How goodr Redefined Success with Centralized Data and Efficient Analytics


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The e-commerce company centralized their data and took steps to optimize campaigns with insights they can trust. 

goodr, the active sunglasses brand, needed quick, actionable analytics and insights that reached business users downstream from their data tools, so teams could work more efficiently and confidently. They leveraged Mozart Data to streamline their data centralization process and eventually redefine success metrics. 

Working towards efficient data centralization

Before onboarding Mozart Data’s platform, goodr struggled to centralize their data and efficiently access (and share) insights. 

Gabby Dionise, a Business Analyst at goodr, explained, “When I onboarded at goodr, we were using a different data centralization software…we were really in silos, working and reporting individually, and it just didn’t allow us to scale the way we wanted to.” goodr struggled with software that required steep learning curves, leaving them siloed and unable to centralize their data effectively.

As goodr confronted these challenges with their existing software, they decided to explore data warehousing solutions that would allow them to not just centralize their data, but also ensure its reliability ahead of sharing with other members of the team for analysis. 

Moving past “just” sales numbers to redefine success

Core reporting on sales performance was an immediate priority for Dionise and the goodr team. 

“Pretty quickly, we were asked to make sales versus target projections and graphs. We were able to do that really easily within Mozart because we were able to push everything into Google Sheets,” said Dionise. goodr automated this reporting, so that business stakeholders receive up-to-date information in the tool of their preference, like Google Sheets or in a slide deck. 

After setting up core reporting on sales performance, Dionise and the team moved on to “redefine success” as they leveraged data to optimize campaigns. goodr frequently launches new styles of sunglasses and limited edition pairs. While they’ve always had the capability to assess how many pairs of these sunglasses they sell, with the implementation of Mozart’s platform they’ve been able to pair the sales data with other data to better understand the success of these launches. 

The goodr team now looks at not just the sales of the particular launched pair of sunglasses, but the “halo effect”. By evaluating the other purchases made alongside the launch units, the team is capable of redefining what a successful launch should look like.

Faster time-to-insight with reliable data

Mozart’s platform helps goodr get to insights much faster than previously possible. goodr didn’t just move their data into the Mozart-managed Snowflake data warehouse — they organized the data in their warehouse in a way that corresponds to how they operate, like labeling tables with data on channels and partnerships in the same manner others actually describe them.

Because of this, Dionise is able to find exactly what she’s looking for in less time. “We’ve restructured our data warehouse to make sense with the way we run our business, so I’m able to find things a lot faster. I’m able to explore what we have a lot easier because everything is in terms that make sense to me. And I’m able to turn those into insights and visuals really quickly,” said Dionise. 

Moving data quickly on to analysts and business practitioners is only valuable when the data is trustworthy, especially at a company like goodr where not every employee that needs to be a data user is an advanced data practitioner

To help ensure everyone is working with clean, reliable data, Dionise and the analytics team used Mozart’s platform to set up data alerts. Dionise receives an email every morning if any reports have not run, meaning other users could turn to outdated or incomplete data if they accessed an impacted report. With this setup, goodr employees can operate with the confidence that the data they need to perform their responsibilities is reliable. 

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