When in-house integrations started eating engineering time, Productive switched to Coupler. Now their data team moves fast, tests freely, and hasn't opened a dev ticket for a pipeline since.
How Productive went from "open a ticket for the dev team" to "pipeline ready in minutes"
When in-house integrations started eating engineering time, Productive switched to Coupler. Now their data team moves fast, tests freely, and hasn't opened a dev ticket for a pipeline since.
10+
Integrations running without developer involvement
20 hrs
Developer time saved on initial setup alone
0
Dev interventions needed to build or fix a pipeline
Company
Productive
Industry
B2B SaaS / All-in-one platform for professional services
Use case
Marketing data infrastructure
Sources connected
HubSpot, Xero, Google Ads, Facebook Ads, LinkedIn Ads
Data sources
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HubSpot
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Xero
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Google Ads
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Facebook Ads
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LinkedIn Ads
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YouTube
Data destinations
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BigQuery
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Google Sheets
Stop manual exports
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The problem
Building in-house integrations sounds reasonable until someone has to maintain them
Like most others, Productive's marketing team relies heavily on data: campaign performance across Google Ads, Facebook Ads, and LinkedIn Ads. CRM activity in HubSpot. Financial data in Xero. Getting all of that connected and flowing — without burning developer hours on custom builds — was the real challenge.
The team had tried building integrations in-house. It worked, until it didn't. Integrations require maintenance, and Productive aims to focus its engineering capacity on the product customers pay for, not marketing tooling.
We build a few things and integrations ourselves, but it takes a lot of time, and someone always has to maintain it — which becomes a problem down the line, and dev hours add up over time.
— Bruno Gudelj, RevOps Specialist, Productive
— Bruno Gudelj, RevOps Specialist, Productive
Why Coupler.io
From engineering dependence to marketing independence
What the marketing team needed was the ability to move on its own. They wanted control over what to sync, how often, and where to send it — starting with low-risk testing, then scaling into ongoing maintenance. Coupler.io fit that pattern exactly.
The deciding factor wasn't just features. It was the build-vs-buy math. In-house integrations require upfront build time and permanent maintenance ownership — and for a product company, development time is precious.
Coupler removes both. Connecting HubSpot, Xero, or any ad platform takes minutes, not a sprint cycle.
We needed an easier way to control what to sync, how often, and where: first for testing, then for ongoing maintenance. Coupler turned out to be exactly what we were looking for.
— Bruno Gudelj, RevOps Specialist, Productive
— Bruno Gudelj, RevOps Specialist, Productive
The impact
20 hours back — and counting
With more than 10 integrations running through Coupler — Google Ads, Facebook Ads, LinkedIn Ads, HubSpot, Xero, and more — the math adds up fast. At roughly two developer hours per integration, that's 20 hours recovered from setup alone. And the number grows every time a new platform is added.
But the bigger shift is behavioral. Building and testing pipelines no longer requires a formal request or developer support. If something doesn't work, starting over takes minutes. There's no fear of breaking anything.
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20+ developer hours saved on integration setup, growing with every new pipeline
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HubSpot, Xero, Google Ads, Facebook Ads, and LinkedIn Ads all connected without custom builds
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New pipelines built and tested independently by the marketing team — no tickets, no waiting
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Faster reporting cycles with easier iteration on advanced ideas
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AI analytics layer surfaces specific insights that static dashboards miss
We can test things really easily and build pipelines in a few clicks — no special procedures or waiting for developers. If something isn't working, it's easy to start over. There's no fear of breaking anything.
— Bruno Gudelj, RevOps Specialist, Productive
— Bruno Gudelj, RevOps Specialist, Productive
The AI layer
When dashboards aren't enough
Productive also uses Coupler's AI analytics as part of their stack — and the use case is specific. Dashboards work well when you already know what you're looking for. They're less useful for exploratory questions or edge cases that don't fit a pre-built view.
The marketing team uses the AI layer to find specific insights on demand, and as a practical helper when building pipelines — for example, locating a specific internal field key in HubSpot needed for an advanced filter. It's the kind of task that used to require deep platform knowledge or a developer lookup.
Dashboards can be too general — sometimes they make sense in theory, but sometimes nobody uses them. It's easy to find specific data or insights when you have an idea of what you're looking for.
— Pave Šturman, Performance Marketing Specialist, Productive
— Pave Šturman, Performance Marketing Specialist, Productive
Advice
One piece of advice for teams considering it
The key is to start small and build from there. A focused first step can help teams understand how the data pipeline works in practice before expanding it across more use cases.
Try it. It's flexible enough that you won't spend a fortune on either the basics or the advanced stuff. Just make sure you know your next step — because once you can control your data pipeline this easily, you'll start seeing a lot of new directions and ideas.
— Bruno Gudelj, RevOps Specialist, Productive
— Bruno Gudelj, RevOps Specialist, Productive
