Brian Nguyen
Director of Digital Marketing at LaunchDarkly

From Confusion to Clarity: LaunchDarkly’s Path to Accurate Marketing Data


Before implementing fixes, LaunchDarkly faced several key marketing data challenges:

  1. Inaccurate UTM Tagging: The company was launching a high volume of campaigns and seeing instances of incorrectly placed utm_source and utm_campaign due to the size of their campaigns.
  2. Launching Campaigns Without Testing: Due to the speed needed to launch new campaigns and a small team, LaunchDarkly would launch ads without conducting test leads, which could result in errors and wasted resources.
  3. Understanding Marketing Automation Flow: The Digital Marketing team wasn’t in sync with the Marketing Operations team that owned the Marketo platform. They weren’t aligned with the Marketo logic that captured the leads into specific programs.


To address these data issues, LaunchDarkly took the following steps:

  1. Improved UTM Tagging: They implemented a quality assurance process for UTM tagging. Before launching any campaign, a second set of eyes would review the tagging to ensure accuracy.
  2. Testing Campaigns: LaunchDarkly began incorporating a test flow before launching campaigns. This step helped identify and rectify issues before campaigns went live in Metadata. The company leveraged Metadata’s test lead feature within the Marketo integration section. It allowed them to pull in all the relevant UTMs and push the lead over to Marketo and then Salesforce. This way, they could see instantly if it went to the right Marketo program and into the right SFDC campaign, with the correct data and routing required.
  3. Understanding Marketing Automation: Regular collaboration with their Marketing Operations team helped LaunchDarkly better understand how leads were captured and reported on in Metadata. They established UTM-based triggers for lead attribution, improving consistency and accuracy.

    For example, since their Marketo didn’t connect to LinkedIn, they couldn’t use certain triggers/filters that were available. They worked with their Marketing Operations team to develop a framework on the logic to capture those leads (e.g., should a smart list looking for utm_campaign contain the “exact” name of a campaign or just “contains” demo if it’s a demo request campaign, etc.) Understanding these concepts is critical for the digital marketer in ensuring there aren’t any gaps.
  4. Tools and Analytics: LaunchDarkly used tools like Terminus for URL building and relied on a strong analytics team for data accuracy. Regular syncs with the Metadata team, along side Looker dashboards, helped identify and address any data anomalies.


The adjustments positively impacted LaunchDarkly’s marketing efforts in several ways:

  • Efficiency and Cost Allocation: Accurate tracking allowed LaunchDarkly to allocate marketing dollars more efficiently by focusing on channels that generated the most pipeline. This precision was crucial for financial planning and resource allocation.
  • Attribution Accuracy: Accurate data ensured that the credit for pipeline generated through paid social efforts was correctly attributed. This helped in making informed decisions and optimizing marketing strategies.

Lesson Learned

Other companies can learn from LaunchDarkly’s experience in improving marketing data:

  1. Data Accuracy is Paramount: Prioritizing data accuracy is crucial for making informed decisions, optimizing resource allocation, and achieving marketing goals.
  2. Collaboration is Key: Close collaboration between marketing teams and operations teams (MOps in this case) can help bridge the gap between data generation and data utilization, leading to more accurate insights.
  3. Continuous Improvement: Building a culture of continuous improvement and habituating best practices is essential for sustaining data accuracy efforts.

Future Growth

As LaunchDarkly continues to grow, maintaining data accuracy will remain a critical aspect of their paid social strategy, ensuring they can make data-driven decisions and achieve efficiency.

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