I’m writing a case study for myself. Why? Because we eat our own dog food.
Let me explain:
In 2017, I dropped out of my college and decided to document my journey and share it on different social media platforms.
As I grew my following on LinkedIn in 2018, two business owners independently from each other reached out to me and basically said: “Hey, these videos you put on LinkedIn are pretty cool. Can you do this for me, and I’ll pay you money for it?”
That’s how Project 33 started.
Me and a friend trying to create LinkedIn content for business owners (we eventually broke up and I started from scratch, alone, six months later).
Since then, it has been a continuous process of:
- systematizing how we create content for customers,
- delivering results more reliably, and
- understanding B2B companies and their sales processes better.
We now use the exact same process we use for customers on ourselves.
And here are some of our results:
Revenue:
As a bootstrapped company, we did $100,000 in cash collected in 2020, just over $250k in 2021, and $490k in 2022. Most of our revenue growth came from the content we posted.
Sales:
Our sales cycles are comparatively short for a $30-50k B2B solution: We close most people within a month and 1-3 calls. It’s mainly because people already know and trust us from our content before they hop on the first call with us. Roughly 70-90% of our customers come directly through our content or referrals.
Hiring:
We found half of our team members through our content because they were following me or someone on the team on social media. They already knew who we were, had seen our processes, and understood how we operated, so the cultural affinity was very high. This made the hiring process very easy and short.
Product improvement:
Using our own processes for ourselves allows us to address the inefficiencies. We don’t always have to wait for our customers to tell us if something is not working. And it’s skin in the game – we practice what we preach. We’re obviously very excited about the process we’ve built. We’ve used it for ourselves and continue to use and improve it. That’s why I’m writing this case study for ourselves.
Last month, we booked $102,000 in net new revenue from 1 Linkedin ad along using our exact system:
1. Create a content engine to post weekly on Linkedin
- Ideate questions around our product, company, and expertise (b2b marketing & demand gen)
- Weekly recording session with Tobias to answer the questions in a conversational way
- Turn the recording & transcript of that conversation into bite-sized video, text and image Linkedin posts
- Repeat weekly for 2-5 posts/week or 8-20/month
2. Determine best-performers
- Do monthly analysis using Shield to determine which posts & messages resonated most with our audience
- Main metric: "total weighted engagements (TWE)" = likes + 2*comments + 4*shares
- Best-performer = any post with TWE greater than at least 1 standard deviation above the average for that data set (you can use other thresholds or just subject feeling)
3. Feed best-performers into targeted Linkedin ad campaign
- Run towards 3 cold targeting groups
1. Targeting ICP based on job titles, geography and company size for top-down approach (for us: any Founder or Head of Marketing of a software business with 10-100 employees)
2. Targeting broad ICP based on job function/department for bottom-up approach (for us: anyone working in the Marketing, Sales, or Biz Dev department of a software business with 10-100 employees)
3. ABM/account-level targeting: I created a list of 200 target accounts who are our dream customers
- Retargeting layer: any website visitor, any Linkedin company page visitor and anyone who engaged with the ads in the cold layer (50% video viewers)
4. Objective: in-feed consumption of the content + drive interested people to website
- Linkedin campaign objective: Engagement
- CTA: Learn more
- Landing page: our website homepage (not a lead gen form, not our demo page)
- Tracking conversions: Linkedin Insight Tag with 30-day "Last touch - Last campaign" attribution
5. Wait for inbound
- We want to drive high-intent demo requests ONLY, not leads
- By sending people to the home page, rather than a lead gen form or a call booking page, we only get people who are REALLY interested
- Installing self-reported attribution: most demo requests are not tracked by Linkedin Insight Tag, so make sure you have a qualitative way to attribute
6. Convert & optimize
- Current cost per qualified demo from ads: $200-300
- In our case, 80% of the demo requests were generated by 1 outlier ad
- Outliers driving most of the conversions is a common pattern we see, that's why you need a content engine to test new messaging & creatives all the time, to find your next outlier
- Total ad spend here was $3,500, so that's a 30x ROI on revenue
- $102,000 revenue generated is just the initial contract value, LTV is higher