We’ve published 18 HockeyStack Labs reports in 18 weeks in 2024, generating nearly 100K unique visitors and millions in pipeline. HockeyStack Labs has become our golden goose—more than half of our pipeline comes from these reports. Our website traffic has more than tripled, and these reports have greatly accelerated our sales cycles.
However, it wasn’t easy, and we had to learn a lot to scale this process. As we kept publishing reports, more and more people started to message us about how we do it, and these messages inspired this very first playbook.
In this playbook, we’ll cover everything we’ve been through and how we managed to scale the Labs process.
The most authentic thing you have is your data. Other companies can copy your structure or processes, but they can never replicate your data and how you present it.
Using your own data as thought leadership content puts you in a unique position among your ICP. These reports are not just for attracting new customers; they are also valuable for your open deals and existing customers. By providing truly unique and educational content, you build unmatched trust.
Additionally:
Some might argue that you need a scalable concept and should plan everything in advance, but my recommendation is the opposite. I suggest treating this project as a one-off initially because you have no idea if it will work for you. If you spend too much time planning and investing resources, and it doesn’t work, it might cause more harm than good.
HockeyStack Labs reports worked really well for us, but this might not be a recipe for success for everyone. I believe they worked for HockeyStack for a couple of reasons:
Organic advantage: We had data from many B2B SaaS companies that we could easily anonymize and analyze. No major company had this—not Linkedin, not Salesforce, nobody. Smaller companies had no idea how to use or analyze their data.
Limited competition: There were not enough reports in the market that our ICP could read. The ones from Gartner or Forrester were mostly geared towards enterprises, not companies with 100-5k employees. However, you need to think strategically here: why was there no report? Is it because nobody can generate one, or is it because there is no demand? This is such a crucial difference.
No recent data: Even among the available reports, there were only a handful of recent ones with limited datasets. As metrics change significantly every quarter in B2B SaaS, reports from two or three years ago were mostly irrelevant.
Understanding the ICP: I was my own ICP, so I knew exactly what data points my ICP needed to see and what they would be curious about.
*If you are uncertain about your ICP, you might need a subject matter expert. There's nothing wrong with this; you may understand your ICP and know their pain points to some extent, but may not fully grasp it. For instance, if you target the procurement persona, you can only empathize with them up to a certain point, unless you’ve been marketing to them for years. Therefore, collaborating with a subject matter expert in creating these reports could be beneficial.
At the end, you should be able to say: “My ICP would need this data as it would make their lives easier.”
I believe the points mentioned above are the main reasons why the Labs worked so well, but until we launched the first report, we had no idea if it would be successful. These reports were one of our experiments, so the initial investment was minimal.
Since this was just one of our many experiments, we were conservative about what success would look like for us. We didn’t want to bet on pipeline or closed-won deals; rather, we focused on vanity metrics like engagement, comments, and website visits.
After seeing that our ICP was genuinely interested in the first report, we decided to continue with the next one. The Recap 2023 report became the most visited pages on the website that month, the bounce rate was low, and the time on page was more than 3 minutes. This clearly indicated that our ICP found the Labs content helpful.
However, we didn’t have a clear process up until the fourth report. This had pros and cons.
Pros: We avoided wasting time on long-term planning, success metrics, or budgeting.
Cons: We spent significant time in structuring data processes, and decided content topics weekly.
Even now, we don’t have a strict process for choosing topics. I choose the topics myself and only write about what I genuinely find interesting; I’m fortunate to be my own ICP. But if i wasn't my own ICP, I'd brainstorm with a subject matter expert or a product power user to understand what would really interest them.
For example, if you are selling to developers and you’re a marketer, the easiest way is to talk with your customers who use your product daily.
Once you have some answers, it’s easier to reverse engineer them to find ideas. For instance, if you’re selling an AI B2C chatbot for customer service directors, and your ICP is customer success directors and above at giant corporations like Lufthansa, Delta, or United, you need to tailor your content to their specific needs and challenges.
If you already have some power users, I recommend reaching out to them directly with utmost personalization. You might consider offering them a discount on your product, unlocking a new feature, or giving more credits.
For example, if they mention, "I don't know how long it takes other companies to solve their customer queries in AI chatbots," or "I don't know how many chats are handled by AI," you can work on a report comparing customer query times across your customers. This data is likely available in your product but might not be easy to find or export, so you’ll likely need a developer or someone with intermediate SQL knowledge.
In our case, Kris took some SQL courses on Udemy, which was enough for him to understand the fundamentals. Then our CTO, Arda, trained him for a few hours. Our plan was to hire an SQL developer for these queries if Labs worked out.
For our first report, we decided to start with the 2023 benchmark data since 2023 had just ended, and as a marketer, I knew other marketers would be interested in that information. I listed out the data points we needed, then Arda and Kris had a call to discuss which data points were possible to retrieve and if we could create a full report with those data points.
Since we didn’t have a process in place, it took Kris a week to gather all this data, followed by a few days for me to organize the data and write the Python scripts to analyze it.
To scale, the first question you need to ask is, 'What's taking the most time in this process?'
In our case, it was gathering the data. We spent days getting some data points, but moving forward we had to be faster. So we started asking some questions; is there a way to get that data faster? how can we reuse this data in another report?
With the fourth report, we realized that Labs was going well, and we needed a game plan to operationalize this process.
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