How I helped Stackify refine its content strategy by building a content performance database
Background & Challenge
At Stackify, we had a wealth of data spread across multiple platforms—Ahrefs, Google Search Console, and Google Analytics, alongside invaluable tribal knowledge from our internal team. However, there was no single source of truth to answer complex questions like:
Which blog content converts senior Java engineers in the middle of the funnel?
Which posts receive low search traffic but convert at a high rate and should be featured in our newsletter?
I needed to aggregate all this data into a single database to drive better keyword research, curate high-performing content for our newsletter, and provide clearer strategic direction for our guest writers.
Role & Objective
As the senior content marketer, my goal wasn’t just to see which blogs or keywords drove the most traffic—it was to uncover why certain content performed well and how to double down on those growth levers. I needed more nuanced insights into conversion data, audience intent, and search behavior to optimize Stackify’s content strategy for both traffic growth and lead generation.
Strategy & Approach
I pulled in a combination of our top 100-performing blogs based on three key metrics:
Traffic volume
Conversion volume
Conversion rate
Given that much of the data entry was manual, I had to limit the dataset while ensuring it was comprehensive enough to generate meaningful insights. I structured everything in Google Sheets, allowing me to sort and filter data efficiently without needing SQL or other database languages. Looking back, if I had done this today, I would have leveraged AI to speed up the process but at the time, rolling up my sleeves and doing the manual work was the best option.
Execution & Adaptation
To extract the right insights, I had to develop a deep understanding of the platforms from which I was pulling data. This project resulted in the most time I’ve ever spent in Google Analytics and Ahrefs, which not only improved the accuracy of my data but also made me significantly more proficient in using these tools.
By combining platform data with in-house tribal knowledge, I was able to identify critical data points that no external tool or analyst would have captured, such as:
What technologies were being discussed in each blog
Which ICP job titles each post targeted
Which stage of the funnel each piece of content influenced
This context was essential in making truly data-driven decisions.
Results & Impact
Stackify’s blog traffic grew by over 250%, driven by a refined keyword strategy informed by this data.
Website conversion rates improved, leading to more qualified leads from organic traffic.
Reduced reliance on expensive contract writers by developing a structured strategy and process to guide guest writers more effectively.
More confident decision-making, backed by quantitative proof rather than gut instincts.
Lessons Learned & Future Application
One of the biggest lessons from this project was realizing that sometimes, doing the manual work others aren’t willing to do can be a massive differentiator. Many marketers rely solely on out-of-the-box tools, but those tools don’t always provide the full picture.
Had we outsourced this project to a third-party analyst and temp data entry professionals, it would have been costly and they wouldn’t have had access to the same tribal knowledge that made this analysis so effective. My knowledge of SEO, our market, and my scrappy, figure-it-out approach allowed me to build something that created lasting impact at Stackify.
For any startup marketer, this is a reminder that rolling up your sleeves and diving deep into the data can unlock insights that no external tool or consultant could provide.