We all know the usual competitor analysis done with #SEO tools.
Today I want to talk about other ideas that can give you a competitive advantage or a different angle when doing analyses.
A thread on how to approach competitors 🧵
Your analysis should depend on the business model and the type of website. For an Ecommerce you would care about the product selection and how they're going to present them.
For an online magazine/blog you would consider different factors, mostly related to topic breadth/depth.
Metrics like DA/DR are kinda useless, as you cannot quantify the value with an integer value alone. For this reason, you should have a better look at the backlink profile.
This is not really my field but I would never present DA/DR!
A handful of lessons I learned (and I am still learning) while trying to apply #DataScience to #SEO. Some of them are not so obvious either.
This is a thread 🧵
1. Communication is hard and you will get mad a lot of times. Non-technical people have no clue what you are talking about and you have to educate them.
Easier said than done, but I think that you should stay strong and keep trying.
2. Data quality is all. In SEO it's way harder as you are working with estimates and you don't even know the original data distribution.
That is why I am very careful when using Machine Learning models for SEO.
Today I tweet about something different. Some things I learned while studying and practicing #SEO that some people may find extremely valuable (or not).
This is a thread about my personal 15 lessons 🧵
1. Soft skills are extremely important. The first time you heard about them they seem fluff, it's actually the opposite.
Convincing stakeholders and negotiation are the most valuable skills for my own experience.
2. Variety is great. This doesn't just apply to your workplace but to skillset as well.
Exploring new things and going over prejudices involving other disciplines is a super valuable learning.