About
Steve Simitzis, former CMO of Wild Earth, lived the “build vs. buy” decision in real time. He started with almost no reliable retention or attribution baseline beyond Shopify exports and a few surface-level dashboards, but the business needed answers it could actually run on.
Over time, Steve went through a familiar four-step path: DIY reporting, pushing Shopify data into BigQuery for cohorting, adding more tools (like Looker Studio and Recharge), hiring a data engineer, and still feeling bottlenecked. Eventually, Wild Earth decided to buy SourceMedium instead of continuing to accumulate technical debt.
Wild Earth is a fast-growing dog care brand. Steve oversaw growth, reporting, and the cross-functional decision-making that depends on trusted numbers.
The challenge
- Retention and attribution reporting was “vibes-based,” with surface-level dashboards hiding what was actually happening
- Decisions on growth, finance, and fundraising required cohort and subscription clarity, not daily “green/red” graphs
- A DIY stack meant constant maintenance (schema changes, tool stitching) and one person fielding everyone’s questions
Piecemealing spreadsheets together
Wild Earth needed the initial technical infrastructure to know what core levers were driving the company forward.
“When I first got here, there was absolutely nothing from a tech and data perspective,” admits Steve.
“I remember we had this Geckoboard on a flat-screen TV in the office showcasing random sales graphs daily. The CEO would walk past it, notice that numbers were in the green, and shout, ‘It’s a good sales day!’ The next day, he’d see numbers in the red and tell us that we should be concerned about sales being down.”
As Steve dug into the details, he discovered that much of the sales data reflected customer subscriptions, creating a misleading narrative about marketing performance. He set out to build a DIY way of pulling data from different sources to understand what was actually happening.
“My first ‘innovation’ at Wild Earth was to take Shopify data, send it into BigQuery, and categorize by first-time customers, new subscriber customers, and recurring customers.”
That simple cohorting decision changed how teams discussed performance, from finance and operations to marketing and ad buying.
Evolving past Shopify reports and roadblocks along the way
“I call it (Shopify reporting) ‘vibes-based’ retention analysis,” Steve grinned.
So, they evolved from a Shopify-only approach. First, they began adding tools like Looker Studio, integrating subscription data from Recharge, running their own SQL queries, and more.
“We were just connecting random tools and hoping to make a narrative out of that.”
After the initial setup, their DIY approach proved unsustainable for the company’s growing demands.
“I was running around like a crazy person meeting with team members, running queries, maintaining the website, working with our head of growth, managing email, helping the CX team, all while keeping up with my day-to-day,” recalls Steve.
They hired a data engineer to help, but the roadblocks didn’t disappear.
The challenges of finding accurate data with a growing team
As the team grew and the company prepared for fundraising, they needed more clarity on how to resolve the data bottlenecks.
A few challenges they faced:
- Confusion on how to define attribution, as each tool had different black-box methods
- Stitching together several horizontal, disjointed data tools that were often too expensive to justify the setup and maintenance
- Pulling and slicing data from various sales channels to neatly create cohorts for retention analysis
Even with a data engineer, they couldn’t do what they needed.
“We realized that the data was becoming a hugely important part of our business. Everyone wanted answers to questions about data, and we only had one team member to field the barrage of requests,” noted Steve.
Steve and his team encountered a critical set of requirements that all cross-functional organizations face:
- The finance team handles the most critical data to allow the business to function and help clarify demand planning, reporting to investors, and fundraising purposes.
- Meanwhile, the marketing team wants everything from attribution and ad conversion to retention analysis, and their needs constantly evolve.
“We’re a dog care brand, not a data company; so when we needed to decide how to allocate resources, we needed to consider whether we should hire another data person or put that budget towards CX, Marketing, or Ops.”
The solution
- A BigQuery foundation and SQL-driven workflow that made cohorting and drill-down reliable
- A CEO- and team-friendly experience that didn’t require one person to run every query
- Less technical debt over time compared to maintaining a stitched-together stack through constant schema changes
Build vs. buy: a burning question
“All I knew is that we wanted answers to data questions at our fingertips.”
You’re a growing company, actively hiring, and you need precise, accurate data to inform decisions that affect the business’s future.
But at what expense?
Ultimately, Steve decided buying software was the more sensible for growing brands like Wild Earth.
From keeping up with constant schema changes to ensuring their transformation layer stayed up to date with Wild Earth’s evolving business needs, accruing technical debt was the last thing Steve wanted.
And, to Steve, what separates SourceMedium from other competition is the platform’s user experience.
“Any great eComm data tool must be both CEO and team friendly.”
He’s not simply referring to dashboards. It’s about filtering and drill-down through pre-built transformations, so technical and non-technical operators can answer questions without rebuilding the logic.
Trial by fire
Steve regrets not discovering SourceMedium sooner, and that feeling underscores the challenges of building custom software.
“The technical debt of building our software has proved quite challenging.”
Building a custom solution sounds ideal until the technical debt starts compounding. For Steve, over-indexing on data hires meant taking budget away from marketing, fulfillment, or growth, which he couldn’t justify.
That’s where SourceMedium enters the conversation. It’s become a data command center for Wild Earth, built to scale with them, even as their omnichannel data needs change.
What changed
- Decision enabled: The team could stop debating whether to keep hiring for data “forever” and instead buy a platform designed to scale with the business.
- How they validated it: A BigQuery foundation and SQL-driven workflow made it possible to move past “vibes-based” reporting and black-box attribution.
- What got faster: Answers to finance and marketing questions without one person running every query.
The results
-
A clearer build-vs-buy path for scaling teams
The story maps the common buyer journey: Shopify-only reporting, DIY BigQuery, more tools, a data hire, and then a decision to buy. -
BigQuery-first thinking improved internal alignment
Sending Shopify data into BigQuery and segmenting cohorts set a standard for how teams discussed performance. -
Reduced operational drag from “random tools”
Moving away from piecemealing dashboards helped the team spend less time creating narratives and more time acting on the data.