One of our clients is well equipped departmentally and it’s showing up in the work. We are working with each of the following: Product, Data Science, and Customer Success to deliver amazing demand gen results. Recently it occurred to me that few people talk about how SEO and Data Science work together so I thought this would be a fun experience to share.
The Client
They’re a SaaS provider that doesn’t have a head of marketing, so they contracted me as a player-coach, doing both high level strategy and some individual contributor button pushing to generate demand for their product. My first point of contact was the Director of Customer Success and since then have begun expanding into other departments to fuel the demand gen (SEO) engine.
The Goal
Our goal from day one was to generate leads for their SaaS product. They thought they just needed SEO so that’s how they found me. But the combination of their needs and the change in market conditions necessitated that we do more than just SEO to generate more demand. We’ve since worked out Product Marketing, Content Marketing, Brand Strategy, and are now beginning to incorporate their Data Science team for SEO purposes.
Where we’re at right meow
In the months since the contract began we’ve created a content machine, delivered a brand strategy that is working, incorporated Technical SEO, and are driving 35,000+ impressions over the last 3 months. When we started they had zero SEO or Product Marketing being done and their site was generating less than 1,000 impressions per month.
But the Clickthrough rate (CTR) was underperforming
I realized that our Clickthrough rate (CTR) was struggling to keep up with the impressions. So I started looking into other departments within the company – Product and Data Science were top of my list. Why? Because if we don’t fix the CTR then the impressions will drop. And more importantly, the best way to fix CTR is with longtail keywords, which something like Product-led SEO would really help with.
Let’s start with SEO and Data Science
I figured we’d best deliver on Product-led SEO if we first get SEO and Data Science in the same room. Here’s how we could be most effective: start building predictive modeling around current users of their product, and from there create keyword models that are directly linked to usage. And then…
Identifying Content Gaps
What are their users using the most? What features? What do those outcomes look like, and can we replicate that in content? I started asking questions so that the Data Scientist could tell me what’s what. Then, and maybe most importantly, I wanted to know what they were using the least. This way I could find gaps in our content – are we oriented in the right direction or the wrong?
Predictive Modeling: SEO and Data Science
- How will our existing content mature?
- Are there concepts we ought to be planning into our work?
And this leads us to Product-led SEO
When the work between SEO and Data Science is complete we’ll use the data and context to scale our Product-led SEO. We’ve not yet started it because we’re not ready. Why aren’t we ready? Because we don’t have the data. In order to bring the PLG framework to SEO we need to abide the fundamentals of the framework, which means starting with the users. Thank you, Data Science.