Python Web Scraping SEO and Digital Marketing Part: 4
My last few articles have been about web scraping with python. In this article I am going to talk about digital marketing and how python can be used to increase your social media presence.
I am going to explain the techniques and tactics I used to increase my social footprint and talk about the struggles I encountered along the way.
I started writing on medium a few years back, at first my articles where a bit ridiculous my follow and view counts where nothing to brag about either. I kept writing and got better and then Jexia’s Editorial Team distributed one of my article’s and viewer numbers started to increase.
From the time my first article got into in a newsletter I was writing on medium for approximately six months. At this point I knew nothing about digital marketing or what SEO even meant. So I started researching about how to use google, keyword analysis, social media marketing, and much more it has been a crazy journey. I thought if I could answer the below questions I could really zone in on my demographic and create a successful social media presence.
I needed historical data about my articles?
I needed data about the demographic I was trying to reach?
I needed data about what my demographic was interested in?
I needed data about where I could find my demographic?
The first place I decided to look was medium, I noticed each article has its own analytics. The below graph shows how many users viewed a specific article on any given day from Jan 2019 to Jan 2020. I thought this would be useful in predicting article success.
I wanted more information I did not just want to know that my react article was successful. I needed to know why it was successful?
I first went to google trends and found want I needed when I discovered npm trends. Npm is a package manager and shows react is dominating the frontend market.
At this point the data is telling me that react is a hot topic and it has been adopted by many frontend developers. I figured react had potential and decided to continue to write about react. As my monthly view count increased I wanted to know more.
I had to figure out how my users are finding my articles? I noticed medium has an external referral section.
When you click on the twitter link above you are redirected to the tweet that tweeted your article, Awesome!!!!!! Thanks https://twitter.com/JavaScriptNewz
Thanks https://twitter.com/WebDesignHat
At this point I started learning about twitter, wow, twitter is crazy. I wanted to know more about user engagement and realized frequency is variable, never found a straight answer. On average I am going to start off posting 10 tweets a day.
I know how I am going to engage with my users but I still do not know my demographic. To find my demographic I decided to search for hashtags and analyze how people create hashtags, why people click on certain tweets and not others, I went down the rabbit hole with this and learned a lot but most importantly, I was ready to create an effective social media strategy.
My strategy at this point was search twitter for keywords, analyze tweet text and if keywords matched, I would like the tweet, follow the user and retweet there tweet. I decided to use python and tweepy to automate interacting with twitter.
In the next article I will explain tweepy and how I used it to increase my social media presence. As alway thanks for reading and if you enjoyed this article give it a couple of claps. If you like this article you might like these as well.
