gifts.
If you've ever looked for a way to share your special moments with loved ones, you've probably come across Artifact Uprising. They enable anyone to create high-quality framed prints and photo albums online. Started in 2012, they've grown by focusing on high-quality design that that lasts a lifetime.
At Artifact Uprising, they had been capturing analytics using Google Tag Manager and Segment. They had been plagued by data issues that caused the company to lose trust in their analytics. Everything from inconsistent data across platforms to development changes breaking analytics in production.
Carly, who is responsible for Business Intelligence at Artifact Uprising, wanted to solve this problem once and for all and enable the company to use data to drive growth. At Artifact Uprising, analytics is used not just for business intelligence but for marketing, personalization, and advertising.
While Carly initially tried to clean things up using a spreadsheet, this quickly got out of hand, and she went looking for a better solution to help govern analytics.
Her primary goal was to find a solution that:
Carly says, "We wanted to improve our overall data quality and standardize how we capture analytics across our products. This was important to enable all teams with clean data." Once Carly discovered Iteratively, she kicked off a pilot in late 2019.
At the same time, Artifact Uprising was rewriting its website and this provided an excellent opportunity to rethink their analytics taxonomy. Carly and the team spent a few weeks migrating their analytics to Iteratively and adding instrumentation across platforms. A few of the features that helped were:
Sergio Mendoza, a developer at Artifact Uprising, had the following to say, "Iteratively has not only taken the guesswork out of our analytics process, but provides an indisputable source of truth that is visible by our entire organization. Any data inconsistencies are immediately caught in our CI/CD pipeline before getting to production which prevents headaches and awkward conversations down the line. The developer toolkit ties in nicely with our existing workflow such that we are able to see exactly what changes were made within our codebase."
Another added benefit that Sergio mentioned was how Iteratively improved the developer relations with the data analytics team. He says, "Now we know exactly what data analysts want to see, and how they plan to use the data without the added time spent having a back and forth through different communication tools." Now analysts can spec out changes to the tracking plan and the team gets notified with exactly what data they need to capture.
Now that Artifact Uprising has Iteratively in place, Carly estimates that they're saving 6 hours a week as a team and preventing future analytics bugs from impacting the business financially.
"With Iteratively, we have a process that's easy for the entire team and ensures that we're capturing good data that we trust."
Carly and the team now have a single source of truth for analytics across the organization that enables everyone with clean data that they can trust. Better yet, they now have a workflow that will help them scale and iterate on their analytics as their business evolves.